We leverage applied machine learning to determine which environmental features are best associated with the “moving” behaviour(s) of a troop of olive baboons (Papio anubis; collared with GPS trackers at Mpala Research Centre, Kenya). Specifically, we develop a behaviour-selection surface informed by classification trees trained using movement trajectories and remotely sensed environmental features. Atop this surface, we simulate agent movement towards set destinations, constrained by the relative extent to which sets of features are associated with behaviour(s). To achieve our goal, we perform: (a) path segmentation using thresholding to label training data; (b) agent-rule extraction using classification trees to associate the relative Euclidean distance of a point from environmental features with behaviour; and (c) implementation of this information into an agent-based model to provide a data-driven simulation of troop movement. We believe this framework can accommodate intensifications in data velocity, veracity, volume, and variety expected from increasingly sophisticated biologgers and data-fusion techniques.

中文翻译：

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将决策边界转移到地理空间：使用分类树从移动数据中提取代理规则

我们利用应用机器学习来确定哪些环境特征与橄榄狒狒（Papio anubis）的“移动”行为最相关; 在肯尼亚姆帕拉研究中心装有 GPS 跟踪器）。具体来说，我们开发了一个行为选择表面，该表面由使用运动轨迹和遥感环境特征训练的分类树提供信息。在这个表面上，我们模拟代理向设定目的地的移动，受特征集与行为关联的相对程度的限制。为了实现我们的目标，我们执行：（a）使用阈值标记训练数据的路径分割；(b) 使用分类树提取代理规则以将点与环境特征的相对欧几里德距离与行为相关联；(c) 将此信息实施到基于代理的模型中，以提供数据驱动的部队移动模拟。我们相信这个框架可以适应数据速度、准确性、