Spatial regression models for demographic analysis

Guangqing Chi, Jun Zhu

Research output: Contribution to journalReview article

91 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)17-42
Number of pages26
JournalPopulation Research and Policy Review
Volume27
Issue number1
DOIs
StatePublished - Feb 1 2008

Fingerprint

regression
spatial data
data analysis
basic concept
population development
analysis
population dynamics
modeling

All Science Journal Classification (ASJC) codes

  • Demography
  • Management, Monitoring, Policy and Law

Cite this

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Spatial regression models for demographic analysis. / Chi, Guangqing; Zhu, Jun.

In: Population Research and Policy Review, Vol. 27, No. 1, 01.02.2008, p. 17-42.

Research output: Contribution to journalReview article

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T1 - Spatial regression models for demographic analysis

AU - Chi, Guangqing

AU - Zhu, Jun

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AB - 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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