Identifying and understanding road-constrained areas of interest (AOIs) through spatiotemporal taxi GPS data: A case study in New York City. (March 2021)

• Record Type: Journal Article
• Authors: Liu, Yunzhe; Singleton, Alex; Arribas-bel, Daniel; Chen, Meixu
• Is Part Of: Computers, environment and urban systems. Volume 86(2021)
• Publication Date: 2021-03
• Subjects: Areas of interest -- ST-DBSCAN -- Public transit -- Taxi GPS -- Urban analytics -- Mobility; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2020.101592
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: Urban areas of interest (AOIs) represent areas within the urban environment featuring high levels of public interaction, with their understanding holding utility for a wide range of urban planning applications. Within this context, our study proposes a novel space-time analytical framework and implements it to the taxi GPS data for the extent of Manhattan, NYC to identify and describe 31 road-constrained AOIs in terms of their spatiotemporal distribution and contextual characteristics. Our analysis captures many important locations, including but not limited to primary transit hubs, famous cultural venues, open spaces, and some other tourist attractions, prominent landmarks, and commercial centres. Moreover, we respectively analyse these AOIs in terms of their dynamics and contexts by performing further clustering analysis, formulating five temporal clusters delineating the dynamic evolution of the AOIs and four contextual clusters representing their salient contextual characteristics. Highlights: An enhanced analytical framework is proposed to integratedly explore urban AOIs in New York City. ST-DBSCAN algorithm is applied on taxi GPS data to detect spatiotemporal hotspots. Road network is used as the carrier of the identified taxi trip hotspots, formulating road-constrained AOIs. Spatiotemporal dynamics and contextual characteristics of the AOIs are investigated through further clustering analysis.