Leveraging Geospatial Data to Understand Social Mixing in Cities
Understanding the impact of dense social interactions in urban areas is crucial for designing inclusive cities, as they contribute to both the benefits and challenges of urban living. Various urban planning paradigms and design approaches, such as the 15 minute city, aim to foster social connection...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/153725 |
| _version_ | 1826200706912092160 |
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| author | Heine, Cate E. |
| author2 | Pentland, Sandy |
| author_facet | Pentland, Sandy Heine, Cate E. |
| author_sort | Heine, Cate E. |
| collection | MIT |
| description | Understanding the impact of dense social interactions in urban areas is crucial for designing inclusive cities, as they contribute to both the benefits and challenges of urban living. Various
urban planning paradigms and design approaches, such as the 15 minute city, aim to foster social connections and accessibility across communities. However, evaluating the effectiveness of these paradigms is difficult given the complexity of measuring social interactions. In the following projects, I leverage large, geospatial datasets which capture human mobility to describe policy and design impacts on social mixing in three different urban contexts.
Study 1 presents a new way to measure social segregation in mobility patterns using sparse, anonymized geolocation data. We identify homophily in the way that Stockholm residents travel
through cities without performing privacy-invasive and data-intensive home location detection. Study 2 calculates this metric with geolocated Twitter data to analyze the impacts of Paris’s “Zones 30” policy, which aimed to enhance human mobility and social mixing by reducing speed limits and implementing street improvements. We find that areas of the city where the policy was implemented fostered more activity from more unique Twitter users who come from more neighborhoods of the city, but not from more socioeconomically diverse neighborhoods of the city. Study 3 employs call detail record data to measure social mixing on a fine spatial and temporal scale across the city of Stockholm, identifying the types of urban amenities that are located in areas where highly income-diverse groups of people gather. We then leverage quasi-random shocks to the road network due to maintenance-based road closures in order to identify causally-interpretable relationships between increasing access to various types of urban amenities and experienced segregation. Study 4 draws on GPS data from mobile phones in order to assess the relationship between local living, as advocated for by the “15-minute city” urban planning
paradigm, and experienced socioeconomic segregation across the US. We find that low-income neighborhoods with high levels of local living see lower levels of experienced diversity, indicating a potential tradeoff between the social and environmental goals of the 15-minute city paradigm.
Collectively, these studies highlight the value of fine-grained mobility data in understanding the types of urban spaces and layouts that foster social interactions between diverse groups of people and those that exacerbate social segregation—questions critical to the design of inclusive, sustainable cities. |
| first_indexed | 2024-09-23T11:40:36Z |
| format | Thesis |
| id | mit-1721.1/153725 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:40:36Z |
| publishDate | 2024 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1537252024-03-14T03:41:47Z Leveraging Geospatial Data to Understand Social Mixing in Cities Heine, Cate E. Pentland, Sandy Santi, Paolo Massachusetts Institute of Technology. Institute for Data, Systems, and Society Understanding the impact of dense social interactions in urban areas is crucial for designing inclusive cities, as they contribute to both the benefits and challenges of urban living. Various urban planning paradigms and design approaches, such as the 15 minute city, aim to foster social connections and accessibility across communities. However, evaluating the effectiveness of these paradigms is difficult given the complexity of measuring social interactions. In the following projects, I leverage large, geospatial datasets which capture human mobility to describe policy and design impacts on social mixing in three different urban contexts. Study 1 presents a new way to measure social segregation in mobility patterns using sparse, anonymized geolocation data. We identify homophily in the way that Stockholm residents travel through cities without performing privacy-invasive and data-intensive home location detection. Study 2 calculates this metric with geolocated Twitter data to analyze the impacts of Paris’s “Zones 30” policy, which aimed to enhance human mobility and social mixing by reducing speed limits and implementing street improvements. We find that areas of the city where the policy was implemented fostered more activity from more unique Twitter users who come from more neighborhoods of the city, but not from more socioeconomically diverse neighborhoods of the city. Study 3 employs call detail record data to measure social mixing on a fine spatial and temporal scale across the city of Stockholm, identifying the types of urban amenities that are located in areas where highly income-diverse groups of people gather. We then leverage quasi-random shocks to the road network due to maintenance-based road closures in order to identify causally-interpretable relationships between increasing access to various types of urban amenities and experienced segregation. Study 4 draws on GPS data from mobile phones in order to assess the relationship between local living, as advocated for by the “15-minute city” urban planning paradigm, and experienced socioeconomic segregation across the US. We find that low-income neighborhoods with high levels of local living see lower levels of experienced diversity, indicating a potential tradeoff between the social and environmental goals of the 15-minute city paradigm. Collectively, these studies highlight the value of fine-grained mobility data in understanding the types of urban spaces and layouts that foster social interactions between diverse groups of people and those that exacerbate social segregation—questions critical to the design of inclusive, sustainable cities. Ph.D. 2024-03-13T13:29:39Z 2024-03-13T13:29:39Z 2024-02 2024-02-05T22:03:28.203Z Thesis https://hdl.handle.net/1721.1/153725 0000-0002-4033-3761 Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Copyright retained by author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Heine, Cate E. Leveraging Geospatial Data to Understand Social Mixing in Cities |
| title | Leveraging Geospatial Data to Understand Social Mixing in Cities |
| title_full | Leveraging Geospatial Data to Understand Social Mixing in Cities |
| title_fullStr | Leveraging Geospatial Data to Understand Social Mixing in Cities |
| title_full_unstemmed | Leveraging Geospatial Data to Understand Social Mixing in Cities |
| title_short | Leveraging Geospatial Data to Understand Social Mixing in Cities |
| title_sort | leveraging geospatial data to understand social mixing in cities |
| url | https://hdl.handle.net/1721.1/153725 |
| work_keys_str_mv | AT heinecatee leveraginggeospatialdatatounderstandsocialmixingincities |