Alonso, W. (1964). Location and land use: Toward a general theory of land rent. Harvard University Press. https://isbnsearch.org/isbn/9780674729568
Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.  https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Barbosa, H., Barthelemy, M., Ghoshal, G., James, C. R., Lenormand, M., Louail, T., Ronaldo Menezes, José J. Ramasco, Filippo Simini, & Marcello Tomasini, M. (2018). Human mobility: Models and applications. Physics Reports, 734, 1–74. https://doi.org/10.1016/j.physrep.2018.01.001
Cheng, T., Haworth, J., Anbaroglu, B., Tanaksaranond, G., & Wang, J. (2014). Spatiotemporal data mining. In M. M. Fischer & P. Nijkamp (Eds.), Handbook of Regional Science (pp. 1173–1193). Berlin, Heidelberg: Springer Berlin Heidelberg. https://isbnsearch.org/isbn/9783642234309
Den Hoed, W. (2024). The unfinished cycling city: Embedding age-inclusion in urban cycling futures. Urban, Planning and Transport Research, 12(1), 2335195.     https://doi.org/10.1080/21650020.2024.2335195  
Friedmann, J. (1966). Regional development policy: A case study of Venezuela. The University of California: M.I.T. Press. https://isbnsearch.org/isbn/9780262560025
Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189–206.  https://doi.org/10.1111/j.1538-4632.1992.tb00261.x
Hägerstraand, T. (1970).  What about people in regional science? Papers in Regional Science, 24(1), 7–21. https://doi.org/10.1111/j.1435-5597.1970.tb01464.x
Han, J. W., Kamber, M., & Pei., J. (2011). Data mining: Concepts and techniques ( 3rd ed ed.). Berlin, Germany: Morgan Kaufmann Publishers. https://isbnsearch.org/isbn/9780123814807
Kaygisiz, Ö., Düzgün, Ş., Yildiz, A., & Senbil, M. (2015). Spatio-temporal accident analysis for accident prevention in relation to behavioral factors in driving: The case of South Anatolian Motorway. Transportation Research Part F: Traffic Psychology and Behavior, 33, 128–140. https://doi.org/10.1016/j.trf.2015.07.002
Kerner, B. S. (2002). Empirical macroscopic features of spatial-temporal traffic patterns at highway bottlenecks. Physical Review E, 65(4).  https://doi.org/10.1103/PhysRevE.65.046138
Kerner, B. S. (2004). The physics of traffic: Empirical freeway pattern features, engineering applications, and theory. Springer. https://isbnsearch.org/isbn/9783662078928
Kerner, B. S., Rehborn, H., Aleksic, M., & Haug, A. (2004). Recognition and tracking of spatial–temporal congested traffic patterns on freeways. Transportation Research Part C: Emerging Technologies, 12(5), 369–400. https://doi.org/10.1016/j.trc.2004.07.015
Komarovsky, S., & Haddad, J. (2025). Spatio-temporal graph convolutional neural network for traffic signal prediction in large-scale urban networks. Transportation Research Interdisciplinary Perspectives, 32, 101482. https://doi.org/10.1016/j.trip.2025.101482
Lanorte, A., Nolè, G., & Cillis, G. (2024). Application of the Getis-Ord correlation index (Gi) for burned area detection improvement in Mediterranean ecosystems (Southern Italy and Sardinia) using Sentinel-2 data. Remote Sensing, 16(16), 2943. https://doi.org/10.3390/rs16162943
Liu, X., Sun, L., Sun, Q., & Gao, G. (2017). Spatial variation of taxi demand using GPS trajectories and POI data. Journal of Advanced Transportation, 2020(1). https://doi.org/10.1155/2020/7621576
Mitchell, A. (2009). The Esri guide to GIS analysis (second edition), Vol. 2: Spatial Measurements and Statistics, Esri Press. https://isbnsearch.org/isbn/9781589486089
Mohammed, S., Alkhereibi, A. H., Abulibdeh, A., Jawarneh, R. N., & Balakrishnan, P. (2023). GIS-based spatiotemporal analysis for road traffic crashes; in support of sustainable transportation Planning. Transportation Research Interdisciplinary Perspectives, 20, 100836. https://doi.org/10.1016/j.trip.2023.100836
Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis, 27(4), 286–306. https://doi.org/10.1111/j.1538-4632.1995.tb00912.x
Peng, H., Shen, N., Cui, A., Cheng, H., & Li, J. (2019). Spatio-temporal pattern of crime in the context of rapid urbanization: A case study in Shenzhen, China. Applied Geography, 103, 37–48.  https://doi.org/10.1016/j.apgeog.2019.01.002
Shekhar, S., Jiang, Z., Ali, R. Y., Eftelioglu, E., Tang, X., Gunturi, V. M. V., & Zhou, X. (2015). Spatiotemporal data mining: A computational perspective. ISPRS Int. J. Geo-Inf., 4(4), 2306-2338.  https://doi.org/10.1109/TKDE.2018.2866809
Soltani, A., & Roohani Qadikolaei, M. (2024). Space-time analysis of accident frequency and the role of built environment in mitigation. Transport Policy, 150, 189–205. https://doi.org/10.1016/j.tranpol.2024.02.006
Treiber, M., & Kesting, A. (2013). Traffic flow dynamics: Data, models, and simulation. Springer. https://isbnsearch.org/isbn/9783642324604
Van Wageningen-Kessels, F., van Lint, H., Vuik, K., & Hoogendoorn, S. (2015). Genealogy of traffic flow models. EURO Journal on Transportation and Logistics, 4(4), 445–473. https://doi.org/10.1007/s13676-014-0045-5
Zhang, D., Lan, H., Wang, M., Yu, J., Jiang, X., & Zhang, S. (2025). Graph convolutional networks with multi-scale dynamics for traffic speed forecasting. Applied Soft Computing, 174, 112966. https://doi.org/10.1016/j.asoc.2025.112966
Zhao, X., Wu, Y. P., Ren, G., Ji, K., & Qian, W. W. (2019). Clustering analysis of ridership patterns at subway stations: A case in Nanjing, China. Journal of Urban Planning and Development, 145(2). https://doi.org/10.1061/(ASCE)UP.1943-5444.0000468
Zhu, L., Zhang, Q., Jian, X., & Yang, Y. (2025). Graph convolutional network for traffic incident duration classification. Engineering Applications of Artificial Intelligence, 151, 110570. https://doi.org/10.1016/j.engappai.2025.110570