Browsing by Author "Liu, Chao"

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    Changing Urban Growth Patterns in a Pro-Smart Growth State: The Case of Maryland, 1973-2002
    (2007) Shen, Qing; Liu, Chao; Liao, Joe; Zhang, Feng; Dorney, Chris
    This paper presents a study of recent urban growth patterns in the state of Maryland, which is known as a leader in the current smart growth movement. Five research questions are addressed in this study. First, what have been the trends in urban growth and land use in Maryland for the past 30 years? Second, to what extent have recent urban development patterns in Maryland matched the typical characterization of sprawl? Third, how have the intensity of urban land uses and the physical forms of urban growth in this state varied among its counties? Fourth, have the smart growth initiatives, especially the “Smart Growth Area Act,” significantly affected urban development patterns? Fifth, does the effectiveness of smart growth initiatives vary significantly across local jurisdictions? To answer these research questions, we measure, analyze, and model urban development patterns in Maryland using land use and land cover (LULC) and demographic data for 1973, 1992, 1997, 2000, and 2002. By calculating several important indicators of urban development patterns, we find that for the past three decades population densities have continued to decrease for the state as a whole. However, this trend has slowed since 1997, when the state implemented the smart growth programs. The land conversion rate has somewhat decreased, which indicates that smart growth initiatives have helped, in a limited way, curtail the growing demand for urban land and residential space. Further, we find that the patterns of urban growth and land use have generally become slightly less fragmented and more continuous since 1997. Additionally, we find significant variations in urban development patterns among local jurisdictions. In general, higher densities, higher levels of compactness, and lower levels of fragmentation are observed in the more urbanized counties. Moreover, by estimating a series of logit models of land conversion, we find that Maryland’s “Smart Growth Area Act” has generally increased the probability of land use change from non-urban to urban for areas designated as “Priority Funding Areas.” The effectiveness of this program, however, varies significantly across the counties. We discuss the implications of these findings and identify the directions for future research.
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    Exploring the Influence of Urban Form on Travel and Energy Consumption, using Structural Equation Modeling
    (2012) Liu, Chao; Ducca, Frederick W; Urban and Regional Planning and Design; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This dissertation has contributed to the current knowledge by gaining additional insights into the linkages of different aspects of the built environments, travel behavior, and energy consumption using Structural Equation Modeling (SEM) that provides a powerful analytic framework for a better understanding of the complex relationships of urban form, travel and energy consumption. Several urban form measurements (density, mixed land use index, street network connectivity, regional accessibility, and distance to transit) were gathered from multiple external sources and utilized for both trip/tour origins and destinations. This dissertation also contributed to the analysis framework by aggregating trips into tours to test whether the tour-based analysis generates better results than the trip-based analysis in terms of model fit, significance, and coefficient estimations. In addition to that, tour-based samples were also stratified into three different classification schemes to investigate the variations of relationship of urban form and travel among auto and transit modes and among various travel types.: (1) by modes (i.e. auto and transit); (2) by travel purposes (i.e. work, mixed, and non-work tours); and (3) by modes and purposes (first by modes, then by purpose). Stratification by purposes and modes provided an in-depth investigation of the linkages of urban form and travel behavior. The research findings are many: (1) urban form does have direct effects on travel distance for all tour types modeled; (2) urban form at the destination ends has more influence than on the origin ends; (3) Urban form has indirect effects on travel distance and energy consumption through affecting driving patterns, mode choice, vehicle type and tour complexity; (4) People tend to drive when they have complicated travel patterns; (5) The effects of intermediate variables (driving patterns, tour complexity, mode choice, and vehicle type) are stronger than the direct effects generated from urban form; (6) Tour-based analyses have better model fit than trip-based analysis; (7) Different types and modes of travel have various working mechanisms for travel behavior. No single transportation technology or land use policy action can offer a complete checklist of achieving deep reductions of travel and energy consumption while preserving mobility of driving.
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    The Impact of Employer Attitude to Green Commuting Plans on Reducing Car Driving: A Mixed Method Analysis
    (2014) Liu, Chao; Ding, Chuan; Lin, Yaoyu; Wang, Yaowu
    The empirical data were selected from Washington-Baltimore Regional Household Travel Survey in 2007-2008, including all the trips from home to workplace during the morning hours. The model parameters were estimated using the simultaneous estimation approach and the integrated model turns out to be superior to the traditional multinomial logit (MNL) model accounting for the impact of employer attitudes towards green commuting. The direct and indirect effects of socio-demographic attributes and employer attitudes towards green commuting were estimated. Through the structural equation modelling with mediating variable, this approach confirmed the intermediary nature of car ownership in the choice process.
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    Mapping Opportunity: A Critical Assessment
    (2014) Knaap, Elijah; Knaap, Gerrit; Liu, Chao
    A renewed interest has emerged on spatial opportunity structures and their role in shaping housing policy, community development, and equity planning. To this end, many have tried to quantify the geography of opportunity and quite literally plot it in a map. In this paper we explore the conceptual foundations and analytical methods that underlie the current practice of opportunity mapping. We find that opportunity maps can inform housing policy and metropolitan planning but that greater consideration should be given to the variables included, the methods in which variables are geographically articulated and combined, and the extent to which the public is engaged in opportunity mapping exercises.
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    Reclassification of Sustainable Neighborhoods: An Opportunity Indicator Analysis in Baltimore Metropolitan Area
    (2013) Knaap, Elijah; Knaap, Gerrit; Liu, Chao
    The “Sustainable neighborhoods” has become widely proposed objective of urban planners, scholars, and local government agencies. However, after decades of discussion, there is still no consensus on the definition of sustainable neighborhoods (Sawicki and Flynn, 1996; Dluhy and Swartz 2006; Song and Knaap,2007; Galster 2010). To gain new information on this issue, this paper develops a quantitative method for classifying neighborhood types. It starts by measuring a set of more than 100 neighborhood sustainable indicators. The initial set of indicators includes education, housing, neighborhood quality and social capital, neighborhood environment and health, employment and transportation. Data are gathered from various sources, including the National Center for Smart Growth (NCSG) data inventory, U.S. Census, Bureau of Economic Analysis (BEA), Environmental Protection Agency (EPA), many government agencies and private vendors. GIS mapping is used to visualize and identify variations in neighborhood attributes at the most detailed level (e.g census tracts). Factor analysis is then used to reduce the number of indicators to a small set of dimensions that capture essential differences in neighborhood types in terms of social, economic, and environmental dimensions. These factors loadings are used as inputs to a cluster analysis to identify unique neighborhood types. Finally, different types of neighborhoods are visualized using a GIS tool for further evaluation. The proposed quantitative analysis will help illustrate variations in neighborhood types and their spatial patterns in the Baltimore metropolitan region. This framework offers new insights on what is a sustainable neighborhood.
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    Understanding the Role of Built Environment in Reducing Vehicle Miles Traveled Accounting for Spatial Heterogeneity
    (2014) Liu, Chao; Ding, Chuan; Wang, Yaowu; Xie, Binglei
    In recent years, increasing concerns over climate change and transportation energy consumption have sparked research into the influences of urban form and land use patterns on motorized travel, notably vehicle miles traveled (VMT). However, empirical studies provide mixed evidence of the influence of the built environment on travel. In particular, the role of density after controlling for the confounding factors (e.g., land use mix, average block size, and distance from CBD) still remains unclear. The object of this study is twofold. First, this research provides additional insights into the effects of built environment factors on the work-related VMT, considering urban form measurements at both the home location and workplace simultaneously. Second, a cross-classified multilevel model using Bayesian approach is applied to account for the spatial heterogeneity across spatial units. Using Washington DC as our study area, the home-based work tour in the AM peak hours is used as the analysis unit. Estimation results confirmed the important role that the built environment at both home and workplace plays in affecting work-related VMT. In particular, the results reveal that densities at the workplace have more important roles than that at home location. These findings confirm that urban planning and city design should be part of the solution in stabilizing global climate and energy consumption.