Matching Items (17)
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
Humans cooperate at levels unseen in other species. Identifying the adaptive mechanisms driving this unusual behavior, as well as how these mechanisms interact to create complex cooperative patterns, remains an open question in anthropology. One impediment to such investigations is that complete, long-term datasets of human cooperative behaviors in small-scale societies are hard to come by; such field research is often hindered both by humans' long lifespans and by the difficulties of collecting data in remote societies. In this study, I attempted to overcome these methodological challenges by simulating individual human cooperative behaviors in a small-scale population. Using an agent-based model tuned to population-level measurements from a real-life marine subsistence population in the southern Philippines, I generated dynamic daily cooperative behaviors in a hypothetical subsistence population over a period of 1500 years and 42 overlapping generations. Preliminary findings from the model suggest that, while the agent-based model broadly captured a number of characteristic population-level patterns in the subsistence population, it did not fully replicate nuances of the population's observed cooperative behaviors. In particular, statistical models of the simulated data identified reciprocity-based and need-based cooperative behaviors but did not detect kinship-motivated cooperation, despite the fact that kin cooperation traits evolved positively and reciprocity cooperation traits evolved negatively over time in the agent population. It is possible that this discrepancy reflects a complex interaction between kinship and reciprocity in the agent-based model. On the other hand, it may also suggest that these types of statistical models, which are frequently utilized in human cooperation studies in the anthropological literature, do not reliably discriminate between kin-based and reciprocity-based cooperation mechanisms when both exist in a population. Even so, the completeness of the simulated data enabled use of more complex statistical methodologies which were able to disentangle the relative effects of cooperative mechanisms operating at different decision levels. By addressing remaining pattern-matching issues, future iterations of the agent-based model may prove to be a useful tool for validating empirical research and investigating novel hypotheses about the evolution and maintenance of cooperative behaviors in human populations.
ContributorsPhelps, Julia R. (Author) / Reiser, Mark (Thesis advisor) / Saul, Steven (Thesis advisor) / Morgan, Thomas (Committee member) / Arizona State University (Publisher)
Created2023
Description
The Gulf of Mexico (or “Gulf”) is of critical significance to the oil and gas industries’ offshore production, but the potential for accidental petrochemical influx into the Gulf due to such processes is high; two of the largest marine oil spills in history, Pemex's Ixtoc I spill (1979) and British Petroleum's (BP) Deepwater Horizon (2010), have occurred in the region. However, the Gulf is also of critical significance to thousands of unique species, many of which may be irreparably harmed by accidental petrochemical exposure. To better manage the conservation and recovery of marine species in the Gulf ecosystem, a Petrochemical Vulnerability Index was developed to determine the potential impact of a petrochemical influx on Gulf marine fishes, therein providing an objective framework with which to determine the best immediate and long term management strategies for resource managers and decision-makers. The resulting Petrochemical Vulnerability Index (PVI) was developed and applied to all bony fishes and shark/ray species in the Gulf of Mexico (1,670 spp), based on a theoretical petrochemical vulnerability framework developed by peer review. The PVI for fishes embodies three key facets of species vulnerability: likelihood of exposure, individual sensitivity, and population resilience, and comprised of 11 total metrics (Distribution, Longevity, Mobility, Habitat, Pre-Adult Stage Length, Pre-Adult Exposure; Increased Adult Sensitivity Due to UV Light, Increased Pre-Adult Sensitivity Due to UV Light; and Abundance, Reproductive Turnover Rate, Diet/Habitat Specialization). The resulting PVI can be used to guide attention to the species potentially most in need of immediate attention in the event of an oil spill or other petrochemical influx, as well as those species that may require intensive long-term recovery. The scored relative vulnerability rankings can also provide information on species that ought to be the focus of future toxicological research, by indicating which species lack toxicological data, and may potentially experience significant impacts.
ContributorsWoodyard, Megan (Author) / Polidoro, Beth (Thesis advisor) / Saul, Steven (Thesis advisor) / Matson, Cole (Committee member) / Arizona State University (Publisher)
Created2020
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
The Salt River wild horses are a historic population of unbranded, unclaimed, wild and free-roaming horses, that were born in the wild and merit protection within our National Forest and protection of the Wild Horse and Burro act of 1970. Terms like undomesticated or feral are thrown around in place of “wild”. The past couple of decades or so, there has been an ongoing debate about the current state of the horses on the range. The horses that are along the Salt River, are considered to be state protected and not federally protected, which has sparked a vast discussion on the social, ethical and moral aspects. There has been an overabundance of horses on the range and are causing potential issues to the environment and other farmland. According to the BLM, wild horse and burro populations have a demonstrated ability to grow at 18-20 percent per year. With the widespread and overabundance that is occurring with the horses and burros, it has been said to have a great ecological cost on the rangeland ecosystem by overgrazing native plants, exacerbating invasive establishment and out-competing other ungulates like cattle. Overabundant free-roaming horse and burro populations have large and growing economic and ecological costs for the American public. Without effective management actions, horse and burro populations will double within the next 4-5 years. In this project, with the help of Dr. Julie Murphree, the Salt River Horse Management group and Arizona’s State Liaison for the Department of Agriculture, I conducted various ride-a-longs and conducted my own literature study to further solidify the knowledge I gained when navigating through the Salt River Wild Horse Management group. I can use their data as well as my own observations in the field to catalog their behaviors and look for any signs that would give reason to why this method of population control may or may not be used. I was able to note the horses in their “natural state” which would give me the opportunity to see any behavior changes in various population groups (or otherwise known as Bands). The main objective of this paper is to understand PZP as a population control tool and the effect it has on the Salt River Horses in Arizona.
ContributorsRendon, Chyna (Author) / Murphree, Julie (Thesis director) / Saul, Steven (Committee member) / Barrett, The Honors College (Contributor) / College of Integrative Sciences and Arts (Contributor)
Created2022-05
Description
When most people think of Phoenix, Arizona, they think of sprawling cityscapesand hot desert mountains full of saguaros and other cacti. They rarely think of water and
fish, and yet, the Arizona landscape is home to many lakes, ponds, rivers and streams,
full of both native fish and sportfish, including in the urban areas. According to the report
by DeSemple in 2006, between the years 2001 and 2006, the Rio Salado Environmental
Restoration Project worked to revitalize the dry river bed that runs through Phoenix, that
included the construction of two urban ponds, the Demonstration Pond and the Reservoir
Pond. At the start of this study, it was unknown what vertebrate species inhabited these
ponds, but it was known that these urban ponds have been used to dump unwanted
aquatic pets. The bluegill Lepomis macrochirus was found to reside in both ponds, and as
it is such an important sportfish species, it was chosen as the focal species for these
studies, which took place over periods in March, May, July, and September of 2021.
Single-season occupancy models were used to attempt to determine how L. macrochirus,
use the microhabitats within the system, and a multi-season model was used to estimate
their recruitment, and seasonal changes in occupancy. In addition, this study also
attempts to understand the size structures of the L. macrochirus population in the
Reservoir Pond and the population in the Demonstration Pond, and if that size structure
varies from March to September. As the populations of these ponds are physically
isolated from one another, statistical tests were also done to determine if the size
structures of the two populations of L. macrochirus differ from one another and found
that the two populations do indeed differ from one another, but only during two of the
sampling periods.
ContributorsKeister, Emily Jan (Author) / Saul, Steven (Thesis advisor) / Bateman, Heather (Committee member) / Suzart de Albuquerque, Fabio (Committee member) / Arizona State University (Publisher)
Created2022
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
Description
Marine ecosystems are currently being impacted by various threats; however, quantification of the impacts of known threats and the population status of species are often conducted at different scales, depending upon stakeholder needs. Global-scale species assessments can mask the impact of local or regional threats within the context of global conservation priorities even as conservation policies are generally implemented at the local or regional scale. This work aims to identify the regional threats currently impacting species present within the Gulf of Mexico as well as the current polices addressing those threats. Species currently impacted by threats were used to build an ecosystem model to estimate food web dynamics in the Gulf of Mexico. This model is the first of its kind to incorporate data from more than 1500 species occurring in the Gulf including all marine bony shorefishes, marine reptiles, complete clades of select marine invertebrates, marine birds, marine mammals, and chondrichthyans. Comprehensive analyses of these groups are important for an improved understanding of the functioning of the Gulf of Mexico food web and the impact of identified threats on food web dynamics. The identification of current threats and food web dynamics will help to inform conservation policy moving forward. Properly framed conservation efforts are more likely to be widely accepted and successful when there is an improved understanding on how policies can impact stakeholders both economically and through changing practices. Finally, an investigation of the legal frameworks currently recognized in the Gulf of Mexico was done to build an example tri-national framework between the United States, Mexico, and Cuba focusing on current conservation gaps allowing for specific regional conservation concerns to be addressed.
ContributorsStrongin, Kyle (Author) / Polidoro, Beth (Thesis advisor) / Saul, Steven (Committee member) / Gerber, Leah (Committee member) / Arizona State University (Publisher)
Created2021
Description
Corynorhinus townsendii, a bat species residing in north-central Arizona, has historically been observed hibernating in highly ventilated areas within caves and abandoned mines, but there is little to no specific data regarding this tendency. Understanding how air movement may influence hibernacula selection is critical in bettering conservation efforts for Arizona bats, especially with white-nose syndrome continuing to devastate bat species populations throughout the United States. My study aimed to begin filling in this knowledge gap. I measured wind speed in three known Arizona hibernacula during the winter hibernation season and combined this data with the locations of bats observed throughout each of the three survey locations. I modeled our findings using a generalized linear model, which confirmed that wind speed is indeed a predictor of C. townsendii roost selection.
ContributorsKitchel, Heidi (Author) / Moore, Marianne (Thesis director) / Saul, Steven (Committee member) / Barrett, The Honors College (Contributor) / College of Integrative Sciences and Arts (Contributor)
Created2022-05