東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ null ]

切換: 標籤 | MARC模式 | ISBD

Leveraging Geospatial Data to Unders...

Heine, Cate E.,

FindBook

Google Book

Amazon

博客來

Leveraging Geospatial Data to Understand Social Mixing in Cities /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Leveraging Geospatial Data to Understand Social Mixing in Cities // Cate E Heine.
作者:	Heine, Cate E.,
面頁冊數:	1 electronic resource (191 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-05, Section: A.
Contained By:	Dissertations Abstracts International86-05A.
標題:	Families & family life. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31652271
ISBN:	9798346387114

Leveraging Geospatial Data to Understand Social Mixing in Cities /
Heine, Cate E.,

Leveraging Geospatial Data to Understand Social Mixing in Cities /Cate E Heine. - 1 electronic resource (191 pages)

Source: Dissertations Abstracts International, Volume: 86-05, Section: A.

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. Variousurban 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 travelthrough 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 planningparadigm, 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.

English

ISBN: 9798346387114Subjects--Topical Terms:

3422406
Families & family life.

Leveraging Geospatial Data to Understand Social Mixing in Cities /
LDR:04003nmm a22004213i 4500 001 2434490
005 20250912102028.5
006 m o d
007 cr|nu||||||||
008 261011s2024 miu||||||m |||||||eng d
020 $a 9798346387114
035 $a (MiAaPQD)AAI31652271
035 $a (MiAaPQD)MIT1721_1_153725
035 $a AAI31652271
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Heine, Cate E., $e author. $3 3824487
245 1 0 $a Leveraging Geospatial Data to Understand Social Mixing in Cities / $c Cate E Heine.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (191 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-05, Section: A.
500 $a Advisors: Pentland, Sandy; Santi, Paolo.
502 $b Ph.D. $c Massachusetts Institute of Technology $d 2024.
520 $a 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. Variousurban 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 travelthrough 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 planningparadigm, 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.
546 $a English
590 $a School code: 0753
650 4 $a Families & family life. $3 3422406
650 4 $a Segregation. $3 1557469
650 4 $a Travel. $3 585743
650 4 $a Censuses. $3 3564174
650 4 $a Parks & recreation areas. $3 3560878
650 4 $a Urban areas. $3 3546012
650 4 $a Neighborhoods. $3 993475
650 4 $a Zoning. $3 3231625
650 4 $a Cities. $3 3544022
650 4 $a Area planning & development. $3 3172430
650 4 $a Individual & family studies. $3 2122770
650 4 $a Web studies. $3 2122754
650 4 $a Recreation. $3 535376
690 $a 0341
690 $a 0801
690 $a 0628
690 $a 0646
690 $a 0814
710 2 $a Massachusetts Institute of Technology. $b Institute for Data, Systems, and Society. $e degree granting institution. $3 3824488
720 1 $a Pentland, Sandy $e degree supervisor.
720 1 $a Santi, Paolo $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-05A.
790 $a 0753
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31652271