1. Evaluating the concentration of animal locations, in space and time, provides insight regarding animal activity and behaviour. Most commonly collected via global positioning system technology, these concentrations are identified using rule sets resulting in location ‘clusters’. Cluster algorithms provide a framework for modelling and predicting behavioural states when paired with field data to evaluate habitat characteristics and validate results.
2. We provide a sequential clustering algorithm package (GPSeqClus) to process location datasets based on user‐defined parameters. GPSeqClus also calculates an array of movement attributes commonly applied as covariates to develop cluster‐based models. Our package maps locations and clusters, with additional functions to assess specific clusters for site investigations and to export GPS Exchange format (.gpx) files for navigation. We highlight the ability of GPSeqClus to modify existing cluster attributes or to append additional attributes, and the flexibility to accommodate archived or near real‐time (satellite‐driven) datasets.
3. Although spatio‐temporal clustering is widely used in animal ecology, clustering routines are commonly applied to assess carnivore movement patterns to predict denning or feeding locations, evaluate prey composition and estimate kill rates and handling times. We demonstrate the applicability of GPSeqClus by constructing clusters using mountain lion Puma concolor location data.
4. Our package provides an efficient data processing routine to build, characterize, visualize and navigate to clusters. Attributes provided within GPSeqClus can be used when developing predictive cluster models, or can be applied to other modelling procedures to advance understandings of animal behaviour.

中文翻译：

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GPSeqClus：一个R包，用于对动物位置数据进行顺序聚类，以进行模型构建，模型应用和现场调查

1. 在空间和时间上评估动物位置的集中度，可以提供有关动物活动和行为的见解。这些浓度最通常是通过全球定位系统技术收集的，使用规则集进行标识，从而得出位置“簇”。群集算法与现场数据配对以评估栖息地特征并验证结果时，提供了用于建模和预测行为状态的框架。
2. 我们提供了一个顺序聚类算法包（GPSeqClus），用于根据用户定义的参数处理位置数据集。GPSeqClus还计算通常用作协变量的运动属性数组，以开发基于聚类的模型。我们的程序包可映射位置和群集，并具有评估特定群集以进行站点调查以及导出GPS Exchange格式（.gpx）文件进行导航的附加功能。我们着重介绍了GPSeqClus修改现有群集属性或附加其他属性的能力，以及适应已归档或接近实时（卫星驱动）数据集的灵活性。
3. 尽管时空聚类在动物生态学中得到了广泛应用，但是聚类例程通常用于评估食肉动物的运动模式，以预测食肉动物的觅食位置或觅食位置，评估猎物的组成，并估计杀灭率和处理时间。我们通过使用美洲狮美洲狮concolor位置数据构建聚类来证明GPSeqClus的适用性。
4. 我们的软件包提供了一个有效的数据处理例程，以构建，表征，可视化和导航到集群。在开发预测性聚类模型时，可以使用GPSeqClus中提供的属性，也可以将其应用于其他建模过程，以加深对动物行为的了解。