Spatial Evaluation of Access to Urban Free Spaces during the Post-Earthquake Periods Using Hub and Genetics Optimization Algorithms (Case Study: City of Gorgan)

Document Type : Research article

Authors

1 Associate Professor of Humanities & Social Science, Department of Geography, Yazd University, Iran

2 PhD Candidate in Geography and Urban Planning, Yazd University, Iran

Abstract

Introduction
Awareness of citizens' access to urban free spaces for temporary residence at post-earthquake period is one of the things that can be critical for crisis management. Identifying these points at pre-earthquake period can lead to decent and reasonable decision making by urban managers at an unstable and uncertain time of post-earthquake. In the present study, the process of access of Gorgan citizens to urban free spaces has been evaluated in the post-earthquake period. For this purpose, the genetic algorithm is used to solve a geospatial problem whose input data is derived from GIS. It should be noted, an important part of the comprehensive national planning process in developed countries is dedicated to the issue of preparedness and security against unexpected accidents. In Iran over the past century, 13 earthquakes of greater than 7 magnitudes have occurred. This is despite the fact that, in most cases the villages of Iran against earthquake of 5 magnitudes and the cities of Iran are also vulnerable to earthquake of 6 magnitudes. Given the fact that during the earthquake, the city structure makes significant changes, the expected functions of this structure also change. In this situation, the optimal locating of citizens in the appropriate places due to the interference of multiple spatial and temporal criteria is very complicated. On the other hand, for various economic, technical and administrative reasons, it is not possible to provide housing to meet the basic needs of the injured during the early hours and days after the earthquake crisis. Therefore, access to free urban spaces can greatly facilitate the process of temporary accommodation of injured people. These spaces can be used to distinguish areas with potential hazard from other areas and cause a decentralization of damages. In fact, due to some restriction including time, place, and resources, usually, the relief process is less accelerated in the early hours and days of the post-earthquake. If at a pre-earthquake time, there is no planning for emergency accommodation and access to free spaces, temporary accommodation will be done in a cross-sectional and experimental manner, so doing this scenario may have very bad results. If the existing buildings do not have the ability to protect residents of the city, the best option is to use the temporary placement process in free spaces. In the 2011, earthquake and tsunami in Japan, about 250,000 of injured people are located in emergency and temporary shelters. These free spaces, in addition to capacity criteria, should also provide safety and access to needed facilities. If the process of choosing these safe places occurs at a time before the earthquake, it can have a significant impact on improving the emergency evacuation process of the population. Simultaneous use of GIS and meta-algorithms can add the capabilities of both to solve the problem. In this process, the initial data are generated in the GIS environment and then in order to provide optimal solutions it is exported to meta-heuristics algorithm.
Methodology
This paper is a practical study with descriptive and analytical method. The data of this research have been gathered by documental and surveying. Regarding the nature of the research, at the beginning, we performed the process of collecting and preparing the required data. The allocation of free spaces to demographic blocks has been accomplished in two distinct but integrated formats. One of these steps is performing the allocation process using ArcGIS 10.3 software. In another part, the allocation process with hub problem subject is based on the genetic algorithm and in the MATLAB 2016 software environment. The objective function of both steps is to minimize the cost of transmission, and such things include capacity of free spaces, accessibility, and maximum coverage is the constraints of the problem. The free spaces that used in this research are considered as two separate scenarios. In Scenario 1 (non-flexible scenario), only the public free spaces of the city are structurally able to accommodate the temporary population. Accordingly, 48 units of lands with an area of about 25 hectares were selected as temporary accommodation options. In the second scenario (flexible scenario), the process of the land selection was easier and the non-public parts were also considered as components with a population potential. In this scenario, 153 units of land with an area of 39 hectares were considered as selective options.
Result and Discussion
In solving the allocation problem using the genetic algorithm, by applying changes to the problem-solving parameters, we considered six different alternatives with the lowest cost of population transfer from demographic block centers to free spaces. 
Conclusion
Most likely, with the allocation of blocks with more populations to closer free spaces, the process of minimizing the cost of transferring from the centers of the demographic blocks to the free spaces is accomplished. But doing this process may have other social implications. According to the specific cultural conditions governing the community and the mental fragility of individuals at this particular time, more citizens tend to reside as close to their current location as possible.
The results of the research include the amount of the population with accesses and non-accesses to free spaces. The distance of the citizens' transferring to free spaces is calculated according to the spatial and temporal distance. Accordingly, the average distance from demographic blocks to free spaces for non-flexible and flexible scenarios, including road obstruction, is 487 and 514 meters, respectively. This issue in the flexible scenario is, respectively, 731 and 642 meters. Similarly, the transfer time for the first mode is 64 and 73 seconds, and for the second mode it will be 158 and 126 seconds. In the first case, 24 and 35 percent of the city's population is covered by free spaces and this amount for the second mode is 32 and 47 percent, respectively. The results of hub problem solving using the genetic algorithm are similar to the flexible scenario without considering the blocking roads.   

Keywords

References

  1. Adeli, M., Ziyari, K., and Givehchi, S., 2018, Evaluation of GORGAN Citizens Vulnerability Against Earthquake Disaster Using GIS and Meta-Heuristic Algorithms, Human Geography Research. DOI: 10.22059/Jhgr.2018.234542.1007469. (In Persian)
  2. Adewole, Ph., Akinwale, A., and Otunbanowo, K., 2011, A Genetic Algorithm for Solving Traveling Saleman Problem, International Journal of Advanced Computer Science and Applications (IJACSA). Vol. 2, No. 1. PP. 26-29.
  3. Ahad Nezhad, M., Meshkini, A., and Nouri, B., 2007, Evaluation of Vulnerability of Rural and Informal Residents from Earthquakes Using Geographic Information Systems (Case Study: Zanjan City), The First Urban GIS Conference, North University. (In Persian)
  4. Asghari Zamani, A., 2014, Evaluation of the Quality of Access to Urban Free Spaces During Natural Disasters (Case Study: Tabriz City), Journal of Geography and Planning, Vol. 18, No. 48, PP. 16-1. (In Persian)
  5. Azizi, M. M., and Homafar, M., 2012, Seismic Pathology of Urban Roads, Case Study: Karmandan Area, Karaj, Journal of Fine Arts, Architecture and Urban Development, Vol. 17, No. 3, PP. 5-15. (In Persian)
  6. Behrou, R., and Guest, J. K., 2017, Topology Optimization for Transient Response of Structures Subjected to Dynamic Loads, In 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference (P. 3657).
  7. Behrou, R., Lawry, M., and Maute, K., 2017, Level Set Topology Optimization of Structural Problems with Interface Cohesion, International Journal for Numerical Methods in Engineering, Vol. 112, No. 8, PP. 990-1016.
  8. Behzadi, S., and Alesheikh, A. A., 2008, Developing a Genetic Algorithm for Solving Shortest Path Problem, Wseas International Conference on Urban Planning and Transportation (UPT'07), Heraklion, Crete Island, Greece, July, PP. 22-24.
  9. Bin Jubeir, M., Almazrooie, M., and Abdullah, R., 2017, Enhanced Selection Method for Genetic Algorithm to Solve Traveling Salesman Problem, Proceedings of the 6th International Conference on Computing and Informatics, ICOCI 2017 25-27April, 2017 Kuala Lumpur, Universiti Utara Malaysia (http://www.uum.edu.my), PP. 69-76.

10. Building and Housing Research Center, 2006, Preparation and Compilation of Earthquake Scenario in Gorgan, Earthquake Section, Third Chapter, PP. 66. (In Persian)

11. Cavuoti, S., Garofalo, M., Brescia, M., Paolillo, M., Pescape, A., Longo, G., and Ventre, G., 2013, Astrophysical Data Mining with GPU a Case Study: Genetic Classification of globaler cluster, printed submitted of New Astrophysical, , PP. 1 -29,

12. Central U.S Earthquake Consortium, 2000, Earthquake Vulnerability of Transportation System in the Central United States, Compiled by the Central U.S Earthquake Consortium.

13. Chen W., and Scawthorn C., 2002, Earthquake Engineering Handbook, CRC Press, Florida.

14. Cooray P. L. N. U. and Thashika D., 2017, Rupasinghe. Machine Learning-Based Parameter Tuned Genetic Algorithm for Energy Minimizing Vehicle Routing Problem, Journal of Industrial Engineering. Vol. 2017 (2017), Article ID 3019523, 13 Pages.

15. EMDAT., 2017, The OFDA/CRED International Disaster Database, Universite Catolique De Louvain-Brussels-Belgium, [Accessed 2017 Jan 1], http://www.emdat.be/emergency Shelter Location, Journal of Natural Disasters, Vol. 24, No. 2, PP. 8-14.

16. Eshraghi, M., 2006, Locating Temporary Accommodation Sites of Earthquake-Affected Populations Using Geographic Information Systems (Tehran 2nd District), Tehran, Second International Conference on the Integrated Management of Crisis Management In Natural Disasters. (In Persian)

17. Ghotbeddini, M., Metkan, A. A., Ali Mohammadi, A., and Mirbagheri, B., 2012, Implementation of Ant Colony Optimization Algorithm (ACO) in Locating Temporary Shelters After Earthquake (Case Study: Kerman City), Master’s Degree Dissertation, Department of Remote Sensing and GIS, Faculty of Earth Sciences, Shahid Beheshti University, Tehran. (In Persian)

18. Givehchi, S., Attar, M. A., Rashidi, A., Hesari, E., and Nasabi, N., 2013, Locating Temporary Post-Earthquake Settlement Using GIS and AHP Technique. Case Study: District 6 of Shiraz, Urban and Regional Studies and Research, Vol. 5, No. 7, PP. 101-118. (In Persian)

19. Gog, A., Dumitrescu, D., and Hirsbruner, B., 2007, New Selection Operator Based on Genetic Relatedness for Evolutionary Algorithms, IEEE Congress on Evolutionary Computation, PP. 4610-4614.

20. Governorate of Golestan Province, 2017, The Results Abstract of General Census of Population and Housing in 2017 At Golestan Province, Planning and Budget Organization, Department of Statistics and Information, PP. 1-20. (In Persian)

21. Haleh, H., Esmaeili A., and Abadi, D., 2015, Improvement of Imperialist Colony Competitive Algorithm Using the Colonial Learner's Operator and Its Application to Solving the Travelling Salesman Problem, Journal of Development Evolution Management, No. 22, PP. 55-61. (In Persian)

22. He, S., Zhang, L., Song, R., Wen, Y., and Wu, D., 2009, Optimal Transit Routing Problem for Emergency Evacuations, The Nataional Acadmies of Sciences Engineering Medicine, Transportation Research Board. Accession Number: 01128717, Report/Paper Numbers: 09-0931.

23. John, A., Arunadevi, J., and Mohan, V., 2009, Intelligent Transport Route Planning Using Genetic Algorithm in Computation Algorithm, European Journal of Scientific Reasearch, ISSN 1450-216X. Vol. 25. No. 3, PP. 463-468.

24. Khomr, Gh., Saleh Gohari, H., and Hosseini, Z., 2014, The Feasibility of Urban Sprawl Location Using Model (IO) and Method (AHP) (Case Study: 13 Districts of Kerman City), Quarterly Journal of Urban Planning, Vol 2, No. 7, PP. 29 - 54. (In Persian)

25. Kumar, J., Arunadevi, J., and Mohan., V., 2009, Intelligent Transport Route Planning Using Genetic Algorithms in Path Computation Algorithms, European Journal of Scientific Research, Vol. 25, No. 3, PP. 463-468.

26. Ma, D.X., Chu, J.Y., Wang, Z., Chen L.L., 2015, Study on Location Model of Disaster Emergency Shelter Based on Multi-Objective Programming, Journal of Natural Disasters, Vol. 24, No. 2, PP.1-7, DOI: 10.13577/J.Jnd.2015.0201.

27. Mandekar, U. H., and Aher, P. B., 2013, Solving the Traveling Saleman Problem Using an Evolutionary Algprithm, International Journal of Computer and Technology, Vol. 4., No. 1, PP. 33- 35.

28. Masum, A. K., Shahjalal, M., Faruque, F., and Sarker, I. H., 2011, Solving the Vehicle Routing Problem Using Genetic Algorithm, (IJACSA). International Journal of Advanced Computer Science and Applications. Vol. 2, No. 7, PP. 126 – 131.

29. Mavrovouniotis, M., Müller Felipe M., and Yang, Sh., 2016, Ant Colony Optimization with Local Search for Dynamic Travelling Salesman Problems, Ieee Transactions on Cybernetics, Vol. Xx, No. Yy, PP. 1 -14.

30. Mokhtarzadeh, S., Sargolzaei, Sh., and Bidram, R., 2010, Methodological Assessment of Earthquake Road Vulnerability, National Earthquake Conference and Vulnerability of Vital Sites and Roads. (In Persian)

31. Nappi, M. M .L., Souza, J. C., 2014, Disaster Management: Hierarchical Structuring Criteria for Selection and Location of Temporary Shelters, Nat Hazards. 75:2421–2436. Doi:10.1007/S11069-014-1437-4, On Multi-Objective Programming, Journal of Natural Disasters, Vol. 24, No. 2, PP. 1-7.

  1. 32.  Narooei, K., and Aghaei Zadeh, I., 2017, Locating the Temporary Residental Site for Earthquake in Cities (Case Study: Zahedan City), Journal of Geography and Urban Space Development, Vol. 4, No. 1, PP. 155-173. (In Persian)

33. Nikpour, A., Lotfi, S., and Reza Zadeh, M., 2017, Analysis of the Relationship Between City Form and Accessibility Index (Case Study: Babolsar), Quarterly Journal of Spatial Planning (Geography), Vol. 7, No. 3, PP. 85- 106. (In Persian)

34. Raichaudhuri, A., and Jain, A., 2010, Generic Algorirhm Based Logistics Route Planning, International Journal of Innovation, Management and Technology, Vol. 1, No. 2, PP. 205-208.

35. Saadatseresht, M., Mansourian, A., and Taleai, M., 2009, Evacuation Planning Using Multiobjective Evolutionary Optimization Approach, European Journal of Operational Research, 198, 305–314. Doi: 10.1016/J.Ejor.2008.07.032.

36. Sallabi, O., 2009, An Improved Genetic Algorithm to Solve the Traveling Salesman Problem, World Academy of Science, Engineering and Technology, International Science Index Vol. 3, No. 4, waste.org/publication/ 15802. PP. 388-391.

37. Samadzadegan, F., Abbaspour, A., and Pahlavani, P., 2006, Locating Emergency Accommodation Places of Citizens in Unexpected Accidents Using Smart Space Information Systems, Tehran, Spatial Information Technology Conference and Natural Disaster Management. (In Persian)

38. Shariat Mohaymeni, A., Maadi, S., and Babaei, M., 2012, Using the Colonial Competition Algorithm for Locating Medical Emergency Centers, Sixth National Congress on Civil Engineering, Semnan University. (In Persian)

39. Sherali, H. D., Carter, T. B., and Hobeika, A. G., 1991, A Location-Allocation Model and Algorithm for Evacuation Planning Under Hurricane/ Flood Conditions. Transportation Research Part B: Methodological, Vol. 25, No. 6, PP. 439-452.

40. Shie, I., Habibi, K., and Ismail, K., 2010, Evaluation the Vulnerability of Urban Communication Networks Against Earthquakes Using GIS and IHWP, Bagh-E-Nazar, Vol. 7, No. 13, PP. 35-48. (In Persian)

41. Taqavi, A., 2010, Reflects the Social Changes in the Spatial Structure of the Ancient City of Gorgan During the Islamic Era, Iranian Social Studies Journal, No. 11. (In Persian)

42. Tzeng, G. H., and Cheng, H. J., 2007, Multi-Objective Optimal Planning For Designing Relief Delivery Systems, Transportation Research Part E, Vol. 43, No. 6, PP. 673-686.

43. Tzung Pei, H., Yuan Ching, P., WenYang, L., and ShyueLiang W., 2017, Empirical Comparison of Level-Wise Hierarchical Multi-Population Genetic Algorithm, Journal of Information and Telecommunication, Vol. 1, No. 1, PP. 66–78. (http://dx.doi.org/10.1080/24751839 .2017.1295662.

44. Widener, M. J., and Horner, M. W., 2011, A Hierarchical Approach to Modeling Hurricane Disaster Relief Goods Distribution, Journal of Transport Geography, Vol. 19, No. 4, PP. 821-828.

  1. 45.  Zhao, X., Graham, C., and Wei, X., 2017, Solving the Earthquake Disaster Shelter Location-Allocation Problem Using Optimization Heuristics, Analytical Modeling and Simulation, Proceedings of The 14th ISCRAM Conference – Albi, France, May 2017. 50 -62.

46. Xu, W., Zhao, X., Yunjia, Ma., Ying, Li., Qin, L., Wang, Y., Du, J., 2018, A Multi-Objective Optimization Based Method for Evaluating Earthquake Shelter Location–Allocation, Geomatics, Natural Hazards and Risk, 2018, Vol. 9, No. 1, PP. 662–677. https://doi.org/10.1080/19475705.2018.1470114.

47. Xu, W., et al., 2014, Collaborative Modelling-Based Shelter Planning Analysis: A Case Study of the Nagata Elementary School Community in Kobe City, Japan. Disasters, No. 38, PP.125–147. Doi:10.1111/Disa.12033.

48. Yuan Y., Liu Y., Zhu, S. H., Wang, J. B., 2015, Maximal Preparedness Coverage Model and Its Algorithm for Emergency Shelter Location, Journal of Natural Disasters. Vol 24, No.  02, Pages 8 -14.

49. Yung Lung L., Ming Chin Ho., Tsung Cheng H., and Cheng An T., 2007, Urban Disaster Prevention Shelter Vulnerability Evaluation Considering Road Network Characteristics, 2nd International Conference on Urban Disaster Reduction.

50. Zhao, X., Xu, W., Yunjia, Ma., and 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): E0144455. Doi: 10.1371/Journal.Pone.0144455.

51. Zhou, C., Zheng, J., and Li, W., 2008, An Improved Heuristic Crossover Operator for TSP, Fourth International Conference on Natural Computation, Jinan, Print ISBN: 978-0-7695-3304-9, INSPEC Accession Number:  10398337, pp. 541-545. doi: 10.1109/ICNC.2008.514,

Geographical Urban Planning Research (GUPR)
Volume 7, Issue 2
July 2019
Pages 311-331

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