1. Introduction
Both academic and non-academic worlds are increasingly concerned about the rising popularity of private cars, a known cause of exacerbated air pollution [
1,
2,
3], greenhouse gas emissions [
4], traffic congestion [
5,
6], and risks to public health [
7,
8]. Admittedly, a growing level of private-car dependence is beneficial to the auto industry and its deriving businesses [
9]. The prosperity of the auto industry considerably benefits local governments and communities by boosting employment rates and overall economic conditions. It is evident that an excessive number of vehicles on roads results in enormous environmental and social issues such as traffic congestion [
10] and air pollution [
11]. The positive correlation between higher levels of auto ownership and aggravating traffic congestion has been justified by numerous studies [
2,
12,
13,
14]. For instance, based on the travel survey conducted in King County in the US, Frank et al. [
15] (2006) found a significantly positive relationship between vehicles per household and environmental indicators, including traffic-related pollutants and volatile organic compounds.
Given the evident relationship between auto ownership and its detrimental impacts on the whole society, studies have focused on the understanding of key factors, including household attributes, built-environment characteristics, and life style indicators, on levels of automobile ownership. This line of inquiry corresponds to the lasting interest in the land use-transportation connection, which is “motivated by the possibility that design policies associated with the built environment can be used to control, manage, and shape individual traveler behavior and aggregate travel demand” [
16].
Additionally, it is also crucial to understand the spatial layout, including global and local spatial clustering, of automobile ownership. The spatial patterns of car ownership are indispensable midpoints of the pathways to investigate the travel behaviors of a person or a group of individuals [
17,
18], city-level policies [
19], regional-level travel demand [
20], land use allocation [
21], and many other interrelated research themes. As mentioned above, high levels of car ownership are negatively associated with societal well-being in terms of energy conservation, public health, and other social benefits, though it may be also beneficial in some aspects (for example, cancer screening and job accessibility) at the individual scale. Wang (2016) [
22] stated that good access to private cars encouraged individuals to have a frequent checkup for potential cancer risks. The author further added that the travel preferences of a person may follow a similar pattern to that observed in his/her neighboring communities. Therefore, using aggregated data or indicators (car ownership) of geographical references is important to elucidate people’s travel behavior under a concrete context.
How car ownership may be spatially and globally aggregated facilitates planners and governments in the process of developing specific transportation policies and land use planning in response to various needs of practice and research. For instance, police makers may restrict the use of private cars when high car ownership is spatially correlated with decreasing trends of physical activity of citizens, increased road crashes, and higher levels of noise pollution [
19]. By contrast, better access to cars, partly represented by high auto ownership, contributes to household-level benefits such as greater coverage of cancer screening uptake, necessitating that the parties with conflicting interests ought to seek a compromised policy on private vehicle usage [
22]. Consequently, the spatial clustering of car ownership is a significant phenomenon in relation to policy making and the coordination of conflicting interests from a broader perspective, demanding additional research endeavors.
A growing body of the literature has highlighted the significance of employing spatial methods, particularly those exceling in detecting spatial heterogeneity at a local level, into analyzing vehicle count data [
20,
23]. Spatially explicit approaches have been increasingly advocated in recent years because of their effectiveness in addressing spatial dependence, which has been a common yet unavoidable issue in transportation research [
24,
25]. Ignorance of the spatial dimension may lead to imprecise and inefficient estimators of regression coefficients [
23,
26] and unreliable inferences. In addition, the delineation of local hot spots regarding high car ownership helps to better understand bicyclists’ preferences [
17], job accessibility [
27], social equity [
28], and other behavioral, economic, and societal topics at a fine scale. In sum, it is equally consequential to pinpoint local spatial autocorrelation of car ownership on top of global measures.
Despite the literature’s stressing of the spatial impacts on transportation simulations [
20], there is no rigorous attempt to incorporate spatial patterns as an explaining factor in addressing the causal mechanism in land use-transportation interaction. In an effort to bridge such gaps, this study explicitly incorporates spatial autocorrelation into the interpretation of spatial heterogeneity of automobile ownership. It develops an integrative framework that aims at understanding (1) whether or not automobile dependence is spatially clustered; (2) whether high levels of automobile ownership are locally correlated; and (3) how the spatial mechanism of automobile ownership is partly explained by the factors associated with households, built-up environments, and the interacting terms of these two categories.
The remainder of this paper is organized as follows. The next section outlines findings of previous studies concerning the level of auto ownership and its driving factors. Following the literature review,
Section 3 advances two primary hypotheses of this work.
Section 4 introduces study areas, data sources, and approaches that were used to assess the hypotheses.
Section 5 highlights several key findings of the analysis. Finally,
Section 6 summarizes the whole study, discusses policy implications of current research, and directs future work.
2. Background
For years, the relationships between land use development and commuters’ travel patterns have been under intensive debates [
29,
30,
31,
32,
33]. Overall, the current literature focuses on two aspects; car ownership as a mediating variable and the exploration of various factors affecting vehicle dependence.
First, studies have primarily explored the connection between a range of variables and vehicle ownership, which is viewed as an intermediate link bridging different factors [
34,
35,
36,
37,
38]. As early as the 1990s, for example, Golob (1990) [
34] investigated a variety of interrelated factors, including vehicle ownership and weekly commuting times by private vehicle, transit, cycling, and walking. Using panel data, the author identified interconnected causal linkages between vehicle reliance and the remaining three variables. It was found that there existed a bidirectional casual effect between travel time by different modes and the number of cars per household. Furthermore, higher levels of car ownership were motivated by the propensity or willingness of households to lower their time expenditure as well as by people’s cost-and-benefit considerations. It was also noted by the author that, in the short term, the shift to a more costly but less time-consuming mode would partly result in a rise in the number of cars. In the long run, the adjustments on car ownership may become a driving force behind the households’ choices regarding residential locations [
34]. Likewise, Raphael et al. (2002) [
35] assessed whether car ownership substantively affects the employment characteristics of a household. Using employment status, work hours, and wages as dependent variables, the authors stated that the coefficients of auto ownership were significant and positive in all of three ordinary least squares regression models. Specifically, obtaining access to a car serves as a crucial factor in affecting labor market outcomes [
35]. Nonetheless, these studies might chiefly concentrate on the interrelations between auto ownership and households’ characteristics, possibly lacking a comprehensive account of the effects of built environment.
Second, recent studies have focused efforts on exploring the factors associated with car ownership, which is regarded as a dependent variable [
39,
40,
41,
42,
43,
44]. For instance, Cao et al. (2007) [
43] evaluated the linkages between vehicle ownership and built environment using ordered probit and static-score models. They concluded that the number of vehicles of an examined family were prevalently determined by demographical and social factors; however, the effects of built environment were extremely limited. In the same year, Guo et al. (2007) [
44] investigated the same issue, but a different definition of the built environment was introduced to their discrete choice models. A whole spectrum of measures, including land use types, urban forms, street networks, land use diversity index, and so on, were considered as built-up attributes [
44]. Their findings are in accordance with Cao et al.’s findings [
43]. Furthermore, not only do these attributes have impacts on the levels of car ownership but on households’ decisions of residential choices as well. Unlike Cao et al. (2007), Guo et al. (2007) [
44] maintained that both socio-demographics and built environment attributes were important determinants in car ownership decisions. However, a major limitation of these studies is that they might inadequately consider the potentially spatial signature of car ownership. Such spatially unobserved components may contain missing information from uncontrolled variables over space and time [
26].
To address this limitation, further research has applied Graphically Weighted Regression (GWR) models that integrate the spatial autocorrelation of regression coefficients in analyzing the spatial distribution of car ownership. Several publications have studied the factors associated with car ownership using the GWR approaches [
41,
45,
46]. GWR technologies are promising in capturing local patterns. These approaches may be further enhanced if future efforts could attempt to improve GWR’s generalizable power.
In this respect, this study contributes to the current literature by developing a modeling framework that targets the understanding of the spatial agglomeration of family’s car ownership levels and the coupling effects of built environments and household attributes on the clustering phenomena. This work also sheds light on the literature by synthesizing different global and local techniques of spatial autocorrelation detection and corrected Poisson regression models. Such a synthesis is scarcely observed in previous studies. Moreover, it adds to transportation planning practices with additional insights by designing a straightforward procedure that planners find easy to implement for various policy purposes. Under this overarching framework, two research hypotheses were posited and will be specified in the next section.
6. Discussion and Conclusions
The current paper illuminates the global and spatial patterns of vehicle ownership levels and explored the factors associated with households’ vehicle counts. It develops a framework that can be used to visualize and explain the spatial heterogeneity of auto ownership at the county level. It validates this framework in Broward, Palm-Beach, and Miami-Dade Counties, southern Florida, USA. This research indicates that the global pattern of households with high rates of vehicle ownership is non-random if population at risk is not taken as a reference. Nevertheless, there is no statistical evidence that households with three or more cars were globally clustered based on standardized data. Moreover, this paper does not find robust evidence that those households with high levels of vehicle ownership were locally clustered if the conclusion is made based on standardized data. In addition, six variables are found to significantly affect car ownership, as shown by the regression results of the Standard and Corrected Poisson models. The most substantial factors are the number of drivers in households, housing tenure, and the number of workers in households. These findings are in accordance with earlier studies [
35,
45,
53]. The contributions of this work to the literature are twofold. First, this paper establishes a refined index to characterize land use diversity based on the approach of Guo et al. [
44], and the measure appears to be scientifically sound to address those data sets with limited information on land use. Second, the application of distinct connectivity models boosts the robustness of hypothesis testing.
This paper also adds additional insights into planning practice. Whilst the socio-demographics of households considerably impact their selection of travel modes, optimizing land use is beneficial to mitigate car dependency [
61]. Compact urban forms and mixed land use structures may counteract households’ propensity to own a car. However, transportation policies and the regulations of private vehicles should be tuned toward specific contexts. As stated in the introduction, regulators should make distinct car usage policies based on different needs.
Figure 6 might indicate that a tendency of high car ownership is observed both in the downtown and rural regions, as represented by the red color. Imposing a strict tax on car use (such as road pricing) over the whole region may cause concerns about social inequity at the individual level. An elderly person with an apparent healthcare need may live in suburban areas, as suggested by previous studies regarding the transportation accessibility of people aged 65 or over [
62,
63]. These people residing in city peripheries may rely on private cars more heavily in order to make more frequent health checkups and cancer screenings than citizens in metropolitan regions. Thus, car ownership and its external consequences should be addressed in a way whereby flexible polices can accommodate the voices of different social groups. For example, when examining those areas of potentially high levels of car ownership, we need to be familiar with their demographic information and land use patterns before arbitrarily discouraging car dependence. For example, in download areas and central business districts, a variety of measures such as congestion pricing, the increase of parking fees, and restrictions on parking space can be deployed to deter people’s desire to own a private car, thereby promoting mass transit, cycling, walking, and other environmentally friendly travel modes. Meanwhile, the elderly, disabled people, and those with frequent healthcare needs can be exempt from those regulations of car usage with free parking space and discounted congestion tolls [
22]. The methods and outcomes of this work can be applied to formulate flexible transportation policies.
Several limitations of this study deserve further investigation. First, this study does not conduct a sensitivity analysis of the quadrat size, which may bias the results. Moreover, the quadrat analysis and the spatial autocorrelation tests fail to consider edge or boundary effects of the study area. This may hamper the testing statistics. Second, the study is based on sample data, limiting its ability to model or predict human behaviors, and the statistical implications are unbiased only when the population at risk is explicitly integrated in the correlation analysis. Third, the under-dispersion issue of the Poisson model requires further scrutiny.
This paper opens several promising avenues for follow up work and future research. First, prospective efforts can improve the techniques of the spatial autocorrelation of car ownership levels by developing innovative ways for edge or boundary corrections. In addition, future studies may employ other types of generalized models, including spatial error, spatial lag, multilevel ordered-response, and system dynamics models, for better revealing the impacts of land use patterns on vehicle ownership. With the rapid development of hardware and computers’ computational capacities, major auto manufacturers expand their investment in electric, hybrid-energy, and autonomous vehicles. Hence, the spatial patterns of the ownership levels of those vehicle types will be a fruitful direction in the near future.