Abstract
A better understanding of the urban spatial interaction is important for optimizing the spatial structure and layout of urban agglomeration (UA). We develop a crawler program to compile online big data for urban spatial interaction analysis. Differing from the previous studies, vectorial, realistic, and high spatiotemporal resolution inter-city, bus-passenger-flow big data instead of statistical and modeled data are used for urban spatial interaction analysis. The Yangtze River Delta (YRD) is selected as a case study region to test the big data approach and to gain insights into the cities’ interaction in China’s largest UA. The results testified the superiorities of the big-data method over the traditional gravity model, confirmed some phenomena discussed or mentioned in the literature and regional plans regarding the urban interaction in YRD, derived policy implications for enhancing the sustainability of UA, and suggested some potentials for improving the limitations of the big-data method.
Highlights
Cities are exchanging various flows such as material, energy, information, transportation, and migration with each [1]
Since urban agglomeration (UA) is recognized as having a stronger ability in aggregating population, capital, and resources compared to a single city [3,4], it is acknowledged as the new power engine in regional development [5,6]
The study of the connection between cities and their spatial interaction will help to strengthen the functional linkage within UA and promote the sustainability of UA
Summary
Cities are exchanging various flows such as material, energy, information, transportation, and migration with each [1]. This phenomenon is called urban spatial interaction, through which cities are connected with each other and integrate as a closely related city network system (namely urban agglomeration, UA) [2]. Converse developed a breaking-point model on the basis of Reilly’s law of retail gravitation to determine the urban attractive range and classify the economic zone [12]. Taylor proposed a concept of “world city network”, which provided a new angle for an urban interaction study and expanded the previous single-city dominated analysis to a city-network investigation [13]. The research focuses covered a wide range of fields, such as inter-city trade, transportation, information, tourism, and logistics [19,20,21,22,23]
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