Identification of Urban Functional Zones Considering Point of Interest Differences: A Case Study of Nanjing City

被引:0
作者
Zhang, Yunpeng [1 ]
Zhu, A-Xing [2 ]
Sun, Yan [3 ]
Guan, Mengfan [1 ]
Gao, Tong [1 ]
机构
[1] Nanjing Tech Univ, Sch Geomatics Sci & Technol, Nanjing, Peoples R China
[2] Univ Wisconsin, Dept Geog, Madison, WI 53706 USA
[3] Nanjing Univ Finance & Econ, Sch Publ Adm, Nanjing, Peoples R China
基金
中国国家自然科学基金;
关键词
Nanjing; POI; spatial co-location pattern; urban functional zones; LAND-USE;
D O I
10.1111/tgis.70066
中图分类号
P9 [自然地理学]; K9 [地理];
学科分类号
0705 ; 070501 ;
摘要
Identification of urban functional zones is of paramount significance to urban planning and construction. Point of Interest (POI) data, a type of geospatial data widely employed in recent years for urban functional zone identification, are often used in urban functional zone identification. However, the variability among different types of POIs is often not explored in existing research. This paper explores a novel approach for identifying urban functional zones that reflects the differential roles of various POIs. This approach involves identifying core POIs that represent significant features within a region and constructing spatial co-location combinations based on these core POIs to identify urban functional zones. The experimental results demonstrate that this method achieves an accuracy of 90.57% in identifying urban functional zones in Nanjing, outperforming methods that do not consider POI differentiation under similar conditions. By considering and analyzing the differential roles of various POIs in the identification process, this study offers a new perspective for research on urban functional zone identification.
引用
收藏
页数:13
相关论文
共 34 条
[1]   Deep learning-based remote and social sensing data fusion for urban region function recognition [J].
Cao, Rui ;
Tu, Wei ;
Yang, Cuixin ;
Li, Qing ;
Liu, Jun ;
Zhu, Jiasong ;
Zhang, Qian ;
Li, Qingquan ;
Qiu, Guoping .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2020, 163 :82-97
[2]   Delineating urban functional areas with building-level social media data: A dynamic time warping (DTW) distance based k-medoids method [J].
Chen, Yimin ;
Liu, Xiaoping ;
Li, Xia ;
Liu, Xingjian ;
Yao, Yao ;
Hu, Guohua ;
Xu, Xiaocong ;
Pei, Fengsong .
LANDSCAPE AND URBAN PLANNING, 2017, 160 :48-60
[3]  
[陈占龙 Chen Zhanlong], 2020, [测绘学报, Acta Geodetica et Cartographica Sinica], V49, P907
[4]   Crowdsourcing urban form and function [J].
Crooks, Andrew ;
Pfoser, Dieter ;
Jenkins, Andrew ;
Croitoru, Arie ;
Stefanidis, Anthony ;
Smith, Duncan ;
Karagiorgou, Sophia ;
Efentakis, Alexandros ;
Lamprianidis, George .
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2015, 29 (05) :720-741
[5]  
Deng Yue, 2019, ISPRS INT J GEO-INF, V8, P283, DOI DOI 10.3390/ijgi8060283
[6]   Extracting urban functional regions from points of interest and human activities on location-based social networks [J].
Gao, Song ;
Janowicz, Krzysztof ;
Couclelis, Helen .
TRANSACTIONS IN GIS, 2017, 21 (03) :446-467
[7]   Identification of Urban Functional Areas Based on POI Data: A Case Study of the Guangzhou Economic and Technological Development Zone [J].
Hu, Yunfeng ;
Han, Yueqi .
SUSTAINABILITY, 2019, 11 (05) :1385
[8]   Integrating Point-of-Interest Density and Spatial Heterogeneity to Identify Urban Functional Areas [J].
Huang, Chong ;
Xiao, Chaoliang ;
Rong, Lishan .
REMOTE SENSING, 2022, 14 (17)
[9]   Discovering colocation patterns from spatial data sets: A general approach [J].
Huang, Y ;
Shekhar, S ;
Xiong, H .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2004, 16 (12) :1472-1485
[10]   GIS and urban data science [J].
Li, Yijing ;
Zhao, Qunshan ;
Zhong, Chen .
ANNALS OF GIS, 2022, 28 (02) :89-92