Geographically weighted regression based on a network weight matrix: a case study using urbanization driving force data in China

AbstractGeographically weighted regression (GWR) is a classical modeling method for dealing with spatial non-stationarity. It incorporates the distance decay effect in space to fit local regression models, where distance is defined as Euclidean distance. Although this definition has been expanded, it remains focused on physical distance. However, in the era of globalization and informatization, where the phenomenon of remotely close association is common, physical distance may not reflect real spatial proximity, and GWR based on physical distance has clear limitations. This paper proposes a geographically weighted regression based on a network weight matrix (NWM GWR) model. This does not rely on geographical location modeling; instead, it uses network distance to measure the proximity between two regions and weights observations by improving the kernel function to achieve distance attenuation. We adopt the population mobility network to establish a network weight matrix, modeling China's urbanization and its multidimensional driving factors using network autocorrelation and NWM GWR methods. Results show that the NWM GWR model has more accurate fit and better stability than ordinary least squares and GWR models, and better reveals relationships between variables, which makes it suitable for modeling economic and social systems more broadly.Keywords: Network weight matrixgeographically weighted regressionnetwork distancespatial non-stationarityordinary least squares Author contributionsJingyi He: methodological design, technical implementation, writing – original draft; Ye Wei: conceptual and methodological design, writing – review & editing, Supervision; Bailang Yu: writing – review & editing, validation.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementData and codes used in the study are available at https://doi.org/10.6084/m9.figshare.21299253.v4.Additional informationFundingThis work is supported by National Natural Science Foundation of China [No. 41971202].Notes on contributorsJingyi HeJingyi He is currently pursuing a doctorate in Urban and regional planning of Northeast Normal University, Changchun 130024, China. Her current research interest focuses on application of complex network and GIS in urban research.Ye WeiYe Wei is Professor of School of Geographic Sciences, Northeast Normal University, Changchun 130024, China. His research interests include urban and regional planning and GIS application.Bailang YuBailang Yu is Professor of Key Lab of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China. His research interests include urban remote sensing, nighttime light remote sensing, LiDAR, and object-based methods.