Aiello, A.E.

Bader, M.D.M.

Galea, S.

Goldmann, E.

Koenen, K.C.

Lovasi, G.S.

Mooney, S.J.

Rundle, A.G.

Sheehan, D.M.

Teitler, J.O.

English

Carolina Digital Repository

Response to Invited CommentaryMooney et al. Respond to “Observing Neighborhood Physical Disorder”Stephen J. Mooney*, Michael D. M. Bader, Gina S. Lovasi, Julien O. Teitler, Karestan C. Koenen,Allison E. Aiello, Sandro Galea, Emily Goldmann, Daniel M. Sheehan, and AndrewG. Rundle*Correspondence to Dr. Stephen J. Mooney, Harborview Injury Prevention & Research Center, University of Washington, 401Broadway, 4th Floor, Seattle, WA 98122 (e-mail: sjm2186@u.washington.edu).Initially submitted December 19, 2016; accepted for publication December 22, 2016.We appreciate Dr. Jackelyn Hwang’s comments (1) on ourpaper in which we compared a virtual audit of Detroit, Michi-gan, using data fromGoogle Street View (Google, Inc.,Moun-tain View, California) with an in-person audit of the same city(2). Broadly, we concur with her observations regarding thebenefits and limitations of virtual audits and share her excite-ment regarding how the technique might be leveraged in thefuture. We elaborate here on key points raised by Dr. Hwang,emphasizing decision points relevant to future research usingvirtual audits.First, we concur that ordinary kriging, the interpolation tech-nique that we and others have used for spatially sampled neigh-borhood data (3–6), does not account for block-by-blockdifferences in neighborhood physical disorder that can arise inrapidly changing neighborhoods (7). More broadly, many builtenvironment characteristics of researcher interest, includingsidewalk accessibility (8), transit access (9–11), many aspects ofurban form (12), and pedestrian safety (13) display microscalespatial patterns (i.e., block-by-block differences) that makespatial interpolation imprecise at that level of aggregation.However, these microscale patterns do not preclude the useof a virtual audit, only interpolation. That is, a virtual audit ofevery block in a neighborhood would capture the granularvariation in these features as well as would an in-person auditof every block. Thus, researchers considering neighborhoodaudits should allow the expected microscale variation of thecharacteristics being audited to inform the sample design.Second, we also concur that “big data” (14), such as 3-1-1(nonemergency) and 9-1-1 (emergency) call records, couldsupplement virtual audits by measuring aspects of social disor-der indicators that virtual audits cannot (15); however, thesedata may introduce biases because some demographic groupsare more engaged with 3-1-1 and 9-1-1 systems than are others(16). Indeed, we expect that researchers will develop modelsthat integrate indicators of disorder from multiple sources infuture research; administrative and audit data used together in asingle spatial model may provide better measurement accuracythan any one source alone. Suchmodels, potentially leveraginguniversal kriging, will benefit from further work comparing thereliability and validity of available assessment techniques forsimilar domains of inquiry (17).Finally, we caution researchers considering virtual audits orusing other online geospatial tools that using subject’s homeaddresses to select block faces to audit may violate the privacyof those study subjects (18). Web browsers and virtual auditsystems such as the Forty Area Study Street View (19) and theComputer Assisted Neighborhood Visual Assessment System(20) send location information to Google to identify the streetsegment to audit, thus passing identifying information to athird party. Spatial interpolation, as used in the present study,and spatial imputation minimize the risks of identity disclo-sure (2, 18).ACKNOWLEDGMENTSAuthor affiliations: Harborview Injury Prevention &Research Center, University of Washington, Seattle,Washington (Stephen J. Mooney); Department ofEpidemiology, Dornsife School of Public Health, DrexelUniversity, Philadelphia, Pennsylvania (Gina S. Lovasi);Department of Epidemiology, Mailman School of PublicHealth, Columbia University, New York, New York (DanielM. Sheehan, Andrew G. Rundle); Department of Sociology,American University, Washington, DC (Michael D. M.Bader); Center on Health, Risk, and Society, AmericanUniversity, Washington, DC (Michael D. M. Bader); Schoolof Social Work, Columbia University, New York, New York(Julien O. Teitler); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts(Karestan C. Koenen); Department of Epidemiology,Gillings School of Public Health, University of NorthCarolina-Chapel Hill, Chapel Hill, North Carolina (AllisonE. Aiello); School of Public Health, Boston University,Boston, Massachusetts (Sandro Galea); Division of SocialEpidemiology, New York University College of GlobalHealth, New York, New York (Emily Goldmann).The Eunice Kennedy ShriverNational Institute of ChildHealth and Human Development provided funding to S.J.M.under grant T32HD057822-07.Conflict of interest: none declared.REFERENCES1. Hwang J. Invited commentary: observing neighborhoodphysical disorder in an age of technological innovation. Am JEpidemiol. 2017;186(3):274–277.2. Mooney SJ, Bader MDM, Lovasi GS, et al. Street audits tomeasure neighborhood physical disorder: virtual or in-person?Am J Epidemiol. 2017;186(3):265–273.3. Keyes KM,McLaughlin KA, Koenen KC, et al. Childmaltreatment increases sensitivity to adverse social contexts:neighborhood physical disorder and incident binge drinking inDetroit.Drug Alcohol Depend. 2012;122(1-2):77–85.4. Bader MDM, Ailshire JA. Creating measures of theoreticallyrelevant neighborhood attributes at multiple spatial scales.Sociol Methodol. 2014;44(1):322–368.5. Mooney SJ, Bader MD, Lovasi GS, et al. Validity of anecometric neighborhood physical disorder measureconstructed by virtual street audit. Am J Epidemiol. 2014;180(6):626–635.6. Auchincloss AH, Diez Roux AV, Brown DG, et al. Filling thegaps: spatial interpolation of residential survey data in theestimation of neighborhood characteristics. Epidemiology.2007;18(4):469–478.7. Hwang J, Sampson RJ. Divergent pathways of gentrification:racial inequality and the social order of renewal in Chicagoneighborhoods. Am Sociol Rev. 2014;79(4):726–751.8. Hara K, Le V, Froehlich J. Combining crowdsourcing andgoogle street view to identify street-level accessibilityproblems. Presented at Proceedings of the SIGCHI Conferenceon Human Factors in Computing Systems, Paris, France, April27–May 2, 2013.9. Appleyard B. Sustainable and healthy travel choices and thebuilt environment: analyses of green and active access to railtransit stations along individual corridors. Transp Res Rec.2012;2303:38–45.10. Hara K, Azenkot S, Campbell M, et al. Improving publictransit accessibility for blind riders by crowdsourcing bus stoplandmark locations with Google Street View: an extendedanalysis. ACM Trans Accessible Comput. 2015;6(2):Article 5.11. Kelly CM,Wilson JS, Baker EA, et al. Using Google StreetView to audit the built environment: inter-rater reliabilityresults. Ann Behav Med. 2013;45(suppl 1):S108–S112.12. Neckerman KM, Lovasi GS, Davies S, et al. Disparities inurban neighborhood conditions: evidence from GIS measuresand field observation in New York City. J Public HealthPolicy. 2009;30(suppl 1):S264–S285.13. Mooney SJ, DiMaggio CJ, Lovasi GS, et al. Use of GoogleStreet View to assess environmental contributions to pedestrianinjury. Am J Public Health. 2016;106(3):462–469.14. Mooney SJ, Westreich DJ, El-Sayed AM. Commentary:epidemiology in the era of big data. Epidemiology. 2015;26(3):390–394.15. O’Brien DT, Sampson RJ. Public and private spheres ofneighborhood disorder: assessing pathways to violence usinglarge-scale digital records. J Res Crime Delinq. 2015;52(4):486–510.16. O’Brien DT. Custodians and custodianship in urbanneighborhoods: a methodology using reports of public issuesreceived by a city’s 311 hotline. Environ Behav. 2013;47(3):304–327.17. Pliakas T, Hawkesworth S, Silverwood RJ, et al. Optimisingmeasurement of health-related characteristics of the builtenvironment: comparing data collected by foot-based streetaudits, virtual street audits and routine secondary data sources.Health Place. 2016;43:75–84.18. BaderMD,Mooney SJ, Rundle AG. Protecting personallyidentifiable informationwhen using online geographic tools forpublic health research.Am J Public Health. 2016;106(2):206–208.19. Griew P, HillsdonM, Foster C, et al. Developing and testing astreet audit tool using Google Street View to measureenvironmental supportiveness for physical activity. Int J BehavNutr Phys Act. 2013;10:103.20. Bader MD,Mooney SJ, Lee YJ, et al. Development anddeployment of the Computer Assisted Neighborhood VisualAssessment System (CANVAS) to measure health-relatedneighborhood conditions.Health Place. 2015;31:163–172.

Mooney et al. Respond to "observing Neighborhood Physical Disorder"

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Mooney et al. Respond to "observing Neighborhood Physical Disorder"

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