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 5 2006 Slide 01
Outline CSISS Tools Commercial Software Open Source Software Programming Languages Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 02
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 5 2006 Slide 03
Special Issue 2006 Vol 38 (1) Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 04
GeoDa can be found at the CSISS site: http://www.csiss.org Under Spatial Tools Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 05
Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 06
GeoDa Webpage: http://geoda.uiuc.edu/default.php Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 07
GeoDa: Features Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 08
GeoDa: Documentation Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 09
GeoDa: Documentation Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 10
Contraceptive Prevalence Rates in Nepal Using GeoDa (Developed by Luc Anselin) Stephen Matthews Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 11
Basic GeoDA Interface Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 12
GeoDA: Creating spatial weights Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 13
GeoDA: Box Plot Maps Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 14
GeoDA: Dynamic Windows Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 15
GeoDA: Exploratory Spatial Data Analysis Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 16
GeoDA: ESDA based on circle selection Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 17
GeoDA: ESDA based on circle selection Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 18
GeoDA: ESDA using boxplot selection Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 19
GeoDA: ESDA using boxplot selection Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 20
Moran Scatterplot High-High Selection Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 21
Moran Scatterplot Low-Low Selection Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 22
Moran Scatterplot Low-Low Selection Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 23
Moran Scatterplot High-Low Selection Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 24
Moran Scatterplot Low-High Selection Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 25
LISA statistics Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 26
Mapping and scatterplot Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 27
Bivariate Moran scatterplots Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 28
GeoDA: Data export functionality Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 29
Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 30
STARS - http://stars-py.sourceforge.net/ Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 31
STARS - http://stars-py.sourceforge.net/ 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 5 2006 Slide 32
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Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 39
Waldo Tobler s FlowMapper http://csiss.ncgia.ucsb.edu/clearinghouse/flowmapper/ Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 40
Waldo Tobler s FlowMapper http://csiss.ncgia.ucsb.edu/clearinghouse/flowmapper/ Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 41
Waldo Tobler s FlowMapper http://csiss.ncgia.ucsb.edu/clearinghouse/flowmapper/ Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 42
GeoVista Studio (Penn State) ESTAT http://www.geovista.psu.edu/estat/ ESTAT Lab on Monday June 12, 2006 Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 43
Geographically Weighted Regression http://ncg.nuim.ie/ncg/gwr/index.htm Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 44
Classic (ONLY) Citation A.S. Fotheringham, C. Brunsdon and M.E. Charlton. Published October 3, 2002. 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 5 2006 Slide 45
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 5 2006 Slide 46
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 5 2006 Slide 47
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 5 2006 Slide 48
Education Attainment in Georgia using Geographically Weighted Regression (Tutorial Data) (Stewart Fortheringham et al): http://ncg.nuim.ie/ncg/gwr/software.htm Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 49
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
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
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
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
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 5 2006 Slide 54
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 = 0.021 SE = 0.025 T = 0.867 Slide 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 for Percent Foreign Born. The local parameter estimates increase towards the north. GLOBAL Pct FB Est = 1.255 SE = 0.309 T = 4.054 Slide 56
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
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 5 2006 Slide 58
CrimeStat A spatial statistics program for the analysis of crime (point) incident locations developed by Ned Levine (funded by NIJ). http://www.nedlevine.com The program, the manual, and sample data sets can be downloaded from the NIJ and ICPSR at: http://www.icpsr.umich.edu/nacjd/crimestat.html This is one of the most, if not the most, frequently downloaded software products from ICPSR Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 59
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 5 2006 Slide 60
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 5 2006 Slide 61
CrimeStat Organization: Data Set up Spatial Description Spatial Modeling Crime Travel Demand Modeling Options Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 62
Data Set up Primary File Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 63
Data Set up Secondary File Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 64
Data Set up Reference File Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 65
Data Set up Measurement Parameters Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 66
Spatial Description Spatial Distribution Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 67
Spatial Modeling Interpolation Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 68
Results Files Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 69
Resulting Shapefiles Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 70
Mapping the Results in ArcGIS basic data layers Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 71
Mapping the Results in ArcGIS Mean Center (MC) Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 72
Results in ArcGIS Mean Center and standard deviational ellipse Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 73
Results in ArcGIS Mean Center and standard deviational ellipse Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 74
Results in ArcGIS Neighborhood Hierarchical Clusters Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 75
Results in ArcGIS Neighborhood Hierarchical Clusters Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 76
Results in ArcGIS Kernel Density Surface (blank) Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 77
Results in ArcGIS Kernel Density Surface Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 78
Results in ArcGIS Kernel Density Surface Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 79
R See the Comprehensive R Archive Network (CRAN) http://cran.r-project.org Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 80
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 5 2006 Slide 81
R See Geographical Analysis 2006 article by Roger Bivand. Lance A. Waller & Carol A. Gotway. 2004. Applied Spatial Statistics for Public Health Data. Holboken, NJ: Wiley. (selected R code to support this book) William N. Venables & Brian D. Ripley. 2004. Modern Applied Statistics with S (4 th Edition) Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 82
Roger Bivand 2006 - 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 5 2006 Slide 83
R Manuals Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 84
R Spatial Data Analysis (from Bivand, 2006) Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 85
R Packages sp Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 86
R Packages spdep Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 87
R Packages geor Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 88
R Packages spgwr Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 89
R News June 2001 September 2001 Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 90
James LeSage - http://www.spatial-econometrics.com/ Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 91
Racial Segregation in Cape Town using Arcview 3.x and SEG (Developed by David Wong) Stephen Matthews Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 92
Basic Arcview and SEG interface Slide 93
SEG: Calculation of index D African vs. Coloured African vs. Indian African vs. White Slide 94
SEG: Spatial D for 2 groups African vs. Coloured African vs. Indian African vs. White Slide 95
SEG: Calculation of Multi-group D index 2001 1996 Slide 96
SEG: Drawing Ellipses Slide 97
SEG: Drawing Ellipses Slide 98
SEG: Drawing Ellipses Slide 99
SEG: Diversity Index H Slide 100
SEG: Mapping the Diversity Index H Slide 101
SEG: Most Diverse Ward 2001 Slide 102
SEG: Most Diverse Ward 2001 Population Group Composition of Ward #59 in 2001 African 20% White 50% Coloured 21% Indian 9% Slide 103
SEG: Least Diverse Ward 2001 Population Group Composition of Ward #89 in 2001 Coloured 0% Indian 0% White 0% African 100% Slide 104
SEG: Least Diverse Ward 2001 Population Group Composition of Ward #89 in 2001 Coloured 0% Indian 0% White 0% African 100% Slide 105
SEG: Ten Most Diverse Ward 2001 Slide 106
SEG: Ten Least Diverse Ward 2001 Slide 107
http://ua.t.u-tokyo.ac.jp/okabelab/freesat/ Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 108
http://www.ai-geostats.org/ Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 109
http://www.ai-geostats.org/ Stephen A. Matthews GISPopSci Monday June 5 2006 Slide 110
E-mail: matthews@pop.psu.edu Stephen A. Matthews GISPopSci Monday June 5 2006 The End
http://www.satscan.org/
http://www.satscan.org/
Geographical Analysis Machine http://www.ccg.leeds.ac.uk/software/gam/