Regionalization for urban air mobility application with analyses of 3D urban space and geodemography in San Francisco and New York

Abstract

In a new era of mobility where the transportation of persons or goods via flying vehicles over urban areas has garnered great interest. As a first step to assess the feasibility of Urban Air Mobility (UAM) in urban areas, we conduct regionalization and correspondence analysis in highly urbanized areas - San Francisco, CA and Manhattan, NY - by incorporating population dataset and urban 3D airspace to delineate the regional boundaries. Regionalization is carried out using graph-based clustering technique called SKATER (Spatial 'K'luster Analysis by Tree Edge Removal) to group the regions having similar characteristics and address the compound effect of both population and spatial information. By classifying the regions into five categories through correspondence analysis, the operational and economic feasibility of each region is evaluated. The results provide the region maps of each city with the most and least attractive regions for UAM application with the temporal notion, whether the clusters are daytime-intensive or nighttime-intensive areas. Our approach can contribute to providing a useful basis for management for UAM in urban areas as well as the process of regulating airspace use.