Using Exploratory Spatial Data Analysis to Examine Neighborhood Structure and Foster Care Entry

1
Using Exploratory Spatial Data Analysis to
Examine Neighborhood Structure and Foster Care
Entry
Bridgette Lery, M.S. Center for Social Services
Research, UC Berkeley
This research was funded by the California
Department of Social Services, the Annie E. Casey
Foundation and the Stuart Foundation.
2
Overview

• Neighborhood Research
• GIS vs. Spatial Statistics
• Techniques for Exploring Spatial Distributions
• Implications for Social Research and Practice

3
Alameda County 2000-2003 First Entries
4
What is spatial data analysis?

• - Examining some process in space and its
  relationship to other spatial phenomena
• - Used to describe relationships between points,
  lines, or areas
• - GIS is not spatial analysis

5
Why use spatial analyses?

• Social problems occur in some place, but place
  often not explicitly explored
• Adjacent spatial units may be correlated (e.g.,
  neighborhood rates of poverty)
• Neighborhood boundaries are permeable

6
Spatial Scale
7
Spatial Scale
8

• GeoDa
• http//sal.agecon.uiuc.edu/csiss/geoda.html

9
Spatial Weights

• 0 320 trt00 ID
• 1 6
• 141 140 129 118 43 44
• 2 4
• 141 43 4 3
• 3 7
• 12 11 6 4 2 42 43
• .
• .
• .
• 314 4
• 319 318 313 312

10
Connectivity
11
Spatial Weights Connection Matrix
12
Spatial Autocorrelation

• Correlations between spatial units
• Violates the assumption of unit independence
• Biases the statistical tests of the coefficients

13
Measuring Spatial Autocorrelation

• Moran Coefficient ( 1/(N-1))
• Approximately bounded by (-1) and (1)
• Can be adapted to examine residuals from
  regression models

14
Positive Spatial Autocorrelation

• Adjacent units have same or similar value on
  measures
• If present, results in Type I errors

15
Negative Spatial Autocorrelation

• Adjacent units have opposite values on measures
• If present, results in Type II errors

16
Movement of People in Places

• Spatial Lags measure how characteristics of
  nearby places or populations influence a local
  area

17
Moran Scatter Plot
18
An Example
Is social organization related to neighborhood
rates of foster care entry?
19
Methods

• - Cross-sectional ecological study (modeled after
  Coulton et al., 1995)
• - of first entries to foster care per thousand
  children (Data Source California Childrens
  Services Archive, CWS/CMS, 2004, Quarter 1
  Extract)
• Geocoded to 46 zip codes, 320 census tracts and
  983 block groups in Alameda County in California
  by the removal address of the child

20
Independent Measures

• 3. Hispanic Immigrants
• Hispanic
• elderly
• Ratio children to adults
• 4. Asian Immigrants
• Asian
• foreign-born
• Population
• Population Density

• 1. Impoverishment
• female-headed households
• poverty
• unemployed
• vacant housing
• African American
• 2. Residential Instability
• moved past 5 years
• moved past year
• moved past 10 years
• Ratio men to women

21
(No Transcript)
22
LISA map
23
Comparison of OLS and GLS Models
p lt .05, p lt .01, p lt .001
24
Conclusions

• Overall
• ESDA can help in the examination of social
  phenomena that occur in a geographic context.
• In the current study
• Spatial autocorrelation is positive and
  significant. Therefore, the spatial model is
  better than the OLS model.
• The LISA map suggests specific areas where high
  foster care entry rates are clustered.

25
Implications for Social Work Practice

• Research Spatial dependence can bias results in
  any ecological study.
• - Policy and practice Neighborhoods share and
  owe some characteristics to nearby areas,
  suggesting a role for policies targeted to
  neighborhoods and communities.