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Historically, coyotes were associated with the western United States. During their expansion eastward, coyotes have become more tolerant of humans and have been able to live in varying degrees of urbanization. One main question ecologists around the country are asking is how coyotes are surviving in anthropogenic environments. To aid in answering this question, I have compared coyote land use preference generally and specifically during coyote breeding season, winter and summer, human tourist seasons, and day and night. I also compared coyote land cover preference for deciduous and evergreen cover types during natural seasons. I found that, in general, there was a high variation of preference between and within land use categories. More broadly however, they prefer natural areas over non-natural areas. They used natural and non-natural land use types equally in winter and summer, and during tourist and off-tourist seasons with increased variation in preference during seasons with higher human activity. They had a higher preference for non-natural land use types at night. There is no difference in coyote preference for deciduous or evergreen cover types during the seasons.

As the sea rises in response to global climate changes, small islands will lose a significant portion of their land through ensuing erosion processes. The particular vulnerability of small island systems led me to choose Martha’s Vineyard (MV), a 248 km2 paraglacial island, 8 km off the south shore of Cape Cod, Massachusetts, as a model system with which to analyze the interrelated problems of sea level rise (SLR) and coastal erosion. Historical data documented ongoing SLR (~3mm/yr) in the vicinity of MV. Three study sites differing in geomorphological and climatological properties, on the island’s south (SS), northwest (NW), and northeastern (NE) coasts, were selected for further study. Mathematical models and spatial data analysis, as well as data on shoreline erosion from almost 1500 transects, were employed to evaluate the roles of geology, surficial geology, wetlands, land use, soils, percent of sand, slope, erodible land, wind, waves, and compass direction in the erosion processes at each site. These analyses indicated that: 1) the three sites manifested different rates of erosion and accretion, from a loss of approximately 0.1 m/yr at the NE and NW sites to over 1.7 m/yr at the SS site; 2) the NE and NW sites fit the ratio predicted by Bruun for the rate of erosion vs. SLR, but the SS site exceeded that ratio more than fivefold; 3) the shoreline erosion patterns for all three sites are dominated by short-range effects, not long-range stable effects; 4) geological components play key roles in erosion on MV, a possibility consistent with the island’s paraglacial nature; and 5) the south side of MV is the segment of the coastline that is particularly vulnerable to significant erosion over the next 100 years. These conclusions were not evident from simple statistical analyses. Rather, the recognition that multiple factors besides sea level positions contribute to the progressive change in coastal landscapes only emerged from more complex analyses, including fractal dimension analysis, multivariate statistics, and spatial data analysis. This suggests that analyses of coastal erosion that are limited to only one or two variables may not fully unravel the underlying processes.

This thesis addresses the pressing need for an efficient public transport system in response to increasing urban population and rising fossil fuel costs. The research aims to satisfy that need by answering the research question guiding this study is: "How can developing an SDI-based conceptual model, leveraging cloud computing, optimize public transport by integrating multiple travel data sources and overcoming data fragmentation and interoperability challenges?". The thesis adopts a design science research methodology. The research strategy employed is theoretical research, utilizing various methods such as literature review, theoretical sampling, theory testing survey research, brainstorming, prototyping, cognitive walkthrough, and expert opinion. The outcome of this study is the development of an integrated conceptual model that leverages SDI and digitalization to optimize public transport systems. The model offers a comprehensive solution by addressing the limitations of traditional transport planning methods, integrating multiple data sources, technologies, and stakeholders within an SDI framework. The model demonstrates efficacy, efficiency, and practical significance, setting the stage for further research and advancements in transport planning and optimization. Limitations of the study include the absence of detailed technical specifications, the confinement to a conceptual model without implementation, and the need for further adaptation and validation in different contexts. In conclusion, this thesis offers a valuable contribution by providing a holistic solution that integrates multiple data sources and technologies within an SDI framework. Stakeholders such as PTAs, planners, and researchers can utilize the findings to enhance transport planning and address data and interoperability challenges.

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Uncertainty in geospatial data fusion is a major concern for scientists because society is increasing its use of geospatial technology and generalization is inherent to geographic representations. Limited research exists on the quality of results that come from the fusion of geographic data, yet there is extensive literature on uncertainty in cartography, GIS, and geospatial data. The uncertainties exist and are difficult to understand because data are overlaid which have different scopes, times, classes, accuracies, and precisions. There is a need for a set of tools that can manage uncertainty and incorporate it into the overlay process. This research explores uncertainty in spatial data, GIS and GIScience via three papers. The first paper introduces a framework for classifying and modeling error-bands in a GIS. Paper two tests GIS users' ability to estimate spatial confidence intervals and the third paper looks at the practical application of a set of tools for incorporating uncertainty into overlays. The results from this research indicate that it is hard for people to agree on an error-band classification based on their interpretation of metadata. However, people are good estimators of data quality and uncertainty if they follow a systematic approach and use their average estimate to define spatial confidence intervals. The framework and the toolset presented in this dissertation have the potential to alter how people interpret and use geospatial data. The hope is that the results from this paper prompt inquiry and question the reliability of all simple overlays. Many situations exist in which this research has relevance, making the framework, the tools, and the methods important to a wide variety of disciplines that use spatial analysis and GIS. / Ph. D.

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In the recent years, abnormal spatial pattern recognition has received a great deal of attention from both industry and academia, and has become an important branch of data mining. Abnormal spatial patterns, or spatial outliers, are those observations whose characteristics are markedly different from their spatial neighbors. The identification of spatial outliers can be used to reveal hidden but valuable knowledge in many applications. For example, it can help locate extreme meteorological events such as tornadoes and hurricanes, identify aberrant genes or tumor cells, discover highway traffic congestion points, pinpoint military targets in satellite images, determine possible locations of oil reservoirs, and detect water pollution incidents. Numerous traditional outlier detection methods have been developed, but they cannot be directly applied to spatial data in order to extract abnormal patterns. Traditional outlier detection mainly focuses on "global comparison" and identifies deviations from the remainder of the entire data set. In contrast, spatial outlier detection concentrates on discovering neighborhood instabilities that break the spatial continuity. In recent years, a number of techniques have been proposed for spatial outlier detection. However, they have the following limitations. First, most of them focus primarily on single-attribute outlier detection. Second, they may not accurately locate outliers when multiple outliers exist in a cluster and correlate with each other. Third, the existing algorithms tend to abstract spatial objects as isolated points and do not consider their geometrical and topological properties, which may lead to inexact results. This dissertation reports a study of the problem of abnormal spatial pattern recognition, and proposes a suite of novel algorithms. Contributions include: (1) formal definitions of various spatial outliers, including single-attribute outliers, multi-attribute outliers, and region outliers; (2) a set of algorithms for the accurate detection of single-attribute spatial outliers; (3) a systematic approach to identifying and tracking region outliers in continuous meteorological data sequences; (4) a novel Mahalanobis-distance-based algorithm to detect outliers with multiple attributes; (5) a set of graph-based algorithms to identify point outliers and region outliers; and (6) extensive analysis of experiments on several spatial data sets (e.g., West Nile virus data and NOAA meteorological data) to evaluate the effectiveness and efficiency of the proposed algorithms. / Ph. D.

A pavement management system (PMS) is a valuable tool and one of the critical elements of the highway transportation infrastructure. Since a vast amount of pavement data is frequently and continuously being collected, updated, and exchanged due to rapidly deteriorating road conditions, increased traffic loads, and shrinking funds, resulting in the rapid accumulation of a large pavement database, knowledge-based expert systems (KBESs) have therefore been developed to solve various transportation problems. This dissertation presents the development of theory and algorithm for a new decision tree induction method, called co-location-based decision tree (CL-DT.) This method will enhance the decision-making abilities of pavement maintenance personnel and their rehabilitation strategies. This idea stems from shortcomings in traditional decision tree induction algorithms, when applied in the pavement treatment strategies. The proposed algorithm utilizes the co-location (co-occurrence) characteristics of spatial attribute data in the pavement database. With the proposed algorithm, one distinct event occurrence can associate with two or multiple attribute values that occur simultaneously in spatial and temporal domains. This research dissertation describes the details of the proposed CL-DT algorithms and steps of realizing the proposed algorithm. First, the dissertation research describes the detailed colocation mining algorithm, including spatial attribute data selection in pavement databases, the determination of candidate co-locations, the determination of table instances of candidate colocations, pruning the non-prevalent co-locations, and induction of co-location rules. In this step, a hybrid constraint, i.e., spatial geometric distance constraint condition and a distinct event-type constraint condition, is developed. The spatial geometric distance constraint condition is a neighborhood relationship-based spatial joins of table instances for many prevalent co-locations with one prevalent co-location; and the distance event-type constraint condition is a Euclidean distance between a set of attributes and its corresponding clusters center of attributes. The dissertation research also developed the spatial feature pruning method using the multi-resolution pruning criterion. The cross-correlation criterion of spatial features is used to remove the nonprevalent co-locations from the candidate prevalent co-location set under a given threshold. The dissertation research focused on the development of the co-location decision tree (CL-DT) algorithm, which includes the non-spatial attribute data selection in the pavement management database, co-location algorithm modeling, node merging criteria, and co-location decision tree induction. In this step, co-location mining rules are used to guide the decision tree generation and induce decision rules. For each step, this dissertation gives detailed flowcharts, such as flowchart of co-location decision tree induction, co-location/co-occurrence decision tree algorithm, algorithm of colocation/co-occurrence decision tree (CL-DT), and outline of steps of SFS (Sequential Feature Selection) algorithm. Finally, this research used a pavement database covering four counties, which are provided by NCDOT (North Carolina Department of Transportation), to verify and test the proposed method. The comparison analyses of different rehabilitation treatments proposed by NCDOT, by the traditional DT induction algorithm and by the proposed new method are conducted. Findings and conclusions include: (1) traditional DT technology can make a consistent decision for road maintenance and rehabilitation strategy under the same road conditions, i.e., less interference from human factors; (2) the traditional DT technology can increase the speed of decision-making because the technology automatically generates a decision-tree and rules if the expert knowledge is given, which saves time and expenses for PMS; (3) integration of the DT and GIS can provide the PMS with the capabilities of graphically displaying treatment decisions, visualizing the attribute and non-attribute data, and linking data and information to the geographical coordinates. However, the traditional DT induction methods are not as quite intelligent as one's expectations. Thus, post-processing and refinement is necessary. Moreover, traditional DT induction methods for pavement M&R strategies only used the non-spatial attribute data. It has been demonstrated from this dissertation research that the spatial data is very useful for the improvement of decision-making processes for pavement treatment strategies. In addition, the decision trees are based on the knowledge acquired from pavement management engineers for strategy selection. Thus, different decision-trees can be built if the requirement changes. / Ph. D.