Identification of Urban Functional Areas Based on POI Data: A Case Study of the Guangzhou Economic and Technological Development Zone

Functional areas are the basic spatial units in which cities or development zones implement urban plans and provide functions. Internet map big data technology provides a new method for the identification and spatial analysis of functional areas. Based on the POI (point of interest) data from AMap (a map application of AutoNavi) from 2017, this paper proposes an urban functional areas recognition and analysis method based on the frequency density and the ratio of POI function types. It takes the Guangzhou Economic and Technological Development Zone as a case study to analyze the main function and spatial distribution characteristics of the detailed functional areas. The research shows the following: (1) The POI frequency density index and the function type ratio can effectively distinguish the functions of the grid units and analyze the spatial distribution characteristics of a complex functional area. (2) The single functional area is the most common area type in the Guangzhou Economic and Technological Development Zone. The largest proportion of all areas is allocated to traditional manufacturing industry functional areas, followed by high-tech enterprises, catering and entertainment, real estate, and education and health care, in descending order. The smallest proportion is allocated to finance and insurance functional areas. (3) The current layout of the functional areas in the Guangzhou Economic and Technological Development Zone conforms to the overall requirements and planning objectives of the central and local government. The layout and agglomeration of different blocks within the economic development zone are consistent with local industry’s target orientation and development history.

[1]  Xin Cheng,et al.  Urban Land Intensive Use Evaluation Study Based on Nighttime Light—A Case Study of the Yangtze River Economic Belt , 2019, Sustainability.

[2]  Ye Zhang,et al.  Comparison of Approaches for Urban Functional Zones Classification Based on Multi-Source Geospatial Data: A Case Study in Yuzhong District, Chongqing, China , 2019, Sustainability.

[3]  Tao Lin,et al.  Towards Sustainable Urban Communities: A Composite Spatial Accessibility Assessment for Residential Suitability Based on Network Big Data , 2018, Sustainability.

[4]  Tao Lin,et al.  Mapping Urban Functional Zones by Integrating Very High Spatial Resolution Remote Sensing Imagery and Points of Interest: A Case Study of Xiamen, China , 2018, Remote. Sens..

[5]  Wei Li,et al.  The Role of Manufacturing in Sustainable Economic Development: A Case of Guangzhou, China , 2018, Sustainability.

[6]  S. Gopal,et al.  Geographically Weighted Regression Models in Estimating Median Home Prices in Towns of Massachusetts Based on an Urban Sustainability Framework , 2018 .

[7]  Jin Chen,et al.  Exploring Determinants of Housing Prices in Beijing: An Enhanced Hedonic Regression with Open Access POI Data , 2017, ISPRS Int. J. Geo Inf..

[8]  Shihong Du,et al.  Hierarchical semantic cognition for urban functional zones with VHR satellite images and POI data , 2017 .

[9]  W. Kuang,et al.  Strategic transformation of regionalization for the agricultural comprehensive development: The example of Ningxia Hui Autonomous Region in China , 2016, Journal of Geographical Sciences.

[10]  Pengfei Jiao,et al.  Identification of multi-attribute functional urban areas under a perspective of community detection: A case study , 2016 .

[11]  Jialiang Chen,et al.  Effective successive POI recommendation inferred with individual behavior and group preference , 2016, Neurocomputing.

[12]  Xiaorui Zhang,et al.  Development and Application of a Planning Support System for Regional Spatial Functional Zoning Based on GIS , 2016 .

[13]  Jorge Gonçalves,et al.  Practical simulation of virtual crowds using points of interest , 2016, Comput. Environ. Urban Syst..

[14]  Francisco C. Pereira,et al.  Mining point-of-interest data from social networks for urban land use classification and disaggregation , 2015, Comput. Environ. Urban Syst..

[15]  Krzysztof Janowicz,et al.  POI Pulse: A Multi-granular, Semantic Signature–Based Information Observatory for the Interactive Visualization of Big Geosocial Data , 2015, Cartogr. Int. J. Geogr. Inf. Geovisualization.

[16]  Chaogui Kang,et al.  Social Sensing: A New Approach to Understanding Our Socioeconomic Environments , 2015 .

[17]  Ai Tinghua,et al.  A Real-time Generalization and Multi-scale Visualization Method for POI Data in Volunteered Geographic Information , 2015 .

[18]  Lai Tu,et al.  Inferring Barriers of Urban City Using Mobile Phone Record , 2013, 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing.

[19]  Xing Xie,et al.  Discovering regions of different functions in a city using human mobility and POIs , 2012, KDD.

[20]  C. Farmer,et al.  Network-Based Functional Regions , 2011 .

[21]  Jianguo Wu,et al.  Spatial pattern of urban functions in the Beijing metropolitan region , 2010 .

[22]  Martin Watts,et al.  Identifying Functional Regions in Australia Using Hierarchical Aggregation Techniques , 2010 .

[23]  L. Sýkora,et al.  The micro-regional nature of functional urban areas (FUAs): lessons from the analysis of the Czech urban and regional system , 2009 .

[24]  Chunhui Zhou,et al.  POI Inquiries and Data Update Based on LBS , 2009, 2009 International Symposium on Information Engineering and Electronic Commerce.

[25]  Bo-sin Tang,et al.  Strategic urban management in China: A case study of Guangzhou Development District , 2006 .

[26]  Bo-sin Tang,et al.  Challenges to the sustainability of ‘development zones’: A case study of Guangzhou Development District, China , 2005 .

[27]  Susanne Boll,et al.  The xPOI Concept , 2005, LoCA.

[28]  Wenli Qin,et al.  The growth characters and spatial distribution of urban functional area in China from 1984 to 2000 , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[29]  G. Cha.  Spatial pattern and industrial characteristics of economic technological development areas in eastern coastal China , 2015 .

[30]  Zhongxiang Yu,et al.  Land Use in Ma'anshan Economic and Technological Development Zone: Based on the Land Survey of Typical Enterprises , 2014 .

[31]  Liu Yang,et al.  Application of Voronoi diagrams and multiangle measurable image in the urban POI location and site generation , 2012 .

[32]  Li Bijun,et al.  Extracting hierarchical landmarks from urban POI data , 2011, National Remote Sensing Bulletin.

[33]  XinzhiHu.  A Study of Sustainable Development of China＇s State-Level Economic and Technological Development Zones , 2005 .

[34]  Guo Qingsheng,et al.  A study on the division of urban economic regions based on weighted Voronoi diagram , 2003 .