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The unprecedented urbanization in China has dramatically changed the urban spatial structure of cities. With the proliferation of individual-level geospatial big data, previous studies have widely used the network abstraction model to reveal the underlying urban spatial structure. However, the construction of network abstraction models primarily focuses on the topology of the road network without considering individual travel flows along with the road networks. Individual travel flows reflect the urban dynamics, which can further help understand the underlying spatial structure. This study therefore aims to reveal the intra-urban hierarchical spatial structure by integrating the road network abstraction model and individual travel flows. To achieve this goal, we 1) quantify the spatial interaction relatedness of road segments based on the Word2Vec model using large volumes of taxi trip data, then 2) characterize the road abstraction network model according to the identified spatial interaction relatedness, and 3) implement a community detection algorithm to reveal sub-regions of a city. Our results reveal three levels of hierarchical spatial structures in the Wuhan metropolitan area. This study provides a data-driven approach to the investigation of urban spatial structure via identifying traffic interaction patterns on the road network, offering insights to urban planning practice and transportation management. [ABSTRACT FROM AUTHOR]

As a vital indicator for measuring urban development, urban areas are expected to be identified explicitly and conveniently with widely available data sets, thereby benefiting planning decisions and relevant urban studies. Existing approaches to identifying urban areas are normally based on midresolution sensing data sets, lowresolution socioeconomic information (e.g., population density) in space (e.g., cells with several square kilometers or even larger towns or wards). Yet, few of these approaches pay attention to defining urban areas with high-resolution microdata for large areas by incorporating morphological and functional characteristics. This article investigates an automated framework to delineate urban areas at the block level, using increasingly available ordnance surveys for generating all blocks (or geounits) and ubiquitous points of interest (POIs) for inferring density of each block. A vector cellular automata model was adopted for identifying urban blocks from all generated blocks, taking into account density, neighborhood condition, and other spatial variables of each block. We applied this approach for mapping urban areas of all 654 Chinese cities and compared them with those interpreted from midresolution remote sensing images and inferred by population density and road intersections. Our proposed framework is proven to be more straightforward, time-saving, and fine-scaled com' pared with other existing ones. It asserts the need for consistency, efficiency, and availability in defining urban areas with consideration of omnipresent spatial and functional factors across cities. [ABSTRACT FROM AUTHOR]