Revealing racial-ethnic segregation with individual experienced segregation indices based on social media data: A case study in Los Angeles-Long Beach-Anaheim

• Published in: Computers, environment and urban systems Vol. 104; p. 102008
• Main Authors: Wu, Meiliu, Liu, Xinyi, Qin, Yuehan, Huang, Qunying
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2023
• Subjects: Experienced segregation; Human mobility; Social media data; Spatial proximity; Spatial segregation measurement; User profiling; Spatial segregation measurement; Experienced segregation; Social media data; Spatial proximity; Human mobility; User profiling
• ISSN: 0198-9715
• DOI: 10.1016/j.compenvurbsys.2023.102008

While recent studies have started to measure the experienced racial-ethnic segregation across activity space (beyond “residential” segregation), insufficient efforts have been devoted to revealing the experienced segregation levels of the racial-ethnic minorities (e.g., Asian, Hispanic, Native, and Multi-races), mainly due to the lack of a comprehensive probe into various individual-level datasets. This issue leads to an unnoted deficiency in this field – the lack of showing the “directed” interactions between any pair of two groups. To bridge these gaps, this work proposed a unified framework of using social media data to infer both individual's mobility patterns and user profiles (i.e., race-ethnicity and economic status), to include more racial-ethnic minorities for a more comprehensive estimation of individual experienced segregation. With these inferred information, we developed two novel individual experienced segregation indices, i.e., individual experienced exposure index (EEI) to each race-ethnicity and individual experienced diversity index (EDI). Moreover, we integrated these two indices with the spatial impacts among the mobility-based activity locations based on distance-decay functions, which are often neglected by existing studies. Using Los Angeles-Long Beach-Anaheim as the study case, we discovered several novel, important findings. First, while experienced isolation (i.e., mainly having intra-group interaction) persists among all groups, Asian is most diverse in inter-group interaction. Second, individuals who live closer tend to have similar levels of inter-group interaction. Moreover, exposures to both White and Black are negatively correlated with exposure to Hispanic. On top of that, individuals with a higher economic status are likely less interested in inter-group interaction (except for those from Hispanic), along with more exposures to both White and Black, but less exposure to Hispanic. •Building a framework to infer individual-level mobility patterns and socio-demographic information with social media data.•Developing novel spatial segregation indices with distance-decay functions to measure individual experienced segregation.•Presenting an integrated methodology for segregation measurement applicable to other individual-level mobility datasets.•Disentangling socio-demographic factors of segregation by examining individual race-ethnicity with economic status.