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ارزیابی مکانی دسترسی به فضاهای باز شهری در مقطع زمانی پس از زلزله با استفاده از الگوریتم‌های بهینه‌سازی هاب و ژنتیک (مطالعۀ موردی: شهر گرگان) | ||
| پژوهش‌های جغرافیای برنامه‌ریزی شهری | ||
| مقاله 6، دوره 7، شماره 2، تیر 1398، صفحه 311-331 اصل مقاله (1.77 M) | ||
| نوع مقاله: پژوهشی - کاربردی | ||
| شناسه دیجیتال (DOI): 10.22059/jurbangeo.2019.265008.968 | ||
| نویسندگان | ||
| محمدحسین سرایی* 1؛ محمدرضا رضایی1؛ محسن عادلی2 | ||
| 1دانشیار گروه جغرافیا، دانشگاه یزد | ||
| 2دانشجوی دکتری جغرافیا و برنامه‌ریزی شهری، دانشگاه یزد | ||
| چکیده | ||
| آگاهی از وضعیت دسترسی به فضاهای باز شهری برای اسکان موقت پس از زلزله، از مواردی است که می‌تواند در مدیریت بحران بسیار حیاتی باشد. شناسایی این نقاط پیش از وقوع زلزله سبب می‌شود مدیران در مقطع زمانی ناپایدار و غیرقطعی پس از زلزله تصمیم‌گیری منطقی داشته باشند. همچنین این مسئله تأثیر محدودیت‌های خاص مکانی و زمانی آن مقطع را کم‌رنگ‌تر می‌کند. در مطالعة حاضر، به ارزیابی چگونگی دسترسی شهروندان شهر گرگان به فضاهای باز شهری پرداخته شده است. بدین‌منظور از الگوریتم ژنتیک برای حل مسئله‌ای مکان‌محور که داده‌های ورودی آن از GIS گرفته شده، استفاده شد. در زمینة تخصیص بهینة مکان در محیط GIS، دو سناریوی سخت‌گیرانه و سهل‌گیرانه با اعمال و بدون اعمال محدودیت انسداد راه مدنظر قرار گرفتند؛ از این‌رو برای سناریوی اول 48 قطعه و برای دیگری 153 قطعه زمین به‌عنوان زمین‌هایی با قابلیت بالقوة اسکان موقت درنظر گرفته شد. در زمینة حل مسئلة تخصیص با استفاده از الگوریتم ژنتیک نیز با اعمال تغییرات در پارامترهای حل مسئله و در قالب دو سناریوی فوق، شش گزینة مختلف انتخاب شدند که کمترین مقدار هزینة انتقال جمعیت از مراکز بلوک‌های جمعیتی را به فضاهای باز داشتند. با پذیرش احتمالی‌بودن شرایط پس از وقوع زلزله، نتایج کلی پژوهش نشان می‌دهند در صورت وقوع زلزلة شدید با احتساب انسداد راه در حالت سخت‌گیرانه، 24 و در حالت سهل‌گیرانه، 35 درصد از جمعیت به فضاهای باز دسترسی دارند. این موضوع در سناریوی سهل‌گیرانه به‌ترتیب 35 و 47 درصد خواهد بود. نتایج حل مسئلة هاب با استفاده از الگوریتم ژنتیک نیز مشابه حالات سهل‌گیرانه و سخت‌گیرانه بدون احتساب انسداد راه است. | ||
| کلیدواژه‌ها | ||
| الگوریتم ژنتیک؛ حل مسئلة هاب؛ زلزله؛ شهر گرگان فضاهای باز | ||
| عنوان مقاله [English] | ||
| Spatial Evaluation of Access to Urban Free Spaces during the Post-Earthquake Periods Using Hub and Genetics Optimization Algorithms (Case Study: City of Gorgan) | ||
| نویسندگان [English] | ||
| Mohammad Hosein Saraei1؛ Mohammad Reza Rezaei1؛ Mohsen Adeli2 | ||
| 1Associate Professor of Humanities & Social Science, Department of Geography, Yazd University, Iran | ||
| 2PhD Candidate in Geography and Urban Planning, Yazd University, Iran | ||
| چکیده [English] | ||
| 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. | ||
| کلیدواژه‌ها [English] | ||
| earthquake, free spaces, hub problem, genetic algorithm, Gorgan City | ||
| مراجع | ||
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