As big data becomes increasingly integral to urban studies, the evolving profile of its applications, including the trends in data, methodologies, and contributions to urban theories, remains unclear. This study systematically reviewed literature from 57 representative journals in the fields of Urban Studies, Geography, and Environmental Studies (JCR Q1) over the past decade (2013–2023), with a focus on quantitative research involving urban spatial big data. A total of 1,425 articles were identified through automated keyword filtering and manual screening, of which 82 were selected for further analysis due to their theoretical contributions. The results revealed key thematic areas, including urban transportation, spatial quality, vitality, and structure, with a predominant focus on developed countries and large cities. The applications of big data and novel methodologies in urban studies have increased from 2013 to 2023, with increasingly higher levels of spatiotemporal resolution. However, studies linking big data to urban theories remained limited, with most quantitative research centered on applying or proposing new methods or uncovering new phenomena. Theoretical contributions primarily explored the mechanisms underlying urban environment formation and their impacts on human activities and behaviors. This study provides a comprehensive understanding of big data applications and advances in theories in urban spatial studies and highlights key directions for future research.
AllamZDhunnyZA (2019) On big data, artificial intelligence, and smart cities. Cities89: 80–91.
2.
BarkiJPBarićABachMP (2022) Urban sustainability and urbanization: a review of research topics. Croatian Regional Development Journal3(2): 1–18.
3.
BattyM (2013) Big data, smart cities and city planning. Dialogues in Human Geography3(3): 274–279.
4.
BenitaFTunçerB (2019) Exploring the effect of urban features and immediate environment on body responses. Urban Forestry and Urban Greening43: 126365.
5.
BiljeckiFChowYSLeeK (2023) Quality of crowdsourced geospatial building information: a global assessment of OpenStreetMap attributes. Building and Environment237: 110295.
6.
BoakyeKBovbjergMSchuna JrJ, et al. (2023) Perceived built environment characteristics associated with walking and cycling across 355 communities in 21 countries. Cities132: 104102.
7.
ChangYW (2022) Capability of non-English-speaking countries for securing a foothold in international journal publishing. Journal of Informetrics16: 101305.
8.
ChenZGongZYangS, et al. (2020) Impact of extreme weather events on urban human flow: a perspective from location-based service data. Computers, Environment and Urban Systems83: 101520.
9.
ChoiKZhangM (2017) The net effects of the built environment on household vehicle emissions: a case study of Austin, TX. Transportation Research Part D: Transport and Environment50: 254–268.
10.
CoxMEllsworthD (1997) Managing big data for scientific visualization. ACM Siggraph97: 1–17.
11.
EnnasGGuardoMCD (2015) Features of top-rated gold open access journals: an analysis of the scopus database. Journal of Informetrics9: 79–89.
12.
GanXHuangLWangH, et al. (2021) Optimal block size for improving urban vitality: an exploratory analysis with multiple vitality indicators. Journal of Urban Planning and Development147(3): 04021027.
13.
GodwinCChenGSinghKK (2015) The impact of urban residential development patterns on forest carbon density: an integration of LiDAR, aerial photography and field mensuration. Landscape and Urban Planning136: 97–109.
14.
HaoJZhuJZhongR (2015) The rise of big data on urban studies and planning practices in China: review and open research issues. Journal of Urban Management4(2): 92–124.
15.
HeHLinXYangY, et al. (2020) Association of street greenery and physical activity in older adults: a novel study using pedestrian-centered photographs. Urban Forestry and Urban Greening55: 126789.
16.
HuangJCuiYLiL, et al. (2023) Re-examining Jane Jacobs’ doctrine using new urban data in Hong Kong. Environment and Planning B: Urban Analytics and City Science50(1): 76–93.
17.
ItoKKangYZhangY, et al. (2024) Understanding urban perception with visual data: a systematic review. Cities152: 105169.
18.
KandtJBattyM (2021) Smart cities, big data and urban policy: towards urban analytics for the long run. Cities109: 102992.
19.
KangCD (2020) Effects of the human and built environment on neighborhood vitality: evidence from Seoul, Korea, using mobile phone data. Journal of Urban Planning and Development146(4): 05020024.
20.
KimYL (2025) Urban vitality measurement through big data and internet of things technologies. ISPRS International Journal of Geo-Information14(1): 14.
21.
KimMChoGH (2021) Analysis on bike-share ridership for origin-destination pairs: effects of public transit route characteristics and land-use patterns. Journal of Transport Geography93: 103047.
22.
KimJIYuCYWooA (2023) The impacts of visual street environments on obesity: the mediating role of walking behaviors. Journal of Transport Geography109: 103593.
23.
KitchinR (2013) Big data and human geography: opportunities, challenges and risks. Dialogues in Human Geography3(3): 262–267.
24.
KongLLiuZWuJ (2020) A systematic review of big data-based urban sustainability research: state-of-the-science and future directions. Journal of Cleaner Production273: 123142.
25.
KousisA1TjortjisC (2023) Investigating the key aspects of a smart city through topic modeling and thematic analysis. Future Internet16(1): 3.
26.
LiDDealBZhouX, et al. (2018) Moving beyond the neighborhood: daily exposure to nature and adolescents’ mood. Landscape and Urban Planning173: 33–43.
27.
LiSMaSTongD, et al. (2022) Associations between the quality of street space and the attributes of the built environment using large volumes of street view pictures. Environment and Planning B: Urban Analytics and City Science49(4): 1197–1211.
28.
LimCKimKJMaglioPP (2018) Smart cities with big data: reference models, challenges, and considerations. Cities82: 86–99.
29.
LiuCXuFGaoC, et al. (2024) Deep learning resilience inference for complex networked systems. Nature Communications15(1): 9203.
30.
LongYHuangCC (2019) Does block size matter? The impact of urban design on economic vitality for Chinese cities. Environment and Planning B: Urban Analytics and City Science46(3): 406–422.
31.
MarquetO (2020) Spatial distribution of ride-hailing trip demand and its association with walkability and neighborhood characteristics. Cities106: 102926.
32.
NedungadiPSurendranSTangKY, et al. (2024) Big data and AI algorithms for sustainable development goals: a topic modeling analysis. IEEE Access12: 188519–188541.
33.
PanYTianYLiuX, et al. (2016) Urban big data and the development of city intelligence. Engineering2(2): 171–178.
34.
ParkKCLeeCH (2019) A study on the research trends for smart city using topic modeling. Journal of Internet Computing and Services20(3): 119–128.
35.
PeiWBiljeckiFStouffsR (2024) Techniques and tools for integrating building material stock analysis and life cycle assessment at the urban scale: a systematic literature review. Building and Environment262: 111741.
36.
TangJGaoFHanC, et al. (2021) Uncovering the spatially heterogeneous effects of shared mobility on public transit and taxi. Journal of Transport Geography95: 103134.
WangCYinL (2023) Defining urban big data in urban planning: literature review. Journal of Urban Planning and Development149(1): 04022044.
39.
WangXZhangYYuD, et al. (2022) Investigating the spatiotemporal pattern of urban vibrancy and its determinants: spatial big data analyses in Beijing, China. Land Use Policy119: 106162.
40.
WangZLuFLiuZ, et al. (2023) Measuring spatial nonstationary effects of POI-based mixed use on urban vibrancy using Bayesian spatially varying coefficients model. International Journal of Geographical Information Science37(2): 339–359.
41.
WeiM (2022) How does the weather affect public transit ridership? A model with weather-passenger variations. Journal of Transport Geography98: 103242.
42.
WuSLiangZLiS (2019) Relationships between urban development level and urban vegetation states: A global perspective. Urban Forestry and Urban Greening38: 215–222.
43.
WuJTaNSongY, et al. (2018a) Urban form breeds neighborhood vibrancy: a case study using a GPS-based activity survey in suburban Beijing. Cities74: 100–108.
44.
WuJWangLLiW (2018b) Usage patterns and impact factors of public bicycle systems: comparison between city center and suburban district in Shenzhen. Journal of Urban Planning and Development144(3): 04018027.
45.
XingXYuBKangC, et al. (2024) The synergy between remote sensing and social sensing in urban studies: review and perspectives. IEEE Geoscience and Remote Sensing Magazine12(1): 108–137.
46.
YangCClarkeKShekharS, et al. (2020) Big spatiotemporal data analytics: a research and innovation frontier. International Journal of Geographical Information Science34(6): 1075–1088.
47.
YangLAoYKeJ, et al. (2021) To walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adults. Journal of Transport Geography94: 103099.
48.
YuHPengZR (2019) Exploring the spatial variation of ridesourcing demand and its relationship to built environment and socioeconomic factors with the geographically weighted poisson regression. Journal of Transport Geography75: 147–163.
49.
ZhangSLiuXTangJ, et al. (2019) Urban spatial structure and travel patterns: analysis of workday and holiday travel using inhomogeneous Poisson point process models. Computers, Environment and Urban Systems73: 68–84.
50.
ZhangEXieHLongY (2023a) Decoding the association between urban streetscape skeletons and urban activities: experiments in Beijing using Dazhong Dianping data. Transactions in Urban Data, Science, and Technology2(1): 3–18.
51.
ZhangYDengQGuoM, et al. (2023b) Using street view imagery to examine the association between urban neighborhood disorder and the long-term recurrence risk of patients discharged with acute myocardial infarction in central Beijing, China. Cities138: 104366.
52.
ZhengYLinYZhaoL, et al. (2023) Spatial planning of urban communities via deep reinforcement learning. Nature Computational Science3(9): 748–762.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
