Doctoral Dissertation Research: The Development and Integration of Spatial Analyses for Search and Rescue Operations in Yosemite National Park

博士生Paul Doherty在加州大学工程学院郭清华教授的指导下，默塞德将开发一个系统，该系统旨在根据过去救援事件中受伤和/或发现人员的历史数据，估计约塞米蒂国家公园中受伤和/或失踪人员的位置。 该项目有三个研究组成部分。 第一个组成部分涉及从基于文本的信息中地理参考历史事件的过程。这是依赖空间数据做出决策的地理信息用户（生态学家、历史学家、博物馆馆长）面临的共同挑战。一种概率场地理参照技术将用于绘制约塞米蒂国家公园历史性搜救事件的地图。该项目的第二个组成部分评估了地理学家所面临的地理单类数据问题，只有存在的测试数据。更具体地说，一个由专家知识（直升机管理人员）定义参数的模型将与一个使用仅存在测试点（已知着陆区）的模型进行比较，以开发约塞米蒂国家公园的直升机着陆区适用性层。该项目将提供一种新颖的测试场景，用于使用机器学习算法处理仅存在或地理一类数据。该项目的第三个组成部分将解决在时空空间中定位移动物体的问题。这需要创建一个旅行成本图层，该图层使用坡度、植被密度和路径网络来估计在约塞米蒂国家公园内穿越已知距离所需的时间。一旦这项工作完成并经过测试，将建立一个面向对象的模型，利用这一旅行费用层生成等时线（时距环）。这将允许用户输入地理点和经过的时间，以确定人类可以从已知位置行进的最大距离。 这项研究所要解决的逻辑问题是一个完全空间的问题，“地理和空间科学的发展如何帮助救援人员更有效地找到和营救约塞米蒂国家公园的游客？“从本质上讲，这项研究将为应急服务提供商开发工具，以便在三个方面更好地完成工作：以空间数字图书馆格式从事件历史中获取机构知识，为紧急通信调度员和直升机飞行员生成可视化工具以定位着陆区域，并为搜索管理人员提供工具，用于定义人员失踪时搜索区域的外部界限。搜索和救援的经典困境是专业救援人员，他们的获救者和亲人非常重视的难题。地理信息系统可以提供一个空间分析的平台，帮助解决问题，使其他人可以生活。这项研究将引入以科学为基础的技术，为社会提供更安全、更有效的救援技术。

Doctoral student Paul Doherty, under the supervision of Professor Qinghua Guo in the School of Engineering at the University of California Merced will develop a system designed to estimate the location of injured and/or lost people in Yosemite National Park based on historical data of where people have been injured and/or found in past rescue events. This project has three research components. The first component involves the process of georeferencing historic incidents from text-based information. This is a common challenge for users of geographic information (ecologists, historians, curators) who rely on spatial data to make decisions. A probability-field georeferencing technique will be used to map historic search and rescue incidents in Yosemite National Park. The second component of this project evaluates the geographic one-class data issue faced by geographers with presence-only test data. More specifically, a model whose parameters are defined by expert knowledge (helicopter managers) will be compared to a model that uses presence-only test points (known landing areas) to develop helicopter landing area suitability layers in Yosemite National Park. This will project will provide a novel testing scenario for dealing with presence-only or geographic one-class data using machine-learning algorithms. The third component of this project will address the problem of locating moving objects in spatiotemporal space. This entails creating a travel-cost layer that uses slope, vegetation density, and path network presence to estimate the time required to cross a known distance within Yosemite National Park. Once this has been completed and tested, an object-oriented model will be constructed to use this travel-cost layer to generate isochrones (time-distance rings). This will allow a user to enter a geographic point and time elapsed to determine the maximum distance a human can travel from a known location. The logical problem to be addressed by this research is an entirely spatial one, "how can developments in geography and spatial sciences help rescuers find and rescue visitors in Yosemite National Park more effectively?" In essence, this research will develop tools for emergency service providers to better do their job in three ways: enable institutional knowledge from incident history in a spatially-enabled digital library format, generate a visualization tool for emergency communication dispatchers and helicopter pilots to locate landing areas, and give search managers a tool for defining the outer limits of their search area when persons go missing. The classic dilemma of search and rescue is a puzzle that professional rescuers, their rescuees, and loved-ones take very seriously. Geographic Information Systems can provide a platform for spatial analyses to help solve problems so that others may live. This research will introduce science-based techniques to provide society with safer and more effective rescue techniques.