Monitoring techniques in the capture and adoption of dogs and cats

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Abstract
The continuous improvement of the information systems of organizations that work toward the control of stray dog and cat populations facilitates the implementation of programs aimed at reducing the number of animals that roam free in public streets. This study aimed to present techniques to improve the understanding of the spatial distribution of stray dogs and cats and of people who adopt these animals and the fate of these animals in zoonosis control centers (ZCC). Ripley’s K function was used with a Euclidean distance graph to detect the distribution pattern of dogs and cats captured and of the people who adopted these animals. An estimate of the kernel density was used to allow a better assessment of the spatial distribution of the phenomenon studied. The results showed that the distribution of captured animals and of the people who adopted these animals form a spatial cluster (p = 0.01). Most of the animals were captured near the premises of the ZCC and near the downtown area. Factors such as the abandonment of animals near animal control agencies and the availability of food sources are the main hypotheses associated to the distribution of the captures. The awareness of the people who live in places where there is a greater number of stray animals and the distribution of the urban population are the main hypotheses to explain the distribution of the adoptions. The results will help to implement control measures over these populations in the most problematic areas.

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