Machine-Learning-Based Classification of  Urban Voids: Case Study of “Balat and  Fener, Istanbul, Turkey”

Mojtaba Samadi; Aysegul Akcay Kavakoglu

doi:10.51596/sijocp.v3i1.33

Authors

Mojtaba Samadi MSc Candidate, Istanbul Technical University, Turkey https://orcid.org/0000-0002-1834-2004
Aysegul Akcay Kavakoglu Assistant Professor, Istanbul Technical University, Turkey https://orcid.org/0000-0002-5910-3560

DOI:

https://doi.org/10.51596/sijocp.v3i1.33

Keywords:

urban voids, interim reuse, decision tree, machine learning, climate change mitigation

Abstract

Climate change is one of the most significant challenges humanity is facing today. As major contributors to greenhouse gas emissions, cities have a crucial role in mitigating its effects. One resource that cities can leverage in this fight is their Urban Voids (UVs) - undermanaged spaces that, when adequately repurposed, can provide a range of benefits, such as supporting urban biological ecosystems and helping to address climate change. This study proposes a machine-learning-based classification of UVs in “Balat” and “Fener” in Istanbul, Turkey. By classifying UVs based on their ability to assist ecological restoration and community growth, this study offers a valuable tool for urban planners and policymakers to prioritise their efforts and spend resources more efficiently in the fight against climate change. The classification in this study is based on five factors - Ownership, Debris, Economic activity, Seal, and Leisure facilities - which were selected based on their significance in previous studies of UVs. A decision tree algorithm was employed to classify the UVs into six categories, and an artificial neural network (ANN) was used to validate the classification with an accuracy of 97%. Overall, this study offers insights into the potential of machine learning in UV classification and provides a valuable tool for urban planners and policymakers to manage and activate UVs effectively.

References

Alpaydin, E. (2004). Introduction to machine learning. MIT Press.

Bezmez, D. (2007). The Politics of Urban Regeneration: The Case of the Fener and Balat Initiative. New Perspectives on Turkey, 37, 59–86.

Bhaskaran, R. (2018). Urban Void—A “Bypassed” Urban Resource (SSRN Scholarly Paper No. 3208217). https://doi.org/10.2139/ssrn.3208217

Bowman, A. O. (2004). Terra incognita: Vacant land and urban strategies. Georgetown University Press.

Branas, C. C., South, E., Kondo, M. C., Hohl, B. C., Bourgois, P., Wiebe, D. J., & MacDonald, J. M. (2018). Citywide cluster randomised trial to restore blighted vacant land and its effects on violence, crime, and fear. Proceedings of the National Academy of Sciences, 115(12), 2946–2951. https://doi.org/10.1073/pnas.1718503115

Carmona, M. (2010). Contemporary Public Space: Critique and Classification, Part One: Critique. Journal of Urban Design, 15(1), 123–148.

Casas, I. (2020). Networks, Neural. In A. Kobayashi (Ed.), International Encyclopedia of Human Geography (Second Edition) (pp. 381–385). Elsevier.

Chaturvedi, V., & De Vries, W. (2021). Machine Learning Algorithms for Urban Land Use Planning: A Review. Urban Science, 5, 68. https://doi.org/10.3390/urbansci5030068

De Girolamo, F. (2013). Time and regeneration: Temporary reuse in lost spaces. Planum. The Journal of Urbanism, 2(27), 68–101.

Elbeah, B., Elshater, A., & Toama, A. (2022). Tactical Urbanism for Improving Livability in Lost Spaces of Cairo. In F. Rosso, C. Fabiani, H. Altan, & M. Amer (Eds.), Advances in Architecture, Engineering and Technology (pp. 3–13). Springer International Publishing.

Fürnkranz, J. (2010). Decision Tree. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning (pp. 263–267). Springer US.

Gardiner, M. M., Burkman, C. E., & Prajzner, S. P. (2013). The Value of Urban Vacant Land to Support Arthropod Biodiversity and Ecosystem Services. Environmental Entomology, 42(6), 1123–1136. https://doi.org/10.1603/EN12275

Goldstein, J., Jensen, M., & Reiskin, E. (2001). Urban vacant land redevelopment: Challenges and progress (Vol. 37). Citeseer.

Hashem, O. M., Wahba, S. M.-E., & Nasr-Eldin, T. I. (2022). Urban voids: Identifying and optimising urban voids potential as a revitalisation source in enhancing developing countries’ city income. Journal of Engineering and Applied Science, 69(1). Scopus.

Hwang, S. W., & Lee, S. J. (2020). Unused, underused, and misused: An examination of theories on urban void spaces. Urban Research & Practice, 13(5), 540–556.

Jain, A. K., Mao, J., & Mohiuddin, K. M. (1996). Artificial neural networks: A tutorial. Computer, 29(3), 31–44.

Kelleher, C., Golden, H. E., Burkholder, S., & Shuster, W. (2020). Urban vacant lands impart hydrological benefits across city landscapes. Nature Communications, 11(1), Article 1.

Khalid, N. S., Hilal, S., Nasrudin, N., & Marzukhi, M. A. (2018). Lost Space in Urban Core Areas of Kuala Lumpur in Relation to Physical Urban Environment. Planning Malaysia Journal, 16(7).

Kim, G., Miller, P. A., & Nowak, D. J. (2015). Assessing urban vacant land ecosystem services: Urban vacant land as green infrastructure in the City of Roanoke, Virginia. Urban Forestry & Urban Greening, 14(3), 519–526.

Kim, G., Miller, P. A., & Nowak, D. J. (2018). Urban vacant land typology: A tool for managing urban vacant land. Sustainable Cities and Society, 36, 144–156.

Kishali, E., & Rosina, E. (2018). Conservation issues in Fener—Balat region in the context of resilience. TECHNE - Journal of Technology for Architecture and Environment, 108–115.

Lee, R. J., & Newman, G. (2019). A classification scheme for vacant urban lands: Integrating duration, land characteristics, and survival rates. Journal of Land Use Science, 14(4–6), 306–319.

López, J. I. M., Kim, G., Lei, Y., Newman, G., & Suppakittpaisarn, P. (2021). An assessment method and typology for the regeneration of vacant land in Quito, Ecuador. Urban Forestry & Urban Greening, 62, 127130.

Lopez-Pineiro, S. (2020). A Glossary of Urban Voids (Annotated edition). JOVIS.

Mhatre, P. (2007). Vacant and abandoned lands: A theory paper. Plan.

Nassauer, J. I., & Raskin, J. (2014). Urban vacancy and land use legacies: A frontier for urban ecological research, design, and planning. Landscape and Urban Planning, 125, 245–253.

Németh, J., & Langhorst, J. (2014). Rethinking urban transformation: Temporary uses for vacant land. Cities, 40, 143–150.

Northam, R. M. (1971). Vacant Urban Land in the American City. Land Economics, 47(4), 345.

Rupprecht, C. D. D., & Byrne, J. A. (2014). Informal urban greenspace: A typology and trilingual systematic review of its role for urban residents and trends in the literature. Urban Forestry & Urban Greening, 13(4), 597–611.

Sanches, P. M., & Mesquita Pellegrino, P. R. (2016). Greening potential of derelict and vacant lands in urban areas. Urban Forestry & Urban Greening, 19, 128–139.

Sarang, P. (2021). Artificial neural networks with TensorFlow 2: ANN architecture machine learning projects. Apress.

Stoquart, R., & Çağlar, N. (1998). Rehabilitation of Balat and Fener Districts (İstanbul Historical Peninsula). Istanbul: Municipality of Fatih.

Talukdar, S., Singha, P., Mahato, S., Shahfahad, Pal, S., Liou, Y.-A., & Rahman, A. (2020). Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review. Remote Sensing, 12(7), 1135.

Trancik, R. (1986). Finding Lost Space: Theories of Urban Design. Van Nostrand Reinhold.

United Nations, Department of Economic and Social Affairs, & Population Division. (2019). World urbanization prospects: The 2018 revision (Vol. 12).

US EPA, O. (2022, August 17). Brownfields in the Pacific Southwest: Vacant to Vibrant, Land Renewal (Southwest) [Overviews and Factsheets]. https://www.epa.gov/brownfields/brownfields-pacific-southwest-vacant-vibrant-land-renewal

Xu, S., & Ehlers, M. (2022). Automatic detection of urban vacant land: An open-source approach for sustainable cities. Computers, Environment and Urban Systems, 91. Scopus.

Žlender, V., & Gemin, S. (2020). Testing urban dwellers’ sense of place towards leisure and recreational peri-urban green open spaces in two European cities. Cities, 98, 102579.