spatial data: an introduction - ssccjraymo/links/soc674/674_22.pdf · spatial data: an introduction...

Spatial Data: An IntroductionSpatial Data: An Introduction

Katherine CurtisKatherine Curtis

Guest Lecture to SOC 674: Elementary Demographic Techniques Guest Lecture to SOC 674: Elementary Demographic Techniques November 25November 25thth, 2008, 2008

WhatWhat Why Why Who Who HowHow

•• What are What are ““spatial dataspatial data””??

○○ Data where, in addition to attribute values relating to Data where, in addition to attribute values relating to the primary phenomenon or phenomena of interest, the primary phenomenon or phenomena of interest, the the relative spatial locationsrelative spatial locations of observations are also of observations are also recordedrecorded

Housing prices for city blocksHousing prices for city blocksChild poverty rates for countiesChild poverty rates for countiesAccident counts by intersectionAccident counts by intersectionCancer incidence for Cancer incidence for geocodedgeocoded addressesaddressesCountyCounty--toto--county migration streams for persons 65+county migration streams for persons 65+


GISGIS

Spatial Data Spatial Data AnalysisAnalysis

Spatial Spatial AnalysisAnalysis

““Spatial StatisticsSpatial Statistics””

GeostatisticalGeostatisticalDataData

LatticeLatticeDataData

PointPoint--PatternPatternDataData

Spatial InteractionSpatial InteractionDataData


GeostatisticalGeostatistical DataDataU.S. TemperaturesU.S. Temperatures


GeostatisticalGeostatistical DataDataWisconsin ElevationWisconsin Elevation

175 - 259

260 - 343

344 - 427

428 - 511

512 - 596

Elevation (Meters)

N

Applied Population LaboratoryUW Extension Basin Educators In ServiceSource: USGS Digitial Elevation Model1 Degree DEM with 75m Cell Size

W isconsinE levation M odel


Lattice DataLattice DataCivil Liberties for NationCivil Liberties for Nation--States from the World Freedom AtlasStates from the World Freedom Atlas


Lattice DataLattice DataMexican Presidential Election, 2006Mexican Presidential Election, 2006


Lattice DataLattice DataLog Odds of Proportion of Children in Poverty for U.S. Counties,Log Odds of Proportion of Children in Poverty for U.S. Counties, 20002000


Lattice DataLattice DataPercent Seasonal Housing for Wisconsin Watersheds, 2000Percent Seasonal Housing for Wisconsin Watersheds, 2000

Percent Seasonal HUSSNL HU / TOT HU

0.1% - 6.3%

6.4% - 16%

16.1% - 30.6%

30.7% - 49.8%

49.9% - 78.6%

PointPoint--Pattern (Event) DataPattern (Event) DataA Week of Crimes in HollywoodA Week of Crimes in Hollywood


PointPoint--Pattern (Event) DataPattern (Event) DataLocation of Deaths from Cholera in Central London for Location of Deaths from Cholera in Central London for

September 1854 (Snow)September 1854 (Snow)



Spatial Flows DataSpatial Flows DataNorthwest Airlines Routes from AmsterdamNorthwest Airlines Routes from Amsterdam


Spatial Flows DataSpatial Flows DataMigration in the UK, Map 5 from Migration in the UK, Map 5 from RavensteinRavenstein 18851885

WhatWhat WhyWhy Who Who HowHow

•• Theoretical ImportanceTheoretical Importance

○○ A means of A means of organizing human activitiesorganizing human activitiesHuman Ecology, Contextual Models, etc.Human Ecology, Contextual Models, etc.

•• Statistical ImportanceStatistical Importance

○○ A means of A means of data integrationdata integrationVariable creationVariable creation

○○ A potentialA potential problemproblemSpatial autocorrelationSpatial autocorrelation


•• ToblerTobler’’ss First Law of GeographyFirst Law of Geography

““Everything is related to everything else, but Everything is related to everything else, but near things are more related than distant thingsnear things are more related than distant things””

Waldo ToblerEconomic Geography (1970:236)


•• Erroneous Statistical Inference and Substantive Erroneous Statistical Inference and Substantive ConclusionsConclusions

““If a naIf a naïïve researcher completes a standard ve researcher completes a standard statistical analysis of statistical analysis of georeferencedgeoreferenced data, it data, it does not follow that the data analytic results does not follow that the data analytic results have turned data into meaningful information have turned data into meaningful information merely because to the inexpert eye they are merely because to the inexpert eye they are indistinguishable from conventional statistical indistinguishable from conventional statistical results!results!””

Daniel Griffith and Larry Layne(Oxford University Press, 1999:vii)

WhatWhat Why Why WhoWho HowHow

•• PublicPublic

•• GeographersGeographers

•• EpidemiologistsEpidemiologists

•• CriminologistsCriminologists

•• Demographers!Demographers!○○ Migration: spatial flowsMigration: spatial flows○○ Fertility: diffusion of innovationFertility: diffusion of innovation○○ Mortality: diffusion of diseaseMortality: diffusion of disease○○ Urbanization: uneven developmentUrbanization: uneven development


•• Goals of Spatial Data AnalysisGoals of Spatial Data Analysis

○○ Increase the Increase the basic understandingbasic understanding of some process of some process operating in spaceoperating in space

○○ Assess the evidence in favor of various Assess the evidence in favor of various hypotheseshypothesesconcerning that processconcerning that process

○○ Predict Predict valuesvalues in areas where observations have not in areas where observations have not been madebeen made

○○ Examine the relationships among attributes Examine the relationships among attributes distributed in space, even when understanding of the distributed in space, even when understanding of the spatial process, spatial process, per seper se, is , is not a goalnot a goal


•• Components of Spatial AnalysisComponents of Spatial Analysis

○○ VisualizationVisualizationShowingShowing interesting patternsinteresting patterns

○○ Exploratory Spatial Data AnalysisExploratory Spatial Data AnalysisFindingFinding interesting patternsinteresting patterns

○○ Spatial ModelingSpatial ModelingExplainingExplaining interesting patternsinteresting patterns

•• Finding Interesting PatternsFinding Interesting Patterns


Map 1Map 1 Map 2Map 2

Exploratory spatial data analysis that does not utilize the Exploratory spatial data analysis that does not utilize the spatial arrangement of the data spatial arrangement of the data

will lead to will lead to identical resultsidentical results for the two mapsfor the two maps


•• Example: Residential SegregationExample: Residential Segregation

““[An] important problem, shared by all existing [An] important problem, shared by all existing measures of residential segregation, is that they measures of residential segregation, is that they take no account of the spatial relationship of the take no account of the spatial relationship of the parcels themselves. Calculations are based on parcels themselves. Calculations are based on parcels as discrete and independent units, parcels as discrete and independent units, ignoring the composition of nearby parcels.ignoring the composition of nearby parcels.””

Michael J. WhiteAJS (1983:1010)


WhitesWhites

BlacksBlacks

Residential Segregation in D.C.Residential Segregation in D.C.


Blacks & WhitesBlacks & Whites

Multiple Multiple Race GroupsRace Groups


i ji j

iS E E

E= −

∩1

WongWong’’ss iiSSjj

Similar to Massey & DentonSimilar to Massey & Denton’’s (1988) Exposure Index: Indicates the s (1988) Exposure Index: Indicates the proportion in social group proportion in social group ii not not spatially exposedspatially exposed to social group to social group jj

WongWong’’s s SS

S E E E EE E E E

k

k= −

∩ ∩ ∩ ∩∪ ∪ ∪ ∪

1 1 2 3

1 2 3

K

K

Compares the Compares the areaarea of intersection of the ellipses and the of intersection of the ellipses and the areaarea of their union, of their union, ranging from 0 (no spatial segregation) to 1 (complete spatial sranging from 0 (no spatial segregation) to 1 (complete spatial segregation)egregation)


IndexIndex WhWh--BlBl WhWh--HH BlBl--HH

S .83 .62 .98S .83 .62 .98

D .70 .56 .79D .70 .56 .79

S: WongS: Wong’’s index based on intersecting s index based on intersecting standard deviational ellipsesstandard deviational ellipses

D: Index of DissimilarityD: Index of Dissimilarity

Residential Segregation in MilwaukeeResidential Segregation in Milwaukee


•• Explaining Interesting PatternsExplaining Interesting Patterns

○○ SpatialSpatial HeterogeneityHeterogeneity: First Order Effects: First Order EffectsExists when the mean, and/or variance, and/or covariance Exists when the mean, and/or variance, and/or covariance structure structure ““driftsdrifts”” over the study regionover the study regionDue to unmeasured or Due to unmeasured or unmeasurableunmeasurable exogenous exogenous factor(sfactor(s))

○○ Spatial DependenceSpatial Dependence: Second Order Effects: Second Order Effects““Spatial dependence can be considered to be the existence Spatial dependence can be considered to be the existence of a functional relationship between what happens at one of a functional relationship between what happens at one point in space and what happens elsewhere.point in space and what happens elsewhere.”” (Luc (Luc AnselinAnselin1988:11)1988:11)Due to interactive, diffusive relationshipDue to interactive, diffusive relationship


•• Example: Child PovertyExample: Child Poverty○○ Spatial Heterogeneity (exogenous factor) or Spatial Spatial Heterogeneity (exogenous factor) or Spatial

Dependence (diffusive process)? Or do I care?Dependence (diffusive process)? Or do I care?


-205.94-259.10-578.94Likelihood

Diagnostics:

155.82Robust LM (lag)

352.20Robust LM (error)

800.09740.47401.27Heteroskedasticity (B-P)

435.88544.211181.88AIC

-0.0480.0790.331Moran’s I (residuals)

0.612 (0.019 )0.351 (0.014)Spatial parameter:

-3.591 (0.092)

3.844 (0.163)

1.296 (0.130)

3.636 (0.261)

0.292 (0.116) *

-0.035 (0.179) ns

0.011 (0.144) ns

1.904 (0.090)

*** (***)

(A) + Spatial Lag

-4.854 (0.088)

3.883 (0.183)

1.415 (0.146)

4.942 (0.289)

0.614 (0.130)

0.246 (0.201) ns

-0.101 (0.162) ns

2.377 (0.099)

*** (***)

(A) OLS

-4.347 (0.096 )

4.129 (0.172)

1.519 (0.132)

3.798 (0.286)

0.295 (0.141)

-0.030 (0.201) ns

-0.069 (0.164) ns

2.080 (0.112)

*** (***)

(A) + Spatial Error

Intercept:

Prop. Female-headed families

Family structure:

Prop. male underemployment

Prop. LF unemployed

Local employment opportunities:

Professional services

Miscellaneous services

Non-durable manufacturing

Extractive

Industrial composition:

(Several control variables)

Variable

spatial data: an introduction - ssccjraymo/links/soc674/674_22.pdf · spatial data: an introduction...

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