Integrating crowdsourced data in the built environment studies: A systematic review

被引:0
作者
Yang, Qiuyi [1 ]
Zhang, Bo [2 ]
Chen, Jiawen [3 ]
Song, Yang [4 ]
Shen, Xiwei [5 ]
机构
[1] Univ Michigan, Sch Environm & Sustainabil, Ann Arbor, MI USA
[2] Oklahoma State Univ, Hort & Landscape Architecture, Stillwater, OK USA
[3] Univ Calif Berkeley, Coll Environm Design, Dept Design, Coll Engn, Berkeley, CA USA
[4] Texas A&M Univ, Coll Architecture, Dept Landscape Architecture & Urban Planning, College Stn, TX USA
[5] Univ Nevada, Dept Landscape Architecture, Las Vegas, NV 89154 USA
关键词
Crowdsourced data; Built environment; Environmental management; Social media data; Participatory planning; SOCIAL-MEDIA DATA; CULTURAL ECOSYSTEM SERVICES; VOLUNTEERED GEOGRAPHIC INFORMATION; URBAN LAND-USE; BIG DATA; PEOPLE; SUSTAINABILITY; PARTICIPATION; OPPORTUNITIES; CHALLENGES;
D O I
10.1016/j.jenvman.2024.123936
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The integration of crowdsourced data has become central to contemporary built environment studies, driven by the rapid growth in digital technologies and participatory approaches that characterize modern urbanism. Despite its potential, a systematic framework for its analysis remains underdeveloped. This review, conducted in accordance with the PRISMA protocol, examines the use of crowdsourced data in shaping the built environment, scrutinizing its applications, crowdsourcing techniques, methodologies, and comparison with other big data forms. From 226 relevant studies, this study uncovers the evolving thematic landscape of crowdsourced data through a longitudinal analysis (2013-2024), revealing the driving forces that have shifted its representation over time. Additionally, by examining the cultural dimensions and contextual variability, this study demonstrates how identical data is interpreted in markedly different ways across diverse geographic and social contexts. These findings underscore the inmportance of adopting context-sensitive and culturally aware approaches to effectively leverage crowdsourced data in the built environment research. The novelty of this review lies in reframing crowdsourced data not merely as an application tool but as a lens for understanding broader cultural and technological shifts, offering both theoretical and practical insights into its role in the built environment. By advancing our understanding of the unique contributions of crowdsourced data and its complementary role to other big data types, this review provides actionable recommendations for urban planners and policymakers. Ultimately, these findings promote more inclusive and sustainable urban development, fostering cities that better respond to the needs of diverse populations.
引用
收藏
页数:14
相关论文
共 136 条
[71]   Classifying urban land use by integrating remote sensing and social media data [J].
Liu, Xiaoping ;
He, Jialv ;
Yao, Yao ;
Zhang, Jinbao ;
Liang, Haolin ;
Wang, Huan ;
Hong, Ye .
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2017, 31 (08) :1675-1696
[72]   Land-use decision support in brownfield redevelopment for urban renewal based on crowdsourced data and a presence-and-background learning (PBL) method [J].
Liu, Yilun ;
Zhu, A-Xing ;
Wang, Jingli ;
Li, Wenkai ;
Hu, Guohua ;
Hu, Yueming .
LAND USE POLICY, 2019, 88
[73]   Social Media data: Challenges, opportunities and limitations in urban studies [J].
Marti, Pablo ;
Serrano-Estrada, Leticia ;
Nolasco-Cirugeda, Almudena .
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS, 2019, 74 :161-174
[74]   Using locative social media and urban cartographies to identify and locate successful urban plazas [J].
Marti, Pablo ;
Serrano-Estrada, Leticia ;
Nolasco-Cirugeda, Almudena .
CITIES, 2017, 64 :66-78
[75]   Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning [J].
Milusheva, Sveta ;
Marty, Robert ;
Bedoya, Guadalupe ;
Williams, Sarah ;
Resor, Elizabeth ;
Legovini, Arianna .
PLOS ONE, 2021, 16 (02)
[76]   Urban mobility and resilience: exploring Boston's urban mobility network through twitter data [J].
Mirzaee, Sahar ;
Wang, Qi .
APPLIED NETWORK SCIENCE, 2020, 5 (01)
[77]  
Moher D., 2009, ANN INTERN MED, V151, P264, DOI [DOI 10.7326/0003-4819-151-4-200908180-00135, 10.7326/0003-4819-151-4-200908180-00135]
[78]   Smart city re-imagined: City planning and GeoAI in the age of big data [J].
Mortaheb, Reza ;
Jankowski, Piotr .
JOURNAL OF URBAN MANAGEMENT, 2023, 12 (01) :4-15
[79]   Characterizing multicity urban traffic conditions using crowdsourced data [J].
Nair, Divya Jayakumar ;
Gilles, Flavien ;
Chand, Sai ;
Saxena, Neeraj ;
Dixit, Vinayak .
PLOS ONE, 2019, 14 (03)
[80]   Crowdsourced data for bicycling research and practice [J].
Nelson, Trisalyn ;
Ferster, Colin ;
Laberee, Karen ;
Fuller, Daniel ;
Winters, Meghan .
TRANSPORT REVIEWS, 2021, 41 (01) :97-114