Cars, Community, and the Urban Landscape: Exploring the Spatial Patterns of Car Ownership and Associated Factors Using Geographically Weighted Regression
Mansour Gomaa, M. (2020). Cars, Community, and the Urban Landscape: Exploring the Spatial Patterns of Car Ownership and Associated Factors Using Geographically Weighted Regression. EKB Journal Management System, 3(1), 77-92. doi: 10.21608/ijaeur.2023.291605
Mohammed Mansour Gomaa. "Cars, Community, and the Urban Landscape: Exploring the Spatial Patterns of Car Ownership and Associated Factors Using Geographically Weighted Regression". EKB Journal Management System, 3, 1, 2020, 77-92. doi: 10.21608/ijaeur.2023.291605
Mansour Gomaa, M. (2020). 'Cars, Community, and the Urban Landscape: Exploring the Spatial Patterns of Car Ownership and Associated Factors Using Geographically Weighted Regression', EKB Journal Management System, 3(1), pp. 77-92. doi: 10.21608/ijaeur.2023.291605
Mansour Gomaa, M. Cars, Community, and the Urban Landscape: Exploring the Spatial Patterns of Car Ownership and Associated Factors Using Geographically Weighted Regression. EKB Journal Management System, 2020; 3(1): 77-92. doi: 10.21608/ijaeur.2023.291605

Cars, Community, and the Urban Landscape: Exploring the Spatial Patterns of Car Ownership and Associated Factors Using Geographically Weighted Regression

International Journal of Architectural Engineering and Urban Research
Volume 3, Issue 1, June 2020, Page 77-92  XML PDF (1016.8 K)
Document Type: Original Article
DOI: 10.21608/ijaeur.2023.291605
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Author
Mohammed Mansour Gomaa
Department of Architectural Engineering, Faculty of Engineering, Aswan University
Abstract
A growing body of literature has delved into the
relationships between the number of cars in households and a range
of relative factors. Most previous research has focused on the
relationships between car ownership and individuals' travel
patterns, socio-demographics, and economic factors. However,
sufficient studies on simultaneously identifying the spatial patterns
and associated factors are lacking. This paper examines the
relationship between the number of cars households own and
various factors, focusing specifically on spatial patterns and
variables that may influence car ownership. Using data from the
2017 National Household Travel Survey, the impact of housing
and population density, as well as transit accessibility, on car
ownership has been analyzed using OLS and GWR regression
models. Moreover, to test the spatial autocorrelation of car
ownership and examine its associative factors, statistical methods
have been used. Global Moran's I and Getis-Ord general G tests
were used to analyze car ownership data's spatial autocorrelation.
Five key variables - housing density, population density, proximity
to bus stops and bus stations, and distance to key locations –
significantly impact car ownership, suggesting that areas with a
high rate of car ownership tend to be clustered together.
Keywords
Urban Landscape; Car ownership; Geographically Weighted Regression; spatial autocorrelation; GIS
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