# Helicopter Takeoff and Landing Point Location in Cities for Emergency Services
## 1 Introduction  
With a vast territory and complex geographical and climatic conditions, China is a country prone to frequent natural disasters. In recent years, the National Health Department in China has made profound plans for low-altitude emergency rescue. The selection of the take-off and landing points for rescue services has become one of the bottlenecks. GIS (Geographical Information Systems), its database and software and numerical analysis may be useful for establishing geographical positions for takeoff and landing points. Geographical locations are very important for many transport systems and vehicles [1, 2]. To know the geographical locations is to know the potential for a rescue operation. ArcGIS 10.8 is software that comprehensively integrates the GIS database, software engineering and AI and IT technology. It can offer a solution package for location selection problems. Scholars used to rely on satellite data and field surveys to decide the location. They were subjective and inefficient. The application of ArcGIS could potentially help with the selection of takeoff and landing points for helicopter rescue operations in cities to make it simpler, more efficient and more scientific. A lot of research was carried out around the rescue site location in order to improve the quality of low-altitude rescue services. In terms of helicopter low-altitude rescue research, it can be roughly divided into two types: land rescue and water rescue. As the land rescue pays more attention to the flatness of the site, this kind of research was mostly developed with GIS-related software [3, 4, 5]. For water rescue, due to the complicated water surface environment, most of the temporary take-off and landing activities were carried out on ships. Therefore, it is necessary to make more detailed research on the attitude of the helicopter, the movement of the ship and the changes of the water surface environment [6, 7, 8, 9, 10]. It can be seen that most of theresearch based on land rescue was focused on the evaluation of the take-off and landing site. Because of its applicability, there was no guarantee that it would actually be timely and effective. In the literature on water rescue, a large number of methods and models were demonstrated to solve the problem of safe and efficient landing on mobile platforms. Most of the research was performed through simulation, focusing on selected aspects of the mission. Therefore, the factors affecting the take-off, landing and operation should be fully considered to maximize the efficiency. Simple implementation methods should be explored. In the research on locations, the interception problem mainly studied the problem of customer demand on the route and was mainly used in traffic planning, service, etc. [11; 12]. Other research directions include research in the direction of total cost minimization [13; 14]. It can be seen that the past research mainly selected locations randomly. The selection was highly subjective, which increased the uncertainty of the results. The location results did not analyze the terrain characteristics and could not be applied to aviation, especially for general aviation and other fields with harsh geographical environment. This paper hopes to further optimize the take-off and landing point selection model based on the existing research, reduce the overall cost and maximize the efficiency of low-altitude emergency rescue. The research on the algorithms on location optimization can be divided into two categories, namely, exact algorithms and heuristic algorithms. [PERSON] [15] proposed an efficient hybrid genetic method including an improved wavelet propagation algorithm for solving multi-path optimization problems and a genetic algorithm for optimizing facility location problems. Inspired by the human immune system, [PERSON] [16] analyzed the optimal location of distributed power stations in the existing grid. [PERSON] [17] proposed a fuzzy multi-objective method based on the two-stage grasshopper optimization algorithm (GOA).