At the Society of Animal Welfare Administrators annual conference this past November, the National Council on Pet Population, SAWA, and several other animal welfare organizations launched Shelter Animals Count: The National Database Project as a platform for the collection and reporting of animal shelter data.
The database is located at www.shelteranimalscount.org. As of March 24, 352 shelters had registered to participate, and all 50 states were represented in the database.
“This database is precisely what the animal welfare world needs to guide good decision-making and help enable a greater understanding of the issues facing shelters in this country,” said Jodi Lytle Buckman, board of directors chair for Shelter Animals Count, in a press release.
“While significant progress has been made, we still need accurate and comprehensive nationwide data for our industry. The numbers really do count when it comes to saving lives.”
As membership increases, the database project will measure progress, inspire collaboration and increased public engagement, and work toward a positive impact on dog and cat homelessness.
Besides the NCPP and SAWA, the following organizations are represented on the Shelter Animals Count board: Animal Assistance Foundation; Animal Humane Society, Minnesota; American Society for the Prevention of Cruelty to Animals; Association of Shelter Veterinarians; Best Friends Animal Society; Humane Society of the Pike’s Peak Region; The Humane Society of the United States; Maddie’s Fund; National Animal Care & Control Association; Petco Foundation; PetSmart Charities; University of Florida College of Veterinary Medicine; University of Wisconsin-Madison School of Veterinary Medicine; and Wisconsin Humane Society.
Once an initial baseline of data has been gathered, Shelter Animals Count plans by midyear to provide comparative reports, including shelter and U.S. census data, through a Tableau Software interface. These reports will be viewable and sortable on the Shelter Animals Count website to allow for community comparisons, using variables such as population, education, and income levels.