Matching Items (17)

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Spatial-Temporal Analysis of Barrett Freshmen 2007-2012: Source Area Analysis and Poisson Regression

Description

In order to help enhance admissions and recruiting efforts, this longitudinal study analyzed the geographic distribution of matriculated Barrett freshmen from 2007-2012 and sought to explore hot and cold spot

In order to help enhance admissions and recruiting efforts, this longitudinal study analyzed the geographic distribution of matriculated Barrett freshmen from 2007-2012 and sought to explore hot and cold spot locations of Barrett enrollment numbers using geographic information science (GIS) methods. One strategy involved   weighted mean center and standard distance analyses for each year of data for non-resident (out-of-state) freshmen home zip codes. Another strategy, a Poisson regression model, revealed recruitment "hot and cold spots" across the U.S. to project the expected counts of Barrett freshmen by zip code. This projected count served as a comparison for the actual admissions data, where zip codes with over and under predictions represented cold and hot spots, respectively. The mean center analysis revealed a westward shift from 2007 to 2012 with similar distance dispersions. The Poisson model projected zero-student zip codes with 99.2% accuracy and non-zero zip codes with 73.8% accuracy. Norwalk, CA (90650) and New York, NY (10021) represented the top out-of-state cold spot zip codes, while the model indicated that Chandler, AZ (85249) and Queen Creek, AZ (85242) had the most in-state potential for recruitment. The model indicated that more students have come from Albuquerque, NM (87122) and Aurora, CO (80015) than anticipated, while Phoenix, AZ (85048) and Tempe, AZ (85284) represent in-state locations with higher correlations between the variables included, especially regarding distance decay, and the than expected numbers of freshmen. The regression also indicated the existence of strong likelihood of attracting Barrett students.

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Created

Date Created
  • 2013-05

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A "Massive Fact" of American Politics: Revisitng Regions in the Contemporary House of Representatives

Description

All politics is local, but some locales practice politics differently than others. Unique, individual relationships between a place and the social institutions of politics modifies and mitigates assumptions of how

All politics is local, but some locales practice politics differently than others. Unique, individual relationships between a place and the social institutions of politics modifies and mitigates assumptions of how politics works across space. This analysis takes into account cultural theory concerning political behavior of place and regions and work by political scientists analyzing the differences in political behavior and preferences and aims to test a hypothesis about spatial patterns in the defections from party line votes in the US House of Representatives.

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Created

Date Created
  • 2013-05

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Open Geospatial Analytics with PySAL

Description

This article reviews the range of delivery platforms that have been developed for the PySAL open source Python library for spatial analysis. This includes traditional desktop software (with a graphical

This article reviews the range of delivery platforms that have been developed for the PySAL open source Python library for spatial analysis. This includes traditional desktop software (with a graphical user interface, command line or embedded in a computational notebook), open spatial analytics middleware, and web, cloud and distributed open geospatial analytics for decision support. A common thread throughout the discussion is the emphasis on openness, interoperability, and provenance management in a scientific workflow. The code base of the PySAL library provides the common computing framework underlying all delivery mechanisms.

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Created

Date Created
  • 2015-06-01

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Essays on space-time interaction tests

Description

Researchers across a variety of fields are often interested in determining if data are of a random nature or if they exhibit patterning which may be the result of some

Researchers across a variety of fields are often interested in determining if data are of a random nature or if they exhibit patterning which may be the result of some alternative and potentially more interesting process. This dissertation explores a family of statistical methods, i.e. space-time interaction tests, designed to detect structure within three-dimensional event data. These tests, widely employed in the fields of spatial epidemiology, criminology, ecology and beyond, are used to identify synergistic interaction across the spatial and temporal dimensions of a series of events. Exploration is needed to better understand these methods and determine how their results may be affected by data quality problems commonly encountered in their implementation; specifically, how inaccuracy and/or uncertainty in the input data analyzed by the methods may impact subsequent results. Additionally, known shortcomings of the methods must be ameliorated. The contributions of this dissertation are twofold: it develops a more complete understanding of how input data quality problems impact the results of a number of global and local tests of space-time interaction and it formulates an improved version of one global test which accounts for the previously identified problem of population shift bias. A series of simulation experiments reveal the global tests of space-time interaction explored here to be dramatically affected by the aforementioned deficiencies in the quality of the input data. It is shown that in some cases, a conservative degree of these common data problems can completely obscure evidence of space-time interaction and in others create it where it does not exist. Conversely, a local metric of space-time interaction examined here demonstrates a surprising robustness in the face of these same deficiencies. This local metric is revealed to be only minimally affected by the inaccuracies and incompleteness introduced in these experiments. Finally, enhancements to one of the global tests are presented which solve the problem of population shift bias associated with the test and better contextualize and visualize its results, thereby enhancing its utility for practitioners.

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Created

Date Created
  • 2013

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Spatiotemporal data mining, analysis, and visualization of human activity data

Description

This dissertation addresses the research challenge of developing efficient new methods for discovering useful patterns and knowledge in large volumes of electronically collected spatiotemporal activity data. I propose to analyze

This dissertation addresses the research challenge of developing efficient new methods for discovering useful patterns and knowledge in large volumes of electronically collected spatiotemporal activity data. I propose to analyze three types of such spatiotemporal activity data in a methodological framework that integrates spatial analysis, data mining, machine learning, and geovisualization techniques. Three different types of spatiotemporal activity data were collected through different data collection approaches: (1) crowd sourced geo-tagged digital photos, representing people's travel activity, were retrieved from the website Panoramio.com through information retrieval techniques; (2) the same techniques were used to crawl crowd sourced GPS trajectory data and related metadata of their daily activities from the website OpenStreetMap.org; and finally (3) preschool children's daily activities and interactions tagged with time and geographical location were collected with a novel TabletPC-based behavioral coding system. The proposed methodology is applied to these data to (1) automatically recommend optimal multi-day and multi-stay travel itineraries for travelers based on discovered attractions from geo-tagged photos, (2) automatically detect movement types of unknown moving objects from GPS trajectories, and (3) explore dynamic social and socio-spatial patterns of preschool children's behavior from both geographic and social perspectives.

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Created

Date Created
  • 2012

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An exploratory toolkit for examining residential movement patterns at a micro scale

Description

Change of residence is a commonly occurring event in urban areas. It reflects how people interact with the social or physical environment. Thus, by exploring the movement patterns of residential

Change of residence is a commonly occurring event in urban areas. It reflects how people interact with the social or physical environment. Thus, by exploring the movement patterns of residential changes, geographers and other scholars hope to learn more about the reasons and impacts associated with residential mobility, and to better understand how humans and the environment mutually interact. This is especially meaningful if exploration is based on micro scale movements, since residential changes within a city or a county reflect how the urban structure and community composition interact. Local differentiation, as an inevitable feature among movements at different places, can best be examined based on data at the micro scale. Such work is meaningful, but there have not been appropriate approaches for assessment and evaluation. The majority of traditional methods concentrate more on aggregate movement data at a national scale. So, in order to facilitate research examining movement patterns from a mass of individual residential changes at a micro scale, a toolkit, implemented by computational programming, is introduced in this dissertation to integrate both exploratory as well as confirmatory methods. This toolkit also employs a creative method to explore the spatial autocorrelation of residential movements, reflecting the local effects involved in this social event. The effectiveness and efficiency of this toolkit is examined through a concrete application involving 2,363 residential movements in Franklin County, Ohio.

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Created

Date Created
  • 2012

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Underutilized Spaces and Marginal Lands for Sustainable Land Use: A Multi-Scale Analysis

Description

Drawn from a trio of manuscripts, this dissertation evaluates the sustainability contributions and implications of deploying underutilized spaces for alternative uses at multiple scales: urban, regional and continental. The first

Drawn from a trio of manuscripts, this dissertation evaluates the sustainability contributions and implications of deploying underutilized spaces for alternative uses at multiple scales: urban, regional and continental. The first paper considers the use of underutilized spaces at the urban scale for urban agriculture (UA) to meet local sustainability goals in Phoenix, Arizona. Through a data-driven analysis, it demonstrates UA can meet 90% of annual demand for fresh produce, supply local produce in all food deserts, reduce areas underserved by public parks by 60%, and displace >50,000 tons of carbon-dioxide emissions from buildings.

The second paper considers marginal agricultural land use for bioenergy crop cultivation to meet future liquid fuels demand from cellulosic biofuels sustainably and profitably. At a wholesale fuel price of $4 gallons-of-gasoline-equivalent, 30 to 90.7 billion gallons of cellulosic biofuels can be supplied by converting 22 to 79.3 million hectares of marginal lands in the Eastern United States (U.S.). Displacing marginal croplands (9.4-13.7 million hectares) reduces stress on water resources by preserving soil moisture. This displacement is comparable to existing land use for first-generation biofuels, limiting food supply impacts. Coupled modeling reveals positive hydroclimate feedback on bioenergy crop yields that moderates the land footprint.

The third paper examines the sustainability implications of expanding use of marginal lands for corn cultivation in the Western Corn Belt, a commercially important and environmentally sensitive U.S. region. Corn cultivation on lower quality lands, which tend to overlap with marginal agricultural lands, is shown to be nearly three times more sensitive to changes in crop prices. Therefore, corn cultivation disproportionately expanded into these lands following price spikes.

Underutilized spaces can contribute towards sustainability at small and large scales in a complementary fashion. While supplying fresh produce locally and delivering other benefits in terms of energy use and public health, UA can also reduce pressures on croplands and complement non-urban food production. This complementarity can help diversify agricultural land use for meeting other goals, like supplying biofuels. However, understanding the role of market forces and economic linkages is critical to anticipate any unintended consequences due to such re-organization of land use.

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Date Created
  • 2020

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Developing new methods for analyzing urban energy use in buildings: historic turnover, spatial patterns, and future forecasting

Description

Energy use within urban building stocks is continuing to increase globally as populations expand and access to electricity improves. This projected increase in demand could require deployment of new

Energy use within urban building stocks is continuing to increase globally as populations expand and access to electricity improves. This projected increase in demand could require deployment of new generation capacity, but there is potential to offset some of this demand through modification of the buildings themselves. Building stocks are quasi-permanent infrastructures which have enduring influence on urban energy consumption, and research is needed to understand: 1) how development patterns constrain energy use decisions and 2) how cities can achieve energy and environmental goals given the constraints of the stock. This requires a thorough evaluation of both the growth of the stock and as well as the spatial distribution of use throughout the city. In this dissertation, a case study in Los Angeles County, California (LAC) is used to quantify urban growth, forecast future energy use under climate change, and to make recommendations for mitigating energy consumption increases. A reproducible methodological framework is included for application to other urban areas.

In LAC, residential electricity demand could increase as much as 55-68% between 2020 and 2060, and building technology lock-in has constricted the options for mitigating energy demand, as major changes to the building stock itself are not possible, as only a small portion of the stock is turned over every year. Aggressive and timely efficiency upgrades to residential appliances and building thermal shells can significantly offset the projected increases, potentially avoiding installation of new generation capacity, but regulations on new construction will likely be ineffectual due to the long residence time of the stock (60+ years and increasing). These findings can be extrapolated to other U.S. cities where the majority of urban expansion has already occurred, such as the older cities on the eastern coast. U.S. population is projected to increase 40% by 2060, with growth occurring in the warmer southern and western regions. In these growing cities, improving new construction buildings can help offset electricity demand increases before the city reaches the lock-in phase.

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Agent

Created

Date Created
  • 2016

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Modeling suitable habitat under climate change for chaparral shrub communities in the Santa Monica Mountains National Recreation Area, California

Description

Species distribution modeling is used to study changes in biodiversity and species range shifts, two currently well-known manifestations of climate change. The focus of this study is to explore how

Species distribution modeling is used to study changes in biodiversity and species range shifts, two currently well-known manifestations of climate change. The focus of this study is to explore how distributions of suitable habitat might shift under climate change for shrub communities within the Santa Monica Mountains National Recreation Area (SMMNRA), through a comparison of community level to individual species level distribution modeling. Species level modeling is more commonly utilized, in part because community level modeling requires detailed community composition data that are not always available. However, community level modeling may better detect patterns in biodiversity. To examine the projected impact on suitable habitat in the study area, I used the MaxEnt modeling algorithm to create and evaluate species distribution models with presence only data for two future climate models at community and individual species levels. I contrasted the outcomes as a method to describe uncertainty in projected models. To derive a range of sensitivity outcomes I extracted probability frequency distributions for suitable habitat from raster grids for communities modeled directly as species groups and contrasted those with communities assembled from intersected individual species models. The intersected species models were more sensitive to climate change relative to the grouped community models. Suitable habitat in SMMNRA's bounds was projected to decline from about 30-90% for the intersected models and about 20-80% for the grouped models from its current state. Models generally captured floristic distinction between community types as drought tolerance. Overall the impact on drought tolerant communities, growing in hotter, drier habitat such as Coastal Sage Scrub, was predicted to be less than on communities growing in cooler, moister more interior habitat, such as some chaparral types. Of the two future climate change models, the wetter model projected less impact for most communities. These results help define risk exposure for communities and species in this conservation area and could be used by managers to focus vegetation monitoring tasks to detect early response to climate change. Increasingly hot and dry conditions could motivate opportunistic restoration projects for Coastal Sage Scrub, a threatened vegetation type in Southern California.

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Created

Date Created
  • 2014

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A spatial decision support system for optimizing the environmental rehabilitation of borderlands

Description

The border policies of the United States and Mexico that have evolved over the previous decades have pushed illegal immigration and drug smuggling to remote and often public lands. Valuable

The border policies of the United States and Mexico that have evolved over the previous decades have pushed illegal immigration and drug smuggling to remote and often public lands. Valuable natural resources and tourist sites suffer an inordinate level of environmental impacts as a result of activities, from new roads and trash to cut fence lines and abandoned vehicles. Public land managers struggle to characterize impacts and plan for effective landscape level rehabilitation projects that are the most cost effective and environmentally beneficial for a region given resource limitations. A decision support tool is developed to facilitate public land management: Borderlands Environmental Rehabilitation Spatial Decision Support System (BERSDSS). The utility of the system is demonstrated using a case study of the Sonoran Desert National Monument, Arizona.

Contributors

Agent

Created

Date Created
  • 2013