A Novel Approach to Identify and Map Kitten Clusters Using Geographic Information Systems (GIS): A Case Study From Tompkins County, NY

Direct and Indirect Factors Influencing Cat Outcomes at an Animal Shelter

Animal shelters play a vital role for pets, such as transitioning animals between homes, from outdoor communities into homes, caring for unadoptable and community animals, and providing a breadth of veterinary and welfare services. The goal of shelters is to move cats to their appropriate outcome as quickly as possible, which for many animals, is to rehome them as quickly as possible through adoption. Therefore, the ability to identify pre-existing factors, particularly those occurring outside the walls of the shelter, which result in specific outcomes is vital. In this study, we used structural equation modeling to test four hypotheses addressing how to predict cat outcome from a shelter in Washington, D.C. We developed four hypotheses that described how cat outcomes could be predicted, based on four general factors: (1) The characteristics of the cats; (2) The location of origin; (3) The type and date of intake; (4) The length of stay. Using 4 years of data from the Humane Rescue Alliance in Washington, D.C., we found support for each of our hypotheses. Additionally, we tested and found support for a global model, which comprised an amalgamation of our all our predictors. From the global model, we can conclude that many factors are at play in predicting cat outcomes in this shelter and very likely in many others as well. Critically, these factors are interconnected, indicating, for example, that cat characteristics mediate the influence of intake location on outcome type. Furthermore, our study highlights the importance of incorporating influences beyond the shelter when attempting to understand cat outcomes. Therefore, to modify cat outcomes most efficiently, such as increasing adoption probabilities, our results show that efforts may be most effective when incorporating multiple factors.

A New Web-Based Tool for RTO-Focused Animal Shelter Data Analysis

Animal shelters are increasingly interested in reducing their intake and helping their communities keep and care for animals. Improving Return-to-Owner (RTO) rates of stray dogs is one path to save significant shelter space, time, and costs and keep animals with their caregivers and communities. Aggregating and visualizing RTO data spatially are useful for identifying trends and highlighting areas for potential interventions. Since shelters collect similar data, an interactive web application was developed to make such an analysis easily reproducible. This paper presents the tool's capabilities via a case study of 2019 data from the Dallas Animal Services shelter, covering the relationship between stray intake and RTO rate, the distances traveled from home by RTOed strays, microchip use across the city and its relationship with RTO rate, and the length of stay of RTOs and other outcome groups. Findings include showing that 70% of RTOed strays traveled at most 1 mile away from home and 42% up to block away, and that at-large, adult strays that had a microchip had a 71% RTO rate compared with 39% without one. The results affected the shelter's hold time for strays, highlighted target areas for microchip programs, and motivated neighborhood-based methods to locate found dogs' owners. Shelters are welcome to use the tool and participate in the development of new analytical lenses and visualizations that would best suit their needs.