Vol. 16 No. 2 (2025): (Regular Issue in Progress)
Research Article

Incorporating Population Dynamics in the Context of Earthquake Shelter Location-Allocation Analysis

Marios Batsaris
University of the Aegean, Greece
Data required for location-allocation analysis

Published 2025-03-15

Keywords

  • location-allocation,
  • earthquake shelter location,
  • areal interpolation,
  • population dynamics

How to Cite

Batsaris, Marios. 2025. “Incorporating Population Dynamics in the Context of Earthquake Shelter Location-Allocation Analysis”. European Journal of Geography 16 (2):52-65. https://doi.org/10.48088/ejg.m.bat.16.2.052.065.
Received 2024-11-03
Accepted 2025-03-05
Published 2025-03-15

Abstract

Location-allocation is a widely used approach to optimally select earthquake shelters and efficiently allocate the population in case of an emergency. A significant limitation often ignored by the vast majority of studies is the utilization of static aggregations of residential population, which may lead to sub-optimal location decisions and inefficient allocations. To overcome this limitation, in this article, an attempt to spatially refine population data, as well as, to capture population fluctuations throughout the day, using areal interpolation methods along with open spatial information, is undertaken. Then, its influence on the shelter location selection and population allocation process is examined. The city of Mytilini, Lesvos, Greece is used as the case study to further investigate three location-allocation scenarios using block-level census population, building-level night and day estimations as input. The results indicate that using spatially refined population data provide reduced distances, better shelter selection and capacity optimization, and finally, more efficient allocations. Moreover, using building-level day estimations of the population distribution reveals significant shifts in sheltering demand from residential areas to mixed and commercial zones. The use of detailed population dynamics data can give insights about the adequacy of shelter provision under different scenarios, and therefore, help civil protection authorities to make much more informed decisions.

Highlights:

  • Areal interpolation using ancillary information can be used to capture population dynamics.
  • Building-level population data in the context of location-allocation provide reduced distances, better shelter selection and capacity management, and efficient allocations.
  • Daytime building-level population indicates significant shifts of sheltering demand.

Downloads

Download data is not yet available.

References

  1. Anhorn, J., & Khazai, B. (2015). Open space suitability analysis for emergency shelter after an earthquake. Natural Hazards and Earth System Sciences, 15(4), 789–803. https://doi.org/10.5194/nhess-15-789-2015
  2. Azarmand, Z., & Jami, E. N. (2009). Location Allocation Problem. 2001, 19–36. https://doi.org/10.1007/978-3-7908-2151-2
  3. Bakillah, M., Liang, S., Mobasheri, A., Jokar Arsanjani, J., & Zipf, A. (2014). Fine-resolution population mapping using OpenStreetMap points-of-interest. International Journal of Geographical Information Science, 28(9), 1940–1963. https://doi.org/10.1080/13658816.2014.909045
  4. Bao, W., Gong, A., Zhang, T., Zhao, Y., Li, B., & Chen, S. (2023). Mapping Population Distribution with High Spatiotemporal Resolution in Beijing Using Baidu Heat Map Data. Remote Sensing, 15(2), 1–22. https://doi.org/10.3390/rs15020458
  5. Batista e Silva, F., Freire, S., Schiavina, M., Rosina, K., Marín-Herrera, M. A., Ziemba, L., Craglia, M., Koomen, E., & Lavalle, C. (2020). Uncovering temporal changes in Europe’s population density patterns using a data fusion approach. Nature Communications, 11(1), 1–11. https://doi.org/10.1038/s41467-020-18344-5
  6. Batsaris, M., & Kavroudakis, D. (2023). populR: an R Package for Population Downscaling. The R Journal, 14(December), 223–234. https://doi.org/https://doi.org/10.32614/RJ-2023-007
  7. Batsaris, M., Kavroudakis, D., Soulakellis, N. A., & Kontos, T. (2019). Location-Allocation Modeling for Emergency Evacuation Planning in a Smart City Context. International Journal of Applied Geospatial Research, 10(4), 28–43. https://doi.org/10.4018/ijagr.2019100103
  8. Batsaris, M., Zafeirelli, S., Vaitis, M., & Kavroudakis, D. (2023). PoD: A Web Tool for Population Downscaling Using Areal Interpolation and Volunteered Geographic Information. European Journal of Geography, 14(4), 22–36. https://doi.org/10.48088/EJG.M.BAT.14.4.022.036
  9. Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., & Toivonen, T. (2022). A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data, 9(1), 1–19. https://doi.org/10.1038/s41597-021-01113-4
  10. Bhaduri, B., Bright, E., Coleman, P., & Urban, M. L. (2007). LandScan USA: A high-resolution geospatial and temporal modeling approach for population distribution and dynamics. GeoJournal, 69(1–2), 103–117. https://doi.org/10.1007/s10708-007-9105-9
  11. Bian, R., & Wilmot, C. G. (2015). Spatiotemporal Population Distribution Method for Emergency Evacuation. Transportation Research Record: Journal of the Transportation Research Board, 2532(1), 99–106. https://doi.org/10.3141/2532-12
  12. Chaidas, K., Tataris, G., & Soulakellis, N. (2021). Seismic damage semantics on post-earthquake lod3 building models generated by uas. ISPRS International Journal of Geo-Information, 10(5). https://doi.org/10.3390/ijgi10050345
  13. Chang, K. H., Pan, Y. J., & Chen, H. (2024). Shelter location–allocation problem for disaster evacuation planning: A simulation optimization approach. Computers and Operations Research, 171(September 2023), 106784. https://doi.org/10.1016/j.cor.2024.106784
  14. Chen, Z., Chen, X., Li, Q., & Chen, J. (2013). The temporal hierarchy of shelters: a hierarchical location model for earthquake-shelter planning. International Journal of Geographical Information Science, 27(8), 1612–1630. https://doi.org/10.1080/13658816.2013.763944
  15. Comber, A., & Zeng, W. (2019). Spatial interpolation using areal features: A review of methods and opportunities using new forms of data with coded illustrations. Geography Compass, 13(10), 1–23. https://doi.org/10.1111/gec3.12465
  16. Dehnavi Eelagh, M., & Ali Abbaspour, R. (2024). A location-allocation optimization model for post-earthquake emergency shelters using network-based multi-criteria decision-making. Decision Analytics Journal, 10(February), 100430. https://doi.org/10.1016/j.dajour.2024.100430
  17. Dijkstra, E. W. (1959). A note on two problems in connection with graphs. Numerische Mathematik, 1(1), 269–271. https://doi.org/10.1007/BF01386390
  18. Freire, S., & Aubrecht, C. (2012a). Integrating population dynamics into mapping human exposure to seismic hazard. Natural Hazards and Earth System Science, 12(11), 3533–3543. https://doi.org/10.5194/nhess-12-3533-2012
  19. Freire, S., & Aubrecht, C. (2012b). Integrating population dynamics into mapping human exposure to seismic hazard. Natural Hazards and Earth System Science, 12(11), 3533–3543. https://doi.org/10.5194/nhess-12-3533-2012
  20. Freire, S., Aubrecht, C., & Wegscheider, S. (2011). Spatio-temporal population distribution and evacuation modeling for improving tsunami risk assessment in the Lisbon Metropolitan Area. Geo-Information for Disaster Management (Gi4DM), May, 1–6. http://elib.dlr.de/74594/1/OP32.pdf
  21. Gallego, F. J. (2010). A population density grid of the European Union. Population and Environment, 31(6), 460–473. https://doi.org/10.1007/s11111-010-0108-y
  22. Geng, S., Hou, H., & Zhou, Z. (2021). A hybrid approach of vikor and bi-objective decision model for emergency shelter location–allocation to respond to earthquakes. Mathematics, 9(16). https://doi.org/10.3390/math9161897
  23. Hellenic Statistical Authority. (2009). 2001 Population and Housing Census of Greece.
  24. Hellenic Statistical Authority. (2014). 2011 Population and Housing Census of Greece (Issue April). https://www.statistics.gr/en/2011-census-pop-hous
  25. Hu, F., Xu, W., & Li, X. (2012). A modified particle swarm optimization algorithm for optimal allocation of earthquake emergency shelters. International Journal of Geographical Information Science, 26(9), 1643–1666. https://doi.org/10.1080/13658816.2011.643802
  26. Jiao, H., & Feng, S. (2024). Towards Resilient Cities: Optimizing Shelter Site Selection and Disaster Prevention Life Circle Construction Using GIS and Supply-Demand Considerations. Sustainability, 16(6), 2345. https://doi.org/10.3390/su16062345
  27. Kaveh, A., Javadi, S. M., & Moghanni, R. M. (2020). Emergency management systems after disastrous earthquakes using optimization methods: A comprehensive review. Advances in Engineering Software, 149(March), 102885. https://doi.org/10.1016/j.advengsoft.2020.102885
  28. Kilci, F., Kara, B. Y., & Bozkaya, B. (2015). Locating temporary shelter areas after an earthquake: A case for Turkey. European Journal of Operational Research, 243(1), 323–332. https://doi.org/10.1016/j.ejor.2014.11.035
  29. Lam, N. S. N. (1983). Spatial interpolation methods: A review. The American Cartographer, 10(2), 129–150. https://doi.org/10.1559/152304083783914958
  30. Ma, Y., Xu, W., Qin, L., & Zhao, X. (2019). Site selection models in natural disaster shelters: A review. Sustainability (Switzerland), 11(2), 1–24. https://doi.org/10.3390/su11020399
  31. Mennis, J. (2003). Generating Surface Models of Population Using Dasymetric Mapping*. The Professional Geographer, 55(1), 31–42. https://doi.org/10.1111/0033-0124.10042
  32. Mennis, J. (2016). Dasymetric Spatiotemporal Interpolation. Professional Geographer, 68(1), 92–102. https://doi.org/10.1080/00330124.2015.1033669
  33. Network Analyst Extension, A. (2024a). Algorithms used by the ArcGIS Network Analyst extension. https://desktop.arcgis.com/en/arcmap/10.5/extensions/network-analyst/algorithms-used-by-network-analyst.htm#GUID-D50336EC-7FBA-43FA-AD31-4272AB544393
  34. Network Analyst Extension, A. (2024b). Location-allocation analysis. https://desktop.arcgis.com/en/arcmap/10.5/extensions/network-analyst/location-allocation.htm
  35. Padgham, M., Lovelace, R., Salmon, M., & Rudis, B. (2017). Osmdata. The Journal of Open Source Software, 2(14), 305. https://doi.org/10.21105/joss.00305
  36. Pebesma, E. (2018). Simple features for R: Standardized support for spatial vector data. R Journal, 10(1), 439–446. https://doi.org/10.32614/rj-2018-009
  37. R Core Team. (2015). R: a Language and Environment for Statistical Computing. http://www.r-project.org/
  38. ReVelle, C. S., & Eiselt, H. A. (2005). Location analysis: A synthesis and survey. European Journal of Operational Research, 165(1), 1–19. https://doi.org/10.1016/j.ejor.2003.11.032
  39. Scott, A. J. (1970). Location‐Allocation Systems: A Review. Geographical Analysis, 2(2), 95–119. https://doi.org/10.1111/j.1538-4632.1970.tb00149.x
  40. Sinnott, R. O., & Wang, W. (2017). Estimating micro-populations through social media analytics. Social Network Analysis and Mining, 7(1). https://doi.org/10.1007/s13278-017-0433-6
  41. Tang, K., & Osaragi, T. (2024). Multi-Objective Distributionally Robust Optimization for Earthquake Shelter Planning Under Demand Uncertainties. GeoHazards, 5(4), 1308–1325. https://doi.org/10.3390/geohazards5040062
  42. Tenerelli, P., Gallego, J. F., & Ehrlich, D. (2015). Population density modelling in support of disaster risk assessment. International Journal of Disaster Risk Reduction, 13, 334–341. https://doi.org/10.1016/j.ijdrr.2015.07.015
  43. Wang, W., Wu, S., Wang, S., Zhen, L., & Qu, X. (2021). Emergency facility location problems in logistics: Status and perspectives. Transportation Research Part E: Logistics and Transportation Review, 154(August), 102465. https://doi.org/10.1016/j.tre.2021.102465
  44. Wu, S., Qiu, X., & Wang, L. (2005). Population Estimation Methods in GIS and Remote Sensing: A Review. GIScience & Remote Sensing, 42(1), 80–96. https://doi.org/10.2747/1548-1603.42.1.80
  45. Xu, J., Yin, X., Chen, D., An, J., & Nie, G. (2016). Multi-criteria location model of earthquake evacuation shelters to aid in urban planning. International Journal of Disaster Risk Reduction, 20(October), 51–62. https://doi.org/10.1016/j.ijdrr.2016.10.009
  46. Xu, W., Ma, Y., Zhao, X., Li, Y., Qin, L., & Du, J. (2018). A comparison of scenario-based hybrid bilevel and multi-objective location-allocation models for earthquake emergency shelters: A case study in the central area of Beijing, China. International Journal of Geographical Information Science, 32(2), 236–256. https://doi.org/10.1080/13658816.2017.1395882
  47. Ye, M., Wang, J., Huang, J., Xu, S., & Chen, Z. (2012). Methodology and its application for community-scale evacuation planning against earthquake disaster. Natural Hazards, 61(3), 881–892. https://doi.org/10.1007/s11069-011-9803-y
  48. Younes, A., Ahmad, A., Hanjagi, A. D., & Nair, A. M. (2023). Understanding Dynamics of Land Use & Land Cover Change Using GIS & Change Detection Techniques in Tartous , Syria. European Journal of Geography, 14(3), 20–41. https://doi.org/10.48088/ejg.a.you.14.3.020.041
  49. Zhang, X., Yu, J., Chen, Y., Wen, J., Chen, J., & Yin, Z. (2020). Supply–Demand Analysis of Urban Emergency Shelters Based on Spatiotemporal Population Estimation. International Journal of Disaster Risk Science, 11(4), 519–537. https://doi.org/10.1007/s13753-020-00284-9
  50. Zhao, L., Li, H., Sun, Y., Huang, R., Hu, Q., Wang, J., & Gao, F. (2017). Planning emergency shelters for urban disaster resilience: An integrated location-allocation modeling approach. Sustainability (Switzerland), 9(11), 1–20. https://doi.org/10.3390/su9112098
  51. Zhao, X., Coates, G., & Xu, W. (2017). Solving the earthquake disaster shelter location-allocation problem using optimization heuristics. 14th ISCRAM Conference, May, 50–62.
  52. Zhao, X., Xu, W., Ma, Y., & Hu, F. (2015). Scenario-based multi-objective optimum allocation model for earthquake emergency shelters using a modified particle swarm optimization algorithm: A case study in Chaoyang District, Beijing, China. PLoS ONE, 10(12), 1–16. https://doi.org/10.1371/journal.pone.0144455
  53. Zouros, N., Pavlides, S., Soulakellis, N., Chatzipetros, A., Vasileiadou, K., Valiakos, I., & Mpentana, K. (2011). Using active fault studies for raising public awareness and sensitisation on seismic hazard: A case study from Lesvos petrified forest Geopark, NE Aegean sea, Greece. Geoheritage, 3(4), 317–327. https://doi.org/10.1007/s12371-011-0044-y