Matching Items (33)
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Change of residence is a commonly occurring event in urban areas. It reflects how people interact with the social or physical environment. Thus, by exploring the movement patterns of residential changes, geographers and other scholars hope to learn more about the reasons and impacts associated with residential mobility, and to

Change of residence is a commonly occurring event in urban areas. It reflects how people interact with the social or physical environment. Thus, by exploring the movement patterns of residential changes, geographers and other scholars hope to learn more about the reasons and impacts associated with residential mobility, and to better understand how humans and the environment mutually interact. This is especially meaningful if exploration is based on micro scale movements, since residential changes within a city or a county reflect how the urban structure and community composition interact. Local differentiation, as an inevitable feature among movements at different places, can best be examined based on data at the micro scale. Such work is meaningful, but there have not been appropriate approaches for assessment and evaluation. The majority of traditional methods concentrate more on aggregate movement data at a national scale. So, in order to facilitate research examining movement patterns from a mass of individual residential changes at a micro scale, a toolkit, implemented by computational programming, is introduced in this dissertation to integrate both exploratory as well as confirmatory methods. This toolkit also employs a creative method to explore the spatial autocorrelation of residential movements, reflecting the local effects involved in this social event. The effectiveness and efficiency of this toolkit is examined through a concrete application involving 2,363 residential movements in Franklin County, Ohio.
ContributorsLiu, Yin (Author) / Murray, Alan (Thesis advisor) / Rey, Sergio (Committee member) / Wentz, Elizabeth (Committee member) / Arizona State University (Publisher)
Created2012
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Factors that explain human mobility and active transportation include built environment and infrastructure features, though few studies incorporate specific geographic detail into examinations of mobility. Little is understood, for example, about the specific paths people take in urban areas or the influence of neighborhoods on their activity. Detailed analysis of

Factors that explain human mobility and active transportation include built environment and infrastructure features, though few studies incorporate specific geographic detail into examinations of mobility. Little is understood, for example, about the specific paths people take in urban areas or the influence of neighborhoods on their activity. Detailed analysis of human activity has been limited by the sampling strategies employed by conventional data sources. New crowdsourced datasets, or data gathered from smartphone applications, present an opportunity to examine factors that influence human activity in ways that have not been possible before; they typically contain more detail and are gathered more frequently than conventional sources. Questions remain, however, about the utility and representativeness of crowdsourced data. The overarching aim of this dissertation research is to identify how crowdsourced data can be used to better understand human mobility. Bicycling activity is used as a case study to examine human mobility because smartphone apps aimed at collecting bicycle routes are readily available and bicycling is under studied in comparison to other modes. The research herein aimed to contribute to the knowledge base on crowdsourced data and human mobility in three ways. First, the research examines how conventional (e.g., counts, travel surveys) and crowdsourced data correspond in representing bicycling activity. Results identified where the data correspond and differ significantly, which has implications for using crowdsourced data for planning and policy decisions. Second, the research examined the factors that influence cycling activity generated by smartphone cycling apps. The best predictors of activity were median weekly rent, percentage of residential land, and the number of people using two or more modes to commute in an area. Finally, the third part of the dissertation seeks to understand the impact of bicycle lanes and bicycle ridership on residential housing prices. Results confirmed that bicycle lanes in the neighborhood of a home positively influence sale prices, though ridership was marginally related to house price. This research demonstrates that knowledge obtained through crowdsourced data informs us about smaller geographic areas and details on where people bicycle, who uses bicycles, and the impact of the built environment on bicycling activity.

ContributorsConrow, Lindsey (Author) / Wentz, Elizabeth (Thesis advisor) / Nelson, Trisalyn (Committee member) / Mooney, Sian (Committee member) / Pettit, Christopher (Committee member) / Arizona State University (Publisher)
Created2018
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In light of climate change and urban sustainability concerns, researchers have been studying how residential landscape vegetation affect household water consumption and heat mitigation. Previous studies have analyzed the correlations among residential landscape practices, household water consumption, and urban heating at aggregate spatial scales to understand complex landscape decision tradeoffs

In light of climate change and urban sustainability concerns, researchers have been studying how residential landscape vegetation affect household water consumption and heat mitigation. Previous studies have analyzed the correlations among residential landscape practices, household water consumption, and urban heating at aggregate spatial scales to understand complex landscape decision tradeoffs in an urban environment. This research builds upon those studies by using parcel-level variables to explore the implications of vegetation quantity and height on water consumption and summertime surface temperatures in a set of single-family residential homes in Tempe, Arizona. QuickBird and LiDAR vegetation imagery (0.600646m/pixel), MASTER temperature data (approximately 7m/pixel), and household water billing data were analyzed. Findings provide new insights into the distinct variable, vegetation height, thereby contributing to past landscape studies at the parcel-level. We hypothesized that vegetation of different heights significantly impact water demand and summer daytime and nighttime surface temperatures among residential homes. More specifically, we investigated two hypotheses: 1) vegetation greater than 1.5 m in height will decrease daytime surface temperature more than grass coverage, and 2) grass cover will increase household water consumption more than other vegetation classes, particularly vegetation height. Bivariate and stepwise linear regressions were run to determine the predictive capacity of vegetation on surface temperature and on water consumption. Trees of 1.5m-10m height and trees of 5m-10m height lowered daytime surface temperatures. Nighttime surface temperatures were increased by trees of 5m-10m height and decreased by grass. Houses that experienced higher daytime surface temperatures consumed less water than houses with lower daytime surface temperatures, but water consumption was not directly related to vegetation cover or height. Implications of this study support the practical application of tree canopy (vegetation of 5m-10m height) to mitigate extreme surface temperatures. The trade-offs between water and vegetation classes are not yet clear because vegetation classes cannot singularly predict household water consumption.

ContributorsJia, Jessica (Co-author) / Larson, Kelli L. (Co-author, Thesis director) / Wentz, Elizabeth (Co-author, Committee member) / Barrett, The Honors College (Contributor) / School of Geographical Sciences and Urban Planning (Contributor) / School of Sustainability (Contributor)
Created2015-05
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Description
Alternative fuel vehicles (AFVs) have seen increased attention as a way to reduce reliance on petroleum for transportation, but adoption rates lag behind conventional vehicles. One crucial barrier to their proliferation is the lack of a convenient refueling infrastructure, and there is not a consensus on how to locate initial

Alternative fuel vehicles (AFVs) have seen increased attention as a way to reduce reliance on petroleum for transportation, but adoption rates lag behind conventional vehicles. One crucial barrier to their proliferation is the lack of a convenient refueling infrastructure, and there is not a consensus on how to locate initial stations. Some approaches recommend placing stations near where early adopters live. An alternate group of methods places stations along busy travel routes that drivers from across the metropolitan area traverse each day. To assess which theoretical approach is most appropriate, drivers of compressed natural gas (CNG) vehicles in Southern California were surveyed at stations while they refueled. Through GIS analysis, results demonstrate that respondents refueled on the way between their origins and destinations ten times more often than they refueled near their home, when no station satisfied both criteria. Freeway interchanges, which carry high daily passing traffic volumes in metropolitan areas, can be appropriate locations for initial stations based on these results. Stations cannot actually be built directly at these interchange sites, so suitable locations on nearby street networks must be chosen. A network GIS method is developed to assess street network locations' ability to capture all traffic passing through 72 interchanges in greater Los Angeles, using deviation from a driver's shortest path as the metric to assess a candidate site's suitability. There is variation in the ability of these locations to capture passing traffic both within and across interchanges, but only 7% of sites near interchanges can conveniently capture all travel directions passing through the interchange, indicating that an ad hoc station location strategy is unlikely to succeed. Surveys were then conducted at CNG stations near freeway interchanges to assess how drivers perceive and access refueling stations in these environments. Through comparative analysis of drivers' perceptions of stations, consideration of their choice sets, and the observed frequency of the use of a freeway to both access and leave these stations, results indicate that initial AFV stations near freeway interchanges can play an important role in regional AFV infrastructure.
ContributorsKelley, Scott (Author) / Kuby, Michael (Thesis advisor) / Wentz, Elizabeth (Committee member) / Pendyala, Ram (Committee member) / Arizona State University (Publisher)
Created2015
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Drawing from the fields of coastal geography, political ecology, and institutions, this dissertation uses Cape Cod, MA, as a case study, to investigate how chronic and acute climate-related coastal hazards, socio-economic characteristics, and governance and decision-making interact to produce more resilient or at-risk coastal communities. GIS was used to model

Drawing from the fields of coastal geography, political ecology, and institutions, this dissertation uses Cape Cod, MA, as a case study, to investigate how chronic and acute climate-related coastal hazards, socio-economic characteristics, and governance and decision-making interact to produce more resilient or at-risk coastal communities. GIS was used to model the impacts of sea level rise (SLR) and hurricane storm surge scenarios on natural and built infrastructure. Social, gentrification, and tourism indices were used to identify communities differentially vulnerable to coastal hazards. Semi-structured interviews with planners and decision-makers were analyzed to examine hazard mitigation planning.

The results of these assessments demonstrate there is considerable variation in coastal hazard impacts across Cape Cod towns. First, biophysical vulnerability is highly variable with the Outer Cape (e.g., Provincetown) at risk for being temporarily and/or permanently isolated from the rest of the county. In most towns, a Category 1 accounts for the majority of inundation with impacts that will be intensified by SLR. Second, gentrification in coastal communities can create new social vulnerabilities by changing economic bases and disrupting communities’ social networks making it harder to cope. Moreover, higher economic dependence on tourism can amplify towns’ vulnerability with reduced capacities to recover. Lastly, low political will is an important barrier to effective coastal hazard mitigation planning and implementation particularly given the power and independence of town government on Cape Cod. Despite this independence, collaboration will be essential for addressing the trans-boundary effects of coastal hazards and provide an opportunity for communities to leverage their limited resources for long-term hazard mitigation planning.

This research contributes to the political ecology of hazards and vulnerability research by drawing from the field of institutions, by examining how decision-making processes shape vulnerabilities and capacities to plan and implement mitigation strategies. While results from this research are specific to Cape Cod, it demonstrates a broader applicability of the “Hazards, Vulnerabilities, and Governance” framework for assessing other hazards (e.g., floods, fires, etc.). Since there is no “one-size-fits-all” approach to mitigating coastal hazards, examining vulnerabilities and decision-making at local scales is necessary to make resiliency and mitigation efforts specific to communities’ needs.
ContributorsGentile, Lauren Elyse (Author) / Bolin, Bob (Thesis advisor) / Wentz, Elizabeth (Committee member) / White, Dave (Committee member) / York, Abigail (Committee member) / Arizona State University (Publisher)
Created2016
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Citizen Science programs create a bi-directional flow of knowledge between scientists and citizen volunteers; this flow democratizes science in order to create an informed public (Bonney et al. 2014; Brown, Kelly, and Whitall 2014). This democratization is a fundamental part of creating a science that can address today’s pressing environmental,

Citizen Science programs create a bi-directional flow of knowledge between scientists and citizen volunteers; this flow democratizes science in order to create an informed public (Bonney et al. 2014; Brown, Kelly, and Whitall 2014). This democratization is a fundamental part of creating a science that can address today’s pressing environmental, economic, and social justice problems (Lubchenco 1998). While citizen science programs create an avenue for sharing knowledge between the public and scientists, the exact program details and dynamics leading to different outcomes have not been studied in detail. The current shortcomings in the literature fall into three categories. First, the concept of ‘volunteer’ is used as a catch-all without considering how different

demographics (e.g. young, old, wealthy, poor, differently abled, local inhabitants, and visitors) affect both volunteer and scientific outcomes of citizen science. The second shortcoming: there are no standards to assess the quality of citizen science datasets. The third shortcoming: the volunteer and scientific outcomes of these programs are not routinely, or strategically, measured, or integrated into policy and planning (Brossard, Lewenstein, and Bonney 2005). This research advances the understanding of tourist volunteers in citizen science by examining these three shortcomings through a case-study in Denali National Park and Preserve. This case study included the development of the Map of Life-Denali citizen science program is a “tourist-friendly” program. Volunteers of the program use the Map of Life- Denali mobile application to record wildlife observations in the park. Research conducted on this program shows that tourists can be successful citizen science volunteers, and when compared to resident volunteers produce similar data, and have positive volunteer outcomes. The development of a fitness for use assessment, called STAAq is also a part of this research. This assessment is shown to be an effective method for assessing citizen science data quality. Throughout the development and launch of the program, stakeholders (the Park Service, and Aramark) were consulted. The Map of Life-Denali program will be integrated into the park’s shuttle and tour bus systems as an educational tool, however, the scientific merits of the program are still disputed.

ContributorsFischer, Heather A (Author) / Wentz, Elizabeth (Thesis advisor) / Gerber, Leah (Committee member) / Yabiku, Scott (Committee member) / Arizona State University (Publisher)
Created2017
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Trees serve as a natural umbrella to mitigate insolation absorbed by features of the urban environment, especially building structures and pavements. For a desert community, trees are a particularly valuable asset because they contribute to energy conservation efforts, improve home values, allow for cost savings, and promote enhanced health and

Trees serve as a natural umbrella to mitigate insolation absorbed by features of the urban environment, especially building structures and pavements. For a desert community, trees are a particularly valuable asset because they contribute to energy conservation efforts, improve home values, allow for cost savings, and promote enhanced health and well-being. The main obstacle in creating a sustainable urban community in a desert city with trees is the scarceness and cost of irrigation water. Thus, strategically located and arranged desert trees with the fewest tree numbers possible potentially translate into significant energy, water and long-term cost savings as well as conservation, economic, and health benefits. The objective of this dissertation is to achieve this research goal with integrated methods from both theoretical and empirical perspectives.

This dissertation includes three main parts. The first part proposes a spatial optimization method to optimize the tree locations with the objective to maximize shade coverage on building facades and open structures and minimize shade coverage on building rooftops in a 3-dimensional environment. Second, an outdoor urban physical scale model with field measurement is presented to understand the cooling and locational benefits of tree shade. The third part implements a microclimate numerical simulation model to analyze how the specific tree locations and arrangements influence outdoor microclimates and improve human thermal comfort. These three parts of the dissertation attempt to fill the research gap of how to strategically locate trees at the building to neighborhood scale, and quantifying the impact of such arrangements.

Results highlight the significance of arranging residential shade trees across different geographical scales. In both the building and neighborhood scales, research results recommend that trees should be arranged in the central part of the building south front yard. More cooling benefits are provided to the building structures and outdoor microclimates with a cluster tree arrangement without canopy overlap; however, if residents are interested in creating a better outdoor thermal environment, open space between trees is needed to enhance the wind environment for better human thermal comfort. Considering the rapid urbanization process, limited water resources supply, and the severe heat stress in the urban areas, judicious design and planning of trees is of increasing importance for improving the life quality and sustaining the urban environment.

ContributorsZhao, Qunshan (Author) / Wentz, Elizabeth (Thesis advisor) / Sailor, David (Committee member) / Wang, Zhi-Hua (Committee member) / Arizona State University (Publisher)
Created2017
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Concerns about Peak Oil, political instability in the Middle East, health hazards, and greenhouse gas emissions of fossil fuels have stimulated interests in alternative fuels such as biofuels, natural gas, electricity, and hydrogen. Alternative fuels are expected to play an important role in a transition to a sustainable transportation system.

Concerns about Peak Oil, political instability in the Middle East, health hazards, and greenhouse gas emissions of fossil fuels have stimulated interests in alternative fuels such as biofuels, natural gas, electricity, and hydrogen. Alternative fuels are expected to play an important role in a transition to a sustainable transportation system. One of the major barriers to the success of alternative-fuel vehicles (AFV) is the lack of infrastructure for producing, distributing, and delivering alternative fuels. Efficient methods that locate alternative-fuel refueling stations are essential in accelerating the advent of a new energy economy. The objectives of this research are to develop a location model and a Spatial Decision Support System (SDSS) that aims to support the decision of developing initial alternative-fuel stations. The main focus of this research is the development of a location model for siting alt-fuel refueling stations considering not only the limited driving range of AFVs but also the necessary deviations that drivers are likely to make from their shortest paths in order to refuel their AFVs when the refueling station network is sparse. To add reality and applicability of the model, the research is extended to include the development of efficient heuristic algorithms, the development of a method to incorporate AFV demand estimates into OD flow volumes, and the development of a prototype SDSS. The model and methods are tested on real-world road network data from state of Florida. The Deviation-Flow Refueling Location Model (DFRLM) locates facilities to maximize the total flows refueled on deviation paths. The flow volume is assumed to be decreasing as the deviation increases. Test results indicate that the specification of the maximum allowable deviation and specific deviation penalty functional form do have a measurable effect on the optimal locations of facilities and objective function values as well. The heuristics (greedy-adding and greedy-adding with substitution) developed here have been identified efficient in solving the DFRLM while AFV demand has a minor effect on the optimal facility locations. The prototype SDSS identifies strategic station locations by providing flexibility in combining various AFV demand scenarios. This research contributes to the literature by enhancing flow-based location models for locating alternative-fuel stations in four dimensions: (1) drivers' deviations from their shortest paths, (2) efficient solution approaches for the deviation problem, (3) incorporation of geographically uneven alt-fuel vehicle demand estimates into path-based origin-destination flow data, and (4) integration into an SDSS to help decision makers by providing solutions and insights into developing alt-fuel stations.

ContributorsKim, Jong-Geun (Author) / Kuby, Michael J (Thesis advisor) / Wentz, Elizabeth (Committee member) / Murray, Alan T. (Committee member) / Arizona State University (Publisher)
Created2010
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Remote sensing has demonstrated to be an instrumental tool in monitoring land changes as a result of anthropogenic change or natural disasters. Most disaster studies have focused on large-scale events with few analyzing small-scale disasters such as tornadoes. These studies have only provided a damage assessment perspective with the continued

Remote sensing has demonstrated to be an instrumental tool in monitoring land changes as a result of anthropogenic change or natural disasters. Most disaster studies have focused on large-scale events with few analyzing small-scale disasters such as tornadoes. These studies have only provided a damage assessment perspective with the continued need to assess reconstruction. This study attempts to fill that void by examining recovery from the 1999 Moore, Oklahoma Tornado utilizing Landsat TM and ETM+ imagery. Recovery was assessed for 2000, 2001 and 2002 using spectral enhancements (vegetative and urban indices and a combination of the two), a recovery index and different statistical thresholds. Classification accuracy assessments were performed to determine the precision of recovery and select the best results. This analysis proved that medium resolution imagery could be used in conjunction with geospatial techniques to capture recovery. The new indices, Shortwave Infrared Index (SWIRI) and Coupled Vegetation and Urban Index (CVUI), developed for disaster management, were the most effective at discerning reconstruction using the 1.5 standard deviation threshold. Recovery rates for F-scale damages revealed that the most incredibly damaged areas associated with an F5 rating were the slowest to recover, while the lesser damaged areas associated with F1-F3 ratings were the quickest to rebuild. These findings were consistent for 2000, 2001 and 2002 also exposing that complete recovery was never attained in any of the F-scale damage zones by 2002. This study illustrates the significance the biophysical impact has on recovery as well as the effectiveness of using medium resolution imagery such as Landsat in future research.

ContributorsWagner, Melissa A (Author) / Cerveny, Randall S. (Thesis advisor) / Myint, Soe W. (Thesis advisor) / Wentz, Elizabeth (Committee member) / Brazel, Anthony J. (Committee member) / Arizona State University (Publisher)
Created2011
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Spatial uncertainty refers to unknown error and vagueness in geographic data. It is relevant to land change and urban growth modelers, soil and biome scientists, geological surveyors and others, who must assess thematic maps for similarity, or categorical agreement. In this paper I build upon prior map comparison research, testing

Spatial uncertainty refers to unknown error and vagueness in geographic data. It is relevant to land change and urban growth modelers, soil and biome scientists, geological surveyors and others, who must assess thematic maps for similarity, or categorical agreement. In this paper I build upon prior map comparison research, testing the effectiveness of similarity measures on misregistered data. Though several methods compare uncertain thematic maps, few methods have been tested on misregistration. My objective is to test five map comparison methods for sensitivity to misregistration, including sub-pixel errors in both position and rotation. Methods included four fuzzy categorical models: fuzzy kappa's model, fuzzy inference, cell aggregation, and the epsilon band. The fifth method used conventional crisp classification. I applied these methods to a case study map and simulated data in two sets: a test set with misregistration error, and a control set with equivalent uniform random error. For all five methods, I used raw accuracy or the kappa statistic to measure similarity. Rough-set epsilon bands report the most similarity increase in test maps relative to control data. Conversely, the fuzzy inference model reports a decrease in test map similarity.

ContributorsBrown, Scott (Author) / Wentz, Elizabeth (Thesis advisor) / Myint, Soe W. (Committee member) / Anderson, Sharolyn (Committee member) / Arizona State University (Publisher)
Created2010