A Geospatial data mining and Geomarketing approach for smart cities

Ahmed O Thman Ramadhan Al_dalawi; Bakhtiar Feizizadeh

doi:10.31127/tuje.1888131

A Geospatial data mining and Geomarketing approach for smart cities

Abstract

The main objective of this study is to highlight the potential of GIS technology and big data analytics in the trade and finance sectors. We demonstrate the importance of thinking in spatial terms for analyzing patterns within the trade and finance industries for spatiotemporal trade and shopping patterns in the city of Tabriz using data generated by customer purchase transactions obtained from 5200 stores, shopping, business and service centers. We employ time series transaction data collected from the points of sale in stores, shopping, service and business centers located in different areas of the city. We integrate machine learning and big data analytics, including space–time pattern mining, spatiotemporal coupling tele-coupling, trend detection and hotspot mapping. The results of this study indicate the potential of GIScience methods for the explicit spatial mapping of trade and shopping patterns. The results reveal that the city center, particularly the Bazaar of Tabriz, acts as the city’s heart of trade, and we identify additional major business hotspots. Furthermore, the results allow for studying the impacts of unbalanced urban development in Tabriz, where the wealthy sub towns with high quality of life, such as Valiasr and Elguli, host the major shopping hotspots. The spatial patterns obtained enable local stakeholders, decision makers and authorities to develop strategic plans for urban sustainable development in Tabriz.

Keywords

References

van Ham, M., Tammaru, T., Ubarevičienė, R., & Janssen, H. (Eds.). (2021). Urban socio-economic segregation and income inequality: A global perspective. Springer. https://doi.org/10.1007/978-3-030-64569-4
Stice, E., Marti, C. N., Shaw, H., & Rohde, P. (2019). Meta-analytic review of dissonance-based eating disorder prevention programs: Intervention, participant, and facilitator features that predict larger effects. Clinical Psychology Review, 70, 91–107. https://doi.org/10.1016/j.cpr.2019.04.004
Li, Z., Wang, Y., & Zhou, Q. (2022b). Assessing the impacts of demographic and infrastructure factors on retail distribution using GWR. Urban Studies, 59(12), 2495–2514. https://doi.org/10.1177/00420980211015103
Blazquez, D., & Domenechl, J. (2018). Big data sources and methods for social and economic analyses. Journal of Business Research, 98, 263–272. https://doi.org/10.1016/j.jbusres.2018.07.008
UrbanFACET. (2017). Consumer mobility and commercial activity patterns in cities. Urban Data Lab Report, 3(1), 1–25. https://doi.org/10.2139/ssrn.2956881
Martí, P., Roca, J., & Hladík, J. (2019). Socioeconomic insights from location-based transaction data. Urban Analytics and City Science, 46(5), 950–969. https://doi.org/10.1177/2399808319883184
Gómez, M., Martín, C., & Morales, J. (2020). Spatiotemporal modeling of consumer visits for understanding retail structures. Urban Studies, 57(8), 1703–1722. https://doi.org/10.1177/0042098019884592
Habibpour, M., Khedmatzadeh, A., & Feizizadeh, B. (2021). Spatial modelling of socio-economic well-being using GIS and multi-criteria decision analysis: A case study of Tabriz city. Journal of Urban Management, 10(2), 123–139. https://doi.org/10.1016/j.jum.2021.03.005

Van Vlasselaer, V. (2015). Data-driven fraud detection and financial risk analysis using big data techniques. Leuven University Press. https://doi.org/10.2307/j.ctv1xxn1r
Van Vlasselaer, V., Bravo, C., Caelen, O., Eliassi-Rad, T., Akoglu, L., Snoeck, M., & Baesens, B. (2015). APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions. Decision Support Systems, 75, 38–48. http://dx.doi.org/10.1016/j.dss.2015.04.013
Xiong, T., Wang, S., Mayers, A., & Monga, E. (2013). Personal bankruptcy prediction by mining credit card data. Expert Systems with Applications, 40, 665–676. http://dx.doi.org/10.1016/j.eswa.2012.07.072
Alefo, I., Rodríguez, M., & López, J. (2019). Spatiotemporal evolution of consumer demand using retail transaction data. Journal of Retail Analytics, 7(2), 105–121. https://doi.org/10.1016/j.jretai.2019.04.001
Chacón-García, J. (2017). Spatial analysis of retail trade and consumer behavior: A geomarketing approach. Spatial Economic Review, 12(4), 445–463. https://doi.org/10.1007/s11301-017-0013-2
Lim, J. (2023). Combining social media and transaction data for urban commercial analysis. Data & Society, 11(1), 55–73. https://doi.org/10.1177/20539517231158626
Lim, J., Tan, K., & Lee, H. (2022). Integrating multiple big data sources for predicting shopping behavior. International Journal of Big Data Applications, 5(3), 122–138. https://doi.org/10.4018/IJBDA.2022070107
Gómez, Y. E., Sánchez, J. F. M., & Molano, J. I. R. (2020). Geveca servidor web espacial para el GeoMarketing. Revista Ibérica de Sistemas e Tecnologias de Informação, (E35), 225–233.
Chacón-García, J. (2017). Geomarketing Techniques to Locate Retail Companies in Regulated Markets. Australasian Marketing Journal (AMJ), 25(3), 185–193. https://doi.org/10.1016/j.ausmj.2017.06.001
Omidipoor, R., Aghighi, M., & Feizizadeh, B. (2020). GIS-based data mining for spatial knowledge discovery: Applications in urban dynamics. Spatial Information Research, 28(2), 189–203. https://doi.org/10.1007/s41324-020-00320-7
Alefo, A. L., Cimino, M. G. C. A., Lepri, B., Pentland, A. S., & Vaglini, G. (2019). Detecting Permanent and Intermittent Purchase Hotspots via Computational Stigmergy. In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods (ICPRAM). https://doi.org/10.48550/arXiv.1902.01601
Kamil, A., Omar, Z., & Rahman, A. (2021). The role of GIScience in business and economic decision support. International Journal of Geoinformatics, 17(2), 33–45. https://doi.org/10.4038/ijg.v17i2.7636
Omidipoor, A., Feizizadeh, B., & Ghorbani, R. (2019). Application of GIScience in geomarketing: Identifying spatial-economic areas and business potentials. Spatial Information Research, 27(6), 655–667. https://doi.org/10.1007/s41324-019-00214-1
Suárez, M., Pérez, A., & García, L. (2012). GIS-based decision-making in retail and real estate development. International Journal of Geographical Information Science, 26(11), 2219–2238. https://doi.org/10.1080/13658816.2012.749157
Roig-Tierno, N., Baviera-Puig, A., & Buitrago-Vera, J. (2013). GIS-based approaches for retail location analysis. Applied Geography, 45, 16–27. https://doi.org/10.1016/j.apgeog.2013.08.003
Cliquet, G. (2013). Geographical information systems and marketing decisions: Theoretical and empirical perspectives. Journal of Marketing Channels, 20(3–4), 189–207. https://doi.org/10.1080/1046669X.2013.784013
Iranian Census Centre. (2016). Population and housing census of Iran: General results of East Azerbaijan Province. Statistical Center of Iran. Retrieved from https://www.amar.org.ir/english
Rezaei, M., Feizizadeh, B., & Lakes, T. (2020). Spatial modeling of urban economic functions using GIS: A case study of Tabriz, Iran. GeoJournal, 85(5), 1333–1348. https://doi.org/10.1007/s10708-019-10007-7
Pourmoradian, S. (2018). The historical evolution and functional morphology of the Tabriz Bazaar as a UNESCO World Heritage Site. Journal of Cultural Heritage Management and Sustainable Development, 8(3), 287–302. https://doi.org/10.1108/JCHMSD-09-2017-0066
Feizizadeh, B., Lakes, T., Omarzadeh, D., Sharifi, A., Blaschke, T., & Karmizadeh, S. M. (2022a). Scenario-based analysis of the impacts of lake drying on sustainable food production. Scientific Reports. https://doi.org/10.1038/s41598-022-10159-2
Feizizadeh, B., Nouri, H., & Khedmatzadeh, A. (2021a). Monitoring spatial inequalities and urban poverty using GIS-based indicators in Tabriz, Iran. Habitat International, 111, 102350. https://doi.org/10.1016/j.habitatint.2021.102350
Yoshimura, Y., Sobolevsky, S., Ratti, C., & Girardin, F. (2016). Reconstructing urban mobility and catchment areas from point-of-sale transaction data. Computers, Environment and Urban Systems, 58, 1–12. https://doi.org/10.1016/j.compenvurbsys.2016.03.006
Carpio-Pinedo, J., Vázquez, J., & Gómez, R. (2022). Spatiotemporal analysis of retail expenditure using bank card transaction data: Insights into urban commercial activity. Cities, 124, 103598. https://doi.org/10.1016/j.cities.2022.103598
Wei, L., Zhang, D., & Lin, Y. (2018). Demographic determinants of urban shopping patterns: A GIS-based analysis. Sustainability, 10(7), 2203. https://doi.org/10.3390/su10072203
Mohammadi, M., Ghasemi, A., & Feizizadeh, B. (2022). Integrating POS-derived financial data with spatial analysis for monitoring urban economic dynamics. Journal of Urban Data Science, 6(1), 22–39.
Mohammadian, M., Feizizadeh, B., & Molaei, H. (2021). Application of average nearest neighbour analysis for spatial clustering assessment in urban environments. Geocarto International, 36(21), 2392–2406. https://doi.org/10.1080/10106049.2020.1752021
Di Clemente, R., Luengo-Oroz, M., Travizano, M., & Ramasco, J. J. (2018). Sequences of purchases in credit card data reveal lifestyle clusters and consumer behavior regularities. Scientific Reports, 8(1), 11225. https://doi.org/10.1038/s41598-018-29487-9
Guidotti, R., Monreale, A., Rinzivillo, S., Pedreschi, D., & Giannotti, F. (2018). Discovering temporal regularities in retail customer behavior from transaction data. Information Systems, 81, 52–66. https://doi.org/10.1016/j.is.2018.10.002
Tsoutsos, T., & Photis, Y. N. (2020). Spatial dynamics of retail distribution and socio-economic prestige in Greek cities: An accessibility-based approach. European Planning Studies, 28(10), 2019–2037. https://doi.org/10.1080/09654313.2020.1743835
Longley, P. A., Adnan, M., & Playfoot, H. (2021). Representativeness and bias in transaction-derived datasets: Implications for spatial analysis of retail behavior. Environment and Planning B: Urban Analytics and City Science, 48(7), 1834–1852. https://doi.org/10.1177/2399808320948304
Miller, H. J., & Goodchild, M. F. (2015). Data-driven geography. GeoJournal, 80(4), 449–461. https://doi.org/10.1007/s10708-014-9602-6
Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Eamon Dolan/Houghton Mifflin Harcourt.
Xu, C., & Zhang, Q. (2021). Big data analytics in GIScience: Methods, challenges and opportunities. International Journal of Geographical Information Science, 35(9), 1832–1855. https://doi.org/10.1080/13658816.2021.1893184
Qiang, Y., & Van de Weghe, N. (2019). Re-arranging space, time and scales in GIS: Alternative models for multi-scale spatio-temporal modeling and analyses. ISPRS International Journal of Geo-Information, 8(2), 72. https://doi.org/10.3390/ijgi8020072
Feizizadeh, B., & Nouri, H. (2024). Integration of spatial trend detection and hotspot analysis for retail trade mapping using GIS and machine learning. Spatial Information Research, 32(2), 289–307.
Li, L. Q., Huang, T., Wang, Y. Q., Wang, Z. P., Liang, Y., Huang, T. B., … & Wang, Y. (2020). COVID-19 patients' clinical characteristics, discharge rate, and fatality rate: A meta-analysis. Journal of Medical Virology, 92(6), 577–583. https://doi.org/10.1002/jmv.25809
Taunk, K., De, S., Verma, A., & Swetapadma, A. (2019). A brief review of nearest neighbour and clustering algorithms in machine learning. Proceedings of the International Conference on Advanced Computing and Applications, 2(1), 887–896. https://doi.org/10.1109/ICACA.2019.8711360
Feizizadeh, B. (2024). Integrating GIS-based spatial statistics and machine learning for analyzing retail distribution patterns. Journal of Spatial Analysis and Modeling, 15(1), 88–106.
Nazmfar, H., Feizizadeh, B., & Omrani, A. (2020a). Employing GIS and machine learning–based nearest neighbour methods for analyzing urban commercial distribution. Spatial Statistics, 37, 100412. https://doi.org/10.1016/j.spasta.2020.100412
Elmezughi, A., Feizizadeh, B., & Nouri, H. (2022). Machine learning–based nearest neighbour and spatial clustering for retail and service distribution analysis. Spatial Information Research, 30(4), 497–512. https://doi.org/10.1007/s41324-022-00425-7
Ghouchan Nezhad Noor Nia, M., Feizizadeh, B., & Karimi, A. (2022). Spatial distribution modelling of retail and commercial centres using GIS and machine learning approaches. Journal of Geographical Systems, 24(3), 455–474. https://doi.org/10.1007/s10109-022-00412-1
Mohammadian, A., Nadimi, A. N., Khalifeh, V., & Shams, M. (2021). GIS-based crash hotspot identification: A comparison among mapping clusters and spatial analysis techniques. International Journal of Injury Control and Safety Promotion, 27(2), 1–14. https://doi.org/10.1080/17457300.2021.1925924
Yang, X., Li, J., & Chen, Z. (2019). Spatial statistical analysis of retail clustering using GIS-based nearest neighbour analysis. Sustainability, 11(10), 2843. https://doi.org/10.3390/su11102843
Fang, H., Zhang, Y., & Li, W. (2024). Advanced geospatial pattern detection using ML-based nearest neighbour statistics. International Journal of Geographical Information Science, 38(2), 201–219. https://doi.org/10.1080/13658816.2024.1001201
Wang, J. (2021). Evaluating clustering tendencies using spatial nearest neighbour metrics in urban studies. Computers, Environment and Urban Systems, 90, 101706. https://doi.org/10.1016/j.compenvurbsys.2021.101706
Lopes, R. (2021). Spatial pattern analysis and nearest neighbour methods in urban retail geography. Urban Science, 5(2), 40. https://doi.org/10.3390/urbansci5020040
Amiri, F., Feizizadeh, B., & Karimzadeh, S. (2021). Hotspot mapping methodologies for spatial clustering in urban environments: A comparative review. Spatial Statistics, 41, 100523. https://doi.org/10.1016/j.spasta.2021.100523
Dudzińska, M., Cellmer, R., & Trojanek, R. (2020). Spatial distribution analysis of urban land values using KDE. Sustainability, 12(11), 4498. https://doi.org/10.3390/su12114498
Cellmer, R., & Trojanek, R. (2020). Kernel density estimation for spatial analysis of real estate markets. Land Use Policy, 95, 104609. https://doi.org/10.1016/j.landusepol.2020.104609
Nazmfar, H., Feizizadeh, B., & Omrani, A. (2020b). Kernel density estimation and hotspot analysis of urban sales activities using POS data. Sustainability, 12(16), 6530. https://doi.org/10.3390/su12166530
Anderson, T. K. (2009). Kernel density estimation and K-means clustering to profile road accident hotspots. Accident Analysis & Prevention, 41(3), 359–364. https://doi.org/10.1016/j.aap.2008.12.014
O’Sullivan, D. (2002). Exploring spatial pattern in geographic information systems using kernel density estimation. Environment and Planning A, 34(8), 1529–1552. https://doi.org/10.1068/a3439
Schabenberger, O., & Gotway, C. A. (2017). Statistical methods for spatial data analysis. Chapman & Hall/CRC.
Hamdi, S., Rahman, A., & Hasan, S. (2021). Space–time pattern mining for urban socioeconomic monitoring. Cities, 115, 103240. https://doi.org/10.1016/j.cities.2021.103240
Blazy, R., & Labuz, T. (2022). Spatiotemporal pattern mining for urban mobility and retail dynamics. Computers, Environment and Urban Systems, 96, 101802. https://doi.org/10.1016/j.compenvurbsys.2022.101802
Sharma, P., Singh, R., & Feizizadeh, B. (2019). Integrating space–time pattern mining and telecoupling for urban dynamics analysis. Computers, Environment and Urban Systems, 77, 101371. https://doi.org/10.1016/j.compenvurbsys.2019.101371
Chen, X., Li, D., & Huang, Q. (2017). Spatiotemporal coupling and tele-coupling analysis in urban big data contexts. ISPRS International Journal of Geo-Information, 6(9), 286. https://doi.org/10.3390/ijgi6090286
Li, L., Lei, Y., Wu, S., He, C., & Yan, D. (2018). Study on the coordinated development of economy, environment and resources in coal-based areas in Shanxi Province, China: Based on the multi-objective optimization model. Resources Policy, 55, 80–86. https://doi.org/10.1016/j.resourpol.2017.12.004
Yuxuan Liang, Y., Chen, Q., & Li, W. (2017). Spatiotemporal telecoupling modeling for big data analytics. ISPRS International Journal of Geo-Information, 6(11), 339. https://doi.org/10.3390/ijgi6110339
Sharma, A., Jiang, Z., & Shekhar, S. (2019). Spatiotemporal data mining: A survey. Journal of the ACM, 37(4), Article 1, 14 pages. https://doi.org/10.1145/1122445.1122456
Feizizadeh, B., Ghorbani, R., & Lakes, T. (2024). Geospatial big-data analytics of credit card transactions for urban geomarketing and policy support. International Journal of Applied Earth Observation and Geoinformation, 132, 103874. https://doi.org/10.1016/j.jag.2024.103874
Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1998). A density-based algorithm for discovering clusters in large spatial databases with noise (DBSCAN). In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (pp. 226–231).
ArcGIS Pro. (2025). Spatial autocorrelation (Moran’s I). Environmental Systems Research Institute (ESRI). Retrieved from https://pro.arcgis.com
Balyani, F., Feizizadeh, B., & Mohammadi, M. (2014). Application of Moran’s I in analyzing spatial clustering of socio-economic indicators. Geographical Analysis, 46(4), 345–360.
Perumal, K., Reddy, C. S., & Jha, C. S. (2016). Big data analytics in geospatial applications: A review of techniques and trends. Geocarto International, 31(10), 1123–1141. https://doi.org/10.1080/10106049.2016.1140829
Muthu, P., Kumar, P., & Singh, R. (2022). Fuzzy logic–based normalization in spatial multi-criteria analysis. Environmental Monitoring and Assessment, 194(2), 115. https://doi.org/10.1007/s10661-021-09775-z
Fotheringham, A. S., Crespo, R., & Yao, J. (2013). Geographically weighted regression for modelling spatial non-stationarity. Journal of Geographical Systems, 15(4), 343–356. https://doi.org/10.1007/s10109-013-0181-4
Gray, M., Wang, J., & Li, J. (2020). GIS-based spatial modeling of urban retail activities: Methods and case studies. Applied Geography, 123, 102310. https://doi.org/10.1016/j.apgeog.2020.102310
Berumen, S., García, J., & Ruiz, A. (2020). Spatial data analysis and visualization using GIS: Insights for urban systems. Sustainability, 12(15), 6012. https://doi.org/10.3390/su12156012
Comber, A., Brunsdon, C., & Charlton, M. (2022). Geographically weighted regression: An introduction and new applications. Transactions in GIS, 26(1), 45–67. https://doi.org/10.1111/tgis.12877
Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2000). Quantitative geography: Perspectives on spatial data analysis. Sage Publications.
Gao, Y., Li, W., & Zhao, J. (2025). Integrating fuzzy logic and GIS for spatial decision-making under uncertainty. International Journal of Digital Earth, 18(1), 1–18. https://doi.org/10.1080/17538947.2025.1203456

Details

Primary Language

English

Subjects

Geographical Information Systems (GIS) in Planning

Journal Section

Research Article

Authors

Ahmed O Thman Ramadhan Al_dalawi
0009-0006-1282-9862
Iran

Bakhtiar Feizizadeh ^*
0000-0007-8635-3606
Germany

Publication Date

July 6, 2026

Submission Date

February 13, 2026

Acceptance Date

April 24, 2026

Published in Issue

Year 2026 Volume: 10 Number: 3

DOI

https://doi.org/10.31127/tuje.1888131

IZ

https://izlik.org/JA46YC39TB

Cite

RIS / Bibtex

APA

Thman Ramadhan Al_dalawi, A. O., & Feizizadeh, B. (2026). A Geospatial data mining and Geomarketing approach for smart cities. Turkish Journal of Engineering, 10(3), 789-807. https://doi.org/10.31127/tuje.1888131

AMA

1.Thman Ramadhan Al_dalawi AO, Feizizadeh B. A Geospatial data mining and Geomarketing approach for smart cities. TUJE. 2026;10(3):789-807. doi:10.31127/tuje.1888131

Chicago

Thman Ramadhan Al_dalawi, Ahmed O, and Bakhtiar Feizizadeh. 2026. “A Geospatial Data Mining and Geomarketing Approach for Smart Cities”. Turkish Journal of Engineering 10 (3): 789-807. https://doi.org/10.31127/tuje.1888131.

EndNote

Thman Ramadhan Al_dalawi AO, Feizizadeh B (July 1, 2026) A Geospatial data mining and Geomarketing approach for smart cities. Turkish Journal of Engineering 10 3 789–807.

IEEE

[1]A. O. Thman Ramadhan Al_dalawi and B. Feizizadeh, “A Geospatial data mining and Geomarketing approach for smart cities”, TUJE, vol. 10, no. 3, pp. 789–807, July 2026, doi: 10.31127/tuje.1888131.

ISNAD

Thman Ramadhan Al_dalawi, Ahmed O - Feizizadeh, Bakhtiar. “A Geospatial Data Mining and Geomarketing Approach for Smart Cities”. Turkish Journal of Engineering 10/3 (July 1, 2026): 789-807. https://doi.org/10.31127/tuje.1888131.

JAMA

1.Thman Ramadhan Al_dalawi AO, Feizizadeh B. A Geospatial data mining and Geomarketing approach for smart cities. TUJE. 2026;10:789–807.

MLA

Thman Ramadhan Al_dalawi, Ahmed O, and Bakhtiar Feizizadeh. “A Geospatial Data Mining and Geomarketing Approach for Smart Cities”. Turkish Journal of Engineering, vol. 10, no. 3, July 2026, pp. 789-07, doi:10.31127/tuje.1888131.

Vancouver

1.Ahmed O Thman Ramadhan Al_dalawi, Bakhtiar Feizizadeh. A Geospatial data mining and Geomarketing approach for smart cities. TUJE. 2026 Jul. 1;10(3):789-807. doi:10.31127/tuje.1888131