1. WorkShop 2007 Final Presentation Lu, Zhixiang July, 30, 2007

2. 2 Intro. to Project ► Title: Spatial Clustering Analysis using LISA (Local Indicator of Spatial Association) ► Type: GIS tool application development  In depth exploration of an existing technology  Software tool development to do implementation ► Application tool:  ArcGIS 9.2 (ArcView)  VBA (Visual Basic for Application)  ArcObject

3. 3 Objectives ► Automatically achieving redistribution of polygons to reduce the numbers of extreme clustering ► Potential Application Area  Partitions of administrative region or electoral districts ► Income, population distribution, occupied houses, sexes, ethnic  Wildlife habit analyses ► Species of animal, duration of sunshine, duration of night, mean monthly precipitation, coverage rate of forest

4. 4 Object (cont.) AB C

5. 5 Data ► Data  Data used in this project is demographic data, which is a statistic characterizing human populations data, (or segments of human populations broken down by age or sex or income etc.).  Population density, age, income, sex, occupied rate of houses etc., need to be treated as clustering variables to do the spatial clustering analysis   Tested data for this project is Dallas County 2000 census tract data downloaded from ESRI

6. 6 Terminology ► LISA:  Abbreviation of Local Indicator of Spatial Association detect significant spatial clustering around individual locations and pinpoint areas that contribute most to an overall pattern of spatial dependence.  For each location, LISA values allow for the computation of its similarity with its neighbors and also to test its significance  Higher LISA, more homogeneous characteristic with its neighbor  Lower LISA, more heterogeneous characteristic with its neighbor  So for higher LISA, delete it or merge it with its neighbor to lower LISA to balance clustering.  Getis-Ord Gi* :  another popular method for local spatial clustering analysis that can distinguish between HH and LL clustering, which local Moran ’ s I CAN NOT.

7. 7 Terminology (cont.) ► Spot and Outlier:  Locations with high values with similar neighbors: high-high, in other words, positive, significant LISAs located near other positive significant LISAs. Also known as "hot spots".  Locations with low values with similar neighbours: low-low. Also known as "cold spots".  Locations with high values with low-value neighbours: high-low. Potential "spatial outliers".  Locations with low values with high-value neighbours: low-high. Potential "spatial outliers".  Locations with no significant local autocorrelation.

8. 8 Methodology ► Methodology (Algorithm) 1. selecting polygon layer and cluster variable 2. re-symbology the map using Natural Breaks, Equal Intervals, Quantile in order to represent clustering area clearly 3. pointing to a clustering area to do research 4. select a method for Local Moran ’ s I or Getis-Ord G i 5. select the directory for output dataset 6. caculate LISA for all of the polygons and list the LISA of polygons which share border with selected clustering area 7. find out the biggest LISA 8. merge the polygons, which has the biggest LISA, with the selected clustering area 9. update data information of the map and re-caculate LISA 10. user can continue to merge or re-select other clustering area and do the merge.

9. 9 Problems Encountered ► Difficult to understand spatial statistics term and formula  Read some papers, books and some existing projects related to this area ► Difficult to obtain other polygons which share the border with the selected polygon 1.Extract selected polygon to generate a new copy 1.Create a new polygon file for that new copy 1.Use "intersect" method in ITopologicalOperator2 to select other polygons 1.Use "IDs" method in ISelectionSet to obtain the FID of those polygons

10. 10 Conclusion ► Objective: Automatically achieving redistribution of polygons to reduce the numbers of extreme clustering  Successfully complete the redistribution through merging some polygons  Successfully reduce the numbers of extreme clustering through using LISA and Getis-Ord Gi*

11. 11 Relevant literature ► Using global Moran ’ s I to test for significant levels of spatial autocorrelation among views of water quality in Salado and Leon Creek; then employ a LISA to identify and map the statistically significant similar responses related to the creeks. ► Spatial correlation and demography, exploring India ’ s demographic patterns. ► Sergio Palladini used global statistics to solve Modifiable Areal Unit Problem (MAUP). Global statistics can tell us whether or not an overall configuration is autocorrelated, but not where the unusual interaction are. However, LISA can provide a way of finding where in the study area there are interesting or anomalous data patterns.

12. 12 Reference 1. David O'Sullivan and David J. Unwin, Geographic Information Analysis, 2003 2. Daniel A. Griffith, Methods: Spatial autocorrelation, 2007, International Encyclopedia of Human Geography 3. Luc Anselin, Ibnu Syabri, Youngihn Kho, GeoDa: An Introduction to Spatial Data Analysis 4. Anselin, L., 1995. Local indicators of spatial association - LISA. Geographical Anal. 27(2), 93 - 115. 5. Brody, S. D., et al. 2005. Exploring the mosaic of perceptions for water quality across watersheds in San Antonio, Texas, Landscape and Urban Planning 73 (2005) 200 - 214 6. Oliveau, S., 2004. Spatial correlation and demography: Exploring India's demographic patterns, UMR Geographic-city, Paris 7. Luc Anselin, 1998, GIS Research Infrastructure for Spatial Analysis of Real Estate Markets, Journal of Housing Research, Volume 9, Issue 1 8. Bong, C. W., et al. 2004. State-of-the-Art Multiobjective Metaheuristic for Redistricting, 0-7803-8669- 8/04, IEEE