Identification of multi-attribute functional urban areas under a perspective of community detection: A case study

Liu, Xin; Jiao, Pengfei; Yuan, Ning; Wang, Wenjun

doi:10.1016/j.physa.2016.06.106

Cited by 10 publications

(4 citation statements)

References 17 publications

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“…In recent years, geoscience big data based on Internet spatiotemporal mega data has brought new ideas and methods, helping us to understand and quantitatively analyze the spatial and temporal pattern characteristics of complex natural regions and socioeconomic systems [20][21][22][23][24][25]. For example, point of interest (POI) data provide a fast and effective alternative to the division of urban functional areas [24,[26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

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

Han

2019

Sustainability

126

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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.

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Section: Introductionmentioning

confidence: 99%

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

Han

2019

Sustainability

126

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“…In addition, a few studies using administrative district or cadastral parcel as basic units on urban functional zone classification [52,53]. The statistics for the number of articles that used 6-unit types are shown in Figure 5.…”

Section: The Basic Unit Of Urban Functional Zone Identificationmentioning

confidence: 99%

Classification Schemes and Identification Methods for Urban Functional Zone: A Review of Recent Papers

Liu

Deng

et al. 2021

Applied Sciences

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Urbanization is accelerating due to economic and societal development. The accurate identification of urban functional zones is significant for urban structure optimization, urban planning, and resource allocation. This paper reviews the scholarly literature on urban functional zone identification. Based on the retrieval results of databases, we analyzed the overview and current status. The identification methods and classification schemes are summarized from the existing research. The following results were obtained: (1) point of interest (POI) data are widely used for functional zone identification; (2) the block is the most common unit for functional zone identification; (3) cluster analysis is the main approach for urban functional zone identification; (4) most of the classification schemes are based on the dominant land use and characteristics of data sources. We predict future trends of urban functional zone identification based on the reviewed literature. Our findings are expected to be valuable for urban studies.

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“…Most people live in urban areas, where the world is undergoing rapid urbanization, particularly in developing countries, posing the urgent need for sustainable urban development [1]. Spatial information such as urban land use and function, which refers to the use of the land, is thus essential for effective urban planning and management [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Identifying Urban Functional Regions from High-Resolution Satellite Images Using a Context-Aware Segmentation Network

Zhao

et al. 2022

Remote Sensing

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The automatic identification of urban functional regions (UFRs) is crucial for urban planning and management. A key issue involved in URF classification is to properly determine the basic functional units, for which popular practices are usually based upon existing land use boundaries or road networks. Such practices suffer from the unavailability of existing datasets, leading to difficulty in large-scale mapping. To deal with this problem, this paper presents a method to automatically obtain functional units for URF classification using high-resolution remote sensing images. We develop a context-aware segmentation network to simultaneously extract buildings and road networks from remote sensing images. The extracted road networks are used for partitioning functional units, upon which five main building types are distinguished considering building height, morphology, and geometry. Finally, the UFRs are classified according to the distribution of building types. We conducted experiments using a GaoFen-2 satellite image with a spatial resolution of 0.8 m acquired in Fuzhou, China. Experimental results showed that the proposed segmentation network performed better than other convolutional neural network segmentation methods (i.e., PSPNet, Deeplabv3+, DANet, and JointNet), with an increase of F1-score up to 1.37% and 1.19% for road and building extraction, respectively. Results also showed that the residential regions, accounting for most of the urban areas, identified by the proposed method had a user accuracy of 94%, implying the promise of the proposed method for deriving the spatial units and the types of urban functional regions.

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Identification of multi-attribute functional urban areas under a perspective of community detection: A case study

Cited by 10 publications

References 17 publications

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

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

Classification Schemes and Identification Methods for Urban Functional Zone: A Review of Recent Papers

Identifying Urban Functional Regions from High-Resolution Satellite Images Using a Context-Aware Segmentation Network

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