Urban Functional Zone Classification Based on POI Data and Machine Learning

Luo, Guowei; Ye, Jiayuan; Wang, Jinfeng; Wei, Yi

doi:10.3390/su15054631

Cited by 14 publications

(13 citation statements)

References 39 publications

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“…For result verification, 353 sample blocks were selected as the test set for identification verification, and then compared with Google satellite images and Baidu maps [12] . The number of correctly identified cells was judged by visual interpretation, and the results showed that the number of correctly identified grid cells was 302, with an overall accuracy of 85.55% (Table 4).…”

Section: Results Verification and Functional Area Classification Displaymentioning

confidence: 99%

Identification and spatio-temporal characterization of urban functional areas based on POI data

Peng,

Yang,

Cai

et al. 2024

Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023)

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Accurate identification of urban functional areas is of great significance to the scientific management and intelligent services of cities. Aiming at the problems of low accuracy and subjectivity of current identification methods, a quantitative identification method combining random forest and frequency density algorithm is proposed. Firstly, the frequency density calculation method is constructed by assigning weights to 19 POI types through random forest, and then the spatiotemporal feature analysis is performed on the distribution of urban functional areas identified in 2016 and 2021, respectively; secondly, the influence of rank scaling is considered, and Jingjiang Palace Street is used as an example for functional area identification; finally, a test set is selected for validation and comparison experiments. The results show that the accuracy rate of the method in this paper reaches 85.55%.

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Section: Results Verification and Functional Area Classification Displaymentioning

confidence: 99%

Identification and spatio-temporal characterization of urban functional areas based on POI data

Peng,

Yang,

Cai

et al. 2024

Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023)

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“…The accuracy rate and recall rate are used as evaluation metrics, and are calculated using Formulas ( 15) and ( 16), respectively. F1 values are used to combine the accuracy and recall rates according to Formula (17).…”

Section: Evaluation Of the Building Function Identification Resultsmentioning

confidence: 99%

“…The arrival of the geographic big data era has brought massive online geographic information resources, such as social perception data from social media [13], cab trajectory data [14], cell phone data [15], street-view image data [16], and point of interest (POI) data [17,18]. These record a large amount of information about human activities and have been widely used in the study of urban spatial structure characteristics.…”

Section: Introductionmentioning

confidence: 99%

Identification of Urban Building Functions Based on Points of Interest and Spatial Relationships between Geographic Entities

Wei,

Luo,

et al. 2024

Applied Sciences

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Knowing the functions of buildings is valuable in urban planning and management. For example, it can be used for the assessment of urban planning implementation and the fine-tuning of community governance. At large scales, determining building functions can be time-consuming and laborious. While point of interest (POI) data can be used to identify urban building functions, it is prone to missing values. The present study proposes combining POIs with the spatial relationships between geographic entities and geographic information systems (GIS) to improve the accuracy of urban building function identification. First, the POIs are reclassified according to building functions. Second, the spatial relationships among road networks, buildings and POIs are analysed, and the frequency density ratios of POI types are calculated to identify the functions of buildings that contain POIs. Finally, buildings that do not contain POIs are identified by calculating the spatial similarity between unrecognised buildings and recognised buildings within the same road network mesh. The method can identify buildings with singular residential, commercial, office, and public services functions, as well as seven mixed functions, with an accuracy, recall, and F1 value of 90.28%, 97.52%, and 93.76%, respectively. Public service buildings and residential buildings have the highest identification precision, while the identification precision of mixed commercial and public service buildings and mixed residential and public service buildings are the lowest. An experiment demonstrates the effectiveness of the method. The results indicate that the spatial relationships between entities can compensate for missing POI data.

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“…Qian et al applied hierarchical cluster method to study the spatial distribution and structure of urban functions in 200 Chinese cities on different time scales [29]. Luo et al identified a relationship diagram of POI data and classification of urban spatial functions, calculated the densities of each type of POI, and used the density values of various POIs in the research unit as feature vectors to recognize urban spatial function by the Kstar algorithm [30].…”

Section: Urban Functionmentioning

confidence: 99%

Analyzing the Direction of Urban Function Renewal Based on the Complex Network

Cheng,

Luo

2023

Sustainability

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Urban function renewal is essential for modern megacities’ urban planning and economic developments. This paper investigates the urban function renewal in Shenzhen, China based on a complex network method. According to the points of interest and the location quotient, the dominant urban functions in each district are discussed. After computing conditional probability, the interdependence of urban functions is analyzed. The complex networks of the functions and the corresponding clusters are presented to examine the relationship and the overall features of the functions, and the features of the function clusters, respectively. The average degree and average weighted degree of the main function categories of the functions are computed to explore the features of the function classification. The urban functions’ renewal potential index is calculated to show the potential of the non-dominant functions renewing to the dominant ones in the coming years. The difficulty index of the urban function renewal in each district is presented, and the difficulty degree of the original d-ominant function group renewing to a new one is obtained. The results show that more dominant urban functions have a significant probability of being dominant ones in a district; the functions of hotels and life services are essential in the planning and development in Shenzhen; and the districts with better economic levels have greater values of the difficulty of the urban function renewal. Then, the function renewal direction in Shenzhen is analyzed, and some policy implications are given.

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Urban Functional Zone Classification Based on POI Data and Machine Learning

Cited by 14 publications

References 39 publications

Identification and spatio-temporal characterization of urban functional areas based on POI data

Identification and spatio-temporal characterization of urban functional areas based on POI data

Identification of Urban Building Functions Based on Points of Interest and Spatial Relationships between Geographic Entities

Analyzing the Direction of Urban Function Renewal Based on the Complex Network

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