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The dynamics of urban water use are characterized by spatial and temporal variability that is influenced by associated factors at different scales. Thus it is important to capture the relationship between urban water use and its determinants in a spatio-temporal framework in order to enhance understanding and management of urban

The dynamics of urban water use are characterized by spatial and temporal variability that is influenced by associated factors at different scales. Thus it is important to capture the relationship between urban water use and its determinants in a spatio-temporal framework in order to enhance understanding and management of urban water demand. This dissertation aims to contribute to understanding the spatio-temporal relationships between single-family residential (SFR) water use and its determinants in a desert city. The dissertation has three distinct papers to support this goal. In the first paper, I demonstrate that aggregated scale data can be reliably used to study the relationship between SFR water use and its determinants without leading to significant ecological fallacy. The usability of aggregated scale data facilitates scientific inquiry about SFR water use with more available aggregated scale data. The second paper advances understanding of the relationship between SFR water use and its associated factors by accounting for the spatial and temporal dependence in a panel data setting. The third paper of this dissertation studies the historical contingency, spatial heterogeneity, and spatial connectivity in the relationship of SFR water use and its determinants by comparing three different regression models. This dissertation demonstrates the importance and necessity of incorporating spatio-temporal components, such as scale, dependence, and heterogeneity, into SFR water use research. Spatial statistical models should be used to understand the effects of associated factors on water use and test the effectiveness of certain management policies since spatial effects probably will significantly influence the estimates if only non-spatial statistical models are used. Urban water demand management should pay attention to the spatial heterogeneity in predicting the future water demand to achieve more accurate estimates, and spatial statistical models provide a promising method to do this job.
ContributorsOuyang, Yun (Author) / Wentz, Elizabeth (Thesis advisor) / Ruddell, Benjamin (Thesis advisor) / Harlan, Sharon (Committee member) / Janssen, Marcus (Committee member) / Arizona State University (Publisher)
Created2013
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Description
It has been identified in the literature that there exists a "spatial mismatch" between geographical concentrations of lower-income or minority people who have relatively lower rates of car ownership, lower skills or educational attainment and who mainly rely on public transit for their travel, and low-skilled jobs for which they

It has been identified in the literature that there exists a "spatial mismatch" between geographical concentrations of lower-income or minority people who have relatively lower rates of car ownership, lower skills or educational attainment and who mainly rely on public transit for their travel, and low-skilled jobs for which they more easily qualify. Given this situation, various types of transportation projects have been constructed to improve public transit services and, alongside other goals, improve the connection between low-skilled workers and jobs. As indicators of performance, measures of job accessibility are commonly used in to gauge how such improvements have facilitated job access. Following this approach, this study investigates the impact of the Phoenix Metro Light Rail on job accessibility for the transit users, by calculating job accessibility before and after the opening of the system. Moreover, it also investigates the demographic profile of those who have benefited from improvements in job accessibility----both by income and by ethnicity. Job accessibility is measured using the cumulative opportunity approach which quantifies the job accessibility within different travel time limits, such as 30 and 45 minutes. ArcGIS is used for data processing and results visualization. Results show that the Phoenix light rail has improved job accessibility of the traffic analysis zones that are along the light rail line and Hispanic and lower-income groups have benefited more than their counterparts.
ContributorsLiu, Liyuan (Author) / Golub, Aaron (Thesis advisor) / Wentz, Elizabeth (Committee member) / Kuby, Michael (Committee member) / Arizona State University (Publisher)
Created2014