Multi-criteria Satisfaction Assessment of the Spatial Distribution of Urban Emergency Shelters Based on High-Precision Population Estimation

Abstract

This article introduces a framework for the multi-criteria satisfaction assessment of the spatial distribution of urban emergency shelters. A GIS-based analytic hierarchy process approach was utilized to conduct the assessment based on selected criteria layers for daytime and nighttime scenarios, respectively. The layers were generated from high-precision land use data based on high-resolution aerial images and census data. Considering the uncertainty in criteria weighting, a spatial sensitivity analysis was undertaken for deriving more accurate results. The feasibility of the framework was tested on a case study in Jing’an District, Shanghai, China. The assessment results show that both at nighttime and during daytime, even if all potentially available shelters are open, the demand in large areas can only be marginally satisfied or not satisfied, especially in the northern, eastern, and central parts of Jing’an District. The quantitative analysis of the satisfaction conditions of the buildings or land parcels and the affected people, especially children and the elderly, shows a low satisfaction level of shelter services in these areas. The satisfaction assessment of emergency shelters can help government decision makers find low satisfaction areas of sheltering services and support further location-allocation optimization of urban emergency shelters.

Keywords

Population estimation / Shanghai / Spatial distribution / Urban emergency shelters

References

[1] Alçada-Almeida L, Tralhão L, Santos L, Coutinho-Rodrigues J. A multiobjective approach to locate emergency shelters and identify evacuation routes in urban areas. Geographical Analysis, 2009, 41(1): 9-29

[2] Pan, A.P. 2010. The applications of maximal covering model in typhoon emergency shelter location problem. 2010 IEEE International Conference on Industrial Engineering and Engineering Management, 7–10 December 2010, 1727–1731. doi:10.1109/IEEM.2010.5674577.

[3] Ara S. Impact of temporal population distribution on earthquake loss estimation: A case study on Sylhet, Bangladesh. International Journal of Disaster Risk Science, 2014, 5(4): 296-312

[4] Bashawri A, Garrity S, Moodley K. An overview of the design of disaster relief shelters. Procedia Economics and Finance, 2014, 18: 924-931

[5] Botev ZI, Grotowski JF, Kroese DP. Kernel density estimation via diffusion. Annals of Statistics, 2010, 38(5): 2916-2957

[6] Chen Y, Paydar Z. Evaluation of potential irrigation expansion using a spatial fuzzy multi-criteria decision framework. Environmental Modelling and Software, 2012, 38: 147-157

[7] Chen, Y., S. Khan, and Z. Paydar. 2007. Irrigation intensification or extensification assessment using spatial modelling in GIS. In MODSIM 2007 International Congress on Modelling and Simulation, December 2007. Modelling and Simulation Society of Australia and New Zealand, ed. L. Oxley and D. Kulasiri, 1321–1327. Christchurch, New Zealand.

[8] Chen Y, Liu R, Barrett D, Gao L, Zhou M, Renzullo L, Emelyanova I. A spatial assessment framework for evaluating flood risk under extreme climates. Science of the Total Environment, 2015, 538: 512-523

[9] Chen Y, Yu J, Khan S. Spatial sensitivity analysis of multi-criteria weights in GIS-based land suitability evaluation. Environmental Modelling & Software, 2010, 25(12): 1582-1591

[10] Chen Y, Yu J, Khan S. The spatial framework for weight sensitivity analysis in AHP-based multi-criteria decision making. Environmental Modelling & Software, 2013, 48: 129-140

[11] Cheng H, Yang XK. Bai Y, Ping WV, Shen LD, Chen X. A Comprehensive Evaluation Model for Earthquake Emergency Shelter. Sustainable transportation systems: Plan, design, build, manage, and maintain, 2012, Reston, VA: American Society of Civil Engineers 412-422

[12] Choi S, Park C, Park JY. A spatio-temporal analysis of population and employment growth for Southern California. Annals of Regional Science, 2014, 52(1): 19-40

[13] Chu J, Su Y. The application of TOPSIS method in selecting fixed seismic shelter for evacuation in cities. Systems Engineering Procedia, 2012, 3: 391-397

[14] Compiling Committee of Jing’an Yearbook Shanghai Jing’an statistical yearbook 2011, 2011, Shanghai: Xuelin Publishing House (in Chinese)

[15] Coutinho-Rodrigues J, Tralhão L, Alçada-Almeida L. Solving a location-routing problem with a multiobjective approach: The design of urban evacuation plans. Journal of Transport Geography, 2012, 22: 206-218

[16] Dai S, He L, Su Y. Problem analysis of planning and construction of emergency disaster mitigation shelter in Shanghai. Shanghai Urban Planning Review, 2013, 4: 40-43 (in Chinese)

[17] Dalal J, Mohapatra PK, Mitra GC. Locating cyclone shelters: A case. Disaster Prevention and Management, 2007, 16(2): 235-244

[18] Dinitz LB, Taketa RA. Applying the land use portfolio model with hazus to analyse risk from natural hazard events. International Journal of Risk Assessment and Management, 2013, 17(1): 69-88

[19] Education Bureau of Jing’an District. 2010. Jing’an Educational Public Service Platform. http://www.shzbjy.cn. Accessed 16 Jun 2014 (in Chinese).

[20] Freire S. Konecny M, Zlatanova S, Bandrova TL. Modeling of spatiotemporal distribution of urban population at high resolution—value for risk assessment and emergency management. Geographic information and cartography for risk and crisis management, 2010, Berlin: Springer 53-67

[21] Freire S, Aubrecht C, Wegscheider S. Advancing tsunami risk assessment by improving spatio-temporal population exposure and evacuation modeling. Natural Hazards, 2013, 68(3): 1311-1324

[22] Hu Y, Lv Z, Wu J, Janowicz K, Zhao X, Yu B. A Multi-stage collaborative 3D GIS to support public participation. International Journal of Digital Earth, 2015, 8(3): 211-233

[23] Jia P, Qiu Y, Gaughan AE. A fine-scale spatial population distribution on the High-resolution Gridded Population Surface and application in Alachua County. Florida. Applied Geography, 2014, 50(2): 99-107

[24] Jun MJ. The effects of Seoul’s greenbelt on the spatial distribution of population and employment, and on the real estate market. Annals of Regional Science, 2012, 49(3): 619-642

[25] Kılcı F, Kara BY, Bozkaya B. Locating temporary shelter areas after an earthquake: A case for Turkey. European Journal of Operational Research, 2015, 243(1): 323-332

[26] Li AC, Nozick L, Xu N, Davidson R. Shelter location and transportation planning under hurricane conditions. Transportation Research Part E: Logistics and Transportation Review, 2012, 48(4): 715-729

[27] Liu Q, Ruan X, Shi P. Selection of emergency shelter sites for seismic disasters in mountainous regions: Lessons from the 2008 Wenchuan Ms 8.0 Earthquake China. Journal of Asian Earth Sciences, 2011, 40(4): 926-934

[28] Lloyd CD, Nejad BF. Surface models and the spatial structure of population variables: Exploring smoothing effects using Northern Ireland grid square data. Computers, Environment and Urban Systems, 2014, 48(8): 64-72

[29] Lwin KK, Murayama Y. A GIS Approach to estimation of building population for micro-spatial analysis. Transactions in GIS, 2009, 13(4): 401-414

[30] Lwin, K.K., and Y. Murayama. 2011. Estimation of building population from LIDAR derived digital volume model. In Spatial analysis and modeling in geographical transformation process. The GeoJournal library, ed. Y. Murayama, and R.B. Thapa, 87–98. Netherlands: Springer.

[31] Masuya A, Dewan A, Corner RJ. Population evacuation: evaluating spatial distribution of flood shelters and vulnerable residential units in Dhaka with geographic information systems. Natural Hazards, 2015, 78(3): 1859-1882

[32] Nappi M, Souza J. Disaster management: hierarchical structuring criteria for selection and location of temporary shelters. Natural Hazards, 2015, 75(3): 2421-2436

[33] Neighbors CJ, Cochran ES, Caras Y, Noriega GR. Sensitivity analysis of FEMA HAZUS earthquake model: Case study from King County. Washington. Natural Hazards Review, 2012, 14(2): 134-146

[34] Ohta K, Kobashi G, Takato S, Kagaya S, Yamada H, Minakami H, Yamamura E. Analysis of the geographical accessibility of neurosurgical emergency hospitals in Sapporo city using GIS and AHP. International Journal of Geographical Information Science, 2007, 21(6): 687-698

[35] Orencio PM, Fujii M. A localized disaster-resilience index to assess coastal communities based on an analytic hierarchy process (AHP). International Journal of Disaster Risk Reduction, 2013, 3: 62-75

[36] Ploeger SK, Atkinson GM, Samson C. Applying the HAZUS-MH software tool to assess seismic risk in downtown Ottawa, Canada. Natural Hazards, 2010, 53(1): 1-20

[37] Qi W, Liu S, Gao X, Zhao M. Modeling the spatial distribution of urban population during the daytime and at night based on land use: A case study in Beijing, China. Journal of Geographical Sciences, 2015, 25(6): 756-768

[38] Quinn P. Road density as a proxy for population density in regional-scale risk modeling. Natural Hazards, 2013, 65(3): 1227-1248

[39] Saaty TL. A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 1977, 15(3): 231-281

[40] Shanghai Bureau of Statistics Bulletin of the Second National Economic Census of Shanghai, 2010, Beijing: China Statistics (in Chinese)

[41] Shanghai Bureau of Statistics Bulletin of the Sixth National Census of Shanghai in 2010, 2011, Beijing: China Statistics (in Chinese)

[42] Shiode N, Morita M, Shiode S, Okunuki K. Urban and rural geographies of aging: A local spatial correlation analysis of aging population measures. Urban Geography, 2014, 35(4): 608-628

[43] Siméoni P, Lebot V. Spatial representation of land use and population density: Integrated layers of data contribute to environmental planning in Vanuatu. Human Ecology, 2012, 40(4): 541-555

[44] Soltani A, Ardalan A, Darvishi Boloorani A, Haghdoost A, Hosseinzadeh-Attar MJ. Site selection criteria for sheltering after earthquakes: A systematic review. PLOS Currents Disasters, 2014

[45] Subramanian D, Salazar J, Duenas-Osorio L, Stein R. Jones CP, Griffis LG. Constructing and validating geographically refined HAZUS-MH4 hurricane wind risk models: A machine learning approach. Advances in hurricane engineering: Learning from our past, 2013, Reston, VA: American Society of Civil Engineers 1056-1066.

[46] Tai C, Lee Y, Lin C. Urban disaster prevention shelter location and evacuation behavior analysis. Journal of Asian Architecture and Building Engineering, 2010, 9(1): 215-220

[47] Tong Z, Zhang J, Liu X. Qian Z, Cao L, Su W, Wang T, Yang H. GIS-based design of urban emergency shelter in Songbei Harbin. Recent advances in computer science and information engineering, 2012, Berlin: Springer 617-622

[48] Wang K, Zhang J, Xu Z, Xu Y. Coordinative and transition-oriented technical regulations for land-use planning: An analysis of the code for the classification of urban land use and planning standards of development land (GB 50137-2011). City Planning Review, 2012, 36(4): 42-48 (in Chinese)

[49] Wei L, Li W, Li K, Liu H, Cheng L. Decision support for urban shelter locations based on covering model. Procedia Engineering, 2012, 43: 59-64

[50] Wood NJ, Schmidtlein MC, Peters J. Changes in population evacuation potential for tsunami hazards in Seward, Alaska, since the 1964 Good Friday earthquake. Natural Hazards, 2014, 70(2): 1031-1053

[51] Xu W, Okada N, Takeuchi Y, Kajitani Y. A diagnosis model for disaster shelter planning from the viewpoint of local people—case study of Nagata Ward in Kobe City, Hyogo Prefecture, Japan. Annuals of Disaster Prevention Research Institute, Kyoto University, 2007, 50: 233-240.

[52] Yang X, Zhou J, Ding J, Zhou Q, Zhang Y. A fuzzy AHP-TFN based evaluation model of flood risk analysis. Journal of Computational Information Systems, 2012, 8(22): 9281-9289.

[53] Ye M, Wang J, Huang J, Xu S, Cheng Z. Methodology and its application for community-scale evacuation planning against earthquake disaster. Natural Hazards, 2012, 61(3): 881-892

[54] Yu B, Liu H, Wu J, Hu Y, Zhang L. Automated derivation of urban building density information using airborne LiDAR data and object-based method. Landscape and Urban Planning, 2010, 98(3–4): 210-219

[55] Zhang D, Wang G. Assessment on agricultural drought risk based on variable fuzzy sets model. Chinese Geographical Science, 2011, 21(2): 167-175

[56] Zhang N, Huang H, Su B, Zhang H. Population evacuation analysis: considering dynamic population vulnerability distribution and disaster information dissemination. Natural Hazards, 2013, 69(3): 1629-1646