In geographical literature, numerous studies have demonstrated the differences that arise if spatial autocorrelation (SAC) is incorporated into a conventional nonspatial modeling procedure, but little is known about when these differences might be magnified. This study addressed this query by conducting two sets of regression modeling for 561 variables representing housing prices, metropolitan industry, health, crime, education, and (un)employment across various parts of the United States: (1) nonspatial ordinary least squares (OLS) using a set of selected independent variables and (2) spatial regression incorporating spatial filters into the nonspatial OLS as additional independent variables. This incorporation generally improved the model outcomes through decreases in residual autocorrelation and Akaike’s information criterion (AIC). The degree of improvement correlated positively with the level of SAC inherent in the dependent variables. That is, strongly autocorrelated socioeconomic variables underwent greater decreases in residual autocorrelation and AIC than those variables with weaker SAC. The results imply that spatial modeling outcomes are sensitive to and potentially predictable by the level of SAC possessed by dependent variables. Therefore, the degree of SAC present in a socioeconomic variable can serve as a direct indicator of how much improvement a nonspatial OLS will experience if that SAC is properly accounted for.

中文翻译：

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通过考虑空间自相关预测模型改进：社会经济视角

在地理文献中，许多研究已经证明，如果将空间自相关 (SAC) 纳入传统的非空间建模程序，则会出现差异，但对于何时会放大这些差异知之甚少。本研究通过对 561 个变量进行两组回归建模来解决这个问题，这些变量代表美国各地的房价、大都市工业、健康、犯罪、教育和（未）就业：(1) 非空间普通最小二乘法 (OLS) ) 使用一组选定的自变量和 (2) 空间回归将空间过滤器合并到非空间 OLS 作为附加自变量。这种合并通常通过减少残差自相关和 Akaike 的信息标准 (AIC) 来改善模型结果。改善程度与因变量中固有的 SAC 水平呈正相关。也就是说，与 SAC 较弱的变量相比，强自相关的社会经济变量在残差自相关和 AIC 方面的下降幅度更大。结果意味着空间建模结果对因变量所拥有的 SAC 水平敏感并可能通过该水平进行预测。因此，社会经济变量中存在的 SAC 程度可以作为非空间 OLS 将经历多少改进的直接指标，如果该 SAC 得到适当考虑。结果意味着空间建模结果对因变量所拥有的 SAC 水平敏感并可能通过该水平进行预测。因此，社会经济变量中存在的 SAC 程度可以作为非空间 OLS 将经历多少改进的直接指标，如果该 SAC 得到适当考虑。结果意味着空间建模结果对因变量所拥有的 SAC 水平敏感并可能通过该水平进行预测。因此，社会经济变量中存在的 SAC 程度可以作为非空间 OLS 将经历多少改进的直接指标，如果该 SAC 得到适当考虑。