İstanbul'da afet sonrası toplanma ve barınma alanlarının erişebilirliği
Accessibility of disaster problems in İstanbul
Yazar: GÖZDE NUR KURU
Danışman: DOÇ. DR. HİMMET KARAMAN
İstanbul Teknik Üniversitesi / Fen Bilimleri Enstitüsü / Geomatik Mühendisliği Ana Bilim Dalı / Geomatik Bilim Dalı
Derece: Yüksek Lisans, 2019
Konu: Jeodezi ve Fotogrametri = Geodesy and Photogrammetry
Dizin: Analiz = Analysis ; Analiz yöntemleri = Analysis methods

Özet:
Afet yönetiminde erişim problemleri günümüzde çözüm arayan sorunlar olarak ülkemizin önünde durmaktadır. Özellikle İstanbul gibi mega kentler için ulaşım, en önemli problemlerdendir. Bu bağlamda bu çalışmada tüm İstanbul'u kapsayacak şekilde bir ağ analizi çalışması yapılmıştır. Afet yaşanan bir bölgede toplanma alanlarında toplanan vatandaşların araç kullanarak geçici barınma alanlarına ulaşabilmeleri için en kısa yollar analiz edilmiştir. Ağ analizinde en yakın tesis analizi yöntemi kullanılarak, toplanma alanlarından barınma alanlarına giden rotalar oluşturulmuştur. En yakın tesis yönteminde toplanma alanları olay alanları, geçici barınma alanları ise tesis alanları olarak analiz çalışmasına dahil edilmiştir. Çalışmada, tüm yolların açık olduğu varsayılarak rota hesabı yapılmıştır. Ayrıca, İstanbul için bilinen kapanan yollar veya kapanması olası olan yollar da hesaba katılarak, alternatif rotalar bulunmuştur. Çalışma sonucunda kapanan yol verisi kullanılarak ve kullanılmadan elde edilen iki farklı analiz sonuçları hesaplanmış ve haritaları oluşturulmuştur.  Yol ve alan mekansal verilerinde topolojik düzeltmeler yapılmıştır. Çizge kuramı ve Dijkstra algoritmalarından yararlanılmıştır.Yol verisi üzerinde ağ yapısı incelenerek kenarlar ve düğüm noktaları arasında bağlantılılık çalışması yapılmıştır. Alanların erişebilirliği için yol ile alanları kesim noktaları tespit edilmiştir. Yol ile kesişmeyen alanlar için yola en yakın nokta hesabı yapılmıştır. En yakın noktaya araçla ulaşıldıktan sonra yürüme mesafesinde alanlara ulaşılabilirliği bulunmaktadır. Bu hesaplamalarda kesişim analizi ve yakınlık analizi yöntemleri kullanılmıştır. Elde edilen tüm nokta verileri birleştirilmiştir. Hesaplanan bu veriler çalışmada girdi olarak kullanılmıştır. Ağ analizinde kullanılan tüm yöntemler incelenerek  en uygun yöntem olarak en yakın tesis analizi yöntemi kullanılmıştır. Yöntem seçilirken mevcut veriler ve yöntemin kullanım şekli dikkate alınmıştır.  Çalışma kapsamında, yol analizi sonucunda erişime kapalı toplanma ve geçici barınma alanları tespit edilmiştir. Erişim olan alanların rotaları ortaya konmuştur. Erişimi olmayan toplanma ve barınma alanları ayrı ayrı istatistiksel olarak raporlanmıştır. Erişimi olmayan bu alanların İstanbul'daki yoğunlukları haritaları oluşturulmuştur. Sonuçlar doğrultusunda İstanbul'da erişim sıkıntısı olan ve güçlendirilmesi gereken ilçeler ve semtler ortaya konmuştur.

Abstract:
Access problems in disaster management stand before our country as solutions seeking problems today. Especially for mega cities like Istanbul, transportation is one of the most important problems. 
Considering the urban structure of Istanbul, it is seen that it is a city that stands out with its transportation difficulties. In addition to the disaster, considering the transportation problems experienced even in the flow of daily life in Istanbul, it is important to transport the citizens who will go from the gathering areas to temporary shelter areas after the disaster. In this study, network analyzes are carried out in order to minimize the crises to be experienced after the disaster. In the event of a disaster, it is a priority to move from the disaster area to the gathering areas. Access to shelter areas should also be ensured by the people gathered in the gathering areas. In such disasters, it is very important to use the fastest and most accessible roads. 
Network analysis is frequently used in geographic information systems and engineering studies related to transportation. Network analysis method is applied according to the type of study. Network analysis application methods, optimal route determination, address determination, resource allocation, less branching tree theory, the closest facility function, the shortest path algorithm.
The traveling salesman problem is a widely used method in computer science. The aim, as the name suggests, is to find the shortest trip, starting from the city where the seller is located, with the condition of stopping only once in all the cities on its route and going back to the city where it started. In addition, based on the shortest distance, the order of the cities to be visited along the way is taken into consideration, and the aim is to find the optimal route.
If we put the problem with the graph theorem, the cities that the seller stops by show the dots and the routes between the cities show the lines. The objective is to determine the shortest Hamilton tour to solve the traveling salesman problem. It is the cycle up to the starting point provided that you pass the points on a graph once.
In the GIS application, the Route Analyzer is accessed by selecting New Route in the Network Analysis Tool. This route solver uses the travelling salesman method and recommends a route for navigating the stops in the problem in the most appropriate order. Using the meta-heuristic algorithm, it finds the optimal sequence to visit stops with the least latency. The solver creates a starting point target cost matrix between all stops to be sorted here. Using the taboo search-based algorithm, the best order of visiting stops is found as quickly as possible.
In the route analysis solution, the solution calculates the route based on the selected impedance value. The impedance value is time and distance. Impedance, if time is selected, the best route is the fastest route temporally. If a time attribute with impedance instant traffic information is selected, the best route is the fastest route calculated based on this information. In this method, the best route is defined as the route with the lowest impedance or lowest cost of the selected impedance. The route is calculated as a result of the analysis. The directions can be viewed after a route has been created in the software.
The OD cost matrix method calculates the lowest cost paths from multiple sources to multiple destinations. As a result of this analysis, the number of targets to be achieved can be determined in advance. Results can be drawn at a specified distance limit in the analysis. The result output is not calculated as routes and directions, calculates the distance of bird flight. Although it is similar to the closest plant method, it differs with output and calculation speed.
The campus locations of the private or public sector organizations that make a profit are a subject that affects the business success very much. In this context, it is necessary to remove the positional relationship between the branches of the organization and to determine the locations where the accessibility is high by keeping the expenses low in terms of business processes of an institution working in the distribution sector. Location allocation method can be used for appropriate location selection in matters such as emergency response, not only in cost but also in public interest studies.
The service area method in the GIS application identifies zones covering all accessible roads for the network around a starting point. These zones are graded according to their accessibility. Here, areas are created that can be accessed according to the given time constraint or zones are created according to points.
Many institutions working for transportation purposes use this method to plan the routes in which their vehicles will go with minimum cost within a certain period of time. Vehicle routing problem solution method is used in order to serve the vehicles on each route, determine the orders according to the vehicle capacity and find the most suitable route by making the ordering of the destinations correctly.
In the closest facility analysis method, there are two input data. These two input data are called events and facilities. It measures the cost of traveling between the two data. It finds the closest facility by comparing these costs with each other. The method creates the best directions and routes according to the cost or distance of the route to be traveled and gives directions.
The shortest path algorithm is the most commonly used algorithm in smart maps, navigation systems and many geographic information systems today. The algorithm is called Dijkstra Algorithm since it is found by Dijkstra, a Dutch mathematician. The basis of the algorithm is the graph theorem.
Graph theory is an approach that makes daily life quite easy by using points and lines and is used in many fields. If the problems encountered are expressed by a set of points, the scheme is created with lines connecting these points together. The schemas refer to the diagram. The Dijkstra algorithm works with a source point (node) and other nodes on that network to find the shortest path.  A network consists of lines (paths), nodes, and vertex. When a person traveling on the network reaches the node, he makes decisions and makes a break. It reaches the last point that it wants to reach by passing through the intermediate and node points on the structure. Refraction is the intersection of two line segments. The two line segments are connected by nodes. When a passenger departing from the starting point arrives at the junction (node) that he has to choose, he needs the algorithm to calculate the optimal route. The algorithm uses features in the data, such as length and cost, related to the segment. The known coordinates of a line are defined as start and end points, nodes. It is intended to detect the shortest paths from the starting node to all other nodes. 
According to the working principle of the algorithm, it is first started with the starting node. On the network, the starting node is known. Another node with the least cost is explored to reach it directly from the source point. The new point detected is kept as the node whose shortest distance is known. From this node, another node that is closest to the source / least costly is investigated. This node, like any other node, is held in a set of known nodes. This cycle continues until all nodes are detected.
In this context, a network analysis study covering the whole of Istanbul was conducted in this study. In a disaster area, the shortest ways for citizens gathering in the gathering areas to reach temporary accommodation areas by means of vehicles were analyzed. In the network analysis, routes from the gathering areas to the shelter areas were created using the closest facility analysis method. In network analysis, spatial decision-result relationship is created by using physical lines. In this study, it is aimed to find alternative routes by using the closed roads data known for Istanbul.
The shortest ways for the citizens gathered in the gathering areas in a disaster area to reach temporary accommodation areas by means of vehicles were analyzed. In the network analysis, the routes from the gathering areas to the sheltering areas were established using the closest facility analysis method. In the closest facility method, gathering areas are used as incident points and temporary shelter areas are used as facility points n the analysis. In the study, the route calculation was made assuming that all roads are open. In addition, alternative routes have been found, taking into account the known closed roads or the roads likely to be closed for Istanbul. At the end of the study, two different analysis results were calculated and maps were obtained by using closed road data.
According to the calculations made without adding the closed road data, the point obtained from 11295 gathering areas was added as the incident point and 4282 temporary shelter areas were added to the analysis as the facility point. As a result of the calculation of the routes operated in the geographic information system application, 11236 routes were calculated for access to the temporary accommodation areas from the gathering areas. Access to the shelter areas could not be found from 59 gathering areas. It was found that the access point could not be reached due to the fact that there are no nodes on the line where the access points cannot be calculated and the line where the point is located is not connected to other ways.
Closed road data on the line in the study area was added as a barrier and the closest facility analysis was run. According to the calculations made by adding closed road data, many roads that are thought to have access cannot be used. 9914 routes were calculated by adding barrier data. Incident points that cannot reach facility points are shown with maps. It was revealed that 1322 routes could not be accessed due to closed roads. The results show that 484 incident points are closed to access and 47 facility points cannot be accessed. Distribution map of unreachable gathering and temporary shelter areas all over Istanbul were created.
In the study, the network diameter should be calculated in the shortest path analysis method, since it represents the shortest path between two nodes in a connected network and the maximum number of steps from one node to the other. As a result of the analysis, it was observed that access was achieved in 14 steps to reach the end point starting from the starting point on the longest route. When the n-1 calculation was made for 15 nodes by counting the starting and ending points, the diameter of the network used in this study was calculated as 14. For the shortest route, it was found that access between the starting point and the end point was achieved using 1 node.
There is no temporary shelter in Kınalıada and Burgazada in the Adalar Region of Istanbul. Therefore, the shortest path analysis could not be performed on these two islands. There is only one temporary shelter in Heybeliada.
When the distribution of the areas with and without access is examined, it is seen that inaccessible areas are in the south of Istanbul. Density of inaccessible areas in the south of Istanbul is due to the increase in construction and population in this area. In the northwest of Istanbul, the density of temporary shelter areas was determined compared to the gathering areas. In contrast, the density of the gathering areas is higher in the northeast compared to the density of the temporary shelter areas. No unreachable area was found in the north of Istanbul.
Inaccessible areas in the south of Istanbul are concentrated on the European side. The historical peninsula on the European side, around Beyoğlu, close to the Büyükçekmece lake, in the district of Beylikdüzü is often seen inaccessible areas. On the Anatolian side, the density of inaccessible areas is seen in Pendik-Kartal regions. There is a need to strengthen access in these areas in Istanbul.
When inaccessible temporary shelters are compared to the shelters, it is seen that the transportation of the shelters is considerably lower than the shelters. In addition to the fact that the gathering areas are lower than the number of temporary shelter areas, the ratio of inaccessible gathering areas is found to be very high.
The existing closed roads have a negative impact on access from the gathering areas in disaster management. In addition, there is a shortage of access to the gathering areas around Büyükçekmece-Beylükdüzü. Access to temporary shelter areas in the result map is weak in Zeytinburnu and Fatih districts.
As a result of the road analysis conducted within the scope of the study, strengthening of the building bridges should be carried out regarding the roads that are closed or likely to be closed for closed access and temporary shelter areas. It has been observed that access problems will be significantly reduced when debris removal logistics is provided to roads that are closed or likely to close. In order to open the closed roads, studies should be carried out considering the necessary financial and moral needs.