Limited knowledge exists of the terrain variables that have an influence on gopher tortoise (Gopherus polyphemus) burrow locations. Previous studies suggest that terrain features may play a role in preference of burrow location. LiDAR- (Light Detection and Ranging) derived terrain features can be evaluated through GIS (Geographic Information System) analysis at a fine spatial scale. LiDAR data acquired at 0.5 meter post spacing over three locations on Camp Shelby Joint Forces Training Center, MS were used to develop DTMs (Digital Terrain Models) for use in burrow site characterization. Terrain variables (e.g. elevation, slope, aspect) were developed from the LiDAR DTM in ArcGIS. Burrows and randomly allocated non-burrow points were used in logistic regression analysis to model the relationship between burrow occurrence and terrain features. Four models correctly classified more than 83% of the burrow locations. The R2 were 34.83%, 49.31%, 28.09%, and 31.51%.

<p>The heating sector accounts for almost half of total global energy consumption, with only 1/10th of heat produced from renewables. The adoption and technological advancement of heat pumps is key to electrifying heating, introducing more renewable sources, and decreasing energy expenditure. However, a range of complex barriers–including upfront costs, electricity costs, outdoor temperature, and building characteristics–hinder widespread heat pump adoption. High- resolution temporal and geospatial analysis is a powerful tool for understanding the patterns of such barriers, improving discernment of issues specific to certain populations. This project characterizes different heat pump technologies’ effects on residential energy use and expenditure with a high-resolution linear regression model of energy demand. We constructed linear regression efficiency models for two types of market-available heat pumps, characterized by refrigerant type and compressor type. With the thermal comfort energy demand estimates and estimated heat pump efficiency, we calculated the census tract, hourly-level energy demand in a 100% heat pump adoption case. We obtained these energy demand estimates for the states of Colorado and California–chosen due to their diverse climates and demographics–and used these energy demand estimates to calculate heat pump cost, electricity grid emissions, and energy burden. We also performed a case study comparison with actual heat pump energy consumption data for a household in West Lafayette, Indiana. </p> <p><br></p> <p>We found that heat pumps reduce total heating energy consumption and overall energy consumption for nearly all census tracts in both Colorado and California. In addition, for heating and overall energy consumption, our market average R410A heat pump model has the lower total variable cost in all census tracts relative to our R32 heat pump model. For cooling energy consumption, the R32 heat pump operates at a lower total variable cost than the R410A heat pump in most census tracts. Heat pumps tend to decrease average energy burden—percentage of household income dedicated to energy expenditure—in the less population-dense areas of both states. However, heat pump adoption leads to increased energy burden within cities. In comparison to our case study West Lafayette household, we obtained a relative root mean squared error for daily energy consumption of 28%, which is higher than studies using detailed engineering models at a single household-level but lower than studies using building simulation models. </p>

Great Smoky Mountains National Park (GRSM) received 12.5 million visitors in 2020. With a high traffic volume, it is imperative roadways remain open and free from obstruction. Annual unanticipated rockfall events in GRSM often obstruct traffic flow. Using the Unstable Slope Management Program for Federal Land Management Agencies (USMP for FLMA) protocols, this study analyzes high priority unstable rock slopes through 1) creation of an unstable slope geodatabase and 2) generation of a final rockfall risk model using Co-Kriging from a preliminary risk model and susceptibility model. A secondary goal of this study is to provide risk estimation for the three most traveled transportation corridors within GRSM, as well as investigate current rockfall hazard warning sign location to ultimately improve visitor safety with regards to rockfall hazards.

SANDIS II is a land warfare simulation and analysis tool developed by the Finnish Defence Research Agency. The Swedish Defence Research Agency has evaluated SANDIS II to have potential as a war gaming aid within education, at the Swedish Defence University. However, operating the tool is considered too difficult to avail that potential. In this report we propose a way of using geographical data for path-finding in terrain and automatically adjusting units’ speeds. We construct a cost raster from various types of geographic data, with each grid in the cost raster storing a value, representing a degree of mobility. Models using cost rasters are then created for adjusting unit speed and finding least-cost paths. We implement the models in Python as a stand-alone module, and describe the module’s internal methods, interface and how it can be used by SANDIS II. / SANDIS II är ett simuleringsoch analysverktyg utvecklat av Finska Försvarsmaktens Forskningsanstalt. Svenska Totalförsvarets forskningsinstitut har utvärderat SANDIS II och funnit ett potentiellt användningsområde för verktyget som stöd vid krigsspel, inom utbildning vid Försvarshögskolan. Verktyget anses dock vara för svårhanterligt för att uppfylla detta syfte. I denna rapport föreslås metoder för att beräkna de snabbaste förflyttningsvägarna i terräng och att automatiskt justera enheters hastighet i simulatorn, baserat på geografisk data. Vi konstruerar ett kostnadsraster av olika typer av terrängdata, där varje ruta i rastret tilldelas ett värde som representerar framkomlighet. Med kostnadsraster som grund skapar vi sedan modeller för att kunna justera enheters hastigheter och beräkna framryckningsrutter med så låg kostnad som möjligt. Vi implementerar modellerna i en separat Python-modul och beskriver modulens interna metoder, gränssnitt och hur det kan användas av SANDIS II.

Geospatial linked data are an emerging domain, with growing interest in research and the industry. There is an increasing number of publicly available geospatial linked data resources, which can also be interlinked and easily integrated with private and industrial linked data on the web. The present paper introduces Geo-L, a system for the discovery of RDF spatial links based on topological relations. Experiments show that the proposed system improves state-of-the-art spatial linking processes in terms of mapping time and accuracy, as well as concerning resources retrieval efficiency and robustness.

No description available.

Many regions of the Amazon are experiencing drastic changes as deforestation and climate change drive the world’s largest continuous rainforest towards a ‘tipping point’. These disturbances are changing natural cycles that once past a critical threshold, will mark an unstoppable transition to an altered ecosystem. Losing areas of the Amazon rainforest will have implications for the global climate, global carbon budget, and global hydrological regimes. Scholars have projected these tipping points for areas of the eastern Amazon rainforest, but much less scholarship focuses on the headwaters of the Western Amazon, an area of great cultural and biological importance. Ecuador is one such country. This study aims to model a tipping point for the Ecuadorian Amazon by investigating the potential outcomes of a warming climate and land cover change through 1. a comprehensive review of regional circulation models and global circulation models in the Ecuadorian Amazon, 2. a comprehensive review of anthropogenic disturbances in the Ecuadorian Amazon and their impact on communities, soil, flora and fauna, and 3. A model projecting the deforestation tipping point of the Ecuadorian Amazon. The results of my study will identify patterns of forest loss and provide quantitative assessments of potential ‘tipping points’ in a future Ecuadorian Amazon. The methods and model created herein can be used by future researchers to evaluate regional drivers of deforestation and predict land cover change under future scenarios.

Communities around the world are encountering unprecedented rates of change due to population growth, land use change, development, and increased social vulnerability to natural hazards. Understanding how physical processes and human vulnerability to natural hazards interact is a primary objective of researchers, policy makers, and disaster risk reduction practitioners in order to combat increases in natural hazard frequency and intensity. Nepal, a landlocked mountainous country spanning the central Himalayan region, has about 28 million inhabitants in 147,181 square kilometers. Nepal is exposed to a multitude of natural hazards, requiring individuals and communities to interact with and make decisions on risk acceptability on a day-to-day basis. In many cases, Nepal's geographic location, available resources (human, economic, and capital), and limited government capacity coalesce to turn natural hazards into disasters, resulting damaged infrastructure, economic disruptions, and death. This dissertation evaluates the geographic distribution of natural hazard mortality, quantifies social vulnerability to natural hazards, and models multi-hazard risk in the data deficient environment of Nepal. Chapter 1 conceptualizes relevant terms such as natural hazards, disaster, vulnerability, and risk before discussing the challenges associated with multi-hazard risk assessment in Nepal. Chapter 2 evaluates the spatial and temporal distribution of natural hazard mortalities at the village level using a publicly available disaster database. Results reveal that landslides were the deadliest disasters between 1971-2011. Chapter 3 identifies major social factors and processes that contribute to the vulnerability of individuals and communities using census data. Adapting the Social Vulnerability Index (SoVI) method developed for the US context, this chapter investigates the spatial distribution and clustering of various social vulnerabilities across the country. 'Renter and Occupation', 'Poverty and Poor Infrastructure', and 'Favorable Social Conditions' are three major components that influence social vulnerability in Nepal. Results indicate an interesting regional difference: the eastern and central Tarai are more vulnerable than western Tarai, whereas the eastern Hills and Mountains are less vulnerable than western Hills and Mountains. In Chapter 4, a model of risk from multiple natural hazards in the city of Dharan, Nepal, is presented. Freely available geospatial data in combination with socio-economic data collected from local government and secondary sources are used. Multi-hazard risk assessment is data intensive and requires considerable financial and human resources, which are lacking in Nepal. Results show that geospatial modeling techniques can be used to fill the gap and assist local officers and emergency managers in risk management. Cumulatively, this work offers new insights on natural hazards, vulnerability, risk, the use of geospatial technologies, and their inter-relationships. Research findings advance scholarly understandings of multi-hazard risk in general and particularly in the Nepali context. Additionally, this work is valuable to disaster practitioners who seek to implement more effective disaster risk reduction programs and policies. / Ph. D. / Natural hazards are earth system processes that pose threats to people and have the capacity to disrupt social and ecological processes. Thus, a consideration of both physical and social dimensions is required to better understand natural hazards. This research evaluates social factors and processes that have significant roles in enhancing the vulnerability of individuals and communities. First, this dissertation explores spatial and temporal patterns of natural hazard fatalities at the village level in Nepal. Research findings identified that landslides were the highest contributor to natural hazard fatalities from 1971-2011. Second, this dissertation assesses which social factors and processes contribute most to social vulnerability in Nepal. Additionally, the spatial distribution and clustering of social vulnerability is explored. Finally, geospatial modeling was performed to analyze cumulative risk to floods, landslides, and earthquakes in the municipality of Dharan, Nepal.

Access to clean water and improved sanitation are basic human right. This quantitative, descriptive study sought to establish current water and sanitation coverage in Chirumanzu and Shurugwi districts in Zimbabwe and develop methods of assessing coverage using Geographic Information Systems. Google Earth was used to identify homesteads. Personal digital assistant-based forms were used to collect geo-referenced data on all water points and selected households. Geospatial analysis methods were used to calculate borehole water coverage. Using Google Earth, 29375 homesteads were identified. The water survey mapped 4134 water points; 821 were boreholes; and only 548 were functional. Functional borehole water coverage was: 57.3%, 46.2%, and 33.5% for distance from household to water point of within 1500 m, 1000 m, and 500 m respectively. Sanitation coverage was 44.3%, but 96% of the latrines did not meet Blair Ventilated Pit latrine standards. / Health Studies / M.A. (Public Health) (Medical Informatics)

Written language provides a snapshot of linguistic, cultural, and current events information for a given time period. Aggregating these snapshots by studying many texts over time reveals trends in the evolution of language, culture, and society. The ever-increasing amount of electronic text, both from the digitization of books and other paper documents to the increasing frequency with which electronic text is used as a means of communication, has given us an unprecedented opportunity to study these trends. In this dissertation, we use hundreds of thousands of books spanning two centuries scanned by Google, and over 100 billion messages, or ‘tweets’, posted to the social media platform, Twitter, over the course of a decade to study the English language, as well as study the evolution of culture and society as inferred from the changes in language. We begin by studying the current state of verb regularization and how this compares between the more formal writing of books and the more colloquial writing of tweets on Twitter. We find that the extent of verb regularization is greater on Twitter, taken as a whole, than in English Fiction books, and also for tweets geotagged in the United States relative to American English books, but the opposite is true for tweets geotagged in the United Kingdom relative to British English books. We also find interesting regional variations in regularization across counties in the United States. However, once differences in population are accounted for, we do not identify strong correlations with socio-demographic variables. Next, we study stretchable words, a fundamental aspect of spoken language that, until the advent of social media, was rarely observed within written language. We examine the frequency distributions of stretchable words and introduce two central parameters that capture their main characteristics of balance and stretch. We explore their dynamics by creating visual tools we call ‘balance plots’ and ‘spelling trees’. We also discuss how the tools and methods we develop could be used to study mistypings and misspellings, and may have further applications both within and beyond language. Finally, we take a closer look at the English Fiction n-gram dataset created by Google. We begin by explaining why using token counts as a proxy of word, or more generally, ‘n-gram’, importance is fundamentally flawed. We then devise a method to rebuild the Google Books corpus so that meaningful linguistic and cultural trends may be reliably discerned. We use book counts as the primary ranking for an n-gram and use subsampling to normalize across time to mitigate the extraneous results created by the underlying exponential increase in data volume over time. We also combine the subsampled data over a number of years as a method of smoothing. We then use these improved methods to study linguistic and cultural evolution across the last two centuries. We examine the dynamics of Zipf distributions for n-grams by measuring the churn of language reflected in the flux of n-grams across rank boundaries. Finally, we examine linguistic change using wordshift plots and a rank divergence measure with a tunable parameter to compare the language of two different time periods. Our results address several methodological shortcomings associated with the raw Google Books data, strengthening the potential for cultural inference by word changes.