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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Arthur Getis (1990) studied this question.
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