Spatial Regression Models for Demographic Analysis

While spatial data analysis has received increasing attention in demographic studies, it remains a difficult subject to learn for practitioners due to its complexity and various unresolved issues. Here we give a practical guide to spatial demographic analysis, with a focus on the use of spatial regr...

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Vydáno v:Population research and policy review Ročník 27; číslo 1; s. 17 - 42
Hlavní autoři: Chi, Guangqing, Zhu, Jun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer 01.02.2008
Springer Netherlands
Springer Nature B.V
Edice:Population Research and Policy Review
Témata:
Autocorrelation
Case studies
Data analysis
Demographic analysis
Demographic change
Demographic transition theory
Demographics
Demography
Ecology
Econometrics
Geography
Geostatistics
Human ecology
Implicit theories
Linear regression
Mathematical models
Migration
Modeling
Neighborhoods
Population
Population dynamics
Population Economics
Regression analysis
Science
Segregation
Social Sciences
Sociology
Soil sciences
Spatial Analysis
Spatial autocorrelation and heterogeneity
Spatial data
Spatial data analysis
Spatial demographic analysis
Spatial models
Spatial regression
Spatial weight matrix
Studies
U.S.A
Wisconsin
United States > US
Wisconsin
Spatial weight matrix
Spatial data analysis
Spatial regression
Spatial autocorrelation and heterogeneity
Spatial demographic analysis
ISSN:0167-5923, 1573-7829
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Shrnutí:While spatial data analysis has received increasing attention in demographic studies, it remains a difficult subject to learn for practitioners due to its complexity and various unresolved issues. Here we give a practical guide to spatial demographic analysis, with a focus on the use of spatial regression models. We first summarize spatially explicit and implicit theories of population dynamics. We then describe basic concepts in exploratory spatial data analysis and spatial regression modeling through an illustration of population change in the 1990s at the minor civil division level in the state of Wisconsin. We also review spatial regression models including spatial lag models, spatial error models, and spatial autoregressive moving average models and use these models for analyzing the data example. We finally suggest opportunities and directions for future research on spatial demographic theories and practice.
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ISSN:0167-5923
1573-7829
DOI:10.1007/s11113-007-9051-8