Identifying urban functional zones by capturing multi-spatial distribution patterns of points of interest

作     者：Quan Qin Shishuo Xu Mingyi Du Songnian Li 

作者机构：School of Geomatics and Urban Spatial InformaticsBeijing University of Civil Engineering and ArchitectureBeijingPeople’s Republic of China Key Laboratory of Urban Spatial InformaticsMinistry of Natural Resources of the People’s Republic of ChinaBeijingPeople’s Republic of China Department of Civil EngineeringToronto Metropolitan UniversityTorontoCanada 

出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)

年 卷 期：2022年第15卷第1期

页      面：2468-2494页

核心收录：

学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学] 0705[理学-地理学] 0816[工学-测绘科学与技术] 

基　　金：supported by the China Scholarship Council the Beijing Categorized Development Quota Project the Beijing University of Civil Engineering and Architecture Young Scholars’Research Ability Improvement Program[X21018] the National Natural Science Foundation of China the Natural Sciences and Engineering Research Council of Canada[RGPIN-2017-05950] 

主　　题：Urban functional zone point of interest spatial distribution pattern natural language processing word2vec 

摘      要：Urban Functional Zone(UFZ)identification is vital for urban planning,renewal,and *** of Interest(POI),as one of the most popular data in UFZ studies,is transformed into a geo-corpus under specific sampling strategies,which can be used with Natural Language Processing(NLP)technology to extract geo-semantic features and identify ***,existing studies only capture a single spatial distribution pattern of POIs,while ignoring the other spatial distribution *** this paper,we developed an integrated geo-corpus construction approach to capture multi-spatial distribution patterns of POIs that were represented by different modal POI ***,random forest model was leveraged to classify UFZs based on those embeddings.A set of combination experiments were designed for performance *** results show that our proposed method can effectively identify UFZs with an accuracy of 72.9%,with an improvement of 8.5%compared to the baseline *** outcome of this study will help urban planners to better understand UFZs through investigating the integrated spatial distribution patterns of POIs embedded in UFZs.