The performance of the digital city projects in urban studies of the megalopolises (the case studies of Kharkiv and Dnipro cities)
Abstract
Introduction of the research problem. Urbanization drives Digital City Projects (DCPs) to create smarter urban environments using advanced technologies. DCPs aim to make cities more connected and responsive, adapting to changing needs. The objective of this paper is to evaluate the performance of DCPs in megalopolises, focusing on Kharkiv and Dnipro in Ukraine.
The previous works done. The various literature sources demonstrate the rise of Digital Cities stemming from Smart Cities. Kharkiv and Dnipro in Ukraine exemplify digitalization's role amid Russian aggression.
Exposition of the main research material. The performance of the theoretical urbogeosystemic approach and its UOM in the provision of practical Digital City projects. This subsection delves into the practical application of the urbogeosystemic approach and its Urban Ontological Model (UOM) in DCPs. The UOM guides urban studies by defining components and relationships. Implementing DCPs begins with building simulation models using LiDAR data.
Case Study First - Kharkiv: A feasible perspective of a full-format DCP implementation. This subsection discusses implementing a DCP in Kharkiv, emphasizing data integration from OpenStreetMap (OSM) and LiDAR. The authors propose that a DCP should serve as a comprehensive model of a real city, encompassing all its structural elements and key objects, going beyond the capabilities of a typical GIS project. Possible user’s scenarios include energy consumption analysis, population estimation, and visibility gradients assessment. The subsection highlights the comprehensive DCP approach with LiDAR data processing software (iQ City CCM) and urban geosituational analysis.
Case Study Second - Kharkiv: a perspective of geomarketing within the “Digital Kharkiv” project as a routine GIS one. This subsection delves into the integration of geomarketing into the "Digital Kharkiv" project. Geomarketing plays a pivotal role in mapping socioeconomic elements tied to market interactions. "Digital Kharkiv," primarily sourced from OSM data, is lauded for its versatility in urban studies during peacetime and war. The text urges exploration of geomarketing within "Digital Kharkiv" in the context of post-Russian aggression rehabilitation, particularly in optimizing humanitarian object placements. Changes in geomarketing potential pre- and post-invasion in various city districts have been analyzed, highlighting areas with stagnation and those witnessing growth due to population resettlement.
Case Study Third - Dnipro: implementation of a typical GIS-project for analyzing provision of the city population with public transportation infrastructural networks. This subsection discusses the implementation of the "Digital Dnipro" project as part of the DCP framework. The project focuses on analyzing the provision of public transportation networks in the city of Dnipro. It utilizes data from OSM to create a virtual model of the city, which includes attribute information for urban objects. This subsection also highlights the impact of war on urban planning and the need for sustainable updates to adapt to changing conditions.
Conclusion. This section summarizes the key findings and takeaways from the research on DCPs in Ukrainian cities like Kharkiv and Dnipro. It highlights the importance of an urbogeosystemic approach in implementing DCPs effectively. The study emphasizes the flexibility and efficiency of the relevant GIS tools in urban research and transformation.
Downloads
References
United Nations. (2018). 68% of the World Population Projected to live in Urban Areas by 2050, Says UN. Department of Economic and Social Affairs. Available at: https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html
Battisti A., Baiani S. (2022). Sustainable Development Dimensions and Urban Agglomeration. IntechOpen, London. DOI: https://doi.org/10.5772/intechopen.94779
Dobbs R. Smit S., Remes J., Manyika J, Roxburgh Ch., Restrepo A. (2011). Urban World: Mapping the Economic Power of Cities. Avaliable at: https://www.mckinsey.com/featured-insights/urbanization/urban-world-mapping-the-economic-power-of-cities
Rezende D. A. (2016). Digital City: Chicago and Schaumburg (USA) Information, Sustainability Indicators and Public Services Projects. GeSec: Revista de Gestão e Secretariado. 7 (2), 55-79. DOI: https://doi.org/10.7769/gesec.v7i2.545
Biljecki F., Stoter J., Ledoux H., Zlatanova S., Çöltekin A. (2015). Applications of 3D City Models: State of the Art Review. ISPRS Int. J. Geo-Inf. 4, 2842–2889. DOI: https://doi:10.3390/ijgi4042842
Eremia M., Toma L., Sanduleac M. (2017). The Smart City Concept in the 21st Century. Procedia Engineering. 181, 12-19. DOI: http://dx.doi.org/10.1016/j.proeng.2017.02.357
Rezende D.A., Procopiuck M., Figueiredo F. (2015). Public Policy and a Strategic Digital City Project: A CaseStudy of the Brazilian Municipality of Vinhedo. Journal of Urban Technology. 22, 63–83. DOI: http://dx.doi.org/10.1080/10630732.2014.971536#2015
Bonini V., Galelli P., Minetto A., Delponte I., Morbiducci R. (2019). Effects of the digital transformation on the contemporary city project. Tema. 5 (1), 61-71. DOI: https://doi.org/10.17410/tema.v5i1.215
Hämäläinen M. (2021). Urban development with dynamic digital twins in Helsinki city. IET Smart Cities. 3, 201-210. DOI: https://doi.org/10.1049/smc2.12015
Dembski F., Wössner U., Letzgus M., Ruddat M., Yamu C. (2020). Urban Digital Twins for Smart Cities and Citizens: The Case Study of Herrenberg, Germany. Sustainability. 12, 2307. DOI: https://doi.org/10.3390/su12062307
Kostrikov S. Niemets L., Sehida K. [and other]. (2018). Geoinformation approach to the urban geographic system research (case studies of Kharkiv region). Visnyk of V.N. Karazin Kharkiv National University. Series “Geology. Geography. Ecology”. 49, 107-121. DOI:. https://doi.org/10.26565/2410-7360-2018-49-09
Kostrikov S., Pudlo R., Bubnov D., Vasiliev V. (2020). ELiT, multifunctional web-software for feature extraction from 3D LiDAR point clouds. ISPRS International Journal of Geo-Information. 9(11), 650-885. DOI: http://dx.doi.org/10.3390/ijgi9110650
Kostrikov S., Seryogin D. (2022). Urbogeosystemic Approach to Agglomeration Study within the Urban Remote Sensing Frameworks. Urban Agglomeration. Edited by A. Battisti and S. Baiani: Intech Open, London, Milan, Zagreb. 251-273. DOI: http://dx.doi.org/10.5772/intechopen.102482
GEO Group on Earth Observation. (2019). Earth Observations for the Benefits of Humankind. GEOSS Portal. Avaliable at: http://www.earthobservations.org/index.php
Weng Q. (2016). Remote Sensing for Sustainability. Boca Raton, FL: CRC Press, Taylor & Francis. 366.
Group on Earth Observations. (2016). The GEO 2016–2025 Strategic Plan: Implementing GEOSS. GEOSS Portal. Avaliable at: https://www.earthobservations.org/geoss_wp.php
Komminos N. (2002). Intelligent Cities: Innovation, Knowledge Systems and Digital Spaces Spon Press, London, New York. 320.
Caragliu A., Del Bo C., Nijkamp P. (2011). Smart Cities in Europe. Journal of Urban Technology. 18, 65-82. DOI: http://dx.doi.org/10.1080/10630732.2011.601117
Filatov, V.M., Kostrikov, S.V. (2019). Towards the consideration of some aspects of the Smart City concept in the Human Geography perspective. REGION 2019: Human–Geographical aspects. Proceedings of the International Conference for young scientists and post–graduate students. Kharkiv, 108–111.
Orlowski A., Romanowska P. (2019). Smart Cities Concept: Smart Mobility Indicator. Cybernetics and Systems. 50, 118-131, DOI: https://doi10.1080/01969722.2019.1565120
David M., Kosh F. (2019). “Smart Is Not Smart Enough!” Anticipating Critical Raw Material Use in Smart City Concepts: The Example of Smart Grids. Sustainability. 11, 4422. DOI: https://doi.org/10.3390/su11164422
Bastidas V., Reychav I., Ofir A., Bezbradica M., Helfert M. (2022). Concepts for Modeling Smart Cities. Business Information Systems and Engineering. 64, 359-373. DOI: https://doi.org/10.1007/s12599-021-00724-w
Nam T., Pardo T.A. (2011). Smart City as Urban Innovation: Focusing on management, Policy, and Context. ICEGOV Conference, Tallinn, Estonia, 26-28 September 2011. 185-194.
Giffinger R., Gudrun H. (2010). Smart Cities Ranking: An Effective Instrument for the Positioning of Cities?. ACE: Architecture, City and Environment. 4(12), 7-25. DOI: http://dx.doi.org/10.5821/ace.v4i12.2483
Hall R. E. (2000). The vision of a smart city. Proceedings of the 2nd International Life Extension Technology Workshop, Paris, France, 28 September. Available at: https://www.osti.gov/servlets/purl/773961/
Hartley J. (2005). Innovation in governance and public services: Past and present. Public Money & Management. 25(1), 27-34. DOI: https://doi.org/10.1111/j.1467-9302.2005.00447.x
Toppeta, D. (2010). The Smart City Vision: How Innovation and ICT Can Build Smart, “Livable”, Sustainable Cities. The Innovation Knowledge Foundation. 5, 1-9.
Washburn D., Sindhu U., Balaouras S., Dines R.A., Hayes N.M., Nelson L.E. (2010). Helping CIOs Understand “Smart City” Initiatives: Defining the Smart City, Its Drivers, and the Role of the CIO. Cambridge, MA: Forrester Research, Inc. Avaliable at: http://public.dhe.ibm.com/partner-world/pub/smb/smarterpla net/forr_help_cios_und_smart_city_initiatives.pdf
Kramers A., Höjer M., Lövehagen N., Wangel J. (2014). Smart sustainable cities – Exploring ICT solutions for reduced energy use in cities. Environmental Modelling & Software. 56, 52-62. DOI: https://doi.org/10.1016/j.envsoft.2013.12.019
Bibri S.E. (2018). The IoT for smart sustainable cities of the future: An analytical framework for sensorbased big data applications for environmental sustainability. Sustainable Cities and Society. 38, 230-253. DOI: https://doi.org/10.1016/j.scs.2017.12.034
Li Y., Phelps N.A. (2017). Knowledge polycentricity and the evolving Yangtze River Delta megalopolis. Regional Studies. 51, 1035-1047. DOI: https://doi.org/10.1080/00343404.2016.1240868
Ma H., Li Y., Huang X. (2021). Proximity and the evolving knowledge polycentricity of megalopolitan science: Evidence from China’s Guangdong-Hong Kong-Macao Greater Bay Area, 1990–2016. Urban Studies. 58, 2405-2423. DOI: https://doi.org/10.1177/0042098020942665
Buck T., While N., While A. (2017). Competitive urbanism and the limits to smart city innovation: The UK Future Cities initiative. Urban Studies. 54, 501-519. DOI: https://doi.org/10.1177/0042098015597162
Parteka, E., Rezende, D. A. (2018). Digital planning of the city of Barcelona and its relations with the strategic digital city. Journal of Technology Management & Innovation. 13, 54-60. DOI: https://doi.org/10.4067/S0718-27242018000400054
Kostrikov, S. V., Sehida K. (2016). GIS-modelling of regional commuting (a case study of Kharkiv region). Actual problems of economics. 12, 399-410. Available at: http://nbuv.gov.ua/UJRN/ape_2016_12_45
Kostrikov, S. (2019). Urban remote sensing with LIDAR for the Smart City concept implementation. Visnyk of V.N. Karazin Kharkiv National University. Series “Geology. Geography. Ecology”. 50, 101-124. DOI: https://doi.org/10.26565/2410-7360-2019-50-08
Serohin D., Kostrikov S. (2023). Towards urbanistic geosituation delineation. Visnyk of V.N. Karazin Kharkiv National University. Series “Geology. Geography. Ecology”. 58, 241-256. DOI: https://doi.org/10.26565/2410-7360-2023-58-19 [in Ukrainian].
Johnson J., Portugali J., Meyer H., Stolk E., Tan E. (2012). Complexity Theories of Cities Have Come of Age. – In: An Overview with Implications to Urban Planning and Design. NY-London: Springer. 1-17. DOI: http://dx.doi.org/10.1007/978-3-642-24544-2_9
Potere D., Schneider A., Shlomol A., Civco D.L. (2009). Mapping urban areas on a global scale: Which of the eight maps now available is more accurate. International Journal of Remote Sensing. 30, 6531-6558. DOI: http://dx.doi.org/10.1080/01431160903121134
Dong P., Chen Q. (2018). LiDAR Remote Sensing and Applications. Boca Raton: CRC Press. 246.
Esch T., Zeidler J., Palacios-Lopez D. [and other]. (2020). Towards a Large-Scale 3D Modeling of the Built Environment – Joint Analysis of TanDEM-X, Sentinel-2 and Open Street Map Data. Remote Sensing. 12, 2391. DOI: https://doi.org/10.3390/rs12152391
Wang M., Li Q., Hu Q., and Zhou M. (2013). Quality analysis of open street map data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Science. XL-2/W1. 155–158. DOI: https://doi.org/10.5194/isprsarchives-XL-2-W1-155-2013
Shi J., Pan Z., Jiang L., Zhai X. (2023). An ontology-based methodology to establish city information model of digital twin city by merging BIM, GIS and IoT. Advanced Engineering Informatics. 57. 102114. DOI: https://doi.org/10.1016/j.aei.2023.102114
Eckle M., de Albuquerque J. P., Herfort B., Wolff R., Zipf A., Leiner R., Jacobs C. (2016). Leveraging OpenStreetMap to support flood risk management in municipalities: a prototype decision support system. In: 13th International Conference on Information Systems for Crisis Response and Management ISCRAM 2016, Rio de Janeiro, Brazil, 22-25 May 2016. Available at: https://wrap.warwick.ac.uk/78697/
Ali M., Macana C.A., Prakash K., Islam R., Colak I., Pota H. (2020). Generating open-source datasets for power distribution network using openstreetmaps. – In: 9th International Conference on Renewable Energy Research and Application (ICRERA). IEEE. 301-308. DOI: https://doi.org/10.1109/ICRERA49962.2020.9242771
Fonte C., Minghini M., Antoniou V., See L., Patriarca J., Brovelli M., Milcinski G. (2016). Automated methodology for converting OSM data into a land use / cover map. In: 6th International Conference on Cartography & GIS, Albena, Bulgaria, 13-17 June 2016. Available at: https://pure.iiasa.ac.at/id/eprint/13152/1/An%20automated%20methodology%20for%20converting%20OSM%20data%20into%20a%20landuse%20cover%20map.pdf
Kostrikov S., Pudlo R., Bubnov D., Vasiliev V., Fedyay Y. (2020). Automated Extraction of Heavyweight and Lightweight Models of Urban Features from LiDAR Point Clouds by Specialized Web-Software. Advances in Science, Technology and Engineering Systems. 5, 72-95. DOI: https://doi.org/10.25046/aj050604
Caoa R., Zhanga Y., Zhao Z. (2017). 3D building roof reconstruction from airborne LiDAR point clouds: a framework based on a spatial database. International Journal of Geographic Information Science. 31, 1359-1380. DOI: http://dx.doi.org/10.1080/13658816.2017.1301456
Fekete A., Cserep M. (2021). Tree segmentation and change detection of large urban areas based on airborne LiDAR. Computers and Geoscience. 156. 104900. DOI: https://doi.org/10.1016/j.cageo.2021.104900
Chen Y., Tao R. (2022). A new urban change detection method based on the local G and local spatial heteroscedasticity. Transactions in GIS. 26, 3315-3329. DOI: http://dx.doi.org/10.1111/tgis.13004
Kostrikov S., Serohin D., Berezhnoy V. (2021). Visibility analysis of the urbanistic environmet as a constituent of the urbogeosystems approach. Human Geography Journal, 30(1), 7-23, DOI: https://doi.org/10.26565/2076-1333-2021- 30-01 [in Ukrainian]
DARPA: Defense Advanced Research Projects Agency. Creating breaking through technologies and capabilities for national security. (2023). Available at: https://www.darpa.mil/
Tsai Y.H. (2005). Quantifying urban form: Compactness versus “sprawl”. Urban Studies. 42, 141-161. DOI: https://doi.org/10.1080/0042098042000309748
Stilla, U., Xu Y. (2023). Change detection of urban objects using 3D point clouds: A review. ISPRS Journal of Photogrammetry and Remote Sensing. 197, 228-255. DOI: https://doi.org/10.1016/j.isprsjprs.2023.01.010
Tian, J., Chaabouni-Chouayakh H., Reinartz P. (2011). 3D building change detection from high resolution spaceborne stereo imagery. 2011th International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping. IEEE. Xiamen, China, 10-12 January 2011. DOI: http://dx.doi.org/10.1109/M2RSM.2011.5697371
Awrangjeb M., Fraser, C., Lu G. (2015). Building change detection from Lidar point cloud data based on connected component analysis. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Science. 2, 393-400. DOI: http://dx.doi.org/10.5194/isprsannals-II-3-W5-393-2015
Anderson V.M. (2006). Business geography and geomarketing - new directions in constructive geography. Scientific Paradigm of Geographic Education in Ukraine in the 21st Century: Collection of Scientific Articles of the 2nd Interuniversity Scientific and Practical Conference, Donetsk, December 22, 2006, 83-94 [in Ukrainian].
Verschuren M. (2006). Geomarketing GIS & Marketing, New Combination of Knowledge / Verschuren M. – Wageningen: Wageningen University and Research Centre, 68.
Resilience Points. Available at: https://nezlamnist.gov.ua/
The Map of Recovery. Available at: https://reukraine.shtab.net//
Official website of the main statistics office in Dnipropetrovsk region. Available at: http://www.dneprstat.gov.ua/statinfo/ds/
Official website of the Dnipro metro. Available at: https://metro.dp.ua/maps/
This work is licensed under a Creative Commons Attribution 4.0 International License.
