<p> Women entrepreneurs are on the rise and their numbers have grown at one and a half times the rate of small enterprises generally over the last 15 years. In spite of this, women are underrepresented in the fields of science, technology, engineering, and mathematics (STEM). Women face additional barriers when forging careers within these fields and obtaining startup capital. This study examines female business ownership within the fields of hi-tech and biotech, and the factors that support startups by women throughout the state of California. As both of these industry sectors are known to cluster geographically around sources of venture capital, university research and development (R&amp;D) investment, and skilled labor, the study explored how these factors influence women entrepreneurs through two methods of analysis, specifically, a quantitative GIS analysis using exploratory geo-statistical tools, and a qualitative analysis using semi-structured interviews of twenty women business leaders. Results from the study demonstrated that factors that encourage hub formation are prone to cluster geographically, that women receive less venture capital than their male counterparts, biotech as a sector is more open than hi-tech to women&rsquo;s participation, high numbers of women starting businesses alongside their alma mater, and a high participation of women in business accelerators and incubators.</p>

<p> The objectives of this study were to test the degree to which anthropogenic factors impact surface water geochemistry by evaluating the connections between the built environment and selected chemical parameters in the storm water runoff system for Lafayette Parish, Louisiana. An approach that combines geospatial modeling and geochemical data collected on samples from natural and concrete lined coulee drainage channels in the parish was utilized to examine (1) the distribution and interactions of different water sources (e.g. surface runoff, alternating drainage flow pathways, groundwater, etc.); and (2) the effect of impervious and agriculture land cover on water chemistry in the engineered drainage network. </p><p> Chemical compositions were analyzed for trace and major cations using an Inductively Coupled Plasma &ndash; Optical Emission Spectrometer (ICP-OES). Anion concentrations were analyzed using an Ion Chromatograph (IC). Other variables, such as dissolved oxygen, temperature, conductivity, salinity, pH, and turbidity, were measured in situ using a YSITM sonde meter. Geographic Information Systems (GIS) software was used to model drainage boundaries at each site. Impervious cover and agricultural surface layers were created from the 2010 National Land Cover database and clipped to each microwatershed drainage area such that zonal statistics could be used to calculate total area and percentages of these land coverage types in each microwatershed. </p><p> Potential groundwater interactions were evidenced to occur near the Francois Coulee Lafayette. When urban and non-urban samples were analyzed together, impervious surface cover was significantly correlated to specific conductivity (&micro;S/cm) and the dissolved ionic species: Ca and SO4 in the microwatershed drainage areas. The exact amount of remedial acreage needed to return runoff waters to a normal baseline was calculated for targeted microwatershed drainage areas using a statistically derived coefficient estimate that suggested a 5.9 (&micro;S/cm) increase in specific conductivity concentrations for every 1% increase in impervious surface cover.</p>

Artificial Intelligence Lab, Department of MIS, University of Arizona / This research examined the applicability of using a neural network approach to the estimation of aqueous activity coefficients of aromatic organic compounds from fragmented structural information. A set of 95 compounds was used to train the neural network, and the trained network was tested on a set of 31 compounds. A comparison was made between the results and those obtained using multiple linear regression analysis. With the proper selection of neural network parameters, the backpropagation network provided a more accurate prediction of the aqueous activity coefficients for testing data than did regression analysis. This research indicates that neural networks have the potential to become a useful analytical technique for quantitative prediction of structure-activity relationships.

Artificial Intelligence Lab, Department of MIS, University of Arizona / This research presents an algorithmic approach to addressing the vocabulary problem in scientific information retrieval and information sharing, using the molecular biology domain as an example. We first present a literature review of cognitive studies related to the vocabulary problem and vocabuiary-based search aids (thesauri) and then discuss techniques for building robust and domain-specific thesauri to assist in cross-domain scientific information retrieval. Using a variation of the automatic thesaurus generation techniques, which we refer to as the concept space approach, we recently conducted an experiment in the molecular biology domain in which we created a C. elegans worm thesaurus of 7,657 worm-specific terms and a Drosofila fly thesaurus of 15,626 terms. About 30% of these terms overlapped, which created vocabulary paths from one subject domain to the other. Based on a cognitive study of term association involving four biologists, we found that a large percentage (59.6-85.6%) of the terms suggested by the subjects were identified in the conjoined fly-worm thesaurus. However, we found only a small percentage (8.4-18.1%) of the associations suggested by the subjects in the thesaurus. In a follow-up document retrieval study involving eight fly biologists, an actual worm database (Worm Community System), and the conjoined flyworm thesaurus, subjects were able to find more relevant documents (an increase from about 9 documents to 20) and to improve the document recall level (from 32.41 to 65.28%) when using the thesaurus, although the precision level did not improve significantly. Implications of adopting the concept space approach for addressing the vocabulary problem in Internet and digital libraries applications are also discussed.

<p> Understanding the causes of specific fire regimes is critical for determining the long term impacts of fire on vegetation cover. Numerous studies using 30 m Landsat data find a relationship between fire timing and vegetation type, but this relationship has not been observed at broader scales. In West Africa land-cover patterns are heterogeneous and patchy at the landscape scale and annual fires often burn mosaic patterns. It is well documented that where fires are known to be small and fragmented, the commonly used coarse-resolution MODIS data cannot give accurate estimates of burned area. Moreover, their inability to capture the spatial pattern of land-cover types burned presents a mixed pixel problem, because vegetation and agricultural fields vary on a scale less than 500 m². To overcome these issues, this study uses medium-resolution Landsat data to map land-cover. Landscape ecological indices are used to observe spatial patterns at 500 m scale.</p>

<p> FEMA&rsquo;s <i>Hazus Earthquake Model</i> (FEMA, 2018) is used frequently in the United States for scenario driven catastrophic planning, prioritizing mitigation funding, and disaster response situational awareness. The Hazus Earthquake Model requires a significant array of data for conducting earthquake loss estimations in terms of population demographics, building construction proxies, built environment estimates, critical facility locations, building infrastructure, and a variety of engineering and analysis parameters. A basic baseline of these required inputs is provided with Hazus for all 50 U.S. States, the District of Columbia, and the 5 U.S. territories. These default baseline datasets and engineering/analysis parameters however are somewhat rudimentary, and the incorporation of local data greatly increases results. Hazus enables the United States natural hazards risk assessment community to quantify risk using a robust suite of analysis options. By adapting the <i>Hazus Earthquake Model</i> for a country outside of the United States, risk management professionals across the globe can leverage the significant financial investment and over 20 years of software and scientific methodology investments undertaken by the U.S. government for their communities. While the methods and models are transferable internationally, the limited available data upon which the model is reliant minimizes its application outside of the U.S. Many global population exposure, building, and construction practice datasets do exist that could be leveraged to enable regional Hazus Earthquake modeling capability globally and a methodology for integrating these datasets into the software can facilitate technology transfer for supporting risk reduction. By conducting a quantitative comparison between modeled and actual losses from the 2015 Gorkha (Nepal) Mw 7.8 earthquake, this study aims to answer whether FEMA&rsquo;s U.S.-centric Hazus Earthquake Model can be adapted for Nepal to estimate building damages, injuries, and fatalities using global exposure datasets.</p><p>

<p> This study performed an assessment of the representational accuracy of the forest class of the GlobeLand30 (GL30) global land cover data sets for the country of Mexico using a robust geographically distributed forest inventory survey of the forests in Mexico. The representational accuracy assessment was carried out for both the 2000 and 2010 GL30 data sets. The detailed attribute data associated with the validation set demonstrates how GL30 classifies specific forest types and how canopy coverage and number for trees per site influence the likelihood of GL30 identifying the sites correctly as forests. The results indicate that producers accuracies range from 72.3% to 97.3%. The tropical forests (89.1%) were better represented by the GL30 forest class than the temperate forest (73.9%). The most poorly represented classes from the temperate (oak: 72.3%) and tropical (low dry deciduous jungle: 74.9%) groups were deciduous. Receiver Operator Curve and Area Under the Curve analyses show that canopy coverage of a site is a better predictor of GL30, correctly identifying the site as forest for temperate forest, and that the number of the trees per site is a better predictor of GL30 correctly identifying a site as forest for tropical forests. The results also indicate a distinct spatial variability in the location of the sample sites that are misidentified as forests by GL30. The results of this thesis will help researchers and professionals better understand the representational accuracy of the GL30 data sets for the forests in Mexico.</p><p>

<p> Archaeologists have been interested in relationship between environmental variability and cultural change for the last six decades. By understanding how, when, and why humans adapt to environmental change, archaeologists and anthropologists can better understand the development and complexity of human cultures. In northwest Alaska, archaeologists hypothesize that environmental variability was a major factor in both growing coastal population density, with large aggregated villages and large houses, between 1000 and 500 years ago (ya), and subsequent decreasing population density between 500 ya and the contact era. After 500 ya people are thought to have dispersed to smaller settlements with smaller house sizes in coastal areas, and perhaps, upriver. This settlement pattern was identified through research at four site locations over 30 years ago. The changing geographic distribution of sites, associated settlement size, and house size has not been examined in detail. A more careful examination of changing northwest Alaskan settlement patterns is needed before larger questions about socio-economic organization can be addressed. I use Geographic Information Systems (GIS) to evaluate the evidence for a geographic redistribution of Arctic peoples during the Late Holocene. </p><p> I constructed a database of settlement location and site attribute information, specifically the number of houses within each settlement and the size (m²). Data were collected from a dataset of Western Arctic National Parklands (WEAR), the Alaska Heritage Resource Survey (AHRS) database of archaeological sites in Alaska, 409 unpublished site reports and field notes curated by the National Park Service (NPS) and Bureau of Indian Affairs (BIA), and the results of recent fieldwork in northwest Alaska. A total of 486 settlements were identified within the northwest Alaska with 128 settlements having temporal and site attribute data. </p><p> I incorporated settlement size data into a GIS database and then carried out global, Moran&rsquo;s I, local Moran&rsquo;s I, and local Getis-Ord spatial analyses to test whether settlement redistribution occurred and if key settlement locations shifted after 500 ya. The site attribute data (number of houses and average size of houses) are used to test the additional aspects of the proposed settlement pattern change after 500 ya. A total of 83 settlements with 465 houses are used to test if the average size of settlements and average house size changed after 500 ya. </p><p> The results of the spatial analyses indicate no statistically significant patterns in the spatial distribution of settlements. Site attribute analysis shows no statistical difference in the average number of houses per village or the average size of houses before or after 500 ya. The results of this work build our understanding of regional settlement patterns during the late Holocene. By testing settlement pattern change, i.e. settlement distribution, settlement size, and house size, future research into settlement pattern change can begin to evaluate likely causes for the observed changes. My method, specifically the use of GIS as a method for testing settlement pattern change, can be applied to other regions and temporal scales.</p><p>

<p> This thesis provides an extensive look at where permitted non-farm uses and dwellings have clustered within Exclusive Farm Use (EFU) zones in the Northern Willamette Valley in Oregon. There is a looming concern that non-farm related uses and dwellings, or non-farm development, are conflicting with agricultural preservation strategies. Specifically, non-farm developments can potentially undermine the critical mass of farmland needed to keep the agricultural economy sustainable, but until now, studies have lacked spatially precise data to systematically track these phenomena. This thesis offers methodological contributions towards analyzing these operations and presents a broad account of what has been occurring in the region. Using permit approval data from the Department of Land Conservation and Development (DLCD) and 2015 county tax lot shapefiles, I geocoded the locations of these uses and dwellings. I used location quotient and spatial autocorrelation coefficients to identify non-farm hotspots in the region and summarized different typologies that have developed. The findings reveal that viticulture operations have amassed near Dundee and Newberg in Yamhill County, while commercial activities and home occupations have clustered near the Salem-Keizer UGB. Concurrently, dwellings have clustered near the Yamhill-Polk County border. Finally, I offer suggestions to improve Oregon&rsquo;s agricultural land use policy and data management process, as well as advocate for more intensive research in the future to generate narratives for our results.</p><p>

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