Exploratory Spatial Data Analysis Using GeoDA: : An Introduction

Transcription

1 Exploratory Spatial Data Analysis Using GeoDA: : An Introduction Prepared by Professor Ravi K. Sharma, University of Pittsburgh Modified for NBDPN 2007 Conference Presentation by Professor Russell S. Kirby, University of Alabama at Birmingham

2 Objectives Using MACDP data on chromosomal abnormalities measured across census tracts, we will demonstrate the use of GeoDa to: Start a project, import data, use basic functions Perform Exploratory Spatial Data Analysis (ESDA) Calculate rates and weights Create spatial weight matrix Perform spatial autocorrelation The examples that follow are based on a dataset for county-level analysis of low birth weight for the state of Pennsylvania.

3 GeoDa GeoDa is a freely available software program for exploratory spatial data analysis (ESDA), developed by Professor Luc Anselin of the University of Illinois It can be downloaded from the following URL:

4 Beginning a Project

5 Opening GeoDa To begin click on the GeoDa Icon

6 Starting a project

7 Open a map with shape file

8 The base map

9 Editing features

10 Menu toolbar features

11 Icon toolbar features

12 Creating Maps and Selecting Features

13 Create choropleth maps

14 Choropleth map steps

15 Creating choropleth (quantile)) maps

16 Creating quantile maps

17 Final choropleth map

18 Open a new copy of base map

19 Create a new choropleth map

20 Dynamic map selection option

21 Selecting map areas

22 Table features

23 Table sorting features

24 Specific table selection

25 Creating new variables

26 Creating shape files from map

27 Polygon to point shape file

28 Create centroids for point files

29 Exploratory Data Analysis (EDA)

30 EDA: plots

31 Variable selection for plots

32 Plots: Histogram

33 Linkage: selecting features

34 Linkage: selecting features (con( con t.)

35 Generate and interpret box plots

36 Calculating Rates

37 Create raw rates

38 Selecting variables for rates

39 Raw rates: by percent

40 Saving rates

41 Identifying outliers from box plots

42 Create excess risk rates

43 Excess risk map

44 Creating Empirical Bayes smoothing

45 Map generated by EB smoothing

46 Creating Weights: Examining spatial relationships

47 Creating weights

48 Loading weight files

49 Creating spatial rates

50 Spatially smoothed map

51 Create weights: Rook

52 Text file of Rook weights

53 Compare weights with map and table

54 View weight characteristics

55 Multiple views: weight histogram, map and table

56 Create weights: Queen

57 Compare multiple features

58 Creating weights: neighbors

59 Reviewing weights

60 Creating weights: nearest neighbors

61 Histogram of neighbor weights

62 Autocorrelation: Identifying clusters

63 Global Moran It is a measure of spatial autocorrelation (feature similarity) based not only on feature locations or attribute values alone but also on both feature locations and feature values simultaneously. Given a set of features and an associated attribute, it evaluates whether the pattern expressed is clustered, dispersed, or random. A Moran's Index value near +1.0 indicates clustering; an index value near -1.0 indicates dispersion

64 Global Moran

65 Autocorrelation: weight file required

66 Global Moran result

67 Randomization feature

68 Randomization: graph result

69 Randomization: Envelope Slopes

70 Local Moran (LISA) The local Moran test (Anselin( 1995), detects local spatial autocorrelation. It can be used to identify local clusters (regions where adjacent areas have similar values) or spatial outliers (areas distinct from their neighbors). The Local Moran statistic decomposes Moran's I (Moran 1950) ) into contributions for each location, Ii.. The sum of Ii for all observations is proportional to Moran's I, an indicator of global pattern. Thus, there can be two interpretations of Local Moran statistics, as indicators of local spatial clusters and as a diagnostic for outliers in global spatial patterns.

71 Local Moran

72 LISA: Significance map

73 LISA: Cluster map