Spatial Data Analysis Using GeoDa. Workshop Goals

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Spatial

Data

Analysis

Using

GeoDa

9 Jan

2014

Frank

Witmer

Computing and Research Services Institute of Behavioral Science

Workshop

Goals

• Enable participants to find and retrieve

geographic data pertinent to their study and

conduct spatial analysis using GeoDa

– Geographic data sources and formats

– Data joins in ArcGIS

– Exploratory spatial data analysis (ESDA) in GeoDa

• Provide experience using ArcGIS and GeoDa

software

• Provide the opportunity for you to work with

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Types

of

Geographic

Data

1)

Spatial

data

• helpful to conceptualize as maps

• necessary for answering “Where…” questions

– used to establish spatial relations (e.g. distance, connectivity, containment)

– used to support spatial analysis

2)

Attribute

data

• helpful to conceptualize as tables • necessary for answering “What…” questions (and metadata too, typically in .xml format)

Geographic

Data

Spatial data and attribute table are ‘linked’ together State

Name Population Governor

New Jersey 7,730,188 C. Whitman

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IBS

Data

Links

Page

• http://www.colorado.edu/ibs/crs/geographic_data_sources .html • Some highlights: – WONDER from CDC • http://wonder.cdc.gov/

– ESRI Data Products

• http://www.esri.com/data/find‐data

• Census data

– American FactFinder

• http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml

– Census Explorer (no direct data download)

• http://www.census.gov/censusexplorer/

– http://blogs.census.gov/

Census

Maps

&

Data

• http://www.census.gov/geo/maps

‐

data/

• TIGER

Products

–

Cartographic

Boundary

Files

|

County

• 500k, 5m, 20m reflect the scale of the data

• scale is the ratio of map distance to earth distance, so 1:500,000 has more detail than 1:20,000,000

–

TIGER/Line

Shapefiles |

2010

|

Download

|

Web

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International

Borders

• For

individual

countries,

can

sometimes

find

a

gov’t

agency

that

provides

geographic

data

• ESRI

borders

–

online

or

from

Data

&

Maps

DVD

• Global

Administrative

Areas

(GADM)

–

http://www.gadm.org/

Joining

Attribute

Data

to

Geodata

• Will

often

find

attribute

data

in

tabular

form

• So

might

need

to

obtain

geodata separately

and

join

the

attribute

data

to

it

• Challenge:

construct

common

field

that

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Joining

Tables

In ArcMap, right-click on the destination to begin a join!

Some

GIS

File

Types

• ESRI Shapefiles

– Very common since file format is open

– Multiple files with different extensions (.shp, .shx, .dbf)

– Display quickly and are editable

• But careful, polygons do not share boundary lines!

• ESRI SDC

– Smart Data Compression, files are compressed for efficient storage

• ESRI Interchangefiles

– Extension .e00

• ESRI GRID

– Attribute table stores number of occurrences/value • ESRI Geodatabases

– Integrated approach for storing & managing all types of geographic data and their relationships

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(ESDA)

• Actively find interesting patterns in the data

• Facilitated by dynamically linked views

• Use statistical measures of spatial association

such as global & local Moran’s I to explore spatial

dependence

– Global: one statistic to summarize the pattern

– Local: location specific statistics

• Moran’s I frequently used to test for spatial

autocorrelation in regression residuals, but it is

also of interest when exploring the spatial

distribution of variables

Moran’s

I

Statistic

• Standardized cov/var • Significance tests Normal distribution Randomization/permutation • Spatial correlation

‐1 ‐> neg. correlation (regularity) 0 ‐> no correlation

1 ‐> pos. correlation (clustering)

2 ij i j i j ij i i j i

w z z

n

I

w

z

=

∑∑

∑

w_ij= weights matrix for contiguity matrix, w_ij= 1 if i and j adjacent

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H₀: spatial independence Normal Distribution: – Assume X’s are identically normally distributed (each value for each region has same distribution) – Use E[I] and VAR[I] to calculate Z‐statistic – If Z‐statistic lies beyond critical value, then reject null Randomization/permutation: – Many times randomly rearrange the data on map and compute I each time. Create a histogram of distribution of I. – Then calculate the mean and variance of the distribution. And then a z‐statistic. – If Z‐statistic lies beyond critical value, then reject null

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Low‐value clustering

High‐value clustering Anti‐correlated

Anti‐correlated

Local

Moran’s

I

• Provides

a

measure

of

spatial

autocorrelation

for

every

areal

unit,

I

_i

c

=

a

constant

of

proportionality

• If

assume

I

_i

is

normally

distributed,

can

be

transformed

into

Z

‐

statistic

to

test

for

significance.

cI

I

n i i

=

∑

=1

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GeoDa Software

• Open source, available for Windows, Mac & Linux

– http://geodacenter.asu.edu/

– project is led by Luc Anselin at ASU

– only supports shapefiles, so must use ArcGIS (or other

GIS software) to convert to shapefiles

• Linking

– selection in one view results in selection in all views

(e.g. maps, tables, scatterplots)

• Brushing

– dynamic version of linking

– click & drag rectangle over map or scatterplot