Socioeconomic Correlates of the Occurrence of Unsafe Buildings
Socioeconomic Correlates of the Occurrence of Unsafe Buildings in Huntington, West Virginia Presenters: Godwin Djietror & Chris Cannoy Affiliation: Marshall University LOCATION Latitude 38 22'N, longitude 82 33'W About an hour east of Charleston, state capital Source http://www.city-data.com/city/Huntington-WestVirginia.html BRIEF HISTORY Founded in 1871 by Collis B. Huntington, a railroad entrepreneur. bought Holderby Landing instrumental in developing it into a railroad terminus on
the Ohio River By 1911, expanded to engulf the City of Guyandotte. RELEVANT LITERATURE Several studies address the issue of unsafe buildings: Vey (2007); Pallagst (2008) housing problems in the industrial Midwest (Shetty, 2009) strategies for dealing with unsafe buildings Littlewood and Munro (1996) - poorly-targeted investment or disinvestment in residential structures RELEVANT LITERATURE Hills and Worthing (2006) motivation for carrying out building maintenance
Fonts and Fan (2006) race and budget allocation for housing expenditure POTENTIAL CORRELATES Education Median household income Proportion of female-headed households Proportion of low-income home owners POTENTIAL CORRELATES Unemployment Monthly owner costs as % of household income Housing tenure Race OBJECTIVES This study addresses two research questions: i.
What is the spatial pattern of distribution of unsafe building situates in the City of Huntington during the period 2000-2010? ii. What are the socio-economic correlates of the occurrence of unsafe buildings in the City? DATA & UNITS OF ANALYSIS Location data from Citys Unsafe Buildings Commission (UBC). Socio-economic data from 2000 United States census. Units of analysis: City of Huntington census tracts. METHODS Nearest Neighbor Analysis tool (in ESRI ArcMAP 10.1) used to determine spatial pattern of distribution:
clustering randomness dispersion Correlation analysis (in SPSS 21) to determine ascertain association of potential correlates with outcome variable RESULTS Nearest Neighbor Ratio: 0.544795 z-score: -20.681287 p-value: 0.000000 Observed Mean 77.840215 Distance: Meters Expected Mean 142.879853 Distance: Meters Z-scores indicate clustering or dispersal. P-value indicates significance. Given the z-score of -20.68, there is a less than 1% likelihood that this clustered pattern could be the result
of random chance. Unsafe/ Demolished Buildings Education Unemployment Median HH income Tenure Unempl Incom Educ. . e Tenur e Age -.116 .110 -.287
-.056 1 -.291 .695** .318 1 -.609** -.430 1 Owner cost Proportion white Female HH Head Zero-order
Race F. HH Head -.129 .518* -.622** .427 .221 -.300 -.056 -.355 .639** -.299
CONCLUSION Clustering factors driving occurrence predominate in certain areas of the City Maintenance costs and race are key SE correlates Female-headed households appear to play a role NEXT STEPS Detailed (individual level) study of the significant correlates in areas where clusters occur Potential collaboration with Women Studies to explore the potential role of female headed households
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