Matching Items (7)
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- All Subjects: Geographic Information Science and Geodesy
- All Subjects: spatial statistics
- Genre: Doctoral Dissertation
- Creators: Murray, Alan T.
- Creators: Nelson, Trisalyn
- Status: Published
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 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.
ContributorsWei, Ran (Author) / Murray, Alan T. (Thesis advisor) / Anselin, Luc (Committee member) / Rey, Segio J (Committee member) / Mack, Elizabeth A. (Committee member) / Arizona State University (Publisher)
Created2013
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. 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.
ContributorsHong, Insu (Author) / Murray, Alan T. (Thesis advisor) / Kuby, Micheal (Committee member) / Rey, Sergio (Committee member) / Arizona State University (Publisher)
Created2015
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 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.
ContributorsFolch, David C. (Author) / Rey, Sergio J (Thesis advisor) / Anselin, Luc (Committee member) / Murray, Alan T. (Committee member) / Arizona State University (Publisher)
Created2012
Description
As urban populations become increasingly dense, massive amounts of new 'big' data that characterize human activity are being made available and may be characterized as having a large volume of observations, being produced in real-time or near real-time, and including a diverse variety of information. In particular, spatial interaction (SI) data - a collection of human interactions across a set of origins and destination locations - present unique challenges for distilling big data into insight. Therefore, this dissertation identifies some of the potential and pitfalls associated with new sources of big SI data. It also evaluates methods for modeling SI to investigate the relationships that drive SI processes in order to focus on human behavior rather than data description.
A critical review of the existing SI modeling paradigms is first presented, which also highlights features of big data that are particular to SI data. Next, a simulation experiment is carried out to evaluate three different statistical modeling frameworks for SI data that are supported by different underlying conceptual frameworks. Then, two approaches are taken to identify the potential and pitfalls associated with two newer sources of data from New York City - bike-share cycling trips and taxi trips. The first approach builds a model of commuting behavior using a traditional census data set and then compares the results for the same model when it is applied to these newer data sources. The second approach examines how the increased temporal resolution of big SI data may be incorporated into SI models.
Several important results are obtained through this research. First, it is demonstrated that different SI models account for different types of spatial effects and that the Competing Destination framework seems to be the most robust for capturing spatial structure effects. Second, newer sources of big SI data are shown to be very useful for complimenting traditional sources of data, though they are not sufficient substitutions. Finally, it is demonstrated that the increased temporal resolution of new data sources may usher in a new era of SI modeling that allows us to better understand the dynamics of human behavior.
A critical review of the existing SI modeling paradigms is first presented, which also highlights features of big data that are particular to SI data. Next, a simulation experiment is carried out to evaluate three different statistical modeling frameworks for SI data that are supported by different underlying conceptual frameworks. Then, two approaches are taken to identify the potential and pitfalls associated with two newer sources of data from New York City - bike-share cycling trips and taxi trips. The first approach builds a model of commuting behavior using a traditional census data set and then compares the results for the same model when it is applied to these newer data sources. The second approach examines how the increased temporal resolution of big SI data may be incorporated into SI models.
Several important results are obtained through this research. First, it is demonstrated that different SI models account for different types of spatial effects and that the Competing Destination framework seems to be the most robust for capturing spatial structure effects. Second, newer sources of big SI data are shown to be very useful for complimenting traditional sources of data, though they are not sufficient substitutions. Finally, it is demonstrated that the increased temporal resolution of new data sources may usher in a new era of SI modeling that allows us to better understand the dynamics of human behavior.
ContributorsOshan, Taylor Matthew (Author) / Fotheringham, A. S. (Thesis advisor) / Farmer, Carson J.Q. (Committee member) / Rey, Sergio S.J. (Committee member) / Nelson, Trisalyn (Committee member) / Arizona State University (Publisher)
Created2017
Description
Factors that explain human mobility and active transportation include built environment and infrastructure features, though few studies incorporate specific geographic detail into examinations of mobility. Little is understood, for example, about the specific paths people take in urban areas or the influence of neighborhoods on their activity. Detailed analysis of human activity has been limited by the sampling strategies employed by conventional data sources. New crowdsourced datasets, or data gathered from smartphone applications, present an opportunity to examine factors that influence human activity in ways that have not been possible before; they typically contain more detail and are gathered more frequently than conventional sources. Questions remain, however, about the utility and representativeness of crowdsourced data. The overarching aim of this dissertation research is to identify how crowdsourced data can be used to better understand human mobility. Bicycling activity is used as a case study to examine human mobility because smartphone apps aimed at collecting bicycle routes are readily available and bicycling is under studied in comparison to other modes. The research herein aimed to contribute to the knowledge base on crowdsourced data and human mobility in three ways. First, the research examines how conventional (e.g., counts, travel surveys) and crowdsourced data correspond in representing bicycling activity. Results identified where the data correspond and differ significantly, which has implications for using crowdsourced data for planning and policy decisions. Second, the research examined the factors that influence cycling activity generated by smartphone cycling apps. The best predictors of activity were median weekly rent, percentage of residential land, and the number of people using two or more modes to commute in an area. Finally, the third part of the dissertation seeks to understand the impact of bicycle lanes and bicycle ridership on residential housing prices. Results confirmed that bicycle lanes in the neighborhood of a home positively influence sale prices, though ridership was marginally related to house price. This research demonstrates that knowledge obtained through crowdsourced data informs us about smaller geographic areas and details on where people bicycle, who uses bicycles, and the impact of the built environment on bicycling activity.
ContributorsConrow, Lindsey (Author) / Wentz, Elizabeth (Thesis advisor) / Nelson, Trisalyn (Committee member) / Mooney, Sian (Committee member) / Pettit, Christopher (Committee member) / Arizona State University (Publisher)
Created2018
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 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.
ContributorsZhang, Yujia (Author) / Turner, Billie (Thesis advisor) / Murray, Alan T. (Committee member) / Myint, Soe W (Committee member) / Middel, Ariane (Committee member) / Arizona State University (Publisher)
Created2018
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 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.
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