Impact of AI-Based Tools and Urban Big Data Analytics on the Design and Planning of Cities
Open accessAbstract
Wide access to large volumes of urban big data and artificial intelligence (AI)-based tools allow performing new analyses that were previously impossible due to the lack of data or their high aggregation. This paper aims to assess the possibilities of the use of urban big data analytics based on AI-related tools to support the design and planning of cities. To this end, the author introduces a conceptual framework to assess the influence of the emergence of these tools on the design and planning of the cities in the context of urban change. In this paper, the implications of the application of artificial-intelligence-based tools and geo-localised big data, both in solving specific research problems in the field of urban planning and design as well as on planning practice, are discussed. The paper is concluded with both cognitive conclusions and recommendations for planning practice. It is directed towards urban planners interested in the emerging urban big data analytics based on AI-related tools and towards urban theorists working on new methods of describing urban change.
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ANN, Deep Neural Network (DNN), SVM, GA, LR, and DT are among the prominent AI techniques that gained strong traction in recent years, as are ML, DL, and cognitive AI models in relation to the planning and development of smart cities.
“As consistent with the findings for the second and third periods, ANN, Deep Neural Network (DNN), SVM, GA, LR, and DT are among the prominent AI techniques that gained strong traction in recent years (Şerban and Lytras 2020; Shrestha and Mahmood 2019), so are ML, DL, and cognitive AI models in relation to the planning and development of smart cities (Kamrowska-Załuska 2021; Şerban and Lytras 2020”
From Environmentally sustainable smart cities and their converging AI, IoT, and big data technologies and solutions: an integrated approach to an extensive literature review (2023) · cites this paper as Kamrowska-Załuska 2021
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Cites
Tranfield, D., Denyer, D., & Smart, P. (2003) · British Journal of Management · doi:10.1111/1467-8551.00375 · Cited by 15,386 · see citing works
The integrative review: updated methodology
Whittemore, R., & Knafl, K. (2005) · Journal of Advanced Nursing · doi:10.1111/j.1365-2648.2005.03621.x · Cited by 10,241 · see citing works
Citizens as sensors: the world of volunteered geography
Goodchild, M. F. (2007) · GeoJournal · doi:10.1007/s10708-007-9111-y · Cited by 4,561 · see citing works
Gradient boosting machines, a tutorial
Natekin, A., & Knoll, A. (2013) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2013.00021 · Cited by 3,887 · see citing works
The real-time city? Big data and smart urbanism
Kitchin, R. (2014) · GeoJournal · doi:10.1007/s10708-013-9516-8 · Cited by 2,794 · see citing works
Citizen Science: A Developing Tool for Expanding Science Knowledge and Scientific Literacy
Bonney, R. E., Cooper, C. B., Dickinson, J. L., Kelling, S., Phillips, T., Rosenberg, K. V., & Shirk, J. (2009) · BioScience · doi:10.1525/bio.2009.59.11.9 · Cited by 2,542 · see citing works
Systematic Reviews: Synthesis of Best Evidence for Clinical Decisions
Cook, D. J., Mulrow, C. D., & Haynes, R. B. (1997) · Annals of Internal Medicine · doi:10.7326/0003-4819-126-5-199703010-00006 · Cited by 2,184 · see citing works
The social dimension of sustainable development: Defining urban social sustainability
Dempsey, N., Bramley, G., Power, S., & Brown, C. (2009) · Sustainable Development · doi:10.1002/sd.417 · Cited by 1,933 · see citing works
Batty, M. (2013) · doi:10.7551/mitpress/9399.001.0001 · Cited by 1,700 · see citing works
Challenges of Big Data analysis
Fan, J., Han, F., & Liu, H. (2014) · National Science Review · doi:10.1093/nsr/nwt032 · Cited by 1,482 · see citing works
On big data, artificial intelligence and smart cities
Allam, Z., & Dhunny, Z. A. (2019) · Cities · doi:10.1016/j.cities.2019.01.032 · Cited by 1,178 · see citing works
A formal definition of Big Data based on its essential features
Mauro, A. D., Greco, M., & Grimaldi, M. (2016) · Library Review · doi:10.1108/lr-06-2015-0061 · Cited by 957 · see citing works
Measuring human perceptions of a large-scale urban region using machine learning
Zhang, F., Zhou, B., Liu, L., Liu, Y., Fung, H. H., Lin, H., & Ratti, C. (2018) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2018.08.020 · Cited by 810 · see citing works
Applications of artificial intelligence in transport: An overview
Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A. (2019) · Sustainability · doi:10.3390/su11010189 · Cited by 769 · see citing works
Self-Organization and the City
Portugali, J. (2000) · Springer series in synergetics · doi:10.1007/978-3-662-04099-7 · Cited by 659 · see citing works
Xiang, X., Li, Q., Khan, S., et al. (2020) · Environmental Impact Assessment Review · doi:10.1016/j.eiar.2020.106515 · Cited by 535 · see citing works
Soft Spaces, Fuzzy Boundaries, and Metagovernance: The New Spatial Planning in the Thames Gateway
Allmendinger, P., & Haughton, G. (2008) · Environment and Planning A Economy and Space · doi:10.1068/a40208 · Cited by 471 · see citing works
Open data: Empowering the empowered or effective data use for everyone?
Gurstein, M. B. (2011) · First Monday · doi:10.5210/fm.v16i2.3316 · Cited by 419 · see citing works
Shi, Q., & Abdel-Aty, M. A. (2015) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2015.02.022 · Cited by 414 · see citing works
Open data: An International comparison of strategies
Huijboom, N., & Broek, T. A. V. D. (2011) · Data Archiving and Networked Services (DANS) · Cited by 376 · see citing works
Smart cities, big data and urban policy: Towards urban analytics for the long run
Kandt, J., & Batty, M. (2021) · Cities · doi:10.1016/j.cities.2020.102992 · Cited by 332 · see citing works
Earth System Science in the Anthropocene
Ehlers, E., & Krafft, T. (2006) · Springer eBooks · doi:10.1007/b137853 · Cited by 283 · see citing works
Remote sensing in urban planning: Contributions towards ecologically sound policies?
Wellmann, T., Lausch, A., Andersson, E., Knapp, S., Cortinovis, C., Jache, J., Scheuer, S., Kremer, P., Mascarenhas, A., Kraemer, R., et al. (2020) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2020.103921 · Cited by 281 · see citing works
Pirouz, B., Haghshenas, S. S., Haghshenas, S. S., & Piro, P. (2020) · Sustainability · doi:10.3390/su12062427 · Cited by 243 · see citing works
Understanding house price appreciation using multi-source big geo-data and machine learning
Kang, Y., Zhang, F., Peng, W., Gao, S., Rao, J., Duarte, F., & Ratti, C. (2021) · Land Use Policy · doi:10.1016/j.landusepol.2020.104919 · Cited by 205 · see citing works
Big Data and Urban Informatics: Innovations and Challenges to Urban Planning and Knowledge Discovery
Thakuriah, P. V., Tilahun, N. Y., & Zellner, M. (2016) · Springer geography · doi:10.1007/978-3-319-40902-3_2 · Cited by 200 · see citing works
Quercia, D., O’Hare, N., & Cramer, H. S. M. (2014) · ACM Conference on Computer-Supported Cooperative Work & Social Computing (CSCW) · doi:10.1145/2531602.2531613 · Cited by 196 · see citing works
Yiğitcanlar, T., Kankanamge, N., & Vella, K. J. (2020) · Journal of Urban Technology · doi:10.1080/10630732.2020.1753483 · Cited by 189 · see citing works
Agent-Based Models and Individualism: Is the World Agent-Based?
O’Sullivan, D., & Haklay, M. (2000) · Environment and Planning A Economy and Space · doi:10.1068/a32140 · Cited by 185 · see citing works
Operationalizing Jane Jacobs’s Urban Design Theory
Sung, H., Lee, S., & Cheon, S. (2015) · Journal of Planning Education and Research · doi:10.1177/0739456x14568021 · Cited by 181 · see citing works
Grekousis, G., Manetos, P., & Photis, Y. N. (2012) · Cities · doi:10.1016/j.cities.2012.03.006 · Cited by 171 · see citing works
Huang, J., Obracht‐Prondzyńska, H., Kamrowska‐Załuska, D., Sun, Y., & Li, L. (2020) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2020.103977 · Cited by 169 · see citing works
Self-citationConstructing gazetteers from volunteered Big Geo-Data based on Hadoop
Gao, S., Li, L., Li, W., Janowicz, K., & Zhang, Y. (2014) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2014.02.004 · Cited by 156 · see citing works
A computational approach to ‘The Image of the City’
Filomena, G., Verstegen, J. A., & Manley, E. (2019) · Cities · doi:10.1016/j.cities.2019.01.006 · Cited by 151 · see citing works
The impacts of open data initiatives on smart cities: A framework for evaluation and monitoring
Neves, F. T., Neto, M. D. C., & Aparício, M. (2020) · Cities · doi:10.1016/j.cities.2020.102860 · Cited by 141 · see citing works
Artificial Intelligence Solutions for Urban Land Dynamics: A Review
Wu, N., & Silva, E. A. (2010) · Journal of Planning Literature · doi:10.1177/0885412210361571 · Cited by 118 · see citing works
The significance of digital data systems for smart city policy
Kourtit, K., Nijkamp, P. J., & Steenbruggen, J. (2016) · Socio-Economic Planning Sciences · doi:10.1016/j.seps.2016.10.001 · Cited by 117 · see citing works
Hao, J., Zhu, J., & Zhong, R. (2015) · Journal of Urban Management · doi:10.1016/j.jum.2015.11.002 · Cited by 115 · see citing works
Batty, M. (2019) · Environment and Planning B Urban Analytics and City Science · doi:10.1177/2399808319839494 · Cited by 97 · see citing works
Artificial intelligence-aided design: Smart Design for sustainable city development
Quan, S. J., Park, J. L., Economou, A., & Lee, S. (2019) · Environment and Planning B Urban Analytics and City Science · doi:10.1177/2399808319867946 · Cited by 96 · see citing works
Genetic-Algorithm-Optimized Sequential Model for Water Temperature Prediction
Stajkowski, S., Kumar, D., Samui, P., Bonakdari, H., & Gharabaghi, B. (2020) · Sustainability · doi:10.3390/su12135374 · Cited by 81 · see citing works
Monthly Electric Load Forecasting Using Transfer Learning for Smart Cities
Jung, S., Park, S., Jung, S., et al. (2020) · Sustainability · doi:10.3390/su12166364 · Cited by 80 · see citing works
Representation and communication: challenges in interpreting large social media datasets
Rost, M., Barkhuus, L., Cramer, H., & Brown, B. (2013) · Proceedings of the 2013 conference on Computer supported cooperative work · doi:10.1145/2441776.2441817 · Cited by 78 · see citing works
Risk-based asset management of water piping networks using neurofuzzy systems
Christodoulou, S. E., Deligianni, A., Aslani, P., et al. (2009) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2008.12.001 · Cited by 75 · see citing works
Amiri, S., Mottahedi, S., Lee, E., & Hoque, S. (2021) · Computers, Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2021.101647 · Cited by 75 · see citing works
Network Scale Travel Time Prediction using Deep Learning
Hou, Y., & Edara, P. K. (2018) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/0361198118776139 · Cited by 73 · see citing works
Sentiment in New York City: A High Resolution Spatial and Temporal ViewPreprint
Bertrand, K. Z., Bialik, M., Virdee, K., Gros, A., & Bar‐Yam, Y. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1308.5010 · Cited by 71 · see citing works
Liao, J., Tang, L., Shao, G., et al. (2014) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2013.869820 · Cited by 71 · see citing works
Cities and the Digital Revolution
Allam, Z. (2019) · Springer eBooks · doi:10.1007/978-3-030-29800-5 · Cited by 70 · see citing works
Cities And Regions as Evolutionary, Complex Systems
Allen, P. M. (1997) · Cited by 62 · see citing works
Measuring visitor satisfaction toward peri-urban green and open spaces based on social media data
Sun, Y., & Shao, Y. (2020) · Urban forestry & urban greening · doi:10.1016/j.ufug.2020.126709 · Cited by 54 · see citing works
Ibrahim, M., Haworth, J., & Cheng, T. (2019) · UCL Discovery (University College London) · Cited by 52 · see citing works
Designing Urban Transformation
Inam, A. (2013) · Routledge eBooks · doi:10.4324/9780203728284 · Cited by 48 · see citing works
Byon, Y.-J., & Liang, S. H. L. (2013) · Journal of Intelligent Transportation Systems · doi:10.1080/15472450.2013.824762 · Cited by 48 · see citing works
Vogiatzaki, M., Zerefos, S. C., & Tania, M. H. (2020) · Sustainability · doi:10.3390/su12156142 · Cited by 44 · see citing works
Leveraging artificial intelligence to analyze citizens’ opinions on urban green space
Ghahramani, M., Galle, N., Duarte, F., Ratti, C., & Pilla, F. (2021) · City and Environment Interactions · doi:10.1016/j.cacint.2021.100058 · Cited by 44 · see citing works
Using Street-level Images and Deep Learning for Urban La ndscape STUDIES
Li, X., Cai, B., & Ratti, C. (2018) · Landscape Architecture Frontiers · doi:10.15302/j-laf-20180203 · Cited by 42 · see citing works
Assi, K., Shafiullah, M., Nahiduzzaman, K. M., & Mansoor, U. (2019) · Sustainability · doi:10.3390/su11164484 · Cited by 40 · see citing works
NEURAL NETWORK MODELS FOR TRAFFIC CONTROL AND CONGESTION PREDICTION
Gilmore, J. F., & Abe, N. (1995) · IVHS journal · doi:10.1080/10248079508903828 · Cited by 39 · see citing works
AI-Based Campus Energy Use Prediction for Assessing the Effects of Climate Change
Fathi, S., Srinivasan, R. S., Kibert, C. J., Steiner, R., & Demirezen, E. M. (2020) · Sustainability · doi:10.3390/su12083223 · Cited by 39 · see citing works
Yin, X., Li, J., Kadry, S., et al. (2020) · Environmental Impact Assessment Review · doi:10.1016/j.eiar.2020.106493 · Cited by 36 · see citing works
Use of Bayesian inference method to model vehicular air pollution in local urban areas
Orun, A., Elizondo, D. A., Goodyer, E. N., et al. (2018) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2018.05.009 · Cited by 34 · see citing works
Varia, H. R., Gundaliya, P. J., & Dhingra, S. L. (2012) · Research in Transportation Economics · doi:10.1016/j.retrec.2012.05.014 · Cited by 32 · see citing works
Ibrahim, M. R., Haworth, J., & Cheng, T. (2019) · Environment and Planning B Urban Analytics and City Science · doi:10.1177/2399808319846517 · Cited by 32 · see citing works
RAFFIA: Short-term Forest Fire Danger Rating Prediction via Multiclass Logistic Regression
Wang, L., Zhao, Q., Wen, Z., et al. (2018) · Sustainability · doi:10.3390/su10124620 · Cited by 31 · see citing works
Haqbeen, J. A., Sahab, S., Itō, T., et al. (2021) · Sustainability · doi:10.3390/su13105453 · Cited by 31 · see citing works
Rienow, A., Stenger, D., & Menz, G. (2014) · Erdkunde · doi:10.3112/erdkunde.2014.02.02 · Cited by 30 · see citing works
Cheng, X., Wang, J., Li, H., Zhang, Y., Wu, L., & Liu, Y. (2020) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2020.1805116 · Cited by 30 · see citing works
Geosimulation Model Using Geographic Automata for Simulating Land-Use Patterns in Urban Partitions
Shen, Z., Kawakami, M., & Kawamura, I. (2009) · Environment and Planning B Planning and Design · doi:10.1068/b34148t · Cited by 29 · see citing works
Machine learning for multi-jurisdictional optimal traffic corridor control
Jacob, C., & Abdulhai, B. (2009) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2009.11.001 · Cited by 27 · see citing works
A Predictive Vehicle Ride Sharing Recommendation System for Smart Cities Commuting
Anagnostopoulos, T. (2021) · Smart Cities · doi:10.3390/smartcities4010010 · Cited by 26 · see citing works
Learning to walk: Modeling transportation mode choice distribution through neural networks
Aschwanden, G., Wijnands, J. S., Thompson, J., Nice, K. A., Zhao, H., & Stevenson, M. (2019) · Environment and Planning B Urban Analytics and City Science · doi:10.1177/2399808319862571 · Cited by 24 · see citing works
Chen, W., Zhao, L., Kang, Q., & Fan, D. (2020) · Cities · doi:10.1016/j.cities.2020.102703 · Cited by 24 · see citing works
Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility
Rosa, L., Silva, F., & Analide, C. (2021) · Smart Cities · doi:10.3390/smartcities4020046 · Cited by 24 · see citing works
Fuzzy Rule-Based System Approach to Combining Traffic Count Forecasts
Stathopoulos, A., Karlaftis, M. G., & Dimitriou, L. (2010) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2183-13 · Cited by 22 · see citing works
Abarca-Álvarez, F. J., Campos-Sánchez, F. S., & Osuna-Pérez, F. (2019) · Sustainability · doi:10.3390/su11236622 · Cited by 18 · see citing works
Management of Demand and Routing in Autonomous Personal Transportation
Bazzan, A. L. C., do Amarante, M. D. B., & Costa, F. (2011) · Journal of Intelligent Transportation Systems · doi:10.1080/15472450.2012.639635 · Cited by 17 · see citing works
Cities and Footlooseness: In Search of Place-Bound Companies and Effective Location Policies
van GEENHUIZEN, M., & Nijkamp, P. J. (2007) · Environment and Planning C Government and Policy · doi:10.1068/c0647 · Cited by 17 · see citing works
The Use of Big Data in Regenerative Planning
Kamrowska‐Załuska, D., & Obracht‐Prondzyńska, H. (2018) · Sustainability · doi:10.3390/su10103668 · Cited by 15 · see citing works
Self-citationNew Entrepreneurship in Urban Diasporas in our Modern World
Kourtit, K., Nijkamp, P. J., & Leeuwen, E. S. V. (2013) · Journal of Urban Management · doi:10.1016/s2226-5856(18)30063-3 · Cited by 14 · see citing works
Beura, S. K., & Bhuyan, P. K. (2018) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/0361198118796350 · Cited by 14 · see citing works
Employing waterborne autonomous vehicles for museum visits: a case study in Amsterdam
Rong, H., Tu, W., Duarte, F., & Ratti, C. (2020) · European Transport Research Review · doi:10.1186/s12544-020-00459-x · Cited by 13 · see citing works
Kedia, A., Sowjanya, D., Salini, P. S., et al. (2017) · Transportation in Developing Economies · doi:10.1007/s40890-017-0038-9 · Cited by 12 · see citing works
Brindley, T., Rydin, Y., & Stoker, G. (2005) · Routledge eBooks · doi:10.4324/9780203989661 · Cited by 12 · see citing works
Wang, W., Wang, W., & Namgung, M. (2009) · Environment and Planning B Planning and Design · doi:10.1068/b35072 · Cited by 11 · see citing works
Jena, S., Chakraborty, A., & Bhuyan, P. K. (2018) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/0361198118782761 · Cited by 11 · see citing works
SHENG, Q., Chen, Z., Karimi, K., et al. (2018) · Landscape Architecture Frontiers · doi:10.15302/j-laf-20180211 · Cited by 11 · see citing works
Real-Time Pedestrian Flow Analysis Using Networked Sensors for a Smart Subway System
Hwang, S., Lee, Z., & Kim, J. (2019) · Sustainability · doi:10.3390/su11236560 · Cited by 11 · see citing works
Conceptualization and Development of a DFuzzy Model for Low-Carbon Ecocities
Hsueh, S., Sun, Y., & Yan, M.-R. (2019) · Sustainability · doi:10.3390/su11205833 · Cited by 10 · see citing works
Digital Excavation of Mediatized Urban Heritage: Automated Recognition of Buildings in Image Sources
Mager, T., & Hein, C. (2020) · Urban Planning · doi:10.17645/up.v5i2.3096 · Cited by 10 · see citing works
Migrant Entrepreneurs as Urban ‘Health Angels’ — Contrasts in Growth Strategies
Kourtit, K., Nijkamp, P. J., & Arribas‐Bel, D. (2014) · International Planning Studies · doi:10.1080/13563475.2014.942496 · Cited by 8 · see citing works
Yan, Z., Li, B., Li, Q., & Yang, M. F. (2019) · Journal of Intelligent Transportation Systems · doi:10.1080/15472450.2019.1652826 · Cited by 7 · see citing works
BCNDataset: Description and Analysis of an Annotated Night Urban Leisure Sound Dataset
Vidaña-Vila, E., Duboc, L., Alsina‐Pagès, R. M., Polls, F., & Vargas, H. (2020) · Sustainability · doi:10.3390/su12198140 · Cited by 7 · see citing works
ANN and ANFIS Modeling of Failure Trend Analysis in Urban Water Distribution Network
Markose, L. P., & Deka, P. C. (2016) · Water science and technology library · doi:10.1007/978-3-319-40195-9_20 · Cited by 6 · see citing works
A Methodology for Urban Sustainability Indicator Design
Baeza, R. A. (2018) · TeMA · doi:10.6092/1970-9870/5795 · Cited by 6 · see citing works
Discovering dynamic patterns of urban space via semi-nonnegative matrix factorization
Liu, Z., Jun, C., Yang, J., & Wang, Q. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8258332 · Cited by 4 · see citing works
Domain ontology for urban land management
Arndt, L. T., Philips, J., Cordeiro, A. D., et al. (2014) · Proceedings of the Institution of Civil Engineers - Urban Design and Planning · doi:10.1680/udap.13.00024 · Cited by 3 · see citing works
Urban Water Consumption Estimation Using Artificial Intelligence Techniques
Surendra, H. J., & Deka, P. C. (2016) · Water science and technology library · doi:10.1007/978-3-319-40195-9_22 · Cited by 2 · see citing works
Cities as they could be: Artificial Life and Urban SystemsPreprint
Raimbault, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.12926 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 9
- Batty, M. Cities and Complexity: Understanding Cities with Cellular Automata, Agent-Based Models, and Fractals; MIT Press: Cambridge, MA, USA, 2005.
- Hopkins, L.D. Urban Development: The Logic of Making Plans; Island Press: Washington, DC, USA, 2001; Volume 166.
- Cranshaw, J.; Schwartz, R.; Hong, J.I.; Sadeh, N. The Livehoods Project: Understanding Collective Activity Patterns of a City from Social Media. In Proceedings of the 6th International AAAI Conference on Weblogs and Social Media, Dublin, Ireland, 4-7 June 2012; pp. 58-65.
- TEMA J. Land Use Mobil. Environ. 2013, 6, 189-200.
- Sarma, A.K., Singh, V.P., Kartha, S.A., Bhattacharjya, R.K., Eds.; Springer International Publishing: New York, NY, USA, 2016; Volume 73, pp. 277-285.
- Intrator, K.; Shivdikar, K. Missing Middle Scenarios: Uncovering Nuanced Conditions in Latin America's Housing Crisis. Cityscape 2017, 19, 31-43.
- Abarca Alvarez, F.J.; Osuna Perez, F. Semantic mapping though neural networks: The self-organizing maps (SOM) as representa- tion of patterns and fields. Rev. Expr. Graf. Arquit. 2013, 22, 154-163.
- Sarma, A.K., Singh, V.P., Kartha, S.A., Bhattacharjya, R.K., Eds.; Springer International Publishing: New York, NY, USA, 2016; Volume 73, pp. 255-264.
- Payal Jain, K. A Review Study on Urban Planning & Artificial Intelligence International. J. Soft Comput. Eng. 2011, 1, 5.
Cited by
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Bibri, S. E., Krogstie, J., Kaboli, A., & Alahi, A. (2024) · Environmental Science and Ecotechnology · doi:10.1016/j.ese.2023.100330 · Cited by 536 · see citing works
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- Authors
- Kamrowska-Załuska, D.
- Affiliations
- Faculty of Architecture, Gdansk University of Technology, 80-233 Gdańsk, Poland
- Venue
- Land
- Publisher
- MDPI AG
- Published
- 2021-11-08
- Volume
- 10
- Issue
- 11
- Pages
- 1209
- ISSN
- 2073-445X (ISSN-L)
- OpenAlex
- W3213917030
- Cited by
- 95
- References
- 99
- Citations by year
- 2026: 19 · 2025: 24 · 2024: 31 · 2023: 17 · 2022: 4
- FWCI
- 5.84
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Land (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Media Technology › Smart Cities and Technologies
- Physical Sciences › Engineering › Building and Construction › Urban Design and Spatial Analysis
- Social Sciences › Urban Studies › Urban Planning and Governance
- Keywords
- AI tools, urban planning, urban design, urban transformation, planning practice, conceptual framework, urban theory
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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