scholarly journals A spatial analysis of supply-demand of public transportation in Jefferson County, Kentucky.

2021 ◽  
Author(s):  
Nastaran Abdoli
Keyword(s):  
Spatial Analysis ◽  
Public Transportation ◽  
Jefferson County

Location Patterns of Section 8 Housing in Jefferson County, Kentucky

2012 ◽  
pp. 170-187
Author(s):  
Wei Song ◽  
Karl Keeling
Keyword(s):  
Low Income ◽  
Public Transportation ◽  
Central City ◽  
Jefferson County ◽  
Predominantly Black ◽  
Section 8 ◽  
Section 8 Housing ◽  
Location Patterns ◽  
Black Residents ◽  
Low Income Families

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 Section 8 housing continues to concentrate in the central city area with predominantly black residents, a high proportion of families in poverty, and abundant low-cost properties. The Section 8 voucher policy has failed to successfully de-concentrate poor families from these urban areas. Residential mobility of low-income families has been restricted by various factors, most important of which is the lack of accessibility to public transportation across the metropolitan area.

scholarly journals Spatial Analysis of Changes in Public Transport Passengers as a result of the Corona Pandemic in the Mashhad Suburban Area

2020 ◽  
Vol 4 (2) ◽  
pp. 149-162
Author(s):  
Mostafa Amirfakhriyan ◽  
◽  
Hadye Hasanneya ◽  
Keyword(s):  
Spatial Analysis ◽  
Public Transport ◽  
Public Transportation ◽  
Rural Areas ◽  
Strong Dependence ◽  
Long Distance ◽  
Transportation Management ◽  
Before And After ◽  
Spatial Differences ◽  
The City

The present study was designed to investigate the spatial variations of public transport passengers before and after Corona in the metropolitan area of ​​Mashhad. For this purpose, after conducting documentary studies, descriptive information about the volume of public transport passengers was obtained from Mashhad Bus Organization separately from each station, and at the same time with preparing a spatial map of the stations, a spatial database was formed. The period under study includes before Corona (March 1997 to February 1998) and after Corona (March 1998 to September 2010). Spatial differences before and after Corona were determined using classical and spatial statistics methods. The results showed significant differences in changes before and after corona. On the other hand, showing the differences showed that in rural areas with a long distance from the city of Mashhad in the west and northwest, unlike other areas, the volume of movements after Corona has increased. Using geographical regression model also showed that among local factors, economic characteristics related to each village show the greatest role in explaining the changes, which in some areas also explains up to 63%. Due to the demographic and economic structure of this area and their strong dependence on public transportation, methods such as closing or reducing operating hours; It brings more segregation of these villages. It is suggested that such studies, focusing on spatial analysis of changes, can assist public transportation management in redesigning programs related to this area.

Location Patterns of Section 8 Housing in Jefferson County, Kentucky

2010 ◽  
Vol 1 (2) ◽  
pp. 1-18 ◽  
Author(s):  
Wei Song ◽  
Karl Keeling
Keyword(s):  
Low Income ◽  
Public Transportation ◽  
Urban Areas ◽  
Spatial Clustering ◽  
Low Cost ◽  
Central City ◽  
Jefferson County ◽  
Housing Program ◽  
Section 8 ◽  
Section 8 Housing

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 Section 8 housing continues to concentrate in the central city area with predominantly black residents, a high proportion of families in poverty, and abundant low-cost properties. The Section 8 voucher policy has failed to successfully de-concentrate poor families from these urban areas. Residential mobility of low-income families has been restricted by various factors, most important of which is the lack of accessibility to public transportation across the metropolitan area.

scholarly journals 1499Health behaviours as risk factors of COVID-19 incidence in South Korea

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Jeehyun Kim ◽  
Daesung Yoo ◽  
Kwan Hong ◽  
Sujin Yum ◽  
Raquel Elizabeth Gómez Gómez ◽  
...  
Keyword(s):  
Risk Factors ◽  
Spatial Analysis ◽  
South Korea ◽  
Public Transportation ◽  
Negative Binomial ◽  
Dental Hygiene ◽  
Health Behaviours ◽  
Individual Vulnerability ◽  
Municipal Level ◽  
Bym Model

Abstract Background Personal health behaviours, which rely on community characteristics, could affect individual vulnerability on disease infection. Due to insufficient study to examine health behaviours as risk factors of COVID-19 infection, we conducted municipal level spatial analysis to investigate association between health behaviours and COVID-19 incidence. Methods We extracted cumulative COVID-19 incidence data from January 20th 2020 to February 25th 2021, health behaviours, health condition, socio-economic factors, and covariates in municipal level from publicly available dataset. We chose variables, which were standardized, considering multicollinearity (VIF<10). Further, we employed bayesian hierarchical negative binomial model with intrinsic conditional autoregressive (iCAR) and Besag, York and Mollié (BYM) model, and used deviance information criterion (DIC) for final model selection. Results The mean cumulative COVID-19 incidence per 10,000 population among 229 municipality was 13.73 (Standard deviation=11.43). iCAR model (DIC=2,825.3) outperformed BYM model (DIC=14,009.4). The results of iCAR model highlighted that incidence was associated with dental hygiene practice (incidence risk ratios [IRR]=0.92, 95% Credible Interval [CI]=0.85–1.00), whether tried to be thin (IRR=1.10, 95% CI = 1.00–1.20), proportion of medical personnel (IRR=1.09, 95% CI = 1.01–1.17), and volume of public transportation (IRR=1.19, 95% CI = 1.05–1.35), even after adjusting for various confounding factors. Conclusions Municipality with lower cumulative incidence was likely to have more people who practiced to keep dental hygiene and less people who tried to be thin. Key messages Municipal level spatial analysis resulted that health behaviours were associated with COVID-19 incidence in South Korea.

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