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首页> 外文期刊>International Journal of Geographical Information Science >Space-time analyses for forecasting future incident occurrence: a case study from Yosemite National Park using the presence and background learning algorithm
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Space-time analyses for forecasting future incident occurrence: a case study from Yosemite National Park using the presence and background learning algorithm

机译：时空分析以预测未来事件的发生：以存在和背景学习算法为例的优胜美地国家公园的案例研究

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摘要

To address a spatiotemporal challenge such as incident prevention, we need information about the time and place where incidents have occurred in the past. Using geographic coordinates of previous incidents in coincidence with spatial layers corresponding to environmental variables, we can produce probability maps in geographic and temporal space. Here, we evaluate spatial statistic and machine learning approaches to answer an important space-time question: where and when are wildland search and rescue (WiSAR) incidents most likely to occur within Yosemite National Park (YNP)? We produced a monthly probability map for the year 2011 based on the presence and background learning (PBL) algorithm that successfully forecasts the most likely areas of WiSAR incident occurrence based on environmental variables (distance to anthropogenic and natural features, vegetation, elevation, and slope) and the overlap with historic incidents from 2001 to 2010. This will allow decision-makers to spatially allocate resources where and when incidents are most likely to occur. In the process, we not only answered questions related to a real-world problem but also used novel space-time analyses that give us insight into machine learning principles. The GIScience findings from this applied research have major implications for best practices in future space-time research in the fields of epidemiology and ecological niche modeling.
机译：为了应对时空挑战，例如事件预防，我们需要有关过去发生事件的时间和地点的信息。使用先前事件的地理坐标与对应于环境变量的空间层相一致，我们可以在地理和时间空间中生成概率图。在这里，我们评估了空间统计和机器学习方法，以回答一个重要的时空问题：优胜美地国家公园（YNP）内最有可能发生荒地搜寻和救援（WiSAR）事件的时间和地点？我们根据存在和背景学习（PBL）算法绘制了2011年的月度概率图，该算法根据环境变量（与人为和自然特征的距离，植被，海拔和坡度）成功预测了WiSAR事件最可能发生的区域）以及与2001年至2010年历史事件的重叠。这将使决策者可以在空间上分配资源，最有可能在何时何地发生事件。在此过程中，我们不仅回答了与现实世界有关的问题，而且还使用了新颖的时空分析，使我们能够洞悉机器学习原理。这项应用研究的GIS科学发现对流行病学和生态位建模领域的未来时空研究的最佳实践具有重大意义。

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