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Journals / Türk Coğrafya Dergisi / 2026 / Sayı: 89

Geography in the age of artificial intelligence: Spatial data, analysis, and disciplinary transformation

Yapay zekâ çağında coğrafya: Mekânsal veri, analiz ve disipliner dönüşüm

Mazlum Ar(AHİ EVRAN ÜNİVERSİTESİ)0000-0003-3937-1637
Pages
219–232
Publication Date
2026-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Ultimately, the study posits that in the era of AI, geography is being repositioned, evolving from a discipline that merely processes data into a critical domain that offers contextual interpretations of spatial information and actively contributes to policymaking processes.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study examines the multidimensional relationship between artificial intelligence (AI) and geography from a holistic perspective, framed by the discipline's historical development, contemporary application areas, epistemological transformations, and ethical implications.
📊
Key Findings & Data:In recent years, technological advancements such as machine learning, deep learning, and big data analytics have fundamentally transformed the processes of geographical knowledge production and spatial analysis.
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Conclusion & Impact:Ultimately, the study posits that in the era of AI, geography is being repositioned, evolving from a discipline that merely processes data into a critical domain that offers contextual interpretations of spatial information and actively contributes to policymaking processes.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Coğrafya · Coğrafi Yapay Zekâ-(GeoAI) · Mekânsal Bilgi Üretimi

Abstract

This study examines the multidimensional relationship between artificial intelligence (AI) and geography from a holistic perspective, framed by the discipline's historical development, contemporary application areas, epistemological transformations, and ethical implications. In recent years, technological advancements such as machine learning, deep learning, and big data analytics have fundamentally transformed the processes of geographical knowledge production and spatial analysis. This transformation has unveiled novel research opportunities across geography and its sub-disciplines. Nevertheless, the integration of AI into geography transcends mere methodological innovation; rather, it signifies a paradigm shift that necessitates a critical rethinking of the modes of geographical knowledge production, the actors involved therein, and the ontological foundations of discipline. By examining the concept of Geospatial Artificial Intelligence (GeoAI) within an interdisciplinary framework, this research evaluates the epistemological and ontological shifts precipitated by AI in geography, contextualized within posthumanist geography debates. Furthermore, it scrutinizes the ramifications of AI-driven spatial knowledge production by analyzing ethical concerns such as algorithmic bias, data privacy, the digital divide, and deepfake geography. Ultimately, the study posits that in the era of AI, geography is being repositioned, evolving from a discipline that merely processes data into a critical domain that offers contextual interpretations of spatial information and actively contributes to policymaking processes.

Özet

Bu çalışma, yapay zekâ (YZ) ile coğrafya arasındaki çok boyutlu ilişkiyi disiplinin tarihsel gelişimi, güncel uygulama alanları, epistemolojik dönüşümleri ve etik sonuçları çerçevesinde bütüncül bir perspektifle incelemektedir. Son yıllarda makine öğrenmesi, derin öğrenme ve büyük veri analitiği gibi teknolojik gelişmeler, coğrafi bilgi üretimi ve mekânsal analiz süreçlerini köklü biçimde dönüştürmüştür. Bu dönüşüm coğrafya ve alt araştırma alanlarında yeni araştırma olanaklarını ortaya çıkarmıştır. Bununla birlikte YZ’nin coğrafyaya entegrasyonu yalnızca metodolojik bir yenilik değil, daha ziyade coğrafi bilginin üretim biçimlerini, bilgi üretimindeki aktörleri ve disiplinin ontolojik temellerini yeniden düşünmeyi gerektiren bir dönüşümü ifade etmektedir. Bu araştırma, GeoAI kavramını disiplinlerarası bir çerçevede ele alarak YZ’nin coğrafyada ortaya çıkardığı epistemolojik ve ontolojik değişimleri posthümanist coğrafya tartışmaları bağlamında değerlendirmektedir. Ayrıca algoritmik önyargı, veri gizliliği, dijital bölünme ve derin sahte coğrafya gibi etik sorunları analiz ederek YZ temelli mekânsal bilgi üretiminin sonuçlarını tartışmaktadır. Çalışma, YZ çağında coğrafyanın rolünün yalnızca veri işleyen bir disiplin olmaktan çıkarak mekânsal bilginin bağlamsal yorumunu sağlayan ve politika üretim süreçlerine katkı sunan kritik bir alan olarak yeniden konumlandığını ileri sürmektedir.

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Cite This Article

Ar, M., *, M. A. (2026). Geography in the age of artificial intelligence: Spatial data, analysis, and disciplinary transformation. Türk Coğrafya Dergisi, Sayı: 89, 219–232. https://doi.org/10.17211/tcd.1912647

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References (75)

  1. An, C., Wang, J., & Zhou, C. (2026). The evolution and current landscape of AI in geographical research: A large-scale systematic review. Geography and Sustainability, 7(1). https://doi.org/10.1016/j.geosus.2025.100392
  2. Ar, M. (2021). Mekân, yer ve yersizlik kavramları üzerine bir inceleme. Şehir ve Medeniyet Dergisi, 7(14), 8-25.
  3. Ar, M. (2025). Coğrafyadan ekrana: Dijitalleşme çağında mekânsal dönüşüm. İçinde Y. Söğüt (Ed.), DijiZAlim dijitalleşen dünyanın yol ayrımları (ss. 163-192). Literatürk.
  4. Ash, J., Kitchin, R., & Leszczynski, A. (2018). Digital turn, digital geographies? Progress in Human Geography, 42(1), 25– 43. https://doi.org/10.1177/0309132516664800
  5. Battle, R., & Kolas, D. (2012). Enabling the geospatial semantic web with Parliament and GeoSPARQL. Semantic Web, 3(4), 355–370. https://doi.org/10.3233/SW-2012-0065
  6. Batty, M. (2018). Artificial intelligence and smart cities. Environment and Planning B: Urban Analytics and City Science, 45(1), 3–6. https://doi.org/10.1177/2399808317751169
  7. Baydemir, R. (2025a). How Can We Utilise Artificial Intelligence (AI) in the Fight Against Climate Change? Opportunities and Challenges. 6th International Conference on Frontiers in Academic Research. Konya.
  8. Baydemir, R. (2025b). Artificial Intelligence (AI) in academic research?. 6th International Conference on Frontiers in Academic Research. Konya.
  9. Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim Code. Polity Press.
  10. Braidotti, R. (2013). Posthuman humanities. European Educational Research Journal, 12(1), 1-19. https://doi.org/10.2304/eerj.2013.12.1.1
  11. Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. Norton.
  12. Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research, 81, 1–15.
  13. Castells, M. (2010). The rise of the network society. Wiley- Blackwell.
  14. Cicerone, G., Faggian, A., Montresor, S., & Rentocchini, F. (2023). Regional artificial intelligence and the geography of environmental technologies: Does local AI knowledge help regional green-tech specialization? Regional Studies, 57(2), 330-343. https://doi.org/10.1080/00343404.2022.2092610
  15. Couclelis, H. (1986). Artificial intelligence in geography: Conjectures on the shape of things to come. The Professional Geographer, 38(1), 1-11. https://doi.org/10.1111/j.0033-0124.1986.00001.x
  16. Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.
  17. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. NAACL.
  18. Döllner, J. (2020). Geospatial artificial intelligence: potentials of machine learning for 3D point clouds and geospatial digital twins. PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 88(1), 15-24.
  19. Du, Z., Wang, Z., Wu, S., Zhang, F., & Liu, Y. (2020). Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity. International Journal of Geographical Information Science, 34(7), 1353-1377. https://doi.org/10.1080/13658816.2019.1707834
  20. Egenhofer, M. J. (1989). A formal definition of binary topological relationships. International Conference on Foundations of Data Organization and Algorithms, 457- 472. Springer.
  21. Fan, Z., Zhang, F., Loo, B. P., & Ratti, C. (2023). Urban visual intelligence: Uncovering hidden city profiles with street view images. Proceedings of the National Academy of Sciences, 120(27), e2220417120. https://doi.org/10.1073/pnas.2220417120
  22. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., & Schafer, B. (2018). AI4People—An ethical framework for a good AI society. Minds and Machines, 28(4), 689–707.
  23. Gendron, Y., Andrew, J., & Cooper, C. (2022). The perils of artificial intelligence in academic publishing. Critical Perspectives on Accounting, 87. https://doi.org/10.1016/j.cpa.2021.102411
  24. Ghahramani, M., Galle, N. J., Duarte, F., Ratti, C., & Pilla, F. (2021). Leveraging artificial intelligence to analyze citizens' opinions on urban green space. City and Environment Interactions, 10, 100058. https://doi.org/10.1016/j.cacint.2021.100058
  25. Goodchild, M. F. (1992). Geographical information science. International Journal of Geographical Information Systems, 6(1), 31–45. https://doi.org/10.1080/02693799208901893
  26. Goodchild, M. F. (2007). Citizens as sensors. GeoJournal, 69(4), 211–221. Goodchild, M. F. (2018). Reimagining the history of GIS. Annals of GIS, 24(1), 1–8. https://doi.org/10.1080/19475683.2018.1424737
  27. Goodchild, M. F., & Janelle, D. (2004). Spatially integrated social science. Oxford University Press.
  28. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031
  29. Graham, M. (2011). Time machines and virtual portals. Progress in Development Studies, 11(3), 211–227. https://doi.org/10.1177/146499341001100303
  30. Graham, M., & Shelton, T. (2013). Geography and the future of big data, big data and the future of geography. Dialogues in Human Geography, 3(3), 255–261. https://doi.org/10.1177/2043820613513121
  31. Hagenauer, J., & Helbich, M. (2022). A geographically weighted artificial neural network. International Journal of Geographical Information Science, 36(2), 215-235. https://doi.org/10.1080/13658816.2021.1871618
  32. Hayles, N. K. (1999). How we became posthuman. University of Chicago Press.
  33. Hilbert, M., & López, P. (2011). The world’s technological capacity to store, communicate, and compute information. Science, 332(6025), 60–65.
  34. Hu, Y., Gao, S., Janowicz, K., Yu, B., Li, W., & Prasad, S. (2015). Extracting and understanding urban areas of interest using geotagged photos. Computers, Environment and Urban Systems, 54, 240-254. https://doi.org/10.1016/j.compenvurbsys.2015.09.001
  35. Huang, X., Wang, S., Wilson, J., & Kedron, P. (2025). GeoAI and human geography: The dawn of a new spatial intelligence era. Springer.
  36. Janowicz, K., Gao, S., McKenzie, G., Hu, Y., & Bhaduri, B. (2020). GeoAI: Spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. International Journal of Geographical Information Science, 34(4), 625-636. https://doi.org/10.1080/13658816.2019.1684500
  37. Janowicz, K., Sieber, R., & Crampton, J. (2022). GeoAI, counter- AI, and human geography: A conversation. Dialogues in Human Geography, 12(3), 446-458. https://doi.org/10.1177/20438206221132510
  38. Jiang, Z., Yu, J., Jin, Y., Ginn, A., Chen, J., & Sun, G. (2023). When artificial intelligence comes to the Chinese calligraphic landscape: The coming transformation. Geography Compass, 17(1), e12670. https://doi.org/10.1111/gec3.12670
  39. Kafy, A. A., Rahman, M. S., Hasan, M. M., & Islam, M. (2020). Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi, Bangladesh. Remote Sensing Applications: Society and Environment, 18, 100314. https://doi.org/10.1016/j.rsase.2020.100314
  40. Kang, Y., Jia, Q., Gao, S., Zeng, X., Wang, Y., Angsuesser, S., Liu, Y., & Fei, T. (2019). Extracting human emotions at different places based on facial expressions and spatial clustering analysis. Transactions in GIS, 23(3), 450-480. https://doi.org/10.1111/tgis.12552
  41. Kinkaid, E. (2020). Can assemblage think difference? A feminist critique of assemblage geographies. Progress in Human Geography, 44(3), 457-472. https://doi.org/10.1177/0309132519836162
  42. Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage.
  43. Kitchin, R. (2017). Thinking critically about and researching algorithms. Routledge.
  44. Kruse, J., Kang, Y., Liu, Y. N., Zhang, F., & Gao, S. (2021). Places for play: Understanding human perception of playability in cities using street view images and deep learning. Computers, Environment and Urban Systems, 90, 101693. https://doi.org/10.1016/j.compenvurbsys.2021.101693
  45. Larkin, A., & Hystad, P. (2019). Evaluating street view exposure measures of visible green space for health research. Journal of exposure science & environmental epidemiology, 29(4), 447-456.
  46. Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. Oxford University press.
  47. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
  48. Li, Z., Zhou, W., Chiang, Y. Y., & Chen, M. (2023). Geolm: Empowering language models for geospatially grounded language understanding. InProceedings of the 2023 conference on empirical methods in natural language processing (pp. 5227-5240).
  49. Lin, Y., & Zhao, B. (2025). Posthuman cartography? Rethinking artificial intelligence, cartographic practices, and reflexivity. Annals of the American Association of Geographers, 115(3), 499-512. https://doi.org/10.1080/24694452.2024.2435920
  50. Lundman, R., & Nordström, P. (2023). Creative geographies in the age of AI: Co-creative spatiality and the emerging techno-material relations between artists and artificial intelligence. Transactions of the Institute of British Geographers, 48(3), 650-664. https://doi.org/10.1111/tran.12608
  51. Maalsen, S. (2025). Digital geographies II: All about AI. What's old? What's new? Where to? Progress in Human Geography, 49(1), 1-12. https://doi.org/10.1177/03091325251398858
  52. Mai, G., Hu, Y., Gao, S., Cai, L., Martins, B., Schlieder, C., & Janowicz, K. (2022). Symbolic and subsymbolic GeoAI: Geospatial knowledge graphs and spatially explicit machine learning. Transactions in GIS, 26(8), 3118-3124. https://doi.org/10.1111/tgis.13012
  53. Mai, G., Xie, Y., Jia, X., Lao, N., Rao, J., Zhu, Q., Liu, Z., Chiang, Y. Y., & Jiao, J. (2025). Towards the next generation of geospatial artificial intelligence. International Journal of Applied Earth Observation and Geoinformation, 136, 104368. https://doi.org/10.1016/j.jag.2025.104368
  54. McDuie-Ra, D., & Gulson, K. (2020). The backroads of AI: The uneven geographies of artificial intelligence and development. Area, 52(3), 626-633. https://doi.org/10.1111/area.12602
  55. Miller, H. J., & Goodchild, M. F. (2015). Data-driven geography. GeoJournal, 80(4), 449–461. Monmonier, M. (1991). How to lie with maps. University of Chicago Press.
  56. O’Neil, C. (2016). Weapons of math destruction. Crown Publishing.
  57. Ogunkolu, A. B., Ajibade, L. T., & Ameh, M. S. (2025). AI, geography and ethics: Exploring the intersections and implications. LASU Journal of Environmental Sciences, 1(2), 1-26. https://doi.org/10.5281/zenodo.17846256
  58. Openshaw, S. (1992). Some suggestions concerning the development of artificial intelligence tools for spatial modelling and analysis in GIS. The Annals of Regional Science, 26(1), 35-51. https://doi.org/10.1007/BF01581479
  59. Qi, Y., Mai, G., Zhu, R., & Zhang, M. (2023). EVKG: An interlinked and interoperable electric vehicle knowledge graph for smart transportation system. Transactions in GIS, 27(4), 949-974. https://doi.org/10.1111/tgis.13064
  60. Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566, 195–204. https://doi.org/10.1038/s41586-019-0912-1
  61. Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach. Pearson.
  62. Shekhar, S. (2021, July). What is special about spatial data science and Geo-AI?. In Proceedings of the 33rd International Conference on Scientific and Statistical Database Management (pp. 271-271).
  63. Simonsen, K. (2012). In quest of a new humanism: Embodiment, experience and phenomenology as critical geography. Progress in Human Geography, 37(1), 10–26. https://doi.org/10.1177/0309132512467573
  64. Smith, T. R. (1984). Artificial intelligence and its applicability to geographical problem solving. The Professional Geographer, 36(2), 147-158. https://doi.org/10.1111/j.0033-0124.1984.00147.x
  65. Su, F., Yan, F., Wu, W., Fu, D., Cao, Y., Lyne, V., Meadows, M., Yao, L., Wang, J., Huang, Y., Huang, C., Qin, J., Fang, S., & Zhang, A. (2025). Advancing intelligent geography: Current status, innovations, and future prospects. Geography and Sustainability, 6(1), 100375. https://doi.org/10.1016/j.geosus.2025.100375
  66. Sundberg, J. (2014). Decolonizing posthumanist geographies. Cultural geographies, 21(1), 33-47. https://doi.org/10.1177/1474474013486067
  67. Ullah, S., Tahir, A. A., Akbar, T. A., Hassan, Q. K., & Dewan, A. (2019). Remote sensing-based quantification of the relationships between land use land cover changes and surface temperature over the lower Himalayan region. Journal of the Indian Society of Remote Sensing, 47(10), 1685-1698. https://doi.org/10.3390/su11195492
  68. Walker, M., Winders, J., & Boamah, E. F. (2021). Locating artificial intelligence: A research agenda. Space and Polity, 25(2), 202-219. https://doi.org/10.1080/13562576.2021.1985868
  69. Wang, Z., Xie, Y., Li, Z., Jia, X., & He, E. (2024). SimFair: Physics- guided fairness-aware learning with simulation models. Proceedings of the AAAI Conference on Artificial Intelligence, 38(20), 22420-22428. https://doi.org/10.1609/aaai.v38i20.30249
  70. Yang, L., Driscol, J., Sarigai, S., Wu, Q., Chen, H., & Lippitt, C. D. (2022). Google Earth Engine and artificial intelligence (AI): a comprehensive review. Remote Sensing, 14(14), 3253. https://doi.org/10.3390/rs14143253
  71. Zhang, K., Chen, Y., & Li, C. (2019). Discovering the tourists' behaviors and perceptions in a tourism destination by analyzing photos' visual content with a computer deep learning model: The case of Beijing. Tourism Management, 75, 595-608. https://doi.org/10.1016/j.tourman.2019.07.002
  72. Zhang, X., Ji, Z., Zheng, Y., Ye, X., & Li, D. (2020). Evaluating the effect of city lock-down on controlling COVID-19 propagation through deep learning and network science models. Cities, 107, 102869. https://doi.org/10.1016/j.cities.2020.102869
  73. Zhao, B., Zhang, S., Xu, C., Sun, Y., & Deng, C. (2021). Deep fake geography? When geospatial data encounter artificial intelligence. Cartography and Geographic Information Science, 48(4), 338-352. https://doi.org/10.1080/15230406.2021.1910075
  74. Zheng, T., Dong, C., Dong, D., & Wang, S. (2021). Traffic volume prediction: A fusion deep learning model considering spatial-temporal correlation. Sustainability, 13(19), 10595. https://doi.org/10.3390/su131910595
  75. Zuboff, S. (2019). The age of surveillance capitalism. PublicAffair.