Advancing translational human dynamics research: bridging space, mind, and computational urban science in the era of GeoAI

Bin Jiang , Tao Cheng , Ming-Hsiang Tsou , Di Zhu , Xinyue Ye

Computational Urban Science ›› 2025, Vol. 5 ›› Issue (1) : 12

PDF
Computational Urban Science ›› 2025, Vol. 5 ›› Issue (1) : 12 DOI: 10.1007/s43762-025-00171-3
Opinion Paper
research-article

Advancing translational human dynamics research: bridging space, mind, and computational urban science in the era of GeoAI

Author information +
History +
PDF

Abstract

Human dynamics research has undergone a significant transformation over the past decade, driven by interdisciplinary collaboration and technological innovation. This opinion paper examines the evolution of the field in the past ten years, focusing on its integration of GIScience (Geographic Information Science), social science, and public health to tackle spatial and societal challenges such as urban sustainability, disaster response, and epidemics. Key advancements include the adoption of living structure theory, which redefines space as a dynamic and interconnected entity linked to human well-being and ecological sustainability, and the application of cutting-edge technologies like GeoAI (Geospatial Artificial Intelligence) and digital twins for adaptive modeling and informed decision-making. Despite these advancements, challenges persist, including incomplete data, mismatched scales, and barriers to equitable access to geospatial information. Addressing these issues necessitates innovative approaches such as multiscale modeling, open data platforms, and inclusive methodologies. Increased funding opportunities offer pathways for accelerating translational research. By integrating advanced theories, user-centered technologies, and collaborative frameworks, human dynamics research is poised to transform urban systems into sustainable, resilient, and equitable environments. This paradigm shift underscores the importance of ethical considerations and inclusivity, offering a holistic approach that aligns with human and ecological needs.

The original online version of this article was revised: The authors Ming-Hsiang Tsou and Di Zhu are switched in their affiliations.

A correction to this article is available online at https://doi.org/10.1007/s43762-025-00202-z.

Keywords

Human dynamics / GIScience / Computational urban science / GeoAI / Living structure theory

Cite this article

Download citation ▾
Bin Jiang, Tao Cheng, Ming-Hsiang Tsou, Di Zhu, Xinyue Ye. Advancing translational human dynamics research: bridging space, mind, and computational urban science in the era of GeoAI. Computational Urban Science, 2025, 5(1): 12 DOI:10.1007/s43762-025-00171-3

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

AlexanderCThe nature of order, book I: The phenomenon of life, 2002Center for Environmental Structure.

[2]

Anselin, L. (1989). What is special about spatial data? Alternative perspectives on spatial data analysis. Technical Report of National Center for Geographic Information and Analysis No. 89–4. https://escholarship.org/uc/item/3ph5k0d4.

[3]

BattyM, LongleyPFractal cities: A geometry of form and function, 1994Academic Press.

[4]

BibriSE, HuangJ, JagatheesaperumalSK, KrogstieJ. The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review. Environmental Science and Ecotechnology., 2024, 20. 100433

[5]

BohmDWholeness and the implicate order, 1980Routledge.

[6]

Cheng, et al. (2022a). Human mobility variations in response to restriction policies during the COVID-19 pandemic: An analysis from the Virus Watch community cohort in England. UK, Frontiers in Public Health. https://doi.org/10.3389/fpubh.2022.999521

[7]

ChengT, AdepejuM. Modifiable Temporal Unit Problem (MTUP) and its effect on space-time cluster detection. PLoS ONE, 2014, 9. e100465

[8]

ChengT, HaworthJ, WangJ. Spatio-temporal autocorrelation of road network data. Journal of Geographical Systems, 2012, 14: 389-413.

[9]

Cheng, T., Zhang, Y., Haworth, J. (2022). Network SpaceTime AI: Concepts, methods and applications. Journal of Geodesy and Geoinformation Science, 5(3), 78–92. http://jggs.chinasmp.com/EN/10.11947/j.JGGS.2022.0309

[10]

ChristallerWCentral places in southern Germany, 1933Prentice Hall.

[11]

FrancisJ, DisneyM, LawS. Monitoring canopy quality and improving equitable outcomes of urban tree planting using LiDAR and machine learning. Urban Forestry & Urban Greening, 2023, 89. 128115

[12]

Gong, W., Lee, C. S., Li, S., Adkison, D., Li, N., Wu, L., & Ye, X. (2025). Cyber victimization in hybrid space: an analysis of employment scams using natural language processing and machine learning models. Journal of Crime and Justice, 1–22. https://doi.org/10.1080/0735648X.2024.2448804

[13]

GoodchildMF. The validity and usefulness of laws in geographic information science and geography. Annals of the Association of American Geographers, 2004, 94(2): 300-303

[14]

GoodchildMF, LiW. Replication across space and time must be weak in the social and environmental sciences. Proceedings of the National Academy of Sciences, 2021, 11835e2015759118

[15]

HillierB, HansonJThe social logic of space, 1984Cambridge University Press.

[16]

IbrahimMR, HaworthJ, ChengT. Understanding cities with machine eyes: A review of deep computer vision in urban analytics. Cities, 2020.

[17]

IbrahimMR, HaworthJ, ChengT. URBAN-i: From urban scenes to mapping slums, transport modes, and pedestrians in cities using deep learning and computer vision. Environment and Planning B: Urban Analytics and City Science, 2021, 48: 76-93.

[18]

IbrahimMR, HaworthJ, ChristieN, ChengT. CyclingNet: Detecting cycling near misses from video streams in complex urban scenes with deep learning. IET Intelligent Trans Sys, 2021, 15: 1331-1344.

[19]

JacobsJThe death and life of great American Cities, 1961Random House.

[20]

JiangB. Geospatial analysis requires a different way of thinking: The problem of spatial heterogeneity. GeoJournal, 2015, 80(1): 1-13

[21]

JiangB. Geography as a science of the earth’s surface founded on the third view of space. Annals of GIS, 2022, 28(1): 31-43

[22]

JiangB, de RijkeCA. Structural beauty: A structure-based approach to quantifying the beauty of an image. Journal of Imaging, 2021, 7578

[23]

JiangB, de RijkeC. Representing geographic space as a hierarchy of recursively defined subspaces for computing the degree of order. Computers Environment and Urban Systems, 2022, 921+2101750

[24]

JiangB, de RijkeC. Living images: A recursive approach to computing the structural beauty of images or the livingness of space. Annals of the Association of American Geographers, 2023, 113(6): 1329-1347

[25]

JiangB, HuangJ. A new approach to detecting and designing living structure of urban environments. Computers, Environment and Urban Systems, 2021, 88101646

[26]

KwanM-P, WangJ, TyburskiM, EpsteinDH, KowalczykWJ, PrestonKL. Uncertainties in the geographic context of health behaviors: A study of substance users’ exposure to psychosocial stress using GPS data. International Journal of Geographical Information Science, 2019, 33: 1176-1195.

[27]

LewickaM. Place attachment: How far have we come in the last 40 years?. Journal of Environmental Psychology, 2011, 31: 207-230

[28]

Li, Z., Xia, L., Tang, J., Xu, Y., Shi, L., Xia, L., Yin, D., & Huang, C. (2024). UrbanGPT: spatio-temporal large language models.

[29]

LiuP, BiljeckiF. A review of spatially-explicit GeoAI applications in urban geography. International Journal of Applied Earth Observation and Geoinformation, 2022, 112. 102936

[30]

MaiG, JanowiczK, HuY, GaoS, YanB, ZhuR, CaiL, LaoN. A review of location encoding for GeoAI: Methods and applications. International Journal of Geographical Information Science, 2022, 36: 639-673.

[31]

Malleson, N., Franklin, R., Arribas-Bel, D., Cheng, T., & Birkin, M. (2024). Digital twins on trial: Can they actually solve wicked societal problems and change the world for better? Environment and Planning B: Urban Analytics and City Science, 51(6), 1181–1186.

[32]

MaronM, LoY. High-order structure modeling in spatial data analysis. Spatial Statistics, 2020, 38100438

[33]

MillerH. Place-based versus people-based geographic information science. Geography Compass, 2007, 1(3): 503-535

[34]

PatelD, JainA. Advancements in interpolating and pattern analysis for spatial big data. Journal of Computational Geography, 2016, 34(2): 201-212

[35]

PearlJCausality: Models, reasoning, and inference, 2009Cambridge University Press.

[36]

RichardsonDB, VolkowND, KwanMP, KaplanRM, GoodchildMF, CroyleRT. Spatial turn in health research. Science, 2013, 339(6126): 1390-1392

[37]

RosserG, DaviesT, BowersKJ, et al.. Predictive crime mapping: Arbitrary grids or street networks?. Journal of Quantitative Criminology, 2017, 33: 569-594.

[38]

SalingarosNA, SussmanA. Biometric pilot-studies reveal the arrangement and shape of windows on a traditional façade to be implicitly “engaging”, whereas contemporary façades are not. Urban Science, 2020, 4226.

[39]

SeamonDLife takes place: Phenomenology, life worlds, and place making, 2018Routledge.

[40]

ShawSL, TsouMH, YeX. Editorial: Human dynamics in the mobile and big data era. International Journal of Geographical Information Science, 2016, 30(9): 1687-1693

[41]

ShawSL, YeX, GoodchildM, SuiD. Human dynamics research in GIScience: Challenges and opportunities. Computational Urban Science, 2024, 4131

[42]

TaylorL, SilverLD, EwingR. An analysis of human settlement patterns and their impacts on water resources in the Western United States. Journal of Environmental Management, 2015, 95(2): 144-152

[43]

ToblerW. A computer movie simulating urban growth in the Detroit region. Economic Geography, 1970, 46: 234-240

[44]

TsouMH. Research challenges and opportunities in mapping social media and Big Data. Cartography and Geographic Information Science, 2015, 42(sup1): 70-74

[45]

TuanYFSpace and place: The perspective of experience, 1977University of Minnesota Press.

[46]

WangS, HuangX, LiuP, ZhangM, BiljeckiF, HuT, FuX, LiuL, LiuX, WangR, HuangY, YanJ, JiangJ, ChukwuM, Reza NaghediS, HemmatiM, ShaoY, JiaN, XiaoZ, TianT, HuY, YuL, YapW, MacatuladE, ChenZ, CuiY, ItoK, YeM, FanZ, LeiB, BaoS. Mapping the landscape and roadmap of geospatial artificial intelligence (GeoAI) in quantitative human geography: An extensive systematic review. International Journal of Applied Earth Observation and Geoinformation, 2024, 128. 103734

[47]

WilsonEOBiophilia, 1984Harvard University Press.

[48]

WuJExamining the new kind of beauty using human beings as a measuring instrument, 2015Master Thesis at the University of Gävle.

[49]

WuTY, YangX, LallyS, RainvilleAJ, FordO, BessireR, DonnellyJ. Using community engagement and geographic information systems to address COVID-19 vaccination disparities. Tropical Medicine and Infectious Disease, 2022, 78177

[50]

Xie, Y., Wang, Z., Mai, G., Li, Y., Jia, X., Gao, S., & Wang, S. (2023). Geo-foundation models: Reality, gaps and opportunities. In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems. Presented at the SIGSPATIAL ’23: 31st ACM International Conference on Advances in Geographic Information Systems, ACM, Hamburg Germany (pp. 1–4). https://doi.org/10.1145/3589132.3625616

[51]

YeX, DuJ, LiX, et al.. Human-centered GeoAI foundation models: Where GeoAI meets human dynamics. Urban Informatics, 2025.

[52]

YeX, LinHSpatial synthesis: Computational social science and humanities, 2020Springer Nature.

[53]

Ye, X., Wu, L., Lemke, M., Valera, P., & Sackey, J. (2022). Defining computational urban science. In New thinking in GIScience (pp. 293–300). Springer Nature Singapore.

[54]

ZhangX, ChengT. The impacts of the COVID-19 pandemic on multimodal human mobility in London: A perspective of decarbonizing transport. Geo-Spatial Information Science, 2022, 26(4): 703-715.

[55]

Zhang, Y., Aslam, N., Lai, J., & Cheng, T. (n.d.). You are how you travel: A multi-task learning framework for geodemographic inference using transit smart card data. Computers, Environment and Urban Systems, 83. https://doi.org/10.1016/j.compenvurbsys.2020.101517

[56]

ZhuD, et al.. Inferring spatial interaction patterns from sequential snapshots of spatial distributions. International Journal of Geographical Information Science, 2018, 32(4): 783-805

[57]

Zhu, D., & Cao, G. (2024). Intelligent spatial prediction and interpolation methods. In Handbook of Geospatial Artificial Intelligence (pp. 121–150). CRC Press.

[58]

ZhuD, LiuY, YaoX, FischerMM. Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions. GeoInformatica, 2022, 26(4): 645-676

RIGHTS & PERMISSIONS

The Author(s)

AI Summary AI Mindmap
PDF

109

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/