Distribution and correlates of feral cat trapping permits in Los Angeles, California

Uncontrolled populations of feral cats in urban settings have become of concern to public officials, wildlife scientists, animal rights advocates and the public in general due to the risks they pose to public health, urban wildlife, and esthetics. Solutions to the problem of unmanaged cat populations in cities have been limited in scope by the lack of actual data on feral cats and the urban geographic ranges they occupy. Full extent censuses and environmental analyses have not been collected or performed due to the resources allocations and costs involved. A method for collecting this data without the use of field crews and research summaries exists in the form of unused paper records. Past studies on the problem have used data mining of available records to model cat territories and densities (Aguilar and Farnworth 2012). This approach mitigates the cost while providing information regarding the distributions of these animals. This thesis investigates the spatial properties of feral cat populations in a large metropolitan area (Los Angeles, California) using a previously non-spatialized dataset as a proxy for concentrations of feral cats. The following case study explores two matters: 1) development of a workflow to create a spatial model of feral cat extents from geographic data brought into an analyzable format and 2) analysis of the model data to determine what, if any, variables are correlated with these distributions. The data used for the model were obtained from the City in the form of paper records and successfully imported into a Geographic Information System. Densities of applications were determined from the cleaned and geocoded records and concentrations of both raw density and patterns of clustering were mapped. Modeling of correlations found positive associations with population density and a weak negative correlation with median income. The analysis was assessed and future work on this type of data was considered.城市环境中不受管控的流浪猫（feral cats）种群，已引发公共官员、野生动物科学家、动物权利倡导者及广大民众的广泛担忧，因其对公共卫生、城市野生动物及市容美观均构成潜在风险。针对城市中流浪猫种群问题的解决方案，因缺乏流浪猫实际分布数据及其占据的城市地理范围相关数据，其应用范围始终受限。由于涉及资源分配与相关成本，完整的全域普查及环境分析工作始终未能开展或收集相关数据。目前存在一种无需外勤人员与研究综述即可采集此类数据的方法，其依托未被利用的纸质档案记录实现。过往针对该问题的相关研究，曾通过对现有记录进行数据挖掘，构建流浪猫领地与种群密度模型（Aguilar与Farnworth，2012）。该方法在降低研究成本的同时，可为流浪猫种群分布情况提供有效数据支撑。本论文以美国加利福尼亚州洛杉矶市这一大型都会区为研究对象，采用此前未做空间化处理的数据集作为流浪猫种群密度的替代指标，探究该区域流浪猫种群的空间分布特征。本案例研究将围绕两项核心内容展开：其一，构建一套工作流程，将可分析格式的地理数据转化为流浪猫活动范围的空间模型；其二，对模型数据开展分析，以明确与流浪猫种群分布存在关联的变量（若存在此类变量）。本模型所使用的数据，以纸质档案形式从洛杉矶市政府获取，并成功导入地理信息系统（Geographic Information System）。基于清洗完成并完成地理编码的记录，可计算相关申请的密度，并将原始密度与聚类模式的分布集中度绘制成专题地图。相关性建模结果显示，流浪猫种群密度与城市人口密度呈正相关，而与家庭收入中位数呈微弱负相关。本研究对上述分析结果进行了评估，并对基于此类数据的后续研究方向进行了展望。