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作者

Kang Liu;Ling Yin;Feng Lu;Naixia Mou

来源

CITIES,Vol.99,Issue1,Article 102610

语言

英文

关键字

Point of interest;Urban region;POI configuration;Semantic space;Word embedding;Visualization

作者单位

Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;University of Chinese Academy of Sciences, Beijing 100049, China;College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China;Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;University of Chinese Academy of Sciences, Beijing 100049, China;College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

摘要

POI configuration of a region indicates a combination of POI counts on various types inside the region. Exploring POI configurations of urban regions facilitates to understand their functionalities, vibrancy, and developments. However, current studies and applications mainly make statistics toward POI counts on various types separately, neglecting the implicit semantic relations between different types and failing to uncover POI-configuration patterns intuitively. This study proposes a novel framework for visualizing and exploring POIs on POI-type semantic space, with semantic relations of the types being considered. Firstly, using a word-embedding technique (i.e., Word2Vec), the embeddings of POI types are learned from their neighborships on geographic space. Secondly, using a dimension-reduction technique (i.e., t-SNE), all POI-type embeddings are mapped onto 2-dimensional semantic space, where related or similar types would be nearer to each other. Finally, taking the POI-type semantic space as a “base map”, POI configuration of each region is rendered as a “thematic map” by POI counts on various types, which can help intuitively understand and conveniently compare urban regions. The proposed framework can be applied to any urban region and any POI data source, which provides an effective information delivery method for urban planning and POI related urban studies and applications.