<p>The main problem that is addressed in this thesis is how to represent complex three-dimensional objects in GIS in order to render a more realistic representation of the real world. The goal is to present an approach for representing complex 3D objects in GIS. This is achieved by using commercial GIS (ArcGIS), applied to 3D properties. In order to get a clear overview of the state-of-the-art of 3D GIS and the current 3D cadastral situation a literature study was carried out. Based on this overview it can be concluded that 3D GIS still is in its initial phase. Current 3D GIS developments are mainly in the area of visualisation and animation, and almost nothing in the area of spatial analysis and attribute handling. Furthermore, the literature study reveals that no complete solution has been introduced that solves the problems involved in 3D cadastral registration. In several countries (e.g. Sweden, Denmark, Norway, Netherlands, Israel, and Australia) 3D properties exists in a juridical framework, but technical issues such as how to represent, store, and visualize 3D properties has not yet been solved. Some countries (Sweden, Norway, and Australia) visualize the footprints of 3D property units in a base map. This approach partly solves some technical issues, but can only represent 3D objects in a 2.5D environment. Therefore, research in how to represent complex objects in GIS as ‘true’ 3D objects is of great need.</p><p>This thesis will emphasize MultiPatch as a geographic representation method to represent complex 3D objects in GIS. A case study will demonstrate that complex objects can be visualized and analysed in a commercial GIS, in this case ArcGIS. Most commercial GIS software available on the market applies a 2.5D approach to represent 3D objects. The 2.5D approach has limitations for representing complex objects. There is therefore a need of finding new approaches to represent complex objects within GIS. The result shows that MultiPatch is not an answer to all the problems within 3D GIS but a solution to some of the problems. It still requires a lot of research in the field of 3D GIS, especially in development of spatial analysis capabilities.</p> / <p>Det huvudsakliga problemet i denna uppsats är hur komplexa tre-dimensionella objekt kan representeras i GIS för att återge verkligheten mer realistiskt. Målet är att presentera ett tillvägagångssätt för att representera komplexa 3D-objekt i GIS. Detta har uppnåtts genom att använda ett kommersiellt GIS tillämpat på 3D-fastigheter. En litteraturstudie har genomförts för att erhålla en klar översikt över det senaste inom 3D-GIS och över den aktuella situationen inom 3D-fastigheter. Grundat på översikten kan slutsatsen dras att 3D-GIS bara är i sin begynnelsefas. Den aktuella utvecklingen inom 3D-GIS har huvudsakligen fokuserat på visualisering och animering och nästan ingenting inom rumsliga analysmetoder och hantering av attribut. Litteraturstudien visar också att ingen fullständig lösning för de problem som finns inom 3D-fastighetsregistrering har introducerats. I flera länder, t.ex. Sverige, Danmark, Norge, Nederländerna, Israel och Australien, existerar 3D-fastigheter idag i juridiska termer, men de tekniska problemen som t.ex. hur 3D-fastigheter ska representeras, lagras och visualiseras har inte ännu lösts. Vissa länder (Sverige, Norge och Australien) visualiserar idag en projektion av 3D-fastigheterna på en fastighetskarta. Den här metoden löser endast några av de tekniska problemen och kan endast representera 3D-objekt i en 2,5D-miljö. Därför är forskning inom hur komplexa objekt kan representeras i GIS som s.k. ”sann” 3D av betydelse.</p><p>Den här uppsatsen framhäver MultiPatch som en datatyp för att representera komplexa 3D-objekt i GIS. En fallstudie visar att komplexa objekt kan visualiseras och analyseras i ett kommersiellt GIS, i det här fallet ArcGIS. De flesta kommersiella GIS som är tillgängliga på marknaden använder 2,5D-metoden för att representera 3D-objekt. 2,5D-metoden har vissa begränsningar för att representera komplexa objekt och därför finns det ett behov att finna nya tillvägagångssätt för att representera komplexa objekt inom GIS. Resultaten kommer att visa att MultiPatch inte är någon fullständig lösning till alla problem inom 3D-GIS men en lösning på några av problemen. Det krävs fortfarande mycket forskning inom 3D-GIS, särskilt inom utveckling av rumsliga analysmetoder.</p>

Dolphinfish (Coryphaena hippurus) is a pelagic species that is ecologically and commercially important in the western Atlantic region. This species has been linked to dominant oceanographic features such as sea surface temperature (SST) frontal regions. This work first explored the linkages between the catch rates of dolphinfish and the oceanography (satellite-derived SST, distance to front calculations, bottom depth and hook depth) using Principal Components Analysis (PCA). It was demonstrated that higher catch rates are found in relation to warmer SST and nearer to frontal regions. This environmental information was then included in standardizations of catch-per-unit-effort (CPUE) indices. It was found that including the satellite-derived SST and distance to front increases the confidence in the index. The second part of this work focused on addressing spatial variability in the catch rate data for a subsection of the sampling area: the Gulf of Mexico region. This study used geostatistical techniques to model and predict spatial abundances of two pelagic species with different habitat utilization patterns: dolphinfish (Coryphaena hippurus) and swordfish (Xiphias gladius). We partitioned catch rates into two components, the probability of encounter, and the abundance, given a positive encounter. We obtained separate variograms and kriged predictions for each component and combined them to give a single density estimate with corresponding variance. By using this two stage approach we were able to detect patterns of spatial autocorrelation that had distinct differences between the two species, likely due to differences in vertical habitat utilization. The patchy distribution of many living resources necessitates a two-stage variogram modeling and prediction process where the probability of encounter and the positive observations are modeled and predicted separately. Such a "geostatistical delta-lognormal" approach to modeling spatial autocorrelation has distinct advantages in allowing the probability of encounter and the abundance, given an encounter to possess separate patterns of autocorrelation and in modeling of severely non-normally distributed data that is plagued by zeros.

All organisms are subjected to the living and non-living influences of their surroundings. They derive their energy and essential materials, such as sunlight and nutrients, from their environment, sharing their world not only with members of their own species but also with members of other species. These interactions are central to the survival of the organism, forming reciprocating and integrated systems with other members of their environment. Paleoecology uses the fossil record to interpret and reconstruct life habits of past organisms and environments. By examining well-preserved fossil populations we can assess the relationship between the organisms and their surrounding environment, their distribution within their environment, and the nature of their interactions. Edrioasteroids, an extinct clade of gregarious, obligate-encrusting echinoderm typical of the Late Ordovician, are rarely encountered in the fossil record as their multi-part skeleton rapidly disarticulates post-mortem. Therefore, the discovery of large pavements encrusted by articulated edrioasteroids indicates that obrution, or a sudden input of sediment that smothers the benthic community, occurred. The near instantaneous nature of obrution allows for the examination of a zero-time-averaged census assemblage rather than a time-averaged death assemblage. This dissertation aims to increase our understanding of the paleoecology and biogeography of Ordovician edrioasteroids in three chapters. The first study examines a carbonate hardground encrusted with four species of isorophid edrioasteroids collected from Upper Ordovician strata near Maysville, Kentucky. Detailed paleoecologic analyses include edrioasteroid age structure, thecal orientation, inter- and intraspecific spatial utilization and settlement patterns, and degree of post-mortem disarticulation. Chapter two examines edrioasteroid paleoecology on Upper Ordovocian shell pavements using a brachiopod shell pavement from Florence, Kentucky and a bivalve shell pavement from Sharonville, Ohio. The results are then compared with those from the Maysville hardground. The final chapter of this dissertation summarizes the paleogeographic distribution patterns of the edrioasteroids during the Ordovician. For this study we collected the geographic distribution data for Ordovician edrioasteroids from published faunal reports and plotted these occurrences on paleogeographic maps with the hope that this information will help better predict localities where additional specimens of Ordovician edrioasteroids may be found.

Exploring elections features from a geographical perspective is the focus of this study. Its primary objective is to develop a scientific approach based on geoinformation technology (GIT) that promotes deeper understanding how geographical settings affect the spatial and temporal variations of voting behaviour and election outcomes. For this purpose, the five parliamentary elections (1991-2005) following the political turnaround in 1990 in the South East European reform country Albania have been selected as a case study. Elections, like other social phenomena that do not develop uniformly over a territory, inherit a spatial dimension. Despite of fact that elections have been researched by various scientific disciplines ranging from political science to geography, studies that incorporate their spatial dimension are still limited in number and approaches. Consequently, the methodologies needed to generate an integrated knowledge on many facets that constitute election features are lacking. This study addresses characteristics and interactions of the essential elements involved in an election process. Thus, the baseline of the approach presented here is the exploration of relations between three entities: electorate (political and sociodemographic features), election process (electoral system and code) and place (environment where voters reside). To express this interaction the concept of electoral pattern is introduced. Electoral patterns are defined by the study as the final view of election results, chiefly in tabular and/or map form, generated by the complex interaction of social, economic, juridical, and spatial features of the electorate, which has occurred at a specific time and in a particular geographical location. GIT methods of geoanalysis and geovisualization are used to investigate the characteristics of electoral patterns in their spatial and temporal distribution. Aggregate-level data modelled in map form were used to analyse and visualize the spatial distribution of election patterns components and relations. The spatial dimension of the study is addressed in the following three main relations: One, the relation between place and electorate and its expression through the social, demographic and economic features of the electorate resulting in the profile of the electorate’s context; second, the electorate-election interaction which forms the baseline to explore the perspective of local contextual effects in voting behaviour and election results; third, the relation between geographical location and election outcomes reflecting the implication of determining constituency boundaries on election results. To address the above relations, three types of variables: geo, independent and dependent, have been elaborated and two models have been created. The Data Model, developed in a GIS environment, facilitates structuring of election data in order to perform spatial analysis. The peculiarity of electoral patterns – a multidimensional array that contains information on three variables, stored in data layers of dissimilar spatial units of reference and scales of value measurement – prohibit spatial analysis based on the original source data. To perform a joint spatial analysis it is therefore mandatory to restructure the spatial units of reference while preserving their semantic content. In this operation, all relevant electoral as well as socio-demographic data referenced to different administrative spatial entities are re-referenced to uniform grid cells as virtual spatial units of reference. Depending on the scale of data acquisition and map presentation, a cell width of 0.5 km has been determined. The resulting fine grid forms the basis of subsequent data analyses and correlations. Conversion of the original vector data layers into target raster layers allows for unification of spatial units, at the same time retaining the existing level of detail of the data (variables, uniform distribution over space). This in turn facilitates the integration of the variables studied and the performance of GIS-based spatial analysis. In addition, conversion to raster format makes it possible to assign new values to the original data, which are based on a common scale eliminating existing differences in scale of measurement. Raster format operations of the type described are well-established data analysis techniques in GIT, yet they have rarely been employed to process and analyse electoral data. The Geovisualization Model, developed in a cartographic environment, complements the Data Model. As an analog graphic model it facilitates efficient communication and exploration of geographical information through cartographic visualization. Based on this model, 52 choropleth maps have been generated. They represent the outcome of the GIS-based electoral data analysis. The analog map form allows for in-depth visual analysis and interpretation of the distribution and correlation of the electoral data studied. For researchers, decision makers and a wider public the maps provide easy-to-access information on and promote easy-to-understand insight into the spatial dimension, regional variation and resulting structures of the electoral patterns defined. / Gegenstand der vorliegenden Studie ist die Erforschung der aus politischen Wahlen resultierenden Raumstrukturen mit Methoden und Techniken der Geoinformationsverarbeitung. Auf der Basis eines gemeinsamen räumlichen Bezuges wird es durch die Verknüpfung der Wahlergebnisse mit ausgewählten wirtschaftlichen, demographischen und sozialen Parametern möglich, die räumliche Verteilung, Kernräume (Hochburgen) und räumlich-strukturelle Verknüpfungen der Wahlergebnisse politischer Parteien zu untersuchen. Die Resultate tragen zu einem besseren Verständnis der Ergebnisse politischer Wahlen und deren räumliche Dimensionen auf nationaler bis lokaler Ebene bei. Die Studie wird am Beispiel der fünf Parlamentswahlen (1991-2005) des südosteuropäischen Reformstaates Albanien durchgeführt, die seit der politischen Wende 1990 stattgefunden haben. Ausgangspunkt der Untersuchung ist die Tatsache, dass Wahlen, wie zahllose andere gesellschaftliche Phänomene auch, eine räumliche Dimension besitzen. Diese kommt in der territorialen Organisation politischer Wahlen in Wahlkreisen explizit zum Ausdruck. In der parlamentarischen Vertretung der politischen Parteien spiegelt sich dies allerdings nur indirekt wider. Zwar waren die parteipolitischen Aspekte politischer Wahlen als auch die parlamentarische Repräsentation sowie die soziodemographischen Strukturen der Wahlbevölkerung Gegenstand einer Vielzahl von Studien aus Politik- und Sozialwissenschaften. Dies auch gilt für die Geographie. Die erwähnte räumliche Dimension politischer Wahlen wurde bislang aber seltener in das Zentrum von Untersuchungen gestellt. Es mangelt insofern auch an spezifischen Methodologien, die eine integrierte Untersuchung aller relevanten Wahlparameter ermöglichen und eine umfassende Bewertung alle Aspekte des Wahlwahlverhaltens einer Wahlbevölkerung bei politischen Wahlen unterstützen. Die vorliegende Studie untersucht strukturelle wie räumliche Merkmale und Zusammenhänge der wesentlichen Faktoren, die bei politischen Wahlen relevant sind. Ausgangspunkt ist die Untersuchung so genannter Wahlmuster, die durch das Zusammenwirken folgender Faktoren entstehen: Wahlprozess (Wahlsystem, Wahlcode), politische und soziodemographische Kenndaten der Wahlbevölkerung, räumliche Ausbreitung und regionale Struktur der Wahlbezirke sowie die räumliche Verteilung und Strukturierung der Wahlbevölkerung. Als Wahlmuster wird die endgültige Repräsentation von Wahlergebnissen, i.d.R. in Tabellen- und Kartenform, betrachtet. Wahlmuster entstehen durch komplexe Interaktion der sozialen, wirtschaftlichen, juristischen und räumlichen Merkmale der Wahlbevölkerung zu einer bestimmten Zeit (Wahltag) in einem bestimmten Raum (Wahlgebiet). Für die Untersuchung der räumlichen und zeitlichen Dimension der Wahlmuster werden Methoden und Techniken der Geoinformationsverarbeitung eingesetzt. Die räumliche Dimension wird dabei in drei Merkmalsgruppen untersucht: Erstens, die Beziehungen zwischen Raum (Standort) und Wahlbevölkerung, wie sie sich in den demographischen, wirtschaftlichen und sozialen Kennwerten der Wahlbevölkerung manifestieren. Zweitens, die Interaktion zwischen Walbevölkerung und Wahl, die die Grundlage bildet, um regionale Kontexteffekte bei Wahlverhalten und Wahlergebnissen zu untersuchen. Drittens, die Verknüpfung von Wahlergebnissen und deren räumlichen Bezügen, wie sie sich in der stetigen Veränderung der Wahlkreisgrenzen niederschlägt. Um die genannten Merkmalsgruppen zu untersuchen, werden drei Variablengruppen gebildet: räumliche, unabhängige, abhängige Variablen. Ihre raumzeitlichen Interaktionen werden mittels zweier raumbezogener Modelle untersucht. Das graphikfreie Datenmodell wird in einem Geoinformationssystem erstellt und erlaubt die Strukturierung der Wahldaten. Dies bildet eine Voraussetzung für die nachfolgende räumliche Analyse. Das besondere Kennzeichen der Wahlmuster – eine mehrdimensionale Matrix der Variableninformation, die in unterschiedlichen, nicht aggregierbaren administrativen Raumbezugseinheiten vorliegt – behindert die räumliche Analyse der Originaldaten. Um dennoch räumliche Analysen durchzuführen, ist es erforderlich, den Raumbezug zu verändern bei gleichzeitiger Beibehaltung der thematischen Merkmale. Hierbei werden alle Wahldaten sowie die relevanten soziodemographischen Daten auf eine gemeinsame Raumbezugseinheit bezogen. Statt unterschiedlich administrativ abgegrenzter Raumeinheiten werden regelmäßige Rasterzellen gleicher Maschenweite als Raumbezugseinheiten definiert und den bisherigen, separaten Raummustern der Variablen überlagert. Auf diese Weise wird die räumliche Gleichverteilung aller Variablen in eine gemeinsame räumliche Bezugsbasis überführt, ohne dass die semantischen Merkmale verändert werden. Entsprechend dem Erfassungs- und Präsentationsmaßstab wurde eine Maschenweite von 0,5 km gewählt. Der hieraus resultierende feingranulare Raumgitter bildet die gemeinsame Basis für die nunmehr möglich integrierte räumliche Analyse aller Merkmalsgruppen. Die hier beschriebene rasterbasierte Raumanalyse stellt eine eingeführte Methode der GIS-basierten Geoinformationsverarbeitung dar. Sie wurde bislang jedoch selten zur Verarbeitung und Analyse von Wahldaten eingesetzt. Das mit dem Datenmodell korrespondierende graphikbezogene Visualisierungsmodell wird in einer Kartenkonstruktionsumgebung erstellt und erlaubt die fachgerechte kartographische Veranschaulichung ausgewählter Analyseergebnisse des Datenmodells. Daten- und Kartenmodell sind durch einen Datenfilter verknüpft, der die erforderliche Datenkonversion ermöglicht. Auf Basis des Visualisierungsmodells wurden zweiundfünfzig Kartenmodelle des Kartogramm- bzw. Kartodiagrammtyps erzeugt. Sie ermöglichen die vertiefte visuelle Exploration, Analyse und Interpretation der räumlichen Verteilung und Korrelation der untersuchten Wahldaten. Komplementär zum graphikfreien Datenmodell eröffnet das Visualisierungsmodell Fachwissenschaftlern, politischen Entscheidungsträgern und - in begrenztem Umfang – einer interessierten Öffentlichkeit einen intuitiven Erkenntniszugang zur den räumlichen Dimensionen, der regionalen Variation der Wahlergebnisse und den resultierenden raumgebundenen Wahlmustern.

The goal of this thesis is to analyze and understand the risks of Chronic Obstructive Pulmonary Disease (COPD) and lung cancer with respect to the provinces of Turkey according to the results of spatial analysis. The insurance sector of the country needs that kind of analysis to make more precise pricing in insurance products. Especially in health and life insurance products, morbidities like COPD and lung cancer may aect the life expectancy as much as the premiums. COPD and lung cancer prevalence may exhibit spatial autocorrelation due to spatial similarity of provinces. Hence understanding of spatial pattern of COPD and lung cancer prevalence may provide better actuarial decisions. In this research, common risk factors of COPD and lung cancer are considered to be tobacco sales, air pollution, urbanization, gross schooling rate, life expectancy, median age and GDP per capita of the provinces. The spatial patterns of these factors in Turkey as well as their correlations to COPD and lung cancer prevalence are explored in this study. The raw data of the morbidities (COPD and lung cancer) are collected from the Social Seiv curity Institution (SGK) and the useful data are selected in these raw data. The data of the independent variables are collected and derived from the Turkish Statistical Institute (TUIK) and Tobacco and Alcohol Market Regulatory Authority (TAPDK). First of all, COPD prevalence ratios and lung cancer prevalence ratios are grouped by 81 provinces of Turkey and every morbidity is separated by gender. Then, it needs to be decided the variables which define prevalence of COPD and that of lung cancer. Age, gender, socio-economic status, urbanization, schooling rate, life expectancy, tobacco sales and air quality may be some of the random variables which are categorized by provinces for both morbidities. After data collection spatial analysis is applied with visualization, explanatory analysis and modeling by using Geographic Information Systems (GIS). In visualization, general spatial patterns are identified for morbidities and variables. In explanatory analysis part, proximity matrices are used to evaluate Moran&rsquo / s I values for understanding the spatial autocorrelation. Then, these Moran&rsquo / s I values are used for plotting correlograms in order to follow the spatial dependence better. After identifying spatial dependence of the variables, Ordinary Linear Regression and Spatial Regression models are established and compared. Finally, as a result of those findings in the analysis, actuarial risk assessments are found for both two morbidities with respect to provinces and gender. The risk assessments are mapped and compared with the explanatory variables in the models which are found in the previous part and the relations between risks and variables are observed. As a result, the parameters show spatial autocorrelation which means that / financial risk assessments of COPD and lung cancer should be taken into account when deciding the pricing of some actuarial products such as health insurance. Generally, spatial correlation is ignored in this kind of calculations, but due to the high autocorrelation the results may indicate serious change. From the actuarial perspective, the results of the analysis are suggested to be used in health insurance premium pricing. Since the analysis could not have been made on the basis of individuals, and financial burden of morbidities for insurance companies are not given clearly, it is not possible to calculate any health insurance product premium, but it is more appropriate to consider the importance of these risk results in the calculations of health insurance products.

This thesis explores crime hot spots and identifies risk factors of expressive and acquisitive crimes in Toronto, Ontario at the census tract scale using official crime offence data from 2006. Four research objectives motivate this thesis: 1) to understand a number of local spatial cluster detection tests and how they can be applied to inform law enforcement planning and confirmatory research, 2) explore spatial regression techniques and applications in past spatial studies of crime, 3) to examine the influence of social disorganization and non-residential land use on expressive crime at the census tract scale, and 4) integrate social disorganization and routine activity theories to understand the small-area risk factors of acquisitive crimes. Research chapters are thematically linked by an intent to recognize crime as a spatial phenomenon, provide insight into the processes and risk factors associated with crime, and inform efficient and effective law enforcement planning.

The objective of this dissertation is to test empirically whether fiscal policy mimicking exists in developing countries and whether such mimicking leads to policy harmonization. This is done by studying the Southern African Development Community (SADC) Region and Sub-Saharan Africa (SSA). The dissertation uses panel data and applies the generalized method of moments (GMM) and the generalized spatial two-stage least squares (GS2SLS) methodologies to a spatial setting to test for the spatial interactions. The study also tests for evidence of spatial interaction in the assessment of government efficiency by voters in neighboring countries, where efficiency is measured using the price/quantity ratio of public goods provision. We find evidence of fiscal policy copycat behavior in both the SSA and SADC regions and mimicking is also present in some tax revenues as well as in expenditure levels. This leads us to conclude that there is some form of fiscal harmonization taking place in these developing countries. We also find evidence of spatial interaction in the assessment of governments’ efficiency in the provision of public goods. Overall, we conclude that there is evidence of some fiscal mimicking behavior as a developing world phenomenon.

Through the years, there has been a great deal of archaeological research focused on the earliest farming cultures of Europe (i.e. Early Neolithic). However, little effort has been expended to uncover the type and nature of daily activities performed within Early Neolithic dwellings, particularly in the Balkans. This thesis conducts a spatial analysis of the Early Neolithic pit house levels of the Foeni-Salaş site in southeast Romania, in the northern half of the Balkans, to determine the kinds and locations of activities that occurred in these pit houses. Characteristic Early Neolithic dwellings in the northern Balkans are pit houses. The data are analyzed using Geographic Information Systems (GIS) technology in an attempt to identify non-random patterns that will indicate how the pit house inhabitants used their space. Both visual and statistical (Nearest Neighbor) techniques are used to identify spatial patterns. Spreadsheet data are incorporated into the map database in order to compare and contrast the results from the two techniques of analysis. Map data provides precise artefact locations, while spreadsheet data yield more generalized quad centroid information. Unlike the mapped data, the spreadsheet data also included artefacts recovered in sieves. Utilizing both data types gave a more complexand fuller understanding of how space was used at Foeni-Salaş. The results show that different types of activity areas are present within each of the pit houses. Comparison of interior to exterior artifact distributions demonstrates that most activities take place within pit house. Some of the activities present include weaving, food preparation, butchering, hide processing, pottery making, ritual, and other activities related to the running of households. It was found that these activities are placed in specific locations relative to features within the pit house and the physical structure of the pit house itself. This research adds to the growing body of archaeological research that implements GIS to answer questions and solve problems related to the spatial dimension of human behaviour. / February 2008

In 2010, the American Association of State Highway and Transportation Officials (AASHTO) released the first edition of the Highway Safety Manual (HSM). The HSM introduces a six-step safety management process which provides engineers with a systematic and scientific approach to managing road safety. The first step of this process, network screening, aims to identify the locations that will most benefit from a safety improvement program. The output obtained from network screening is simply a list of locations that have a high concentration of collisions, based on their potential for safety improvement. The ranking naturally tends to lead to the assumption that the most highly ranked locations are the obvious target locations where road authorities should allocate their often-limited road safety resources. Though these locations contain the highest frequency of collisions, they are often spatially unrelated, and scattered throughout the roadway network. Allocating safety resources to these locations may not be the most effective method of increasing road safety. The purpose of this research is to investigate and validate a two-step method of post-network screening analysis, which identifies collision hotzones (i.e., groups of neighboring hotspots) on a road network. The first step is the network screening process described in the HSM. The second step is new and involves network-constrained kernel density estimation (KDE), a type of spatial analysis. KDE uses expected collision counts to estimate collision density, and outputs a graphical display that shows areas (referred to here as hotzones) with high collision densities. A particularly interesting area of application is the identification of high-collision corridors that may benefit from a program of systemic safety improvements. The proposed method was tested using five years of collision data (2005-2009) for the City of Regina, Saskatchewan. Three different network screening measures were compared: 1) observed collision counts, 2) observed severity-weighted collision counts, and 3) expected severity-weighted collision counts. The study found that observed severity-weighted collision counts produced a dramatic picture of the City's hotzones, but this picture could be misleading as it could be heavily influenced by a small number of severe collisions. The results obtained from the expected severity-weighted collision counts smoothed the effects of the severity-weighting and successfully reduced regression-to-the-mean bias. A comparison was made between the proposed approach and the results of the HSM’s existing network screening method. As the proposed approach takes the spatial association of roadway segments into account, and is not limited to single roadway segments, the identified hotzones capture a higher number of expected EPDO collisions than the existing HSM methodology. The study concludes that the proposed two-step method can help transportation safety professionals to prioritize hotzones within high-collision corridors more efficiently and scientifically. Jurisdiction-specific safety performance functions (SPFs) were also developed over the course of this research, for both intersections (three-leg unsignalized, four-leg unsignalized, three and four-leg signalized), and roadway segments (major arterials, minor arterials, and collectors). These SPFs were compared to the base SPFs provided in the HSM, as well as calibrated HSM SPFs. To compare the different SPFs and find the best-fitting SPFs for the study region, the study used statistical goodness-of-fit (GOF) tests and cumulative residual (CURE) plots. Based on the results of this research, the jurisdiction-specific SPFs were found to provide the best fit to the data, and would be the best SPFs for predicting collisions at intersections and roadway segments in the City of Regina.

Quality and error assessment is an essential part of spatial analysis which with the increasingamount of applications resulting from today’s extensive access to spatial data, such as satelliteimagery and computer power is extra important to address. This study evaluates the impact ofinput errors associated with satellite sensor noise for a spatial method aimed at characterisingaspects of landscapes associated with the historical village structure, called the HybridCharacterisation Model (HCM), that was developed as a tool to monitor sub goals of theSwedish Environmental Goal “A varied agricultural landscape”. The method and errorsimulation method employed for generating random errors in the input data, is implemented andautomated as a Python script enabling easy iteration of the procedure. The HCM is evaluatedqualitatively (by visual analysis) and quantitatively comparing kappa index values between theoutputs affected by error. Comparing the result of the qualitative and quantitative evaluationshows that the kappa index is an applicable measurement of quality for the HCM. Thequalitative analysis compares impact of error for two different scales, the village scale and thelandscape scale, and shows that the HCM is performing well on the landscape scale for up to30% error and on the village scale for up to 10% and shows that the impact of error differsdepending on the shape of the analysed feature. The Python script produced in this study couldbe further developed and modified to evaluate the HCM for other aspects of input error, such asclassification errors, although for such studies to be motivated the potential errors associatedwith the model and its parameters must first be further evaluated.