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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Veröffentlicht in:	PloS one Jg. 20; H. 7; S. e0305154
Hauptverfasser:	Porras, Guillermo, Diaz, Elvis W., De la Puente-León, Micaela, Gavidia, Cesar M., Castillo-Neyra, Ricardo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 31.07.2025 Public Library of Science (PLoS)
Schlagworte:	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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Abstract	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.
AbstractList	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. 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.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.
Audience	Academic
Author	Porras, Guillermo Diaz, Elvis W. Gavidia, Cesar M. Castillo-Neyra, Ricardo De la Puente-León, Micaela
AuthorAffiliation	UFSJ: Universidade Federal de Sao Joao del-Rei, BRAZIL 2 Facultad de Medicina Veterinaria, Universidad Nacional Mayor de San Marcos, Lima, Perú 3 Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine at University of Pennsylvania, Philadelphia, Pennsylvania, United States of America 4 Department of Pathobiology, School of Veterinary Medicine at University of Pennsylvania, Philadelphia, Pennsylvania, United States of America 1 Zoonotic Disease Research Lab, One Health Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Lima, Peru
AuthorAffiliation_xml	– name: 1 Zoonotic Disease Research Lab, One Health Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Lima, Peru – name: 4 Department of Pathobiology, School of Veterinary Medicine at University of Pennsylvania, Philadelphia, Pennsylvania, United States of America – name: 3 Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine at University of Pennsylvania, Philadelphia, Pennsylvania, United States of America – name: 2 Facultad de Medicina Veterinaria, Universidad Nacional Mayor de San Marcos, Lima, Perú – name: UFSJ: Universidade Federal de Sao Joao del-Rei, BRAZIL
Author_xml	– sequence: 1 givenname: Guillermo orcidid: 0009-0001-9107-2143 surname: Porras fullname: Porras, Guillermo – sequence: 2 givenname: Elvis W. surname: Diaz fullname: Diaz, Elvis W. – sequence: 3 givenname: Micaela surname: De la Puente-León fullname: De la Puente-León, Micaela – sequence: 4 givenname: Cesar M. surname: Gavidia fullname: Gavidia, Cesar M. – sequence: 5 givenname: Ricardo orcidid: 0000-0001-6629-2139 surname: Castillo-Neyra fullname: Castillo-Neyra, Ricardo
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/40743124$$D View this record in MEDLINE/PubMed
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Title	Estimation of free-roaming dog populations using Google Street View: A methodological study
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