One of the major themes of geography is human and environment interaction. Human activities shape and influence the natural environment in numerous ways, and the effects of these activities vary across space and time. Conservation planning is concerned with the mitigation of human-induced habitat degradation and frequently involves spatial analysis of human impacts on natural ecosystems for the purpose of formulating conservation strategies. The goal of this thesis is to employ spatial analysis tools, namely Geographic Information Systems (GIS) and spatial multiple criteria decision-making methods, to prioritize a network of potential conservation sites according to their relative conservation value. I compared state-managed roadsides within Leon County, Florida in terms of their relative suitability for ground-nesting bees in order to determine which roadsides would best meet the goals of pollinator conservation through the Florida Wildflower Program. Using GIS, in combination with the analytic hierarchy process (AHP), I created a spatial model depicting nesting suitability for ground-nesting bees in Leon County according to relevant site characteristics. The analysis combined both quantitative information sources and qualitative judgements applied through the AHP weighting procedure, which determines the relative significance of different factors through pair-wise comparisons. This model provided a means of comparing the relative habitat suitability of all state-managed roadsides in the county for ground-nesting bees based on the chosen factors. The suitability model was validated through a statistical analysis involving presence/absence data for three genera of ground-nesting bees. The model was found to be significant for two of the three genera, indicating that the suitability values generated through the modelling process are meaningful. I conclude that the method employed for this analysis is effective in assessing the relative suitability of potential insect pollinator conservation sites and can aid in the process of determining the best locations to focus future conservation efforts. / A Thesis submitted to the Department of Geography in partial fulfillment of the requirements for the degree of Master of Science. / Spring Semester 2018. / April 6, 2018. / AHP, conservation, GIS, habitat suitability, MCDA, native bees / Includes bibliographical references. / Xiaojun Yang, Professor Directing Thesis; Victor Mesev, Committee Member; Tingting Zhao, Committee Member.

<p> In this thesis, path planning for an autonomous ground vehicle (AGV) in an urban environment is considered. The following problem is considered. starting from an initial location, the AGV desires to reach a final location by taking the shortest distance, while minimizing the AGVs position estimation error and guaranteeing that the AGVs position estimation uncertainty is below a desired threshold. The AGV is assumed to be equipped with receivers capable of producing pseudodange measurements on Global Navigation Satellite System (GNSS) satellites and cellular long-term evolution (LTE) towers. Using a geographic information system (GIS) three-dimensional (3-D) building map of the urban environment, a signal reliability map is introduced, which provides information about regions where large errors due to cellular signal multipath or poor GNSS line-of-sight (LOS) are expected. The vehicle uses the signal reliability map to calculate the position estimation mean-squared error (MSE). An analytical expression for the AGV's state estimates is derived for a weighted nonlinear least-squares (WNLS) estimator, which is used to find an analytical upper bound on the position bias due to multipath. A path planning approach based on Dijkstra's algorithm is proposed to optimize the AGV's path while minimizing the path length and the position estimation MSE, subject to keeping the position estimation uncertainty and position estimation bias due to multipath being below desired thresholds. The path planning approach yields the optimal path together with a list of feasible paths. Simulation results are presented demonstrating that utilizing ambient cellular LTE signals together with GNSS signals (1) reduces the uncertainty about the AGV's position, (2) increases the number of feasible paths to choose from, which could be useful if other considerations arise, e.g., traffic jams and road blockages due to construction, and (3) yields significantly shorter feasible paths, which would otherwise be infeasible with GNSS signals alone. Experimental results on a ground vehicle navigating in downtown Riverside, California, are presented demonstrating a close match between the simulated and experimental results.</p><p>

Given the large volume of data that is regularly accumulated, the need to properly manage, efficiently display and correctly interpret, becomes more important. Complex analysis of data is best performed using statistical models and in particular those with a geographical element are best analysed using Spatial Statistical Methods, including local regression. Spatial Statistical Methods are employed in a wide range of disciplines to analyse and interpret data where it is necessary to detect significant spatial patterns or relationships. The topic of the research presented in this thesis is an exploration of the most effective methods of visualising results. A human being is capable of processing a vast amount of data as long as it is effectively displayed. However, the perceptual load will at some point exceed the cognitive processing ability and therefore the ability to comprehend data. Although increases in data scale did increase the cognitive load and reduce processing, prior knowledge of geographical information systems did not result in an overall processing advantage. The empirical work in the thesis is divided into two parts. The first part aims to gain insight into visualisations which would be effective for interpretation and analysis of Geographically Weighted Regression (GWR), a popular Spatial Statistical Method. Three different visualisation techniques; two dimensional, three dimensional and interactive, are evaluated through an experiment comprising two data set sizes. Interactive visualisations perform best overall, despite the apparent lack of researcher familiarity. The increase in data volume can present additional complexity for researchers. Although the evaluation of the first experiment augments understanding of effective visualisation display, the scale at which data can be adequately presented within these visualisations is unclear. Therefore, the second empirical investigation seeks to provide insight into data scalability, and human cognitive limitations associated with data comprehension. The general discussion concludes that there is a need to better inform researchers of the potential of interactive visualisations. People do need to be properly trained to use these systems, but the limits of human perceptual processing also need to be considered in order to permit more efficient and insightful analysis.

This project is focused on the goal of improving access to the Water Resources Institute (WRI) archives using a web-based GIS architecture. This project uses the newest version of ArcGIS Server as a method to support an internet-based map search environment, improved information management and data sharing.

The Geographic Information Systems (GIS) have become popular tool, used in different fields. The launching of Google Maps offered a new approach of building web based GIS systems; making it possible to integrate external geographically referenced data with the powerful map service supplied by Google. This thesis demonstrates the design and implementation of creating a web based geographic information system. The system is built by adapting the Google Maps API library and building a web server to display and explore agricultural data.

In Lombok Island (Indonesia), the ratio of land area to population is already low, and is likely to decrease because of the increasing size of the human population. The management of land resources is, therefore, important, to ensure the wise and sustainable use of the available land in meeting population demands, especially for food. Geographic Information Systems (GIS) have been used successfully in resource management, and this area of their application has been a major driver in the development of GIS. Because agrosystems need to be tuned to the specific characteristics of regional environments, regionalisation is one way to improve agricultural production and the management of agrotechnology development. The identification of potential areas for growing soybean, peanut and common bean was conducted based on two tools: GIS analysis and the 30 group regionalisation. Both techniques have advantages and disadvantages. Using GIS, exact mapping of the potential category of each grid cell can be done, but it cannot be used to estimate the total humid periods and suitable planting times in a year. Using a regionalisation, these can be done, but only when purposes is suggested as more widely applicable than using GIS analysis. Both techniques have a role to play. Based on an initial validation of the techniques employed and the results obtained, further work is suggested, either for the optimum application of the results presently obtained or for the improvement of the techniques of analysis and thence the production of results for future use. / Master of Science (Hons)

Thesis (Ph.D.)--University of South Carolina, 2003. / Includes bibliographical references.

Thesis (M.A.)--University of Alberta, 2002. / Adviser: Charles Schweger. Includes bibliographical references.

The objective of this study is to develop a quantifying model that predicts the appraised unit value of parcels in San Francisco County based on number of LEED-NC Public Transportation Access (PTA) qualified bus, light rail and commuter rail stops, distance to closest bus, light rail and commuter rail stops, zoning class and parcel size. As a population of interest, San Francisco County was chosen since it is known as a region having well-organized transportation systems including bus, light rail and commuter rail systems. According to the correlation results, for mixed zone, an appraised unit value increases as the number of LEED qualified transit stops increases (bus, light rail, and commuter rail). In addition, the appraised unit value increases as the distance to LEED qualified bus stops light rail stops decreases. For residential zone, the appraised unit value increases as the number of LEED qualified bus and light rail stations increases. Furthermore, the appraised unit value increases as the distance to LEED qualified bus stops decreases. When it comes to the predictive regression model for mixed zone, the adjusted R-square of the transformed model was 0.713, which indicates that 71.3 percent variability in transformed unit value of parcels could be explained by these variables. In addition, for the predictive model of residential zone, the adjusted R-square for the model was 0.622 thus the independent variables together accounted for 62.2 percent variability in the transformed unit value of parcels. The predicting models for mixed and residential zones were significant that suggests that the components of LEED-NC PTA criteria, number and distance from parcels, this could affect land development strategies. In addition, an appraised unit value of parcels in San Francisco County can be estimated by using the predictive models developed in this study. Therefore, the findings of this study could encourage real-estate developers to site their projects according to the LEED-NC PTA criteria.

This study aimed to examine how the characteristics of the road network and the built environment influence the frequency of pedestrian-vehicle crashes. Pedestrian crashes (2000 – 2007) on major roads in DeKalb County of Georgia were obtained from Georgia Department of Transportation. Hotspot analysis was performed on locations with frequent pedestrian incidents to determine their built environment characteristics. Using Geographic Information Systems, the built environment was characterized using road grade, curvature, population density, the amount stores and restaurants, bars, and public transit stops nearby. A negative binomial regression model was used to examine the influence of the built environment characteristics on pedestrian crashes. The results showed that all the variables except for road grade were positively associated with increased number of pedestrian crashes. Findings provided insights into the influence of built environment characteristics which is important for injury prevention to improve pedestrian safety.