an approach towards urban form analysis and landuse ... · sustainability in developing countries...

An approach towards urban form analysis and landuse classification: A case of Ahmedabad, India

Mousumi Chakraborty June, 2009

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AN APPROACH TOWARDS URBAN FORM ANALYSIS AND LANDUSE CLASSIFICATION

The research presented in this thesis is part of the research project “Land, urban form and the ecological footprint of transport: application of geo-information to measure transport-related urban sustainability in developing countries with a case study of Ahmedabad, India.”, which received a project grant (SP-2006-09) from Volvo Research and Educational Foundations (VREF).

Disclaimer

This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

i

Abstract

Till only a decade ago, cities were perceived as physical artefacts but today a more profound understanding of urban form is gradually emerging. Today not only identification but quantification of spatial patterns in urban landscape is gaining its importance. Detailed information regarding urban form and urban landuse characteristics has become imperative for urban planning and management. The term ‘form’ is used extensively. It implies the manner in which cities can be observed and understood in terms of their spatial pattern. Spatial metrics, today, has become a trend in urban studies as numeric indices that can quantify these spatial patterns of urban landscape.

This research explores the potential of urban form being a simplistic determinant of urban landscape thereby analysing the relation between ‘form’ and ‘function’. Spatial metric analysis has been employed as a tool for quantifying indicators of urban form to derive a landuse classification for the city of Ahmedabad. The metrics have been used to describe the character and form of the city of Ahmedabad, its spatial configurations, spatial clustering, capture the subtle variations across the region, identify the patterns differentiating the old city from the new, thereby developing distinct definitions of its form and use.

From of a city is an integration of its ‘physical form’ and its ‘social form’. A form that a city acquires is influenced to a great extent by a multitude of social and economic processes. Thus, a complete characterisation of urban form requires taking this facet into consideration. Following that, this study not only emphasises the use of spatial metrics for describing the structure and pattern of urban landscape but also link socio-economic factors with the patterns of use.

In a nut shell, this research puts forth a different approach towards a new understanding of the form of cities, wherein its spatial structure and function are correlated.

ii

Acknowledgements This thesis would not be complete without acknowledgement of the teachers, friends and

colleagues, from whom I have sought guidance, advice and criticisms from time to time. I acknowledge the guidance of my supervisors, Dr.Mark Zuidgeest and Ms. Monika Kuffer. I am incredibly indebted to them for their constant encouragement. I would also like to thank Mr. Frans van den Bosch for his timely help.

I would also like to take this opportunity to thank Mr. Talat Munshi and Mr.Ajay Katuri for his support and also extend my gratitude to the people back home who helped us out with our field survey and made life easier.

Apart from all these people, there is one other person, without acknowledging whom it would remain incomplete. My little brother, the constant source of strength and encouragement in my life.

I just extend my gratitude to all those who made this research less difficult than it seemed. Thank you.

iii

Table of contents

Abstract i Acknowledgement ii List of figures v List of tables vii

1� Introduction: Urban, Urban form and landuse ........................................................................... 9�

1.1� . Justification ......................................................................................................................... 10�

1.2� Research problem: .................................................................................................................. 10�

1.3� Research objective: ................................................................................................................. 12�

1.3.1� Main objective: ............................................................................................................... 12�

1.3.2� Sub objectives: ............................................................................................................... 12�

1.4� Research questions: ................................................................................................................ 12�

1.5� Study area: Ahmedabad .......................................................................................................... 13�

1.6� Research Design: .................................................................................................................... 13�

1.6.1� Phase 1: Quantification and analysis of indicators of urban form and their linkages .... 13�

1.6.2� Phase 2: Land use classification and a framework for validation .................................. 15�

1.7� Conclusion: ............................................................................................................................. 16�

2� Literature review .......................................................................................................................... 17�

2.1� Remote Sensing of Urban Areas ............................................................................................ 17�

2.2� Spatial Metrics ........................................................................................................................ 17�

2.3� Urban form and Spatial Metrics ............................................................................................. 19�

2.4� Landuse and Spatial Metrics .................................................................................................. 21�

2.5� Inference: Spatial Metrics and Planning- Where lies the relevance. ...................................... 21�

3� Methodology ................................................................................................................................. 23�

3.1� Data Collection and Preparation ............................................................................................. 23�

3.1.1� Building Footprints to Neighbourhoods ......................................................................... 23�

3.1.2� Conceptual Framework .................................................................................................. 25�

3.2� Spatial Metrics and Indicator Quantification: ........................................................................ 27�

3.2.1� Landscape, a Class and a Patch, the three levels of metrics: .......................................... 27�

3.2.2� Selection of Metrics, the indicators of urban form: ....................................................... 27�

3.2.3� Definition of spatial domain ........................................................................................... 30�

4� Quantification of indicators of urban form using Spatial Metrics .......................................... 33�

iv

4.1� Residential use Metrics: ......................................................................................................... 33�

4.2� Commercial use Metrics ........................................................................................................ 36�

4.3� Mixed use metrics: ................................................................................................................ 40�

4.4� Institutional Metrics ............................................................................................................... 42�

4.5� Comparison between the four classes: A summary ............................................................... 43�

4.6� Height Metrics: ...................................................................................................................... 44�

5� Spatial Regression and Correlation analysis: Form and Use .................................................. 46�

5.1� Hypothesis: ............................................................................................................................ 46�

5.2� Geographically weighted regression...................................................................................... 47�

5.2.1� Residential and Height ................................................................................................... 48�

5.2.2� Commercial and Height: ................................................................................................ 53�

5.2.3� Mixed use and Height .................................................................................................... 56�

5.2.4� Institutional and Height: ................................................................................................ 58�

6� Urban form and social form ....................................................................................................... 61�

6.1� Residential and Income: ........................................................................................................ 61�

6.2� Commercial and Jobs: ........................................................................................................... 63�

6.3� Mixed use, Income and jobs: ................................................................................................. 64�

6.3.1� JOBS: ............................................................................................................................. 64�

6.3.2� INCOME: ...................................................................................................................... 65�

6.4� Institutional and Jobs ............................................................................................................. 67�

6.5� Vehicle ownership and landuse metric: An analysis of compactness and clustering ............ 68�

7� Validation ..................................................................................................................................... 70�

7.1� Methodology .......................................................................................................................... 70�

7.1.1� Phase 1: Conversion of DSM into building heights ...................................................... 70�

7.1.2� Phase 2: Calculation of spatial metrics and socio-economic factors: ............................ 71�

7.1.3� Phase 3: Validation ........................................................................................................ 71�

7.2� Conclusion ............................................................................................................................. 72�

8� Inferences and Conclusion .......................................................................................................... 73�

8.1� LANDUSE CLASSIFICATION SCHEME .......................................................................... 73�

8.2� Limitations: ............................................................................................................................ 75�

8.3� Recommendations ................................................................................................................. 75�

References 73 Appendices 76

v

List of figures Figure 1.1 Contrast between east and west Ahmedabad ........................................................................ 11 Figure 1.2. Ahmedabad City, the study area………………………………………………………… 11 Figure 1.3. Research Design……………………………………………………………………… 12 Figure 2.1Urban growth of Santa Barbara urban area 1930-2030 shown with spatial distribution of local contagion ....................................................................................................................................... 18�Figure 2.2.Schematic map of spatial metrics ......................................................................................... 20�Figure 3.1. Landuse of the buildings surveyed in Ahmedabad .............................................................. 24�Figure 3.2. Division of dataset into Neighbourhoods- Test and Validation ........................................... 24�Figure 3.3. Conceptual Framework ........................................................................................................ 26�Figure 4.1Graph showing mean value of metrics for residential areas- Density Class 1 ....................... 33�Figure 4.2Graph showing mean value of metrics for residential areas- Density Class 2 ....................... 34�Figure 4.3 Graph showing mean value of metrics for residential areas- Density class 3 ....................... 34�Figure 4.4 Graph showing mean value of metrics for commercial areas- Density Class 1 .................... 37�Figure 4.5Graph showing mean value of metrics for commercial areas- Density Class 2 ..................... 37�Figure 4.6. Graph showing mean value of metrics for commercial areas- Density Class 3 ................... 37�Figure 4.7. Graph showing mean value of metrics for mixed use- Low Intensity of Mix (class 1) ....... 40�Figure 4.8. Graph showing mean value of metrics for mixed use- Low Intensity of Mix (class 2) ....... 40�Figure 4.9.Graph showing mean value of metrics for Institutional areas ............................................... 42�Figure 4.10. Comparison of metrics among the four landuse types ....................................................... 43�Figure 4.11. Graph showing mean value of metrics for low rise class ................................................... 44�Figure 4.12. Graph showing mean value of metrics for medium rise class ........................................... 44�Figure 4.13. Graph showing mean value of metrics for high rise class ................................................. 45�Figure 5.1. PLAND metrics of residential classes & Figure 5.2. PLAND metrics of height classes ..................................................................................................................................................... 48�Figure 5.3. GWR between residential and height metrics ...................................................................... 48�Figure 5.4. Representation of the equation between residential form and use ....................................... 52 Figure 5.5. Building categories of residential landuse………………………………………………... 50 Figure 6.1. Scatter diagram showing relation between Residential PLAND and Average Income ....... 61�Figure 6.2. Scatter diagram showing relation between Height PLAND in residential areas and Average Income .................................................................................................................................................... 62�Figure 6.3. Box-plots showing distribution of average income as per residential density class ........... 62�Figure 6.4. Scatter diagram showing the relation between Commercial PLAND and Total Jobs ......... 63�Figure 6.5. Scatter diagram showing the relation between Height PLAND in commercial areas and Total Jobs ............................................................................................................................................... 63�Figure 6.6. Distribution of total jobs as per commercial density classes ............................................... 64�Figure 6.7. Scatter diagram showing the relation between Mixed PLAND and Total Jobs ................... 64�Figure 6.8. Scatter diagram showing the realtion betwen Height PLAND in mixed landuse areas and Total Jobs ............................................................................................................................................... 65�Figure 6.9. Scatter diagram showing the relation between Mixed PLAND and Average Income ......... 66�Figure 6.10. Scatter diagram showing the relation between Height PLAND in mixed landuse areas and Average Income ..................................................................................................................................... 66�Figure 6.11. Scatter diagram showing relation between Insitutional PLAND and Total Jobs ............... 67�

vi

Figure 6.12. Scatter diagram showing relation between H PLAND in insitutional areas and Total Jobs ............................................................................................................................................................... 67�Figure 6.13. Correlation between Urban form metrics and Vehicle ownership .................................... 68�Figure 7.1. Methodology for validation ................................................................................................. 70�

vii

List of tables Table 1:Mean value of Residential metrics (Source:SPSS) .................................................................. 34�Table 2:Karl Pearson’s correlation value between Residential metrics ................................................ 35�Table 3: Mean value of Commercial metrics ........................................................................................ 38�Table 4:Cross-correlation between Commercial metrics ...................................................................... 39�Table 5: Mean value of Mixed use metrics ........................................................................................... 41�Table 6: Cross-correlation Mixed use metrics ........................................................................................ 42�Table 7: Mean value of Institutional metrics .......................................................................................... 43�Table 8: Mean value of Height metrics .................................................................................................. 45�Table 9: Spatial Regression and correlation results (GWR) between R PLAND and H PLAND ......... 49�Table 10: Comparison of means between Residential and Height correlation metrics as per analysis classes ..................................................................................................................................................... 51�Table 11:Spatial Regression and correlation results (GWR) between Commercial and Height metrics ................................................................................................................................................................ 53�Table 12: Comparison of means between Commercial and Height metrics as per analysis classes ...... 54�Table 13: Spatial Regression and correlation results (GWR) between Mixed use and Height metrics . 56�Table 14: Comparison of means between Mixed and Height correlation metrics as per analysis classes ................................................................................................................................................................ 57�Table 15: Spatial Regression and correlation results (GWR) between Institutional and Height metrics ................................................................................................................................................................ 59�Table 16: Comparison of means between Institutional and Height correlation metrics as per analysis classes ..................................................................................................................................................... 59�Table 17: Comparison of mean Residential PLAND values between Income categories ..................... 61�Table 18:Comparison of mean C PLAND values between Job categories ............................................ 63�Table 19: Comparison of mean HPLAND (commercial areas) values between Job categories ............ 63�Table 20:Comparison of mean Commercial PLAND values between Job categories ........................... 65�Table 21:Comparison of mean H PLAND (of residential areas) values between Job categories ......... 65�Table 22:Comparison of mean M PLAND values between Income categories ..................................... 66�Table 23:Comparison of mean H PLAND (in mixed use) values between Income categories ............. 66�Table 24:Comparison of mean Institutional PLAND between Job categories ...................................... 67�Table 25: Comparison of mean H PLAND (in institutional areas) between Job categories .................. 67�Table 26:Correlation between Urban form metrics and Total Vehicle ownership: Analysis of clustering ................................................................................................................................................ 68�Table 27……………………………………………………………………………………………... 21

AN APPROACH TOWARDS URBAN FORM ANALYSIS AND LANDUSE CLASSIFICATION�

9

1 Introduction: Urban, Urban form and landuse

An urban area is an area with an increased density of human-created structures in comparison to the areas surrounding it. Urban areas are created and further developed by the process of urbanization. There are various ways to define what “urban” is and what is part of “urban areas” in different countries. In Britain, for example, open space that is completely surrounded by other land use types, v.i.z. residual, industrial, and commercial, etc., belongs to an urban area. In India, an urban area is defined in terms of population, density and the work composition. Areas with a population of more than 5000people, a density of more than 400 persons/sq km and 75% of the male population being engaged in non-primary activities, are designated as urban areas in India.

Perhaps there is no topic more central to the study of urbanism than urban form. The term form implies shape or more specifically, it is the way in which cities can be observed and understood in terms of their spatial pattern. Urban form refers to the pattern of development in a city, considering aspects like density, use of land, transportation, degree to which development is scattered or contiguous. Thus, the concept encompasses not only the space, but also the processes and function. Therefore, an urban area can also achieve its form from the manner in which it has grown, that is whether developed in a planned manner or without any planning interventions or both. Urban form is therefore a combination of spatial form and social form. It includes not only the physical form but also the underlying social, economic and demographic processes that shape it and render it a distinct character. A growing body of literature has been looking into the concepts of “a good city form” or “sustainable city forms” to enhance the economic and social vitality of the cities and reduce the deterioration of the environment.

Form is a broader concept than shape per se, although an immediate understanding and measurement of its is through the notion of shape i.e. the outward appearance of things (Batty 1994).

“In terms of the study of cities, form will represent the spatial pattern of elements composing the city in terms of its networks, building spaces, defined through its geometry mainly, but not exclusively, in two rather than three dimensions.”(Batty 1994)

Urban form of a city can be understood in two aspects: the external form and the internal form. The external form is applicable for inter-city comparisons. It refers to mainly the shape of the city (linear, radial, etc) and its size. The internal form more refers to the structure of the city, e.g. the intensity of land use, compactness, density, decentralization of activities, accessibility, etc. An interaction of the socio-economic landscape of a city with its spatial structure gives rise to an urban form, which is distinct for that particular city. This further influences the landuse in the area. Thus, an indicator of urban form will provide a measure of these processes and phenomenon. The research aims to focus on extracting these urban form characteristics using spatial analytical techniques with reference to Ahmedabad.

Cities today are experiencing massive alterations in their character of urban form and landuse, with increasing levels of economic development. Remotely sensed data have a substantial role to play in investigating these alterations. Satellite remote sensing offers a tremendous advantage over historical maps or air photos, as it reveals recurrent and explicit patterns of landuse and presents a synoptic view of the landscape (Schneider and Woodcock 2008).


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Through this research, the researcher tries to adopt an alternative method to land use classification. Quantified indicators of urban form have been derived using Spatial Metrics, incorporating the influence of socio-economic factors, thus, and trying to portray the internal variations of urban form in the city of Ahmedabad. Varying from the traditional approaches, in this study, an attempt has been made to predict the landuse, applying spatial statistical techniques, for the entire city of Ahmedabad . Of much significance in study of urban form is to explore and evaluate the quantitative descriptors of spatial urban form (Herold, Liu, and Clarke 2003) on the basis of the distinct relationship between the physical form and the landuse, socio-economic, demographic, and ecological characteristics of a region. This research, thus, attempts to establish a link between remote sensing technology, the land use classification approach, inter- relationships between urban form and social form of the city.

1.1 . Justification

The framework in this research not only provides an innovative approach to land use classification of an urban area but also attempts to quantify certain indicators of its form and structure, the major instrument being remote sensing. Urban form is also determined or influenced by its social form. Here lies the need to validate the physical variations in urban form with the intra urban socio-demographics. Such a quantitative analysis can prove to be an important tool for the planners in the decision making process. The approach adopted in the research explores the influence of urban form on urban landscape.

Whether form defines use or use defines form is a chicken and egg story all together. However, it sounds more logical to believe that within the boundaries of a city’s varying urban form, lies a rich mix of heterogeneous activities and uses. Therefore, landuse becomes a subset of urban form. Hence, urban form can be a crude but simplistic descriptor of urban landuse. However, deriving objective and consistent definitions of either urban form or different categories of urban landuse, with precision, is difficult (Batty 1994). Use of quantified indicators in such a scenario can prove to be a significant approach.

Traditionally, lands use maps have been prepared through surveys or in the later years, large scale air photos have been employed to obtain such information. Today, however, availability of high resolution satellite imagery opens up a potential new avenue for extracting detailed urban landuse information (Herold, Liu, and Clarke 2003). This research thus heavily focuses on the application of remote sensing to urban analysis. Apart from serving as an excellent source of spatial information, satellite images more than often complement and other data sources and help to create an accurate database for decision making. “The spatial dimension of urban characteristics makes it possible the derivation of indicators from remotely sensed data” (Dalumpines 2008). To illustrate, residential proximity to transport networks can be computed by using pixels depicting residential landuse and transport network from, of course, a classified image. So, instead of following traditional methods for generating a landuse map, in this age of technical advancement, Spatial Metrics could open up promising avenues.

1.2 Research problem:

Urban studies still suffer from a lack of knowledge and understanding of the physical and socio economic drivers that contribute to the pattern and dynamics of urban areas (Herold, Goldstein, and Clarke 2003). The age old perception of cities has undergone a gradual change. Hundred years ago, cities were perceived as planning artefacts where the predominant focus was on architecture. It was generally agreed that the physical form of cities was a major determinant of their social and

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1.3 Research objective:

1.3.1 Main objective:

To understand the varying urban form of Ahmedabad city and thereby derive a classification of landuse through Spatial Metrics analysis

1.3.2 Sub objectives:

1. To generate a set of metrics using Spatial Metrics to develop quantified indicators of urban

form for studying the urban form of Ahmedabad. 2. To explore the relationship between building form and use. 3. To correlate urban form indicators with socio-economic indicators of Ahmedabad and drawing

a comparison between different parts of the city of Ahmedabad using the indicators, thereby reflecting the variations.

4. To derive a classification of the land use for the city (within the study area) using the indicators derived through spatial metrics.

5. To develop a conceptual framework to predict the landuse for the remaining areas of the city, as a part of validation, using a Digital Surface Model.

1.4 Research questions:

In response to the specific objectives identified above, the following questions have been formulated that would be answered in the course of the research:

1. To generate a set of metrics using Spatial Metrics to develop quantified indicators of urban form for studying the urban form of Ahmedabad.

� Why apply Spatial Metrics? � How to apply and interpret the derived metrics for analysis? � What are the appropriate indicators to describe the urban form of Ahmedabad? � What is the nature of relationship between the indicators generated? � Do the values of the various indicators or the nature of relationships follow varied

patterns in different parts of the city? 2. To explore the relationship between building form and use.

� Is there any correlation between the metrics generated for building height and use? � Is there any particular pattern? � Is spatial structure a proxy for land use?

3. To correlate urban form indicators with socio-economic indicators of Ahmedabad and drawing a comparison between different parts of the city of Ahmedabad using the indicators, thereby reflecting the variations.

� Do the socio economic factors influence the urban form in the city? � What kind of relation do spatial metrics of urban form have with socio economic

factors like income, job concentration, vehicle ownership, land value, etc? � What patterns emerge from the indicators for the various parts of the city, when

compared? Is it possible to identify specific patterns, e.g. along highways, city centre, periphery, etc?

� How is the urban form of older part of the city different from its counterpart?

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15

Index, Largest Patch Index, etc. Choice of the metrics that are to be computed will surely be guided by previous studies that have been done in this field of interest. So far, there is no standard set of appropriate metrics to be used in urban environment, which makes it even more challenging to take up. Secondly, it is useful to be able to quantify the characteristics of urban, other than mere visual interpretations, in order to understand the built up dynamics. A lot of studies have proved that critical in the description, analysis, modelling of urban form and its changes are spatial metrics.

The character of urban form is likely to vary across the region. Thus, the indicators that would be developed cannot have a universal applicability. Thus, in order to capture this varied character, metrics would be computed for a number of samples taken from different parts of the city (based on satellite images), for example, city centre, periphery, along highways, along major nodes, etc. This is not only going to give an impression of the pattern of the city’s form but also help in identify the corridors and directions of growth. Thus, while selection of indicators of urban form, it is imperative to keep this particular variation in mind. Moreover, the landuse map would be in grids, it could further be divided into neighbourhoods to generate patches for metrics calculations.

The final step in this stage would be synthesis of the indicators of urban form which would include relational analysis. The research at this stage also intends to explore the comparability, consistency, chances of covariance, interactive effects, etc, among the indicators, ultimately culminating into a statistical analysis.

Stage 3: The first relational analysis would be between building form and use i.e. whether there is any

relation between for example high rise building patches with the landuse in that area. The correlation so derived would also form the rules for the last phase of the research.

The next stage would involve a correlation analysis of urban form metrics with the socio-economic indicators, derived from secondary data sources. The process would be to overlay the grid layer having metric values with grid layer having the socio-economic attributes. The socio-economic parameters, useful in this case could be population, income level, vehicular ownership (also can serve as an indirect indicator of income level), the commercial activities, their concentrations, accessibility, proximity to transportation, etc. A regression analysis between the urban form indicators and socio-economic parameters would help in understanding their inter relationships, and thereby, formulate composite indicators of urban form and landuse for the city. The indicators hence developed would paint a certain picture of the city’s form, shape, pattern, etc. To illustrate,

� An indicator of ‘Compactness’ is a combination of the indices like compactness, clustering, shape, fragmentation coupled with a socio-economic indicator like car ownership. The assumption here is that lesser the motorisation the greater the compactness of an area. As, a greater car ownership supports dispersal.

� An indicator identifying ‘Medium density single unit residential clusters’ (Herold, Liu, and Clarke 2003) could be measured as a combination of medium density housing, areas with medium population density, average to large residential buildings (size), distinct spatial arrangements along roads, landscape dominated by vegetation cover. This would be the phenomenon witnessed in the outer fringes of the city of Ahmedabad, towards, Gandhinagar and Bopal.

1.6.2 Phase 2: Land use classification and a framework for validation

With the results generated in stage 1, a detailed landuse classification would be developed, for the sampled areas, based on the indicators generated though the course of this stage i.e. the metrics, their inter relations and their relations with socio-economic factors.


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This stage also delves into the possibilities of predicting a landuse classification of the entire city for which there is no landuse information thereby attempting to validate the results obtained. Development of a conceptual framework for this process is also an objective of this study. The input to this phase of research is a Digital Surface Model, created from a CARTOSAT 1 image of Ahmedabad. As is evident the layer only has height information for the entire city. Spatial metrics would be generated from this DSM layer. In the previous phase of research spatial metrics has been calculated based on the building height and land use information of the sampled area. The relations derived from that analysis would form the basis of prediction at this stage. The basic idea is to explore whether spatial metrics can be used to derive the landuse of the areas for which there is no information other than height. The researcher gets to know that patches with this particular attributes conform to a particular landuse. For illustration, a patch (of grids) with low values (of heights) showing a sudden increase in value in one part can depict a low lying area with a shopping mall of greater height in the vicinity. Further, a patch of low value (height) grids, with low density could probably be a slum area. Such a trend analysis could help in predicting the land use of the entire city i.e. relating the identified patterns to the rest of the city. This, of course would involve some detailed geo-statistical analysis. This way, spatial structure could prove to be a useful proxy for land use.

1.7 Conclusion:

The research aims to bridge the gap in the use of remote sensing technology and geo-statistical analysis in urban studies, as the most basic underlying fact is that that spatial dimension of urban characteristics makes it very challenging to derive indicators form satellite images. What would be worthy to be mentioned here is that if multi-scale or multi- temporal data is available, the above mentioned techniques could produce excellent trend scenarios.


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2 Literature review

2.1 Remote Sensing of Urban Areas

With rapid urbanization changing the face of the earth, it has become all the more imperative to constantly monitor these changes. The dramatic demographic shift has been accompanied by expansion of built up urbanized land. Though urbanization is a worldwide phenomenon, it s especially prevalent in India, where urban areas have experienced an unprecedented rate of growth over the last three decades (H. Taubenböck 2008). Yet city planners lack proper tools to measure, monitor and understand urban processes. Apart from simple visual interpretations it is also important to adopt a quantitative approach to the understanding of urban dynamics.

Cities today are experiencing massive alterations in their character of urban form and landuse, with increasing levels of economic development. For applications related to urban management and planning detailed information on urban land use is essential. Landuse regions are spatially distinct homogeneous structure that are composed of an aggregation of land cover objects (Herold, Liu, and Clarke 2003). Urban areas, today, are not only defined by the movement of goods and people within and across the city but also as having multiple nuclei developing at varying distances from the urban core and as an economic unit including small towns depending on the larger city (Schneider and Woodcock 2008). Thus the very concept of urban, its form and character have undergone a gradual change.

Remotely sensed data have a substantial role to play in investigating these alterations. Satellite remote sensing offers a tremendous advantage over historical maps or air photos, as it reveals recurrent and explicit patterns of landuse and presents a synoptic view of the landscape (Schneider and Woodcock 2008). Application, performance and outputs analysing development of urban form depends strongly on the data available for parameterization. Remote sensing techniques have already proven useful for mapping urban areas (Batty and Howes 2001). Recent researches have used remote sensing images to quantitatively describe the spatial structure of urban environments and thereby characterize patterns of urban morphology(H. Taubenböck 2008). Spatial metrics are critical in the description, analysis and modelling of urban form and its changes (Herold, Goldstein, and Clarke 2003). A study carried out by Taubenbock et al a combination of zonal statistics, landscape metrics and gradient analysis has been used to characterize types of urban development in the 12 largest Indian cities. They have used two parameters, v.i.z. built-up density and shape index, with respect to locations as city centre versus periphery(H. Taubenböck 2008). The result paints a distinct picture of spatial pattern which helps in understanding the spatial growth and urban development in India. These indices developed through spatial metrics could, therefore, be very aptly utilized to quantify the structure and pattern of an urban environment.

2.2 Spatial Metrics

“Spatial Metrics can be defined as quantitative and aggregate measurements derived from digital analysis of thematic categorical maps showing spatial heterogeneity at a specific scale and resolution”(Herold, Goldstein, and Clarke 2003). If applied to multi-temporal data, spatial metrics can be used to analyse change in the degree of spatial heterogeneity. Various researches have justified the use of spatial metrics for not only describing structure and pattern in urban landscape but also to link economic processes with patterns of landuse. Spatial metrics holds a huge potential in providing an


18

improved representation of spatial urban characteristics and enhanced interpretation of the results of modelling.

Calculation of spatial metrics is based on a “categorical, patch-based representation of the landscape within individual landuse regions”(Herold, Liu, and Clarke 2003). Patches are generally defined as homogeneous regions for a “specific landscape property of interest” such as “buildings”, “high-density residential zone” or “vegetation”. Spatial metrics can be used to quantify the spatial heterogeneity of the individual patches, all patches in the same class, and the landscape as a collection of patches. This perspective assumes abrupt transitions between individual patches that result in distinct edges. “Patches are therefore maximally internally and minimally internally variable”(Herold, Goldstein, and Clarke 2003).

Most metrics have fairly intuitive values like the percentage of landscape covered by the class (PLAND), patch density (PD), the mean patch size (AREA_MN). For example, the largest patch index (LPI) metrics describes the total area covered by the class concentrated in the largest patch of that class (McGarigal 2002). Other metrics like contagion and fragmentation are useful for the purpose of analyzing heterogeneity.

Herold et al have used these metrics extensively in analyzing and modelling of urban growth in Santa Barbara, California (Herold, Goldstein, and Clarke 2003).

Figure 2.1Urban growth of Santa Barbara urban area 1930-2030 shown with spatial distribution of local


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The figure above shows the historical and projected growth patterns for the urban sub areas spatially presented with local contagion metrics (i.e. within a 100m radius).

The green areas depicting a low contagion index represent the more fragmented and heterogeneous areas at the urban fringe. The contagion map of 1970 shows some growth in relation to the extension of the urban cores (in yellow) but most of the new developments in the area indicate a low contagion (in green). This can be attributed to the fragmented sprawl in during the 1960s. By 1990, most of the sprawl areas aggregate into compact patches by infilling of vacant land. The output of the model predicts substantial growth in urban areas from 2000 to 2030 including allocation of new developments in Goleta and Carpinteria with occupation of larger tracts of vacant land. Analysis of contagion maps demonstrates the importance of applying spatial metrics in urban growth analysis. It also helps in identifying the ‘hot-spots’ in terms of future development.

The conventional mapping and surveying techniques in planning are expensive and time consuming for estimation of such phenomena as urban sprawl and urban growth. Moreover such information is also not always available in the developing countries. As a result more and more research is being done in mapping and monitoring of urban growth using GIS and remote sensing techniques (Jat, Garg, and Khare 2008). Remote sensing and spatial metrics have been used along with collateral data for analysing the growth, pattern and extent of sprawl (Sudhira, Ramachandra, and Jagadish 2004) for the Udupi region in Karnataka state, India. Spatial and temporal analysis along with modelling has enabled them to identify the pattern and extent of sprawl.

FRAGSTATS, a public domain spatial metrics program, developed in the mid 1990s provides a wide variety of metrics (McGarigal 2002).

2.3 Urban form and Spatial Metrics

Time and often there has been a lot of research on the description, mapping, characterization, measurement, understanding and explanation of urban form and morphology. The classical theories of urban morphology define urban patterns as concentric rings with different landuse types (Burgess’s Concentric Zone Theory, 1925); the concentric zone patterns modified by transportation networks to form sectors having different landuse categories (Hoyt’s Sector Theory, 1939) and further the Multiple Nuclei theory (Harris and Ullman, 1945) that model an urban form with multiple centres of specialized landuse. However, since 1960s various modern theories have been experimented with to characterize urban form, v.i.z. fractals, cellular automata landscape metrics, etc. But the inadequacy with the other models in comparison to spatial metrics is that these models fail to interact with the causal factors such as population characteristics, availability of land and proximity to city centres and highways (Sudhira, Ramachandra, and Jagadish 2004). Spatial metrics indicators offer improved description and representation of heterogeneous urban areas. It provides a link between the physical landscape structure and urban form, functionality and processes (Barnsley, and Barr 1997). The very spatial dimension of an urban area enables indicators to be developed using remote sensing analysis techniques.

Huang et al have attempted to develop “a global comparative analysis urban form”(Huang, Lu, and Sellers 2007) using spatial metrics and remote sensing. The study has tried to capture five distinct dimensions of urban form for metropolitan areas in Asia, US, Europe, Latin America and Australia. The seven spatial metrics calculated for that purpose are compactness, centrality, complexity, porosity and density.


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a. Complexity: Index measuring irregularity of a patch shape. The two complexity metrics employed in this case are the area weighted mean shape index (AWMSI) and the area weighted mean patch fractal dimension (AWMPFD). The former represents shape irregularity while the later describes the “raggedness” of the boundary.

Figure 2.2.Schematic map of spatial metrics

b. Complexity: Index measuring irregularity of a patch shape. The two complexity metrics employed in this case are the area weighted mean shape index (AWMSI) and the area weighted mean patch fractal dimension (AWMPFD). The former represents shape irregularity while the later describes the “raggedness” of the boundary.

c. Centrality: It measures the average distance of the dispersed parts of the city to the city centre, which has been defined as the centroid of the largest patch. It could be interpreted as the degree to which the urban development is close to the CBD or the central business district. Centrality in this research, therefore, measures the overall shape of the city. i.e. whether it is elongated or circular(Huang, Lu, and Sellers 2007). The centrality index displays a higher value if the city is more elongated and vice versa.

d. Compactness: The compactness index not only measures the shape of an individual shape but also the fragmentation of the entire landscape. The more regular the shape of the patch and smaller its size, the bigger the CI value.

e. Porosity: It measures the ratio of open space compared to the total urban area. These “holes” of open spaces comprising vegetation, water bodies, etc., are crucial both as an amenity for residents and for the sustainability of cities. The indicator of porosity is also designated as the “ratio of open space” (ROS). The study referred to above also explored the factors behind the variations in urban form

through comparisons with socio-economic development indicators and historical trajectories in urban development (Huang, Lu, and Sellers 2007). The study attempted to examine the correlation between spatial metrics and socio-economic factors.

In this research conducted by Huang et al, higher purchasing power shows positive correlation with more complex landscapes and larger proportions of open space, while negative correlation with

Source: (Huang, Lu, and Sellers 2007)


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Density and Compactness (Huang, Lu, and Sellers 2007). This is very logical as wealthier people can afford more private vehicles and therefore aiding dispersal. Further, number of telephone lines per capita correlates positively with more complex urban form and open space and negatively with density and compactness. However, Centrality does not show a significant correlation with the socio-economic variables. This may be due to the role that transportation advancements play in the evolution of urban form.

Considering socio-economic factors like rent, population gradient, income level, vehicular ownership, etc., along with basic urban form indicators is a significant approach towards understanding a city’s character and morphology. As stated by Clifton et al “The economic structure perspective on urban form focuses on economic efficiency. According to economists, cities exist because there are advantages to concentrating economic activities spatially. Urban form questions of concern to economists include the following. How big should cities be and what industries should they contain? Should they be organized with one or many centres? How should development types and intensities vary across metropolitan areas?”(Clifton et al. 2008) Evidences provided by various studies have suggested that economic benefits accrue to “urban size and diversity”. Even after the cost of living is adjusted, incomes tend to be higher in relatively diverse parts of the city. At a broader level, cities with such characteristics tend to attract a diverse set of industries thereby favouring economic development strategies.

2.4 Landuse and Spatial Metrics

Urban landuse is a subset of urban form. Thus, indicators of urban form can aid in describing the characteristics of landuse. Detailed information on urban landuse is essential from the perspective of urban management and planning.

Landuse regions can be defined as spatially distinct areas with homogeneous structure that are composed of an aggregation of land cover objects representing a specific type of landuse(Herold, Scepan, and Clarke 2002). Various remote sensing and urban modelling approaches exist for deriving land use regions. However grid based approaches tend to smooth the boundaries between discreet land use parcels and more over it is also difficult to determine the optimum kernel size. “A rectangular window represents an artificial area that does not conform to real parcels or landuse units.”(Herold, Liu, and Clarke 2003) In contrast to that, area based approaches provide a discreet characterization of areas that are thematically and functionally defined.

There has always been an increasing interest in quantification and spatial pattern characterization of land cover classes in order to relate pattern and processes. The paper on “Land cover mapping with patch-derived landscape indices”(Chust, Ducrot, and Pretus 2004) explores how spatial metrics can be used to enhance land cover classification reliability. The study has primarily used four patch indices: area, perimeter, shape index and fractal dimension. The results of classification proved that patch indices along with topographic features significantly improved the discrimination of land cover classes.

2.5 Inference: Spatial Metrics and Planning- Where lies the relevance.

Spatial pattern statistics, including spatial metrics, are important quantitative tools that can incorporate scientific principles into planning in a more rigorous manner. Spatial metrics can be valuable planning tools that provide for a rich source of objective, quantitative and replicable information (Leitao 2006). Planning anticipates and guides changes. Therefore, in order to manage


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change, the planner needs to be aware of the characteristics that are going to undergo that change and there lies the importance of measuring these characteristics. However, the fact remains that a fixed set of metrics suitable for application in urban areas, does not exist so far. But researches, some of which have also been stated above, have shown that spatial metrics is a huge step towards it, if not the solution. Spatial metrics can provide these quantified indicators that would reflect these patterns and processes in urban landscape. Spatial metrics therefore provides a measure of form, pattern and processes.

There lie specific differences in spatial urban form between the different urban land use categories. Significant features are the sizes of buildings, their shape and their spatial configuration. For example, areas of low, medium and high density residential land use would represent a spatial built up structure ranging from “detached irregular structure to the regular high density arrangement of buildings” (Herold, Liu, and Clarke 2003). Commercial, industrial and institutional land uses in urban areas would depict larger buildings and more aggregated spatial configurations. As presented by Herold et al, “Building configuration is best characterized by area coverage, the regularity of the spatial arrangement (Nearest neighbor metrics), the dominance of one large building structure (Largest patch index), and the spatial heterogeneity of the individual building objects (Edge density)” (Herold, Liu, and Clarke 2003).The different patches would also vary in terms of their spatial extent and fragmentation. What is encouraging is “several high order interpretation elements” obtained from visual image analysis “within a digital environment” can help to not only capture but also quantify these distinct variations with the aid of spatial analytical techniques. Spatial metrics if well supported by socio economic databases can open potential avenues for such researches regarding the distinct character of a city’s form and morphology.


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3 Methodology

This chapter comprises two parts: The first one gives the description of the data collection plan and the initial preparation of the data. The Second part deals in details about Spatial Metrics which is the method adopted in this study for developing the indicators of urban form.

3.1 Data Collection and Preparation

The basic input data to this study includes the landuse and height information of buildings in Ahmedabad, which required an extensive field survey. The classification of building categories decided upon is as follows:

3.1.1 Building Footprints to Neighbourhoods

The buildings have been broadly divided into 6 categories and further into sub classes keeping in mind the character of Ahmedabad city. Samples were taken distributed across the city, from the west as well as the east. Selection of samples or patches is not random but based on a particular rational.

The samples have been chosen in such a manner so as to capture the variation and also that they are well distributed and representative. Spatial development of Ahmedabad city has largely been on the shoulder of roads. The clusters selected for survey are mainly based on location along the major roads in Ahmedabad, distributed at the core as well as the periphery. The samples have been selected keeping in mind the landuse and urban form variations across the city. For example, residential areas along C.G. Road are different from those along relief road. Some would represent low rise high density settlements while the others high rise low density. Again the kind of landuse in the old city is quite different from that in the old city which in turn influences the urban form and vice versa. Information collected includes landuse and number of storeys in the building in a neighbourhood.

Building footprints have been digitized and a database for a total of 12652 buildings has been created. 80 % of the sample would be used for the calculation and the remaining for validation based on the rules established in the former.

1 2 3 4 5 6Residential Commercial Industrial Institutional Infrastructure Mixed

1 Slum� Big�Markets� Factory� Hospital� Railway�station� Residential�+�Commercial�2 Apartment� Retail�mall� Small�manufacturing�units� Educational Bus�station� Residential�+�Institutional�3 Bungalow� Office�buildings� Government�offices� Airport� Commercial�+�Institutional�4 Tenement� Hotels� Religious�building� Res�+�Com�+�Insti�5 Shop�building� Bank�6 Shop�+�Office� Others�7 Others�

Landuse�classification�&�building�categories

Table 27: Building categories for field survey


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Figure 3.1. Landuse of the buildings surveyed in Ahmedabad

Figure 3.2. Division of dataset into Neighbourhoods- Test and Validation

The entire dataset has been divided into 67 Neighbourhoods. These have further been divided into Test neighbourhoods-57 (orange coloured in the map) and Validation neighbourhoods-10 (marked with red colour in the map).


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In this research, Spatial Metrics has been used to extract and quantify indicators of urban form (for the surveyed area) and later to be used in combination with socio-economic factors to provide a landuse classification. One issue with spatial metrics is “their aggregate nature as summary descriptors of landscape heterogeneity” (Herold, Goldstein, and Clarke 2003). Averaging of metric value over the area may lead to incorrect interpretations, as changes reflected in one metrics cannot be related to specific locations. Therefore, it is more appropriate to break the study into smaller units and regionalize the metric to an appropriate level of analysis.

For this purpose, the footprints have been divided into neighbourhoods (converting buildings to areas). The neighbourhoods have been divided on the basis of road hierarchy. The patches bounded by the road of the highest hierarchy and the next form one patch. However, following this scheme the size of all the patches would not be same. This is the unit for further metrics calculation and analysis. The dataset contains a total of 67 neighbourhoods. 57 hoods have been used as a training dataset for analysis and the remaining 10 for validation. The validation hoods comprise Hood 6, 13, 19, 20, 40, 48, 53, 57, 64 and 67.

3.1.2 Conceptual Framework

Figure 3.3 shows the conceptual framework developed for this study .i.e. for the purpose of quantification of urban form indicators using spatial metrics and eventually using them to develop a landuse classification for the city of Ahmedabad. The initial phases of data preparation have been explained before in this chapter.

The core elements of this research involve the quantification of indicators of urban form, and exploring their applicability as urban landuse classifiers. Each stage in the conceptual framework has been discussed through the course of this chapter.


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Validation

LANDUSE CLASSIFICATION OF UNKNOWN NEIGHBOURHOODS (VALLIDATION)

Spatial Metrics DSM

Computation of Metrics (height)

Rul

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Employment (jobs)

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Landuse

Building footprints

Identification of Neighbourhoods

Road layer

SPATIAL METRICS

Form - Height

DEVELOPMENT OF A CONCEPTUAL FRAMEWORK

Socio-economic factors

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CONCEPTUAL FRAMEWORK

Figure 3.3. Conceptual Framework


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3.2 Spatial Metrics and Indicator Quantification:

Spatial Metrics involves the analysis of spatial structures and patterns. Spatial Metrics can be used to quantify the spatial heterogeneity of individual patches. Spatially explicit metrics can be computed as patch-based indices (e.g. size, shape, edge length, Patch density, fractal dimension) or as pixel based indices (e.g. contagion) for a patch (Herold, Couclelis, and Clarke 2005).

3.2.1 Landscape, a Class and a Patch, the three levels of metrics:

Spatial Metrics measure and describe the spatial structure of patches, classes of patches, or entire patch mosaics i.e. the landscape (Leitao 2006).

i. Patch level metrics: A patch level metrics defines the spatial character of a patch. A patch is a relatively homogeneous area. In vector data, a patch is a polygon classified as a specific land cover type. Patch level metrics quantify characteristics of individual patches such as size, shape. It provides a unique value for each patch.

ii. Class level metrics: Class level metrics are integrated over all the patches of a given type. Thus a class is a set of patches of the same land cover type. In vector data, a class is a set of polygons classified as the same patch type (Leitao 2006). Class metrics quantify the characteristics for the entire class such as degree of aggregation and clumping and produce on unique result for each class. In more simple terms a class level metrics is derived by summing up the patches.

iii. Landscape level metrics: These are integrated over all patch types or classes over the entire landscape. In vector dataset, a landscape is the entire collection of polygons, regardless of patch types. Most of the landscape level metrics can be interpreted broadly as landscape heterogeneity indices because they measure the overall landscape pattern. Landscape level metrics describe the pattern i.e. composition and configuration of the entire landscape (Leitao 2006).

In a nutshell, Patch level metrics represent the spatial character and context of individual patches. Class level metrics represent the amount and spatial distribution of a single patch type and may be interpreted as fragmentation indices (Leitao 2006). Landscape level metrics represent the spatial pattern of the entire landscape mosaic. Hence, it is important to interpret each metric in a manner appropriate to its level i.e. patch, class and landscape. In most applications, class level metrics are often the primary focus especially for planners where the extent and fragmentation of a particular class is the principle concern. Thus, in this research too, class level metrics have been computed.

3.2.2 Selection of Metrics, the indicators of urban form:

I. Patch Density (PD): PD equals the number of patches of the corresponding patch type divided by total landscape area (m2), multiplied by 10,000 and 100 (to convert to 100 hectares) (McGarigal 2002).

�� ……………………………………………………………….1

where, N= Total number of patches in the landscape

A= Total landscape area (m2).

Patch Density denotes the number of patches on a per unit area basis which facilitates comparisons among landscapes of varying size. Urban areas with greater patch density indicate greater spatial heterogeneity.


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II. Percentage of Landscape (PLAND): PLAND denotes the percentage of landscape comprising the corresponding patch type. It is the sum of the areas of all the patches divded by the total landscape area. The metric value approaches 0 when the corresponding patch type (class) becomes increasingly rare in the landscape. PLAND is 100 when the entire landscape comprises a single patch.

� �� ……………………………………………………………2

where, a ij = area (m2 ) of patch ij and A= total landscape area (m2).

The urban form with a greater PLAND value, therefore occupies a larger, homogeneous area, thereby being the more dominant one.

III. Proximity Index Distribution (PROX): Proximity index considers the size and proximity of all the patches whose edges are within a particular radius (McGarigal 2002). It denotes the spatial context of a patch in relation to its neighbour. It distinguishes sparse distribution of small patches from a complex cluster of larger patches (McGarigal 1995). In this case a mean proximity index or PROX_MN has been applied.

PROX tends to be 0 if a patch has no neighbours of the same patch type within the specified radius. PROX increases as the neighbourhood gets increasingly occupied with similar patches and also as they become less fragmented in distribution.

IV. Euclidean Nearest-Neighbour Distance (ENN_MN): ENN denotes the distance from a patch to the nearest neighbouring patch of the same type. ENN_MN is the mean distance (meters) over all patches of a class to the nearest neighbouring patch based on a shortest edge-to-edge distance, computed from cell centre to cell centre (Herold, Liu, and Clarke 2003).

ENN is reported as “N/A” or is undefined when a patch has no neighbours. This metric helps in identifying the spatial arrangement of urban form over space i.e. identifying spatial clustering and dispersion.

V. Fragmentation Index: Fractal dimension describes the complexity and fragmentation of a patch by a perimeter area proportion. Fractal dimension values range between 1 and 2. Low values are derived when a patch has a compact rectangular form with a relatively small perimeter relative to the area (Herold, Liu, and Clarke 2003). For more complex and fragmented patches, the perimeter increases, yielding a higher fractal dimension. The fractal dimension can be applied as a derived metric called area weighted mean patch fractal dimension (AWMPFD).

…………………………………………3

Where, m= No. of patch type (classes), n= No. of patches of a class, P(ij)= Perimeter of patch type ij, a(ij)= area of patch ij, A= Total landscape area (Herold, Goldstein, and Clarke 2003).

AWMPFD averages the fractal dimension of all patches by weighting larger land cover patches.

VI. Percentage of Like Adjacencies (PLADJ): PLADJ shows the frequency with which different pairs of patch types appear side-by-side on the map. Thus, it is almost like a measure of class specific contagion. Regardless of how much of the landscape is comprised by the focal class,


29

PLADJ is low if the patch type is maximally disaggregated or when there are no like adjacencies and maximum if the patch type is more contagious.

� ��

� �) ……………………………………………………………………4

where, gii = number of like adjacencies (joins) between pixels f patch type (class) i based on a double count method; and

gik= number of adjacencies between pixels of patch types i and k based on double count method.

This metric is a measure of spatial heterogeneity, a measure of aggregation of ‘dissimilar patches). An urban landscape consisting of large contiguous patches is more homogeneous. More heterogeneous an urbanized area, due to fragmentation, dispersed arrangement of similar patch types, i.e. more individual urban units, lower the PLADJ value.

VII. Aggregation Index (AI): AI is a measure of compactness. It takes into account only like adjacencies involving the focal class and not adjacencies with other patches.

�� ! "��# �………………………………………………………………………5

where, gii = number of like adjacencies (joins) between pixels of patch type (class) i based on a single count method; and

gik= maximum number of adjacencies between pixels of patch types i based on single count method.

Maximum aggregation is achieved when the urban forms consists of a single compact patch.

VIII. COHESION: Cohesion is proportional to the area weighted mean perimeter area ratio divided by the area weighted mean patch shape index (Herold, Liu, and Clarke 2003). In simple terms, patch cohesion measures the physical connectedness of the corresponding patch type (McGarigal 2002).

$%&'(�%� � ) * � +�� +��,��

- � * ./�# 0 ��…………………………………………..6

where, pij = perimeter of patch ij in terms of number of cell surfaces.

aij = area of patch ij in terms of number of cells.

A = total number of cells in the landscape (McGarigal 2002).

Cohesion increases as the patches which comprise a class become more aggregated and hence is more physically connected and the index approaces 0 when this connectivity decreases.

Landcapes consisting of patches of relatively large and contiguous classes show a high contagion index. The more heterogenous the urbanized area becomes, e.g. resulting from higher fragmentation or individual urban units, the lower the contagion index (Herold, Goldstein, and Clarke 2003).

-1


30

IX. Class Distribution Statistics: i. Mean patch area (AREA_MN): It is the sum across all patches of the corresponding

metric value divide by the total number of the patches belonging to the same type

1� � � 2��3� …………………………………………………………………………...7

ii. Coefficient of variation in patch area (AREA_CV): This depicts the relative variation

about mean or uniformity in patch area.

$4 � 5678�3 ��………………………………………………………………………8

The quantification of these metrics will be implemented in the public statistical package

FRAGSTATS (McGarigal 2002). FRAGSTATS has been adopted for several reasons: firstly, it contains very relevant metrics; secondly, it supports distribution statistics such as median, median, standard deviation; thirdly, the inputs and outputs are compatible with the GIS platform and due its extensive documentation(Leitao 2006).

3.2.3 Definition of spatial domain

A basic issue in the metrics calculation is the definition of the spatial domain. Metrics can characterize structures or features of an individual patch (McGarigal 2002) and can also describe properties of patch classes and some can summarize properties of the entire landscape (Herold, Couclelis, and Clarke 2005). In this research metrics would therefore be calculated as per landuse classes as mentioned above. However, the original classes of field survey have not been used for the metric calculation. This is also because the basic assumption of spatial metrics is that patches are homogeneous regions with a specific property.

Based on the field survey and the digitized building footprints a set of homogeneous categories have been developed for each land use type (Residential, Commercial, Institutional and mixed landuse) as well as height. These categories are a kind of proxy to the original landuse classification of Bungalows, Tenements, Shop building, etc. The basis for classification of each category is as follows:

i. Residential: The basis of classification is built-up density

9:;<=�:>�?@AB;=C � �D@E�FG�H:;<?;AI�GFF=>D;A= J �F0 FG�B=FD@CB�D@E�FG�A@;IKHF:DKFF?

The classes are as follows: � Low density single unit residential � Medium density residential � High density multi-unit residential

ii. Commercial: The bases for classification in this landuse type are built up density and

landuse. The classes are:

� Low density commercial � High density commercial � Malls and markets


31

iii. Mixed: The basis for classification is Intensity of Mix of each building having mixed landuse

�A=@AB;=C�FG�1;L � $FMM@DN;E<�:B@ O �AB=;=:=;FAE<�:B@PF=E<�ED@E

Where, Commercial use= No. of storeys having commercial use*footprint area Institutional use= No. of storeys having institutional use* footprint area Total area = (Residential storeys+footprint area) + Area of commercial use +Area of institutional use

The classes are as follows: � Low Intensity of Mix (Less than 40% mix) � High Intensity of Mix

iv. Institutional: One homogeneous class for metrics computation.

v. Height:

� Low rise: 1-3 storeys � Medium rise: 4-7 storeys � High rise: 8-16 storeys

Thus, spatial metrics have been used to generate indicators of urban form which would be applied in turn to develop a landuse classification. Metrics have been calculated based on the homogeneous ‘classes’ developed above. In this study, each neighbourhood is a ‘landscape’ while the categories developed with urban form attributes are the ‘classes’ for metrics calculation. As mentioned before, for this study class level metrics have been computed. Metrics have been calculated for the use in the category as well as heights. The focus is to draw a correlation between the form and use of each category of landuse and urban form. For example, if there is a relation between an area being covered by bungalows and the height prevailing in that area. These relationships would be rules for the prediction or rather deriving landuse classification for the neighbourhoods for which there is no information.

An urban form of an area is also influenced to a great extent by its socio economic landscape. The spatial form is shaped by its social form. Thus, this research also focuses on drawing a correlation between the indicators of urban form derived and certain socio-economic indicators. The socio economic factors considered are employment, vehicle ownership and income. For instance, the Compactness indicator can be correlated with vehicular ownership, LPI with Land value, population density with patch density, fragmentation and contagion index with employment, etc. Such a correlation analysis would depict the role that social form or socio-economic factors play to define urban form and landuse. Secondary data for this analysis has been made available through CEPT. These were initially available at the Electoral Block level and have been aggregated at the neighbourhood level for the purpose of this study.

This research thus expects to put forward an alternative approach to understanding of urban form and landuse classification and interactions. An important question that it tries to investigate is whether spatial structure can be a proxy for land use? The accuracy of the technique is a matter of


32

debate, and should be investigated if time permits. The challenge in the process lies in the fact that Spatial Metrics has been widely used in landscape ecology and not so extensively in urban studies. Landscape Metrics are based on a patch based representation of landscape, wherein patches are defined as homogeneous regions for a specific land use property. Patches are therefore maximally externally and minimally internally variable. (Herold, Goldstein, and Clarke 2003) Now, such a case is very difficult to designate in urban areas, in which heterogeneity is a basic character. In practice also, there are no standard set of metrics to be used in urban areas. Thus, for validation of both the approaches, correlation with the socio-economic factors on ground needs to be done. At the end, the research expects to produce not only a set of quantified indicators appropriately defining the city’s urban form and not merely visual interpretations, but also applying those indicators in order to describe the distinct character of the city’s form, capturing its variations and similarities. While in most such land use maps are used as a source of information for studying urban fabric, this research tries to explore how the outputs of urban landscape analysis can improve landuse classification of the urban areas it self- linking form, process and function.


33

4 Quantification of indicators of urban form using Spatial Metrics

Spatial metrics (indices) are numeric measurements that can quantify spatial pattern of urban landscape and has thus, become a trend in urban change studies (Ji et al. 2006). Spatial Metrics provides significant information about the composition and configuration of a landscape. The indicators of urban form in this research focuses on the spatial structure of an urban area, its form, character, arrangement and composition to a certain extent.

This phase of research comprises two stages: the first deals with the computation of the metrics and the second deals with the correlation analysis to explore the relationships and interactions that give urban its form. The description of the metrics and the classes has been provided before in chapter 3. Metrics have been calculated as per the classes designed, for each neighbourhood. 12 metrics have been calculated. The set of metrics have been divided into two sets:

1. Relation and Hypothesis: PLAND, AREA_MN and AI-The correlations between these metrics

with the same of height, over space are being used to establish the rules and thereby derive a scheme of landuse classification using spatial metrics.

2. Analysis Metrics: For the second part correlation among the metrics themselves have been computed to capture the character of urban form. They are:

Area_Coefficent of Variation, Fragmentation index, Proximity index, Probability of like adjacencies, Cohesion, Aggregation index and Euclidean Nearest Neighbour Distance.

COMPUTATION OF SPATIAL METRICS

4.1 Residential use Metrics:

Residential areas in Ahmedabad are experiencing rapid expansion, with gradual extension towards the satellite city of Gandhinagar. While the residences in the west consist of sprawling bungalows with gardens or high rise towers, the ones on the east are medium storied apartments with very little open space between them.

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Figure 4.1Graph showing mean value of metrics for residential areas- Density Class 1


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Figure 4.2Graph showing mean value of metrics for residential areas- Density Class 2

Figure 4.3 Graph showing mean value of metrics for residential areas- Density class 3

Density class PLAND

AREA_CV

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1 Mean 19.6821 114.997 1.2058 45.5912 82.9695 93.2475 85.2156 15.0896 N 57 57 57 57 57 57 57 572 Mean 5.4378 43.528 1.0854 11.0836 83.9152 90.2618 89.8120 38.2724 N 50 50 50 50 50 50 50 503 Mean 3.8330 18.919 1.4711 6.1352 87.7867 92.3722 94.2689 40.2678 N 27 27 27 27 27 27 27 27Total Mean 11.1736 68.971 1.2143 24.7651 84.2930 91.9571 88.7549 28.8131 N 134 134 134 134 134 134 134 134 Std.

Deviation 11.80583 59.2775 .95640 30.29369 4.11091 3.57154 5.35385 38.41098

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Table 1:Mean value of Residential metrics (Source:SPSS)


35

Table 1 provided above summarizes what is portrayed by the graphs i.e. the distribution of metrics in each residential class. The low density residential class occupies the maximum percentage of land as is shown by the PLAND value. Therefore, higher the built-up density, lower the PLAND. This may be due to the fact that low density residential units have a larger horizontal expanse while the higher density buildings expand vertically. Following that, variation in area is also higher for the low density residential class. However, the higher density buildings are more aggregated than its counterpart. The fragmentation index ranges from 1 to 1.5 in all cases denoting a generally compact, less complex rectangular shape of residential areas. Proximity index and the Euclidean nearest neighbour distance index a directly proportional to density. Within a radius of 80 meters, the proximity index is the highest and ENN_Mn the lowest for low density residential units. As per the definition of the indices this depicts that similar patches (of low density) are closely spaced i.e. at a very small distance to each other, unlike the high density settlements. The distance between high density residential units is higher. This relationship truly reflects the actual picture on the ground. In Ahmedabad there are continuous areas of sprawling bungalows, tenements and other low density residential units unlike the residential towers that occur in patches. Moreover, more or less similar PLADJ values show that patches of similar classes tend to cluster together over space further supporting the earlier argument.

The compactness and clustering of the residential units can be analysed through a correlation test among its metrics, v.i.z. PLADJ, AI, COHESION, PROX_MN, ENN_MN and FRAC_AM. Pearson’s correlation test was conducted in SPSS. Correlations

FRAC_AM

PROX_MN

ENN_MN PLADJ COHESION

AI

FRAC_AM

Pearson Correlation 1 .049 -.091 .180(*) -.059 -.030 Sig. (2-tailed) .571 .297 .037 .500 .727 N 134 134 134 134 134 134

PROX_MN

Pearson Correlation .049 1 -.303(**) -.068 .418(**) -.433(**) Sig. (2-tailed) .571 .000 .432 .000 .000 N 134 134 134 134 134 134

ENN_MN Pearson Correlation -.091 -.303(**) 1 .096 -.122 .098 Sig. (2-tailed) .297 .000 .272 .159 .258 N 134 134 134 134 134 134

PLADJ Pearson Correlation .180(*) -.068 .096 1 .518(**) .707(**) Sig. (2-tailed) .037 .432 .272 .000 .000 N 134 134 134 134 134 134

COHESION

Pearson Correlation -.059 .418(**) -.122 .518(**) 1 .216(*) Sig. (2-tailed) .500 .000 .159 .000 .012 N 134 134 134 134 134 134

AI

Pearson Correlation -.030 -.433(**) .098 .707(**) .216(*) 1 Sig. (2-tailed) .727 .000 .258 .000 .012 N 134 134 134 134 134 134

*Correlation significant at 0.05 level(2- tailed) Source: SPSS ** Correlation significant at 0.01 level(2- tailed)

Considering the significant relationships there lie a positive correlation between PLADJ, AI and COHESION. COHESION and Proximity index share a significant relation though the effect is

Table 2:Karl Pearson’s correlation value between Residential metrics


36

only medium (with r being around .4 which is falls under the category of medium correlation). However, all put together, this means that similar patches are clustered together over space. AI and FRAC_AM share a negative relationship implying that aggregation or compactness decreases as fragmentation increases. Also as the distance between the patches increase physical connectivity (COHESION) over space decreases, which is very obvious, off course.

The pattern of the metrics, the compactness and clustering is not homogeneous for the entire cityscape. It is possible to identify variations across the eastern and western parts of the city. There are subtle differences in the character of residential buildings between the eastern ‘older’ city and the west ‘newer’ city. An ‘independent T Test’ was conducted to test the variation in terms of a comparison of their mean of metric values.

T test would help in judging whether there is significant difference between the metrics derived for the neighbourhoods in the west and the east. For the purpose of this test the hoods in the western part have been grouped as 1 and those in the eastern part as 2. The hypothesis for the test is:

H0 = There is no significant difference between the mean values

H1 = There is significant difference between the patches

The output from the independent T test contains two tables.

The summary statistics provides the mean value of all the metrics for the two groups which is useful for apparent comparisons.

The main t statistic table provides information in two rows: one is labeled as Equal variance assumed and the other as Equal variances not assumed. Levene’s test results are provided along with T statistics. Levene’s test is similar to t-test in that it tests the hypothesis that the variances in the groups are equal (Field 2005). Therefore, if Levene’s test is significant at p�.05 then assumption of homogeneity of variances is violated. In such a case the t statistics of the second category would be considered (i.e. Equal variances not assumed).

In the present case, Levene’s test for PLAND values is significant (i.e. p<0.05). Therefore there lie differences in variances in the PLAND values of east and west, at 95% confidence level. Thus, the t statistics in the row labeled equal variances not assumed. The two tailed value of t in this case is .027, which is smaller than 0.05, therefore H1 is accepted. So it could be concluded that there is significant difference between the mean PLAND values of the western and the eastern residential units. The mean PLAND and Patch Density values are higher in the east. Variation in area is more or less same in both parts of the city. The mean AI values are also significantly different in the west and in the east. Aggregation Index is higher in the east which explains it’s clustering while the west is more dispersed. This is also further supported by a lower ENN_MN. Apart from these there are no significant differences between the other metric values of both parts of the city.

4.2 Commercial use Metrics

The major commercial hub in Ahmedabad is along C.G. Road which runs almost through the heart of the city. Although Ahmedabad is today experiencing a shifting of its CBD towards S.G. Road, C.G. Road still remains a dominant hub of all commercial activities. In spite of the gradual degeneration of the core, the old city still retains its quintessential character with traditional shopping areas, whole sale markets, dense, haphazard but highly integrated. The commercial areas in Ahmedabad seem to be highly influenced by the presence of major roads, e.g. Relief road in old city,


37

C.G. road and S.G. road in the west and Ashram road in the centre crossing the road right from south to the north of the city. The metric values of the commercial areas reflect very well the character and the influence of these specific commercial hubs in Ahmedabad, e.g. that of C.G. Road.

Figure 4.4 Graph showing mean value of metrics for commercial areas- Density Class 1

Figure 4.5Graph showing mean value of metrics for commercial areas- Density Class 2

Figure 4.6. Graph showing mean value of metrics for commercial areas- Density Class 3

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CLASS PLAND AREA_CV

FRAC_AM

PROX_MN

PLADJ COHESION

AI ENN_MN

1

Mean 7.1348 61.6202 1.1002 6.2712 80.8922 91.3063 86.5220 66.8572 N 50 50 50 50 50 50 50 50

2

Mean 4.4898 17.0976 1.0846 5.4538 87.5022 94.3229 94.3776 56.0070 N 23 23 23 23 23 23 23 23

3

Mean 2.5923 25.6350 1.0807 .0271 87.0578 93.4527 94.7415 88.1963 N 14 14 14 14 14 14 14 14

Total

Mean 5.7046 44.0592 1.0929 5.0503 83.6319 92.4492 89.9215 67.4226 N 87 87 87 87 87 87 87 87

Source: SPSS

The table provided above summarizes the scenarios portrayed by the graphs for the three commercial categories. PLAND is generally low for commercial areas in comparison to the residential areas. However, PLAND metric value almost gets halved as the density class increases. For example, PLAND (percentage of landscape) for class 3 commercial is about 2 which is almost half of class 2 PLAND. This again is justified as Ahmedabad has a majority of low density commercial units. Although there are high density commercial establishments and malls sprucing up but relatively there percentages are still less. The variation is also more and highly fluctuating for low density commercials. Apparently the variations are higher for the eastern city. Hoods ranging from 41 to 58 show high area variations for high density commercials. Those hoods correspond to areas along Ashram Road and Geeta Mandir Road. The whole stretch along these roads, especially Ashram Road is highly commercial. The high density establishments located along the road e.g. The Reserve Bank of India, Gurjari, Times of India building, etc, varies immensely in their areas. Again if one looks at the Figure 4.5 for class 3 commercials (representing Malls and Markets), one can notice two peaks in AREA_CV. These two areas coincide with the patches along Satellite road and C.G. Road, two other important roads in Ahmedabad. The second peak includes hoods 33-41 which includes areas in proximity to C.G. Road and Drive-in Road, the parts of Ahmedabad that house some of the biggest shopping centres, largely varying in scale which explains the variation in areas. The metric values, thus, aptly represent the actual picture on the ground.

PROX_MN and ENN_MN here too represent an inverse relation (search radius considered here is 100 meters). Higher the distance between similar patches, lower the proximity index. The distance between ENN_MN values for class 3 commercials is around 88 meters. This shows that malls and markets are located in isolation in Ahmedabad and not clustered together in one place. The proximity index is only 0.027, implying the patches belonging to this category are more disaggregated and fragmented. However, PROX_MN reaches higher values for the hoods 35-40 for high density commercial class. These hoods coincide with again, the areas along C.G. Road. As mentioned before this entire stretch is densely built with commercial establishments. Another peak appears between hood 4 and 9, which largely correspond to the areas along S.G and a major stretch along Ashram road. Road which is again dominated by a long strip of commercial establishments. Eastern part of the city or the old city shows high proximity index for its low density commercial units. This explains its very highly clustered commercial areas.

Table 3: Mean value of Commercial metrics


39

Talking about the variations in the character of commercial areas of the western and the eastern city, t-test was conducted between the metrics of the two groups, the hypothesis (H1) being that the means of metrics for both these areas are different. The t-test shows significant difference between the mean PLAND values and ENN_MN of the east and west (p<0.05). This has also been explained earlier. For metrics like PD, PROX_Mn and AI the hypothesis of homogeneity of variance is violated (Levene’s test p>0.05) but the difference between the means is not significant (p>0.05). This means that the basic density and aggregation of similar patches of commercial classes is more or less same all over the cityscape. Uniform PLADJ values also corroborate this argument. Commercial units belonging to a particular category generally tend to cluster together. The t test shows no significant difference for the metrics like COHESION or PLADJ between the west and the east. All the three graphs, too, clearly portray this situation. The lines for PLADJ, COHESION and AI move almost in a similar manner with very little variation across space.

The relation between these indicators of compactness and clustering for commercial areas has also been tested through Pearson’s correlation.

Correlations FRAC_M

NPROX_M

NENN_M

NPLADJ COHESIO

NAI

FRAC_MN Pearson Correlation 1 .219(*) -.162 -.169 .294(**) -.461(**) Sig. (2-tailed) .042 .135 .117 .006 .000 N 87 87 87 87 87 87

PROX_MN Pearson Correlation .219(*) 1 -.246(*) -.003 -.050 -.314(**) Sig. (2-tailed) .042 .022 .976 .642 .003 N 87 87 87 87 87 87

ENN_MN Pearson Correlation -.162 -.246(*) 1 -.015 -.051 .016 Sig. (2-tailed) .135 .022 .891 .639 ��4 N 87 87 87 87 87 87

PLADJ Pearson Correlation -.169 -.003 -.015 1 .674(**) .713(**) Sig. (2-tailed) .117 .976 .891 .000 .000 N 87 87 87 87 87 87

COHESION

Pearson Correlation .294(**) -.050 -.051 .674(**) 1 .441(**) Sig. (2-tailed) .006 .642 .639 .000 .000 N 87 87 87 87 87 87

AI Pearson Correlation -.461(**) -.314(**) .016 .713(**) .441(**) 1 Sig. (2-tailed) .000 .003 .884 .000 .000 N 87 87 87 87 87 87

* Correlation is significant at the 0.05 level (2-tailed). Source:SPSS **Correlation is significant at the 0.01 level (2-tailed)

R values between COHESION and PLADJ and AI are 0.674 and 0.441 respectively, at 99% confidence level. It shows a highly significant relation. A positive and significant correlation between COHESION and PLADJ implies that patches of similar classes are adjacent to one other and the physical connectivity is also high among them. This along with a positive correlation with AI shows that the commercial areas in each class are in general highly aggregated and have a compact form.

Therefore, the instances of high PLAND values or High PROX_Mn values, AI, etc, analysed earlier are not any particular trend for the western or older parts of the city. They are only

Table 4:Cross-correlation between Commercial metrics


40

characteristics specific to certain hoods or pockets of the commercial landscape of city (highly influenced by integration of major roads) which have actually given rise to the major commercial hubs or cores that Ahmedabad city has.

4.3 Mixed use metrics:

Mixed landuse is an inherent character of Indian cities, Ahmedabad being no exception. Mixed landuse involves integration of different uses which apart from strengthening the commercial base of a city also brings in other economic benefits. In this case mixed landuse implies having more than one landuse types in one building which is later on aggregated on an area level with the help of classes designed on the basis of intensity of mix.

Figure 4.7. Graph showing mean value of metrics for mixed use- Low Intensity of Mix (class 1)

Figure 4.8. Graph showing mean value of metrics for mixed use- Low Intensity of Mix (class 2)

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Source: SPSS

Table 5 above provides a summary of the information provided by the graphs for two scenarios: Landuse with low intensity of mix and high intensity of mix. Higher mean PLAND value of class 2 shows that majority of the area in the city has high intensity of mix. This is even reflected in the graphs (Fig 4.7 & 4.8). More over relatively higher PLAND values seem to occur in the eastern part or the old city. From the early times, Western Ahmedabad has had more of residential while eastern more of commercial and institutional. So the intensity of mix is higher on the eastern side than on the west. Though mixed landuse is present in the whole of Ahmedabad it is the most dominant and dense in the old city areas. The metric values also support this fact. Variation in area is also higher in the east e.g. from hood 55-66, in both the cases.

To explain this variation further an independent t-test has been conducted with the metrics for the two groups i.e. neighbourhoods in the west and those in the east. The hypothesis is same as before:

H1: There is significant difference between the means of the metrics computed for the two areas.

H0 : H1 is wrong and the mean value of the metrics is similar

The results of the t-test show that there lies significant difference in the mean PLAND values between the two groups. The significance value is .045, p<0.05, therefore H1 accepted. Mean patch density values are also higher on the east but according to the t-test the difference is not significant.

T statistics also shows highly significant difference in the mean values of PROX_MN and ENN_MN between the two groups. The significance values are 0.000 and 0.004 respectively, p<0.05, hence, H1 accepted. In real terms, mean value of PROX_MN metric in the east (21.15) is almost 4 times that in the west (4.6). The situation with ENN_Mn is exactly opposite. The mean value in the west is about 96 meters while that in the east is around 26. Thus, the areas with mixed land uses are more closely spaced in the east and rather dispersed in the west. This beautifully depicts the true picture on the ground. If one follows the first graph this relationship can be easily visualized. For instance, at hood 9, lines representing ENN_MN and PROX_MN intersect. The inverse relationship between these two metrics can be clearly seen. The relation between these metrics can be clearer if the R values are considered.

Even in the case of mixed land uses, the COHESION, PLADJ and AI values remain almost the same for the whole city. There is a significant positive relation among these metrics. This again indicates that similar land uses tend to cluster together i.e. buildings having residential and commercial

CLASS PLAND AREA_CV

FRAC_AM

PROX_MN

PLADJ COHESION

AI ENN_MN

1 Mean 10.9388 52.8684 1.0898 7.2699 82.0632 93.0490 88.0697

85.3567

N 33 33 33 33 33 33 33 33 2 Mean 16.4279 57.4125 1.0814 13.074 82.7388 93.9656 83.704

5 60.9111

N 19 19 19 19 19 19 19 19 Total Mean 12.9444 54.5288 1.0867 9.3910 82.3101 93.3839 86.474 76.4247

N 52 52 52 52 2 52 52 52 Std. Deviation

15.6776 30.9202 .09354 15.4687 3.39083 3.01222 12.632 82.030

Table 5: Mean value of Mixed use metrics


42

land uses together, are likely to be surrounded by similar buildings, in an area. Buildings having higher mixed intensities, say comprising commercial and institutional tend to concentrate in a similar surrounding and not in a residential dominating area.

Source:SPSS

4.4 Institutional Metrics

Institutional land uses include government offices, hospitals, educational institutes, religious buildings, etc. Figure 4.9 shows the distribution of urban form metrics computed to define institutional landuse.

Figure 4.9.Graph showing mean value of metrics for Institutional areas

FRAC_AM PROX_MN ENN_MN PLADJ COHESION

AI

FRAC_AM

Pearson Correlation

1 -.197 .029 -.111 .469(**) .809(**)

Sig. (2-tailed) .162 .839 .433 .000 .000 N 52 52 52 52 52 52

PROX_MN

Pearson Correlation

-.197 1 -.382(**) .476(**) .027 -.546(**)

Sig. (2-tailed) .162 .005 .000 .847 .000 N 52 52 52 52 52 52

ENN_MN

Pearson Correlation

.029 -.382(**) 1 -.164 .118 .161

Sig. (2-tailed) .839 .005 .246 .403 .255 N 52 52 52 52 52 52

PLADJ

Pearson Correlation

-.111 .476(**) -.164 1 .332(*) -.120

Sig. (2-tailed) .433 .000 .246 .016 .397 N 52 52 52 52 52 52

COHESION

Pearson Correlation

.469(**) .027 .118 .332(*) 1 .461(**)

Sig. (2-tailed) .000 .847 .403 .016 .001 N 52 52 52 52 52 52

AI

Pearson Correlation

.809(**) -.546(**) .161 -.120 �461(**) 1

Sig. (2-tailed) .000 .000 .255 .397 .001 N 52 52 52 52 52 52

** Correlation is significant at the 0.01 level (2-tailed).* Correlation is significant at the 0.05 level (2-tailed).

Correlations

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Table 6: Cross-correlation Mixed use metrics

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44

comparatively lesser for residential class. This argument can be supported by PLADJ, which represents clustering of similar patches. PLADJ is higher for residential. This is also logical because it is common to see one homogenous patch of low density bungalow housing rather than one of Malls and markets. This only means that there is less clustering in commercial and mixed classes in comparison to residential. However, even though the fragmentation is high, high COHESION values indicate that they are well aggregated and therefore are compact to a certain extent.

However, in a nut shell, in all the cases metrics like AI, COHESION and PLADJ have depicted that similar uses tend to cluster together, but the distance between residential units is the least. This also reflects on the character of the city in general, it being compact and clustered to a certain extent, even though the western part is experiencing gradual sprawl.

4.6 Height Metrics:

The metrics calculated for height are more related to its area and aggregation. Since relation with height will be dealt in much detail in the later chapters, only a short description of the distribution of the metrics has been given here.

0

50

100

150

200

250

300

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55

AREA_MN

AI

PLAND

AREA_CV

0

50

100

150

200

250

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52

AREA_MN

AI

PLAND

AREA_CV

Figure 4.11. Graph showing mean value of metrics for low rise class

Figure 4.12. Graph showing mean value of metrics for medium rise class

Neighbourhoods

Neighbourhoods


45

Table 8 summarizes the picture portrayed by the graphs. Low rise class seems to occupy the maximum percentage of urban landscape as shown by the PLAND values. This is also true because Ahmedabad is not yet a vertical city, majority of the area is low rise or medium rise. There does exist high rise towers but there share is less in the coverage. PLAND values also seem to be higher in the eastern side of the city. T-test conducted between the height metrics of the hoods in the west and those in the east show that there is significant difference between the PLAND values in the two parts of the city. Same is the case with AI and AREA_MN. However, AI index seems to be higher for the high rise class which implies that the buildings that increase vertically tend to better aggregated than the low rise ones, which tend to be more dispersed. Therefore, the low rise class may have a larger share of area but the high rise class is more compact.

The main purpose of these form metrics is to be correlated eventually with ‘use-metrics’ to establish rules for defining the character of urban form of the city and thereby landuse.

HT_CODE_HO PLAND AREA_MN AREA_CV AI 1 Mean 16.871 .1842 122.1307 86.5879 Std. Deviation 11.58 .12004 56.47640 3.896072 Mean 11.55 .1993 92.1515 87.5087 Std. Deviation 10.81 .27236 44.44253 3.515953 Mean 4.407 .1445 45.6398 89.7012 Std. Deviation 3.794 .05575 29.63585 4.08400Total Mean 11.573 .1786 90.5523 87.7675 Std. Deviation 10.902 .18027 55.07212 3.99757

0

50

100

150

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

AREA_MN

AI

PLAND

AREA_CV

Figure 4.13. Graph showing mean value of metrics for high rise class

Neighbourhoods

Source: SPSS Table 8: Mean value of Height metrics


46

5 Spatial Regression and Correlation analysis: Form and Use

The aim of this stage of research is correlate urban form (height) with its use (residential, commercial, etc). Regression and correlation analysis allows modeling and exploring spatial relationships and in the process explaining the factors behind observed spatial patterns. Correlation analysis tests the strength of the relationship between two variables. Regression analysis on the other hand attempts to examine the degree to which one or more variables induce positive or negative change in another variable.

The rules derived through this stage of analysis would be used to establish definitions of various land use categories. So the process is from Form to Landuse i.e. from low rise, low density residential unit to a bungalow. So the urban form indicators are to be used for a land use classification. There is a set of hypotheses framed for this purpose. If those hypotheses are proved during the course of this analysis, then it would be justified to say that urban form indicators could be land use classifiers.

5.1 Hypothesis:

While framing the hypotheses not only the definition or characteristics of each class in terms of metrics has been considered, but also its probable coincidence with the original land use categories has also been considered. The set of hypothesis adopted against which the correlation analysis would be evaluated are:

1. Residential:

� Low density single unit residential: Low rise, high PLAND, high AREA_MN, low AI, varying income levels ( high for bungalows but low for tenements)

-----------------Bungalow, Tenements and low rise apartments

� Medium density residential unit: Medium rise, medium PLAND, medium to high AI, lower AREA_MN, low income level

-----------------Medium rise apartment buildings

� High density multi unit residential: High rise, Low PLAND, high AI, lower AREA_MN, High income levels

----------------High rise apartments and towers 2. Commercial:

� Low density commercial units: Low rise, high PLAND, Low AI, medium job availability -----------------shops, hotels and small offices

� High density commercial establishments: High rise, low PLAND, high AI, high job

-----------------shop and office together, large office buildings

� Malls and large Markets: Medium rise, high PLAND, high AREA_MN, highest AI, low job 3. Mixed landuse:

� Low intensity of mix: high height, lower PLAND, high AI, lower AREA_MN, higher income levels and lower jobs --------------- More residential


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� High intensity of mix: lower height, higher PLAND, Higher AREA_MN, lower AI, lower income levels and more jobs -------------- More commercial and institutional 4. Institutional:

� Low rise institutional class: Low to medium rise, medium to high PLAND, low AI-------------------mainly Government offices, educational institutes

� High rise institutional class: High height, low PLAND and high AI------------------Banks, Administrative buildings, etc.

These hypotheses mention certain socio-economic factors like income, jobs, etc. Correlation with socio-economic factors will be dealt with in the next chapter.

Like any other dynamic process in geography, urban development is a “continuous diffusion process” (Liu 2009) in space and time. Therefore there cannot be any crisp theory about how to define form with the help of spatial metrics, the very reason behind that being heterogeneity of urban areas. Therefore, to achieve an optimum level of precision, an area will be assigned a particular form metric based on its percentage of likelihood to be of that class. Following that, to improve the classification and to develop rules considering height and density equation, for each category three factors have been taken into account: Correlation value, mean value of metrics in each class and the range of the metric value. Correlation values could be an important aid in determining the type of development in that area, low rise or high rise. It explains how much variability in the particular use can be explained by form (height). Spatial correlation has been done using Geographically Weighted Regression function in Spatial Statistics tool of ArcGIS 9.3.

5.2 Geographically weighted regression

Geographically weighted regression is one of several spatial statistical techniques. It provides a local model of the variable that is being predicted by fitting a regression to every feature in the dataset. In GWR an observation is weighted in accordance with its proximity to location i so that the weighting of observation varies with i (Fotheringham 2005). Each data point is weighed by its distance from the regression point. Hence, data points that are closer to the regression point are weighted more heavily. In this way the regression model is calibrated by moving the regression model all across the region.

Apart from the attribute table of the feature class, GWR also produces a supplementary table showing the overall results. For example, regression analysis has been done between residential PLAND and height PLAND (according to three height categories). To make a judgment regarding the relation between the two variables the fields of information that are important are Local R2, R2 and residuals.

� Residual squares: it is the sum of the squared residuals in the model. Residual is the difference between an observed Y value and its estimated value produced by GWR. The smaller the measure the closer the model fits to the observed data.

� R2: R2 is a measure of goodness of fit. Its value varies from -1 to +1. It is the amount of variation in the dependent variable explained by the regression model.

� Local R2: These values range between 0-1 and indicate how well the local regression model fits the observed y values. Very low values indicate poor performance of the local model.

Variables for regression: Dependent: Use Metrics (residential, commercial, mixed and institutional). Independent Variables: Height Metrics (H PLAND, H-AI, H-AREA_MN)


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Figure 5.1. PLAND metrics of residential classes Figure 5.2. PLAND metrics of height classes

5.2.1 Residential and Height

Figure 5.3. GWR between residential and height metrics

These figures are an illustration of The Geographically Weighted Regression method. In this case regression has been shown between Residential PLAND metric and Low height PLAND metric. In Figure the areas with blue colour show the highest local R2 value while the lowest correlations are shown by red. The interpretations have been given in the following sections.


49

Source: Spatial Staistics, Arc GIS 9.3

The table above shows the ‘local R2’ provided by GWR. It is necessary to look at the correlation values over space i.e. in different areas, which is the main idea behind doing spatial correlation. This would also help in identifying areas of high rise, high density development and vice versa. For this purpose groups have been grouped according to the major road in its proximity (as was done for the field survey plan) for the ease of analysis. Correlation has been done individual metrics with each height class.

PLAND:

The first inference that can be drawn from the overall R values is that residential PLAND metric and height metric are positively related. Test in SPSS also proves that the correlation is significant (p<0.05). GWR between residential PLAND and PLAND value of low rise shows maximum spatial correlation in the hoods that are around S.G.road, Judges Bungalow area and Satellite road (all to the extreme west) and southern parts of C.G.road and Ashram road. Lowest correlation values are found in the east around Kankaria. Similarly for GWR with medium rise PLAND value, highest correlation values are observed in the hoods around Ashram road, C.G.road and Relief road and the lowest in the extreme western part. Correlation values for GWR with high rise PLAND are the highest in the hoods that are around S.G.road and Satellite road and the lowest again around Relief road and Kankaria. The correlation values for relief road and Kankaria still seem valid as these are the areas of low rise. But based on this it would be wrong to stamp the entire area in the west as low rise or only high rise for the sole reason that there are overlaps and, as mentioned before, it is not possible to have crisp boundaries in an urban area. Therefore, to further support this, the correlations in individual patches have also been analysed for each category.

So if this data is analysed in the horizontal direction, most of the areas show higher correlation with low rise PLAND values (all being positive). This could be interpreted as that the city in general has a low rise low density development with high rise buildings located in certain areas (and is not a general trend).

Aggregation Index (AI): Residential AI and Height AI have a significant correlation, as per SPSS. GWR between

residential AI values and AI values of low rise shows highest correlation in the hoods in and around S.G.-Satellite-Judges bungalow patch and Kankaria. Conversely, GWR between residential AI and low rise as well as medium rise AI show high correlation value for the hoods around Relief road and Kankaria. Even if the individual R2 values are analysed almost all show high correlation with high rise, including the Relief road patch. However, if this is compared to the ground truth AI does not give the

Groups R2 (PLAND) R2 (AI) R2 (AREA_MN) low Medium High low Medium High low Medium High

S.G. road-hoods 17-19 0.5-1 13-17 0.6-0.8 0.25-0.3 1.5-2.7 1-6 0.6-1.3 Variable Judges Bungalow- 15-19 0.3-1 11-14 0.5-0.6 0.25-0.4 0.7-1.5 3-6 0.6-1.3 14-30 Satellite road- hoods 15-19 0.3-1 13-17 0.5-0.8 0.3-0.4 1.5-5.3 3-11 0.6-1.3 8-22 Drive-in road -hoods 13-17 0.3-1 9-14 0.3-0.5 0.25-0.4 0.7-1.5 1-6 0-1.3 0-8 C.G. road- hoods 8-14 1-3 4-8 0.01-0.3 0.4-1.3 2.6-5.3 Variable 0-3 Variable Ashram road -hoods 8-13 2-3 4-12 0.01-0.1 0.4-1.3 5.3-8 3-11 0-1 0-3 Relief road- hoods 5-13 1-2 1-5 0.01-0.3 1.2-3 8-11 0-3 3-10 0-3 Kankaria road- hoods 5-13 0.3-1 1-5 0.3-0.6 3-4 5.3-8 6-11 0.6-6 0-3

Table 9: Spatial Regression and correlation results (GWR) between R PLAND and H PLAND


50

true picture for Relief road-hoods. Relief road does not have many high rise buildings except for a few at the beginning of the area. It is predominantly a medium rise area.

AREA_MN:

GWR between residential AREA_MN and AREA_MN values for low rise category show higher R2 value in the hoods around Kankaria, central part of C.G. road and certain areas of C.G. road. Similarly, GWR between residential AREA_MN and high rise AREA_MN show higher values in the southern parts of C.G. road area and Satellite-Judges bungalow patch. In the table provided above, three cells have been marked as variable. That is because the R2 values in these hoods vary i.e. there is no single value applicable to the whole area. One such area is C.G.road. The anomalies between its south and north have been already mentioned. The areas to the north of C.G. road have a correlation of about 6-19% with height metrics and only 0-8% with high rise. This proves that this area is primarily low rise development. The southern areas of C.G.road have a correlation of 21-30% with high rise metrics while only 0-6% with low rise. This in turn proves that the south is an area of high rise high density development. In terms of the individual R2, Ashram road-hoods and Kankaria have highest correlation with low rise AREA_MN while relief road with medium rise. AREA_MN gives a relatively clearer picture than AI.

Correlation tests were also conducted in SPSS with these three metrics e.g. residential PLAND with PLAND value of height (considering height factor as a whole and not its sub categories of low rise and high rise) to check which out of them explain the maximum variability. R2 value for PLAND is 16.3%, AI = 5.1% and AREA_MN = 10%. Therefore, PLAND and AREA_MN explain the maximum variability. So, these two could be given more importance because, as seen before, AI is not really giving a true picture.

So if all the correlation results are combined, the relationships that come out are: � Neighbourhoods around S.G. road Judges Bungalow Satellite road = mainly a

combination of high rise and low rise, low rise being more predominant. There patches with high rise and high residential PLAND and AI probably are more common along the major roads and mainly along S.G. Road.

� Neighbourhoods around Drive-in road: In this case, the correlation between residential PLAND and Low height is much more than the correlation between residential AREA_MN and high height. This could be interpreted as the area having mainly a low rise residential development but there are instances of high rise establishments in certain areas.

� Neighbourhoods around C.G. road and Ashram road: Low to medium rise and low to medium PLAND. If AREA_MN is followed, the residential areas to the south of C.G.road have more of high rise development (R2= 21-30%) while the rest of it medium to low development.

� Neighbourhoods in the vicinity of Relief road: Low to medium rise and low to medium PLAND, considering both PLAND and AI correlations.

� Neighbourhoods around Kankaria: low rise residential development.

The second factor comprises the mean of metric values for the three categories of analysis in

height and use.


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Density class

Mean Height class Mean PLAND AI AREA_MN PLAND AI AREA_MN

Low 18.6 84.89 0.18 Low 19.11 87.4 0.21 Medium 5.7 88.21 0.12 Medium 12.22 87.02 0.21 High 4.4 94.087 0.3 High 4.64 88.85 0.05

Source: SPSS

The inferences that can be drawn from this table are:

i. PLAND:

� Low density has higher residential PLAND values on an average; High to medium

density have lower PLAND values

� Low height areas have high PLAND; High height areas have low PLAND values

ii. AI:

� Low density have low residential AI on an average; Medium to High density have a

higher AI

� Low rise areas have low AI; high rise areas have high AI.

iii. AREA_MN:

� High density has higher AREA_MN; Low density and medium density have lower

AREA_MN

� High rise areas correspond to low AREA_MN; Low and medium rise areas correspond

to higher AREA_MN

The range for ‘high’ and ‘low’ metric value has been provided in the appendix. Based on the inferences derived from above certain equations could be derived which could

be used to define the different land use categories. It is actually also possible to gauge the original landuse category i.e. whether it is a bungalow or a tenement, thereby even analyzing whether the hypotheses set before are correct. However, as mentioned before, it is difficult to have one universal equation for the entire city. So what has been done is that different neighbourhoods have been scanned to see if they conform to the inferences or conditions derived so far. The equation that majority of them conformed to has been established as the rule. Based on this the rules established to define the landuse categories are: (Letter H before a metrics denotes that it belongs to height factor and R for residential).

1. Low height, High H PLAND, low H-AI, high H-AREA_MN Medium to high R PLAND, very low R-AI, low to medium R-AREA_MN = Low density residential class--------------------------------------------Bungalows

2. Medium rise, low H PLAND, medium to high H-AI, high H-AREA_MN High R PLAND, Low R-AI, medium R-AREA_MN = Low to medium density residential class-------------------Medium rise apartment buildings

3. Low to medium rise, high H PLAND, low H-AI (east- very High AI), medium to high H-AREA_MN

Table 10: Comparison of means between Residential and Height correlation metrics as per analysis classes


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The figure above represents the equations developed above, for a particular part of the study area. The white box represents a ‘Low density residential class’ while the orange box denotes the ‘High residential density class’. If at the same time the building categories are referred to, it will be seen that do spatially coincide with bungalows and high rise apartments respectively.

Figure 5.4. Representation of the equation between residential form and use

Figure 5.5. Building categories of residential landuse


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Medium to high R PLAND, low R-AI (east-very high AI), medium R-AREA_MN =Low residential density class--------------------------------------------Tenements

4. High rise, low to medium H PLAND (mainly low), high H-AI, low H-AREA_MN Low R PLAND, High R-AI, medium to low R-AREA_MN = High to medium density residential class---------------------High rise apartment buildings

5. Low rise, low H PLAND, high H-AI, Very low R PLAND, high R-AI =Medium to high density class …………………………………… Slums (Character of slums is highly variable. Slums to the east have higher PLAND)

5.2.2 Commercial and Height:

The table above shows the local R2 values provided by GWR between commercial and height metric. The cells marked variable imply that there is no single correlation value for the whole area and considering that is important to understand its character.

PLAND:

The PLAND metric of height can explain 4.35 of the variability in commercial PLAND, according correlation test in SPSS (relation significant as p<0.05). If the overall results of the GWR are interpreted, eastern part of the city shows very high correlation with low rise PLAND values. Hoods around Relief road and Kankaria show a R2 value of almost about 6%. Hoods along S.G.road, hoods to the north of C.G. road (2-5%) and hoods along Ashram road show high correlation with medium height values. The remaining hoods along C.G.road show higher correlation (1.4-2.6%) with low rise PLAND. ALL the hoods in the Satellite to Drive-in patch show high correlation with high rise PLAND values. GWR between commercial PLAND and height metric show the highest correlation values for the S.G. road-hoods.

AI:

The local R2 provided by GWR between commercial AI and low height AI (5.5%) decreases from west to east. Hoods along S.G. road, Satellite, etc., show the highest correlation (around 5-6%) with low rise AI values. S.G. road hoods also show a high correlation with high rise metrics. The


S.G. road-hoods 0-0.4 1-2 1.3-2 5.6-5.7 All range between 3.82-3.83

5-7 1.9-13 2-15 1-2 Judges Bungalow-hoods 0-0.4 0-0.2 0.4-1.3 5-5.6 1.4-5 2-13 14-32 0.4-1.1 Satellite road- hoods 0-0.4 0.2-2 0-0.4 5-5.6 0-1.4 10-13 14-32 0-0.4 Drive-in road -hoods 0-0.4 0-0.2 0.4-1.3 5-5.6 0.4-2.9 2-10 2-15 0-1.1 C.G. road- hoods Variable Variable 0-0.1 4.8-5 0.4-3 0-0.2 0-2 1-3 Ashram road -hoods 0-4 2-5 0-0.7 4.6-4.8 0.1-3 0-0.2 0-2 2-5 Relief road- hoods 4.9-6.5 0-0.2 0-0.7 4-4.6 0-0.14 0-0.6 Variable Variable Kankaria road- hoods 2.7-6.5 0.2-1 0-0.1 4-4.6 0-0.4 0.6-2 0-8.6 0-0.4

Table 11:Spatial Regression and correlation results (GWR) between Commercial and Height metrics


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correlation values for both are almost the same. In this case, to come to a conclusion the overall local R2 values could be considered. The local R2 for high rise AI is 10.2% while that of low rise AI is 5.5%. Following this logic, the S.G. road hoods could be marked as having high rise commercial establishments as a majority with few low rise commercial units. Neighbourhoods near Satellite road, Judges Bungalow and Drive-in road show high correlation with low rise commercial units. In Relief road, the hoods located at the beginning of the road correspond to higher R2 values while the rest lower.

AREA_MN:

GWR between commercial AREA_MN and height metrics produce the highest local R2 for the medium rise class (27.3%). local R2 for the low rise class is the highest for the hoods in the Judges Bungalow and Satellite-hoods and decreases towards the hoods located in the eastern city. local R2 for medium rise class is found majorly in the satellite area and that of high rise class is found in the hoods near Ashram road and C.G.road. However, SPSS shows that the relation between commercial AREA_MN and that of height is not significant.

PLAND and AI of both the factors correlate at 4.3 and 5.8% (through SPSS). The value for AREA_MN being non-significant, the former two metrics have been given more importance in defining the commercial characteristics if the hoods. Combining the above correlations, the following relationships can be derived in order to portray the characteristics of these hoods:

� Neighbourhoods around S.G. road: Mainly high rise commercial development with a few low rise commercial units.

� Neighbourhoods near Judges Bungalow, Satellite and Drive-in road: Low to medium rise commercial development.

� Neighbourhoods near C.G.road and Ashram road: Medium to low rise commercial development, with instances of high commercial establishments in some areas.

� Neighbourhoods near Relief road: Low to medium rise commercial development, with the higher value being for the initial ones.

� Neighbourhood near Kankaria: Low rise commercial development. The second factor comprises the mean of metric values for the three categories of analysis in

height and use.


i. PLAND:

� Low density class has higher commercial PLAND values on an average; High density

class has medium PLAND values while the class containing Malls and markets have

lower PLAND values.

Density class


Low 7.09 86.37 0.094 Low 14.89 86.37 0.17 Medium 4.48 93.94 0.23 Medium 13.22 87.34 0.21 High 2.46 94.45 0.27 High 5.46 87.12 0.12 Table 12: Comparison of means between Commercial and Height metrics as per analysis classes (Source:SPSS)


55

� Low and medium height areas have high PLAND; High height areas have low PLAND

values

ii. AI:

� Low density have low commercial AI on an average; Malls and markets and High

density class have a higher AI

� Low rise class has lower AI; Medium and High rise class have higher AI.

iii. AREA_MN:

� Malls and markets and High rise class have a higher AREA_MN; Low density has

lower AREA_MN

� Medium rise areas correspond to higher AREA_MN; Low rise class corresponds to

medium AREA_MN while the class of high height has the lowest area mean.

The range for ‘high’ and ‘low’ metric values has been provided in the appendix B.

Based on the above relationships inferred upon, the following equations have been derived based on the same logic by which residential land use was done (Letter H before a metrics denotes that it belongs to height factor and C for commercial):

1. Low height, High H PLAND, low H-AI, low H-AREA_MN Medium to high C PLAND, low C-AI, high C-AREA_MN = Low density commercial class--------------------------low rise low density commercial units: shops and offices in many cases. This equation holds good for both the east as well as west. However, in cases where shops and offices are combined, the height metric tends to be on the higher side.

2. Low rise, high H PLAND, low H-AI, Low to medium (east) C PLAND, high C-AI = Malls and Markets

3. High rise, low H PLAND, high H-AI, low H-AREA_MN Low to medium C PLAND, high C-AI, and high C-AREA_MN =High density commercial class------------------------High rise high density commercial establishments, comprises hotels, offices, shop buildings, etc. However, in the same category, certain buildings containing shop and offices have a higher height metric.

4. High rise, low H PLAND, high H-AI, Medium to low C PLAND, low C-AI =Low density commercial class--------------------------High rise low density commercial units largely including office buildings.

Now, if the hypotheses set at the beginning are judged now, most of them stand to be proved if these equations are followed, though AREA_MN does not prove to be a very appropriate indicator. However, these equations themselves do not always follow the inferences drawn from the mean values earlier. This may be a problem with the mean values itself, as mean is highly influenced by extreme values. Further, the commercial classes are even fuzzier than the residential classes. The basic


56

character of density wise classes remains almost the same all over but wide variations come up when referring to the actual landuse classes e.g. shop, office, etc. However, this also seems logical because the character of bungalows all over can be expected to be the same, but the characteristics of shops would vary (especially between the dense old city and the sprawling western city).

5.2.3 Mixed use and Height

The table given above summarizes the correlation results derived through GWR between mixed metrics and height metrics, for individual areas. The overall R2 values have been provided in the appendix. The same metrics were also tested in SPSS to check the significance. There was no significant correlation mixed-AI and height AI (p>0.5).

PLAND:

Mixed land use PLAND and height are correlated with a R2 of 8.3% (SPSS). To analyse the spatial correlation the results of GWR with the sub categories of height are important. GWR results with low height PLAND shows highest spatial correlation on the eastern side in the hoods near Relief road. That is the general feature of the hoods on the eastern side of the city as Kankaria-hoods too have a fairly high correlation with low rise PLAND. However, the hoods around relief road also have high correlation with high rise PLAND values. This means relief road also has considerable high rise mixed units, probably more in the hoods located at the beginning of Relief road. The final conclusion, therefore, regarding the form of this area can be derived after comparing the results of all the three metrics. The mixed land use classes of the hoods in the Judges Bungalow area correlate most with low rise PLAND values. It’s similar with the hoods in Satellite and Drive-in patch. Hoods along S.G. road, though, show high spatial correlation with medium rise PLAND.

AI

Local R2 increases from east to west, the highest correlation values found over hoods in Kankaria and relief road. The local R2 shows high correlation with low rise in almost all the hoods. However, as mentioned before the relationship is not significant therefore, AI could be given led importance while defining mixed urban form.

AREA_MN:

The local R2 values provided by GWR between Mixed AREA_MN and height metrics is almost around 45%, with very little variation between the low and the high rise class. Correlation


S.G. road-hoods 0.3- 1-3 0-0.3 1.6-1.8 0.4-0.5 0.3-0.4 0-0.6 0-1.3 3.3-6.7 Judges Bungalow-hoods 1-1.7 0-1 0-1 1.6-2 0.5-0.6 0.41-0.43 0-1.7 0-0.4 2-5.5 Satellite road- hoods 0.3- 0-0.4 0-0.3 1.8-2.3 0.5-0.6 0.38-0.4 0.2-1.7 0.4-1.3 1-2 Drive-in road -hoods 1.7-4 0-0.4 0.3-2 1.6-2 0.5-0.6 0.35-0.37 0.6-1.7 0.4-1.3 3-5.5 C.G. road- hoods 0-0.3 0-0.4 1-2 2.3-2.8 0.6-1.4 0.41-0.45 0-0.6 0.4-1.3 0-2 Ashram road -hoods 0.4-1 0.2-1 1-3 2-2.8 0.6-1.2 0.38-0.43 1.7-5 0-1.3 0-1 Relief road- hoods 4-8 1-3 3-4.5 2.7-3.4 1.2-1.7 0.44-0.45 0-1.7 0.4-5.5 0-3 Kankaria road- hoods 1-4 0-0.1 1-3 2.7-3.4 1.7-2 0.46-0.5 5-7 0-1.3 0-1

Table 13: Spatial Regression and correlation results (GWR) between Mixed use and Height metrics


57

results with low rise AREA_MN show higher spatial correlation for the hoods near Kankaria and Ashram Road. Conversely, R2 values for high rise metrics are higher for the mixed use classes in the hoods along S.G.road, along Drive-in road and in the Judges Bungalow area. Mixed uses in these areas, thus, are probably of high rise.

Therefore combining the results from all the individual correlations, the character of urban form for these particular areas can be defined as:

� Neighbourhoods around S.G. road: Medium to high mixed use � Neighbourhoods near Judges Bungalow, Satellite and Drive-in road: high rise

commercial development. � Neighbourhoods near C.G.road: Medium to high rise mixed development � Ashram road: Mainly high rise mixed development, with low rise establishments in

some areas. � Neighbourhoods near Relief road: Low to medium rise mixed development, with the

higher value being for the initial ones. � Neighbourhood near Kankaria: Low rise mixed development. The second factor comprises the mean of metric values for the three categories of analysis in

height and use.


i. PLAND:

� Low intensity of mix has lower PLAND; High intensity of mix has higher PLAND

� Low height areas have high PLAND; High height areas have low PLAND values

ii. AI:

� Low intensity of mix has high AI; High intensity of mix has low AI

� Low rise class has lower AI; High rise class has higher AI

iii. AREA_MN:

� Category of high intensity of mix has a slightly higher AREA_MN

� Low and medium rise areas correspond to medium AREA_MN;

The range for ‘high’ and ‘low’ metric value has been provided in the appendix B. Based on the above relationships inferred upon, the following equations have been derived

in based on the same logic by which residential landuse was done (Letter H before a metrics denotes that it belongs to height factor and M for mixed):

1. Low height, High H PLAND (or medium height paired with low H PLAND), low H-AI, low H-AREA_MN

Density class


Low 8.83 87.78 0.1 Low 18.71 87.23 0.21 High 13.53 85.84 0.14 Medium 13.50 87.15 0.22 High 4.15 89.24 0.14

Table 14: Comparison of means between Mixed and Height correlation metrics as per analysis classes (Source:SPSS)


58

High M PLAND, low M-AI, high M-AREA_MN = High intensity of mix----------------------------------------- low rise high intensity of mix largely comprising residential+commercial wherein proportion of commercial is likely to be more. This is the most dominant scene all over. However, in the east it is mostly

2. High rise, low H PLAND, high H-AI, low H-AREA_MN

Low M PLAND, high M-AI, high M-AREA_MN =Low intensity of mix------------------------High rise units with low intensity of mix, comprises residential and commercial use but wherein the proportion of residential is more. However, in the same category, certain hoods along S.G.road have high M PLAND and that comprises commercial and institutional use. This may be because of the general character of S.G. road, which being commercial tends to attract less of residential in mixed uses, especially along the road.

3. High rise, low H PLAND, high H-AI, Low M PLAND, low M-AI =High intensity of mix--------------------------Largely comprises institutional with commercial or institutional with residential. A general trend that can be noticed in the data is that where ever institutional is in the mix, intensity of mix is higher.

4. High rise, high H PLAND, high H-AI, Low M PLAND, low M-AI =low intensity of mix--------------------------comprises residential, commercial and institutional.

The equations developed above are based on the relationship that majority of the different classes follow. Yet it should be mentioned here, the relationships are not as clear or crisp as that of residential. There are wide variations. Variation also lies between the east and the west. For example, most of the mixed classes along relief road follow the 1st equation but there are instances, like those at the beginning of relief road, where medium height is coupled with high H PLAND and M PLAND, resulting in low intensity of mix. And even that combination corresponds to a residential-commercial mix. So this is one of the limitations of spatial metrics that it fails to capture this heterogeneity of urban areas, which is at the detailed-building level. As far as deciphering low and high intensity of mix is concerned, that can be done using these metrics (because class heterogeneity can be captured by spatial metrics), but confusions arise when one tries to extract building level information, where mix exists among different floors.

5.2.4 Institutional and Height:

The table below shows the local R2 values derived through GWR between institutional metrics and height metrics. Correlations between the metrics were also done in SPSS to carry to test its significance. Both PLAND and AI have significant correlations but correlation with AREA_MN was not found to be significant (p>0.05).


59

PLAND:

Correlation between Institutional PLAND and height metrics is higher than that of other metrics. Low rise PLAND values show highest correlation values for the hoods in Kankaria in the east and those near Drive-in road and Judges Bungalow in the west. Hoods along S.G road shows widely varying correlation values but to the north the values are fairly high. Medium rise PLAND values have a narrow range and thus, show very little variations. Relief road-hoods show a higher correlation with high rise R2. In the old city, this area houses large government institutions and other administrative buildings. So correlation with high rise values can be justified. High rise values also show high spatial correlation in the hoods around Ashram road and C.G. road.

AI:

Hoods around Drive-in road, Relief road and Kankaria show high correlation with low rise AI. Spatial correlation values for both low rise and high rise are highest over the hoods in Kankaria. Correlation with medium rise AI is also fairly high in the hoods along S.G. road and those in the Satellite area. C.G. road and Ashram road show high correlation with high rise AI values, though C.G. shows some variation with lower values in the hoods to the south of it.

AREA_MN:

If correlation values between institutional metrics and height metrics are followed all the hoods correspond to high rise values. However, the overall correlation value produced by SPSS is only about 0.04% and that too non-significant. Therefore, institutional form cannot be appropriately defined by this metric, in this case.

The second factor comprises the mean of metric values for the three categories of analysis in height and use.



S.G. road-hoods 2.2-12.7 All ranging between 2.2217 and 2.224

0-3.4 1.6-4.6 3.1-10.6 0-0.4 All ranging between 0.1093 and 0.1108

0-0.77 3.5-5 Judges Bungalow-hoods 5.6-12.7 0-9.3 1.6-4.6 3.1-10.6 0-0.4 0.77-1.7 3-6.7 Satellite road- hoods 2.2-5.6 0-0.3 2.5-4.6 3.2-4.4 0-0.4 0-1.1 3-6.7 Drive-in road -hoods 5.6-12.7 0.3-1 1.6-2.5 0.6-3.2 0-1 0-1.1 3-6.7 C.G. road- hoods 0-2.2 1-1.9 0.4-0.8 0-0.6 1-3.5 0-0.77 6.7-10.6 Ashram road -hoods 0-8.6 1-3 0.8-1.6 0-0.6 1.9-5.2 0.76-1.7 9.4-10.6 Relief road- hoods 0-2.2 3-4.5 0.8-4.6 0-2.1 0.9-1.9 2.6-7 10.6-13.7 Kankaria road- hoods 5.6-12.7 2-3 4.6-9.7 4.4-10.6 0-0.4 2.6-5 10.6-13.7

Class Mean Height class 23233�PLAND AI AREA_MN PLAND AI AREA_MN

Institutional 8.49 88.08 0.10 Low 15.24 86.56 0.18 Medium 12.93 86.85 0.23 High 4.77 89.24 0.15

Table 15: Spatial Regression and correlation results (GWR) between Institutional and Height metrics

Table 16: Comparison of means between Institutional and Height correlation metrics as per analysis classes (Source:SPSS)


60

1. PLAND:

� Institutions with low height areas have high PLAND; High height institutional class

have low PLAND values

2. AI:

� Low rise class has lower AI; High rise class has higher AI

3. AREA_MN:

� Category of high rise institutions have lower AREA_MN

� Medium rise class correspond to highest AREA_MN; Low rise class corresponds to

medium AREA_MN

The range for ‘high’ and ‘low’ metric value has been provided in the appendix B. Based on the above relationships inferred upon, the following equations have been derived

in (Letter H before a metrics denotes that it belongs to height factor and I for institutional): 1. Low height, High H PLAND, high H-AI,

High I PLAND, high I-AI = Low rise institutional---------------------------------------This is the most common relation found comprising religious buildings, government offices, libraries and other institutional categories.

2. Low to medium height, low H PLAND, low H-AI,

High I PLAND, low I-AI = Low rise institutional---------------------------------------Educational Institutes

3. Medium rise, high H PLAND, low H-AI, high I PLAND, low I-AI =Medium rise institutional------------------------Largely comprises hospitals

4. High rise, high H PLAND, high H-AI, High I PLAND, high I-AI =High rise institutional--------------------------Largely comprises Government institutions and administrative buildings

5. Medium to high rise, low H PLAND, high H-AI (low if height is medium), Low I PLAND, high I-AI =Medium rise institutional--------------------------largely comprises banks

All the equations derived in this chapter for the four categories, describing the urban form of particular uses, will be combined further with socio-economic factors and eventually be used to establish rules for generating a landuse classification for the city of Ahmedabad.


61

6 Urban form and social form

As urban life has expanded the form which the cities have acquired are not only based on its spatial form but also its social form. These two components together make up the morphology of an urban area.

To explore the relation between socio-economic factors and urban form metrics, an analysis of distribution of the factors and correlation values has been done using SPSS. This analysis is an important tool to understand the subtle underlying factors of urban form characterization. Further, it would also help in distinguishing the different uses. The socio-economic factors considered are:

� Average income � Jobs � Vehicle ownership

The data for these factors were available at the EB layer and were later on aggregated to the

neighbourhood level. Income has been analysed in relation to residential and mixed use, jobs with commercial use, mixed and institutional.

6.1 Residential and Income:

Residential metric PLAND and Average Income have been correlated to identify areas of low income residential, high income residential, etc. The scatter diagram provided below shows a negative relation, having a R2 of 0.7%. This means that income can explain a very small percentage of the variation in the percentage of landscape occupied by the residential density classes. A negative relation indicates: � Lower the area occupied by a

residential class, Higher is the average income

� Higher the area occupied by a residential class, lower is the average income

This can be further explained by the mean PLAND values as per income classes.

Income groups Mean(PLAND) Low 13.16328 High 10.87651

Average�Income�(mean)1000080006000400020000

Reside

ntial�m

etrics�(P

LAND)

60

40

20

0

R Sq Linear = 0.007

Figure 6.1. Scatter diagram showing relation between Residential PLAND and Average Income (Source:SPSS)

Table 17: Comparison of mean Residential PLAND values between Income categories (Source:SPSS)


62

The scatter diagram provided here represents the regression analysis between height metrics and average income. The percentage of landscape occupied by a particular height class of residential category is positively related to average income, however, with a very small R2 value of 0.4%. the mean PLAND values in each income group also does not show much variation. However, the inferences that can be drawn from this analysis is: � Lower the area occupied by a

residential class of a particular height, lower is the average income

� Higher the area occupied by a height class, Higher is the average income

From analysis earlier, Low density residential has been correlated with High PLAND values and vice versa. Correspondingly, an analysis of the distribution of income values as per density classes would help support that inference.

The boxplots show the lowest and the highest score for each class. The distance between the horizontal axis and the lowest edge of the box is the range between which 25% of the scores fall. The box itself includes 50% of the scores.

The high density residential class has a higher range of income values in comparison to the others. 50% of the class has an average income above 2500 rupees. The median itself is above 5000 which is fairly higher than the other two classes. The low density class has the lowest average it seems. Majority of the class, here, corresponds to less than 600- rupees approximately. This is a highly skewed distribution i.e. top 25% are spread over a wider range than the lower 25% or only 25% of the class (4th quartile) has reached higher income values. The distribution is more symmetrical for the high density class, because whiskers on either side of the bar are more or less equal.

Thus, combining all the inferences obtained so far, the following relations can be derived: 1. low average income, high R PLAND -----low residential density 2. medium to low average income, medium R PLAND----------medium density residential

1 2 3

Residential�class

0.000

2500.000

5000.000

7500.000

Average�Inco

me�(m

ean)

Height�metrics�(PLAND)6050403020100

Average�income�(m

ean)

10000

8000

6000

4000

2000

0

R Sq Linear = 0.004

Figure 6.2. Scatter diagram showing relation between Height PLAND in residential use and Average Income (Source:SPSS)

Figure 6.3. Box-plots showing distribution of average income as per residential density class (Source:SPSS)


63

3. High average income, low R PLAND-----high residential density

4. Low average income, low H PLAND -------High rise residential 5. High average income, high H PLAND ----- low rise residential

6.2 Commercial and Jobs:

Commercial use has been correlated with total job available. Average job in the commercial units is about 1850. The scatter diagram provided below shows a positive relation between total jobs and the percentage of landscape (PLAND) occupied by the three commercial classes, having a correlation value of 8%. This implies that:

� Higher the PLAND of a commercial class, higher the total job available

� Lower the PLAND, lower the total job available.

This can also be proved from the mean PLAND values in each job category (jobs<4000= category low; jobs>4000= category high)

Job category Mean(C PLAND) Low 5.65 High 11.77

To analyse the relation between

jobs and height metrics, regression analysis was done in SPSS along with a comparison of their means.

The inferences that can be drawn from this analysis are: � Low jobs correlate with commercial

units having lower H PLAND � High jobs correlate with commercial

units having higher H PLAND From earlier analysis, it has already been inferred that low density commercial class has higher PLAND values, and Malls and markets, least PLAND values. Conversely, low height has high H PLAND and vice versa. Following that, jobs and the commercial categories can also be equated.

Job category Mean(H PLAND) Low 12.059 High 14.846

Commercial�metric�(PLAND)403020100

Total�job

s

30000

25000

20000

15000

10000

5000

0

R�Sq�Linear�=�0.08

Height�PLAND50403020100

Total�job

s

30000

25000

20000

15000

10000

5000

0


Figure 6.4. Scatter diagram showing the relation between Commercial PLAND and Total Jobs (Source:SPSS)

Figure 6.5. Scatter diagram showing the relation between Height PLAND in commercial areas and Total Jobs (Source:SPSS)

Table 18:Comparison of mean C PLAND values between Job categories

Table 19: Comparison of mean HPLAND (commercial areas) values between Job categories (Source:SPSS)


64

The boxplots further explain the distribution of jobs as per commercial classes. Low density commercial units have a higher proportion of jobs. It averages at about 2250 jobs. 25% of the class Malls and markets have the least share with an average of 824 jobs. This is very obvious because malls are large scale commercial establishments having individual shops; therefore, total employment is less than the other commercial units. Also low density commercial units being a majority in Ahmedabad, the share in jobs is ought to be high. Apart from this, there are few circles above the boxes. These are the cases that are deemed to be outliers. An outlier is a score very different from the rest of the data (Field 2005). These points mostly correlate with the hoods around Relief road and C.G. road. These are the areas in Ahmedabad which have large scale commercial activities. Therefore, it is not unnatural to find few extreme values in those areas.

Thus, combining all the inferences obtained so far, the following relations can be derived: 1. low job available, low C PLAND-----Malls and Markets 2. Medium to high jobs, medium to high C PLAND----------High density commercial 3. High job availability, high C PLAND-----Low density commercial

4. low job available, low H PLAND-----High rise commercial class 5. Medium to high jobs, Medium to high PlAND----------low to medium rise commercial

class.

6.3 Mixed use, Income and jobs:

Mixed use, here, is a integration of residential, commercial and institutional use at varying intensities. Therefore, it has been correlated with income as well as jobs.

6.3.1 JOBS:

Mixed use classes have an average of 2177 jobs. The scatter diagram below depicts the relationship between jobs and commercial metrics. The percentage of landscape occupied by a mixed use class is positively correlated (R2=3.6 %) with total jobs. A comparison of means as per

1 2 3

Commercial�classes

0

10000

20000

Total�job

s

��

�

��

��

Mixed�metric�(PLAND)

806040200

Total�job

s

40000

30000

20000

10000

0


Figure 6.6. Distribution of total jobs as per commercial density classes (Source:SPSS)

Figure 6.7. Scatter diagram showing the relation between Mixed PLAND and Total Jobs (Source:SPSS)


65

category of job availability represents the same statistics:

This implies that: � Higher the PLAND of a mixed intensity class, higher the total job available � Lower the PLAND, lower is the total jobs available.

Similarly, percentage area occupied by a mixed use class with a particular height and total jobs also show a positive relationship. R2

value is around 4.3%.

The inferences that can be drawn from this are: � Lower the percentage of area

covered by a mixed intensity height-class, lower is the total jobs available.

� Higher the percentage of area covered by a height class, higher is the number of jobs available.

From earlier analysis, the equations between mixed intensity classes and PLAND and that between height class and PLAND have been seen. This relation between height classes and jobs would be vital when validation is done later in the research. However, as far as use intensity classes are concerned, low intensity class has an average of 1360 jobs while the high intensity class has an average of 3780 jobs. This is also logical, as a low intensity class has more of residential so it is likely to generate lesser jobs in comparison to its counterpart. Therefore, combining the above inferences, the following equations can be established:

1. low jobs available, low M PLAND-----Low intensity of mix 2. High jobs, high M PLAND----------High intensity of mix

3. low jobs available, low H PLAND-----High rise mixed use 4. High job availability, high H PLAND-----Low rise mixed use

6.3.2 INCOME:

Mixed use classes have a mean ‘average income’ of Rs.3800. The scatter diagram provided below depicts the relation between average income and metrics of mixed use. The percentage of landscape occupied by a class of mixed intensity is positively related to average income, with an R2 of 1.2%. A comparison of mean as per income groups show:

Job groups Mean(M PLAND) Low (<1000) 9.6408 High(>1000) 12.9096

Job groups Mean(H PLAND) Low 10.90 High 18.74

Height�metric�(PLAND)6050403020100

Total�job

s

40000

30000

20000

10000

0


Figure 6.8. Scatter diagram showing the realtion betwen Height PLAND and Total Jobs (Source:SPSS)

Table 20:Comparison of mean Commercial PLAND values between Job categories (Source:SPSS)

Table 21:Comparison of mean H PLAND (of residential areas) values between Job categories


66

Therefore, both the statistics imply that: � Higher the PLAND of a mixed intensity

class, higher the average income � Lower the PLAND, lower is the

average income Similarly, average income has also been

correlated with height metrics. However, in this case the percentage of area occupied by a particular height of mixed intensity class is negatively related to average income, with a R2 value of only about 0.6%.

A comparison of the mean H PLAND for each income group depicts the same statistics:

Therefore, the inferences that can be drawn

from this are: � Lower the percentage of area covered

by a mixed intensity height-class, higher is the average income

� Higher the percentage of area covered by a height class, lower is the average income

From earlier analysis, the equations between mixed intensity classes and PLAND and that between height class and PLAND have been seen. If the distribution of data is seen, the class of low intensity of mix has lower average income, about Rs.3600 while the class of high intensity shows an average of Rs.4430. Therefore, combining the above inferences, the following equations can be established:

1. low average income, low M PLAND-----Low intensity of mix 2. High average income, high M PLAND----------High intensity of mix

3. low average income, high H PLAND-----low rise mixed use 4. High average income, low H PLAND-----high rise mixed use

Correlation between these two socio-economic factors and mixed metrics as per categories of mixed intensities yield very interesting results. Average income has a higher correlation with the

Income groups Mean(M PLAND) Low (<4000Rs.) 9.755 High (>4000Rs.) 11.05

Income groups Mean(H PLAND) Low (<4000Rs.) 13.97 High (>4000Rs.) 11.58

Mixed�metrics�(PLAND)806040200


ean)

10000

8000

6000

4000

2000

0


Height�metric�(PLAND)

6050403020100


ean)

10000

8000

6000

4000

2000

0


Figure 6.9. Scatter diagram showing the relation between Mixed PLAND and Average Income (Source:SPSS)

Figure 6.10. Scatter diagram showing the relation between Height PLAND in mixed landuse and Average Income (Source:SPSS)

Table 22:Comparison of mean M PLAND values between Income categories (Source:SPSS)

Table 23:Comparison of mean H PLAND (in mixed use) values between Income


67

metric of low intensity of mix-class while job has a much higher correlation of about 5% with high intensity of mix- class (as against 0.1% for its counterpart in the same category). This further justifies the basis of definition of the two classes of mixed use. Class 2 has higher proportion of commercial and institutional uses; therefore, it is only logical that job has a higher correlation value for it.

6.4 Institutional and Jobs

Institutional areas have been correlated with Jobs. Institutional use shows an average of 1100 jobs.The scatter diagram provided below depicts the positive relation between the percentage of landscape occupied by institutional use and total jobs with a R2 value of 11%. This is but logical. As seen earlier, higher PLAND values correspond to government offices and large administrative buildings which also in turn employ a large number of people. The overall correlation value is fairly high between jobs and institutional use in comparison to commercial and mixed use. This is perhaps because institutions are large scale units with a larger share in employment in comparison to commercial, where there are individual units too.

A comparison of the mean PLAND values in each job class depicts the following relationships:

� Higher the PLAND of a institutional class, higher the total jobs

� Lower the PLAND, lower is the total jobs

Similarly, job has also been correlated with height metrics. Percentage of area occupied by a particular height class of institutional use is negatively correlated to total jobs. However, the R2 value is very less, only about 0.3%. This means height is not a very significant factor which explains the variability in jobs in institutional use. Also, the means across the two job classes has got very little difference. Nevertheless, the inference that can be drawn from these statistics is:

Job groups Mean(H PLAND) Low 13.407 High 13.385

Job groups Mean(M PLAND)

Low (Job<1000) 7.036

High (Job>1000) 12.0938

Institutional�metrics(PLAND)100806040200

Total�job

s

12500

10000

7500

5000

2500

0


Height�metric�(PLAND)6050403020100

Total�job

s

12500

10000

7500

5000

2500

0


Figure 6.11. Scatter diagram showing relation between Insitutional PLAND and Total Jobs (Source:SPSS)

Figure 6.12. Scatter diagram showing relation between H PLAND in institutional areas and Total Jobs (Source:SPSS

� Lower the area occupied by a particular height class in institutional areas, higher is the job availability.

Table 24:Comparison of mean Institutional PLAND between Job categories (Source:SPSS)

Table 25: Comparison of mean H PLAND (in institutional areas) between Job categories (Source:SPSS)


68

6.5 Vehicle ownership and landuse metric: An analysis of compactness and clustering

Vehicle ownership is an important indicator of compactness and clustering. High motorization contributes directly to the ease of living in the outlying suburban area(Huang, Lu, and Sellers 2007). With low motorization, residents cannot afford to live away from their place of work, which in turn leads to a more compact form. Vehicle ownership has been correlated with four urban form metrics for residential, commercial and mixed use classes (height metrics not considered here). The metrics considered are:

1. Aggregation index (AI) 2. Fractal dimension (FRACT_AM) 3. COHESION 4. Proximity index (PROX_MN)

The simple hypothesis is higher the AI, lower the FRACT_MN, higher the PROX_MN, higher the COHESION and lower the vehicle ownership, the more compact the class is.

* Not significant according to SPSS

Use Form Metrics – R AI COHESION PROX_MN FRACT_AM

Residential -0.263 0.317 0.478 -0.019 Commercial -0.324 -0.086 0.292 0.169 *

Mixed 0.046 * 0.298 0.488 0.369

aicohesionprox_mnfrac_am

aicohesionprox_mnfrac_mn

aicohesionprox_mnfrac_am

Figure 6.13. Correlation between Urban form metrics and Vehicle ownership (Source:SPSS)

Table 26:Correlation between Urban form metrics and Total Vehicle ownership: Analysis of clustering (SPSS)

Tota

l veh

icle

ow

ners

hip

RESIDENTIAL

COMMERCIAL

MIXED


69

For the residential classes, with increasing vehicular ownership AI decreases while proximity index increases. Correlation between vehicle ownership and proximity index is fairly high, about 0.5.This means as vehicular ownership decreases the class becomes more closely spaced and aggregated or clustered. However, the scatter for cohesion shows a positive relation, implying as the class becomes more compact or less fragmented, vehicular ownership seemingly increases. Therefore, an inverse relation would have supported the hypothesis. However what could be said here in its defence is that, As the residential class becomes one huge homogenous or clustered patch, vehicle ownership increase probably because of the need to gain access to other landuse, e.g. Commercial centres, offices, etc.

Commercial class, however, displays an inverse relation between COHESION and vehicle ownership. Which means as the class becomes more compact or less fragmented vehicle ownership is likely to decrease. The simple reason behind this is that increasing vehicular ownership will encourage fragmentation of land as motorization can increase accessibility. But the general character of commercial areas is to come up near to residential areas or other commercial area. There locations are demand driven. Therefore, vehicular ownership here is also a deciding factor (accessibility being an important ingredient) rather than only being a distinguishing factor.

For mixed uses, however, correlation with AI is not very significant while that with FRACT_AM and PROX_MN s very high compared to the other classes. Mixed landuse includes an integration of different types of uses. Therefore, higher the mix, more compact the mix, less is likely to be the vehicle ownership. That is because in such a case the place to live and the place to work or shop are in close proximity to each other. Thus, it is also said that mixed uses promote pedestrianisation.

Hence, judging from the correlation analysis, residential classes seem to be the most compact i.e. it best portrays the relationship, followed by mixed use. Certain metrics do not show significant relation with vehicle ownership. This may be due to the generally, increasing role of transportation in the evolution of urban form, irrespective of whether the development is compact or dispersed.

Apart from physical factors like location, topography, regional patterns of social and economic development bears a significant relation with urban form. This chapter probes into that relation between spatial form and social form, two different faces of the same coin, urban form.


70

7 Validation

In the chapters so far, analysis of urban form through indicators and their relationship with certain socio-economic factors have been described. Equations have been developed in the course of the analysis, by correlating urban form and use metrics, and further with socio-economic factors. The aim was to prove whether urban form can be a determinant of urban use. The relationships derived through the analysis can be further proved by applying them on the validation hoods. The basic idea in this stage is to see that if there is only height information for an area, whether a landuse classification can be derived for that area, based on certain rules established.

The conceptual framework for the validation process has been described in this chapter.

7.1 Methodology

Fig : Conceptual framework for validation process

7.1.1 Phase 1: Conversion of DSM into building heights

The only input available for this analysis is the Digital Surface Model, available from earlier research in ITC (Bock 2008).To be used for metrics calculation homogeneous classes have to developed, as done for the test dataset. First, the DSM is converted to building heights. For this purpose, height of one floor has been considered as 3 meters. Bases on this the elevations in DSM have been converted to number of storeys in a building. The highest building comes to be of 23

Figure 7.1. Methodology for validation


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storeys. The buildings have been classified into categories of low rise, medium rise and high rise, similar to that done for the test neighbourhoods.

Low rise= 1 to 3 storeys Medium rise=4 to 7 storeys High rise= 8 storeys and above.

7.1.2 Phase 2: Calculation of spatial metrics and socio-economic factors:

The newly created building storey layer has to be divided into neighbourhoods, similar to the test neighbourhoods. 10 neighbourhoods have been selected for validation. Spatial metrics is to be calculated for these neighbourhoods as per the height classes. The metrics that are to be calculated are:

Percentage of landscape (PLAND),

AREA_MN, and

Aggregation index (AI)

In previous analysis, socio-economic factors have been used in conjunction with metrics to define urban form and use. Similarly, for validation, average income, population density and jobs have to be calculated for the validation neighbourhoods. Moreover, it is difficult to identify the classes solely on the basis of height. Thus, some additional information is required to identify the classes. In this case, the socio-economic factors can be used. Apart from this some attributes like open space or vegetation areas, etc can also be obtained from the satellite image, to provide the additional information.

The equations derived from earlier analysis will form the rules for validation. Hence, following that mean value of metrics for each height category and correlation of metrics with socio-economic factors have to be done, for example, mean PLAND value for each height category or correlation of PLAND value for height categories with socio-economic factors.

7.1.3 Phase 3: Validation

From the above table the following inferences can be drawn:

� Low rise areas have a larger landscape coverage(PLAND), low aggregation (AI) and low average patch size (AREA_MN)

� High rise areas have a smaller landscape coverage, high aggregation index and high average patch size

At this stage, the relation between PLAND values and height is established. This equation derived has to be integrated with the rules established earlier between form and use. To apply the rest of the equations, it is important to identify whether the area belongs to residential, commercial, mixed or institutional use. The steps for validation suggested are as follows:

� Correlation of height metrics with population density. This step would help in discriminating the residential areas. Correlation between these two factors and mean population density in each height class has been established before for the test neighbourhoods. The rule, then established, i.e. the range, mean and R2 would now help in identifying a particular area either as residential or mixed landuse.


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� The probable residential areas can be further established by correlating with average income. Average income has already been established as a socio-economic indicator of residential form. From earlier analysis, it has been established that residential areas have higher average income and also residential areas of a particular height have a higher correlation with income, in comparison to mixed landuse. This would help in identifying the residential landuse. For illustration, an area with low population density (less than 18 people/hectare), low rise, higher correlation with income, low average income (less than 4000 Rs.), H PLAND values higher than 11% (mean PLAND high), H-AI higher than 88 (mean AI high), etc. is most likely to be a low density residential area or more precisely tenements. Further, mixed landuse would also show a high correlation with total jobs.

� Further, to identify the classes of mixed landuse, i.e. high intensity mixed use or low intensity mixed use, the equations derived between mixed form and use based on R2 and comparison of means, can now be applied. For example, areas of high rise with high average income (above Rs.4000), H PLAND values less than 11%, H AREA_MN higher than 0.20 meters would probably be an area of high intensity mixed landuse. This could be determined on the equations or rules established before. (The values have in provided in Appendix B)

� The remaining areas would either be commercial or institutional. Apart from the relation that can be derived between the height class and height metrics of the validation hoods and there conformation with the rules established before, correlation with total jobs and mean job availability is a useful discriminator. Areas with a particular height having a higher correlation with total jobs and having higher mean jobs are likely to be commercial, as per the rules. Further classifications within the landuse can be obtained by applying the equations established before. For illustration, an area having high correlation with jobs, with low height, H PLAND higher than 11% ( high mean PLAND), H-AI lower than 87% (lower mean AI), H-AREA_MN lower than 15 meters (medium mean AREA_MN) is most like to be of low density residential commercial class, comprising shops or small offices.

Based on the relationships analysed and equations drawn above, a landuse classification of validation hoods can be derived.

7.2 Conclusion

The landuse information of these validation hoods are actually available from field survey. In this way, the accuracy of landuse classification in the validation hoods can also be computed.

The same methodology can be applied to derive a landuse classification of the entire city of Ahmedabad. A landuse classification can be derived for areas for which there is no landuse information, by using indicators of spatial structure, based on a set of rules established, using spatial metrics analysis.


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8 Inferences and Conclusion

Is spatial structure a proxy to landuse? This is the question with which this research started

off. The aim was to prove that urban form can be a determinant of urban landuse, thereby putting forward a very different approach towards landuse classification for urban areas, the city of Ahmedabad specifically.

A mentioned all through this research, urban form is a combination of spatial form and social form. Following that, spatial metrics, i.e. the indicators of urban form, and socio-economic factors have been integrated and correlated to develop certain equations that depict the relationships over space. The final step of this study was to develop a landuse classification incorporating all these relationships or rules derived so far.

8.1 LANDUSE CLASSIFICATION SCHEME

Residential: 1. Low density single unit residential: Most dominant category in residential landuse as per

percentage of landscape covered. Low built-up density, predominantly low rise housing, mainly comprising bungalows and tenements. Low population density, larger patch sizes, compact rectangular shape, relatively lesser aggregation but very compact arrangement over space.

2. Medium density residential unit: This class consists of medium rise apartment buildings with medium to lower patch size, relatively higher population density, medium to low average income, very compact shape, higher aggregation and clustering on an average but fairly less compact spatial arrangement. This is especially a dominant character of the eastern part of the city.

3. High density multi-unit residential: Characterised by high rise apartment buildings and towers, average population density but high income, smaller patch size, distinct but irregular shape, highly aggregated but dispersed arrangement over space (greater distance between similar patches).

Commercial:

4. Low density commercial: This is the most dominant class of commercial landuse as depicted by the percentage of landscape occupied (PLAND) with the maximum number of jobs. Low to medium rise with highly variable average patch size, compact rectangular shape, less clustering and aggregation due to high fragmentation of patches but comparatively compact spatial arrangement. This class largely comprises small shops and offices.

5. High density commercial landuse: Large, regular high rise commercial establishments comprising offices, shop buildings, hotels, etc., with moderate jobs. This class has comparatively less coverage over landscape. Large patch size, highly aggregated but rather dispersed arrangement of similar patches over space.


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6. Malls and Markets: Characterised by very large, low density, low to medium rise commercial establishments and low job availability. Highly regular shape, compact and well aggregated but generally isolated arrangement over space.

Mixed landuse:

7. Low intensity mixed landuse: Larger proportion of residential integrated with commercial or institutional occupying a smaller percentage of landscape. Low average income, low jobs, High rise, average patch size, compact, highly aggregated but generally dispersed arrangement over space.

8. Low rise high intensity mixed use: Comprises a larger proportion of commercial or institutional and less of residential. This is the most dominant class in the category of mixed use, occupying the maximum percentage of landscape. Higher average income and number of jobs. Low rise, higher average patch size, lower aggregation, relatively less dispersed arrangement over space with some amount of clustering.

9. High rise high intensity mixed use: Generally, a combination of institutional and commercial or institutional and residential but lower percentage of landscape covered. High job availability and average income. High average patch size, disaggregated but relatively less dispersed spatial arrangement.

Institutional:

10. Institutional: Low rise government offices, educational institutes, hospitals, religious buildings having a higher expanse or high rise administrative buildings having a comparatively lower expanse. Widely varying average patch size, simple regular shape, spatially aggregated structures but isolated location over space, with moderate job availability.

The indicators used to formulate these classes include the size or coverage area of the classes, their compactness, shape, clustering, spatial arrangement etc. along with income and employment level.

Almost nine spatial metrics have been examined in the study. The results show that spatial metrics contribute significantly to landuse classification. Metrics describing spatial structure and configuration seem to be most informative. The metrics like PLAND has been used to indicate area coverage or expanse, AREA_MN and CV to indicate the average patch size and its variations. Fractal dimension indicating shape, AI indicating the aggregation of individual classes and compactness while COHESION, the Nearest Neighbour Distance metric or even Proximity index indicating chances of spatial clustering and regularity of spatial arrangement and further, spatial heterogeneity being indicated by PLADJ, which is the class level CONTAGION measure, provide other important landuse discriminators. Therefore, apart from forming the basis for a landuse classification, the metrics have also been used to identify different patterns over the city, between neighbourhoods and also discriminate between different urban form characteristics. The metrics have proved residential form to be the most compact in Ahmedabad while commercial is the most segregated. However, physical connectivity between the commercial patches is very high, so even though they are fragmented implying spatial heterogeneity, they are well aggregated. Most importantly, spatial metrics has provided some very accurate results for residential landuse. Further, Mixed urban form stands out as the most dominant scenario in the urban landscape of Ahmedabad, like most Indian cities. In a nut


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shell, results of all the metrics put together portray Ahmedabad as a very heterogeneous and compact city, with the weight being slightly higher on its residential side.

8.2 Limitations:

This study has also brought forth some problems associated with the use of metrics, along its course.

i. The basic premise of spatial metrics is that patches are homogeneous regions, for example, parks or high density residential zone etc. Therefore, when trying to define these classes based on the metrics, it is simple. However, trying to define categories like bungalows or apartment buildings directly poses problems, as then the clause of homogeneity is no longer maintained. Thus, in such a case it is worthwhile to use proxy classes as low density residential or high intensity of mix etc.

ii. Spatial metrics is an area approach. In this study, therefore the building footprints were aggregated to areas. This approach has a few limitations as far as mixed use is concerned unlike residential or commercial. That is because the definition of mix here is integration of more than one use in one building, i.e. different uses in each floor. This kind of heterogeneity is not easy to capture using spatial metrics. Proxy classes like high intensity of mix and low intensity of mix have been used here, instead. Though this also serves the purpose, but it underestimates the heterogeneity, which is so typical of Ahmedabad, to a certain extent. In such cases, an object oriented classification would be worth a try.

iii. The absolute value of metrics is dependent to a great extent on the spatial resolution and the detail in the initial landscape classification, which would be the input to the metric quantification. For example, fractal dimension or the mean nearest neighbour are highly sensitive to the aggregation of patches. A change in the spatial resolution or thematic definition of classes significantly changes the metric and limits the ease of quantitative comparison.

iv. Providing objective and highly precise definition of urban landuse is difficult, as mentioned before. Therefore, these definitions do come with some amount of fuzziness.

8.3 Recommendations

i. It would be in interesting to apply an object oriented classification to the same data and then compare it with the results of this study.

ii. Computing spatial metrics using satellite data. iii. Applying the same set of equations to some other Indian city, for example Hyderabad, and

examine the outputs. iv. Apply the same metrics to a time series data to explore the change.

In spite of the above limitations, spatial metrics, in this study, has been able to quantify

form, identify patterns and characteristics of different urban form, captured its heterogeneity, identify the differences in urban form pattern between the old city and the new and provide a detailed landuse classification of Ahmedabad city, capable of inter-city comparisons. In the course, it has also been established that form can be a simplistic determinant of urban landuse. Therefore, spatial metrics can be used for an improved representation of spatial urban characteristics and provide an innovative tool to planning and decision making process.


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Appendices

A. Units and Range of Metrics- The standard limits

Metric Units Range

PLAND-Percentage of landscape

Percent 0 < PLAND � 100

AREA_MN- Mean patch size

Meters/Hectares AREA_MN � 0, no limit.

AREA_CV- Patch size coefficient of variation

Percent 0 < AREA_CV � 100

PROX_MN- Mean proximity

None PROX � 0

ENN_MN- Euclidian nearest neighbour distance mean

Meters ENN_MN > 0, no limit.

FRAC_AM- Area weighted mean patch fractal dimension

None 1 � FRAC_AM � 2

AI- Aggregation index Percent 0 � AI � 100

PLADJ- Percentage of like adjacencies

Percent 0 � PLADJ � 100

COHESION None 0 < COHESION < 100

PD- Patch density Numbers per 100 ha PD � 1, no limit

B. Range of the calculated Metrics value:

The values computed have been scaled as low, medium and high for applying in the equations developed.

i. Urban landuse Metrics

Residential Use

PLAND (%) AREA_MN AI (%) 0-11 0-0.12 0-88 11-30 0.13-0.45 88-100 30-65 0.46-1.45

Commercial Use

PLAND AI AREA_MN 0.25-3 77-87 0.03-0.1 3.1-12 87.1-100 0.2-0.8 12.1-35


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ii. Height Metrics

Mixed Use

PLAND AI AREA_MN 0.9-6 0-88 0.03-0.1 6-77 88-100 0.1-0.5

Institutional Use

PLAND AI AREA_MN 0.85-4.5 81-88 0.01-0.08 4.5-89 88-100 0.08-0.3

Height (Residential Areas)

PLAND (%) AI (%) AREA_MN

Height (Commercial Areas)

PLAND (%) AI (%) AREA_MN 0-10.6 77-87 0.06-0.13 10.6-50 87-100 0.14-2

Height (Mixed use Areas)

PLAND (%) AI (%) AREA_MN 0-10 75-88 0.06-0.15 10.1-55 88-100 0.16-2.5

Height (Institutional Areas)

PLAND (%) AI (%) AREA_MN 0-11.5 80-88 0.06-0.13 11.5-55 88-100 0.14-2.5


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C. Independent Samples ‘t test’ results for the neighbourhoods in the east and the west of the city

i. RESIDENTIAL

Levene's Test for Equality of Variances

t-test for equality of means

F Sig. t df Sig. (2-tailed) Mean

Difference PLAND

Equal variances assumed 40.215 .000 -3.207 132 .002 -7.33972

Equal variances not assumed -2.300 36.934 .027 -7.33972

AREA_MN

Equal variances assumed 12.954 .000 -2.054 132 .042 -.08738

Equal variances not assumed -1.662 41.174 .104 -.08738

AREA_CV

Equal variances assumed 1.880 .173 .130 132 .897 1.5547

Equal variances not assumed .119 47.277 .906 1.5547

FRAC_AM

Equal variances assumed 1.226 .270 .670 132 .504 .12877

Equal variances not assumed 1.170 102.009 .245 .12877

PROX_MN

Equal variances assumed 3.430 .066 .406 132 .686 2.47094


PLADJ

Equal variances assumed 16.188 .000 -.357 132 .722 -.29516

Equal variances not assumed -.292 41.629 .772 -.29516

COHESION



AI

Equal variances assumed .179 .673 -2.292 132 .023 -2.42229


ENN_MN

Equal variances assumed .513 .475 .419 132 .676 3.23817


PD




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ii. COMMERCIAL

Levene's Test for

Equality of Variances t-test for equality of means


Difference PLAND Equal variances

assumed 3.410 .068 -2.150 85 .034 -3.2800135


PD Equal variances assumed 7.734 .007 -2.092 85 .039 -57.836747


AREA_MN Equal variances assumed .606 .439 -.256 85 .798 -.0092103


AREA_CV Equal variances assumed 1.133 .290 .201 85 .842 1.7242062


FRAC_AM Equal variances assumed 2.778 .099 1.182 85 .241 .0110799


PROX_MN Equal variances assumed 2.754 .101 1.230 85 .222 2.6704173

Equal variances not assumed 1.574 62.427 .121 2.6704173

ENN_MN Equal variances assumed 3.829 .054 1.606 85 .112 36.2616055


PLADJ Equal variances assumed .007 .934 -.287 85 .775 -.3589752


COHESION Equal variances assumed .447 .505 -.376 85 .708 -.3391093


AI Equal variances assumed 1.293 .259 -1.413 85 .161 -1.8109915



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iii. MIXED

Levene's Test for



Difference PLAND

Equal variances assumed .050 .825 -2.059 50 .045 -9.580462


PD



AREA_MN

Equal variances assumed 38.242 .000 -2.149 50 .036 -.0536297

Equal variances not assumed -1.414 14.534 .179 -.0536297

AREA_CV



FRAC_AM


Equal variances not assumed .268 14.345 .793 .0119350

PROX_MN



ENN_MN

Equal variances assumed 10.830 .002 3.030 50 .004 70.6257335


PLADJ



COHESION



AI




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iv. INSTITUTIONAL

Levene's Test for



Difference PLAND



PD

Equal variances assumed 15.196 .000 -2.000 43 lts (-688.66925 -688.66925


AREA_MN

Equal variances assumed .593 .446 .804 43 .426 .0178543


AREA_CV



FRAC_AM

Equal variances assumed 1.253 .269 2.360 43 .023 .0313457


PROX_MN

Equal variances assumed .114 .738 .495 43 .623 3.567


ENN_MN



PLADJ



COHESION



AI




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D. Spatial Regression and correlation results (done with spatial statistics analyst tool in

Arc GIS 9.3):

* Relation not significant according to SPSS

R square values- In percentage

Relation between landuse metrics and height metrics Low Medium High rise Residential Res_PLAND & Ht_PLAND 20 11 18 Res_AREA_MN & Ht AREA_MN 25.4 15.1 40.1 Res_AI & Ht_AI 3.1 3.4 7.7 Commercial Com_PLAND & Ht_PLAND 13.8 17 18 Com_AREA_MN & Ht AREA_MN * 11.6 27.3 15.8 Com_AI & Ht_AI 5.5 3.8 10.2 Mixed Use Mix_PLAND & Ht_PLAND 20 20 17 Mix_AREA_MN & Ht AREA_MN 44.2 45 44.8 Mix_AI & Ht_AI * 5 5 1.3

Institutional Inst_PLAND & Ht_PLAND 23.5 2.2 18.5 Inst_AREA_MN & Ht AREA_MN * Inst_AI & Ht_AI 15 15.4 11.4

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