Delineating urban functional use from points of interest data with neural network embedding: A case study in Greater London

Niu, Haifeng; Silva, Elisabete

doi:10.1016/j.compenvurbsys.2021.101651

Cited by 52 publications

(38 citation statements)

References 45 publications

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“…For example, Beijing moved the city administrative office from the city center to the Tongzhou District, which is far away from the city center, indicating that some public service may be withdrawn from the city center with the development of the city. Additionally, studies have also shown that some urban centers are mainly mixed-function areas [36,37].…”

Section: Applicability Of the Mining Methodsmentioning

confidence: 99%

Mining Spatial Correlation Patterns of the Urban Functional Areas in Urban Agglomeration: A Case Study of Four Typical Urban Agglomerations in China

Zheng

Zhang

et al. 2022

Land

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Urban agglomeration is a higher stage of urban development. Exploring the spatial correlation of functional areas is important for promoting high-quality urban development. However, recently the research on urban functional areas is mainly focused on how to identify urban functional areas, and they lack some methods to analyze the spatial correlation patterns of urban functional areas. Therefore, firstly, this study uses POI data and a deep learning model to identify the urban functional areas of four typical urban agglomerations in China. Then, we create a new method to mine the spatial correlation patterns of urban functional areas from two levels (city and cities in one urban agglomeration). Moreover, we find that various graphs well express the spatial correlation patterns. Based on the above, we establish a new technical process for mining the spatial correlation of urban functional areas. The main conclusions are as follows: (1) The multilayer detailed division of the functional area is helpful to mine the spatial correlation pattern of the functional area. (2) The rank of each city in the urban agglomeration can be divided according to the urban functional area; there are great differences in richness and scale of the mixed-functional areas in the urban agglomeration, but there is little difference among the urban agglomerations. (3) The spatial correlation patterns of the functional areas in the first-rank cities of each urban agglomeration area are highly similar. (4) There is a certain spatial correlation pattern of functional areas in Chinese urban agglomerations. (5) There are great differences in the similarity of spatial correlation patterns between cities in one urban agglomeration, and the spatial relationship of similarity may not surround the most developed cities. This research will help urban planners to develop functional areas in different cities.

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Section: Applicability Of the Mining Methodsmentioning

confidence: 99%

Mining Spatial Correlation Patterns of the Urban Functional Areas in Urban Agglomeration: A Case Study of Four Typical Urban Agglomerations in China

Zheng

Zhang

et al. 2022

Land

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“…For example, methods for the classification of building functions have been investigated by classifying the types through POI-weighted frequency density (FD) ratios. It was hypothesised that the distribution of functional areas in cities could be reflected in the classification of building functions [9,28,29].…”

Section: Literature Reviewmentioning

confidence: 99%

Identification of Urban Functional Areas and Governance Measures Based on Point of Interest Data: A Case Study of the Shenyang Railway Station Area in Shenyang City

Sheng-jun

et al. 2022

Buildings

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Inventory extraction and governance measures in urban land use have become important topics in urban regeneration research. This study aimed to inform design governance in urban regeneration through a point of interest (POI) data-based case study. An approximately 15 km2 rectangular development area was assessed for its characteristics and deficiencies. Frequency density analysis was conducted based on data identification of urban built environments and governance from an integrated planning perspective, using field research, spatial autocorrelation, Getis–Ord Gi* analysis, and SPSS. We (1) applied POI data to assess the frequency ratios of the function types for the 6008 buildings in the study area; (2) analysed layouts of POI densities in hotspots for different formats; and (3) applied an evidence-based approach and overlay analysis to identify the area’s functional morphological zones. Finally, the urban physical and morphological properties were identified and compared with the identification result of the urban functional areas to qualitatively evaluate the differences. Global Moran’s I of the POI density of Retail Business (B11), Restaurants (B13), Hotels (B14), Entertainment and Recreation (B3), and Residential Services (R22) were 0.35, 0.35, 0.06, 0.20, and 0.15, respectively, displaying a significant spatial clustering feature. However, for land types including Administrative Offices (A1), Finance and Insurance (B21), and Other Business Facilities (B29), the p-values between the POI density and the random pattern were 0.23, 0.71, 0.56, respectively, showing no significant difference. Our study provides recommendations for governance and integrated urban redevelopment planning to coordinate and guide further regeneration.

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“…In contrast, POI data have the advantages of simple acquisition and convenient processing, meaning that POI data are widely used in geographical research. At present, the research on POI mainly focuses on a recommendation model [19][20][21] and urban functional zoning [22], and the research on POI itself mainly focuses on automatic classification [23] and classification accuracy by using machine learning. Nak introduced POI into eye-tracking data analysis [24], making the research scope of POI broader.…”

Section: Introductionmentioning

confidence: 99%

Identification of Urban Functional Areas and Their Mixing Degree Using Point of Interest Analyses

Liu

2022

Land

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With the rise of smart cities and geographic big-data applications, the refined identification of urban functional areas is of great significance for decision-makers to formulate scientific and reasonable urban planning. In this paper, a random forest algorithm was adopted to analyze Point of Interest (POI) data, with the aim of identifying the functional zoning of Chongqing’s central urban area and to quantify the functional mixing degree by combining POI data with Open Street Map (OSM) road networks. The main conclusions include: (1) Due to the topography and previous urban planning strategies, the central urban area of Chongqing has a significant cluster development that radiates outward from the center of each district. Mixed functional areas account for about 40% of the total area, excluding non-functional areas. The land-use intensity of the central urban area is significant. (2) The mixing degree of the inner ring is generally high, while the aggregation characteristics of the outer ring are weaker. The functions of catering and transportation are dispersed and are mutually exclusive from other functions. (3) The identification of residential service and green spaces and squares was the best, while the identification of catering service areas was slightly less accurate. The overall identification accuracy of the single-function areas was 82%. The results of functional zoning provide valuable information for understanding the downtown area of Chongqing and represent a new method for the study of urban structures in the future.

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Delineating urban functional use from points of interest data with neural network embedding: A case study in Greater London

Cited by 52 publications

References 45 publications

Mining Spatial Correlation Patterns of the Urban Functional Areas in Urban Agglomeration: A Case Study of Four Typical Urban Agglomerations in China

Mining Spatial Correlation Patterns of the Urban Functional Areas in Urban Agglomeration: A Case Study of Four Typical Urban Agglomerations in China

Identification of Urban Functional Areas and Governance Measures Based on Point of Interest Data: A Case Study of the Shenyang Railway Station Area in Shenyang City

Identification of Urban Functional Areas and Their Mixing Degree Using Point of Interest Analyses

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