Geospatial Technologies and Advancing Geographic Decision Making
DOI: 10.4018/978-1-4666-0258-8.ch013
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Location Patterns of Section 8 Housing in Jefferson County, Kentucky

Abstract: The controversial Section 8 Housing Choice Voucher program is the largest federal low-income housing program. Using GIS-based spatial clustering analysis (Getis–Ord’s Gi statistic) and multiple linear regressions, in this paper, the authors examine the locational patterns of more than 13,600 Section 8 housing units in Jefferson County, Kentucky, and explore key social, economic, demographic, and locational factors underlying the spatial distribution of Section 8 housing. The findings reveal that Sectio… Show more

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Cited by 3 publications
(3 citation statements)
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“…The LISA statistics assess the local association between data by comparing local averages to global averages (Anselin, 1995) and are able to outline “an area that has a greater than average number of criminal or disorder events, or an area where people have a higher than average risk of victimization.” They help define crime hot spots and place a spatial limit on those areas of highest crime event concentration (Harries, 1999). To date, more researchers have attempted to use the LISA statistics to detect the concentration or clusters of crimes or other spatial phenomena and have produced convincing results (Chainey & Ratcliffe, 2005; Craglia, Haining, & Wiles, 2000; Mencken & Barnett, 1999; Messner et al., 1999; Murray, McGuffog, & Western, 2001; Song & Keeling, 2010; Wang, 2006). With the rapid development of GIS technology, statistically detecting hot sports using LISA and visualizing results in the GIS environment prove promising in conducting spatial analysis of urban crimes.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The LISA statistics assess the local association between data by comparing local averages to global averages (Anselin, 1995) and are able to outline “an area that has a greater than average number of criminal or disorder events, or an area where people have a higher than average risk of victimization.” They help define crime hot spots and place a spatial limit on those areas of highest crime event concentration (Harries, 1999). To date, more researchers have attempted to use the LISA statistics to detect the concentration or clusters of crimes or other spatial phenomena and have produced convincing results (Chainey & Ratcliffe, 2005; Craglia, Haining, & Wiles, 2000; Mencken & Barnett, 1999; Messner et al., 1999; Murray, McGuffog, & Western, 2001; Song & Keeling, 2010; Wang, 2006). With the rapid development of GIS technology, statistically detecting hot sports using LISA and visualizing results in the GIS environment prove promising in conducting spatial analysis of urban crimes.…”
Section: Literature Reviewmentioning
confidence: 99%
“…These findings indicate that the highest foreclosure rates are found in the central city, or at the very least neighborhoods with “urban” characteristics (Immergluck & Smith, 2005; Pedersen & Delgadillo, 2007). The strategy of looking at neighborhoods within cities has proven to be accepted by scholars (Ambrosius, Gilderbloom, & Hanka, 2010; Song & Keeling, 2010). However, in the aftermath of the recent housing boom and the beginning reemergence and gentrification of neighborhoods closer to downtown, anecdotal evidence suggests that foreclosure rates are also high in outlying suburbs and exurbs (Lloyd, 2008; also see MHC, 2008).…”
Section: Research Questions and Hypothesesmentioning
confidence: 99%
“…The voucher program funds the difference between the federal government's established fair market rent and actual rent. This provision extends the location of Section 8 families into previously unaffordable neighborhoods (Song & Keeling, 2010). Research on government studies has noted that providing voucher holders with increased mobility improves Section 8 housing recipients' lives considerably (Anthony, 2005;Brooks, Zugazaga, Wolk, & Adams, 2005).…”
mentioning
confidence: 99%