SCREENING FOR SPATIAL DEPENDENCE IN REGRESSION ANALYSIS

Arthur Getis

105 citations0/0 files storedPapers of the Regional Science Association

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Spatial and Panel Data Analysis Crime Patterns and Interventions Regional Economic and Spatial Analysis

Abstract

A technique of analysis is presented that is designed to circumvent the problem of finding wasy to estimate parameters of spatially stochastic independent variables. It is based on 1) a type of second-order analysis that describes the spatial association among weighted observations, and 2) a screening procedure that removes most of the spatial dependence in the dependent variable. The approach is illustrated by a study of the incidence of certain crimes in 49 districts of Columbus, Ohio. It is concluded that spatial justaposition of observations plays a large role in regression analyses that are based on spatial series.

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References

1988Spatial Econometrics: Methods and Models 1981Spatial statistics 1981Spatial Statistics 1988DO SPATIAL EFFECFS REALLY MATTER IN REGRESSION ANALYSIS?1985Multivariate Spatial Correlation: A Method for Exploratory Geographical Analysis 1988Do spatial effects really matter in regression analysis 1988Advanced Spatial Statistics 1988Model Validation in Spatial Econometrics: A Review and Evaluation of Alternative Approaches

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2012Accounting for Spatial Autocorrelation in Linear Regression Models Using Spatial Filtering with Eigenvectors 2013Spatial Autocorrelation and Spatial Filtering 2020Principal component analysis for geographical data: the role of spatial effects in the definition of composite indicators 2008Spatial Filtering in a Regression Framework: Examples Using Data on Urban Crime, Regional Inequality, and Government Expenditures

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