School of Biological and Chemical Sciences, Queen Mary University of London, London, E1 4NS, UK.
Centre for Advanced Spatial Analysis, University College London, London, W1T 4TJ, UK.
Ecol Appl. 2021 Jul;31(5):e02329. doi: 10.1002/eap.2329. Epub 2021 May 4.
Geographic profiling, a mathematical model originally developed in criminology, is increasingly being used in ecology and epidemiology. Geographic profiling boasts a wide range of applications, such as finding source populations of invasive species or breeding sites of vectors of infectious disease. The model provides a cost-effective approach for prioritizing search strategies for source locations and does so via simple data in the form of the positions of each observation, such as individual sightings of invasive species or cases of a disease. In doing so, however, classic geographic profiling approaches fail to make the distinction between those areas containing observed absences and those areas where no data were recorded. Absence data are generated via spatial sampling protocols but are often discarded during the inference process. Here we construct a geographic profiling model that resolves these issues by making inferences via count data, analyzing a set of discrete sentinel locations at which the number of encounters has been recorded. Crucially, in our model this number can be zero. We verify the ability of this new model to estimate source locations and other parameters of practical interest via a Bayesian power analysis. We also measure model performance via real-world data in which the model infers breeding locations of mosquitoes in bromeliads in Miami-Dade County, Florida, USA. In both cases, our novel model produces more efficient search strategies by shifting focus from those areas containing observed absences to those with no data, an improvement over existing models that treat these areas equally. Our model makes important improvements upon classic geographic profiling methods, which will significantly enhance real-world efforts to develop conservation management plans and targeted interventions.
地理概况分析是一种最初在犯罪学中发展起来的数学模型，现在越来越多地被应用于生态学和流行病学领域。地理概况分析有广泛的应用，例如寻找入侵物种的源种群或传染病媒介的繁殖地。该模型提供了一种具有成本效益的方法，可以优先选择源位置的搜索策略，方法是使用每个观察位置的简单数据，例如入侵物种的个体目击或疾病的病例。然而，在这样做的过程中，经典的地理概况分析方法无法区分包含观察到的缺失区域和没有记录数据的区域。缺失数据是通过空间采样协议生成的，但在推理过程中通常会被丢弃。在这里，我们构建了一个地理概况分析模型，通过使用计数数据进行推断来解决这些问题，分析了一组离散的哨兵位置，记录了遇到的次数。至关重要的是，在我们的模型中，这个数字可以为零。我们通过贝叶斯功效分析验证了该新模型估计源位置和其他实际感兴趣参数的能力。我们还通过美国佛罗里达州迈阿密戴德县的蔓藤植物中蚊子繁殖地的实际数据来衡量模型性能。在这两种情况下，我们的新模型通过将重点从包含观察到的缺失区域转移到没有数据的区域，从而产生更有效的搜索策略，这优于同等对待这些区域的现有模型。我们的模型对经典地理概况分析方法进行了重要改进，这将极大地增强现实世界中制定保护管理计划和有针对性干预措施的努力。
