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

Abstract

Geographically 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 matrix
• geographically weighted regression
• network distance
• spatial non-stationarity
• ordinary least squares

Author contributions

Jingyi 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 statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

Data and codes used in the study are available at https://doi.org/10.6084/m9.figshare.21299253.v4.

Additional information

Funding

This work is supported by National Natural Science Foundation of China [No. 41971202].

Notes on contributors

Jingyi He

Jingyi 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 Wei

Ye 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 Yu

Bailang 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.