Mapping Block-Level Urban Areas for All Chinese Cities

Bibliographic Data

ID3775656
AuthorsYing Long (0000-0002-8657-6988, Beijing Municipal Commission of Urban Planning), Yao Shen (0000-0002-7261-4234, Space Syntax (United Kingdom)), Xiaobin Jin (0000-0003-1862-5837, Nanjing University)
Year2016
Volume106
Issue1
Pages96-113
Publication date2016-01-02
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueAnnals of the American Association of Geographers (JOURNAL)
Journal identifiersISSN: 2469-4452 • E-ISSN: 2469-4460
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/00045608.2015.1095062
OpenAlexW3102417830
LanguageEN
Citations received22
References cited35

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, low-resolution 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 compared 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

Cartography · Cellular automaton · City block · Civil engineering · Geography · Point of interest · Population · Remote sensing · Urban area · Urban density · Urban planning · Computer Science · Land Use and Ecosystem Services · Remote Sensing and Land Use · Remote-Sensing Image Classification · Artificial Intelligence

Tools

Open DOISci-Hub