Alcohol Beverage Control, Privatization and the Geographic Distribution of Alcohol Outlets

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Description

Background: With Pennsylvania currently considering a move away from an Alcohol Beverage Control state to a privatized alcohol distribution system, this study uses a spatial analytical approach to examine potential impacts of privatization on the number and spatial distribution of alcohol…

Background: With Pennsylvania currently considering a move away from an Alcohol Beverage Control state to a privatized alcohol distribution system, this study uses a spatial analytical approach to examine potential impacts of privatization on the number and spatial distribution of alcohol outlets in the city of Philadelphia over a long time horizon.

Methods: A suite of geospatial data were acquired for Philadelphia, including 1,964 alcohol outlet locations, 569,928 land parcels, and school, church, hospital, park and playground locations. These data were used as inputs for exploratory spatial analysis to estimate the expected number of outlets that would eventually operate in Philadelphia. Constraints included proximity restrictions (based on current ordinances regulating outlet distribution) of at least 200 feet between alcohol outlets and at least 300 feet between outlets and schools, churches, hospitals, parks and playgrounds.

Results: Findings suggest that current state policies on alcohol outlet distributions in Philadelphia are loosely enforced, with many areas exhibiting extremely high spatial densities of outlets that violate existing proximity restrictions. The spatial model indicates that an additional 1,115 outlets could open in Philadelphia if privatization was to occur and current proximity ordinances were maintained.

Conclusions: The study reveals that spatial analytical approaches can function as an excellent tool for contingency-based “what-if” analysis, providing an objective snapshot of potential policy outcomes prior to implementation. In this case, the likely outcome is a tremendous increase in alcohol outlets in Philadelphia, with concomitant negative health, crime and quality of life outcomes that accompany such an increase.

Date Created
2012-11-21
Agent

Ecological, environmental and hydrological integrity in sustainable water resource management for river basins

Description
This dissertation presents a new methodology for the sustainable and optimal allocation of water for a river basin management area that maximizes sustainable net economic benefit over the long-term planning horizon. The model distinguishes between short and long-term planning horizons…

This dissertation presents a new methodology for the sustainable and optimal allocation of water for a river basin management area that maximizes sustainable net economic benefit over the long-term planning horizon. The model distinguishes between short and long-term planning horizons and goals using a short-term modeling component (STM) and a long term modeling component (LTM) respectively. An STM optimizes a monthly allocation schedule on an annual basis in terms of maximum net economic benefit. A cost of depletion based upon Hotelling’s exhaustible resource theory is included in the STM net benefit calculation to address the non-use value of groundwater. An LTM consists of an STM for every year of the long-term planning horizon. Net economic benefits for both use and non-use values are generated by the series of STMs. In addition output from the STMs is measured in terms of sustainability which is quantified using a sustainability index (SI) with two groups of performance criteria. The first group measures risk to supply and is based on demand-supply deficits. The second group measures deviations from a target flow regime and uses a modified Hydrologic Alteration (HA) factor in the Range of Variability Approach (RVA). The STM is a linear programming (LP) model formulated in the General Algebraic Modeling System (GAMS) and the LTM is a nonlinear programming problem (NLP) solved using a genetic algorithm. The model is applied to the Prescott Active Management Area in north-central Arizona. Results suggest that the maximum sustainable net benefit is realized with a residential population and consumption rate increase in some areas, and a reduction in others.
Date Created
2015
Agent

Spatial and Social Inequities in HIV Testing Utilization in the Context of Rapid Scale-Up of HIV/AIDS Services in Rural Mozambique

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Description

The massive scale-up of HIV counseling, testing, and treatment services in resource-limited sub-Saharan settings with high HIV prevalence has significant implications for the course of the HIV/AIDS epidemic. It also offers important broader policy lessons for improving access to critical…

The massive scale-up of HIV counseling, testing, and treatment services in resource-limited sub-Saharan settings with high HIV prevalence has significant implications for the course of the HIV/AIDS epidemic. It also offers important broader policy lessons for improving access to critical health services. Applying GIS-based methods and multilevel regression analysis to unique longitudinal three-wave survey data from rural Mozambique, this study investigates the impact of a rapid expansion of HIV-related services on access to and utilization of HIV testing. The results illustrate the declining importance of spatial barriers to utilization of HIV testing services as these services expanded. In addition, the expansion of HIV-related services decreased the spatial variability of HIV testing among the survey respondents. At the same time, some important non-spatial variation, such as that in educational level, persisted despite the expansion of services. These results illustrate the process and consequences of health service diffusion.

Date Created
2014-07-01
Agent

Essays on space-time interaction tests

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Description
Researchers across a variety of fields are often interested in determining if data are of a random nature or if they exhibit patterning which may be the result of some alternative and potentially more interesting process. This dissertation explores a…

Researchers across a variety of fields are often interested in determining if data are of a random nature or if they exhibit patterning which may be the result of some alternative and potentially more interesting process. This dissertation explores a family of statistical methods, i.e. space-time interaction tests, designed to detect structure within three-dimensional event data. These tests, widely employed in the fields of spatial epidemiology, criminology, ecology and beyond, are used to identify synergistic interaction across the spatial and temporal dimensions of a series of events. Exploration is needed to better understand these methods and determine how their results may be affected by data quality problems commonly encountered in their implementation; specifically, how inaccuracy and/or uncertainty in the input data analyzed by the methods may impact subsequent results. Additionally, known shortcomings of the methods must be ameliorated. The contributions of this dissertation are twofold: it develops a more complete understanding of how input data quality problems impact the results of a number of global and local tests of space-time interaction and it formulates an improved version of one global test which accounts for the previously identified problem of population shift bias. A series of simulation experiments reveal the global tests of space-time interaction explored here to be dramatically affected by the aforementioned deficiencies in the quality of the input data. It is shown that in some cases, a conservative degree of these common data problems can completely obscure evidence of space-time interaction and in others create it where it does not exist. Conversely, a local metric of space-time interaction examined here demonstrates a surprising robustness in the face of these same deficiencies. This local metric is revealed to be only minimally affected by the inaccuracies and incompleteness introduced in these experiments. Finally, enhancements to one of the global tests are presented which solve the problem of population shift bias associated with the test and better contextualize and visualize its results, thereby enhancing its utility for practitioners.
Date Created
2013
Agent

Locating counting sensors in traffic network to estimate origin-destination volumes

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Description
Improving the quality of Origin-Destination (OD) demand estimates increases the effectiveness of design, evaluation and implementation of traffic planning and management systems. The associated bilevel Sensor Location Flow-Estimation problem considers two important research questions: (1) how to compute the best…

Improving the quality of Origin-Destination (OD) demand estimates increases the effectiveness of design, evaluation and implementation of traffic planning and management systems. The associated bilevel Sensor Location Flow-Estimation problem considers two important research questions: (1) how to compute the best estimates of the flows of interest by using anticipated data from given candidate sensors location; and (2) how to decide on the optimum subset of links where sensors should be located. In this dissertation, a decision framework is developed to optimally locate and obtain high quality OD volume estimates in vehicular traffic networks. The framework includes a traffic assignment model to load the OD traffic volumes on routes in a known choice set, a sensor location model to decide on which subset of links to locate counting sensors to observe traffic volumes, and an estimation model to obtain best estimates of OD or route flow volumes. The dissertation first addresses the deterministic route flow estimation problem given apriori knowledge of route flows and their uncertainties. Two procedures are developed to locate "perfect" and "noisy" sensors respectively. Next, it addresses a stochastic route flow estimation problem. A hierarchical linear Bayesian model is developed, where the real route flows are assumed to be generated from a Multivariate Normal distribution with two parameters: "mean" and "variance-covariance matrix". The prior knowledge for the "mean" parameter is described by a probability distribution. When assuming the "variance-covariance matrix" parameter is known, a Bayesian A-optimal design is developed. When the "variance-covariance matrix" parameter is unknown, Markov Chain Monte Carlo approach is used to estimate the aposteriori quantities. In all the sensor location model the objective is the maximization of the reduction in the variances of the distribution of the estimates of the OD volume. Developed models are compared with other available models in the literature. The comparison showed that the models developed performed better than available models.
Date Created
2013
Agent

An exploratory toolkit for examining residential movement patterns at a micro scale

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Description
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…

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.
Date Created
2012
Agent
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