Urban planning and GeoAI in smart city policies
Abstract
Starting from the relationship between urban planning and mobility management, TeMA has gradually expanded the view of the covered topics, always following a rigorous scientific in-depth analysis. This section of the Journal, Review Notes, is a continuous update about emerging topics concerning relationships among urban planning, mobility, and environment, thanks to a collection of short scientific papers written by young researchers. The Review Notes are made up of five parts. Each section examines a specific aspect of the broader information storage within the main interests of the TeMA Journal. In particular: the Town Planning International Rules and Legislation. Positive Energy Districts has entered the scientific and policy arena to accelerate urban transitions in Europe, however their implementation remains challenging in planning processes. The PED incorporates socio-economic, technological, environmental, political and institutional challenges that need to be addressed simultaneously as part of a holistic urban strategy. The theme of PEDs finds its first application implications in renewable energy communities on a local scale. This review focuses its attention on Renewable Energy Directive Recast which also provides for financial support for the production and self-consumption of electricity from renewable sources and on the Italian legislation on renewable energy communities governed by the Milleproroghe decree.
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References
Abadía, J. J. P., Walther, C., Osman, A. & Smarsly, K. (2022). A systematic survey of Internet of Things frameworks for smart city applications. Sustainable Cities and Society, 83, 103949. https://doi.org/10.1016/j.scs.2022.103949
Albino, V., Berardi, U. & Dangelico, R. M. (2015). Smart cities: Definitions, dimensions, performance, and initiatives. Journal of urban technology, 22(1), 3-21. https://doi.org/10.1080/10630732.2014.942092
Batty, M. (2013). Big data, smart cities and city planning. Dialogues in human geography, 3(3), 274-279. https://doi.org/10.1177/2043820613513390
Batty, M., Axhausen, K.W., Giannotti, F., Pozdnov, A., Bazzani, A., Wachowiez, M., Ouzounis. and Portugali, Y. (2012), “Smart cities of the future”, The European Physical Journal Special Topics, Vol. 214 No. 1, pp. 476-481. https://doi.org/10.1140/epjst/e2012-01703-3
Caragliu, A., Del Bo, C. & Nijkamp, P. (2013). Smart cities in Europe. In Creating Smart-er Cities (pp. 65-82). Routledge.
Cottrill, C. D. & Derrible, S. (2015). Leveraging big data for the development of transport sustainability indicators. Journal of Urban Technology, 22(1), 45-64. https://doi.org/10.1080/10630732.2014.942094
COM/2018/795 final.Communication from the commission to the european parliament, the european council, the council, the european economic and social committee and the committee of the regions coordinated plan on artificial intelligence. Retrivied from: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52018DC0795
COM/2021/206 final. Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Retrivied from: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206
Deren, L., Wenbo, Y. & Zhenfeng, S. (2021). Smart city based on digital twins. Computational Urban Science, 1, 1-11. https://doi.org/10.1007/s43762-021-00005-y
Gaglione, F. & Etigo, D. A. A. (2022). Accelerate urban sustainability through European action, optimization models and decision support tools for energy planning. TeMA - Journal of Land Use, Mobility and Environment, 15(2), 325-334. https://doi.org/10.6093/1970-9870/9240
Gao, S. (2021). Geospatial artificial intelligence (GeoAI). New York: Oxford University Press.
Gargiulo C. & Papa R. (2021). Chaos and chaos: the city as a complex phenomenon. TeMA - Journal of Land Use, Mobility and Environment, 14 (2), 261-270. https://doi.org/10.6093/1970-9870/8273
Gibson, D. V., Kozmetsky, G. & Smilor, R. W. (Eds.). (1992). The technopolis phenomenon: Smart cities, fast systems, global networks. Rowman & Littlefield. Publishers, Inc: Lanham, MD, USA, 1992.
Giffinger, R., Fertner, C., Kramar, H., Kalasek, R., Pichler-Milanovic, N. & Meijers, E. J. (2007). Smart cities. Ranking of European medium-sized cities. Final Report. https://doi.org/10.34726/3565
Gordon, S. N., Murphy, P. J., Gallo, J. A., Huber, P., Hollander, A., Edwards, A. & Jankowski, P. (2021). People, projects, organizations, and products: Designing a knowledge graph to support multi-stakeholder environmental planning and design. ISPRS International Journal of Geo-Information, 10(12), 823. https://doi.org/10.3390/ijgi10120823
Huang, W., Zhang, Y. & Zeng, W. (2022). Development and application of digital twin technology for integrated regional energy systems in smart cities. Sustainable Computing: Informatics and Systems, 36, 100781. https://doi.org/ 10.1016/j.suscom.2022.100781
Kitchin, R. (2015). Making sense of smart cities: addressing present shortcomings. Cambridge journal of regions, economy and society, 8(1), 131-136. https://doi.org/10.1093/cjres/rsu027
Lazzeretti, L., Innocenti, N., Nannelli, M. & Oliva, S. (2023). The emergence of artificial intelligence in the regional sciences: a literature review. European Planning Studies, 31(7), 1304-1324. https://doi.org/10.1080/09654313.2022.2101880
Letaifa, S. B. (2015). How to strategize smart cities: Revealing the SMART model. Journal of business research, 68 (7), 1414-1419. https://doi.org/10.1016/j.jbusres.2015.01.024
Li, W. & Hsu, C. Y. (2022). GeoAI for large-scale image analysis and machine vision: Recent progress of artificial intelligence in geography. ISPRS International Journal of Geo-Information, 11(7), 385. https://doi.org/10.3390/ijgi11070385
Li, W., Batty, M. & Goodchild, M. F. (2020). Real-time GIS for smart cities. International Journal of Geographical Information Science, 34 (2), 311–324. https://doi.org/10.1080/13658816.2019.1673397
Liu, P. & Biljecki, F. (2022). A review of spatially-explicit GeoAI applications in Urban Geography. International Journal of Applied Earth Observation and Geoinformation, 112, 102936.
Mahmood, H. (2022). Strategic foresight to applications of Geospatial Artificial Intelligence (GeoAI) to achieve disaster-related sustainable development goals. https://hdl.handle.net/20.500.12870/5172
Melkonyan, A., Gruchmann, T., Lohmar, F. & Bleischwitz, R. (2022). Decision support for sustainable urban mobility: A case study of the Rhine-Ruhr area. Sustainable Cities and Society, 80, 103806. https://doi.org/10.1016/j.scs.2022.103806
Moghadam, S. T. & Lombardi, P. (2019). An interactive multi-criteria spatial decision support system for energy retrofitting of building stocks using CommuntiyVIZ to support urban energy planning. Building and Environment, 163, 106233. https://doi.org/10.1016/j.buildenv.2019.106233
Orsetti, E., Tollin, N., Lehmann, M., Valderrama, V. A. & Morató, J. (2022). Building resilient cities: climate change and health interlinkages in the planning of public spaces. International journal of environmental research and public health, 19 (3), 1355. https://doi.org/10.3390/ijerph19031355
Papa, R., Galderisi, A., Majello, M. C. V. & Saretta, E. (2015). Smart and resilient cities. A systemic approach for developing cross-sectoral strategies in the face of climate change. TeMA - Journal of Land Use, Mobility and Environment, 8 (1), 19-49. https://doi.org/10.6092/1970-9870/2883
Papa, R., Gargiulo, C. & Battarra, R. (2016). Città Metropolitane e Smart Governance: Iniziative di successo e nodi critici verso la Smart City (Vol. 1). FedOA-Federico II University Press.
Ramaprasad, A., Sánchez-Ortiz, A. & Syn, T. (2017). A unified definition of a smart city. In Electronic Government: 16th IFIP WG 8.5 International Conference, EGOV 2017, St. Petersburg, Russia, September 4-7, 2017, Proceedings 16 (pp. 13-24). Springer International Publishing.
Raspotnik, A., Grønning, R. & Herrmann, V. (2020). A tale of three cities: the concept of smart sustainable cities for the Arctic. Polar Geography, 43 (1), 64-87. https://doi.org/10.1080/1088937X.2020.1713546
Razavi, S. (2021). Deep learning explained: Fundamentals, explainability, and bridgeability to process-based modelling. Environmental Modelling & Software, 144, 105159. https://doi.org/10.1016/j.envsoft.2021.105159
Thomas, M. R. (2002). A GIS-based decision support system for brownfield redevelopment. Landscape and Urban Planning, 58(1), 7-23. https://doi.org/10.1016/S0169-2046(01)00229-8
Ullah, F., Qayyum, S., Thaheem, M. J., Al-Turjman, F. & Sepasgozar, S. M. (2021). Risk management in sustainable smart cities governance: A TOE framework. Technological Forecasting and Social Change, 167, 120743. https://doi.org/10.1016 /j.techfore.2021.120743
Zhu, H. (2020). Big data and artificial intelligence modeling for drug discovery. Annual review of pharmacology and toxicology, 60, 573-589. https://doi.org/10.1146/annurev-pharmtox-010919-023324
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