Identifying Land Use Functions in Five New First-Tier Cities Based on Multi-Source Big Data

Wangmin Yang, Yang Ye, Bowei Fan, Shuang Liu and Jingwen Xu ()
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Wangmin Yang: College of Resources, Sichuan Agricultural University, Chengdu 611130, China
Yang Ye: College of Resources, Sichuan Agricultural University, Chengdu 611130, China
Bowei Fan: College of Resources, Sichuan Agricultural University, Chengdu 611130, China
Shuang Liu: Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610299, China
Jingwen Xu: College of Resources, Sichuan Agricultural University, Chengdu 611130, China

Land, 2024, vol. 13, issue 3, 1-22

Abstract: With the continuous development of big data technology, semantic-rich multi-source big data provides broader prospects for the research of urban land use function recognition. This study relied on POI data and OSM data to select the central urban areas of five new first-tier cities as the study areas. The TF-IDF algorithm was used to identify the land use functional layout of the study area and establish a confusion matrix for accuracy verification. The results show that: (1) The common feature of these five cities is that the total number and area of land parcels for residential land, commercial service land, public management and service land, and green space and open space land all account for over 90%. (2) The Kappa coefficients were all in the range [0.61, 0.80], indicating a high consistency of accuracy evaluation. (3) Chengdu and Tianjin have the highest land use function mixing degree, followed by Xi‘an, Nanjing, and Hangzhou. (4) Among the five new first-tier cities, Hangzhou and Nanjing have the highest similarity in land use function structure layout. This study attempts to reveal the current land use situation of five cities, which will provide a reference for urban development planning and management.

Keywords: POI data; TF-IDF; urban function identification; spatial analysis (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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