Connected Digital Twins; Digital Twins; Geospatial; National Digital Twins; Connected digital twin; Data engineering; Digital representations; Geo-spatial; Geo-spatial data; Geographic information science; National digital twin; Physical world; Public values; Two ways; Instrumentation; Environmental Science (miscellaneous); Earth and Planetary Sciences (miscellaneous)
Abstract :
[en] Digital Twins are realistic digital representations of the physical world, frequently characterised by a two way link between digital and physical. Originating in manufacturing, they are now expanding to city and national scales. In this paper we explore connections between Geographic Information Science and National Digital Twins. Six different viewpoints and perspectives are presented on the topic, highlighting the importance of: geospatial data engineering; metadata engineering; standards; challenges facing mapping agencies; governance and public values; and a ”reality check” that explores the gaps between what is required and what can currently be achieved. We present 22 recommendations and summarise the findings by presenting a high level research agenda to enable better understanding and articulation of the link between the two.
Agnusdei, G. P., Elia, V., Gnoni, M. G., 2021. A classification proposal of digital twin applications in the safety domain. Computers & Industrial Engineering, 154, 107137.
Agrawal, A., Thiel, R., Jain, P., Singh, V., Fischer, M., 2023. Digital Twin: Where do humans fit in? Automation in Construction, 148, 104749.
AlAmir, N., 2022. How digital twins will help to unlock united nations sustainable development goals. Abu Dhabi International Petroleum Exhibition and Conference, SPE, D021S065R002.
Bill, R., Blankenbach, J., Breunig, M., Haunert, J.-H., Heipke, C., Herle, S., Maas, H.-G., Mayer, H., Meng, L., Rottensteiner, F. et al., 2022. Geospatial Information Research: State of the Art, Case Studies and Future Perspectives. PFG-Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 90(4), 349-389.
Bolton, A., Butler, L., Dabson, I., Enzer, M., Evans, M., Fenemore, T., Harradence, F., Keaney, E., Kemp, A., Luck, A. et al., 2018. Gemini principles.
Broo, D. G., Bravo-Haro, M., Schooling, J., 2022. Design and implementation of a smart infrastructure digital twin. Automation in construction, 136, 104171.
Callcut, M., Cerceau Agliozzo, J.-P., Varga, L., McMillan, L., 2021. Digital twins in civil infrastructure systems. Sustainability, 13(20), 11549.
CDBB National Digital Twin Programme, n.d. https://www.cdbb.cam.ac.uk/what-we-did/nationaldigital-twin-programme, note = Accessed: 28/01/2024.
Duckham, M., Sun, Q. C., Worboys, M. F., 2023. GIS: a computing perspective. CRC press.
Elsehrawy, R., Kumar, B., Watson, R., 2021. A digital twin uses classification system for urban planning & city infrastructure management. Journal of Information Technology in Construction, 26, 832-862.
Errami, S. A., Hajji, H., El Kadi, K. A., Badir, H., 2023. Spatial big data architecture: From Data Warehouses and Data Lakes to the LakeHouse. Journal of Parallel and Distributed Computing, 176, 70-79.
Goodchild, M., Appelbaum, R., Crampton, J., Herbert, W., Janowicz, K., Kwan, M.-P., Michael, K., Alvarez León, L., Bennett, M., Cole, D. G. et al., 2022. A white paper on locational information and the public interest.
Goodchild, M. F., 2011. Challenges in geographical information science. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 467(2133), 2431-2443.
Goodchild, M. F., Guo, H., Annoni, A., Bian, L., De Bie, K., Campbell, F., Craglia, M., Ehlers, M., Van Genderen, J., Jackson, D. et al., 2012. Next-generation digital earth. Proceedings of the National Academy of Sciences, 109(28), 11088-11094.
Hall, J. W., Tran, M., Hickford, A. J., Nicholls, R. J., 2016. The future of national infrastructure: A system-of-systems approach. Cambridge University Press.
Hassani, H., Huang, X., MacFeely, S., 2022. Enabling Digital Twins to Support the UN SDGs. Big Data and Cognitive Computing, 6(4), 115.
Henriksen, H. J., Schneider, R., Koch, J., Ondracek, M., Troldborg, L., Seidenfaden, I. K., Kragh, S. J., Bogh, E., Stisen, S., 2022. A New Digital Twin for Climate Change Adaptation, Water Management, and Disaster Risk Reduction (HIP Digital Twin). Water, 15(1), 25.
Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., Melo, G. D., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S. et al., 2021. Knowledge graphs. ACM Computing Surveys (Csur), 54(4), 1-37.
Jamshidi, M., 2017. Introduction to system of systems. Systems of Systems Engineering, CRC Press, 1-36.
Lamb, K., 2019. Research Landscape: Scoping Review.
Lamb, K., 2021. Modelling across the built and natural environment interface.
Linden, A., Fenn, J., 2003. Understanding Gartner's hype cycles. Strategic Analysis Report No R-20-1971. Gartner, Inc, 88, 1423.
Longley, P. A., Goodchild, M. F., Maguire, D. J., Rhind, D. W., 2015. Geographic information science and systems. John Wiley & Sons.
Lu, Q., Parlikad, A. K., Woodall, P., Don Ranasinghe, G., Xie, X., Liang, Z., Konstantinou, E., Heaton, J., Schooling, J., 2020. Developing a digital twin at building and city levels: Case study of West Cambridge campus. Journal of Management in Engineering, 36(3), 05020004.
Maimour, M., Ahmed, A., Rondeau, E., 2024. Survey on digital twins for natural environments: A communication network perspective. Internet of Things, 101070.
Mons, B., 2018. Data stewardship for open science: Implementing FAIR principles. CRC Press.
Muller, M., Lange, I., Wang, D., Piorkowski, D., Tsay, J., Liao, Q. V., Dugan, C., Erickson, T., 2019. How data science workers work with data. Conference on Human Factors in Computing Systems-Proceedings, 86-94.
Nochta, T., Parlikad, A., Schooling, J., Badstuber, N., Wahby, N., 2019. The local governance of digital technology-Implications for the city-scale digital twin.
Quantifying the UK Data Skills Gap, 2021. https://www.gov.uk/government/publications/quantifyingthe-uk-data-skills-gap/quantifying-the-uk-dataskills-gap-full-report. Accessed: 2024-01-30.
Reis, J., Housley, M., 2022. Fundamentals of Data Engineering. "O'Reilly Media, Inc.".
Rinaldi, S. M., Peerenboom, J. P., Kelly, T. K., 2001. Identifying, understanding, and analyzing critical infrastructure interdependencies. IEEE control systems magazine, 21(6), 11-25.
Schooling, J., Enzer, M., Broo, D. G., 2021. Flourishing systems: Re-envisioning infrastructure as a platform for human flourishing. Proceedings of the Institution of Civil Engineers-Smart Infrastructure and Construction, 173(1), 166-174.
Shahzad, M., Shafiq, M. T., Douglas, D., Kassem, M., 2022. Digital twins in built environments: an investigation of the characteristics, applications, and challenges. Buildings, 12(2), 120.
Sharma, A., Kosasih, E., Zhang, J., Brintrup, A., Calinescu, A., 2022. Digital twins: State of the art theory and practice, challenges, and open research questions. Journal of Industrial Information Integration, 100383.
Tomko, M., Winter, S., 2019. Beyond digital twins-A commentary. Environment and Planning B: Urban Analytics and City Science, 46(2), 395-399.
Tzachor, A., Sabri, S., Richards, C. E., Rajabifard, A., Acuto, M., 2022. Potential and limitations of digital twins to achieve the sustainable development goals. Nature Sustainability, 5(10), 822-829.
Uggla, M., Olsson, P., Abdi, B., Axelsson, B., Calvert, M., Christensen, U., Gardevärn, D., Hirsch, G., Jeansson, E., Kadric, Z. et al., 2023. Future Swedish 3D city models-Specifications, test data, and evaluation. ISPRS International Journal of Geo-Information, 12(2), 47.
Xu, H., Nyamsuren, E., Scheider, S., Top, E., 2023. A grammar for interpreting geo-analytical questions as concept transformations. International Journal of Geographical Information Science, 37(2), 276-306.
Zabala, A., Masó, J., Bastin, L., Giuliani, G., Pons, X., 2021. Geospatial User Feedback: How to Raise Users' Voices and Collectively Build Knowledge at the Same Time. ISPRS international journal of geo-information, 10(3), 141.
Zrhal, M., Bucher, B., Van Damme, M.-D., Hamdi, F., 2021. Spatial dataset search: Building a dedicated knowledge graph. AGILE: GIScience Series, 2, 43.
