Estimation of free-roaming dog populations using Google Street View: A methodological study - Porras, Guillermo; Diaz, Elvis W.; De la Puente-León, Micaela; Gavidia, Cesar M.; Castillo-Neyra, Ricardo (PloS one, 2025)
Controlling and eliminating zoonotic pathogens such as rabies virus, Echinococcus granulosus , and Leishmania spp . require quantitative knowledge of dog populations. Dog population estimates are fundamental for planning, implementing, and evaluating public health programs. However, dog population estimation is time-consuming, requires many field personnel, may be inaccurate and unreliable, and is not without danger. Our objective was to evaluate a remote method for estimating the population of free-roaming dogs using Google Street View (GSV). Adopting a citizen science approach, participants from Arequipa and other regions in Peru were recruited using social media and trained to use GSV to identify and count free-roaming dogs in 20 urban and 6 periurban communities. We used correlation metrics and negative binomial models to compare the counts of dogs identified in the GSV imagery with accurate counts of free-roaming owned dogs estimated via door-to-door (D2D) survey conducted in 2016. Citizen scientists detected 862 dogs using GSV. After adjusting by the proportion of streets that were scanned with GSV we estimated 1,022 free-roaming dogs, while the 2016 D2D survey estimated 1,536 owned free-roaming dogs across those 26 communities. We detected a strong positive correlation between the number of dogs detected by the two methods in the urban communities (r = 0.85; p < 0.001) and a weak correlation in periurban areas (r = 0.36; p = 0.478). Our multivariable model indicated that for each additional free-roaming dog estimated using GSV, the expected number of owned free-roaming dogs decreased by 2% in urban areas (p < 0.001) and increased by 2% in peri-urban areas (p = 0.004). The type of community (urban vs periurban) had an effect on the predictions, and fitting the models in periurban communities was difficult because of the sparsity of high-resolution GSV images. Using GSV imagery for estimating dog populations is a promising tool, especially in urban areas. Citizen scientists can help to generate information for disease control programs in places with insufficient resources.