Understanding the dynamics of polycentric urbanization is important for urban studies and management. This paper proposes an analytical model that uses multisource big geospatial data to characterize such dynamics to facilitate policy making. There are four main steps: 1) main centers and subcenters are identified using spatial cluster analysis and geographically weighted regression (GWR) based on Visible Infrared Imaging Radiometer Suite (VIIRS)/NPP and social media check-in data; 2) the built-up areas are extracted by using Defense Meteorological Satellite Program – Operational Linescan System (DMSP/OLS) gradient images; 3) the economic corridors that connect the main center and subcenters are constructed using road network data from Open Street Map (OSM) with the least-cost distance method; and 4) the major urban development direction is identified by analyzing the changes in built-up areas within the economic corridors. The model is applied to three major cities in northeastern, central, and northwestern China (Shenyang, Wuhan, and Xi'an) from 1992 to 2012.

Isotropic homogeneity does not hold in urban areas. Street networks exert a great influence on human mobility. As a result, city structure is largely shaped by this network, especially the streets that carry a higher volume of traffic. In practice, small areas along network edges often need to be grouped into regions for management purposes. This work formalizes the extension to the P-regions problem that takes the network as the underlying constraint and proposes a heuristic-based approach to solve the problem to near optimality. The network is subdivided into aggregator edges that attract regions and separator regions that divide areas apart. Two types of regions emerge in the region formation process: regions that grow along a certain network edge (network regions) and regions that grow from areas that are far away from all the network edges (planar regions). The heuristic solution effectively uses pre-computed spatial contiguity and distance matrices. The global objective function consists of the original heterogeneity factor and the discounted network proximity factor. This approach is elaborated with both a simulated and a real-world dataset. The regionalization results help design, study, and service regions that explicitly consider the network configuration with flexible parameter controls.

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