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GIS Demographics Spatial Analysis and Modeling

GIS Demographics Spatial Analysis and Modeling. by Wadembere Mugumbu Ismail Supervisor: Assoc. Prof. Dr. Lee Lik Meng School of Housing, Building and planning Universiti Sains Malaysia May 2001. The Study.

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GIS Demographics Spatial Analysis and Modeling

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  1. GIS Demographics Spatial Analysis and Modeling by Wadembere Mugumbu Ismail Supervisor: Assoc. Prof. Dr. Lee Lik Meng School of Housing, Building and planning Universiti Sains Malaysia May 2001

  2. The Study • Objective: Investigate how GIS can be used to generate demographics for planning analysis • Research questions • What is needed by the planner from demographics • How demographics analysis is being carried • What are the available GIS tools • How to geocode/georeference demographic data • Which GIS techniques to apply for specific demographic analysis • Which GIS dimension to apply for demographic analysis • What should a characterization of demographics achieve • How should and what are surface terms to characterize demographics • How to carry out multi-vertical GIS demographics

  3. Methodology • GIS Spatial Analysis Requirements • GIS Spatial Analysis Techniques • Target of GIS Demographic Spatial Analysis • 2D GIS Demographic Spatial Analysis • 2.5D GIS Demographic Spatial Analysis • 3D (solid) GIS Demographic Spatial Analysis • GIS Spatial Analysis Requirements • Geocoding (georeferencing) • Geoprocessing • Linking tabular data • Visualization

  4. GIS Spatial Analysis Techniques • Single layer operations • Multiple-layer operations • Spatial modeling (explanatory and predictive models) • Point pattern analysis • Network analysis • Surface analysis (continuous spatial analysis) • Solid modeling • Spatial analysis tools • Spatial overlay, • Boundary analysis, • Proximity analysis, • Buffer analysis, • Clustering

  5. Target of GIS Demographic Spatial Analysis • Show spatial distribution of demographic characteristics (DCs) • Show spatial variation of DCs. • Show spatial relation between DCs. • Show spatial relation between DCs and other features • How DCs can be represented as continuous variables. • Represent and visualize spatial influence of DCs (surface analysis) • How the demographic quantities vary spatially. • Multi-vertical representation of DCs.

  6. 2D GIS Demographic AnalysisGIS analysis preparation (base map, bldg, pop)Available GIS demographics spatial analysis Unable to accomplish demographics Position and functions of demographics analyst

  7. With Demographics Analyst we are able to achieve • Demographics nearest neighbor analysis • Demographics spatial change analysis • Spatial progressive similarity clustering • Demographics spatial alternative analysis • Demographics spatial segregation and integration • Selection of analysis variables

  8. 2.5D Demographic Spatial Analysis • 2D unable • Represent and visualize influence of DCs • Show how the demographic quantities vary spatially • Spatially analyze multi-vertical demographics • Visualize DCs in relation with other features. • Spatially to represent DCs which have only two factors differentiating them • Geo-demographic interpolation and extrapolation • Demographics holes, breaks and boundaries • Generation of demographics points at the boundaries • MODC extrapolation at boundaries • TIN and Delaunay Triangulation

  9. Population Overlaid on Buildings

  10. GIS Surface Demographics Analysis • Demographic Quantitative Analysis • Iso-lines and vertical demographic • Quantitative spatial effect analysis • Quantitative spatial analysis • Transparent neighborhood analysis • Demographic Visibility Analysis • demographic viewshed • demographic spatial limit (horizon) • linear spatial demographic variation • Demographic Variation Analysis • Demographic spatial variation (slope) • Demographic directional variation • Others on the figure

  11. Demographic Surface Features

  12. 3D Demographic Spatial Analysis • 2.5D unable • Spatially analyze multi-vertical demographics • Spatially to represent DCs which have only two factors differentiating them • Set out to carry out 3D Modeling • Consideration in 3D • 3D spatial interpolation • Generation of 3rd dimension • Multiresolution • Techniques to use • Use of objects • Constrained Delaunay triangulation • Use Polyhedrons to for incremental demographic solid modeling • Employ GMV, MV and DV to build 3D

  13. Conclusion • Findings • We need to use 2D, 2.5D and 3D GIS to accomplish GIS-DSA • Current GIS need more modules in order to accomplish GIS-DSA • Developers need to adopt true 3D in addition to 2.5D for GIS-DSA • Contributions • Developed demographics analyst • Documented a methodology for demographics visualization • Developed geo-demographic interpolation and extrapolation procedure • Coined demographics terms for GIS analysis • Incremental 3D demographics modeling has been introduced • Publications and thesis (www.hbp.usm.my/thesis/heritageGIS) • Future work • use objects, uncertainty, automation, temporal – 4D GIS

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