Original Article
Spatial optimisation of fire service coverage: a case study of Brisbane, Australia
Kiran KC,
Jonathan Corcoran,
Prem Chhetri,
Corresponding Author
Kiran KC
Queensland Centre for Population Research, School of Earth and Environmental Sciences, The University of Queensland, Brisbane, Queensland, Australia
Corresponding author. Email: [email protected]Search for more papers by this authorJonathan Corcoran
Queensland Centre for Population Research, School of Earth and Environmental Sciences, The University of Queensland, Brisbane, Queensland, Australia
Search for more papers by this authorPrem Chhetri
School of Business IT and Logistics, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, Victoria, Australia
Search for more papers by this authorKiran KC,
Jonathan Corcoran,
Prem Chhetri,
Corresponding Author
Kiran KC
Queensland Centre for Population Research, School of Earth and Environmental Sciences, The University of Queensland, Brisbane, Queensland, Australia
Corresponding author. Email: [email protected]Search for more papers by this authorJonathan Corcoran
Queensland Centre for Population Research, School of Earth and Environmental Sciences, The University of Queensland, Brisbane, Queensland, Australia
Search for more papers by this authorPrem Chhetri
School of Business IT and Logistics, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, Victoria, Australia
Search for more papers by this authorAbstract
In the context of changing demand for fire services, spatial optimisation of fire coverage has attracted little scholarly attention despite its potential to improve emergency response and to inform future service planning for fire stations. Drawing on small area population forecasts, this paper extends the application of the Maximum Coverage Location Model to compute and delineate the spatial coverage of current and proposed new fire stations to align with population growth estimates for Brisbane, Australia. Our results reveal important gaps in fire cover that are likely to emerge as a result of predicted population growth, the spatial patterns of which varies across the Brisbane metropolitan area. We draw on these results to delineate a series of new potential sites for fire stations to ameliorate the reduction in spatial coverage as a consequence of predicted population growth demonstrating the utility of our analytic approach for decision-making and operational planning in the fire services.
References
- ABS, 2011. Australian statistical geography standard (ASGS): Volume 1. Main Structure and Greater Capital City Statistical Areas Australia July. Available at: http://www.ausstats.abs.gov.au/Ausstats/subscriber.nsf/0/D3DC26F35A8AF579CA257801000DCD7D/$File/1270055001_july%202011.pdf. [Accessed 04/06/2017].
- ABS, 2014. ABS regional population growth. Available at:http://www.abs.gov.au/AUSSTATS/[email protected]/Previousproducts/3218.0Main%20Features152013-14?opendocument%26tabname. [Accessed 04/06/2017].
- Aktaş, E., Özaydın, Ö., Bozkaya, B., Ülengin, F. and Önsel, Ş., 2013. Optimizing fire station locations for the Istanbul metropolitan municipality. Interfaces, 43(3), pp. 240–255.
- Alexandris, G., Karadimas, N.V. and Doukas, N., 2011, Coverage models in military operations. In: Proceedings of the 5th WSEAS International Conference on Communications and Information Technology. World Scientific and Engineering Academy and Society (WSEAS). pp. 301–306.
- Badri, M.A., Mortagy, A.K. and Alsayed, C.A., 1998. A multi-objective model for locating fire stations. European Journal of Operational Research, 110(2), pp. 243–260.
- Başar, A., Çatay, B. and Ünlüyurt, T., 2012. A taxonomy for emergency service station location problem. Optimization Letters, 6(6), pp. 1147–1160.
- Beraldi, P. and Ruszczynski, A., 2002. The probabilistic set-covering problem. Operations Research, 50(6), pp. 956–967.
- Brunsdon, C., Corcoran, J. and Higgs, G., 2007. Visualising space and time in crime patterns: a comparison of methods. Computers, Environment and Urban Systems, 31(1), pp. 52–75.
- Catay, B., 2011. Siting new fire stations in Istanbul: a risk-based optimization approach. OR Insight, 24(2), pp. 77–89.
10.1057/ori.2011.3 Google Scholar
- Chevalier, P., Thomas, I., Geraets, D., Goetghebeur, E., Janssens, O., Peeters, D. and Plastria, F., 2012. Locating fire stations: an integrated approach for Belgium. Socio-Economic Planning Sciences, 46(2), pp. 173–182.
10.1016/j.seps.2012.02.003 Google Scholar
- Chhetri, P., Corcoran, J., Stimson, R.J. and Inbakaran, R., 2010. Modelling potential socio-economic determinants of building fires in south east Queensland. Geographical Research, 48(1), pp. 75–85.
- Church, R. and ReVelle, C., 1974. The maximal covering location problem. Papers of the Regional Science Association, 32(1), pp. 101–118.
10.1111/j.1435-5597.1974.tb00902.x Google Scholar
- Corcoran, J., Higgs, G., Brunsdon, C., Ware, A. and Norman, P., 2007. The use of spatial analytical techniques to explore patterns of fire incidence: a South Wales case study. Computers, Environment and Urban Systems, 31(6), pp. 623–647.
- Daskin, M., 1983. A maximum expected covering location model: formulation, properties and heuristic solution. Transportation Science, 17(1), pp. 48–70.
- Daskin, M.S. and Stern, E.H., 1981. A hierarchical objective set covering model for emergency medical service vehicle deployment. Transportation Science, 15(2), pp. 137–152.
- Galvão, R.D., Chiyoshi, F.Y. and Morabito, R., 2005. Towards unified formulations and extensions of two classical probabilistic location models. Computers & Operations Research, 32(1), pp. 15–33.
- Gendreau, M., Laporte, G. and Semet, F., 2001. A dynamic model and parallel tabu search heuristic for real-time ambulance relocation. Parallel Computing, 27(12), pp. 1641–1653.
- Gunawardene, G., 1982. Dynamic versions of set covering type public facility location problems. European Journal of Operational Research, 10(2), pp. 190–195.
- Hogan, K. and ReVelle, C., 1986. Concepts and applications of backup coverage. Management Science, 32(11), pp. 1434–1444.
- Hogg, J.M., 1968. The siting of fire stations. Operational Research Society, 19(3), pp. 275–287.
10.1057/jors.1968.71 Google Scholar
- Iannoni, A.P. and Morabito, R., 2007. A multiple dispatch and partial backup hypercube queuing model to analyze emergency medical systems on highways. Transportation research part E: logistics and transportation review, 43(6), pp. 755–771.
- Iannoni, A.P., Morabito, R. and Saydam, C., 2008. A hypercube queueing model embedded into a genetic algorithm for ambulance deployment on highways. Annals of Operations Research, 157(1), pp. 207–224.
- KC, K. and Corcoran, J., 2017. Modelling residential fire incident response times: a spatial analytic approach. Applied Geography, 84, pp. 64–74. https://doi.org/10.1016/j.apgeog.2017.03.004
- Keane, J.A. and Ward, T.A., 2002. A computational framework for location analysis. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 32(5), pp. 574–581.
10.1109/TSMCA.2002.804819 Google Scholar
- Krockenberger, M., 2015, ‘ Population growth in Australia’, Australia Institute.
- Larson, R.C., 1975. Approximating the performance of urban emergency service systems. Operations Research, 23(5), pp. 845–868.
- Li, X., Zhao, Z., Zhu, X. and Wyatt, T., 2011. Covering models and optimization techniques for emergency response facility location and planning: a review. Mathematical Methods of Operations Research, 74(3), pp. 281–310.
- Liu, N., Huang, B. and Chandramouli, M., 2006. Optimal siting of fire stations using GIS and ANT algorithm. Journal of Computing in Civil Engineering, 20(5), pp. 361–369.
- Marianov, V. and Serra, D., 2002. Location problems in the public sector. In: Z. Drezner and H. Hamacher, eds. Facility Location Application and Theory, Vol. 2002. New York: Springer. pp. 119–143.
10.1007/978-3-642-56082-8_4 Google Scholar
- Marianov, V. and Revelle, C., 1994. The queuing probabilistic location set covering problem and some extensions. Socio-Economic Planning Sciences, 28(3), pp. 167–178.
- McGuirk, P. and Argent, N., 2011. Population growth and change: implications for Australia's cities and regions. Geographical Research, 49(3), pp. 317–335.
- Murray, A.T., 2013. Optimising the spatial location of urban fire stations. Fire Safety Journal, 62(Part A), pp. 64–71.
- Tong, D. and Murray, A.T., 2009. Maximising coverage of spatial demand for service. Papers in Regional Science, 88(1), pp. 85–97.
- Murray, A.T., Tong, D. and Grubesic, T.H., 2012. Spatial optimization: expanding emergency services to address regional growth and development. In: R. Stimson and K. E. Haynes, eds. Studies in Applied Geography and Spatial Analysis: Addressing Real World Issues. Glos: Edward Elgar Publishing Ltd. p. 109.
10.4337/9781781007969.00012 Google Scholar
- Murray, A.T., Tong, D. and Kim, K., 2010. Enhancing classic coverage location models. International Regional Science Review, 33(2), pp. 115–133.
- Owen, S.H. and Daskin, M.S., 1998. Strategic facility location: a review. European Journal of Operational Research, 111(3), pp. 423–447.
- Plane, D.R. and Hendrick, T.E., 1977. Mathematical programming and the location of fire companies for the Denver fire department. Operations Research, 25(4), pp. 563–578.
- Queensland Treasury and Trade, 2014. Annual report. Available at:https://www.treasury.qld.gov.au/publications-resources/annual-reports/2013-14/pdf/docs/complete-treasury-annual-report-2013-14.pdf. [Accessed 23 July 2015].
- Rafi, M.M., Wasiuddin, S. and Siddiqui, S.H., 2012. Assessment of fire hazard in Pakistan. Disaster Prevention and Management, 21(1), pp. 71–84.
- Rajagopalan, H.K., Saydam, C. and Xiao, J., 2008. A multi period set covering location model for dynamic redeployment of ambulances. Computers & Operations Research, 35(3), pp. 814–826.
- ReVelle, C. and Hogan, K., 1989a. The maximum availability location problem. Transportation Science, 23(3), pp. 192–200.
- ReVelle, C. and Hogan, K., 1989b. The maximum reliability location problem and α-reliable p-center problem: derivatives of the probabilistic location set covering problem. Annals of Operations Research, 18(1), pp. 155–173.
10.1007/BF02097801 Google Scholar
- Saydam, C. and Aytuğ, H., 2003. Accurate estimation of expected coverage: revisited. Socio-Economic Planning Sciences, 37(1), pp. 69–80.
10.1016/S0038-0121(02)00004-6 Google Scholar
- Schilling, D., Elzinga, D.J., Cohon, J., Church, R. and ReVelle, C., 1979. The TEAM/FLEET models for simultaneous facility and equipment siting. Transportation Science, 13(2), pp. 163–175.
10.1287/trsc.13.2.163 Google Scholar
- Schilling, D.A., ReVelle, C., Cohon, J. and Elzinga, D.J., 1980. Some models for fire protection locational decisions. European Journal of Operational Research, 5(1), pp. 1–7.
- Sufianto, H. and Green, A.R., 2012. Urban fire situation in Indonesia. Fire Technology, 48(2), pp. 367–387.
- Takeda, R.A., Widmer, J.A. and Morabito, R., 2007. Analysis of ambulance decentralization in an urban emergency medical service using the hypercube queueing model. Computers & Operations Research, 34(3), pp. 727–741.
- Toregas, C., Swain, R., ReVelle, C. and Bergman, L., 1971. The location of emergency service facilities. Operations Research, 19(5), pp. 1363–1373.
- Yin, P. and Mu, L., 2012. Modular capacitated maximal covering location problem for the optimal siting of emergency vehicles. Applied Geography, 34, pp. 247–254. https://doi.org/10.1016/j.apgeog.2011.11.013
- Zhang, L. and Rushton, G., 2008. Optimizing the size and locations of facilities in competitive multi-site service systems. Computers & Operations Research, 35(2), pp. 327–338.
