Improving Urban Cooling in the Semi-arid Phoenix Metropolis: Land System Science, Landscape Ecology and Urban Climatology Approaches

156599-Thumbnail Image.png
Description
The global increase in urbanization has raised questions about urban sustainability to which multiple research communities have entered. Those communities addressing interest in the urban heat island (UHI) effect and extreme temperatures include land system science, urban/landscape ecology, and urban

The global increase in urbanization has raised questions about urban sustainability to which multiple research communities have entered. Those communities addressing interest in the urban heat island (UHI) effect and extreme temperatures include land system science, urban/landscape ecology, and urban climatology. General investigations of UHI have focused primarily on land surface and canopy layer air temperatures. The surface temperature is of prime importance to UHI studies because of its central rule in the surface energy balance, direct effects on air temperature, and outdoor thermal comfort. Focusing on the diurnal surface temperature variations in Phoenix, Arizona, especially on the cool (green space) island effect and the surface heat island effect, the dissertation develops three research papers that improve the integration among the abovementioned sub-fields. Specifically, these papers involve: (1) the quantification and modeling of the diurnal cooling benefits of green space; (2) the optimization of green space locations to reduce the surface heat island effect in daytime and nighttime; and, (3) an evaluation of the effects of vertical urban forms on land surface temperature using Google Street View. These works demonstrate that the pattern of new green spaces in central Phoenix could be optimized such that 96% of the maximum daytime and nighttime cooling benefits would be achieved, and that Google Street View data offers an alternative to other data, providing the vertical dimensions of land-cover for addressing surface temperature impacts, increasing the model accuracy over the use of horizontal land-cover data alone. Taken together, the dissertation points the way towards the integration of research directions to better understand the consequences of detailed land conditions on temperatures in urban areas, providing insights for urban designs to alleviate these extremes.
Date Created
2018
Agent

Optimizing Green Space Locations to Reduce Daytime and Nighttime Urban Heat Island Effects in Phoenix, Arizona

141439-Thumbnail Image.png
Description

The urban heat island effect is especially significant in semi-arid climates, generating a myriad of problems for large urban areas. Green space can mitigate warming, providing cooling benefits important to reducing energy consumption and improving human health. The arrangement of

The urban heat island effect is especially significant in semi-arid climates, generating a myriad of problems for large urban areas. Green space can mitigate warming, providing cooling benefits important to reducing energy consumption and improving human health. The arrangement of green space to reap the full potential of cooling benefits is a challenge, especially considering the diurnal variations of urban heat island effects. Surprisingly, methods that support the strategic placement of green space in the context of urban heat island are lacking. Integrating geographic information systems, remote sensing, spatial statistics and spatial optimization, we developed a framework to identify the best locations and configuration of new green space with respect to cooling benefits. The developed multi-objective model is applied to evaluate the diurnal cooling trade-offs in Phoenix, Arizona. As a result of optimal green space placement, significant cooling potentials can be achieved. A reduction of land surface temperature of approximately 1–2 °C locally and 0.5 °C regionally can be achieved by the addition of new green space. 96% of potential day and night cooling benefits can be achieved through simultaneous consideration. The results also demonstrate that clustered green space enhances local cooling because of the agglomeration effect; whereas, dispersed patterns lead to greater overall regional cooling. The optimization based framework can effectively inform planning decisions with regard to green space allocation to best ameliorate excessive heat.

Date Created
2017-07-31
Agent

Comparative Approaches for Assessing Access to Alcohol Outlets: Exploring the Utility of a Gravity Potential Approach

129079-Thumbnail Image.png
Description

Background: A growing body of research recommends controlling alcohol availability to reduce harm. Various common approaches, however, provide dramatically different pictures of the physical availability of alcohol. This limits our understanding of the distribution of alcohol access, the causes and consequences

Background: A growing body of research recommends controlling alcohol availability to reduce harm. Various common approaches, however, provide dramatically different pictures of the physical availability of alcohol. This limits our understanding of the distribution of alcohol access, the causes and consequences of this distribution, and how best to reduce harm. The aim of this study is to introduce both a gravity potential measure of access to alcohol outlets, comparing its strengths and weaknesses to other popular approaches, and an empirically-derived taxonomy of neighborhoods based on the type of alcohol access they exhibit.

Methods: We obtained geospatial data on Seattle, including the location of 2402 alcohol outlets, United States Census Bureau estimates on 567 block groups, and a comprehensive street network. We used exploratory spatial data analysis and employed a measure of inter-rater agreement to capture differences in our taxonomy of alcohol availability measures.

Results: Significant statistical and spatial variability exists between measures of alcohol access, and these differences have meaningful practical implications. In particular, standard measures of outlet density (e.g., spatial, per capita, roadway miles) can lead to biased estimates of physical availability that over-emphasize the influence of the control variables. Employing a gravity potential approach provides a more balanced, geographically-sensitive measure of access to alcohol outlets.

Conclusions: Accurately measuring the physical availability of alcohol is critical for understanding the causes and consequences of its distribution and for developing effective evidence-based policy to manage the alcohol outlet licensing process. A gravity potential model provides a superior measure of alcohol access, and the alcohol access-based taxonomy a helpful evidence-based heuristic for scholars and local policymakers.

Date Created
2016-08-02
Agent

Deriving an obstacle-avoiding shortest path in continuous space: a spatial approach

153527-Thumbnail Image.png
Description
The shortest path between two locations is important for spatial analysis, location modeling, and wayfinding tasks. Depending on permissible movement and availability of data, the shortest path is either derived from a pre-defined transportation network or constructed in continuous space.

The shortest path between two locations is important for spatial analysis, location modeling, and wayfinding tasks. Depending on permissible movement and availability of data, the shortest path is either derived from a pre-defined transportation network or constructed in continuous space. However, continuous space movement adds substantial complexity to identifying the shortest path as the influence of obstacles has to be considered to avoid errors and biases in a derived path. This obstacle-avoiding shortest path in continuous space has been referred to as Euclidean shortest path (ESP), and attracted the attention of many researchers. It has been proven that constructing a graph is an effective approach to limit infinite search options associated with continuous space, reducing the problem to a finite set of potential paths. To date, various methods have been developed for ESP derivation. However, their computational efficiency is limited due to fundamental limitations in graph construction. In this research, a novel algorithm is developed for efficient identification of a graph guaranteed to contain the ESP. This new approach is referred to as the convexpath algorithm, and exploits spatial knowledge and GIS functionality to efficiently construct a graph. The convexpath algorithm utilizes the notion of a convex hull to simultaneously identify relevant obstacles and construct the graph. Additionally, a spatial filtering technique based on intermediate shortest path is enhances intelligent identification of relevant obstacles. Empirical applications show that the convexpath algorithm is able to construct a graph and derive the ESP with significantly improved efficiency compared to visibility and local visibility graph approaches. Furthermore, to boost the performance of convexpath in big data environments, a parallelization approach is proposed and applied to exploit computationally intensive spatial operations of convexpath. Multicore CPU parallelization demonstrates noticeable efficiency gain over the sequential convexpath. Finally, spatial representation and approximation issues associated with raster-based approximation of the ESP are assessed. This dissertation provides a comprehensive treatment of the ESP, and details an important approach for deriving an optimal ESP in real time.
Date Created
2015
Agent

A spatial decision support system for optimizing the environmental rehabilitation of borderlands

152293-Thumbnail Image.png
Description
The border policies of the United States and Mexico that have evolved over the previous decades have pushed illegal immigration and drug smuggling to remote and often public lands. Valuable natural resources and tourist sites suffer an inordinate level of

The border policies of the United States and Mexico that have evolved over the previous decades have pushed illegal immigration and drug smuggling to remote and often public lands. Valuable natural resources and tourist sites suffer an inordinate level of environmental impacts as a result of activities, from new roads and trash to cut fence lines and abandoned vehicles. Public land managers struggle to characterize impacts and plan for effective landscape level rehabilitation projects that are the most cost effective and environmentally beneficial for a region given resource limitations. A decision support tool is developed to facilitate public land management: Borderlands Environmental Rehabilitation Spatial Decision Support System (BERSDSS). The utility of the system is demonstrated using a case study of the Sonoran Desert National Monument, Arizona.
Date Created
2013
Agent

Addressing geographic uncertainty in spatial optimization

151538-Thumbnail Image.png
Description
There exist many facets of error and uncertainty in digital spatial information. As error or uncertainty will not likely ever be completely eliminated, a better understanding of its impacts is necessary. Spatial analytical approaches, in particular, must somehow address data

There exist many facets of error and uncertainty in digital spatial information. As error or uncertainty will not likely ever be completely eliminated, a better understanding of its impacts is necessary. Spatial analytical approaches, in particular, must somehow address data quality issues. This can range from evaluating impacts of potential data uncertainty in planning processes that make use of methods to devising methods that explicitly account for error/uncertainty. To date, little has been done to structure methods accounting for error. This research focuses on developing methods to address geographic data uncertainty in spatial optimization. An integrated approach that characterizes uncertainty impacts by constructing and solving a new multi-objective model that explicitly incorporates facets of data uncertainty is developed. Empirical findings illustrate that the proposed approaches can be applied to evaluate the impacts of data uncertainty with statistical confidence, which moves beyond popular practices of simulating errors in data. Spatial uncertainty impacts are evaluated in two contexts: harvest scheduling and sex offender residency. Owing to the integration of spatial uncertainty, the detailed multi-objective models are more complex and computationally challenging to solve. As a result, a new multi-objective evolutionary algorithm is developed to address the computational challenges posed. The proposed algorithm incorporates problem-specific spatial knowledge to significantly enhance the capability of the evolutionary algorithm for solving the model.  
Date Created
2013
Agent

Spatial optimization approaches for solving the continuous Weber and multi-Weber problems

151286-Thumbnail Image.png
Description
Facility location models are usually employed to assist decision processes in urban and regional planning. The focus of this research is extensions of a classic location problem, the Weber problem, to address continuously distributed demand as well as multiple facilities.

Facility location models are usually employed to assist decision processes in urban and regional planning. The focus of this research is extensions of a classic location problem, the Weber problem, to address continuously distributed demand as well as multiple facilities. Addressing continuous demand and multi-facilities represents major challenges. Given advances in geographic information systems (GIS), computational science and associated technologies, spatial optimization provides a possibility for improved problem solution. Essential here is how to represent facilities and demand in geographic space. In one respect, spatial abstraction as discrete points is generally assumed as it simplifies model formulation and reduces computational complexity. However, errors in derived solutions are likely not negligible, especially when demand varies continuously across a region. In another respect, although mathematical functions describing continuous distributions can be employed, such theoretical surfaces are generally approximated in practice using finite spatial samples due to a lack of complete information. To this end, the dissertation first investigates the implications of continuous surface approximation and explicitly shows errors in solutions obtained from fitted demand surfaces through empirical applications. The dissertation then presents a method to improve spatial representation of continuous demand. This is based on infill asymptotic theory, which indicates that errors in fitted surfaces tend to zero as the number of sample points increases to infinity. The implication for facility location modeling is that a solution to the discrete problem with greater demand point density will approach the theoretical optimum for the continuous counterpart. Therefore, in this research discrete points are used to represent continuous demand to explore this theoretical convergence, which is less restrictive and less problem altering compared to existing alternatives. The proposed continuous representation method is further extended to develop heuristics to solve the continuous Weber and multi-Weber problems, where one or more facilities can be sited anywhere in continuous space to best serve continuously distributed demand. Two spatial optimization approaches are proposed for the two extensions of the Weber problem, respectively. The special characteristics of those approaches are that they integrate optimization techniques and GIS functionality. Empirical results highlight the advantages of the developed approaches and the importance of solution integration within GIS.
Date Created
2012
Agent

The centralization index as a measure of local spatial segregation

151109-Thumbnail Image.png
Description
Decades ago in the U.S., clear lines delineated which neighborhoods were acceptable for certain people and which were not. Techniques such as steering and biased mortgage practices continue to perpetuate a segregated outcome for many residents. In contrast, ethnic enclaves

Decades ago in the U.S., clear lines delineated which neighborhoods were acceptable for certain people and which were not. Techniques such as steering and biased mortgage practices continue to perpetuate a segregated outcome for many residents. In contrast, ethnic enclaves and age restricted communities are viewed as voluntary segregation based on cultural and social amenities. This diversity surrounding the causes of segregation are not just region-wide characteristics, but can vary within a region. Local segregation analysis aims to uncover this local variation, and hence open the door to policy solutions not visible at the global scale. The centralization index, originally introduced as a global measure of segregation focused on spatial concentration of two population groups relative a region's urban center, has lost relevancy in recent decades as regions have become polycentric, and the index's magnitude is sensitive to the particular point chosen as the center. These attributes, which make it a poor global measure, are leveraged here to repurpose the index as a local measure. The index's ability to differentiate minority from majority segregation, and its focus on a particular location within a region make it an ideal local segregation index. Based on the local centralization index for two groups, a local multigroup variation is defined, and a local space-time redistribution index is presented capturing change in concentration of a single population group over two time periods. Permutation based inference approaches are used to test the statistical significance of measured index values. Applications to the Phoenix, Arizona metropolitan area show persistent cores of black and white segregation over the years 1990, 2000 and 2010, and a trend of white segregated neighborhoods increasing at a faster rate than black. An analysis of the Phoenix area's recently opened light rail system shows that its 28 stations are located in areas of significant white, black and Hispanic segregation, and there is a clear concentration of renters over owners around most stations. There is little indication of statistically significant change in segregation or population concentration around the stations, indicating a lack of near term impact of light rail on the region's overall demographics.
Date Created
2012
Agent

Location of refueling stations for alternative fuel vehicles considering driver deviation behavior and uneven consumer demand: model, heuristics, and GIS

149488-Thumbnail Image.png
Description

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

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.

Date Created
2010
Agent