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The objective of this dissertation is to empirically analyze the results of the retail location decision making process and how chain networks evolve given their value platform. It employs one of the largest cross-sectional databases of retailers ever assembled, including 50 US retail chains and over 70,000 store locations. Three

The objective of this dissertation is to empirically analyze the results of the retail location decision making process and how chain networks evolve given their value platform. It employs one of the largest cross-sectional databases of retailers ever assembled, including 50 US retail chains and over 70,000 store locations. Three closely related articles, which develop new theory explaining location deployment and behaviors of retailers, are presented. The first article, "Regionalism in US Retailing," presents a comprehensive spatial analysis of the domestic patterns of retailers. Geographic Information Systems (GIS) and statistics examine the degree to which the chains are deployed regionally versus nationally. Regional bias is found to be associated with store counts, small market deployment, and the location of the founding store, but not the age of the chain. Chains that started in smaller markets deploy more stores in other small markets and vice versa for chains that started in larger markets. The second article, "The Location Types of US Retailers," is an inductive analysis of the types of locations chosen by the retailers. Retail locations are classified into types using cluster analysis on situational and trade area data at the geographical scale of the individual stores. A total of twelve distinct location types were identified. A second cluster analysis groups together the chains with the most similar location profiles. Retailers within the same retail business often chose similar types of locations and were placed in the same clusters. Retailers generally restrict their deployment to one of three overall strategies including metropolitan, large retail areas, or market size variety. The third article, "Modeling Retail Chain Expansion and Maturity through Wave Analysis: Theory and Application to Walmart and Target," presents a theory of retail chain expansion and maturity whereby retailers expand in waves with alternating periods of faster and slower growth. Walmart diffused gradually from Arkansas and Target grew from the coasts inward. They were similar, however, in that after expanding into an area they reached a point of saturation and opened fewer stores, then moved on to other areas, only to revisit the earlier areas for new stores.
ContributorsJoseph, Lawrence (Author) / Kuby, Michael (Thesis advisor) / Matthews, Richard (Committee member) / Ó Huallacháin, Breandán (Committee member) / Kumar, Ajith (Committee member) / Arizona State University (Publisher)
Created2013
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Description
In today's world, unprecedented amounts of data of individual mobile objects have become more available due to advances in location aware technologies and services. Studying the spatio-temporal patterns, processes, and behavior of mobile objects is an important issue for extracting useful information and knowledge about mobile phenomena. Potential applications across

In today's world, unprecedented amounts of data of individual mobile objects have become more available due to advances in location aware technologies and services. Studying the spatio-temporal patterns, processes, and behavior of mobile objects is an important issue for extracting useful information and knowledge about mobile phenomena. Potential applications across a wide range of fields include urban and transportation planning, Location-Based Services, and logistics. This research is designed to contribute to the existing state-of-the-art in tracking and modeling mobile objects, specifically targeting three challenges in investigating spatio-temporal patterns and processes; 1) a lack of space-time analysis tools; 2) a lack of studies about empirical data analysis and context awareness of mobile objects; and 3) a lack of studies about how to evaluate and test agent-based models of complex mobile phenomena. Three studies are proposed to investigate these challenges; the first study develops an integrated data analysis toolkit for exploration of spatio-temporal patterns and processes of mobile objects; the second study investigates two movement behaviors, 1) theoretical random walks and 2) human movements in urban space collected by GPS; and, the third study contributes to the research challenge of evaluating the form and fit of Agent-Based Models of human movement in urban space. The main contribution of this work is the conceptualization and implementation of a Geographic Knowledge Discovery approach for extracting high-level knowledge from low-level datasets about mobile objects. This allows better understanding of space-time patterns and processes of mobile objects by revealing their complex movement behaviors, interactions, and collective behaviors. In detail, this research proposes a novel analytical framework that integrates time geography, trajectory data mining, and 3D volume visualization. In addition, a toolkit that utilizes the framework is developed and used for investigating theoretical and empirical datasets about mobile objects. The results showed that the framework and the toolkit demonstrate a great capability to identify and visualize clusters of various movement behaviors in space and time.
ContributorsNara, Atsushi (Author) / Torrens, Paul M. (Thesis advisor) / Myint, Soe W (Committee member) / Kuby, Michael (Committee member) / Griffin, William A. (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Regional differences of inventive activity and economic growth are important in economic geography. These differences are generally explained by the theory of localized knowledge spillovers, which argues that geographical proximity among economic actors fosters invention and innovation. However, knowledge production involves an increasing number of actors connecting to non-local partners.

Regional differences of inventive activity and economic growth are important in economic geography. These differences are generally explained by the theory of localized knowledge spillovers, which argues that geographical proximity among economic actors fosters invention and innovation. However, knowledge production involves an increasing number of actors connecting to non-local partners. The space of knowledge flows is not tightly bounded in a given territory, but functions as a network-based system where knowledge flows circulate around alignments of actors in different and distant places. The purpose of this dissertation is to understand the dynamics of network aspects of knowledge flows in American biotechnology. The first research task assesses both spatial and network-based dependencies of biotechnology co-invention across 150 large U.S. metropolitan areas over four decades (1979, 1989, 1999, and 2009). An integrated methodology including both spatial and social network analyses are explicitly applied and compared. Results show that the network-based proximity better defines the U.S. biotechnology co-invention urban system in recent years. Co-patenting relationships of major biotechnology centers has demonstrated national and regional association since the 1990s. Associations retain features of spatial proximity especially in some Midwestern and Northeastern cities, but these are no longer the strongest features affecting co-inventive links. The second research task examines how biotechnology knowledge flows circulate over space by focusing on the structural properties of intermetropolitan co-invention networks. All analyses in this task are conducted using social network analysis. Evidence shows that the architecture of the U.S. co-invention networks reveals a trend toward more organized structures and less fragmentation over the four years of analysis. Metropolitan areas are increasingly interconnected into a large web of networked environment. Knowledge flows are less likely to be controlled by a small number of intermediaries. San Francisco, New York, Boston, and San Diego monopolize the central positions of the intermetropolitan co-invention network as major American biotechnology concentrations. The overall network-based system comes close to a relational core/periphery structure where core metropolitan areas are strongly connected to one another and to some peripheral areas. Peripheral metropolitan areas are loosely connected or even disconnected with each other. This dissertation provides empirical evidence to support the argument that technological collaboration reveals a network-based system associated with different or even distant geographical places, which is somewhat different from the conventional theory of localized knowledge spillovers that once dominated understanding of the role of geography in technological advance.
ContributorsLee, Der-Shiuan (Author) / Ó Huallacháin, Breandán (Thesis advisor) / Anselin, Luc (Committee member) / Kuby, Michael (Committee member) / Lobo, Jose (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Bicycle sharing systems (BSS) operate on five continents, and they change quickly with technological innovations. The newest “dockless” systems eliminate both docks and stations, and have become popular in China since their launch in 2016. The rapid increase in dockless system use has exposed its drawbacks. Without the order imposed

Bicycle sharing systems (BSS) operate on five continents, and they change quickly with technological innovations. The newest “dockless” systems eliminate both docks and stations, and have become popular in China since their launch in 2016. The rapid increase in dockless system use has exposed its drawbacks. Without the order imposed by docks and stations, bike parking has become problematic. In the areas of densest use, the central business districts of large cities, dockless systems have resulted in chaotic piling of bikes and need for frequent rebalancing of bikes to other locations. In low-density zones, on the other hand, it may be difficult for customers to find a bike, and bikes may go unused for long periods. Using big data from the Mobike BSS in Beijing, I analyzed the relationship between building density and the efficiency of dockless BSS. Density is negatively correlated with bicycle idle time, and positively correlated with rebalancing. Understanding the effects of density on BSS efficiency can help BSS operators and municipalities improve the operating efficiency of BSS, increase regional cycling volume, and solve the bicycle rebalancing problem in dockless systems. It can also be useful to cities considering what kind of BSS to adopt.
ContributorsCui, Wencong (Author) / Kuby, Michael (Thesis advisor) / Salon, Deborah (Committee member) / Thigpen, Calvin (Committee member) / Arizona State University (Publisher)
Created2018
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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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Description
Spatial regression is one of the central topics in spatial statistics. Based on the goals, interpretation or prediction, spatial regression models can be classified into two categories, linear mixed regression models and nonlinear regression models. This dissertation explored these models and their real world applications. New methods and models were

Spatial regression is one of the central topics in spatial statistics. Based on the goals, interpretation or prediction, spatial regression models can be classified into two categories, linear mixed regression models and nonlinear regression models. This dissertation explored these models and their real world applications. New methods and models were proposed to overcome the challenges in practice. There are three major parts in the dissertation.

In the first part, nonlinear regression models were embedded into a multistage workflow to predict the spatial abundance of reef fish species in the Gulf of Mexico. There were two challenges, zero-inflated data and out of sample prediction. The methods and models in the workflow could effectively handle the zero-inflated sampling data without strong assumptions. Three strategies were proposed to solve the out of sample prediction problem. The results and discussions showed that the nonlinear prediction had the advantages of high accuracy, low bias and well-performed in multi-resolution.

In the second part, a two-stage spatial regression model was proposed for analyzing soil carbon stock (SOC) data. In the first stage, there was a spatial linear mixed model that captured the linear and stationary effects. In the second stage, a generalized additive model was used to explain the nonlinear and nonstationary effects. The results illustrated that the two-stage model had good interpretability in understanding the effect of covariates, meanwhile, it kept high prediction accuracy which is competitive to the popular machine learning models, like, random forest, xgboost and support vector machine.

A new nonlinear regression model, Gaussian process BART (Bayesian additive regression tree), was proposed in the third part. Combining advantages in both BART and Gaussian process, the model could capture the nonlinear effects of both observed and latent covariates. To develop the model, first, the traditional BART was generalized to accommodate correlated errors. Then, the failure of likelihood based Markov chain Monte Carlo (MCMC) in parameter estimating was discussed. Based on the idea of analysis of variation, back comparing and tuning range, were proposed to tackle this failure. Finally, effectiveness of the new model was examined by experiments on both simulation and real data.
ContributorsLu, Xuetao (Author) / McCulloch, Robert (Thesis advisor) / Hahn, Paul (Committee member) / Lan, Shiwei (Committee member) / Zhou, Shuang (Committee member) / Saul, Steven (Committee member) / Arizona State University (Publisher)
Created2020
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Description
Geographically Weighted Regression (GWR) has been broadly used in various fields to

model spatially non-stationary relationships. Classic GWR is considered as a single-scale model that is based on one bandwidth parameter which controls the amount of distance-decay in weighting neighboring data around each location. The single bandwidth in GWR assumes that

Geographically Weighted Regression (GWR) has been broadly used in various fields to

model spatially non-stationary relationships. Classic GWR is considered as a single-scale model that is based on one bandwidth parameter which controls the amount of distance-decay in weighting neighboring data around each location. The single bandwidth in GWR assumes that processes (relationships between the response variable and the predictor variables) all operate at the same scale. However, this posits a limitation in modeling potentially multi-scale processes which are more often seen in the real world. For example, the measured ambient temperature of a location is affected by the built environment, regional weather and global warming, all of which operate at different scales. A recent advancement to GWR termed Multiscale GWR (MGWR) removes the single bandwidth assumption and allows the bandwidths for each covariate to vary. This results in each parameter surface being allowed to have a different degree of spatial variation, reflecting variation across covariate-specific processes. In this way, MGWR has the capability to differentiate local, regional and global processes by using varying bandwidths for covariates. Additionally, bandwidths in MGWR become explicit indicators of the scale at various processes operate. The proposed dissertation covers three perspectives centering on MGWR: Computation; Inference; and Application. The first component focuses on addressing computational issues in MGWR to allow MGWR models to be calibrated more efficiently and to be applied on large datasets. The second component aims to statistically differentiate the spatial scales at which different processes operate by quantifying the uncertainty associated with each bandwidth obtained from MGWR. In the third component, an empirical study will be conducted to model the changing relationships between county-level socio-economic factors and voter preferences in the 2008-2016 United States presidential elections using MGWR.
ContributorsLi, Ziqi (Author) / Fotheringham, A. Stewart (Thesis advisor) / Goodchild, Michael F. (Committee member) / Li, Wenwen (Committee member) / Arizona State University (Publisher)
Created2020
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Description
Since the mid-2000s, the domestic aviation industry has been influenced by new, rapidly growing ultra low-cost carriers (ULCCs) such as Allegiant Air, Spirit Airlines, and Frontier Airlines. These carriers augment the existing low-cost airline model by operating largely point-to-point routes with a minimum of passenger amenities. Existing literature, however, is

Since the mid-2000s, the domestic aviation industry has been influenced by new, rapidly growing ultra low-cost carriers (ULCCs) such as Allegiant Air, Spirit Airlines, and Frontier Airlines. These carriers augment the existing low-cost airline model by operating largely point-to-point routes with a minimum of passenger amenities. Existing literature, however, is limited for North American ULCCs, often lumping them together with mainstream low-cost carriers. The pattern of markets served by ULCCs is incongruous with the models of other airlines and requires further research to examine causal factors. This paper sought to establish conclusions about ULCCs and the relevant market factors used for airport choice decisions.The relationship between ULCC operations and airport choice factors was analyzed using three methods: a collection of 2019 flight data to establish existing conditions and statistics, two regression analyses to evaluate airport market variables, and three case studies examining distinct scenarios through qualitative interviews with airport managers. ULCC enplanement data was assembled for every domestic airport offering scheduled ULCC service in 2019. Independent variable data informed by previous research were collected for every Part 139 airport in the U.S. The first regression analysis estimated a OLS regression model to analyze the log of enplanements. The second model estimated a binary logistic equation for ULCC service as a 0-1 dependent variable. Case studies for Bellingham, Washington, Waco, Texas, and Lincoln, Nebraska were selected based on compelling airport factors and relevant ULCC experience. Results of the research methods confirm certain theories regarding ULCC airport choice, but left others unanswered. Maps of enplanements and market share revealed concentrations of ULCC operations on the East Coast. Each regression analysis showed a strong and positive relationship between population figures and the existence and quantity of ULCC operations. Tourism employment was only significantly related to enplanements. Other factors including distance and competition variables were significantly associated to ULCC service. Case studies revealed the importance of airport fees and costs in ULCC decision-making; factors that proved difficult to investigate quantitatively in this research. Further research may shed light on this complex and ever- changing subset of the domestic commercial aviation industry.
ContributorsTaplin, Drew (Author) / Kuby, Michael (Thesis advisor) / King, David (Committee member) / Salon, Deborah (Committee member) / Arizona State University (Publisher)
Created2021
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Description
Equity concerning food access has gained a lot of attention in the past decades. This problem can be seen in the dearth of supermarkets offering healthy food at reasonable prices in disadvantaged neighborhoods. Numerous studies show that the disparity in the distribution of food outlets has resulted in disparities in

Equity concerning food access has gained a lot of attention in the past decades. This problem can be seen in the dearth of supermarkets offering healthy food at reasonable prices in disadvantaged neighborhoods. Numerous studies show that the disparity in the distribution of food outlets has resulted in disparities in health outcomes. To mitigate the issue, various intervention strategies have been proposed and implemented, including introducing new supermarkets, mobile food markets, community gardens, and city farms in these neighborhoods. Among these strategies, mobile food markets have gained the attention of practitioners and policymakers for their low costs and service flexibility. Challenges remain in identifying the sites for best serving the people in need given limited resources. In this study, a new spatial optimization model is proposed to determine the best locations for mobile food markets in the City of Phoenix. The new model aims to cover the largest number of people with food access challenges while minimizing transportation costs. Compared with the existing mobile market sites, the sites provided by the new model can increase the coverage of low-food access residents with a shorter transportation distance. The new model has also been applied to help expand the service provider of the existing mobile food markets. In addition to mobile food markets, the method provided in this study can be extended to support the planning of other food outlets and food assistance services.
ContributorsLu, Junzhou (Author) / Tong, Daoqin (Thesis advisor) / Connor, Dylan (Committee member) / Kuby, Michael (Committee member) / Arizona State University (Publisher)
Created2022
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Description
Transportation infrastructure facilitates humans in moving themselves and material goods, and thereby supports the functioning of human society. Transportation planners, engineers, and decision makers in the 20th century largely excluded local stakeholders from planning processes; the resultant built environment has perpetuated inequity and social division. Transportation system planning has often

Transportation infrastructure facilitates humans in moving themselves and material goods, and thereby supports the functioning of human society. Transportation planners, engineers, and decision makers in the 20th century largely excluded local stakeholders from planning processes; the resultant built environment has perpetuated inequity and social division. Transportation system planning has often been conducted in specialized departments with little interdisciplinary collaboration. Integration of diverse perspectives and ontologies throughout transportation planning processes can produce robust, resilient, equitable, and sustainable transportation systems. Geodesign is a framework for planning the built environment that necessarily involves voices from multiple perspectives including local stakeholders, design professionals, geographic scientists, and information technology coordinators. Geodesign uses geographic information systems to create designs that reflect stakeholder needs, values, and priorities while addressing the study area’s geographic context. Geodesign has been used primarily for land use planning and has only addressed transportation planning concerns in relation to land use.This dissertation consists of an introduction, three projects that apply the geodesign framework to transportation planning and a concluding chapter. The introduction details the rationale for this research. The first project is a systematic review of geodesign projects that address transportation systems. The review seeks to identify epistemological alignment between the geodesign framework and participatory transportation planning. The results demonstrate that geodesign comports with transportation planners’ existing practices and uses of planning support systems. The combination of geodesign and transportation planning methods for stakeholder engagement could produce a synergistic framework for transportation infrastructure planning. The second project applies geodesign to locating refueling stations for hydrogen fuel cell vehicles around Hartford, Connecticut. Network designs generated by workshop participants were compared to networks generated by optimization models. The third project applies geodesign to locating sites for micromobility hubs in Tempe, Arizona, via short-form workshop series format. Participants considered the format conducive to collaborative public participatory design. These three projects demonstrate the suitability of the geodesign framework for node-based transportation facility planning via communicative rationality. The conclusion summarizes these three projects and highlights the reproducibility of the geodesign method for node-based transportation facility location planning in other study areas.
ContributorsLopez Jaramillo, Oscar (Author) / Kuby, Michael (Thesis advisor) / Wentz, Elizabeth (Committee member) / Ruddell, Darren (Committee member) / Arizona State University (Publisher)
Created2023