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Twitter data. (A) Density of tweets across Stockholm. (B) Trajectories of the geotagged tweets of four random users. User one's geotagged tweets (red) span eight different stadsdelar across the city, while user four's tweets (blue) are confined to just two. https://doi.org/10.1371/journal.pone.0247996.g001
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We present a novel metric for measuring relative connection between parts of a city using geotagged Twitter data as a proxy for co-occurrence of city residents. We find that socioeconomic similarity is a significant predictor of this connectivity metric, which we call “linkage strength”: neighborhoods that are similar to one another in terms of res...
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Context 1
... order to understand human mobility across the city, we analyze a set of geotagged tweets in the municipality of Stockholm between January 1, 2016 andApril 30, 2019. By looking at successive geotagged tweets, we can understand users' general mobility spaces- Fig 1 shows the density of Twitter activity across Stockholm as well as the tweet trajectories of four randomly selected users. After filtering out bots, businesses, and other types of uninformative tweets (see S1 Appendix), we analyze 281,863 tweets from 14,478 users. ...
Context 2
... looking at successive geotagged tweets, we can understand users' general mobility spaces- Fig 1 shows the density of Twitter activity across Stockholm as well as the tweet trajectories of four randomly selected users. After filtering out bots, businesses, and other types of uninformative tweets (see S1 Appendix), we analyze 281,863 tweets from 14,478 users. ...
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Citations
... Similarly, in Sweden, Hedman et al. [2021] find that individuals from areas with a higher proportion of non-Europeans generally stay within or move towards immigrant-dense areas. Social network data in Stockholm, Sweden, reveals the tendency of individuals to move through neighborhoods of similar income, education levels, and immigrant makeup, even when other factors, such as transit time and opportunities for interaction, are controlled [Heine et al., 2021]. In short, apart from geographically distant areas, people are less likely to travel between socioeconomically distant areas [Park et al., 2021. ...
... Studies using emerging data sources, compared to those relying on traditional data, provide evidence based on actual behavioral data on mobility rather than stated preferences and offer broader spatial and population coverage with high spatiotemporal granularity [Nilforoshan et al., 2023]. These emerging-data studies typically quantify multi-dimensional aspects of socio-spatial segregation, simultaneously examining race/ethnicity, nativity, income, and other factors [e.g., Heine et al., 2021]. In contrast, traditional data-driven studies often focus on a single aspect of segregation, e.g., housing [Wang et al., 2012], linking it to other relevant and readily available explanatory variables in the applied data. ...
... The abundance and intricacy of big data have spurred the development of innovative analytical methods and tools in spatial segregation research. For example, the theory of mobility homophily, confirmed across multiple regions [Xu et al., 2019, Heine et al., 2021, Huang et al., 2022, extends its relevance beyond residential segregation to include daily segregation experiences. Leveraging emerging data sources allows for integrating extensive mobility geolocation data with social network data, unveiling patterns previously unobservable at such refined scales [Xu et al., 2019, Silm et al., 2021. ...
Social segregation, the spatial and social separation between individuals from different backgrounds, can affect sustainable urban development and social cohesion. The literature has traditionally focused on residential segregation, examining how individuals' residential locations are distributed differently across neighborhoods based on income, ethnicity, and education. However, this approach overlooks the complexity of spatial segregation because daily activities often extend far beyond residential areas. Over the past one to two decades, emerging mobility data sources have enabled a new understanding of socio-spatial segregation by considering daily activities such as work, school, shopping, and leisure visits. From traditional surveys to GPS trajectories, diverse data sources reveal that day-today movements can impact segregation by reducing or amplifying segregation levels obtained when considering residential aspects alone. This literature review focuses on three critical questions: (a) to what extent do individual mobility patterns contribute to segregation? (b) Which factors explain the role played by mobility in segregation? and (c) What insights are gained by incorporating extensive mobility data into segregation research? Our literature review contributes to an improved understanding of socio-spatial segregation at the individual level and offers actionable insights into reducing segregation and addressing research gaps in the field.
... Our study also complements a growing literature in urban studies using emerging technologies such as sensors, Wi-Fi sniffing, Bluetooth tracking, and street imagery to study human dynamics in cities (Ben-Joseph, 2011;. One branch of this literature exploits passively collected cellphone location data from mobile applications (Ratti et al., 2006;Shoval, 2008;González et al., 2008;Jiang, Ferreira, & González, 2012;Louail et al., 2014;Tan, Zegras, Wilhelm, & Arcaya, 2018;Heine et al., 2021;Salazar Miranda et al., 2021;Bongiorno et al., 2021). Other studies leverage data from static sensors to measure the number of pedestrians using urban infrastructure (Williams et al., 2019). ...
... Land use change has specific and cumulative effects on air and water quality, waste generation, climate and human health (Lu, Y. et al. 2021;Zhao, Y. et al. 2021). Increased socio-economic activities have an impact on population mobility based on the pattern of origin and destination of travel (Graells-Garrido, E. et al. 2021;Heine, C. et al. 2021). The spatial dynamics of suburban areas which are dominantly developed for housing and settlement development are positively related to an increase in traffic volume on main roads, traffic congestion and disturbances to residents' travel patterns (Nair, DJ. et al. 2019;Nozdrovická, J. et al. 2020;Surya, B. et al. 2020a). ...
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... The idea that human activity in streets is an important dimension of city life that slow zones could significantly impact stems from previous 2 This measure is inspired by the "linkage strength" measure in Heine et al. (2021), which calculates the strength of connection between neighborhood pairs using shared users. In this paper, we follow a similar approach but look at the unique number of neighborhoods connected to each street segment instead of strength of connection between pairs. ...
... This paper also complements a large body of literature that has used social media data as a proxy for human activity to study mobility and the use of urban spaces. Existing studies have used Twitter data specifically to estimate home and work locations (Kurkcu et al., 2016;Steiger et al., 2015), measure visitation to urban spaces (Hamstead et al., 2018;Hasan et al., 2013), analyze social mixing in cities (Heine et al., 2021;Wang et al., 2018), and identify land use patterns (Soliman et al., 2017), among other applications. Several studies have used Twitter data to measure visitation patterns to urban places, especially in urban parks (Abbiasov, 2020;Hamstead et al., 2018;Roberts et al., 2017;Zhang & Zhou, 2018). ...
Streets are fundamental to social and economic exchange in cities. Despite their importance for urban life, streets continue to dedicate more space to cars than people, raising concerns about their ability to host social exchange. In this paper, we study the extent to which slow zones (areas designed to be more pedestrian-friendly via speed limit reductions) affect human activity in streets. We study this question in the context of Paris, which implemented several slow zones covering a large portion of the city between 2010 and 2019. We exploit differences in the effects of the policy at the boundaries of the slow zones and their staggered introduction over time to identify their causal effect on human activity. Comparing street segments immediately within the slow zone boundary (our treatment group) to street segments immediately outside the slow zone (our control group) shows that human activity measured using Twitter is 44% higher in slow zones. This effect is driven by an increase in both the number of users and in the number of tweets per user, suggesting that slow zones attract more people and that people are tweeting more in these areas. We also show that slow zones draw visitors from a wider geographic range of neighborhoods, contributing to social mixing.
... Notable efforts include Kwan (1999), who investigated the disparity of travel links due to gender differences in Columbus, Ohio, U.S., and Limtanakool et al. (2006), who explored the impact of the spatial configuration of land use and transport systems on travel patterns in the Netherlands. Despite that survey-based investigation can retrieve detailed demographic and socioeconomic information from respondents, the difficulties in data collection (e.g., labor-intensive and time-consuming) largely limited the scope of these studies, which were usually limited to a small sampling size of participants during a short period of time (Heine et al., 2021). The advent of location-aware techniques coupled with the emerging concepts of "Web 2.0" (Murugesan, 2007) and "Citizen as Sensors" (Catlin-Groves, 2012) led to the emergence of many new human mobility datasets which detail fine-grained whereabouts of a massive number of users. ...
As described in the proverb “birds of a feather flock together”, the term homophily narrates the principle that stronger spatial interactions tend to be formed among locations with similar characteristics. Taking advantage of mobility networks derived from around 45 million mobile devices in the U.S. and targeting the top twenty most-populated U.S. Metropolitan Statistical Areas (MSAs), we extract human mobility structures by detecting communities formed by strong spatial links and unravel the homophily effect at the community level using information entropy that measures the chaoticness of societal settings within communities. The results suggest that the power-law still, to a large extent, governs the travel patterns in MSAs. However, communities featured by strong human interactions can sometimes transcend geographic proximity in modern metropolitans. The entropy varies across communities, and a community can exhibit variation of homophily levels when different sociodemographic settings are investigated. Our study proves the ubiquity of the homophily phenomenon in modern metropolitans and documents its variation from different social perspectives from a mobility-oriented setting. The conceptual and analytical knowledge, as well as the results of this study, are expected to facilitate better policymaking to promote social integration in metropolitan areas.
... Estes rastros digitais dos usuários podem trazer novas formas de observar as dinâmicas urbanas e atividades humanas nestes espaços (Martino et al, 2019;Ratti e Claudel, 2018), como uma espécie de "macroscópio" poderoso para observar padrões na cidade. Assim, um enorme conjunto de dados sobre usuários de celulares tem sido explorado na percepção de fenômenos nas áreas urbanas, como os padrões de mobilidade e contagem diária de passos (Sevtsuk e Ratti, 2010;Song et al., 2010;Lenormand et al., 2020), o uso de espaços urbanos e sua potencialidade de atratividade (Reades et al., 2007;Girardin et al., 2009;Schläpfer et al., 2021), determinação da localização geográfica de casas e locais de trabalho (Ahas et al., 2010;Isaacman et al., 2011;Heine et al., 2021). O sensoriamento também é capaz de capturar comportamentos humanos na cidade, podendo até medir o grau de atratividade de uma área, como as cachoeiras de Nova York (Girardin et al., 2009). ...
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... Fueled by these individual-level mobility data sources, recent studies attempt to re-assess social segregation by incorporating individuals' activities and mobility patterns into their analytical frameworks (Candipan et al., 2021;Liu, 2021;Xu et al., 2019;Yip et al., 2016). Four types of individual-level mobility data sources have been intensively utilized in existing studies, including mobility/activity surveys (Farber et al., 2015;Park & Kwan, 2018), social media (Heine et al., 2021;Q. Wang et al., 2018), call details records (CDRs) (Amini et al., 2014;Xu et al., 2019), and activity-tracking mobile apps (Yip et al., 2016). ...
The notion of social segregation refers to the degrees of separation between socially different population groups. Many studies have examined spatial and residential separations among different socioeconomic or racial populations. However, with the advancement of transportation and communication technologies, people’s activities and social interactions are no longer limited to their residential areas. Therefore, there is a growing necessity to investigate social segregation from a mobility perspective by analyzing people’s mobility patterns. Taking advantage of crowdsourced mobility data derived from 45 million mobile devices, we innovatively quantify social segregation for the twelve most populated U.S. metropolitan statistical areas (MSAs). We analyze the mobility patterns between different communities within each MSA to assess their separations for two years. Meanwhile, we particularly explore the dynamics of social segregation impacted by the COVID-19 pandemic. The results demonstrate that New York and Washington D.C. are the most and least segregated MSA respectively among the twelve MSAs. Since the COVID-19 began, six of the twelve MSAs experienced a statistically significant increase in segregation. This study also shows that, within each MSA, the most and least vulnerable groups of communities are prone to interacting with their similar communities, indicating a higher degree of social segregation.
... The idea that human activity in streets is an important dimension of city life that slow zones could significantly impact stems from previous 2 This measure is inspired by the "linkage strength" measure in Heine et al. (2021), which calculates the strength of connection between neighborhood pairs using shared users. In this paper, we follow a similar approach but look at the unique number of neighborhoods connected to each street segment instead of strength of connection between pairs. ...
... This paper also complements a large body of literature that has used social media data as a proxy for human activity to study mobility and the use of urban spaces. Existing studies have used Twitter data specifically to estimate home and work locations (Kurkcu et al., 2016;Steiger et al., 2015), measure visitation to urban spaces (Hamstead et al., 2018;Hasan et al., 2013), analyze social mixing in cities (Heine et al., 2021;Wang et al., 2018), and identify land use patterns (Soliman et al., 2017), among other applications. Several studies have used Twitter data to measure visitation patterns to urban places, especially in urban parks (Abbiasov, 2020;Hamstead et al., 2018;Roberts et al., 2017;Zhang & Zhou, 2018). ...
... Notable efforts include Kwan (1999), who investigated the disparity of travel links due to gender differences in Columbus, Ohio, U.S., and Limtanakool et al. (2006), who explored the impact of the spatial configuration of land use and transport systems on travel patterns in the Netherlands. Despite that survey-based investigation can retrieve detailed demographic and socioeconomic information from respondents, the difficulties in data collection (e.g., labor-intensive and time-consuming) largely limited the scope of these studies, which were usually limited to a small sampling size of participants during a short period of time (Heine et al., 2021). The advent of location-aware techniques coupled with the emerging concepts of "Web 2.0" (Murugesan, 2007) and "Citizen as Sensors" (Catlin-Groves, 2012) led to the emergence of many new human mobility datasets which detail fine-grained whereabouts of a massive number of users. ...
... For instance, the theory of homophily states that people who share characteristics (e.g., age, sexuality, hobbies) tend to connect and interact with each other online and to congregate offline ("birds of a feather flock together"; e.g., McPherson et al., 2001). Inspired by this theory, Heine et al. (2021) used geotagged tweets of residents in Stockholm to analyze how their movements around the city connected them to different neighborhoods. The authors found that the socioeconomic similarity of neighborhoods in Stockholm (i.e., similarity in residents' income, education level, and immigration history) could predict how individuals moved through those areas (i.e., residents of socioeconomically similar neighborhoods were likely to adopt similar movement patterns throughout the city). ...
Understanding people’s movement patterns has many important applications, from analyzing habits and social behaviors, to predicting the spread of disease. Information regarding these movements and their locations is now deeply embedded in digital data generated via smartphones, wearable sensors, and social-media interactions. Research has largely used data-driven modeling to detect patterns in people’s movements, but such approaches are often devoid of psychological theory and fail to capitalize on what movement data can convey about associated thoughts, feelings, attitudes, and behavior. This article outlines trends in current research in this area and discusses how psychologists can better address theoretical and methodological challenges in future work while capitalizing on the opportunities that digital movement data present. We argue that combining approaches from psychology and data science will improve researchers’ and policy makers’ abilities to make predictions about individuals’ or groups’ movement patterns. At the same time, an interdisciplinary research agenda will provide greater capacity to advance psychological theory.
