1 introduction to spatial analyses and tools estp course on geographic information systems (gis):...
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Introduction to spatial analyses and tools
ESTP course on Geographic Information Systems (GIS): Use of GIS for making statistics in a production environmentStatistics Norway, Oslo, 26th to 30th of March 2012
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Mrs Diana Makarenko-PiirsaluMSc in Landscape Ecology and Environmental ProtectionGeolytics OÜMere tee 15, Saviranna, Jõelähtme vald, Harjumaa, [email protected]. +372 556 19 636
Topics
• What is spatial analyses?
• What are important and fundamental issues in spatial statistics?
• Examples of the spatial analyses types
• gvSIG incorporated spatial analyses and introduction SEXTANTE
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What is spatial analyses?
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• In statistics, spatial analysis or spatial statistics includes any of the formal techniques which study entities using their topological, geometric, or geographic properties.
(Source: http://wiki.gis.com/wiki/index.php/Spatial_analysis)
• The process of examining the locations, attributes, and relationships of features in spatial data through overlay and other analytical techniques in order to address a question or gain useful knowledge. Spatial analysis extracts or creates new information from spatial data. GIS Dictionary (Source:http://support.esri.com/en/knowledgebase/GISDictionary/term/spatial%20analysis )
• In a very broad sense: answering to the question : „What happens where? “
Main steps of analysing reality spatially
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• The aim is to create new knowledge
• Extracting or creating new information from spatial data
Reality Data ModelResults
Data collection Conceptualize
Spatial analyses
Raw Data
What is important in spatial analyses?
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• How geographic phenomena are arranged in the real world?
• We should consider the arrangement of geographic phenomena along discrete – continuous and abrupt – smooth continua
• Discrete phenomena • occur at distinct locations with space in between• Example: individual person in a city . Location can be specified for each person, with
space between individuals
• Continuous phenomena • occur throughout a geographic region of interest • Example: elevation, every longitude and latitude position has a value above or below
sea level.
• Discrete and continuous phenomena can also be considered as either abrupt or smooth.
• Example: Number of votes in local municipalities is abrupt phenomena and precipitations in a humid region are smooth.
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Continuos- discrete – abrupt – smooth phenomena
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• Considering the distribution of geographic phenomena is important in selecting proper spatial analyses or appropriate method of symbolisation in visualising data in thematic mapping
Source: Thematic cartography and geovisualization, T A. Solcum et al, 2009
Fundamental issues in spatial analyses
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• A fundamental concept in geography is that nearby entities often share more similarities than entities which are far apart.
• This idea is known as „Tobler´s first law of geography„ - everything is related to everything else, but near things are more related than distant things„. Source: Tobler, W. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46,
• Spatial auto-correlation – correlation of variables with itself through the space
• Possible causes:
• Simple correlation- whatever is causing an observation in one location also causes similar observations in nearby locations
• Causality - something at a given location directly influences the characteristics of nearby locations
Source: http://wiki.gis.com/wiki/index.php/Spatial_analysis
Fundamental issues in spatial analyses
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• Spatial dependency or auto-correlation – correlation of variables with itself though the space
• Standard statistical techniques assume independence among observations
• Standard regression analyses may result in unreliable significance tests.
• Spatial regression models (for example - Geographically weighted regression - GWR ) capture these relationships and do not suffer from these weaknesses.
• It is also appropriate to view spatial dependency as a source of information rather than something to be corrected.
Source: http://wiki.gis.com/wiki/index.php/Spatial_analysis
Spatial autocorrelation statistics
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• Measure the strength of spatial autocorrelation• Test the assumption of independence or randomness
• Classic spatial autocorrelation statistics are:
• Moran´s I - compares the value of the variable at any one location with the variable at all other locations . The value of Moran´s I lies between 1 and +1. The higher the coeficient the stronger the aotocorrelation is. A random arrangement of square colors would give Moran's I a value that is close to 0.
• Geary ´s C – Geary's C is inversely related to Moran´s I, but it is not identical. The value of Geary's C lies between 0 and 2. 1 means no spatial autocorrelation. Smaller than 1 means positive spatial autocorrelation
• Moran's I is a measure of global spatial autocorrelation, while Geary's C is more sensitive to local spatial autocorrelation.
Negative None Positive
Source:http://en.wikipedia.org
Fundamental issues in spatial statistics – MAUP
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• Modifiable areal unit problem MAUP
• is an issue in the analysis of spatial data arranged in zones, where the conclusion depends on the particular shape or size of the zones used in the analysis.
• spatial units are therefore arbitrary or modifiable and contain artifacts related to the degree of spatial aggregation or the placement of boundaries
• Example : Statistical units as NUTS, LAU etc
• MAUP can cause random variables to appear as if there is a significant association, when there is not. Multivariate regression parameters are more sensitive to MAUP than correlation coefficients
• http://wiki.gis.com/wiki/index.php/Modifiable_areal_unit_problem#MAUP_sensitivity_analysis
Fundamental issues in spatial statistics
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• Scale
• Spatial and temporal scale are still under the research in spatial analysis.
• ensuring that the conclusion of the analysis does not depend on any arbitrary scale.
• Using quantitative metrics which do not depended on the scale at which they were measured are the solution
What components of spatial dimensionscan be analysed?
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Source: GITTA, 2012
• Geometry
• Topology
• Pattern
• Proximity
• Accesibilty
• Dynamics
Steps in spatial analyses
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Source:http://www.spatialanalysisonline.com/output/
Examples of spatial analyses types
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• One of the GIS power is to cobine spatial data
• Overlay analyses – „What is on above what?“
• Joining and viewing together separate data sets that share all or part of the same area
• The result of overlay analyses is a new data set that identifies the spatial relationships
Overlay analyse tools available in gvSIG:
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Clip Difference
Intersection UnionSpatial selection
Examples of spatial analyses types
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• Proximity analyses – „What is close to ? „ How far is ..?“
• Proximity analyse tools available in gvSIG
Buffer Spatial join
Examples of spatial analyses types
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• Network analyses – the spatial analysis of linear (line) features
• analyzing structure (connectivity pattern) of networks
• analyzing movement (flow) over the network system
• Costs (weights) can be analysed
• Network analyse tools available in gvSIG
Service area Shortest path Closest facility
Examples of spatial analyses types
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• Interpolation
• Spatial interpolation - estimating the value of properties at unsampled sites within the area covered by existing observations
• can be thought of as the reverse of the process used to select the few points from a DEM which accurately represent the surface
• rationale behind spatial interpolation Tobler´s first law of geographySource:http://www.geog.ubc.ca/courses/klink/gis.notes/ncgia/u40.html#SEC40.2
Examples of spatial analyses types
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• Point pattern analyses
• The spatial pattern of distribution of point featrues
• Valid measure of the distribution are the number of occurances in the pattern and respective geographic location
• Spatial pattern of all points in the study area
Examples of spatial analyses types
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• Neighbourhood analyses
• analyzes the relationship between an object and similar surrounding objects in a surface
• is based on local or neighborhood characteristics of the data
• computes an output grid where the value at each location is a function of the input cells within a specified neighborhood of the location
• computes an output grid where the value at each location is a function of the input cells within a specified neighborhood of the
Source of pictures:http://www.esri.com
Examples of spatial analyses types
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• Neighbourhood analyses algorithms in gvSIG can be found from SEXTANTE – Focal statistics
SEXTANTE
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• http://www.sextantegis.com/• Developed by Victor Olaya
Main tools to use SEXTANTE in gvSIG
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Main tools to use SEXTANTE in gvSIG
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THANK YOU!
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ESTP course on Geographic Information Systems (GIS): Use of GIS for making statistics in a production environmentStatistics Norway, Oslo, 26th to 30th of March 2012
Mrs Diana Makarenko-PiirsaluMSc in Landscape Ecology and Environmental ProtectionGeolytics OÜMere tee 15, Saviranna, Jõelähtme vald, Harjumaa, [email protected]. +372 556 19 636