This article proposes a novel method for constructing an asymmetric spatial weight matrix and applies it to improve spatial econometric modeling. As opposed to traditional spatial weight matrices that simply consider geographic or economic proximity, the spatial weight matrix proposed in this study is based on large-volume daily population flow data. It can more accurately reflect the socioeconomic interactions between cities over any given period. To empirically test the validity and accuracy of this proposed spatial weight matrix, we apply it to a spatial econometric model that analyzes COVID-19 transmission in Mainland China. Specifically, this matrix is used to address spatial dependence in outcome and explanatory variables and to calculate the direct and indirect effects of all predictors. We also propose a practical framework that combines instrumental variable regressions and a Hausman test to validate the exogeneity of this matrix. The test result confirms its exogeneity; hence, it can produce consistent estimates in our spatial econometric models. Moreover, we find that spatial econometric models using our proposed population flow–based spatial weight matrix significantly outperform those using the traditional inverse distance weight matrix in terms of goodness of fit and model interpretation, thus providing more reliable results. Our methodology not only has implications for national epidemic control and prevention policies but can also be applied to a wide range of research to better address spatial autocorrelation issues. Key Words: COVID-19 transmission, endogeneity, population flow, spatial dependence (autocorrelation), spatial weight matrix.