<p>Previous research on firmography microsimulation model for the City of Hamilton, Ontario, has been conducted by estimating several crucial statistical models, which would serve as the basis for an operational computer simulation model.</p> <p>Based on the previous research, this thesis illustrates the implementation of the firmography microsimulation model for the City of Hamilton. This implementation includes the development of separate computer modules for the survival submodel, the mobility submodel, the location choice submodel and the firm formation submodel, as well as the integration of all these submodels. Meanwhile, the data storage mechanism, the simulation results visualization and analysis functions have been implemented by the support of GIS technology.</p> <p>The microsimulation model starts with the 1990 firm micro data for the City of Hamilton as the base year and proceeds year by year with the simulation. The simulation results of firm distribution are validated by 1997 firm micro data for the City of Hamilton. The validation has proved that the developed firmography model is able to capture the overall trend of urban development processes in terms of firms at the micro level.</p> <p>The limitations of the current model, especially those caused by the requirement for detailed data, are discussed, and some directions for the future research are indicated.</p> / Master of Arts (MA)

Many regions around the world suffer from a lack of authoritatively-collected data on factors critical to understanding human well-being. This challenges our ability to understand the progress society is making towards reducing poverty, improving lifespans, or otherwise improving livelihoods. A growing body of research is exploring how deep learning algorithms can be used to produce novel estimates of sparse development data, and how access to such data can impact development efforts. This dissertation contributes to this literature in three parts. First, using Landsat 8 satellite imagery and data from the Armed Conflict Location & Event Data Project, convolutional neural networks are trained to predict locations where conflict is likely to result in fatalities for one year. Second, building on the findings in chapter 1, this dissertation explores the potential to extend predictions to a time series using both yearly and six month intervals. Finally, chapter 3 introduces GeoQuery, a dynamic web application which utilizes a High Performance Computing cluster and novel parallel geospatial data processing methods to overcome challenges associated with integrating, and distributing geospatial data within research communities.

Coastal inundation initiated via storm surge by hurricanes and nor'easters along the U.S. East Coast is a substantial threat to residential properties, community infrastructure, and human life. During and after the storm, compounding with heavy precipitation and upland drainage, inundation can be caused by the combination of storm surge and river-induced inland flooding in various locations throughout the coastal plain. Thus, coastal inundation can be expanded from the open coast upstream into the tributaries of the New York Bay including the Hudson and East River systems. Given the cross-disciplinary nature of the dynamics (encompassing hydraulics, oceanography, and hydrology), and the complexity of the atmospheric forcing, a numerical model is the optimal approach for a comprehensive study of the hydrodynamics of coastal inundation.;This study will utilize the large-scale parallel SELFE model to simulate the storm surge and inundation caused by 2012 Hurricane Sandy utilizing different forecast wind and pressure fields. The large-scale numerical model made use of multiple inputs for atmospheric forcing and spatially covered a large domain area to account for large-scale oceanographic processes and output accurate model simulation of water levels. In a simultaneous effort, a street-level sub-grid inundation model coupled with Lidar-derived topography (UnTRIM 2) was employed to simulate localized flooding events in the New York Harbor.;Sub-grid modeling is a novel method by which water level elevations are efficiently calculated on a coarse computational grid, with discretized bathymetric depths and topographic heights stored on a sub-grid nested within each base grid cell, capable of addressing local friction parameters without resorting to solve the full set of equations. Sub-grid technology essentially allows velocity to be rationally and efficiently determined at the sub-grid level. This salient feature enables coastal flooding to be addressed in a single cross-scale model from the ocean to the upstream river channel without overly refining the grid resolution. to this end, high-resolution Digital Elevation Models (DEMs) were developed utilizing GIS from Lidar-derived topography for incorporation into a sub-grid model, for research into the plethora of practical research applications related to urban inundation in New York City.;SELFE large-scale storm tide simulations were successfully conducted for 2012 Hurricane Sandy using both the North American Regional Reanalysis (NARR), and the Regional Atmospheric Modeling System (RAMS) atmospheric hindcast model results as atmospheric inputs. Overall statistics using the 24km resolution NARR inputs observed an average R2 value of 0.8994, a relative error of 11.77%, and a root-mean-squared error of 32.69cm for 10 NOAA observation stations. The 4km RAMS inputs performed noticeably better at all 10 stations with aggregate statistics yielding an average R2 value of 0.9402, a relative error of 4.08%, and a rootmean-squared error of 19.22 cm. Since the RAMS atmospheric inputs possessed a higher spatial and temporal resolution than the NARR inputs for air pressure and wind speed, it was concluded that generally superior storm tide predictions could be expected from utilizing more reliable or better resolution atmospheric forecast products.;UnTRIM2 results were obtained via sub-grid simulation of 2012 Hurricane Sandy in the New York Harbor with high-resolution topography and building heights embedded in the model sub-grid for New York City. Model performance was assessed via comparison with various verified field measurements: (1) Temporal comparison of NOAA and USGS permanent water level gauges, (2) USGS rapid deployment water level gauges, along with a spatial inundation comparison using (3) USGS-collected high water marks, (4) FEMA-collected data regarding inundated schools, (5) calculated area and distance differentials using FEMA's maximum extent of inundation map, and (6) known locations of inundated subway entrances. Temporal results verified the effectiveness of the sub-grid model's wetting and drying scheme via seven over land rapid deployment gauges installed and collected by the USGS with a mean R2 of 0.9568, a relative error of 3.83%, and a root-mean-squared error of 18.15cm.;Spatial verification of the inundation depths predicted by the UnTRIM 2 model were addressed by comparison with 73 high water mark measurements collected by the USGS and by 80 FEMA-reported water level thicknesses at inundated schools throughout the sub-grid domain separated by state. Average statistics for the 73 USGS-recorded high water marks for New York and New Jersey were: 0.120+/-0.085m and 0.347+/-0.256m for root-mean-squared error +/- standard deviation, respectively. The larger differences and errors reported in the point to point comparisons for New Jersey relative to New York were largely due to the lack building representation in the sub-grid DEM for the New Jersey side of the Hudson River, and was a significant indication that the representation of buildings as a physical impediment to fluid flow is critical to urban inundation modeling.;A maximum difference threshold was imposed for distance and area comparisons with FEMA's Hurricane Sandy flood map using the average distance differential rounded to 40m. This was done to minimize the impact of missing or added infrastructure such as highway overpasses along with Lidar-derived data limitations of physical impediments to fluid flow not accounted for in the model's DEM. The difference in the absolute mean distance between the maximum extent predicted by the street-level sub-grid model and the FEMA maximum inundation observation was 21.207m or ≈4 sub-grid pixels at 5m resolution for the entire sub-grid domain. The final area comparison resulted in an 85.17% area (49,253,687m 2) spatial match, with 7.57% area (4,376,726m2) representing model over-prediction, and under-prediction area accounting for 7.27% (4,202,376m 2), with differences being attributed to lack of building representation in the FEMA maximum inundation map. Additionally, the implementation of the FEMA's spatial flood map data as a "bathtub" model derivative product of USGS interpolated high water marks and elevation data without regard for strong water current velocities or estuarine circulation can also account for regions with significant discrepancies.;Keywords: Sub-Grid, Stotul Surge, Inundation, New York Harbor, New York City, Jersey City, Conveyance Approach, Unstructured Grids, UnTRIM, SELFE, Lidar-Derived Topography.

This research uses GIS methods to contextualize a Florida Cracker community in the Ocala National Forest. My case study is drawn from two seasons of fieldwork and supporting desktop surveys associated with Pat's Island, the home of multiple Cracker families. Cracker culture is historically categorized by its efforts to avoid modernization and prevent the commercialization of traditional lifeways in the quickly shifting society of late-19th and early 20th-century Florida. The geospatial expressions of these Cracker values are an emphasis on semi-remote living, adaptations to a unique environmental context, and the development of a semi-self-sufficient community. This research evaluates how GIS can be used in conjunction with limited datasets to draw meaningful conclusions. In other words, how can geospatial approaches to sparse historical datasets reveal useful insights about the past? Specifically, how the combination of General Land Office patents, census records, and Florida Master Site records combine with archaeological data to conduct better understand community formation, development, and dissolution. The resultant study shows the efficacy with which these datasets, when combined and analyzed using GIS can add clarity to otherwise disparate and scarce data. Furthermore, Pat's Island has received relatively little attention. This research thus hopes to begin the process of creating a foundation by which the history of Florida homesteaders can be contextualized and understood. Using a spatial approach, the space which homesteaders inhabited, altered, and experienced can be understood. Furthermore, this research will explore the efficacy of a digital anthropological approach to analyzing and exploring anthropological questions.

Depictions of movement in Late Postclassic and early Colonial Mesoamerican art, maps, and documents show a multi-faceted conception of movement as not only a means of physical travel through the landscape of the mundane world, but also a means to reach the cosmic realms of divine forces. In this thesis, I explored the intersection of movement and ritual in Late Postclassic Oaxaca by modelling a hypothetical ritual circuit around the Manialtepec Lagoon: a bioluminescent lagoon near Oaxaca's Pacific Coast that is significant in oral histories of the Indigenous Chatinos. The Manialtepec Basin lacks the level of continuous occupation or historic documentation that have proven integral in reconstructing prehispanic landscapes in other regions of Oaxaca. In a step towards determining whether sacred landscapes of Late Postclassic Oaxaca can be confirmed or predicted by a Geographic Information System (GIS), this project created a network of Least Cost Paths and natural features to predict ritual circuits and the sanctuaries visited along those circuits. Using a portion of the Nochixtlán Valley and the region of the Valley of Oaxaca containing the city of Mitla as well-studied test regions, the model's predictive capabilities were compared to the findings of archaeological surveys. The model predicted locations that coincided with ceremonial-use archaeological sites slightly better than archaeological sites in general, and twice the rate at which it predicted the locations of a spatially random dataset of polygons. Using the generated paths as well as ones that exist today, I proposed a hypothetical ritual circuit around the Manialtepec Lagoon. The circuit, as well as the predictive model itself, are demonstrative as proof-of-concept. Based on promising initial prediction results, this project will serve as inspiration for improved models and eventual use in recreating Postclassic Oaxacan landscapes and providing spatial focus for non-site archaeological surveys.

<p>Blood in Canada is donated by a volunteer base that is increasingly challenged, through a combination of demographic aging and immigration, to meet the needs of the health sector. Canadian Blood Services, the agency with the mandate to manage blood products in Canada with the exception of Quebec, is therefore actively involved in the development of programs to help increase the number of donors, to improve the retention of existing donors, and to increase the frequency of donation of repeat donors. An important factor that influences blood donation is the accessibility to clinics. Accessibility to clinics is determined by the location of clinics, the resources allocated to each clinic in terms of number of beds and hours of operation, and the distribution of the population in the areas serviced by the clinics. The objective of this research is to investigate, given a set of fixed sites for clinic locations and population characteristics, the potential for increasing the donor yield as a function of accessibility. A case study is presented of the Hamilton Census Metropolitan Area, in Canada. Using donor and clinic data provided by Canadian Blood Services, and census information, an objective function is derived by estimating a generalized linear model of donations. The objective function is maximized globally using Genetic Algorithm techniques, subject to total resources available for clinic operations. The results suggest that an optimized allocation of resources to clinic sites has the potential to increase the donor yield by approximately 50% of the current donor base.</p> / Master of Arts (MA)

Climate change studies and examinations of increasing sea levels and temperatures show storm intensity and frequency are increasing. As these storms are increasing in intensity and frequency, the effects of these storms must be monitored to determine the probable damages or impacts to critical infrastructure [2, 35]. These storms suddenly create new demands and requirements upon already stressed critical infrastructure sectors [1]. A combined and interdisciplinary effort must be made to identify these stresses and to mitigate any failures. This effort is needed so that the 21st Century Smart Grid is robust and resilient enough to ensure that the grid is secured against all hazards. This project focuses on anticipating loss of above ground electrical power due to extreme wind speeds. This thesis selected a study region of Indiana, Illinois, Kentucky, and Tennessee to investigate the skill of fragility curve generation for this region, during Hurricane Irene, in the Fall of 2011. Three published fragility techniques are compared within the Midwest study region to determine the best skilled technique for the low wind speeds experienced in this region in August 2011. The three techniques studied are: 1) Powerline Technique [6], a correlation between “as published” state based construction standards and surface wind speeds sustained for greater than one minute; 2) the ANL Headout Technique [37], a correlation of Hurricane Irene three second wind gusts with DOE situation reports of outages; and 3) the Walker Technique [1], a correlation of utility reported outages in the Eastern Seaboard counties with three second surface gusts. The deliverable outcomes for this project include: 1) metrics for determining the method best for the study region, from the archival data during Hurricane Irene timeframe; 2) a fragility curve methodology description for each technique; and 3) a mathematical representation for each technique suitable for inclusion in automated forecast algorithms. Overall, this project combines situational awareness modeling to provide distinct fragility techniques that can be used by the public and private sectors to improve emergency management, restoration processes, and critical infrastructure all-hands-preparedness. This work was supported by Western Kentucky University (WKU) and the National Oceanic Atmospheric Administration (NOAA)

High-wind events (HWE) occur across every region of the United States (U.S.) and result in hundreds of fatalities, as well as thousands of dollars in damages annually. HWEs are classified as sustained high-winds or high-wind gusts and can be generated from convective or non-convective weather systems. This study investigates high-wind observations across the eastern U.S. during a 43-year climatological period (1973-2015) for spatial and temporal variations in wind speed and direction. Hourly surface wind observations were gathered from the National Centers for Environmental Information Data Center Integrated Surface Database (NCEI-ISD). This dataset includes qualitycontrolled wind observations from 391 first-order weather stations in the eastern U.S. Findings show that HWEs were most concentrated in the High Plains and fewer convective HWEs occurred during the study period compared to non-convective. Convective and non-convective sustained HWE frequency and mean wind-speeds declined during the study period while gust HWE frequencies and speeds increased. The purpose of this study is to develop an extensive climatological understanding of convective and non-convective high-wind events to mitigate associated damages and fatalities caused by these events.

This study examines the spatial impact various socio-demographic and housing factors might have in the foreclosure lis pendens rate within various Hillsborough County, Florida tracts as well as comparing those results with past research. Hopefully the techniques used in this study can be implemented elsewhere in order to better study the foreclosure crisis. The methods used within this research were chosen carefully in order to best understand what is being observed. One method is OLS regression which helps see the impact of each variable and if that impact has a negative or positive effect on the rate of foreclosure. Bivariate Maps were created to spatially examine each variable when compared to the foreclosure rate as well as Effect plots from regression in order to see how the true relationship of a variable affects the foreclosure rate.

Industry clusters can be important components of regional development. The effects of industry clusters on growth typically vary across geography, which has implications for targeted development strategies. Employment and business establishment growth in the Appalachian region (2000 – 2008) was regressed on industry cluster concentration indexes controlling for local determinants. The hypothesis that local response to growth determinants is geographically heterogeneous was tested using Smooth Transition spatial process models. This class of models exhibiting regime switching behavior is useful for identifying regional clusters, providing another tool for exploring relationships between geographical determinants and economic growth.