CSISS Tools and Spatial Analysis Software
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1 CSISS Tools and Spatial Analysis Software June 5, 2006 Stephen A. Matthews Associate Professor of Sociology & Anthropology, Geography and Demography Director of the Geographic Information Analysis Core Population Research Institute Stephen A. Matthews GISPopSci Monday June Slide 01
2 Outline CSISS Tools Commercial Software Open Source Software Programming Languages Stephen A. Matthews GISPopSci Monday June Slide 02
3 Outline Commercial Software GWR (nominal fee) Open Source Software GeoDa GeoVISTA Studio/ESTAT CrimeStat Programming Languages R STARS FlowMapper Stephen A. Matthews GISPopSci Monday June Slide 03
4 Special Issue 2006 Vol 38 (1) Stephen A. Matthews GISPopSci Monday June Slide 04
5 GeoDa can be found at the CSISS site: Under Spatial Tools Stephen A. Matthews GISPopSci Monday June Slide 05
6 Stephen A. Matthews GISPopSci Monday June Slide 06
7 GeoDa Webpage: Stephen A. Matthews GISPopSci Monday June Slide 07
8 GeoDa: Features Stephen A. Matthews GISPopSci Monday June Slide 08
9 GeoDa: Documentation Stephen A. Matthews GISPopSci Monday June Slide 09
10 GeoDa: Documentation Stephen A. Matthews GISPopSci Monday June Slide 10
11 Contraceptive Prevalence Rates in Nepal Using GeoDa (Developed by Luc Anselin) Stephen Matthews Stephen A. Matthews GISPopSci Monday June Slide 11
12 Basic GeoDA Interface Stephen A. Matthews GISPopSci Monday June Slide 12
13 GeoDA: Creating spatial weights Stephen A. Matthews GISPopSci Monday June Slide 13
14 GeoDA: Box Plot Maps Stephen A. Matthews GISPopSci Monday June Slide 14
15 GeoDA: Dynamic Windows Stephen A. Matthews GISPopSci Monday June Slide 15
16 GeoDA: Exploratory Spatial Data Analysis Stephen A. Matthews GISPopSci Monday June Slide 16
17 GeoDA: ESDA based on circle selection Stephen A. Matthews GISPopSci Monday June Slide 17
18 GeoDA: ESDA based on circle selection Stephen A. Matthews GISPopSci Monday June Slide 18
19 GeoDA: ESDA using boxplot selection Stephen A. Matthews GISPopSci Monday June Slide 19
20 GeoDA: ESDA using boxplot selection Stephen A. Matthews GISPopSci Monday June Slide 20
21 Moran Scatterplot High-High Selection Stephen A. Matthews GISPopSci Monday June Slide 21
22 Moran Scatterplot Low-Low Selection Stephen A. Matthews GISPopSci Monday June Slide 22
23 Moran Scatterplot Low-Low Selection Stephen A. Matthews GISPopSci Monday June Slide 23
24 Moran Scatterplot High-Low Selection Stephen A. Matthews GISPopSci Monday June Slide 24
25 Moran Scatterplot Low-High Selection Stephen A. Matthews GISPopSci Monday June Slide 25
26 LISA statistics Stephen A. Matthews GISPopSci Monday June Slide 26
27 Mapping and scatterplot Stephen A. Matthews GISPopSci Monday June Slide 27
28 Bivariate Moran scatterplots Stephen A. Matthews GISPopSci Monday June Slide 28
29 GeoDA: Data export functionality Stephen A. Matthews GISPopSci Monday June Slide 29
30 Stephen A. Matthews GISPopSci Monday June Slide 30
31 STARS - Stephen A. Matthews GISPopSci Monday June Slide 31
32 STARS - Open source written in Python Supports dynamic ESDA time Designed for regional income dynamics but can be applied to a wide set of socioeconimc processes with data measured for areal units over multiple periods of time. Stephen A. Matthews GISPopSci Monday June Slide 32
33 Stephen A. Matthews GISPopSci Monday June Slide 33
34 Stephen A. Matthews GISPopSci Monday June Slide 34
35 Stephen A. Matthews GISPopSci Monday June Slide 35
36 Stephen A. Matthews GISPopSci Monday June Slide 36
37 Stephen A. Matthews GISPopSci Monday June Slide 37
38 Stephen A. Matthews GISPopSci Monday June Slide 38
39 Stephen A. Matthews GISPopSci Monday June Slide 39
40 Waldo Tobler s FlowMapper Stephen A. Matthews GISPopSci Monday June Slide 40
41 Waldo Tobler s FlowMapper Stephen A. Matthews GISPopSci Monday June Slide 41
42 Waldo Tobler s FlowMapper Stephen A. Matthews GISPopSci Monday June Slide 42
43 GeoVista Studio (Penn State) ESTAT ESTAT Lab on Monday June 12, 2006 Stephen A. Matthews GISPopSci Monday June Slide 43
44 Geographically Weighted Regression Stephen A. Matthews GISPopSci Monday June Slide 44
45 Classic (ONLY) Citation A.S. Fotheringham, C. Brunsdon and M.E. Charlton. Published October 3, Geographically Weighted Regression: The Analysis of Spatially Varying Relationships Chichester: Wiley, UK. (Price = $95.00). CONTENTS 1. Local Statistics and Local Models for Spatial Data 2. Geographically Weighted Regression: the Basics 3. Extensions to the Basic GWR Model 4. Statistical Inference and Geographically Weighted Regression 5. GWR and Spatial Autocorrelation 6. Scale Issues and Geographically Weighted Regression 7. Geographically Weighted Local Statistics 8. Extensions of Geographically Weighting 9. Software for Geographically Weighted Regression 10. Summary and Future Research Stephen A. Matthews GISPopSci Monday June Slide 45
46 Geographically Weighted Regression Based on the premise that relationships between variables measured at different locations might not be constant over space (CF: conditional scatterplots) If relations do vary significantly over space then serious questions are raised about the reliability of traditional global-level analysis. Stephen A. Matthews GISPopSci Monday June Slide 46
47 Local vs. Global Statistical Models There is a growing literature and techniques on examining local relationships in aspatial data: LOWESS regression Kernel regression Drift Analysis of Regression Parameter (DARP) There are emerging techniques for local analysis of spatial data: Local Indicators of Spatial Association (LISA) Geographically Weighted Regression (GWR) Stephen A. Matthews GISPopSci Monday June Slide 47
48 Global vs. Local statistics Global Summarize data for a whole region Single-value statistic Non-mappable GIS-unfriendly Aspatial or spatially limited Emphasis on similarities across space Search for regularities or laws EXAMPLE Classic regression techniques Local Local disaggregations of global statistics Multiple value Mappable GIS-friendly Spatial Emphasis on differences across space Search for exception or local hot-spots Geographically Weighted Regression Stephen A. Matthews GISPopSci Monday June Slide 48
49 Education Attainment in Georgia using Geographically Weighted Regression (Tutorial Data) (Stewart Fortheringham et al): Stephen A. Matthews GISPopSci Monday June Slide 49
50 Setting up a GWR Model DV = Pct with bachelors degree. Are there geographical variations in the relationship between educational attainment and a series of six independent variables? Slide 50
51 Parameter Estimates and Standard Errors from a Global model fitted to the data & then GWR model results The reduction in the AIC from the global model suggests that the local model is better even accounting for differences in degrees of freedom. Slide 51
52 The ANOVA tests the null hypotheses that the GWR represents no improvement over the global model (see F-test). The main output from GWR is a set of local parameter estimates regression points (saved to a file). A convenient indication of the extent of the variability in local parameter estimates is a five number summary. Slide 52
53 The ANOVA tests the null hypotheses that the GWR represents no improvement over the global model (see F-test). Both % Black and % Foreign Born exhibit significant spatial nonstationarity. Slide 53
54 Geographically Weighted Regression & Mapping The main output from GWR is a set of local parameter estimates and associated diagnostics. Unlike the single global values these values can be mapped. Indeed, in large datasets, mapping or visualizing the data is the only way to make sense of the large volume of output. Stephen A. Matthews GISPopSci Monday June Slide 54
55 Examples of displaying GWR results as a point map. Points are proportional symbols and lie at the geographic centroids of the counties. Local parameter estimates associated with the variable Percent Black. The local parameter estimates change sign over space, being negative in the NE and positive in the south. GLOBAL PctBlack Est = SE = T = Slide 55
56 Examples of displaying GWR results as a point map. Points are proportional symbols and lie at the geographic centroids of the counties. Local parameter estimates for Percent Foreign Born. The local parameter estimates increase towards the north. GLOBAL Pct FB Est = SE = T = Slide 56
57 Examples of displaying GWR results for areas. The local parameter estimates for Pct Black change sign over space, being negative in the NE and positive in the south. Local parameter estimates for Percent Foreign Born. The local parameter estimates increase towards the north. Slide 57
58 Citation: Ned Levine, CrimeStat III: A Spatial Statistics Program for the Analysis of Crime Incident Locations (version 3.0). 2004, Ned Levine & Associates: Houston, TX/ National Institute of Justice: Washington, DC. Stephen A. Matthews GISPopSci Monday June Slide 58
59 CrimeStat A spatial statistics program for the analysis of crime (point) incident locations developed by Ned Levine (funded by NIJ). The program, the manual, and sample data sets can be downloaded from the NIJ and ICPSR at: This is one of the most, if not the most, frequently downloaded software products from ICPSR Stephen A. Matthews GISPopSci Monday June Slide 59
60 CrimeStat CrimeStat is Windows-based and interfaces with most desktop GIS programs. Inputs: The program inputs incident locations (e.g., robbery locations) in 'dbf', 'shp', ASCII or ODBC-compliant (Open DataBase Connectivity Microsoft) formats using either spherical or projected coordinates. Outputs: It calculates various spatial statistics and writes graphical objects to ArcView/ArcGIS, MapInfo, Atlas*GIS, Surfer for Windows, and Spatial Analyst. Stephen A. Matthews GISPopSci Monday June Slide 60
61 CrimeStat is a spatial statistics package which can analyze crime incident location data. Its purpose is to provide a variety of tools for the spatial analysis of crime incidents or other point locations. CrimeStat has a collection of statistical tools for the analysis of point/incident locations and includes a range of diagnostic and modeling spatial statistics, including statistics for measuring spatial distribution, for examining distances between incident locations, for detecting hot spots, for interpolating one-variable and two-variable density surfaces, and for analyzing space-time interactions. Stephen A. Matthews GISPopSci Monday June Slide 61
62 CrimeStat Organization: Data Set up Spatial Description Spatial Modeling Crime Travel Demand Modeling Options Stephen A. Matthews GISPopSci Monday June Slide 62
63 Data Set up Primary File Stephen A. Matthews GISPopSci Monday June Slide 63
64 Data Set up Secondary File Stephen A. Matthews GISPopSci Monday June Slide 64
65 Data Set up Reference File Stephen A. Matthews GISPopSci Monday June Slide 65
66 Data Set up Measurement Parameters Stephen A. Matthews GISPopSci Monday June Slide 66
67 Spatial Description Spatial Distribution Stephen A. Matthews GISPopSci Monday June Slide 67
68 Spatial Modeling Interpolation Stephen A. Matthews GISPopSci Monday June Slide 68
69 Results Files Stephen A. Matthews GISPopSci Monday June Slide 69
70 Resulting Shapefiles Stephen A. Matthews GISPopSci Monday June Slide 70
71 Mapping the Results in ArcGIS basic data layers Stephen A. Matthews GISPopSci Monday June Slide 71
72 Mapping the Results in ArcGIS Mean Center (MC) Stephen A. Matthews GISPopSci Monday June Slide 72
73 Results in ArcGIS Mean Center and standard deviational ellipse Stephen A. Matthews GISPopSci Monday June Slide 73
74 Results in ArcGIS Mean Center and standard deviational ellipse Stephen A. Matthews GISPopSci Monday June Slide 74
75 Results in ArcGIS Neighborhood Hierarchical Clusters Stephen A. Matthews GISPopSci Monday June Slide 75
76 Results in ArcGIS Neighborhood Hierarchical Clusters Stephen A. Matthews GISPopSci Monday June Slide 76
77 Results in ArcGIS Kernel Density Surface (blank) Stephen A. Matthews GISPopSci Monday June Slide 77
78 Results in ArcGIS Kernel Density Surface Stephen A. Matthews GISPopSci Monday June Slide 78
79 Results in ArcGIS Kernel Density Surface Stephen A. Matthews GISPopSci Monday June Slide 79
80 R See the Comprehensive R Archive Network (CRAN) Stephen A. Matthews GISPopSci Monday June Slide 80
81 R Supports a very wide range of statistical techniques and is easily extensible via userdefined functions written in its own language, or using dynamically linked modules written in C, C++, Fortran. R can be used with Linux, Unix, and MicroSoft. Stephen A. Matthews GISPopSci Monday June Slide 81
82 R See Geographical Analysis 2006 article by Roger Bivand. Lance A. Waller & Carol A. Gotway Applied Spatial Statistics for Public Health Data. Holboken, NJ: Wiley. (selected R code to support this book) William N. Venables & Brian D. Ripley Modern Applied Statistics with S (4 th Edition) Stephen A. Matthews GISPopSci Monday June Slide 82
83 Roger Bivand summary section Advancing spatial data analysis in R 1. Vitality and rapid development of R (open source) 2. Reproduciblity of research 3. Link to applied statisticians 4. Spatial data analysis community working together and exchanging ideas Stephen A. Matthews GISPopSci Monday June Slide 83
84 R Manuals Stephen A. Matthews GISPopSci Monday June Slide 84
85 R Spatial Data Analysis (from Bivand, 2006) Stephen A. Matthews GISPopSci Monday June Slide 85
86 R Packages sp Stephen A. Matthews GISPopSci Monday June Slide 86
87 R Packages spdep Stephen A. Matthews GISPopSci Monday June Slide 87
88 R Packages geor Stephen A. Matthews GISPopSci Monday June Slide 88
89 R Packages spgwr Stephen A. Matthews GISPopSci Monday June Slide 89
90 R News June 2001 September 2001 Stephen A. Matthews GISPopSci Monday June Slide 90
91 James LeSage - Stephen A. Matthews GISPopSci Monday June Slide 91
92 Racial Segregation in Cape Town using Arcview 3.x and SEG (Developed by David Wong) Stephen Matthews Stephen A. Matthews GISPopSci Monday June Slide 92
93 Basic Arcview and SEG interface Slide 93
94 SEG: Calculation of index D African vs. Coloured African vs. Indian African vs. White Slide 94
95 SEG: Spatial D for 2 groups African vs. Coloured African vs. Indian African vs. White Slide 95
96 SEG: Calculation of Multi-group D index Slide 96
97 SEG: Drawing Ellipses Slide 97
98 SEG: Drawing Ellipses Slide 98
99 SEG: Drawing Ellipses Slide 99
100 SEG: Diversity Index H Slide 100
101 SEG: Mapping the Diversity Index H Slide 101
102 SEG: Most Diverse Ward 2001 Slide 102
103 SEG: Most Diverse Ward 2001 Population Group Composition of Ward #59 in 2001 African 20% White 50% Coloured 21% Indian 9% Slide 103
104 SEG: Least Diverse Ward 2001 Population Group Composition of Ward #89 in 2001 Coloured 0% Indian 0% White 0% African 100% Slide 104
105 SEG: Least Diverse Ward 2001 Population Group Composition of Ward #89 in 2001 Coloured 0% Indian 0% White 0% African 100% Slide 105
106 SEG: Ten Most Diverse Ward 2001 Slide 106
107 SEG: Ten Least Diverse Ward 2001 Slide 107
108 Stephen A. Matthews GISPopSci Monday June Slide 108
109 Stephen A. Matthews GISPopSci Monday June Slide 109
110 Stephen A. Matthews GISPopSci Monday June Slide 110
111 Stephen A. Matthews GISPopSci Monday June The End
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114 Geographical Analysis Machine
The CrimeStat Program: Characteristics, Use, and Audience
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