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- All Subjects: Geography
- All Subjects: spatial statistics
- Genre: Academic theses
- Creators: Kuby, Michael
Description
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
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 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
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. 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
Description
Gerrymandering is a central problem for many representative democracies. Formally, gerrymandering is the manipulation of spatial boundaries to provide political advantage to a particular group (Warf, 2006). The term often refers to political district design, where the boundaries of political districts are “unnaturally” manipulated by redistricting officials to generate durable advantages for one group or party. Since free and fair elections are possibly the critical part of representative democracy, it is important for this cresting tide to have scientifically validated tools. This dissertation supports a current wave of reform by developing a general inferential technique to “localize” inferential bias measures, generating a new type of district-level score. The new method relies on the statistical intuition behind jackknife methods to construct relative local indicators. I find that existing statewide indicators of partisan bias can be localized using this technique, providing an estimate of how strongly a district impacts statewide partisan bias over an entire decade. When compared to measures of shape compactness (a common gerrymandering detection statistic), I find that weirdly-shaped districts have no consistent relationship with impact in many states during the 2000 and 2010 redistricting plan. To ensure that this work is valid, I examine existing seats-votes modeling strategies and develop a novel method for constructing seats-votes curves. I find that, while the empirical structure of electoral swing shows significant spatial dependence (even in the face of spatial heterogeneity), existing seats-votes specifications are more robust than anticipated to spatial dependence. Centrally, this dissertation contributes to the much larger social aim to resist electoral manipulation: that individuals & organizations suffer no undue burden on political access from partisan gerrymandering.
ContributorsWolf, Levi (Author) / Rey, Sergio J (Thesis advisor) / Anselin, Luc (Committee member) / Fotheringham, A. Stewart (Committee member) / Tam Cho, Wendy K (Committee member) / Arizona State University (Publisher)
Created2017
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 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
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 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
Description
Walking and bicycling bring many merits to people, both physically and mentally.
However, not everyone has an opportunity to enjoy healthy and safe bicycling and
walking. Many studies suggested that access to healthy walking and bicycling is heavily
related to socio-economic status. Low income population and racial minorities have
poorer transportation that results in less walking and bicycling, as well as less access to
public transportation. They are also under higher risks of being hit by vehicles while
walking and bicycling. This research quantifies the relationship between socioeconomic
factors and bicyclist and pedestrian involved traffic crash rates in order to establish an
understanding of how equitable access to safe bicycling and walking is in Phoenix. The
crash rates involving both bicyclists and pedestrians were categorized into two groups,
minor crashes and severe crashes. Then, the OLS model was used to analyze minor and
severe bicycle crash rates, and minor and severe pedestrian crash rates, respectively.
There are four main results, (1) The median income of an area is always negatively
related to the crash rates of bicyclists and pedestrians. The reason behind the negative
correlation is that there is a very small proportion of people choosing to walk or ride
bicycles as their commuting methods in the high-income areas. Consequently, there are
low crash rates of pedestrians and bicyclists. (2) The minor bicycle crash rates are more
related to socio-economic determinants than the severe crash rates. (3) A higher
population density reduces both the minor and the severe crash rates of bicyclists and
pedestrians in Phoenix. (4) A higher pedestrian commuting ratio does not reduce bicyclist
and pedestrian crash rates in Phoenix. The findings from this study can provide a
reference value for the government and other researchers and encourage better future
decisions from policy makers.
However, not everyone has an opportunity to enjoy healthy and safe bicycling and
walking. Many studies suggested that access to healthy walking and bicycling is heavily
related to socio-economic status. Low income population and racial minorities have
poorer transportation that results in less walking and bicycling, as well as less access to
public transportation. They are also under higher risks of being hit by vehicles while
walking and bicycling. This research quantifies the relationship between socioeconomic
factors and bicyclist and pedestrian involved traffic crash rates in order to establish an
understanding of how equitable access to safe bicycling and walking is in Phoenix. The
crash rates involving both bicyclists and pedestrians were categorized into two groups,
minor crashes and severe crashes. Then, the OLS model was used to analyze minor and
severe bicycle crash rates, and minor and severe pedestrian crash rates, respectively.
There are four main results, (1) The median income of an area is always negatively
related to the crash rates of bicyclists and pedestrians. The reason behind the negative
correlation is that there is a very small proportion of people choosing to walk or ride
bicycles as their commuting methods in the high-income areas. Consequently, there are
low crash rates of pedestrians and bicyclists. (2) The minor bicycle crash rates are more
related to socio-economic determinants than the severe crash rates. (3) A higher
population density reduces both the minor and the severe crash rates of bicyclists and
pedestrians in Phoenix. (4) A higher pedestrian commuting ratio does not reduce bicyclist
and pedestrian crash rates in Phoenix. The findings from this study can provide a
reference value for the government and other researchers and encourage better future
decisions from policy makers.
ContributorsWu, Feiyi (Author) / Nelson, Trisalyn (Thesis advisor) / Salon, Deborah (Committee member) / Kuby, Michael (Committee member) / Arizona State University (Publisher)
Created2020
Description
With the acceleration of urbanization in many parts of the world, transportation challenges such as traffic congestion, increasing carbon emissions, and the “first/last-mile” connectivity problems for commuter travel have arisen. Transport experts and policymakers have proposed shared transportation, such as dockless e-scooters and bike-sharing programs, to solve some of these urban transportation issues. In cities with high population densities, multimodal mobility hubs designed to integrate shared and public transportation can be implemented to achieve faster public connections and thus increase access to public transport on both access and egress sides. However, haphazard drop-offs of these dockless vehicles have led to complaints from community members and motivated the need for neighborhood-level parking areas (NLPAs). Simultaneously, concerns about the equitable distribution of transportation infrastructure have been growing and have led to the Biden Administration announcing the Justice40 Initiative which requires 40% of certain federal investments to benefit disadvantaged communities. To plan a system of NLPAs to address not only the transportation shortcomings while elevating these recent equity goals, this thesis develops a multi-objective optimal facility location model that maximizes coverage of both residential areas and transit stations while including a novel constraint to satisfy the requirements of Justice40. The model is applied to the City of Tempe, Arizona, and uses GIS data and spatial analyses of the existing public transportation stops, estimates of transit station boardings, population by census block, and locations of disadvantaged communities to optimize NLPA location. The model generates Pareto optimal tradeoff curves for different numbers of NLPAs to find the non-dominated solutions for the coverage of population nodes and boardings. The analysis solves the multi-objective model with and without the equity constraint, showing the effect of considering equity in developing a multimodal hub system, especially for disadvantaged communities. The proposed model can provide a decision support tool for transport and public authorities to plan future investments and facilitate multimodal transport.
ContributorsQuan, Hejun (Author) / Kuby, Michael (Thesis advisor) / Frazier, Amy (Thesis advisor) / Tong, Daoqin (Committee member) / Arizona State University (Publisher)
Created2022
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 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
Description
Embedded within the regression framework, local models can estimate conditioned relationships between observed spatial phenomena and hypothesized explanatory variables and help infer the intangible spatial processes that contribute to the observed spatial patterns. Rather than investigating averaged characteristics corresponding to processes over space as global models do, these models estimate a surface of spatially varying parameters with a value for each location. Additionally, some models such as variants within the Geographically Weighted Regression (GWR) framework, also estimate a parameter to represent the spatial scale across which the processes vary representing the inherent heterogeneity of the estimated surfaces. Since different processes tend to operate at unique spatial scales, some extensions to local models such as Multiscale GWR (MGWR) estimate unique scales of association for each predictor in a model and generate significantly more information on the nature of geographic processes than their predecessors. However, developments within the realm of local models are fairly nascent and hence an understanding around their correct application as well as recognizing their true potential in exploring fundamental spatial science issues is under-developed. The techniques within these frameworks are also currently limited thus restricting the kinds of data that can be analyzed using these models. Therefore the goal of this dissertation is to advance techniques within local multiscale modeling specifically by coining new diagnostics, exploring their novel application in understanding long-standing issues concerning spatial scale and by expanding the tool base to allow their use in wider empirical applications. This goal is realized through three distinct research objectives over four chapters, followed by a discussion on the future of the developments within local multiscale modeling. A correct understanding of the capability and promise of local multiscale models and expanding the fields where they can be employed will not only enhance geographical research by strengthening the intuition of the nature of geographic processes, but will also exemplify the importance and need for using such tools bringing quantitative spatial science to the fore.
ContributorsSachdeva, Mehak (Author) / Fotheringham, A. Stewart (Thesis advisor) / Goodchild, Michael Frank (Committee member) / Kedron, Peter (Committee member) / Wolf, Levi John (Committee member) / Arizona State University (Publisher)
Created2022