Characterizing People's Daily Activity Patterns in the Urban Environment: A Mobility Network Approach with Geographic Context-Aware Twitter Data. Issue 7 (10th November 2021)

• Record Type: Journal Article
• Authors: Yin, Junjun; Chi, Guangqing
• Is Part Of: Annals of the American Association of Geographers. Volume 111:Issue 7(2021)
• Page Start: 1967
• Page End: 1987
• Publication Date: 2021-11-10
• Subjects: activity motif -- activity pattern -- geographic context -- geolocated tweets -- mobility network; 活动主题 -- 活动模式 -- 地理背景 -- 地理定位推文 -- 移动网络; contexto geográfico -- motivo de actividad -- patrón de actividad -- red de movilidad -- tuits geolocalizados; Geography -- Periodicals; Environmental sciences -- Periodicals; Geography; Electronic journals; Periodicals; 550
• DOI: Https://www.tandfonline.com/doi/10.1080/24694452.2020.1867498
• Languages: English
• ISSNs: 2469-4452

Abstract

Abstract : People's daily activities in the urban environment are complex and vary by individual. Existing studies using mobile phone data revealed distinct and recurrent transitional activity patterns, known as mobility motifs, in people's daily lives. The limitation in using only a few inferred activity types hinders our ability to examine general patterns in detail. We proposed a mobility network approach with geographic context-aware Twitter data to investigate granular daily activity patterns in the urban environment. We first used publicly accessible geolocated tweets to track the movements of individuals in two major U.S. cities, Chicago and Greater Boston, where each recorded location is associated with its closest land use parcel to enrich its geographic context. A direct mobility network represents the daily location history of the selected active users, where the nodes are physical places with semantically labeled activity types and the edges represent the transitions. Analyzing the isomorphic structure of the mobility networks uncovered sixteen types of location-based motifs, which describe over 83 percent of the networks in both cities and are comparable to those from previous studies. With detailed and semantically labeled transitions between every two activities, we further dissected the general location-based motifs into activity-based motifs, where sixteen common activity-based motifs describe more than 57 percent of transitional behaviors in the daily activities in the two cities. The integration of geographic context from the synthesis of geolocated Twitter data with land use parcels enables us to reveal unique activity motifs that form the fundamental elements embedded in complex urban activities.