Identification of Urban Functional Regions Based on Floating Car Track Data and POI Data

Abstract

Along with the rapid development of China’s economy as well as the continuing urbanization, the internal spatial and functional structures of cities within this country are also gradually changing and restructuring. The study of functional region identification of a city is of great significance to the city’s functional cognition, spatial planning, economic development, human livability, and so forth. Backed by the emerging urban Big Data, and taking the traffic community as the smallest research unit, a method is proposed to identify urban functional regions by combining floating car track data with point of interest (POI) data recorded on an electronic map. It provides a new perspective for the study of urban functional region identification. Firstly, the main functional regions of the city studied are identified through clustering analysis according to the passenger’s spatial-temporal travel characteristics derived from the floating car data. Secondly, the fine-grained identification of the functional region attributes of the traffic communities is achieved using the label information from POI data. Finally, the AND-OR operation is performed on the recognition results derived by the clustering algorithm and the Delphi method, to obtain the identification of urban functional regions. This approach is verified by applying it to the main urban zone within Chengdu’s Third Ring Road. The results show that: (1) There are fewer single functional regions and more mixed functional regions in the main urban zone of Chengdu, and the distribution of the functional regions are roughly concentric centering in the city center. (2) Using the traffic community as a research unit, combined with dynamic human activity trajectory data and static urban interest point data, complex urban functional regions can be effectively identified.

Spatial Distribution of Travel Activities and its Relationship with Points of Interest

“…Scholars studied the functional structure of the city by mainly using POI data and proposing frameworks to identify the functional regions of a city. For instance, Long and Shen [33] and Yu et al [34] established a method to discover zones with different functions by combining POI data with smart card data and floating car track data, respectively. Yuan et al [35] proposed a framework for discovering functional zones in a city through the analysis of human mobility trajectories among regions and POIs within regions.…”

Tempo‐spatial variability of urban leisure functional zones: An analysis based on geo‐big data

“…Tu et al (2018) discerned urban functional regions via combining with human activity metrics (i.e., in‐home activity, 0:00–6:00; working activity, 9:00–12:00 & 14:00–17:00; social activity) based on mobile phone positioning data as well as remote sensing images. Yu et al (2019) identified urban functional zones based on dynamic human activity, namely, combining with the taxi trajectory data for deriving passengers’ tempo‐spatial commuting characteristics. Liu et al (2020) identified urban functional regions concerning dynamic human activities as well as landscape features, based on crowdsourced data (i.e., POIs, taxi trajectory data etc.)…”

A Multiple Subspaces-Based Model: Interpreting Urban Functional Regions with Big Geospatial Data

“…Chen et al [20] proposed a dynamic time warping (DTW) distance-based kmedoids method to delineate functional regions based on building-level social media data. Yu et al [21] identified functional regions through the clustering method according to the travel characteristics derived from the taxi data and utilized POI data to label functionality.…”