Estimation of free-roaming dog populations using Google Street View: A methodological study

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 e...

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Vydáno v:PloS one Ročník 20; číslo 7; s. e0305154
Hlavní autoři: Porras, Guillermo, Diaz, Elvis W., De la Puente-León, Micaela, Gavidia, Cesar M., Castillo-Neyra, Ricardo
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 31.07.2025
Public Library of Science (PLoS)
Témata:
Analysis
Animal bites
Animal Distribution
Animal populations
Animals
Biology and Life Sciences
Citizen Science
Community
Control programs
Correlation
Disease control
Distribution
Dogs
Earth Sciences
Ecology and Environmental Sciences
Engineering and Technology
Estimation
Feral dogs
Forecasts and trends
Geospatial imaging
Households
Image resolution
Medicine and Health Sciences
Methods
People and Places
Peru
Physical Sciences
Population
Population statistics
Public health
Rabies
Science Policy
Scientists
Social Sciences
Streets
Surveys
Urban areas
Veterinary medicine
Peru
ISSN:1932-6203, 1932-6203
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Shrnutí: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.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0305154