Matching Items (2)
Filtering by
- All Subjects: Geographic information systems
- All Subjects: spatial statistics
- Genre: Academic theses
- Creators: Brewis-Slade, Alexandra
Description
Despite the arid climate of Maricopa County, Arizona, vector-borne diseases have presented significant health challenges to the residents and public health professionals of Maricopa County in the past, and will continue to do so in the foreseeable future. Currently, West Nile virus is the only mosquitoes-transmitted disease actively, and natively, transmitted throughout the state of Arizona. In an effort to gain a more complete understanding of the transmission dynamics of West Nile virus this thesis examines human, vector, and environment interactions as they exist within Maricopa County. Through ethnographic and geographic information systems research methods this thesis identifies 1) the individual factors that influence residents' knowledge and behaviors regarding mosquitoes, 2) the individual and regional factors that influence residents' knowledge of mosquito ecology and the spatial distribution of local mosquito populations, and 3) the environmental, demographic, and socioeconomic factors that influence mosquito abundance within Maricopa County. By identifying the factors that influence human-vector and vector-environment interactions, the results of this thesis may influence current and future educational and mosquito control efforts throughout Maricopa County.
ContributorsKunzweiler, Colin (Author) / Boone, Christopher (Thesis advisor) / Wutich, Amber (Committee member) / Brewis-Slade, Alexandra (Committee member) / Arizona State University (Publisher)
Created2013
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