Geology topics

Reid Ewing

Publications and source records attributed to Reid Ewing.

7 recordsLinked to original sources

USGS2020

Conceptual frameworks

The chapter starts by addressing some of the issues that come from not using a conceptual framework. This point is illustrated using an example with causal factors. The chapter then goes on to explain the mechanics of establishing conceptual frameworks. Lastly, it lays out a step-by-step guide on how to create a framework—generating a set of concepts, specifying the relations between concepts, writing a narrative for the conceptual framework, and rethinking the framework through the entire research project.

Book chapter↗

USGS2020

Structural equation modeling

This chapter introduces background and historical information on how structural equation modeling (SEM) came to be developed. Then, the main differences between SEM and earlier multivariate methods are explained. The chapter describes three main applications of SEM: path analysis, factor analysis, and hybrid models. Some computer programs are recommended for these applications. The step-by-step section goes over how to estimate structural models with AMOS and R. The chapter concludes with two example applications of SEM in the planning field.

Book chapter↗

USGS2018

Associations between urban sprawl and life expectancy in the United States

In recent years, the United States has had a relatively poor performance with respect to life expectancy compared to the other developed nations. Urban sprawl is one of the potential causes of the high rate of mortality in the United States. This study investigated cross-sectional associations between sprawl and life expectancy for metropolitan counties in the United States in 2010. In this study, the measure of life expectancy in 2010 came from a recently released dataset of life expectancies by county. This study modeled average life expectancy with a structural equation model that included five mediators: annual vehicle miles traveled (VMT) per household, average body mass index, crime rate, and air quality index as mediators of sprawl, as well as percentage of smokers as a mediator of socioeconomic status. After controlling for sociodemographic characteristics, this study found that life expectancy was significantly higher in compact counties than in sprawling counties. Compactness affects mortality directly, but the causal mechanism is unclear. For example, it may be that sprawling areas have higher traffic speeds and longer emergency response times, lower quality and less accessible health care facilities, or less availability of healthy foods. Compactness affects mortality indirectly through vehicle miles traveled, which is a contributor to traffic fatalities, and through body mass index, which is a contributor to many chronic diseases. This study identified significant direct and indirect associations between urban sprawl and life expectancy. These findings support further research and practice aimed at identifying and implementing changes to urban planning designed to support health and healthy behaviors.

International Journal of Environmental Research an↗

USGS2015

Compact development and VMT: environmental determinism, self-selection, or some of both?

There is a long-running debate in the planning literature about the effects of the built environment on travel behavior and the degree to which apparent effects are due to the tendency of households to self-select into neighborhoods that reinforce their travel preferences. Those who want to walk will choose walkable neighborhoods, and those who want to use transit will choose transit-served neighborhoods. These households might have walked or used transit more than their neighbors wherever they lived. Most previous studies have shown that individual attitudes attenuate the relationship between the residential environment and travel choices, and so the effect of the built environment on travel may be overestimated. But there are other researchers who argue the reverse, claiming that residential preferences reinforce built environmental influences. This study assesses the relative importance of the built environment and residential preferences/travel attitudes for a sample of 962 households in the Greater Salt Lake region using structural equation modeling. For the sake of simplicity, we extracted two factors using principal component analysis, one representing the built environment and the other representing residential preferences/attitudes. Our findings are consistent with the view that the neighborhood built environment and residential preferences both influence household’s travel, that the built environment is the stronger influence, and that the built environment affects travel through two causal pathways, one direct and the other indirect, through attitudes.

Environment and Planning B: Planning and Design↗

USGS2014

Urban sprawl as a risk factor in motor vehicle crashes

A decade ago, compactness/sprawl indices were developed for metropolitan areas and counties which have been widely used in health and other research. In this study, we first update the original county index to 2010, then develop a refined index that accounts for more relevant factors, and finally seek to test the relationship between sprawl and traffic crash rates using structural equation modelling. Controlling for covariates, we find that sprawl is associated with significantly higher direct and indirect effects on fatal crash rates. The direct effect is likely due to the higher traffic speeds in sprawling areas, and the indirect effect is due to greater vehicle miles driven in such areas. Conversely, sprawl has negative direct relationships with total crashes and non-fatal injury crashes, and these offset (and sometimes overwhelm) the positive indirect effects of sprawl on both types of crashes through the mediating effect of increased vehicle miles driven. The most likely explanation is the greater prevalence of fender benders and other minor accidents in the low speed, high conflict traffic environments of compact areas, negating the lower vehicle miles travelled per capita in such areas.

Urban Studies↗

USGS2014

Structural equation models of VMT growth in US urbanised areas.

Vehicle miles travelled (VMT) is a primary performance indicator for land use and transportation, bringing with it both positive and negative externalities. This study updates and refines previous work on VMT in urbanised areas, using recent data, additional metrics and structural equation modelling (SEM). In a cross-sectional model for 2010, population, income and freeway capacity are positively related to VMT, while gasoline prices, development density and transit service levels are negatively related. Findings of the cross-sectional model are generally confirmed in a more tightly controlled longitudinal study of changes in VMT between 2000 and 2010, the first model of its kind. The cross-sectional and longitudinal models together, plus the transportation literature generally, give us a basis for generalising across studies to arrive at elasticity values of VMT with respect to different urban variables.

Urban Studies↗

USGS2013

Combined effects of compact cevelopment, transportation investments, and road user pricing on vehicle miles traveled in urbanized areas

Vehicle miles traveled (VMT) is the primary determinant of traffic congestion, vehicle crashes, greenhouse gas emissions, and other effects of transportation. Two previous studies have sought to explain VMT levels in urbanized areas. This study updates and expands on previous work with more recent data, additional metrics, and structural equation modeling (SEM) to explain VMT levels in 315 urbanized areas. According to SEM, population, income, and gasoline prices are primary exogenous drivers of VMT. Development density is a primary endogenous driver. Urbanized areas with more freeway capacity are significantly less dense and have significantly higher VMT per capita. Areas with more transit service coverage and service frequency have higher development densities and per capita transit use, which leads to lower VMT per capita. The indirect effect of transit on VMT through land use, the so-called land use multiplier, is more than three times greater than the direct effect through transit ridership.

Transportation Research Record↗