The Scale Effect on Spatial Interaction Patterns: An Empirical Study Using Taxi O-D Data of Beijing and Shanghai
Abstract
Spatial scale is a fundamental issue for geographical phenomena because the size of the spatial unit adopted for analysis can have a significant effect on aggregated spatial data and the corresponding analytical results. There exists much research on the scale issue of spatial distribution data while a few has paid attention to the scale effect on spatial interactions. Big geo-data with micro-level individual movements provide an unprecedented opportunity to explore spatial interactions from the bottom up and to understand the scale effect from the perspective of interaction patterns. In this paper, we introduced an empirical framework to explore spatial interaction data across scales. By incorporating two data sets of taxi trajectories in Beijing and Shanghai, we aggregated the taxi O-D trips under multiple regular grids with different cell sizes and analyzed the properties of spatial interactions in three aspects: the statistical distribution, the distance decay mechanism, and the structure pattern. Our research demonstrated the feasibility of analyzing spatial interactions from a multi-scale view and offered a basic empirical framework for future geographical research that is interested in scale and spatial interactions.
Di Zhu
Professors
I am an Assistant Professor of Geographic Information Science in the Department of Geography, Environment, and Society at the University of Minnesota, Twin Cities. My research interests center around Geospatial Artificial Intelligence (GeoAI), Spatial Analytics, Social Sensing, and Urban Complexities.
Ximeng Cheng
Postdoctoral Researcher
My main research interests are spatial-temporal big data mining, explainable artificial intelligence (XAI), GeoAI, time series analysis, and social sensing.
