Skip to main content

Capturing Neighborhood Physical Disorder Using Small, Unmanned Aerial Systems

  • Chapter
  • First Online:
UAVs and Urban Spatial Analysis

Abstract

Neighborhood physical disorder manifests in many different ways, from litter and graffiti, to abandoned cars and broken windows. The effects of physical disorder are also varied, from limiting recreational activity for both young and elderly neighborhood residents, to creating a discount effect on housing prices. Methods for capturing physical disorder in neighborhoods often include in-person observation, the use of virtual street audits, windshield tours, and satellite imagery. The purpose of this chapter is to explore how small unmanned aerial systems can be used to capture physical disorder in neighborhoods. In addition, we discuss a basic analytical framework for quantifying disorder and detecting problematic areas within the community. Limitations and implications for urban and environmental planning are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Anselin, L. (2002). Under the hood Issues in the specification and interpretation of spatial regression models. Agricultural Economics, 27(3):247–267.

    Article  Google Scholar 

  • Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.

    Article  Google Scholar 

  • Anselin, L., & Rey, S. (2014). Modern spatial econometrics in practice: A guide to GeoDa, GeoDaSpace and PySAL. Chicago: GeoDa Press.

    Google Scholar 

  • Brewer, C., & Campbell, A. J. (1998). Beyond graduated circles: Varied point symbols for representing quantitative data on maps. Cartographic Perspectives, (29):6–25.

    Google Scholar 

  • Caughy, M. O., O’Campo, P. J., & Patterson, J. (2001). A brief observational measure for urban neighborhoods. Health & Place, 7(3), 225–236.

    Article  Google Scholar 

  • Charreire, H., Mackenbach, J. D., Ouasti, M., Lakerveld, J., Compernolle, S., Ben-Rebah, M., & Oppert, J. M. (2014). Using remote sensing to define environmental characteristics related to physical activity and dietary behaviours: A systematic review (the SPOTLIGHT project). Health & Place, 25, 1–9.

    Article  Google Scholar 

  • Cohen, D., Spear, S., Scribner, R., Kissinger, P., Mason, K., & Wildgen, J. (2000). “ Broken windows” and the risk of gonorrhea. American Journal of Public Health, 90(2), 230.

    Google Scholar 

  • Douglas, J. A., Briones, M. D., Bauer, E. Z., Trujillo, M., Lopez, M., & Subica, A. M. (2018). Social and environmental determinants of physical activity in urban parks: Testing a neighborhood disorder model. Preventive Medicine, 109, 119–124.

    Article  Google Scholar 

  • Dykes, J., MacEachren, A. M., & Kraak, M. J. (2005). Exploring geovisualization. Amsterdam: Elsevier.

    Google Scholar 

  • Estes, C. (2018). Can phoenix’s sunnyslope community become a tourist destination? KJZZ. Retrieved March 14, 2019, from https://tinyurl.com/ybcgs5j6

  • Fischer, H. (2016). New law allows drones to photograph people basking in their back yards. Arizona Capitol Times. Retrieved April 24, 2018, from https://tinyurl.com/y7vvxbnq

  • Franzini, L., Caughy, M. O. B., Nettles, S. M., & O’Campo, P. (2008). Perceptions of disorder: Contributions of neighborhood characteristics to subjective perceptions of disorder. Journal of Environmental Psychology, 28(1), 83–93.

    Article  Google Scholar 

  • Franzini, L., Elliott, M. N., Cuccaro, P., Schuster, M., Gilliland, M. J., Grunbaum, J. A., & Tortolero, S. R. (2009). Influences of physical and social neighborhood environments on children’s physical activity and obesity. American Journal of Public Health, 99(2), 271–278.

    Article  Google Scholar 

  • Getis, A., & Ord, J. K. (1996). Local spatial statistics: An overview. In Spatial analysis: Modelling in a GIS environment (Vol. 374, pp. 261–277). Bristol: GeoInformation International.

    Google Scholar 

  • Grubesic, T. H. (2008). Zip codes and spatial analysis: Problems and prospects. Socio-Economic Planning Sciences, 42(2), 129–149.

    Article  Google Scholar 

  • Grubesic, T. H., Wallace, D., Chamberlain, A. W., & Nelson, J. R. (2018). Using unmanned aerial systems (UAS) for remotely sensing physical disorder in neighborhoods. Landscape and Urban Planning, 169, 148–159.

    Article  Google Scholar 

  • Hallgren, K. A. (2012). Computing inter-rater reliability for observational data: An overview and tutorial. Tutorials in Quantitative Methods for Psychology, 8(1), 23.

    Article  Google Scholar 

  • Harcourt, B.E. (2001). Illusion of Order. The False Promise of Broken Windows Policing. Harvard University Press, Cambridge, MA.

    Google Scholar 

  • Herwig, C. (2016). Keeping Earth up to date and looking great. Googleblog. Retrieved May 16, 2018, from https://tinyurl.com/y9gj3dqb

  • Hill, T. D., Ross, C. E., & Angel, R. J. (2005). Neighborhood disorder, psychophysiological distress, and health. Journal of Health and Social Behavior, 46(2), 170–186.

    Article  Google Scholar 

  • Hoeben, E. M., Steenbeek, W., & Pauwels, L. J. (2018). Measuring disorder: Observer bias in systematic social observations at streets and neighborhoods. Journal of Quantitative Criminology, 34(1), 221–249.

    Article  Google Scholar 

  • Inzerillo, L., Di Mino, G., & Roberts, R. (2018). Image-based 3D reconstruction using traditional and UAV datasets for analysis of road pavement distress. Automation in Construction, 96, 457–469.

    Article  Google Scholar 

  • Janetsky, M. (2018). ‘Highway for bad things’: Phoenix tries closing alleys after neighbors complain of crime. Arizona Republic. Retrieved from https://tinyurl.com/y8tor8cl

  • Jelinski, D. E., & Wu, J. (1996). The modifiable areal unit problem and implications for landscape ecology. Landscape Ecology, 11(3), 129–140.

    Article  Google Scholar 

  • Jones, M., Pebley, A. R., & Sastry, N. (2011). Eyes on the block: Measuring urban physical disorder through in-person observation. Social Science Research, 40(2), 523–537.

    Article  Google Scholar 

  • Kelling, G. L., & Coles, C. M. (1997). Fixing broken windows: Restoring order and reducing crime in our communities. Simon and Schuster.

    Google Scholar 

  • Keyes, K. M., McLaughlin, K. A., Koenen, K. C., Goldmann, E., Uddin, M., & Galea, S. (2012). Child maltreatment increases sensitivity to adverse social contexts: Neighborhood physical disorder and incident binge drinking in Detroit. Drug and Alcohol Dependence, 122(1–2), 77–85.

    Article  Google Scholar 

  • LaDeau, S. L., Allan, B. F., Leisnham, P. T., & Levy, M. Z. (2015). The ecological foundations of transmission potential and vector-borne disease in urban landscapes. Functional Ecology, 29(7), 889–901.

    Article  Google Scholar 

  • Leibler, J. H., Zakhour, C. M., Gadhoke, P., & Gaeta, J. M. (2016). Zoonotic and vector-borne infections among urban homeless and marginalized people in the United States and Europe, 1990–2014. Vector-Borne and Zoonotic Diseases, 16(7), 435–444.

    Article  Google Scholar 

  • Manfreda, S., McCabe, M. F., Miller, P. E., Lucas, R., Pajuelo Madrigal, V., Mallinis, G., & Müllerová, J. (2018). On the use of unmanned aerial systems for environmental monitoring. Remote sensing, 10(4), 641.

    Google Scholar 

  • Molnar, B. E., Gortmaker, S. L., Bull, F. C., & Buka, S. L. (2004). Unsafe to play? Neighborhood disorder and lack of safety predict reduced physical activity among urban children and adolescents. American Journal of Health Promotion, 18(5), 378–386.

    Article  Google Scholar 

  • Mooney, S. J., Bader, M. D., Lovasi, G. S., Neckerman, K. M., Teitler, J. O., & Rundle, A. G. (2014). Validity of an ecometric neighborhood physical disorder measure constructed by virtual street audit. American Journal of Epidemiology, 180(6), 626–635.

    Article  Google Scholar 

  • Ogden, N. H. (2016). Emerging challenges of vector-borne diseases and cities: Vector-borne disease, climate change and urban design. Canada Communicable Disease Report, 42(10), 202.

    Article  Google Scholar 

  • Openshaw, S. (1984). Ecological fallacies and the analysis of areal census data. Environment and Planning A, 16(1), 17–31.

    Article  Google Scholar 

  • Paquet, C., Cargo, M., Kestens, Y., & Daniel, M. (2010). Reliability of an instrument for direct observation of urban neighbourhoods. Landscape and Urban Planning, 97(3), 194–201.

    Article  Google Scholar 

  • Park, Y., & Rogers, G. O. (2015). Neighborhood planning theory, guidelines, and research: Can area, population, and boundary guide conceptual framing? Journal of Planning Literature, 30(1), 18–36.

    Article  Google Scholar 

  • Patino, J. E., & Duque, J. C. (2013). A review of regional science applications of satellite remote sensing in urban settings. Computers, Environment and Urban Systems, 37, 1–17.

    Google Scholar 

  • Pliakas, T., Hawkesworth, S., Silverwood, R. J., Nanchahal, K., Grundy, C., Armstrong, B., et al. (2017). Optimising measurement of health-related characteristics of the built environment: Comparing data collected by foot-based street audits, virtual street audits and routine secondary data sources. Health & Place, 43, 75–84.

    Article  Google Scholar 

  • Ross, C. E., & Mirowsky, J. (1999). Disorder and decay the concept and measurement of perceived neighborhood disorder. Urban Affairs Review, 34(3), 412–432.

    Google Scholar 

  • Sampson, R. J., & Groves, W. B. (1989). Community structure and crime: Testing social-disorganization theory. American Journal of Sociology, 94(4), 774–802.

    Google Scholar 

  • Sampson, R. J., & Raudenbush, S. W. (1999). Systematic social observation of public spaces: A new look at disorder in urban neighborhoods. American Journal of Sociology, 105(3), 603–651.

    Article  Google Scholar 

  • Sampson, R. J., & Raudenbush, S. W. (2004). Seeing disorder: Neighborhood stigma and the social construction of “broken windows”. Social Psychology Quarterly, 67(4), 319–342.

    Article  Google Scholar 

  • Schaefer-McDaniel, N., Caughy, M. O. B., O’Campo, P., & Gearey, W. (2010). Examining methodological details of neighbourhood observations and the relationship to health: A literature review. Social Science & Medicine, 70(2), 277–292.

    Article  Google Scholar 

  • Skogan, W. G. (1990). Disorder and decline: Crime and the spiral of decay in American neighborhoods. Berkeley: University of California Press.

    Google Scholar 

  • Taylor, R. B. (2001). Breaking away from broken windows: Evidence from Baltimore neighborhoods and the nationwide fight against crime, grime, fear and decline. New York, NY: Westview.

    Google Scholar 

  • Wallace, D., & Schalliol, D. (2015). Testing the temporal nature of social disorder through abandoned buildings and interstitial spaces. Social science research, 54, 177–194.

    Google Scholar 

  • Weiss, L., Ompad, D., Galea, S., & Vlahov, D. (2007). Defining neighborhood boundaries for urban health research. American Journal of Preventive Medicine, 32(6), 154–S159.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Grubesic, T.H., Nelson, J.R. (2020). Capturing Neighborhood Physical Disorder Using Small, Unmanned Aerial Systems. In: UAVs and Urban Spatial Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-35865-5_7

Download citation

Publish with us

Policies and ethics