Browsing by Subject "Geospatial analysis"

Now showing 1 - 4 of 4

Climate change and migration in Cambodia : an analysis of spatiotemporal trends in water availability and migration
(2020-05-14) Sigelmann, Laura Emilia; Scanlon, Bridget R.; Busby, Joshua W.
Cambodia is a small fragile state in Southeast Asia that is highly exposed to the effects of climate change. While there is a burgeoning body of research on the effects of climate change on security outcomes, there is limited research on the relationship between climate change and migration, particularly in Cambodia. The purpose of this study is twofold: first, to analyze subnational climate vulnerabilities in Cambodia with a specific emphasis on water availability; second, to analyze the relationship between water availability and migration in Cambodia. The study first uses a mix of quantitative and qualitative data to establish the political, economic, and social conditions necessary for climate change to affect migration. Then, the study uses Esri’s Emerging Hot Spot Analysis tool to identify precipitation trends on a subnational level. Finally, the study uses qualitative and quantitative data, including focus group interviews, to analyze subnational migration patterns in relation to subnational precipitation patterns and provide a holistic picture of Cambodia’s climate-migration nexus. The study finds that precipitation is decreasing in the northwest provinces of Banteay Meanchey, Battambang, Oddar Meanchey, and Siem Reap, where the bulk of the population is reliant on traditional rice agriculture, which is highly vulnerable to the effects of climate change. The study also concludes that households that have experienced crop loss, drought, and poor rainfall are more likely to have a family member migrate the following year. If the northwest continues to experience a drying trend, it is likely that more individuals will migrate from these provinces in the future. Future research should address two things: first, how climate change projections for the country vary spatially and temporally; and second, how climate change and migration are quantitatively linked. Finally, the Cambodian government and international organizations should direct funding towards research to better understand the situation in northwestern Cambodia and policies that increase the resilience of the region’s agricultural communities
Demographic and demand characteristics of carsharing : a case study of Austin, Texas
(2008-05) Thomen, Martin K.; Zhang, Ming, 1963 April 22-
Demographic and Demand Characteristics of Carsharing: A Case Study of Austin, Texas explores the use of geospatial analysis in order to understand the demand characteristics and market for carsharing services. A literature review was performed and the demographic characteristics of typical users of carsharing were established. A series of maps was created to geospatially identify concentrations of typical users and their location and access in reference to carsharing vehicle locations. The greater urbanized area of Austin, Texas located within Travis County was used as a case study for this analysis. The report demonstrates that geospatial analysis is a valuable tool to understand the spatial relationship between typical carshare users, nontypical carshare users and the placement of carshare vehicles.
Mapping the denial of space : Latinos and United States immigration law
(2017-08-09) Flynn, Paul Conan; Torres, Rebecca Maria
Immigration legal spaces such as courtrooms and national borders are constructed by the geopolitical tensions that exist between U.S. nationalism and foreign bodies traversing its territory. Mexican, Guatemalan, Salvadorian, and Hondurans—referred to as Latinos in this research—constitute nearly all of deportation from the United States in recent decades. In addition to these deportation trends, Latinos are also less likely to receive some form of relief from deportation despite increasing violence and political instability in Latin America. Immigration law is federal and therefore is supposed to provide the same standards and protocols for all nationalities in every immigration court. This thesis investigates how the immigration and asylum process is in fact spatialized and biased to regional politics. The connection between rituals, myths, and symbols of nationalism in the judgement of Latinos are also examined. A third component explores how migrant and refugee bodies are codified in immigration law through their experiences with immigration legal spaces. This thesis uses a mixed methods approach to understanding the spatial processes involved in the judgement and deportation of Latinos from the United States. GIS is used to validate the uneven geography of immigrant justice and identify specific locations of inequality. Reflected in the geospatial analysis, Texas’ courts are places of increased deportations and denials of asylum. Ethnographic observations in San Antonio and Pearsall’s courtrooms were conducted to extract qualitative information elucidating the asymmetric use of immigration enforcement. A second field site in Chicago was chosen to compare the impacts of border politics on Latinos in removal hearings. My research finds that immigration legal spaces are constructed through the use of nationalist myths and symbols to control the mobility of Latino bodies. Moreover, deportations are significantly influenced by geopolitics and the spatial relationship of immigration legal spaces.
Modeling electric grid vulnerability induced by natural events using machine learning and geospatial analysis
(2023-04-21) Agarwal, Khushboo; Pierce, Suzanne Alise, 1969-; Mobley, William; Kutanoglu, Erhan
The growing frequency of weather-induced power outages in recent decades has put the electric grid infrastructure of the United States at risk. Natural hazards, like hurricanes, floods, heat waves, and winter storms, can cause millions of dollars of loss to the grid infrastructure. Any damage to the electric grid can further impact other critical infrastructure, like water distribution and transportation. Past instances show that these events have more impact on low-income communities. Therefore, modeling the grid vulnerability to weather extremes is vital to protect these communities. This research involves the creation of a multi-step modeling method to predict the spatial extent and intensity of electric power outages for a case study in the Rio Grande Valley region of Texas. The study focuses on the impact of hydrologic flood events on the power grid using a step-wise workflow that scales geospatial analyses and applies a machine-learning approach to inform prevention, mitigation, and restoration strategies. The initial analysis generates a flood inundation model using the Height Above Nearest Drainage (HAND) method. This Python workflow uses the Stampede2 supercomputer to produce the HAND flood extent for one input DEM tile in 16 seconds and is made scalable for 135,000 DEM tiles in the Rio Grande Valley. The implementation presents ways to scale up and time-bound such hydrologic models on high-performance computing systems. Combining the HAND data with the Precipitation Frequency Estimate generated a Flood Vulnerability Raster (FVR) to provide the base dataset for subsequent steps in the analysis. The areas most prone to outages are identified using a spatial power outage model that combines information from the grid infrastructure maps, FVR, Social-Vulnerability Index (SVI) data, and the Weather Events dataset. The same set of input datasets with a total of 47 features for each of the 17 counties in the Rio Grande, along with their historical power outage data, is used to train a Random Forest model to predict the power outage intensity for a county. The Random Forest model performs well with a low normalized RMSE of 11.8%. Also, an analytical model for future outage prediction is developed based on linear regression. The simplified power-outage analytical model is created by using feature selection and utilizes only four important variables for an R-squared of 0.88. Furthermore, this research discusses possible practices that can improve power system resilience, such as deploying microgrids, expansion of transmission capacity and grid hardening. Research results show promise for use by urban planners, operators and decision-makers that make decisions related to resource allocation, critical infrastructure protection, investments, and managing emergency preparedness.