Multiple studies have documented declines of forest breeding birds in the eastern United States, but the temporal and spatial scales of most studies limit inference regarding large scale bird-habitat trends. A potential solution to this challenge is integrating existing long-term datasets such as the U.S. Forest Service Forest Inventory and Analysis (FIA) program and U.S. Geological Survey Breeding Bird Survey (BBS) that span large geographic regions. The purposes of this study were to determine if FIA metrics can be related to BBS population indices at multiple spatial and temporal scales and to develop predictive models from these relationships that identify forest conditions favorable to forest songbirds. I accumulated annual route-level BBS data for 4 species guilds (canopy nesting, ground and shrub nesting, cavity nesting, early successional), each containing a minimum of five bird species, from 1966-2004. I developed 41 forest variables describing forest structure at the county level using FIA data from for the 2000 inventory cycle within 5 physiographic regions in 14 states (AL, GA, IL, IN, KY, MD, NC, NY, OH, PA, SC, TN, VA, and WV). I examine spatial relationships between the BBS and FIA data at 3 hierarchical scales: 1) individual BBS routes, 2) FIA units, and 3) and physiographic sections. At the BBS route scale, I buffered each BBS route with a 100m, 1km, and 10km buffer, intersected these buffers with the county boundaries, and developed a weighted average for each forest variable within each buffer, with the weight being a function of the percent of area each county had within a given buffer. I calculated 28 variables describing landscape structure from 1992 NLCD imagery using Fragstats within each buffer size. I developed predictive models relating spatial variations in bird occupancy and abundance to changes in forest and landscape structure using logistic regression and classification and regression trees (CART). Models were developed for each of the 3 buffer sizes, and I pooled the variables selected for the individual models and used them to develop multiscale models with the BBS route still serving as the sample unit. At the FIA unit and physiographic section scales I calculated average abundance/route for each bird species within each FIA unit and physiographic section and extrapolated the plot-level FIA variables to the FIA unit and physiographic section levels. Landscape variables were recalculated within each unit and section using NCLD imagery resampled to a 400 m pixel size. I used regression trees (FIA unit scale) and general linear models (GLM, physiographic section scale) to relate spatial variations in bird abundance to the forest and landscape variables. I examined temporal relationships between the BBS and FIA data between 1966 and 2000. I developed 13 forest variables from statistical summary reports for 4 FIA inventory cycles (1965, 1975, 1989, and 2000) within NY, PA, MD, and WV. I used linear interpolation to estimate annual values of each FIA variable between successive inventory cycles and GLMs to relate annual variations in bird abundance to the forest variables. At the BBS route scale, the CART models accounted for > 50% of the variation in bird presence-absence and abundance. The logistic regression models had sensitivity and specificity rates > 0.50. By incorporating the variables selected for the models developed within each buffer (100m, 1km, and 10km) around the BBS routes into a multiscale model, I was able to further improve the performance of many of the models and gain additional insight regarding the contribution of multiscale influences on bird-habitat relationships. The majority of the best CART models tended to be the multiscale models, and many of the multiscale logistic models had greater sensitivity and specificity than their single-scale counter parts. The relatively fine resolution and extensive coverage of the BBS, FIA, and NLCD datasets coupled with the overlapping multiscale approach of these analyses allowed me to incorporate levels of variation in both habitat and bird occurrence and abundance into my models that likely represented a more comprehensive range of ecological variability in the bird-habitat relationships relative to studies conducted at smaller scales and/or using data at coarser resolutions. At the FIA unit and physiographic section scales, the regression trees accounted for an average of 54.1% of the variability in bird abundance among FIA units, and the GLMs accounted for an average of 66.3% of the variability among physiographic sections. However, increasing the observational and analytical scale to the FIA unit and physiographic section decreased the measurement resolution of the bird abundance and landscape variables. This limits the applicability and interpretive strength of the models developed at these scales, but they may serve as indices to those habitat components exerting the greatest influences on bird abundance at these broader scales. The GLMs relating average annual bird abundance to annual estimates of forest variables developed using statistical report data from the 1965, 1975, 1989, and 2000 FIA inventories explained an average of 62.0% of the variability in annual bird abundance estimates. However, these relationships were a function of both the general habitat characteristics and the trends in bird abundance specific to the 4-state region (MD, NY, PA, and WV) used for these analyses and may not be applicable to other states or regions. The small suite of variables available from the FIA statistical reports and multicollinearity among all forest variables further limited the applicability of these models. As with those developed at the FIA unit and physiographic sections scales, these models may serve as general indices to the habitat components exerting the greatest influences on bird abundance trends through time at regional scales. These results demonstrate that forest variables developed from the FIA, in conjunction with landscape variables, can explain variations in occupancy and abundance estimated from BBS data for forest bird species with a variety of habitat requirements across spatial and temporal scales. / Ph. D.

Over the last 20 years, Lyme disease has grown to become the most common vector-borne disease affecting Americans. Spread in the eastern U.S. primarily by the bite of Ixodes scapularis, the black-legged tick, the disease affects an estimated 329,000 Americans per year. Originally confined to New England, it has since spread across much of the east coast and has become endemic in Virginia. Since 2010 the state has averaged 1200 cases per year, with 200 annually in the New River Health District (NRHD), the location of our study. Efforts to geographically model Lyme disease primarily focus on landscape and climatic variables. The disease depends highly on the survival of the tick vector, and white-footed mouse, the primary reservoir. Both depend on the existence of forest-herbaceous edge-habitats, as well as warm summer temperatures, mild winter lows, and summer wetness. While many studies have investigated the effect of forest fragmentation on Lyme, none have made use of high-resolution land cover data to do so at the peridomestic level. To fill this knowledge gap, we made use of the Virginia Geographic Information Network’s 1-meter land cover dataset and identified forest-herbaceous edge-habitats for the NRHD. We then calculated the density of these edge-habitats at 100, 200 and 300-meter radii, representing the peridomestic environment. We also calculated the density of <2-hectare forest patches at the same distance thresholds. To avoid confounding from climatic variation, we also calculated mean summer temperatures, total summer rainfall, and number of consecutive days below freezing of the prior winters. Adding to these data, elevation, terrain shape index, slope, and aspect, and including lags on each of our climatic variables, we created environmental niche models of Lyme in the NRHD. We did so using both Boosted Regression Trees (BRT) and Maximum Entropy (MaxEnt) modeling, the two most common niche modeling algorithms in the field today. We found that Lyme is strongly associated with higher density of developed-herbaceous edges within 100-meters from the home. Forest patch density was also significant at both 100-meter and 300-meter levels. This supports the notion that the fine scale peridomestic environment is significant to Lyme outcomes, and must be considered even if one were to account for fragmentation at a wider scale, as well as variations in climate and terrain. / M.S. / Lyme disease is the most common vector-borne disease in the United States today. Infecting about 330,000 Americans per year, the disease continues to spread geographically. Originally found only in New England, the disease is now common in Virginia. The New River Health District, where we did our study, sees over 200 cases per year. Lyme disease is mostly spread by the bite of the black-legged tick. As such we can predict where Lyme cases might be found if we understand the environmental needs of these ticks. The ticks themselves depend on warm summer temperatures, mild winter lows, and summer wetness. But they are also affected by forest fragmentation which drives up the population of white-footed mice, the tick’s primary host. The mice are particularly fond of the interface between forests and open fields. These edge habitats provide food and cover for the mice, and in turn support a large population of ticks. Many existing studies have demonstrated this link, but all have done so across broad scales such as counties or census tracts. To our knowledge, no such studies have investigated forest fragmentation near the home of known Lyme cases. To fill this gap in our knowledge, we made use of high-resolution forest cover data to identify forest-field edge habitats and small isolated forest patches. We then calculated the total density of both within 100, 200 and 300 meters of the homes of known Lyme cases, and compared these to values from non-cases using statistical modeling. We also included winter and summer temperatures, rainfall, elevation, slope, aspect, and terrain shape. We found that a large amount of forest-field edges within 100 meters of a home increases the risk of Lyme disease to residents of that home. The same can be said for isolated forest patches. Even after accounting for all other variables, this effect was still significant. This information can be used by health departments to predict which neighborhoods may be most at risk for Lyme. They can then increase surveillance in those areas, warn local doctors, or send out educational materials.

Die Nutzung von terrestrischen Ökosystemen zur Befriedigung der Grundbedürfnisse der Menschheit hat tiefgreifende Auswirkungen auf das Erdsystem und führte zur Ausprägung von anthropogen dominierten Landsystemen. Diese sind von hoher Komplexität, da sie aus einer Vielzahl von unterschiedlichsten Einflussfaktoren angetriebenen Landnutzungsveränderungen hervorgegangen sind. Aktuelle Forderungen nach einer nachhaltigen zukünftigen Landnutzung erfordern ein fundiertes und integratives Verständnis dieser Komplexität. Das Hauptziel dieser Arbeit ist es, ein besseres Verständnis der raum-zeitlichen Muster und Determinanten des Landsystemwandels, insbesondere der Landnutzungsintensität, in Europa zwischen 1990 und 2010 zu erlangen. Europa ist ein interessantes Studiengebiet, da es jüngst starke Landnutzungsveränderungen erlebte und seine Heterogenität zu einer Vielfalt von Landsystemen und Landsystemveränderungen führte. Das Ziel der Arbeit wurde durch (i) die Kartierung von Intensitätsmustern und deren Veränderungen in Forst- und Agrarsystemen sowie der Ermittlung der dafür einflussreichsten räumlichen Determinanten und (ii) die Kartierung und Charakterisierung archetypischer Muster und Entwicklungsverläufe von Landsystemen untersucht. Die Ergebnisse dieser Arbeit zeigten einen deutlichen Ost-West-Unterschied in Landsystemmustern und -veränderungen in Europa, mit intensiv genutzten und intensivierenden Regionen vor allem in Westeuropa. Dennoch wurde Europa vor allem durch relativ stabile Landsystemmuster gekennzeichnet und (De-)Intensivierungstrends waren nur von untergeordneter Bedeutung. Intensitätsmuster und -veränderungen waren stark an Standortbedingungen gebunden, vor allem an edaphische, klimatische, und länderspezifische Besonderheiten. Diese Arbeit erweitert das Verständnis des Landsystemwandels in Europa und kann zur Entwicklung wissenschaftlicher und politikbezogener Maßnahmen sowie zur Erreichung einer nachhaltigeren Landnutzung in Europa beitragen. / The utilisation of terrestrial ecosystems to satisfy the basic needs of humankind has profound impacts on the Earth System and led to the development of human-dominated land systems. These are substantially complex as they evolved from a multitude of land-change pathways driven by a variety of influential factors. Current calls for a more sustainable future land-use require a sound and integrative understanding of this complexity. The main goal of this thesis is to better understand the spatio-temporal patterns and the determinants of land-system change in Europe between 1990 and 2010, especially with regard to land-use intensity. Europe serves as an interesting study region as it recently experienced a period of marked land-use change, and since its large environmental, political, and socio-economic heterogeneity resulted in a diversity of land systems and land-change pathways. Land-system changes in Europe were examined by (i) mapping patterns and changes in forestry and agricultural intensity and identifying the most influential spatial determinants related to these changes, and (ii) mapping and characterising archetypical patterns and trajectories of land systems considering both land-use extent and intensity indicators. Results revealed a distinct east-west divide in Europe’s land-system patterns and change trajectories, with intensively used and intensifying regions particularly located in Western Europe. However, Europe was mainly characterised by relatively stable land-systems patterns with (de-) intensification trends being only of minor importance. Land-use intensity levels and changes were strongly related to site conditions, especially with regard to soil and climate, as well as to country-specific characteristics. By fostering the understanding of land-system change, this thesis has the potential to contribute to scientific and policy-related actions that address current efforts to guide future land systems in Europe to a more sustainable use.

Agriculture is becoming more dependent on computerized solutions to make thefarmer’s job easier. The big step that many companies are working towards is fullyautonomous vehicles that work the fields. To that end, the equipment fitted to saidvehicles must also adapt and become autonomous. Making this equipment autonomoustakes many incremental steps, one of which is developing an accurate and reliable tanklevel measurement system. In this thesis, a system for tank level measurement in a seedplanting machine is evaluated. Traditional systems use load cells to measure the weightof the tank however, these types of systems are expensive to build and cumbersome torepair. They also add a lot of weight to the equipment which increases the fuel consump-tion of the tractor. Thus, this thesis investigates the use of radar sensors together witha number of Machine Learning algorithms. Fourteen radar sensors are fitted to a tankat different positions, data is collected, and a preprocessing method is developed. Then,the data is used to test the following Machine Learning algorithms: Bagged RegressionTrees (BG), Random Forest Regression (RF), Boosted Regression Trees (BRT), LinearRegression (LR), Linear Support Vector Machine (L-SVM), Multi-Layer Perceptron Re-gressor (MLPR). The model with the best 5-fold crossvalidation scores was Random For-est, closely followed by Boosted Regression Trees. A robustness test, using 5 previouslyunseen scenarios, revealed that the Boosted Regression Trees model was the most robust.The radar position analysis showed that 6 sensors together with the MLPR model gavethe best RMSE scores.In conclusion, the models performed well on this type of system which shows thatthey might be a competitive alternative to load cell based systems.

Le but de cette thèse est d’expliquer la délinquance prolifique de certains délinquants. Nous avançons la thèse que la délinquance prolifique s’explique par la formation plus fréquente de situations criminogènes. Ces situations réfèrent au moment où un délinquant entre en interaction avec une opportunité criminelle dans un contexte favorable au crime. Plus exactement, il s’agit du moment où le délinquant fait face à cette opportunité, mais où le crime n’a pas encore été commis. La formation de situations criminogènes est facilitée par l’interaction et l’interdépendance de trois éléments : la propension à la délinquance de la personne, son entourage criminalisé et son style de vie. Ainsi, la délinquance prolifique ne pourrait être expliquée adéquatement sans tenir compte de l’interaction entre le risque individuel et le risque contextuel. L’objectif général de la présente thèse est de faire la démonstration de l’importance d’une modélisation interactionnelle entre le risque individuel et le risque contextuel afin d’expliquer la délinquance plus prolifique de certains contrevenants. Pour ce faire, 155 contrevenants placés sous la responsabilité de deux établissements des Services correctionnels du Québec et de quatre centres jeunesse du Québec ont complété un protocole d’évaluation par questionnaires auto-administrés. Dans un premier temps (chapitre trois), nous avons décrit et comparé la nature de la délinquance autorévélée des contrevenants de notre échantillon. Ce premier chapitre de résultats a permis de mettre en valeur le fait que ce bassin de contrevenants est similaire à d’autres échantillons de délinquants en ce qui a trait à la nature de leur délinquance, plus particulièrement, au volume, à la variété et à la gravité de leurs crimes. En effet, la majorité des participants rapportent un volume faible de crimes contre la personne et contre les biens alors qu’un petit groupe se démarque par un lambda très élevé (13,1 % des délinquants de l’échantillon sont responsables de 60,3% de tous les crimes rapportés). Environ quatre délinquants sur cinq rapportent avoir commis au moins un crime contre la personne et un crime contre les biens. De plus, plus de 50% de ces derniers rapportent dans au moins quatre sous-catégories. Finalement, bien que les délinquants de notre échantillon aient un IGC (indice de gravité de la criminalité) moyen relativement faible (médiane = 77), près de 40% des contrevenants rapportent avoir commis au moins un des deux crimes les plus graves recensés dans cette étude (décharger une arme et vol qualifié). Le second objectif spécifique était d’explorer, au chapitre quatre, l’interaction entre les caractéristiques personnelles, l’entourage et le style de vie des délinquants dans la formation de situations criminogènes. Les personnes ayant une propension à la délinquance plus élevée semblent avoir tendance à être davantage entourées de personnes criminalisées et à avoir un style de vie plus oisif. L’entourage criminalisé semble également influencer le style de vie de ces délinquants. Ainsi, l’interdépendance entre ces trois éléments facilite la formation plus fréquente de situations criminogènes et crée une conjoncture propice à l’émergence de la délinquance prolifique. Le dernier objectif spécifique de la thèse, qui a été couvert dans le chapitre cinq, était d’analyser l’impact de la formation de situations criminogènes sur la nature de la délinquance. Les analyses de régression linéaires multiples et les arbres de régression ont permis de souligner la contribution des caractéristiques personnelles, de l’entourage et du style de vie dans l’explication de la nature de la délinquance. D’un côté, les analyses de régression (modèles additifs) suggèrent que l’ensemble des éléments favorisant la formation de situations criminogènes apporte une contribution unique à l’explication de la délinquance. D’un autre côté, les arbres de régression nous ont permis de mieux comprendre l’interaction entre les éléments dans l’explication de la délinquance prolifique. En effet, un positionnement plus faible sur certains éléments peut être compensé par un positionnement plus élevé sur d’autres. De plus, l’accumulation d’éléments favorisant la formation de situations criminogènes ne se fait pas de façon linéaire. Ces conclusions sont appuyées sur des proportions de variance expliquée plus élevées que celles des régressions linéaires multiples. En conclusion, mettre l’accent que sur un seul élément (la personne et sa propension à la délinquance ou le contexte et ses opportunités) ou leur combinaison de façon simplement additive ne permet pas de rendre justice à la complexité de l’émergence de la délinquance prolifique. En mettant à l’épreuve empiriquement cette idée généralement admise, cette thèse permet donc de souligner l’importance de considérer l’interaction entre le risque individuel et le risque contextuel dans l’explication de la délinquance prolifique. / The purpose of this dissertation is to explain the prolific delinquency of certain offenders. We suggest that prolific delinquency is explained by the formation of a greater number of criminogenic situations. A criminogenic situation makes reference to situations wherein an offender may interact with a criminal opportunity in an environment which is conducive to crime. More precisely, a criminogenic situation is the moment when an offender faces this opportunity, but the crime has not yet been committed. The formation of criminogenic situations facilitated by the interaction and interdependence of three elements: criminal propensity, criminal social environment and deviant lifestyle. Thus, prolific delinquency cannot be adequately explained without accounting for the interaction between individual and contextual risk. The overall objective of this dissertation is to demonstrate the importance of a model based on the interaction between individual and contextual risk to explain the prolific delinquency of some offenders. To accomplish this objective, one hundred and fifty-five offenders, under the responsibility of Services Correctionels du Québec and four Centres Jeunesse, completed an evaluation through self-administered questionnaires. The first objective of this study was to describe and compare, in Chapter Three, the delinquent nature of the offenders in our sample. Our results revealed that our sample of offenders is similar to that of other samples of delinquents; we found this with respect to the nature of their delinquency, and in particular, the volume, diversity and severity of their crimes. Indeed, the majority of the participants reported a small volume of crimes against a person and property. A small group distinguished themselves with a very high lambda (13.1% of offenders in the sample are responsible for 60.3 % of all crimes reported). Additionally, more than four out of five offenders reported having committed at least one crime against a person and one crime against property. Moreover, 50 % reported having committed crimes in at least four subcategories. Finally, although the offenders in our sample have a relatively low IGC (gravity scale) mean (median = 77), nearly 40 % of the offenders reported having committed at least one of the two most serious crimes identified in this study (discharging firearm and robbery). The second specific objective was to explore, in Chapter four, the interaction between personal characteristics, social environment and the lifestyle of offenders which may lead to criminogenic situation. People with a higher propensity to crime tend to be surrounded by other criminalized people and have a more idle lifestyle. The criminalized social environment tends to also influence the lifestyle of these offenders. Thus, the interdependence between these three elements can lead to criminogenic situations and can create a climate conducive to the emergence of prolific delinquency. The last specific objective of this dissertation, covered in Chapter Five, is to analyze the impact of factors leading to situational crime on the nature of the delinquency. Analyses of multiple linear regression and regression trees highlighted the contribution of personal characteristics, social environment and lifestyle in explaining the nature of the crime. On the one hand, regression analyses (additive models) suggest that all the elements leading to situational crime make a unique contribution to the explanation of delinquency. However, on the other hand, regression trees allowed us to better understand the interaction between the elements which underlie prolific delinquency. For example, a lower position on certain items may be offset by a higher position on others. Moreover, the accumulation of risk factors which lead to situational crime does not happen in a linear fashion. These conclusions are supported by the proportions of explained variance which were higher for the regression trees than for the multiple linear regressions. In conclusion, focusing simply on one element (the person and their criminal propensity or the context and its opportunities) or on their combination in a simply additive manner does not represent the reality of criminal phenomenon. This dissertation therefore serves to highlight the importance of considering the interaction between the individual risk and the contextual risk in explaining prolific delinquency.

Le but de cette thèse est d’expliquer la délinquance prolifique de certains délinquants. Nous avançons la thèse que la délinquance prolifique s’explique par la formation plus fréquente de situations criminogènes. Ces situations réfèrent au moment où un délinquant entre en interaction avec une opportunité criminelle dans un contexte favorable au crime. Plus exactement, il s’agit du moment où le délinquant fait face à cette opportunité, mais où le crime n’a pas encore été commis. La formation de situations criminogènes est facilitée par l’interaction et l’interdépendance de trois éléments : la propension à la délinquance de la personne, son entourage criminalisé et son style de vie. Ainsi, la délinquance prolifique ne pourrait être expliquée adéquatement sans tenir compte de l’interaction entre le risque individuel et le risque contextuel. L’objectif général de la présente thèse est de faire la démonstration de l’importance d’une modélisation interactionnelle entre le risque individuel et le risque contextuel afin d’expliquer la délinquance plus prolifique de certains contrevenants. Pour ce faire, 155 contrevenants placés sous la responsabilité de deux établissements des Services correctionnels du Québec et de quatre centres jeunesse du Québec ont complété un protocole d’évaluation par questionnaires auto-administrés. Dans un premier temps (chapitre trois), nous avons décrit et comparé la nature de la délinquance autorévélée des contrevenants de notre échantillon. Ce premier chapitre de résultats a permis de mettre en valeur le fait que ce bassin de contrevenants est similaire à d’autres échantillons de délinquants en ce qui a trait à la nature de leur délinquance, plus particulièrement, au volume, à la variété et à la gravité de leurs crimes. En effet, la majorité des participants rapportent un volume faible de crimes contre la personne et contre les biens alors qu’un petit groupe se démarque par un lambda très élevé (13,1 % des délinquants de l’échantillon sont responsables de 60,3% de tous les crimes rapportés). Environ quatre délinquants sur cinq rapportent avoir commis au moins un crime contre la personne et un crime contre les biens. De plus, plus de 50% de ces derniers rapportent dans au moins quatre sous-catégories. Finalement, bien que les délinquants de notre échantillon aient un IGC (indice de gravité de la criminalité) moyen relativement faible (médiane = 77), près de 40% des contrevenants rapportent avoir commis au moins un des deux crimes les plus graves recensés dans cette étude (décharger une arme et vol qualifié). Le second objectif spécifique était d’explorer, au chapitre quatre, l’interaction entre les caractéristiques personnelles, l’entourage et le style de vie des délinquants dans la formation de situations criminogènes. Les personnes ayant une propension à la délinquance plus élevée semblent avoir tendance à être davantage entourées de personnes criminalisées et à avoir un style de vie plus oisif. L’entourage criminalisé semble également influencer le style de vie de ces délinquants. Ainsi, l’interdépendance entre ces trois éléments facilite la formation plus fréquente de situations criminogènes et crée une conjoncture propice à l’émergence de la délinquance prolifique. Le dernier objectif spécifique de la thèse, qui a été couvert dans le chapitre cinq, était d’analyser l’impact de la formation de situations criminogènes sur la nature de la délinquance. Les analyses de régression linéaires multiples et les arbres de régression ont permis de souligner la contribution des caractéristiques personnelles, de l’entourage et du style de vie dans l’explication de la nature de la délinquance. D’un côté, les analyses de régression (modèles additifs) suggèrent que l’ensemble des éléments favorisant la formation de situations criminogènes apporte une contribution unique à l’explication de la délinquance. D’un autre côté, les arbres de régression nous ont permis de mieux comprendre l’interaction entre les éléments dans l’explication de la délinquance prolifique. En effet, un positionnement plus faible sur certains éléments peut être compensé par un positionnement plus élevé sur d’autres. De plus, l’accumulation d’éléments favorisant la formation de situations criminogènes ne se fait pas de façon linéaire. Ces conclusions sont appuyées sur des proportions de variance expliquée plus élevées que celles des régressions linéaires multiples. En conclusion, mettre l’accent que sur un seul élément (la personne et sa propension à la délinquance ou le contexte et ses opportunités) ou leur combinaison de façon simplement additive ne permet pas de rendre justice à la complexité de l’émergence de la délinquance prolifique. En mettant à l’épreuve empiriquement cette idée généralement admise, cette thèse permet donc de souligner l’importance de considérer l’interaction entre le risque individuel et le risque contextuel dans l’explication de la délinquance prolifique. / The purpose of this dissertation is to explain the prolific delinquency of certain offenders. We suggest that prolific delinquency is explained by the formation of a greater number of criminogenic situations. A criminogenic situation makes reference to situations wherein an offender may interact with a criminal opportunity in an environment which is conducive to crime. More precisely, a criminogenic situation is the moment when an offender faces this opportunity, but the crime has not yet been committed. The formation of criminogenic situations facilitated by the interaction and interdependence of three elements: criminal propensity, criminal social environment and deviant lifestyle. Thus, prolific delinquency cannot be adequately explained without accounting for the interaction between individual and contextual risk. The overall objective of this dissertation is to demonstrate the importance of a model based on the interaction between individual and contextual risk to explain the prolific delinquency of some offenders. To accomplish this objective, one hundred and fifty-five offenders, under the responsibility of Services Correctionels du Québec and four Centres Jeunesse, completed an evaluation through self-administered questionnaires. The first objective of this study was to describe and compare, in Chapter Three, the delinquent nature of the offenders in our sample. Our results revealed that our sample of offenders is similar to that of other samples of delinquents; we found this with respect to the nature of their delinquency, and in particular, the volume, diversity and severity of their crimes. Indeed, the majority of the participants reported a small volume of crimes against a person and property. A small group distinguished themselves with a very high lambda (13.1% of offenders in the sample are responsible for 60.3 % of all crimes reported). Additionally, more than four out of five offenders reported having committed at least one crime against a person and one crime against property. Moreover, 50 % reported having committed crimes in at least four subcategories. Finally, although the offenders in our sample have a relatively low IGC (gravity scale) mean (median = 77), nearly 40 % of the offenders reported having committed at least one of the two most serious crimes identified in this study (discharging firearm and robbery). The second specific objective was to explore, in Chapter four, the interaction between personal characteristics, social environment and the lifestyle of offenders which may lead to criminogenic situation. People with a higher propensity to crime tend to be surrounded by other criminalized people and have a more idle lifestyle. The criminalized social environment tends to also influence the lifestyle of these offenders. Thus, the interdependence between these three elements can lead to criminogenic situations and can create a climate conducive to the emergence of prolific delinquency. The last specific objective of this dissertation, covered in Chapter Five, is to analyze the impact of factors leading to situational crime on the nature of the delinquency. Analyses of multiple linear regression and regression trees highlighted the contribution of personal characteristics, social environment and lifestyle in explaining the nature of the crime. On the one hand, regression analyses (additive models) suggest that all the elements leading to situational crime make a unique contribution to the explanation of delinquency. However, on the other hand, regression trees allowed us to better understand the interaction between the elements which underlie prolific delinquency. For example, a lower position on certain items may be offset by a higher position on others. Moreover, the accumulation of risk factors which lead to situational crime does not happen in a linear fashion. These conclusions are supported by the proportions of explained variance which were higher for the regression trees than for the multiple linear regressions. In conclusion, focusing simply on one element (the person and their criminal propensity or the context and its opportunities) or on their combination in a simply additive manner does not represent the reality of criminal phenomenon. This dissertation therefore serves to highlight the importance of considering the interaction between the individual risk and the contextual risk in explaining prolific delinquency.

The incidence of many arboviral diseases is largely associated with social and environmental conditions. Ross River virus (RRV) is the most prevalent arboviral disease in Australia. It has long been recognised that the transmission pattern of RRV is sensitive to socio-ecological factors including climate variation, population movement, mosquito-density and vegetation types. This study aimed to assess the relationships between socio-environmental variability and the transmission of RRV using spatio-temporal analytic methods. Computerised data files of daily RRV disease cases and daily climatic variables in Brisbane, Queensland during 1985-2001 were obtained from the Queensland Department of Health and the Australian Bureau of Meteorology, respectively. Available information on other socio-ecological factors was also collected from relevant government agencies as follows: 1) socio-demographic data from the Australia Bureau of Statistics; 2) information on vegetation (littoral wetlands, ephemeral wetlands, open freshwater, riparian vegetation, melaleuca open forests, wet eucalypt, open forests and other bushland) from Brisbane City Council; 3) tidal activities from the Queensland Department of Transport; and 4) mosquito-density from Brisbane City Council. Principal components analysis (PCA) was used as an exploratory technique for discovering spatial and temporal pattern of RRV distribution. The PCA results show that the first principal component accounted for approximately 57% of the information, which contained the four seasonal rates and loaded highest and positively for autumn. K-means cluster analysis indicates that the seasonality of RRV is characterised by three groups with high, medium and low incidence of disease, and it suggests that there are at least three different disease ecologies. The variation in spatio-temporal patterns of RRV indicates a complex ecology that is unlikely to be explained by a single dominant transmission route across these three groupings. Therefore, there is need to explore socio-economic and environmental determinants of RRV disease at the statistical local area (SLA) level. Spatial distribution analysis and multiple negative binomial regression models were employed to identify the socio-economic and environmental determinants of RRV disease at both the city and local (ie, SLA) levels. The results show that RRV activity was primarily concentrated in the northeast, northwest and southeast areas in Brisbane. The negative binomial regression models reveal that RRV incidence for the whole of the Brisbane area was significantly associated with Southern Oscillation Index (SOI) at a lag of 3 months (Relative Risk (RR): 1.12; 95% confidence interval (CI): 1.06 - 1.17), the proportion of people with lower levels of education (RR: 1.02; 95% CI: 1.01 - 1.03), the proportion of labour workers (RR: 0.97; 95% CI: 0.95 - 1.00) and vegetation density (RR: 1.02; 95% CI: 1.00 - 1.04). However, RRV incidence for high risk areas (ie, SLAs with higher incidence of RRV) was significantly associated with mosquito density (RR: 1.01; 95% CI: 1.00 - 1.01), SOI at a lag of 3 months (RR: 1.48; 95% CI: 1.23 - 1.78), human population density (RR: 3.77; 95% CI: 1.35 - 10.51), the proportion of indigenous population (RR: 0.56; 95% CI: 0.37 - 0.87) and the proportion of overseas visitors (RR: 0.57; 95% CI: 0.35 - 0.92). It is acknowledged that some of these risk factors, while statistically significant, are small in magnitude. However, given the high incidence of RRV, they may still be important in practice. The results of this study suggest that the spatial pattern of RRV disease in Brisbane is determined by a combination of ecological, socio-economic and environmental factors. The possibility of developing an epidemic forecasting system for RRV disease was explored using the multivariate Seasonal Auto-regressive Integrated Moving Average (SARIMA) technique. The results of this study suggest that climatic variability, particularly precipitation, may have played a significant role in the transmission of RRV disease in Brisbane. This finding cannot entirely be explained by confounding factors such as other socio-ecological conditions because they have been unlikely to change dramatically on a monthly time scale in this city over the past two decades. SARIMA models show that monthly precipitation at a lag 2 months (=0.004,p=0.031) was statistically significantly associated with RRV disease. It suggests that there may be 50 more cases a year for an increase of 100 mm precipitation on average in Brisbane. The predictive values in the model were generally consistent with actual values (root-mean-square error (RMSE): 1.96). Therefore, this model may have applications as a decision support tool in disease control and risk-management planning programs in Brisbane. The Polynomial distributed lag (PDL) time series regression models were performed to examine the associations between rainfall, mosquito density and the occurrence of RRV after adjusting for season and auto-correlation. The PDL model was used because rainfall and mosquito density can affect not merely RRV occurring in the same month, but in several subsequent months. The rationale for the use of the PDL technique is that it increases the precision of the estimates. We developed an epidemic forecasting model to predict incidence of RRV disease. The results show that 95% and 85% of the variation in the RRV disease was accounted for by the mosquito density and rainfall, respectively. The predictive values in the model were generally consistent with actual values (RMSE: 1.25). The model diagnosis reveals that the residuals were randomly distributed with no significant auto-correlation. The results of this study suggest that PDL models may be better than SARIMA models (R-square increased and RMSE decreased). The findings of this study may facilitate the development of early warning systems for the control and prevention of this widespread disease. Further analyses were conducted using classification trees to identify major mosquito species of Ross River virus (RRV) transmission and explore the threshold of mosquito density for RRV disease in Brisbane, Australia. The results show that Ochlerotatus vigilax (RR: 1.028; 95% CI: 1.001 - 1.057) and Culex annulirostris (RR: 1.013, 95% CI: 1.003 - 1.023) were significantly associated with RRV disease cycles at a lag of 1 month. The presence of RRV was associated with average monthly mosquito density of 72 Ochlerotatus vigilax and 52 Culex annulirostris per light trap. These results may also have applications as a decision support tool in disease control and risk management planning programs. As RRV has significant impact on population health, industry, and tourism, it is important to develop an epidemic forecast system for this disease. The results of this study show the disease surveillance data can be integrated with social, biological and environmental databases. These data can provide additional input into the development of epidemic forecasting models. These attempts may have significant implications in environmental health decision-making and practices, and may help health authorities determine public health priorities more wisely and use resources more effectively and efficiently.

Background Recently, Taiwan has been faced with the migration of numbers of women from Southeast Asian (SEA) countries. It was estimated that the aggregate number of SEA wives in Taiwan was more than 131,000 in 2007 (Ministry of Foreign Affairs, 2006).These women are often colloquially called, “foreign brides” or “alien brides”; most of them are seen as commodities of the marriage trade, whose marriages are arranged by marriage brokers. Some women can be regarded as being sold for profit by their families. These young Vietnamese immigrant women come to Taiwan alone, often with a single suitcase, and are culturally and geographically distinct from Taiwanese peoples; the changes in culture, interpersonal relationships, personal roles, language, value systems and attitudes exert many negative impacts on their health, so greater levels of acculturation stress can be expected. This particular group of immigrant women are highly susceptible and vulnerable to health problems, due to language barriers, cultural conflicts, social and interpersonal isolation, and lack of support systems. The aims of this study were to examine the relationships between acculturation and immigrantspecific distress and health outcomes among Vietnamese transnational married women in Taiwan. This study focuses on Vietnamese intermarriage immigrants, the largest immigrant group in the period from1994 through to 2007. Methodology The quantitative study was divided into two phases: the first was a pilot study and the second the main study. This study was conducted in a communitybased health centre in the south of Taiwan, targeting Taiwanese households with Vietnamese wives, including the Tanam, Kaohsiung, and Pentong areas. This involved convenience sampling with participants drawn from registration records at the Public Health Centre of Kaohsiung and used the snowball technique to recruit 213 participants. The instruments included the following measures: (1) Socio-demographic information (2) Acculturation Scale (3) Acculturative Distress Scale, and (4) HRQOL. Questions related to immigrant women’s acculturation level and health status were modified. Quantitative data was coded and entered into the SPSS and SAS program for statistical analysis. The data analysis process involved descriptive, bivariate, multivariate multiple regression, and classification and regression trees (CART). Results Six hypotheses of this study were validated. Demographic data was presented and it revealed that there are statically significant differences between levels of acculturation and years of residency in Taiwan, number of children, marital status, education, religion of spouse, employment status of spouse and Chinese ethnic background by Pearson correlation and Kendall’s Tau-b or Spearman test. The correlations of daily activity, language usage, social interaction, ethnic identity, and total of acculturation score with DI tend to be negatively significant. In addition, the result of the one-way ANOVA supported the hypothesis that the different types of acculturation had a differential effect on immigrant distress. The marginalized group showed a greater immigrant distresses in comparison with the integrated group. Furthermore, the comparison t-test revealed that the Vietnamese immigrant women showed a lower score than Taiwanese women in HRQOL. The result showed higher acculturative stress associated with lower score of HRQOL on bodily pain, vitality, social functioning, mental health, and mental component summary. The CART procedure to the conclusion that the predictive variables for the physical component of the SF-36 (PCS) were: alienation, occupation, loss, language, and discrimination (predicted 28.8% of the total variance explained). The predictive variables for the mental component of the SF-36 (MCS) were: alienation, occupation, loss, language, and novelty (predicted 28.4% of the total variance explained). Conclusion As these Vietnamese immigrant women become part of Taiwanese communities and society, the need becomes apparent to understand how they acculturate to Taiwan and to the health status they acquire. The findings have implications for nursing practice, research, and will assist the Taiwanese government to formulate appropriate immigrant health policies for these SEA immigrant women. Finally, the application of this research will positively contribute to the health and well being of thousands of immigrant women and their families.

Thesis (MScFor)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: Site Index is used extensively in modern commercial forestry both as an indicator of current and future site potential, but also as a means of site comparison. The concept is deeply embedded into current forest planning processes, and without it empirical growth and yield modelling would not function in its present form. Most commercial forestry companies in South Africa currently spend hundreds of thousands of Rand annually collecting growth stock data via inventory, but spend little or no money on the default compartment data (specifically Site Index) which is used to estimate over 90% of the product volumes in their long term plans. A need exists to construct reliable methods to determine Site Index for sites which have not been physically measured (the socalled "default", or indirect Site Index). Most previous attempts to model Site Index have used multiple linear regression as the model, alternative methods have been explored in this thesis: Regression tree analysis, random forest analysis, hybrid or model trees, multiple linear regression, and multiple linear regression using regression trees to identify the variables. Regression tree analysis proves to be ideally suited to this type of data, and a generic model with only three site variables was able to capture 49.44 % of the variation in Site Index. Further localisation of the model could prove to be commercially useful. One of the key assumptions associated with Site Index, that it is unaffected by initial planting density, was tested using linear mixed effects modelling. The results show that there may well be role played by initial stocking in some species (notably E. dunnii and E. nitens), and that further work may be warranted. It was also shown that early measurement of dominant height results in poor estimates of Site Index, which will have a direct impact on inventory policies and on data to be included in Site Index modelling studies. This thesis is divided into six chapters: Chapter 1 contains a description of the concept of Site Index and it's origins, as well as, how the concept is used within the current forest planning processes. Chapter 2 contains an analysis on the influence of initial planted density on the estimate of Site Index. Chapter 3 explores the question of whether the age at which dominant height is measured has any effect on the quality of Site Index estimates. Chapter 4 looks at various modelling methodologies and compares the resultant models. Chapter 5 contains conclusions and recommendations for further study, and finally Chapter 6 discusses how any new Site Index model will effect the current planning protocol. / AFRIKAANSE OPSOMMING: Hedendaagse kommersiële bosbou gebruik groeiplek indeks (Site Index) as 'n aanduiding van huidige en toekomstige groeiplek moontlikhede, asook 'n metode om groeiplekke te vergelyk. Hierdie beginsel is diep gewortel in bestaande beplanningsprosesse en daarsonder kan empiriese groeien opbrengsmodelle nie in hul huidige vorm funksioneer nie. SuidAfrikaanse bosboumaatskappye bestee jaarliks groot bedrae geld aan die versameling van groeivoorraad data deur middel van opnames, maar weinig of geen geld word aangewend vir die insameling van ongemete vak data (veral groeiplek indeks) nie. Ongemete vak data word gebuik om meer as 90% van die produksie volume te beraam in langtermyn beplaning. 'n Behoefte bestaan om betroubare metodes te ontwikkel om groeiplek indeks te bereken vir groeiplekke wat nog nie opgemeet is nie. Die meeste vorige pogings om groeiplek indeks te beraam het meervoudige linêre regressie as model gebruik. Alternatiewe metodes is ondersoek; naamlik regressieboom analise, ewekansige woud analise, hibriedeof modelbome, meervoudige linêre regressie en meervoudige linêre regressie waarin die veranderlike faktore bepaal is deur regressiebome. Regressieboom analise blyk geskik te wees vir hierdie tipe data en 'n veralgemeende model met slegs drie groeiplek veranderlikes dek 49.44 % van die variasie in groeiplek indeks. Verdere lokalisering van die model kan dus van kommersiële waarde wees. 'n Sleutel aanname is gemaak dat aanvanklike plantdigtheid nie 'n invloed op groeiplek indeks het nie. Hierdie aanname is getoets deur linêre gemengde uitwerkings modelle. Die toetsuitslag dui op 'n moontlikheid dat plantdigtheid wel 'n invloed het op sommige spesies (vernaamlik E. dunnii en E. nitens) en verdere navorsing kan daarom geregverdig word. Dit is ook bewys dat metings van jonger bome vir dominante hoogtes gee aanleiding tot swak beramings van groeiplek indekse. Gevolglik sal hierdie toestsuitslag groeivoorraad opname beleid, asook die data wat vir groeiplek indeks modellering gebruik word, beïnvloed. Hierdie tesis word in ses hoofstukke onderverdeel. Hoofstuk een bevat 'n beskrywing van die beginsel van groeiplek indeks, die oorsprong daarvan, asook hoe die beginsel tans in huidige bosbou beplannings prosesse toegepas word. Hoofstuk twee bestaan uit ń ontleding van die invloed van aanvanklike plantdigtheid op die beraming van groeplek indeks. In hoofstuk drie word ondersoek wat die moontlike invloed is van die ouderdom waarop metings vir dominante hoogte geneem word, op die kwaliteit van groeplek indeks beramings het. Hoofstuk vier verken verskeie modelle metodologieë en vergelyk die uitslaggewende modelle. Hoofstuk vyf bevat gevolgtrekkings en voorstelle vir verdere studies. Afsluitend, is hoofstuk ses ń bespreking van hoe enige nuwe groeiplek indeks modelle die huidige beplannings protokol kan beïnvloed.

Supervised learning of piecewise linear models is a well studied problem in machine learning community. The key idea in piecewise linear modeling is to properly partition the input space and learn a linear model for every partition. Decision trees and regression trees are classic examples of piecewise linear models for classification and regression problems. The existing approaches for learning decision/regression trees can be broadly classified in to two classes, namely, fixed structure approaches and greedy approaches. In the fixed structure approaches, tree structure is fixed before hand by fixing the number of non leaf nodes, height of the tree and paths from root node to every leaf node of the tree. Mixture of experts and hierarchical mixture of experts are examples of fixed structure approaches for learning piecewise linear models. Parameters of the models are found using, e.g., maximum likelihood estimation, for which expectation maximization(EM) algorithm can be used. Fixed structure piecewise linear models can also be learnt using risk minimization under an appropriate loss function. Learning an optimal decision tree using fixed structure approach is a hard problem. Constructing an optimal binary decision tree is known to be NP Complete. On the other hand, greedy approaches do not assume any parametric form or any fixed structure for the decision tree classifier. Most of the greedy approaches learn tree structured piecewise linear models in a top down fashion. These are built by binary or multi-way recursive partitioning of the input space. The main issues in top down decision tree induction is to choose an appropriate objective function to rate the split rules. The objective function should be easy to optimize. Top-down decision trees are easy to implement and understand, but there are no optimality guarantees due to their greedy nature. Regression trees are built in the similar way as decision trees. In regression trees, every leaf node is associated with a linear regression function. All piece wise linear modeling techniques deal with two main tasks, namely, partitioning of the input space and learning a linear model for every partition. However, Partitioning of the input space and learning linear models for different partitions are not independent problems. Simultaneous optimal estimation of partitions and learning linear models for every partition, is a combinatorial problem and hence computationally hard. However, piecewise linear models provide better insights in to the classification or regression problem by giving explicit representation of the structure in the data. The information captured by piecewise linear models can be summarized in terms of simple rules, so that, they can be used to analyze the properties of the domain from which the data originates. These properties make piecewise linear models, like decision trees and regression trees, extremely useful in many data mining applications and place them among top data mining algorithms. In this thesis, we address the problem of supervised learning of piecewise linear models for classification and regression. We propose novel algorithms for learning piecewise linear classifiers and regression functions. We also address the problem of noise tolerant learning of classifiers in presence of label noise. We propose a novel algorithm for learning polyhedral classifiers which are the simplest form of piecewise linear classifiers. Polyhedral classifiers are useful when points of positive class fall inside a convex region and all the negative class points are distributed outside the convex region. Then the region of positive class can be well approximated by a simple polyhedral set. The key challenge in optimally learning a fixed structure polyhedral classifier is to identify sub problems, where each sub problem is a linear classification problem. This is a hard problem and identifying polyhedral separability is known to be NP complete. The goal of any polyhedral learning algorithm is to efficiently handle underlying combinatorial problem while achieving good classification accuracy. Existing methods for learning a fixed structure polyhedral classifier are based on solving non convex constrained optimization problems. These approaches do not efficiently handle the combinatorial aspect of the problem and are computationally expensive. We propose a method of model based estimation of posterior class probability to learn polyhedral classifiers. We solve an unconstrained optimization problem using a simple two step algorithm (similar to EM algorithm) to find the model parameters. To the best of our knowledge, this is the first attempt to form an unconstrained optimization problem for learning polyhedral classifiers. We then modify our algorithm to find the number of required hyperplanes also automatically. We experimentally show that our approach is better than the existing polyhedral learning algorithms in terms of training time, performance and the complexity. Most often, class conditional densities are multimodal. In such cases, each class region may be represented as a union of polyhedral regions and hence a single polyhedral classifier is not sufficient. To handle such situation, a generic decision tree is required. Learning optimal fixed structure decision tree is a computationally hard problem. On the other hand, top-down decision trees have no optimality guarantees due to the greedy nature. However, top-down decision tree approaches are widely used as they are versatile and easy to implement. Most of the existing top-down decision tree algorithms (CART,OC1,C4.5, etc.) use impurity measures to assess the goodness of hyper planes at each node of the tree. These measures do not properly capture the geometric structures in the data. We propose a novel decision tree algorithm that ,at each node, selects hyperplanes based on an objective function which takes into consideration geometric structure of the class regions. The resulting optimization problem turns out to be a generalized eigen value problem and hence is efficiently solved. We show through empirical studies that our approach leads to smaller size trees and better performance compared to other top-down decision tree approaches. We also provide some theoretical justification for the proposed method of learning decision trees. Piecewise linear regression is similar to the corresponding classification problem. For example, in regression trees, each leaf node is associated with a linear regression model. Thus the problem is once again that of (simultaneous) estimation of optimal partitions and learning a linear model for each partition. Regression trees, hinge hyperplane method, mixture of experts are some of the approaches to learn continuous piecewise linear regression models. Many of these algorithms are computationally intensive. We present a method of learning piecewise linear regression model which is computationally simple and is capable of learning discontinuous functions as well. The method is based on the idea of K plane regression that can identify a set of linear models given the training data. K plane regression is a simple algorithm motivated by the philosophy of k means clustering. However this simple algorithm has several problems. It does not give a model function so that we can predict the target value for any given input. Also, it is very sensitive to noise. We propose a modified K plane regression algorithm which can learn continuous as well as discontinuous functions. The proposed algorithm still retains the spirit of k means algorithm and after every iteration it improves the objective function. The proposed method learns a proper Piece wise linear model that can be used for prediction. The algorithm is also more robust to additive noise than K plane regression. While learning classifiers, one normally assumes that the class labels in the training data set are noise free. However, in many applications like Spam filtering, text classification etc., the training data can be mislabeled due to subjective errors. In such cases, the standard learning algorithms (SVM, Adaboost, decision trees etc.) start over fitting on the noisy points and lead to poor test accuracy. Thus analyzing the vulnerabilities of classifiers to label noise has recently attracted growing interest from the machine learning community. The existing noise tolerant learning approaches first try to identify the noisy points and then learn classifier on remaining points. In this thesis, we address the issue of developing learning algorithms which are inherently noise tolerant. An algorithm is inherently noise tolerant if, the classifier it learns with noisy samples would have the same performance on test data as that learnt from noise free samples. Algorithms having such robustness (under suitable assumption on the noise) are attractive for learning with noisy samples. Here, we consider non uniform label noise which is a generic noise model. In non uniform label noise, the probability of the class label for an example being incorrect, is a function of the feature vector of the example.(We assume that this probability is less than 0.5 for all feature vectors.) This can account for most cases of noisy data sets. There is no provably optimal algorithm for learning noise tolerant classifiers in presence of non uniform label noise. We propose a novel characterization of noise tolerance of an algorithm. We analyze noise tolerance properties of risk minimization frame work as risk minimization is a common strategy for classifier learning. We show that risk minimization under 01 loss has the best noise tolerance properties. None of the other convex loss functions have such noise tolerance properties. Empirical risk minimization under 01 loss is a hard problem as 01 loss function is not differentiable. We propose a gradient free stochastic optimization technique to minimize risk under 01 loss function for noise tolerant learning of linear classifiers. We show (under some conditions) that the algorithm converges asymptotically to the global minima of the risk under 01 loss function. We illustrate the noise tolerance of our algorithm through simulations experiments. We demonstrate the noise tolerance of the algorithm through simulations.