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The impacts of rail transit system on residential property values have been examined for many metropolitan areas in the U.S. But there are few studies on the effects of light rail in a non-zoning city. As the rail transit in the largest non-zoning city, Houston's light rail transit line, or the so-called METRORail, has not received much attention from the planning research society since it opened to the public in 2004. A previous study by the author utilized 2007 household data to analyze the impacts of Houston's METRORail line and found the net effects of the rail transit line change significantly at different distances from the rail stations. One limitation of that study was that the physical environment and neighborhood characteristics of the station areas may not have had notable changes over a relatively short time span, i.e., three years after the opening of the light rail. This study employs 2010 InfoUSA household data to re-examine the effects of Houston's METRORail line. Similar to the previous studies, the author adopts a traditional ordinary linear regression (OLS) to investigate the contribution of a set of variables representing the physical, neighborhood, and accessibility characteristics of properties, and also employs a multi-level regression model (MLR) to examine the hierarchical structures of spatial data explicitly. In addition, this study tests the spatial autocorrelation in the modeling process and analyzes its effects on the results. The modeling results suggest that the METRORail line has had significant net positive effects on residential property values. The MLS model captures the difference of these effects with more spatial details. The spatial regression model improves model fit, but spatial autocorrelation is not completely eliminated.

Accessibility is a fundamental concept in theories of metropolitan spatial structure. Urban economic models explain urban structure as a function of access to jobs; accessibility is capitalised into land values, which in turn explain the population distribution. Studies of residential land values show that many factors contribute to the value of a given location: the characteristics of the housing unit, its location with respect to social and environmental amenities, as well as access to jobs, services and other economic opportunities. Empirical studies typically use job access as a proxy for more generalised access to economic activities. However, jobs represent many different activities, from retail shopping to heavy manufacturing, and the value of access to these activities may be positive or negative. In this paper, accessibility measures based on industry sectors have been developed, allowing the separating out of possible different effects. Their impacts are tested on residential land values using data from the Los Angeles region. A multilevel modelling approach is used in order to control for neighbourhood-level attributes common to multiple properties. It is found that the various access measures have different and significant effects on land values, but attributes of the dwelling unit, together with access to the coast, explain most of the variation. The multilevel model is confirmed; there is significant correlation among properties within the same neighbourhood.

This book discusses a range of planning and management issues related to building urban resiliency. It covers such topics as urban, environmental, and transportation planning, historical preservation, emergency relief and management, geographic information systems (GIS) and other technological applications. The book includes case studies of several cities and districts in China, including Shanghai, and a number of cities in the United States of America. Urban resiliency in the face of uncertainty is a priority for planning and governance in communities worldwide. In China, which has suffered

Particulate matter (PM) 2.5 generates a variety of negative effects on health, such as heart and lung disease, asthma, and respiratory symptoms. The pollutants in the atmosphere primarily result from human activities, and, in urban settings, increases in traffic volume and higher building density can elevate the level of PM2.5. Building on previous research, this study primarily focuses on two highly developed urban areas in the Texas Triangle region: Travis County in the Austin Metropolitan Area and Harris County in the Greater Houston Area. It explores different types of urban features, such as urban structures, land use/land cover, traffic volume, and distance from roads, that affect the PM2.5 concentration in urban environments at the local scale. Throughout this study, we use various research methods, including geographically weighted regression, to estimate the PM2.5 concentrations at local scales, 3D city models to derive urban characteristics, and the random forest algorithm to predict the effects of urban features on PM2.5 concentrations. Our findings suggest that developed land use, tall buildings in dense areas, and major traffic networks are the key contributors to PM2.5. However, we also find that tree canopy cover can significantly reduce PM2.5 concentrations.

There is only a sparse empirical literature that examines the emergence and growth of employment centres in large metropolitan areas. And only a few of the available studies analyse the determinants of the growth of centre(s) (and subcentres) over time. There are even fewer studies that compare different metropolitan regions in terms of how their employment centres grow or decay over several decades. This research aims to fill the gap by exploring changes in employment centres for two decades in two large US metropolitan areas of comparable size– Houston and Dallas. It employs 1990 and 2000 Census Transportation Planning Package (CTPP) data and American Community Survey (ACS) 2006–2010 5-year estimates to test factors suggested in the literature as possibly affecting the emergence and growth of employment centres. A revised two-stage minimum cut-off point approach is utilised to identify employment centres. We adopt an OLS model to find the driving forces for job changes in the employment centres of Houston and Dallas. A step-wise regression method is also adopted to find the major contributors to observed changes in job centres. Based on the results, it is possible to identify job changes and also a better understanding of the major factors associated with the changes. The findings are conceivably valuable for urban planners and policy makers to help them understand trends and allocate resources in an informed manner.

This book discusses a range of planning and management issues related to building urban resiliency. It covers such topics as urban, environmental, and transportation planning, historical preservation, emergency relief and management, geographic information systems (GIS) and other technological applications. The book includes case studies of several cities and districts in China, including Shanghai, and a number of cities in the United States of America. Urban resiliency in the face of uncertainty is a priority for planning and governance in communities worldwide. In China, which has suffered many of the world?s most devastating floods, earthquakes, and typhoons, preparing for the threat of disaster has long been an important planning objective. Recent calamities, such as the 2008 Winter Storms, the 2008 Wenchuan Earthquake, and the 2012 Beijing Floods have only made planning for resiliency more urgent. As planners work to prepare for such events, interdisciplinary collaboration becomes increasingly important. Planners need the tools and insights offered by other fields, including both the natural and social sciences. At the same time, these interdisciplinary relationships help shape the identity of urban-rural planning in its new role as one of China?s primary academic disciplines. Thus, the nexus between planning and science is critically important in building resilient cities in China, and the Chinese planning experience can serve as an example to and benefit countries around the world.

This book presents a multiregional input-output model for the metropolitan area of Southern California, which helps to estimate the economic impact of simulated terrorist attacks on seaports, malls etc. as well as of natural disasters such as earthquakes and tsunamis. The authors also analyze the economic and social effects of metropolitan policies such as growth controls, neighborhood gentrification or road-congestion charges. The model presented in the book has evolved over a period of 25 years and requires a very substantial computer capacity

This book presents a multiregional input-output model for the metropolitan area of Southern California, which helps to estimate the economic impact of simulated terrorist attacks on seaports, malls etc. as well as of natural disasters such as earthquakes and tsunamis. The authors also analyze the economic and social effects of metropolitan policies such as growth controls, neighborhood gentrification or road-congestion charges. The model presented in the book has evolved over a period of 25 years and requires a very substantial computer capacity.