Geospatial Analytics Platforms and Hazard Assessment in Urban Drainage Systems

Natsuki Takahashi

Authors

Natsuki Takahashi Department of Computer Science, School of Computing, Institute of Science Tokyo, Tokyo, Japan Author

Keywords:

Urban Drainage, Geospatial Analytics, Hazard Assessment, Survey Analysis, Software Engineering

Abstract

Urban drainage systems are increasingly stressed by rapid urbanization and extreme weather events induced by global climate change. To manage the resulting pluvial flooding hazards, municipal authorities and engineering practitioners rely on geospatial analytics platforms that integrate geographic information systems with dynamic hydraulic models. However, while the technical capabilities of these systems have advanced significantly, the human factors governing their operational utility and decision-making efficacy remain understudied. This paper presents a comprehensive survey-based assessment of geospatial analytics platforms in urban drainage hazard assessment, gathering empirical evidence from a diverse cohort of municipal planners, design engineers, and academic researchers. The analysis evaluates user perceptions regarding model accuracy, interface usability, and decision-making confidence across various platform architectures. The findings indicate that while platforms excel at high-resolution static hazard mapping, significant usability bottlenecks persist regarding system latency, interface clutter, and dynamic simulation visualization. These challenges increase cognitive load and can delay critical emergency and design interventions. Based on the survey insights, we outline policy and technical recommendations aimed at standardizing spatial data frameworks, implementing role-based adaptive user interfaces, and establishing robust practitioner training protocols to maximize the disaster risk reduction potential of these analytical tools.

References

1. Konvitz, J.W. Cities & the Sea: Port City Planning in Early Modern Europe; JHU Press: Baltimore, MD, USA, 2020.

2. Goetschalckx, M.; Vidal, C.J.; Dogan, K. Modeling and design of global logistics systems: A review of integrated strategic and tactical models and design algorithms. Eur. J. Oper. Res. 2002, 143, 1–18.

3. Song, Z.; Chadha, S.; Byalik, A.; Tilevich, E. Programming support for sharing resources across heterogeneous mobile devices. In Proceedings of the 5th International Conference on Mobile Software Engineering and Systems (MOBILESoft ’18), Gothenburg, Sweden, 27–28 May 2018; pp. 105–116.

4. Nunkesser, R. The Past, Present, and Future of Mobile Software Engineering. In Software Technologies; Fill, H.G., Domínguez Mayo, F.J., van Sinderen, M., Maciaszek, L.A., Eds.; Springer: Cham, Switzerland, 2026.

5. Cidell, J. Distribution centers among the rooftops: The global logistics network meets the suburban spatial imaginary. Int. J. Urban Reg. Res. 2011, 35, 832–851.

6. Avvaru, V.; Bruno, G.; Chiabert, P.; Traini, E. Integration of PLM, MES and ERP Systems to Optimize the Engineering, Production and Business. In Proceedings of the 17th International Conference on Product Lifecycle Management-PLM-Annual, Rapperswil, Switzerland, 5–8 July 2020; pp. 70–82.

7. Losacco, C.; Pugliese, G.; Forte, L.; Tufarelli, V.; Maggiolino, A.; De Palo, P. Digital Transition as a Driver for Sustainable Tailor-Made Farm Management: An Up-to-Date Overview on Precision Livestock Farming. Agriculture 2025, 15, 1383.

8. Su, Z.; Liu, Y.; Gao, Y.; Park, K.S.; Su, M. Critical success factors for green port transformation using digital technology. J. Mar. Sci. Eng. 2024, 12, 2128.

9. Corbalan, L.; Fernandez, J.; Cuitiño, A.; Delia, L.; Cáseres, G.; Thomas, P.; Pesado, P. Development frameworks for mobile devices: A comparative study about energy consumption. In Proceedings of the 5th International Conference on Mobile Software Engineering and Systems (MOBILESoft ’18), Gothenburg, Sweden, 27–28 May 2018; pp. 191–201.

10. Wang, T.; Jin, H.; Sieverding, H.L. Factors affecting farmer perceived challenges towards precision agriculture. Precis. Agric. 2023, 24, 2456–2478.

11. Heavin, C., & Power, D. J. (2018). Challenges for digital transformation—Towards a conceptual decision support guide for managers. Journal of Decision System, 27(Suppl. S1), 38–45.

12. Bailey, J.; Budgen, D.; Turner, M.; Kitchenham, B.; Brereton, P.; Linkman, S. Evidence relating to object-oriented software design: A survey. In Proceedings of the First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007), Madrid, Spain, 20–21 September 2007; pp. 482–484.

13. Bachmann, N., & Jodlbauer, H. (2023). Iterative business model innovation: A conceptual process model and tools for incumbents. Journal of Business Research, 168, 114177.

14. Basu, S.; Fernald, J. Information and Communications Technology as a General-Purpose Technology: Evidence from US Industry Data. Ger. Econ. Rev. 2007, 8, 146–173.

15. Jin, M.; Chen, Y. Has green innovation been improved by intelligent manufacturing?—Evidence from listed Chinese manufacturing enterprises. Technol. Forecast. Soc. Change 2024, 205, 123487.

16. Hein, C.; van Mil, Y.; Momirski, L.A. (Eds.) Port City Atlas: Mapping European Port City Territories: From Understanding to Design; TU Delft OPEN Publishing: Delft, The Netherlands, 2023.

17. Yang, Y.C.; Hsieh, Y.H. The critical success factors of smart port digitalization development in the post-COVID-19 era. Case Stud. Transp. Policy 2024, 17, 101231.

18. Żywiołek, J.; Szymonik, A.; Smal, T. Navigating Supply Chain Turbulence: Leveraging Modern Technologies for Safety, Resilience, and Risk Reduction; CRC Press: Boca Raton, FL, USA, 2025.

19. Inkinen, T.; Helminen, R.; Saarikoski, J. Port digitalization with open data: Challenges, opportunities, and integrations. J. Open Innov. Technol. Mark. Complex. 2019, 5, 30.