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Bogomolov Y, Belyi A, Sobolevsky S. Urban delineation through a prism of intraday commute patterns. Front Big Data 2024; 7:1356116. [PMID: 38504749 PMCID: PMC10948430 DOI: 10.3389/fdata.2024.1356116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 02/16/2024] [Indexed: 03/21/2024] Open
Abstract
Introduction Urban mobility patterns are crucial for effective urban and transportation planning. This study investigates the dynamics of urban mobility in Brno, Czech Republic, utilizing the rich dataset provided by passive mobile phone data. Understanding these patterns is essential for optimizing infrastructure and planning strategies. Methods We developed a methodological framework that incorporates bidirectional commute flows and integrates both urban and suburban commute networks. This comprehensive approach allows for a detailed representation of Brno's mobility landscape. By employing clustering techniques, we aimed to identify distinct mobility patterns within the city. Results Our analysis revealed consistent structural features within Brno's mobility patterns. We identified three distinct clusters: a central business district, residential communities, and an intermediate hybrid cluster. These clusters highlight the diversity of mobility demands across different parts of the city. Discussion The study demonstrates the significant potential of passive mobile phone data in enhancing our understanding of urban mobility patterns. The insights gained from intraday mobility data are invaluable for transportation planning decisions, allowing for the optimization of infrastructure utilization. The identification of distinct mobility patterns underscores the practical utility of our methodological advancements in informing more effective and efficient transportation planning strategies.
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Affiliation(s)
- Yuri Bogomolov
- Department of Mathematics and Statistics, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Alexander Belyi
- Department of Mathematics and Statistics, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Stanislav Sobolevsky
- Department of Mathematics and Statistics, Faculty of Science, Masaryk University, Brno, Czech Republic
- Center for Urban Science and Progress, New York University, New York, NY, United States
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2
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Abbiasov T, Heine C, Sabouri S, Salazar-Miranda A, Santi P, Glaeser E, Ratti C. The 15-minute city quantified using human mobility data. Nat Hum Behav 2024; 8:445-455. [PMID: 38316977 DOI: 10.1038/s41562-023-01770-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 10/24/2023] [Indexed: 02/07/2024]
Abstract
Amid rising congestion and transport emissions, policymakers are embracing the '15-minute city' model, which envisions neighbourhoods where basic needs can be met within a short walk from home. Prior research has primarily examined amenity access without exploring its relationship to behaviour. We introduce a measure of local trip behaviour using GPS data from 40 million US mobile devices, defining '15-minute usage' as the proportion of consumption-related trips made within a 15-minute walk from home. Our findings show that the median resident makes only 14% of daily consumption trips locally. Differences in access to local amenities can explain 84% and 74% of the variation in 15-minute usage across and within urban areas, respectively. Historical data from New York zoning policies suggest a causal relationship between local access and 15-minute usage. However, we find a trade-off: increased local usage correlates with higher experienced segregation for low-income residents, signalling potential socio-economic challenges in achieving local living.
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Affiliation(s)
- Timur Abbiasov
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Cate Heine
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sadegh Sabouri
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Paolo Santi
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
- Instituto di Informatica e Telematica del CNR, Pisa, Italy
| | - Edward Glaeser
- Department of Economics, Harvard University, Cambridge, MA, USA
| | - Carlo Ratti
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department ABC, Politecnico di Milano, Milano, Italy
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3
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Napoli L, Sekara V, García-Herranz M, Karsai M. Socioeconomic reorganization of communication and mobility networks in response to external shocks. Proc Natl Acad Sci U S A 2023; 120:e2305285120. [PMID: 38060564 PMCID: PMC10723118 DOI: 10.1073/pnas.2305285120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 10/23/2023] [Indexed: 12/17/2023] Open
Abstract
Socioeconomic segregation patterns in networks usually evolve gradually, yet they can change abruptly in response to external shocks. The recent COVID-19 pandemic and the subsequent government policies induced several interruptions in societies, potentially disadvantaging the socioeconomically most vulnerable groups. Using large-scale digital behavioral observations as a natural laboratory, here we analyze how lockdown interventions lead to the reorganization of socioeconomic segregation patterns simultaneously in communication and mobility networks in Sierra Leone. We find that while segregation in mobility clearly increased during lockdown, the social communication network reorganized into a less segregated configuration as compared to reference periods. Moreover, due to differences in adaption capacities, the effects of lockdown policies varied across socioeconomic groups, leading to different or even opposite segregation patterns between the lower and higher socioeconomic classes. Such secondary effects of interventions need to be considered for better and more equitable policies.
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Affiliation(s)
- Ludovico Napoli
- Department of Network and Data Science, Central European University, Vienna110Austria
| | - Vedran Sekara
- Department of Computer Science, Information Technology, University of Copenaghen, Copenhagen2300, Denmark
| | - Manuel García-Herranz
- Frontier Data Tech Unit, Chief Data Office, United Nations International Children’s Emergency Fund, New York, NY10017
| | - Márton Karsai
- Department of Network and Data Science, Central European University, Vienna110Austria
- National Laboratory for Health Security, Alfréd Rényi Institute of Mathematics, Budapest1053, Hungary
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4
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Nilforoshan H, Looi W, Pierson E, Villanueva B, Fishman N, Chen Y, Sholar J, Redbird B, Grusky D, Leskovec J. Human mobility networks reveal increased segregation in large cities. Nature 2023; 624:586-592. [PMID: 38030732 PMCID: PMC10733138 DOI: 10.1038/s41586-023-06757-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 10/17/2023] [Indexed: 12/01/2023]
Abstract
A long-standing expectation is that large, dense and cosmopolitan areas support socioeconomic mixing and exposure among diverse individuals1-6. Assessing this hypothesis has been difficult because previous measures of socioeconomic mixing have relied on static residential housing data rather than real-life exposures among people at work, in places of leisure and in home neighbourhoods7,8. Here we develop a measure of exposure segregation that captures the socioeconomic diversity of these everyday encounters. Using mobile phone mobility data to represent 1.6 billion real-world exposures among 9.6 million people in the United States, we measure exposure segregation across 382 metropolitan statistical areas (MSAs) and 2,829 counties. We find that exposure segregation is 67% higher in the ten largest MSAs than in small MSAs with fewer than 100,000 residents. This means that, contrary to expectations, residents of large cosmopolitan areas have less exposure to a socioeconomically diverse range of individuals. Second, we find that the increased socioeconomic segregation in large cities arises because they offer a greater choice of differentiated spaces targeted to specific socioeconomic groups. Third, we find that this segregation-increasing effect is countered when a city's hubs (such as shopping centres) are positioned to bridge diverse neighbourhoods and therefore attract people of all socioeconomic statuses. Our findings challenge a long-standing conjecture in human geography and highlight how urban design can both prevent and facilitate encounters among diverse individuals.
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Affiliation(s)
- Hamed Nilforoshan
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Wenli Looi
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Emma Pierson
- Department of Computer Science, Cornell Tech, New York, NY, USA
| | - Blanca Villanueva
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Nic Fishman
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Yiling Chen
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - John Sholar
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Beth Redbird
- Institute for Policy Research, Northwestern University, Evanston, IL, USA
- Department of Sociology, Northwestern University, Evanston, IL, USA
| | - David Grusky
- Department of Sociology, Stanford University, Stanford, CA, USA
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA, USA.
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5
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Luca M, Campedelli GM, Centellegher S, Tizzoni M, Lepri B. Crime, inequality and public health: a survey of emerging trends in urban data science. Front Big Data 2023; 6:1124526. [PMID: 37303974 PMCID: PMC10248183 DOI: 10.3389/fdata.2023.1124526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 05/10/2023] [Indexed: 06/13/2023] Open
Abstract
Urban agglomerations are constantly and rapidly evolving ecosystems, with globalization and increasing urbanization posing new challenges in sustainable urban development well summarized in the United Nations' Sustainable Development Goals (SDGs). The advent of the digital age generated by modern alternative data sources provides new tools to tackle these challenges with spatio-temporal scales that were previously unavailable with census statistics. In this review, we present how new digital data sources are employed to provide data-driven insights to study and track (i) urban crime and public safety; (ii) socioeconomic inequalities and segregation; and (iii) public health, with a particular focus on the city scale.
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Affiliation(s)
- Massimiliano Luca
- Mobile and Social Computing Lab, Bruno Kessler Foundation, Trento, Italy
- Faculty of Computer Science, Free University of Bolzano, Bolzano, Italy
| | | | | | - Michele Tizzoni
- Department of Sociology and Social Research, University of Trento, Trento, Italy
| | - Bruno Lepri
- Mobile and Social Computing Lab, Bruno Kessler Foundation, Trento, Italy
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6
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Abella D, San Miguel M, Ramasco JJ. Aging effects in Schelling segregation model. Sci Rep 2022; 12:19376. [PMID: 36371496 PMCID: PMC9653388 DOI: 10.1038/s41598-022-23224-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 10/27/2022] [Indexed: 11/13/2022] Open
Abstract
The Schelling model has become a paradigm in social sciences to explain the emergence of residential spatial segregation, even in the presence of high tolerance to mixed neighborhoods by the side of citizens. In particular, we consider a noisy constrained version of the Schelling model, in which agents maximize its satisfaction, related to the composition of the local neighborhood, by infinite-range movements towards satisfying vacancies. We add to it an aging effect by making the probability of agents to move inversely proportional to the time they have been satisfied in their present location. This mechanism simulates the development of an emotional attachment to a location where an agent has been satisfied for a while. The introduction of aging has several major impacts on the model statics and dynamics: the phase transition between a segregated and a mixed phase of the original model disappears, and we observe segregated states with a high level of agent satisfaction even for high values of tolerance. In addition, the new segregated phase is dynamically characterized by a slow power-law coarsening process similar to a glassy-like dynamics.
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Affiliation(s)
- David Abella
- grid.507629.f0000 0004 1768 3290Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122 Palma de Mallorca, Spain
| | - Maxi San Miguel
- grid.507629.f0000 0004 1768 3290Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122 Palma de Mallorca, Spain
| | - José J. Ramasco
- grid.507629.f0000 0004 1768 3290Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122 Palma de Mallorca, Spain
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7
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Li QQ, Yue Y, Gao QL, Zhong C, Barros J. Towards a new paradigm for segregation measurement in an age of big data. URBAN INFORMATICS 2022; 1:5. [PMID: 36124239 PMCID: PMC9458482 DOI: 10.1007/s44212-022-00003-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/11/2022] [Accepted: 07/19/2022] [Indexed: 11/24/2022]
Abstract
Recent theoretical and methodological advances in activity space and big data provide new opportunities to study socio-spatial segregation. This review first provides an overview of the literature in terms of measurements, spatial patterns, underlying causes, and social consequences of spatial segregation. These studies are mainly place-centred and static, ignoring the segregation experience across various activity spaces due to the dynamism of movements. In response to this challenge, we highlight the work in progress toward a new paradigm for segregation studies. Specifically, this review presents how and the extent to which activity space methods can advance segregation research from a people-based perspective. It explains the requirements of mobility-based methods for quantifying the dynamics of segregation due to high movement within the urban context. It then discusses and illustrates a dynamic and multi-dimensional framework to show how big data can enhance understanding segregation by capturing individuals’ spatio-temporal behaviours. The review closes with new directions and challenges for segregation research using big data.
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8
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Liu C, Yang Y, Chen B, Cui T, Shang F, Fan J, Li R. Revealing spatiotemporal interaction patterns behind complex cities. CHAOS (WOODBURY, N.Y.) 2022; 32:081105. [PMID: 36049958 DOI: 10.1063/5.0098132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 08/01/2022] [Indexed: 06/15/2023]
Abstract
Cities are typical dynamic complex systems that connect people and facilitate interactions. Revealing general collective patterns behind spatiotemporal interactions between residents is crucial for various urban studies, of which we are still lacking a comprehensive understanding. Massive cellphone data enable us to construct interaction networks based on spatiotemporal co-occurrence of individuals. The rank-size distributions of dynamic population of locations in all unit time windows are stable, although people are almost constantly moving in cities and hot-spots that attract people are changing over time in a day. A larger city is of a stronger heterogeneity as indicated by a larger scaling exponent. After aggregating spatiotemporal interaction networks over consecutive time windows, we reveal a switching behavior of cities between two states. During the "active" state, the whole city is concentrated in fewer larger communities, while in the "inactive" state, people are scattered in smaller communities. Above discoveries are universal over three cities across continents. In addition, a city stays in an active state for a longer time when its population grows larger. Spatiotemporal interaction segregation can be well approximated by residential patterns only in smaller cities. In addition, we propose a temporal-population-weighted-opportunity model by integrating a time-dependent departure probability to make dynamic predictions on human mobility, which can reasonably well explain the observed patterns of spatiotemporal interactions in cities.
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Affiliation(s)
- Chenxin Liu
- UrbanNet Lab, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Yu Yang
- UrbanNet Lab, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Bingsheng Chen
- UrbanNet Lab, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Tianyu Cui
- UrbanNet Lab, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Fan Shang
- UrbanNet Lab, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Jingfang Fan
- School of Systems Science/Institute of Nonequilibrium Systems, Beijing Normal University, Beijing 100875, China
| | - Ruiqi Li
- UrbanNet Lab, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
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9
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Li X, Huang X, Li D, Xu Y. Aggravated social segregation during the COVID-19 pandemic: Evidence from crowdsourced mobility data in twelve most populated U.S. metropolitan areas. SUSTAINABLE CITIES AND SOCIETY 2022; 81:103869. [PMID: 35371911 PMCID: PMC8964479 DOI: 10.1016/j.scs.2022.103869] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 02/18/2022] [Accepted: 03/25/2022] [Indexed: 05/25/2023]
Abstract
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.
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Affiliation(s)
- Xiao Li
- Texas A&M Transportation Institute, Bryan, TX, USA
| | - Xiao Huang
- Department of Geosciences, University of Arkansas, Fayetteville, USA
| | - Dongying Li
- Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX, USA
| | - Yang Xu
- Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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10
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Jr ANL, Monteiro LHA. A complex network model for a society with socioeconomic classes. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:6731-6742. [PMID: 35730280 DOI: 10.3934/mbe.2022317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
People's attitudes and behaviors are partially shaped by the socioeconomic class to which they belong. In this work, a model of scale-free graph is proposed to represent the daily personal contacts in a society with three social classes. In the model, the probability of having a connection between two individuals depends on their social classes and on their physical distance. Numerical simulations are performed by considering sociodemographic data from France, Peru, and Zimbabwe. For the complex networks built for these three countries, average values of node degree, shortest-path length, clustering coefficient, closeness centrality, betweenness centrality, and eigenvector centrality are computed. These numerical results are discussed by taking into account the propagation of information about COVID-19.
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Affiliation(s)
- A N Licciardi Jr
- Universidade de São Paulo, Escola Politécnica, São Paulo, SP, Brazil
- Universidade Presbiteriana Mackenzie, Escola de Engenharia, São Paulo, SP, Brazil
| | - L H A Monteiro
- Universidade de São Paulo, Escola Politécnica, São Paulo, SP, Brazil
- Universidade Presbiteriana Mackenzie, Escola de Engenharia, São Paulo, SP, Brazil
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11
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Reme BA, Kotsadam A, Bjelland J, Sundsøy PR, Lind JT. Quantifying social segregation in large-scale networks. Sci Rep 2022; 12:6474. [PMID: 35440681 PMCID: PMC9018874 DOI: 10.1038/s41598-022-10273-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 03/17/2022] [Indexed: 11/30/2022] Open
Abstract
We present a measure of social segregation which combines mobile phone data and income register data in Oslo, Norway. In addition to measuring the extent of social segregation, our study shows that social segregation is strong, robust, and that social networks are particularly clustered among the richest. Using location data on the areas where people work, we also examine whether exposure to other social strata weakens measured segregation. Lastly, we extend our analysis to a large South Asian city and show that our main results hold across two widely different societies.
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Affiliation(s)
- Bjørn-Atle Reme
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway.
| | | | | | | | - Jo Thori Lind
- Department of Economics, University of Oslo, Oslo, Norway
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12
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Heine C, Marquez C, Santi P, Sundberg M, Nordfors M, Ratti C. Analysis of mobility homophily in Stockholm based on social network data. PLoS One 2021; 16:e0247996. [PMID: 33690698 PMCID: PMC7943013 DOI: 10.1371/journal.pone.0247996] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 02/18/2021] [Indexed: 11/18/2022] Open
Abstract
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 residents' median income, education level, and (to a lesser extent) immigration history are more strongly connected in terms of the of people who spend time there, indicating some level of homophily in the way that individuals choose to move throughout a city's districts.
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Affiliation(s)
- Cate Heine
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, United States of America
- * E-mail:
| | | | - Paolo Santi
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, United States of America
- Instituto di Informatica e Telematica del CNR, Pisa, Italy
| | - Marcus Sundberg
- Department of Urban Planning and Environment, Division of Systems Analysis and Economics, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Miriam Nordfors
- Strategi- och utvecklingsenheten, Stockholms Stad, Stockholm, Sweden
| | - Carlo Ratti
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, United States of America
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13
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Mobile Phone Data for Urban Climate Change Adaptation: Reviewing Applications, Opportunities and Key Challenges. SUSTAINABILITY 2020. [DOI: 10.3390/su12041501] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Climate change places cities at increasing risk and poses a serious challenge for adaptation. As a response, novel sources of data combined with data-driven logics and advanced spatial modelling techniques have the potential for transformative change in the role of information in urban planning. However, little practical guidance exists on the potential opportunities offered by mobile phone data for enhancing adaptive capacities in urban areas. Building upon a review of spatial studies mobilizing mobile phone data, this paper explores the opportunities offered by such digital information for providing spatially-explicit assessments of urban vulnerability, and shows the ways these can help developing more dynamic strategies and tools for urban planning and disaster risk management. Finally, building upon the limitations of mobile phone data analysis, it discusses the key urban governance challenges that need to be addressed for supporting the emergence of transformative change in current planning frameworks.
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