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Looking at Google Street View can be amusing – as with the image of a cow with its face blurred out by Google’s algorithm for anonymising humans (see here). But this tool can help with research – as we report in a just published review in the journal “Tobacco Control”. In this blog we briefly consider some of the research possibilities of this tool of relevance to public health.

Using Google Street View (GSV) for research is becoming increasingly common, as per a recent review [1]. But in our just published work [2] we focused particularly on its potential for studying tobacco control-related issues. In general, it seems that GSV is good for identifying or evaluating larger objects (eg, large signs, buildings) but less effective for smaller ones, or those objects distant from roads.

GSV for studying tobacco control

We found two areas where GSV was useful for studying tobacco-related issues in the literature. One was smokefree signage at school grounds in NZ, with GSV being efficient and with high specificity (97%), albeit modest sensitivity (44%) because of the difficulty of seeing small signs at a distance [3]. Another was smokefree signage at hospital grounds in NZ (100% sensitivity and specificity) [4], but the sample was small.

Research on other issues also suggests some other potential areas of relevance to tobacco control research using GSV for identifying: signage/advertising/window displays (n=10 studies), retail outlets/stores (n=9), and bars/pubs (n=5) (see references in the Online Appendix to our published work [2]). This work could suggest its relevance for studying the food and alcohol environments – with one such NZ study being done for alcohol in urban streetscapes [5]. Such studies could also usefully be conducted over time, as imagery is routinely updated on GSV.

Studying neighbourhood conditions, behaviours

The literature on GSV seems to be rapidly growing – but a few other examples are as follows:

  • Urban measures of “neighborhood physical disorder” (eg, litter, graffiti, and abandoned buildings) have been studied in the USA [6].
  • Cycling infrastructure eg, a NZ study [7] and a study in Spain [8]. Cycling routes to school have also been studied in Belgium [9].
  • Walking infrastructure (eg, these studies [10] [11], including a NZ study [7]). Some of the features at the entrances to walkways, could also potentially be studied using GSV (see this field study in NZ: [12]).
  • Obesogenic neighbourhood features of the built environment, as per this Dutch study [13].
  • Assessment of building features that reflect guidelines for people with disabilities (ie, as per the Americans with Disabilities Act Access Guidelines) and social activity in a rural community in the USA [14].
  • In disaster preparedness in Japan – to help residents of areas at risk for natural disasters to learn escape in their “real” contexts [15]. (This would seem very relevant to NZ as well).

We have also used GSV for studying drinking fountains in public places in NZ (of modest benefit – in work yet to be published) and are currently exploring its value in studying shade provision in childrens’ playgrounds. But GSV is often only able to show features of parks and playgrounds that are near roads (ie, only a few NZ parks have “footpath views”).

Future improvements in GSV?

We suspect that if Google continues to expand “footpath views” and interior shop views it will become even more useful to researchers. Similarly if it updates its imagery more frequently (images were a median of 1.9 years old in one NZ study [3]). Improved resolution might also help with studying small items such as litter, tobacco packs on café tables etc. But we note that in some places biologists have already used GSV to study bird nests [16], and insects with silk nests [17]. In the meantime, we suggest that NZ researchers continue to explore the utility of GSV for researching social and built environments as they relate to public health.


Prof Nick Wilson
Dr Amber Pearson
Amanda Rzotkiewicz
A/Prof George Thomson


  1. Schootman M, Nelson EJ, Werner K, Shacham E, Elliott M, Ratnapradipa K et al. Emerging technologies to measure neighborhood conditions in public health: implications for interventions and next steps. Int J Health Geogr. 2016;15(1):20.
  2. Wilson N, Pearson A, Thomson G, Edwards R. Actual and potential use of google street view for studying tobacco issues: A brief review. Tob Control 2017(E-publication 30 June).
  3. Wilson N, Thomson G, Edwards R. The potential of Google Street View for studying smokefree signage. Aust N Z J Public Health. 2015;39(3):295-296.
  4. Wilson N, Thomson G. Suboptimal smokefree signage at some hospitals: Field observations and the use of Google Street View. N Z Med J. 2015;128(1415):56-59.
  5. Clews C, Brajkovich-Payne R, Dwight E, Ahmad Fauzul A, Burton M, Carleton O et al. Alcohol in urban streetscapes: a comparison of the use of Google Street View and on-street observation. BMC Public Health. 2016;16:442.
  6. Mooney SJ, Bader MD, Lovasi GS, Neckerman KM, Teitler JO, Rundle AG. Validity of an ecometric neighborhood physical disorder measure constructed by virtual street audit. Am J Epidemiol. 2014;180(6):626-635.
  7. Badland HM, Opit S, Witten K, Kearns RA, Mavoa S. Can virtual streetscape audits reliably replace physical streetscape audits? J Urban Health. 2010;87(6):1007-1016.
  8. Gullon P, Badland HM, Alfayate S, Bilal U, Escobar F, Cebrecos A et al. Assessing Walking and Cycling Environments in the Streets of Madrid: Comparing On-Field and Virtual Audits. J Urban Health. 2015;92(5):923-939.
  9. Vanwolleghem G, Van Dyck D, Ducheyne F, De Bourdeaudhuij I, Cardon G. Assessing the environmental characteristics of cycling routes to school: a study on the reliability and validity of a Google Street View-based audit. Int J Health Geogr. 2014;13:19.
  10. Chiang YC, Sullivan W, Larsen L. Measuring Neighborhood Walkable Environments: A Comparison of Three Approaches. Int J Environ Res Public Health. 2017;14(6).
  11. Adu-Brimpong J, Coffey N, Ayers C, Berrigan D, Yingling LR, Thomas S et al. Optimizing Scoring and Sampling Methods for Assessing Built Neighborhood Environment Quality in Residential Areas. Int J Environ Res Public Health. 2017;14(3).
  12. Wilson N, Brander B, Mansoor OD, Pearson AL. Building a reliable measure for unobtrusive observations of street-connecting pedestrian walkways. J Urban Health. 2014;91(6):1129-1135.
  13. Bethlehem JR, Mackenbach JD, Ben-Rebah M, Compernolle S, Glonti K, Bardos H et al. The SPOTLIGHT virtual audit tool: a valid and reliable tool to assess obesogenic characteristics of the built environment. Int J Health Geogr. 2014;13:52.
  14. Seekins T, Rennie B, Hammond J. Exploring the Feasibility of Using “Google Street View” to Assess the Accessibility of Community Environments: Developing Definitions and Observational Protocol for Image Recognition and Classification. In: Environmental Contexts and Disability (pp. 123-139). Emerald Group Publishing Limited, 2014.
  15. Mitsuhara H, Inoue T, Yamaguchi K, Takechi Y, Morimoto M, Iwaka K et al. Web-Based system for designing game-based evacuation drills. Procedia Computer Science. 2015;72:277-284.
  16. Olea PP, Mateo-Tomas P. Assessing species habitat using Google Street View: a case study of cliff-nesting vultures. PLoS One. 2013;8(1):e54582.
  17. Rousselet J, Imbert CE, Dekri A, Garcia J, Goussard F, Vincent B et al. Assessing species distribution using Google Street View: a pilot study with the Pine Processionary Moth. PLoS One. 2013;8(10):e74918.


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