ArcGIS For Land

AdministrationCUSTOMARY TENURE GEOSPATIAL STATISTICAL

ANALYSIS

By Nyerembe D Munasa(PHD student) University of Bagamoyo

deus.munasa@gmail.com.mobile 0767682041

1.INTRODUCTION

• Land administration definition-process-determining-recording-diserminating-information-ownership-value-use-assess-impact-plans-manage-related-land-policies

• Land tenure definition-relationship-people- land-rules-regulating-behavior-management-mode -land - held-adjudication-systematic-sporadic.

• Land and person or persons(organization)relationship

• Modeling spatial land tenure

• Geo-statistical prediction of land tenure system and values

• Relationship and correlation between land and people

• Predicting security of land tenure-inclusion-access-interest-land-rights.

2.Land Tenure Geo-database

• Can support land administration life cycle data capture, management,

processing and land information dissemination in open environment that

support IT standards and interoperability with the existing system.

• An automated work flow modelled and implemented in GIS improves

accuracy and quality and saves labor, time and money.

• Assessing and evaluating the security of customary tenure using geospatial

statistical analysis can be useful in effective and efficient Land Administration

3.Models for Property Formalization

• Replacement Model

• Desoto Model

• Multiple or Dual Tenure Models

• Adaptation Model

4.Adaptation Related Approaches to

Formalization of Customary Rights

• Locally Driven Tenure Models

• Incremental or Gradualist Tenure Models

• Unified Tenure Model

6.Examples of Innovative Approaches to

Formalization

• Community Land Trust of Kenya

• Certificate of Customary Right of Occupancy

• Certificate of Granted Right of Occupancy

• Certificate of Building Right

• Unit Title

• Residential License

• Derivative Rights

• Social Tenure Domain Model

7.Formalization

• Essential part of transition towards structured governance order and civilization(Benjamin Sen, et al:2009)

• Replacement formalization have been influenced by activities of donor agencies(Arko –Adjei:2011)

• Efficiency, equity and sustainability of private property compared to common property right.(Ostrom:1999)

• Traditional tenure system need to be codified(World Bank:1989)

• Capitalism can work to the advantage of the poor if their rights in land are formalized(Peters:2009)

8.Categories of Property Right

• Private Property

• State Property

• Common Property

• Open Access(Tuladhar: 2004)

9.German Administration.

• 1895- “all land crown land vested in German empire”

10.British Administration.

• 1926- all land under the control of governor.

11.Deemed Right of Occupancy.

• Customary law(URT:1997)

• Individualized freehold

• Superior replacement for customary land rights(Tsikata:2001)

• Freehold were abolished and converted into government leases

• Freehold title (conversion) and government lease Act Cap 523 0f 1963

• Degraded into right of occupancy under government leaseholds

• Conversion of right of occupancy Act no 44 of 1969

12.Ujamaa Villagization

• Grouping of moral communities in Ujamaa Villages 1968

• Well-intentioned resettlement policy

• Inhibited the development of the development of customary tenure system

• Viable living law and a system of land relationship

• Intensification of insecurity of land tenure

• Paralysis of land development in rural and urban area

• Weakening social and production relations( Dalal-Clayton and Dent:2001)

13.Certificate of Customary Right of

Occupancy(CCRO).

• Land Reform Project/Land Administration Project granting Certificate of

Customary Right of Occupancy to resulting rights enforce the weakening of

social and production relations.

• Villagization, nationalization and privatization reduced the status of land

owner to more Land Occupant(Dalal-Clayton and Dent:2001) not Land

Owner.

14.Tenure Transformation and Trends in

Geo-statistical Analysis

• Tenure data-normally distributions.

• Tenure data-skewed-the distribution is lopsided-transformation to make it normal.

• Exploratory Spatial Data Analysis(ESDA)-graphically investigate tenure dataset for better understanding.

• Histogram ,voronoi map, normal QQ plot, generalQQ plot, covariance cloud,crosscovariance cloud and trend analysis-explore the effect of different transformation on distribution of the tenure data set.

• Interpolated surface.

15.Results of ESDA Tool.

• Histogram tool ; univariate-calculate summary statistics

• Voronoi mapping tool-analyzes stationarity and spatial variability of tenure

data set

• Data view.

16.Summary Statistics.

• Provides important features of tenure distribution

• Location.

• Spread.

• Shape.

• Normal QQ plot checks for normality of tenure dataset

• Trend analysis identifies global tenure trends in a tenure dataset

17.Normal and General QQ plot.

• Checks normality of tenure data set(NQQP)

• Explores whether two tenure data sets have the same distribution(GQQP)

• Cross Covariance Cloud used to understand the cross covariance between

two tenure datasets.

• Semi Variogram /Covariance Cloud-used to analyze the spatial dependencies

in a tenure dataset.

18.Geo-statistical Techniques.

• Variography-describe and model tenure pattern

• Kriging-predict tenure values at unmeasured locations.

• Co-kriging-to assess Tenure Insecurity(Tenure Uncertainity )associated with Predicted Tenure Value at unmeasured locations.

• Ordinary kriging-Observed Tenure Pattern - result of Autocorrelation/scientific problem.

• Universal Kriging can use Semivariograms/Covariances to express tenure autocorrelation.

• Polynomial Regression with spatial coordinates as explanatory variables

• Can use Tenure Transformations in which Tenure Trends should be removed and allow for Tenure Measurement Error.

19.Summary Statistics.

• Measure of tenure location.

• Measure of tenure spread.

• Measure of tenure shape.

• Tenure Voronoi map.

• Tenure Semivariogram.

• Tenure Covariance Cloud.

• Tenure Cross Covariance Cloud.

20.Valuation Trending.

• Value and Security of Land Tenure.

• Market Value Trending.

• Replacement Cost Trending

• Residual Value Trending

• Profit Value Trending

• Opinion of Value Trending.

• Lacks forecasting and prediction.

• They depict Stationarity.

• They lack trending.

21.Value and Security of Land Tenure Trending

and Prediction.

• With geo-statistics it is possible to ascertain Value and Security of Land

tenure Trending and Prediction.

• Customary Social Tenure Domain Model can be predicted and forecasted.

• Security of Land Tenure and coherence can be mapped and predicted.

• Land Tenure Attributes can be mapped to ascertain their pattern.

22.Process of Land Tenure Kriging

Interpolation

• The process of Land Tenure Kriging Interpolation has been undertaken .

• The methodology that was adopted from the case study was having the main objective of tenure prediction and analysis tenure coherence in the study case unit.

• We determined tenure location and spatial variation among the land owners .

• This was done using kriging interpolation method.

• We were guided by the research question to which extent customary tenure system can be identified and tenure coherence assessed using owners identified and analyzed.

23.Methodology

• Methodology had the assumption that in kriging interpolation method explicitly makes assumption that things that are close to one another are more a like than those that are further apart.

• The methodology also enabled us to predict the value for any unmeasured location, kriging in this case as a method used the measured tenure value surrounding g the prediction location.

• The measured tenure value closest to the prediction were found to have more influence on the predicted tenure value than those further away.

• In classical statistics, observation are assumed to be independent that is there is no correlations.

• In geo-statistics the information in spatial location allows you to complete distances between observation and to model autocorrelation as function of distance

• Kriging and co-kriging are prediction methods and the ultimate is to produce a surface of predicted tenure values in case study pilot villages.

• For critical analysis we would like to now how good are the tenure predictions. In this case three deferent of Tenure Prediction Maps (TPM) can be produced and two of them have standard error associated with them.

• The tenure system prediction distribution pattern changes for each location

• Thus, when holding threshold value constant , a probability map is produced for the whole surface .when holders the tenure probability constant , a Tenure Quantile Map(TQM) is produced for the whole village land surface.

• Standard error tenure maps can be produced for tenure prediction and tenure probability maps.

• The various methods and output tenure maps, along with major assumption are given the following table

Table of Various methods and out put tenure

maps ,along with major assumption

Kriging and

cokriging

prediction Prediction

standard error

Quantile

maps

Probability

map

Standard error of

indication

Ordinary Yes Yes Yes* Yes*

Universal Yes Yes Yes* Yes*

Simple Yes Yes Yes* Yes*

Indicator Yes Yes

Probability Yes Yes

disjunctive Yesx Yesx Yesx Yesx

Key

• * Requires assumption of multivariate normal distribution

• x Requires assumption of pairwise bivariate normality

• Analysis and out put

1. Creating tenure kriging map using defauts

2. Creating tenure map using ordinary kriging

3. Creating training and test datasets to validate the predictions

4. Creating a tenure prediction standard error map

5. Creating a tenure prediction map using validation

6. Creating a tenure prediction map while applying a transformation

7. Creating a tenure prediction map using detrending.

24.Material and method.

• Material and method that was used include;

• Differential GPS.

• Software such as ArcGIS and Google earth image

• The method that was used included data processing that incorporated

• Data check .

• Data manipulation.

• Graphic representation.

• Interpolation method through using of kriging interpolation method was used.

• The method used is ordinary kriging and for searching neighbor was smooth.

• The result were as indicated in geo statistical wizard kriging step 4 of 5 search neighborhood

• The point in the eclipse showing tenure similarities and layers showing variation (decrease or increase of tenure variation) but the variation decrease from the center of the eclipse was observed.

25. Methodology conceptual model.

• The methodology conceptual model was designed and used accordingly.

• The flow that (conceptual model) showed the data and activities that were involve in

achieving the objectives of the study starting from the data collection to the final

result.

• After obtaining land information data check and manipulation was done to achieve

kriging interpolation. Then the ordinary kriging was performed to achieve maps and

attribute table after semivariogram and smooth process was done .

• The result was tenure map and tenure attribute table showing tenure coherence.

26.Findings.

• The tenure map showing land tenure system at Nselewa village in Mbozi indicate tenure coherence of Mwashela clan land pattern.

• Coherence of Mwashelas clanland and some disintegration towards the southern part of their tenure pattern.

• There is a confirmation of Mwashela clan land tenure pattern

• There is an indication of tenure interference of Siwale Mwampashe, Giru and Mzilio clan.

• Mwashela clan land indicate tenure continuation and dominance suggesting tenure cohension to a great extent.

• The occurancy of Zawadi Mwampashe , Marry Mwaipopo, Sarah Uziho suggest that gender segregation is non existence.

• The occurancy of Damiana Mwansela indicates that female clan members are considered equally in customary system.

27. Summary and conclusion.

• Land administration is a millennium civilization.

• Inclusion of all people to land relationship is vital

• National land Administration System (LAS), incremental improve mental improvements to insecurity of tenure inclusion of indigenous and

management and application of social tenure customary ,

• domain model can assist the approach

• Certificate of Customary Right of Occupancy(CCRO) facilitates the removal of customary land tenure from the domain of customary law enable it

to be assimilated in the statutory system of right of occupancy .The trend of Certificate of Granted Right of Occupancy (CGRO) is observed to

replace CCRO in village land and CGRO and Residential License (RL) in informal areas and is preferred by financial institution to CCRO and RL.

• The CCRO and CGRO are distinct and fail to benefit from the clashes and insecurity of tenure of villagers and hunter gatherers.

28.PBFP AND BEST

• Successful attempts reforms to transport familiar Land Administration Tools

• Did not adopt the theoretical Maturity of Land Administration(MOLA)

• Did not explain how land Consolidation, Restitution, Reconfiguration and Redistribution could be carried on and for whose interest

• They did influence to a lesser extent new forms such as good governance dimensions;Paricipation,Accountability,Transparency,Equity,Efficiency and Effectiveness of Land Administration System(LAS)

31.GEOSTATISTICAL ANALYSIS

• Can be used to analyze and predict customary tenure in un sampled areas

• Can use tenure predicted maps in communal land

• Can use predicted hunting blocks for indigenous communities

• Hunter gatherers security of land tenure can be mapped predicted and

validated

SPATIAL STATISTICAL ANALYSIS

• Useful in the construction of representation models

• Models attempting to stimulate processes in village land

• Land scape processes and indigenous models and indigenous lands

• Representation model describe the objects

• Process model describe the interaction of the objects that are modelled

33.TENURE UNDERSTANDING

• Understanding and mapping with;

• Ordinary,simple,universal,indicator,probability, and disjunctive kriging and Co-Kriging.

• Deep insight systems

• Advanced tenure inventory analysis

• Investigating tenure spatial analysis

• Tenure crossvalidation

• Removing tenure trends

• Tenure bivariate normal distribution

34.Security of Land Tenure

• Spatial tenure pattern

• Coherence and distortion of historical tenure pattern

• Correlation for checking tenure bivariate normal distribution

• Land tenure validation and trending can be executed

35.LAND REFORM GOVERNANCE

• Influenced by the activities of donor agency

• Sustainability is questionable

• Spatial tenure pattern using names of owners or occupiers

• Tenure bivariate normal distribution

• Names and land size or term checks tenure bivariate normal distribution

• We need stable and sustainable LAS in enterprise platform