Environmental problems are more abundant because of the rapid increase in urbanization, climate change, and population growth leading to the depletion of natural resources and endangerment of some species. The availability of infrastructure as well as socio-economic factors facilitate the illicit trade of wildlife through supply chain networks, adding further threats to species. Ecosystem conservation and protection of wildlife from illegal trade and poaching is fundamental to guarantee the survival of endangered species. Conservation efforts require a landscape approach that incorporates spatial features for the effective functionality of the selected reserve. This dissertation studies combinatorial optimization problems with application to two classes of societal problems: landscape conservation and disruption of illicit supply chains. The first and second chapter propose a mixed-integer formulation to model the reserve design problem with budget and ecological constraints. The first uses the radius of the smallest circle enclosing the selected areas as a metric of compactness. An extension of the model is proposed to solve the multi reserve design problem and the reserve expansion problem. The solution approach includes warm start heuristic, separation problem and cuts to improve model performance. The enhanced model outperforms the linearized and the equivalent nonlinear model. The second chapter uses the Reock’s metric as a metric of compactness. The solution approach includes warm start heuristic, knapsack based separation problem to inject solutions, and cuts to improve model performance. The enhanced model outperforms the default model. The third chapter proposes an integer programming model to solve the wildlife corridor design problem with minimum width requirement and a budget constraint. A separation algorithm is proposed to identify boundary patches and violations in the corridor width. A branch-and-cut approach is proposed to induce the corridor width and is tested on real-life landscape. The fourth chapter proposes an integer programming formulation to model the disruption of illicit supply chain problem. The proposed model enforces that at least x paths must be disrupted for an Origin-Destination pair to be disrupted and at least y arcs must be disrupted for a path to be disrupted. The proposed model is tested on real-life road networks.

Networks are a versatile modeling tool for the cyber and physical infrastructure that characterize society. They can be used to describe system spatiotemporal dynamics, including distribution of commodities, movement of agents, and data transmission. This flexibility has resulted in the widespread use of network optimization techniques for decision-making in telecommunications, transportation, commerce, among other systems. However, realistic network problems are typically large-scale and require the use of integer variables to incorporate design or logical system constraints. This makes such problems hard to solve and precludes their wide applicability in the solution of applied problems. This dissertation studies four large-scale optimization problems with underlying network structure in different domain applications, including wireless sensor networks, wastewater monitoring, and scheduling. The problems of interest are formulated using mixed-integer optimization formulations. The proposed solution approaches in this dissertation include branch-and-cut and heuristic algorithms, which are enhanced with network-based valid inequalities and network reduction techniques. The first chapter studies a relay node placement problem in wireless sensor networks, with and without the presence of transmission obstacles in the deployment region. The proposed integer linear programming approach leverages the underlying network structure to produce valid inequalities and network reduction heuristics, which are incorporated in the branch-and-bound exploration. The solution approach outperforms the equivalent nonlinear model and solves instances with up to 1000 sensors within reasonable time. The second chapter studies the continuous version of the maximum capacity (widest) path interdiction problem and introduces the first known polynomial time algorithm to solve the problem using a combination of binary search and the discrete version of the Newton’s method. The third chapter explores the service agent transport interdiction problem in autonomous vehicle systems, where an agent schedules service tasks in the presence of an adversary. This chapter proposes a single stage branch-and-cut algorithm to solve the problem, along with several enhancement techniques to improve scalability. The last chapter studies the optimal placement of sensors in a wastewater network to minimize the maximum coverage (load) of placed sensors. This chapter proposes a branch-and-cut algorithm enhanced with network reduction techniques and strengthening constraints.

Research administrators (RAs) are integral to universities and corporations as the first point of contact for faculty in research proposal submissions. RAs are also the intermediary between the university or the institution and the office sponsoring the project. The multiple demands placed upon RAs could potentially lead to burnout. The objective of this mixed-methods action research study was to understand better how incorporating mindfulness practices (e.g., breathing exercises, meditation) may allow RAs to manage or potentially eliminate burnout. Participants learned about mindfulness through a smartphone meditation application, which also shared various coaching techniques for reducing stress in their work-life. Results obtained from the quantitative and qualitative pre- and post-intervention data showed RAs might benefit from managing daily work life by incorporating mindfulness practices. While many were aware of the concept of mindfulness and university trainings, they expressed their demanding work environment is continually changing, and a solution in reducing burnout may need to be continuously redefined. The understanding gained from this action research study is RAs can benefit from mindfulness tools and techniques. Furthermore, other colleges or institutions with pre-award research administrators may benefit from how to aid in lowering burnout in their daily work environments.

This dissertation focuses on three large-scale optimization problems and devising algorithms to solve them. In addition to the societal impact of each problem’s solution, this dissertation contributes to the optimization literature a set of decomposition algorithms for problems whose optimal solution is sparse. These algorithms exploit problem-specific properties and use tailored strategies based on iterative refinement (outer-approximations). The proposed algorithms are not rooted in duality theory, providing an alternative to existing methods based on linear programming relaxations. However, it is possible to embed existing decomposition methods into the proposed framework. These general decomposition principles extend to other combinatorial optimization problems.

The first problem is a route assignment and scheduling problem in which a set of vehicles need to traverse a directed network while maintaining a minimum inter-vehicle distance at any time. This problem is inspired by applications in hazmat logistics and the coordination of autonomous agents. The proposed approach includes realistic features such as continuous-time vehicle scheduling, heterogeneous speeds, minimum and maximum waiting times at any node, among others.

The second problem is a fixed-charge network design, which aims to find a minimum-cost plan to transport a target amount of a commodity between known origins and destinations. In addition to the typical flow decisions, the model chooses the capacity of each arc and selects sources and sinks. The proposed algorithms admit any nondecreasing piecewise linear cost structure. This model is applied to the Carbon Capture and Storage (CCS) problem, which is to design a minimum-cost pipeline network to transport CO2 between industrial sources and geologic reservoirs for long-term storage.

The third problem extends the proposed decomposition framework to a special case of joint chance constraint programming with independent random variables. This model is applied to the probabilistic transportation problem, where demands are assumed stochastic and independent. Using an empirical probability distribution, this problem is formulated as an integer program with the goal of finding a minimum-cost distribution plan that satisfies all the demands with a minimum given probability. The proposed scalable algorithm is based on a concave envelop approximation of the empirical probability function, which is iteratively refined as needed.

I study the problem of locating Relay nodes (RN) to improve the connectivity of a set

of already deployed sensor nodes (SN) in a Wireless Sensor Network (WSN). This is

known as the Relay Node Placement Problem (RNPP). In this problem, one or more

nodes called Base Stations (BS) serve as the collection point of all the information

captured by SNs. SNs have limited transmission range and hence signals are transmitted

from the SNs to the BS through multi-hop routing. As a result, the WSN

is said to be connected if there exists a path for from each SN to the BS through

which signals can be hopped. The communication range of each node is modeled

with a disk of known radius such that two nodes are said to communicate if their

communication disks overlap. The goal is to locate a given number of RNs anywhere

in the continuous space of the WSN to maximize the number of SNs connected (i.e.,

maximize the network connectivity). To solve this problem, I propose an integer

programming based approach that iteratively approximates the Euclidean distance

needed to enforce sensor communication. This is achieved through a cutting-plane

approach with a polynomial-time separation algorithm that identies distance violations.

I illustrate the use of my algorithm on large-scale instances of up to 75 nodes

which can be solved in less than 60 minutes. The proposed method shows solutions

times many times faster than an alternative nonlinear formulation.

Coastal areas are susceptible to man-made disasters, such as oil spills, which not

only have a dreadful impact on the lives of coastal communities and businesses but also

have lasting and hazardous consequences. The United States coastal areas, especially

the Gulf of Mexico, have witnessed devastating oil spills of varied sizes and durations

that resulted in major economic and ecological losses. These disasters affected the oil,

housing, forestry, tourism, and fishing industries with overall costs exceeding billions

of dollars (Baade et al. (2007); Smith et al. (2011)). Extensive research has been

done with respect to oil spill simulation techniques, spatial optimization models, and

innovative strategies to deal with spill response and planning efforts. However, most

of the research done in those areas is done independently of each other, leaving a

conceptual void between them.

In the following work, this thesis presents a Spatial Decision Support System

(SDSS), which efficiently integrates the independent facets of spill modeling techniques

and spatial optimization to enable officials to investigate and explore the various

options to clean up an offshore oil spill to make a more informed decision. This

thesis utilizes Blowout and Spill Occurrence Model (BLOSOM) developed by Sim

et al. (2015) to simulate hypothetical oil spill scenarios, followed by the Oil Spill

Cleanup and Operational Model (OSCOM) developed by Grubesic et al. (2017) to

spatially optimize the response efforts. The results of this combination are visualized

in the SDSS, featuring geographical maps, so the boat ramps from which the response

should be launched can be easily identified along with the amount of oil that hits the

shore thereby visualizing the intensity of the impact of the spill in the coastal areas

for various cleanup targets.

Elections in the United States are highly decentralized with vast powers given to the states to control laws surrounding voter registration, primary procedures, and polling places even in elections of federal officials. There are many individual factors that predict a person's likelihood of voting including race, education, and age. Historically disenfranchised groups are still disproportionately affected by restrictive voter registration and ID laws which can suppress their turnout. Less understood is how election-day polling place accessibility affects turnout. Absentee and early voting increase accessibility for all voters, but 47 states still rely on election-day polling places. I study how the geographic allocation of polling places and the number of voters assigned to each (polling place load) in Maricopa County, Arizona has affected turnout in primary and general elections between 2006 and 2016 while controlling for the demographics of voting precincts. This represents a significant data problem; voting precincts changed three times during the time studied and polling places themselves can change every election. To aid in analysis, I created a visualization that allows for the exploration of polling place load, precinct demographics, and polling place accessibility metrics in a map view of the county. I find through a spatial regression model that increasing the load on a polling place can decrease the election-day turnout and prohibitively large distances to the polling place have a similar effect. The effect is more pronounced during general elections and is present at varying levels during each of the 12 elections studied. Finally, I discuss how early voting options appear to have little positive effect on overall turnout and may in fact decrease it.

The connections between different entities define different kinds of networks, and many such networked phenomena are influenced by their underlying geographical relationships. By integrating network and geospatial analysis, the goal is to extract information about interaction topologies and the relationships to related geographical constructs. In the recent decades, much work has been done analyzing the dynamics of spatial networks; however, many challenges still remain in this field. First, the development of social media and transportation technologies has greatly reshaped the typologies of communications between different geographical regions. Second, the distance metrics used in spatial analysis should also be enriched with the underlying network information to develop accurate models.

Visual analytics provides methods for data exploration, pattern recognition, and knowledge discovery. However, despite the long history of geovisualizations and network visual analytics, little work has been done to develop visual analytics tools that focus specifically on geographically networked phenomena. This thesis develops a variety of visualization methods to present data values and geospatial network relationships, which enables users to interactively explore the data. Users can investigate the connections in both virtual networks and geospatial networks and the underlying geographical context can be used to improve knowledge discovery. The focus of this thesis is on social media analysis and geographical hotspots optimization. A framework is proposed for social network analysis to unveil the links between social media interactions and their underlying networked geospatial phenomena. This will be combined with a novel hotspot approach to improve hotspot identification and boundary detection with the networks extracted from urban infrastructure. Several real world problems have been analyzed using the proposed visual analytics frameworks. The primary studies and experiments show that visual analytics methods can help analysts explore such data from multiple perspectives and help the knowledge discovery process.

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.