Traffic from major hurricane evacuations is known to cause severe gridlocks on evacuation routes. Better prediction of the expected amount of evacuation traffic is needed to improve the decision-making process for the required evacuation routes and possible deployment of special traffic operations, such as contraflow. The objective of this dissertation is to develop prediction models to predict the number of daily trips and the evacuation distance during a hurricane evacuation. Two data sets from the surveys of the evacuees from Hurricanes Katrina and Ivan were used in the models' development. The data sets included detailed information on the evacuees, including their evacuation days, evacuation distance, distance to the hurricane location, and their associated socioeconomic characteristics, including gender, age, race, household size, rental status, income, and education level. Three prediction models were developed. The evacuation trip and rate models were developed using logistic regression. Together, they were used to predict the number of daily trips generated before hurricane landfall. These daily predictions allowed for more detailed planning over the traditional models, which predicted the total number of trips generated from an entire evacuation. A third model developed attempted to predict the evacuation distance using Geographically Weighted Regression (GWR), which was able to account for the spatial variations found among the different evacuation areas, in terms of impacts from the model predictors. All three models were developed using the survey data set from Hurricane Katrina and then evaluated using the survey data set from Hurricane Ivan. All of the models developed provided logical results. The logistic models showed that larger households with people under age six were more likely to evacuate than smaller households. The GWR-based evacuation distance model showed that the household with children under age six, income, and proximity of household to hurricane path, all had an impact on the evacuation distances. While the models were found to provide logical results, it was recognized that they were calibrated and evaluated with relatively limited survey data. The models can be refined with additional data from future hurricane surveys, including additional variables, such as the time of day of the evacuation.

This student thesis in General Management addresses how leaders can bridge the gap between work groups and teams in geographically dispersed organizations and partially distributed teams. These types of organizational structures are increasingly common in the globalized world of business, and bring benefits to many organizations by for example connecting skilled workers regardless of their location through the means of information and communications technology. However, previous research within the field of work in dispersed settings has identified several challenges that these settings entail, including areas like for example group cohesion and motivation. If not handled, these challenges may have negative effects on team performance and organizational effectiveness. Previous studies have mostly targeted the challenges in isolation. The purpose of this study is to provide a holistic perspective, connecting different challenges in order to pinpoint reasons and effects. By identifying consequences that follow from being geographically dispersed and investigating how the challenges affect a real-world organization, the study aims to suggest countermeasures to deal with these consequences. Theory is built using Informed Grounded Theory, based on primary data from 21 in-depth interviews conducted at a Swedish high tech company. Through an analysis combining the primary data with secondary data stemming from relevant literature, the study presents conclusions including suggested countermeasures to overcome challenges imposed by work in dispersed settings. The study identifies communication as the key factor with possibility to affect group cohesion and motivation directly, and thereby also performance indirectly. Thoughtful use of different types of communication can in fact counteract challenges and lead to increased productivity and well-being. The study has implications for organizations that are planning for, or currently utilizing a dispersed organizational structure, and aids in understanding the collected effects of the challenges involved. The study is conducted at one company, which can be seen as a limitation. To counteract for this limitation, the researchers have put in effort to emphasize generalizable factors.

China has undergone significant urban growth and industrialization over the last 30 years and its incredible development continues to move ahead at an increasingly rapid pace. In terms of urban expansion, China has just recently surpassed the world’s average urbanization rate of 50%, as it moves its massive population from rural to urban areas at an astonishing speed. It’s massive population and fast urbanizing speed aside, China is also unique in terms of its socio-political system and historical-cultural context: it is a hybrid of government planning and market forces. Since it encompasses a large part of the global population and has had a vastly different urbanization experience than that of Western countries, around which most theories are based, studying China’s urbanization is an opportunity to contribute to the field of urban studies in an unprecedented manner. However, these differences also make it difficult to develop a comprehensive study of China’s urban system since the predominant theories in the field are best suited to Western cities. This research rises to this challenge by systematically studying the relationship between the socioeconomic and biophysical processes in the Chinese urban system to understand the interaction between human and physical factors, and the landscape patterns that result from these interactions. This complex urban system is examined using a hierarchical, top-down approach. At the highest level is a Macro-scale analysis of the national urban system, followed by a study of the regional urban system: the JingJinJi Metropolitan Area at the Meso-scale, and finally a Micro-scale examination with a focus on the city of Beijing. Since urban systems develop over both time and space, the urban system is analyzed spatio-temporally on all three levels. Research at the national scale is composed of two parts. First, the challenges and opportunities of China’s urban development since the foundation of the People’s Republic of China in 1949 are investigated in a general context. The institutional barriers that impede the management and continuation of China’s urban development are also discussed. Rank-size Analysis and satellite images are used to present the structural transitions of city scaling and urban clusters. These changes come with a series of challenges that are also iterated and discussed. This is followed by an analysis of the spatial distribution and transition patterns of China’s urban system using Centrographic Analysis, particularly since the post-1979 reforms. Second, the Macro-scale research focuses on a study of the urban hierarchy that is based on inter-city interactions as determined by the Synthesized Gravity Model (SGM). Under this model socioeconomic variables are synthesized and represented by the Influential Factor, while the Function Distance is derived from a Network Analysis that is based on multiple transportation methods. As an improvement on the conventional Gravity Model (GM), the SGM is used to accurately establish and represent the nodal structure of China’s urban system, the evolution of its hierarchical structure, and the relationships that exist between the nodal structure and socioeconomic factors. The results based on the SGM indicate that China’s national urban system is characterized by the emergence of urban clusters with stronger inter-city interactions since the 1990s. However, development among cities within certain urban clusters is not even, although the general pattern indicates a lessening inequality among cities. Spatially, while most cities at the top of the hierarchy are located in the east of China, cities in the middle and west of the country are also gaining higher positions in the hierarchy over time. On the Meso-scale, the applicability of the Cellular Automata (CA)-based SLEUTH model for regional urban growth pattern is studied through a focus on the JingJinJi Metropolitan Area (Beijing-Tianjin-Hebei). By integrating socioeconomic factors into a modified SLEUTH model, the urban growth dynamics and future development scenarios of the area are simulated and predicted. The results based on the CA model show that this region is characterized by a dynamic development pattern with high spreading and breeding growth rules that relies greatly on the growing transportation systems. It also allows for the projection of three possible future urban growth scenarios, each occurring under different environmental and development conditions, showing the future urban growth with or without further intervention. This research confirms that four factors play essential roles in the formulation of the urban growth mechanism of the JingJinJi Metropolitan Area: Urban policies, Industry restructuring, Rural-urban migration, and Reclassification of urban boundaries. The Micro-scale study of Beijing is conducted from two perspectives: the social and natural. The social aspect adopts the factorial ecology approach to identify the social landscape patterns and the factors that have shaped Beijing’s social space in 1990 and 2000. The social mosaic has experienced a significant change due to suburbanization, resulting in a more dynamic and complex internal structure since the 2000s. From a natural perspective, Beijing’s physical landscape patterns are extracted by processing remotely sensed images that have the same temporal span. The physical change through landscape metrics demonstrates that Beijing’s expansion has generated a more complex and fragmented land use/cover pattern. Meanwhile, transportation systems play a significant role in urban expansion, although the expansion across the space (zonal rings and directional sectors) is not even. Finally, the relationship between the social and physical landscapes is quantitatively defined by the Geographically Weighted Regression (GWR) technique, using physical landscape metrics as dependent variables and social areas as independent variables. The GWR is able to demonstrate the relationship between the social and physical landscapes at this level: as a city’s social mosaic becomes more varied over time it results in the fragmentation of that city’s physical space.

Northeastern forests are being impacted by unprecedented environmental stressors, including acid deposition, invasive pests, and climate change. Forest health monitoring at a landscape scale is necessary to evaluate the changing condition of forest resources and to inform management of forest stressors. Traditional forest health monitoring is often limited to specific sites experiencing catastrophic decline or widespread mortality. Satellite remote sensing can complement these efforts by providing comprehensive forest health assessments over broad regions. Subtle changes in canopy health can be monitored over time by applying spectral vegetation indices to multitemporal satellite imagery. This project used historical archives of Landsat-5 TM imagery and geographic information systems to examine forest health trends in the northern Green Mountains of Vermont from 1984 to 2009. Results indicate that canopy health has remained relatively stable across most of the landscape, although decline was present in localized areas. Significant but weak relationships were discovered between declining forest health and spruce-fir-paper birch forests at high elevations. Possible causes of decline include the interacting effects of acid deposition, windthrow, and stressful growing environments typical of montane forests.

Doctor of Philosophy / Department of Geography / Douglas G. Goodin / Jesse A. Poland / Kevin Price / Wheat breeders are in a race for genetic gain to secure the future nutritional needs of a growing population. Multiple barriers exist in the acceleration of crop improvement. Emerging technologies are reducing these obstacles. Advances in genotyping technologies have significantly decreased the cost of characterizing the genetic make-up of candidate breeding lines. However, this is just part of the equation. Field-based phenotyping informs a breeder’s decision as to which lines move forward in the breeding cycle. This has long been the most expensive and time-consuming, though most critical, aspect of breeding. The grand challenge remains in connecting genetic variants to observed phenotypes followed by predicting phenotypes based on the genetic composition of lines or cultivars. In this context, the current study was undertaken to investigate the utility of UAS in assessment field trials in wheat breeding programs. The major objective was to integrate remotely sensed data with geospatial analysis for high throughput phenotyping of large wheat breeding nurseries. The initial step was to develop and validate a semi-automated high-throughput phenotyping pipeline using a low-cost UAS and NIR camera, image processing, and radiometric calibration to build orthomosaic imagery and 3D models. The relationship between plot-level data (vegetation indices and height) extracted from UAS imagery and manual measurements were examined and found to have a high correlation. Data derived from UAS imagery performed as well as manual measurements while exponentially increasing the amount of data available. The high-resolution, high-temporal HTP data extracted from this pipeline offered the opportunity to develop a within season grain yield prediction model. Due to the variety in genotypes and environmental conditions, breeding trials are inherently spatial in nature and vary non-randomly across the field. This makes geographically weighted regression models a good choice as a geospatial prediction model. Finally, with the addition of georeferenced and spatial data integral in HTP and imagery, we were able to reduce the environmental effect from the data and increase the accuracy of UAS plot-level data. The models developed through this research, when combined with genotyping technologies, increase the volume, accuracy, and reliability of phenotypic data to better inform breeder selections. This increased accuracy with evaluating and predicting grain yield will help breeders to rapidly identify and advance the most promising candidate wheat varieties.

Despite the rich knowledge surrounding risk factors for malaria, the spatial processes of malaria transmission and vector control interventions are underexplored. This thesis aims 1) to describe the spatial variation of risk factor effects on malaria infection, and 2) to determine the presence and range of any community effect from malaria vector control interventions. Data from a cluster-randomized control trial in Tanzania were analyzed to determine the geographically-weighted odds of malaria infection in children at trial baseline and post-intervention. The spatial range of intervention effects on malaria infection was estimated post-intervention using semivariance models. Spatial heterogeneities in malaria infection and each covariate under study were found. The median effective semivariance range of intervention effects was approximately 1200 meters, suggesting the presence of a community effect that may cause contamination between trial clusters. Trials should consider these spatial effects when examining interventions and ensure that clusters are adequately insulated from contamination.

<p>Today’s information systems engineering involves large number of stakeholders, wide geographical distribution and wide range of tools. Success in system engi-neering depends on effective human communication. Early understanding and modelling of the problem domain is a key to manage large scale systems and pro-jects. This requires stakeholders to reach a certain level of shared interpretation of the domain referred throughout the development</p><p>We propose a method for semantics driven change impact assessment. In our method, first a collaborative problem analysis is conducted. The problem analysis results in an agreed and committed common understanding of the prob-lem domain, expressed in a conceptual domain model. The constructed concep-tual domain-specific model is then actively used as a communication medium, e.g., to abstract development objects from representation format in order to expli-cate their semantics. Stakeholders browse the domain model and interactively as-sociate to product fragments by selecting concept clusters that best describe the contents (intended meaning) of the product fragments.</p><p>Associations of the development objects with concepts from domain model, as well as the domain model itself constitute the basis for change impact assess-ment throughout the development. Every revision of a development object in-vokes change impact notifications that are either confirmed or rejected. Accumu-lated statistics are used to refine associations via the domain model to the direct dependency links among development objects.</p><p>The method has been implemented in a prototype system CO2SY and has been evaluated in an experiment, where a set of test users has been provided with a problem domain description including a domain model and a set of develop-ment objects. The experiment was based on two real world cases. Users were asked to perform tasks using the prototype and two comparative tools. The method and prototype have been evaluated with respect to actual performance and users perceptions. The result shows actual effectiveness, perceived ease of use and usefulness comparing to other tools used in the experiment, as well as intention of the subjects to use the method in future.</p><p>A discussion of future research directions and possible revisions of the method concludes the thesis.</p>

Today’s information systems engineering involves large number of stakeholders, wide geographical distribution and wide range of tools. Success in system engi-neering depends on effective human communication. Early understanding and modelling of the problem domain is a key to manage large scale systems and pro-jects. This requires stakeholders to reach a certain level of shared interpretation of the domain referred throughout the development We propose a method for semantics driven change impact assessment. In our method, first a collaborative problem analysis is conducted. The problem analysis results in an agreed and committed common understanding of the prob-lem domain, expressed in a conceptual domain model. The constructed concep-tual domain-specific model is then actively used as a communication medium, e.g., to abstract development objects from representation format in order to expli-cate their semantics. Stakeholders browse the domain model and interactively as-sociate to product fragments by selecting concept clusters that best describe the contents (intended meaning) of the product fragments. Associations of the development objects with concepts from domain model, as well as the domain model itself constitute the basis for change impact assess-ment throughout the development. Every revision of a development object in-vokes change impact notifications that are either confirmed or rejected. Accumu-lated statistics are used to refine associations via the domain model to the direct dependency links among development objects. The method has been implemented in a prototype system CO2SY and has been evaluated in an experiment, where a set of test users has been provided with a problem domain description including a domain model and a set of develop-ment objects. The experiment was based on two real world cases. Users were asked to perform tasks using the prototype and two comparative tools. The method and prototype have been evaluated with respect to actual performance and users perceptions. The result shows actual effectiveness, perceived ease of use and usefulness comparing to other tools used in the experiment, as well as intention of the subjects to use the method in future. A discussion of future research directions and possible revisions of the method concludes the thesis.

China has undergone significant urban growth and industrialization over the last 30 years and its incredible development continues to move ahead at an increasingly rapid pace. In terms of urban expansion, China has just recently surpassed the world’s average urbanization rate of 50%, as it moves its massive population from rural to urban areas at an astonishing speed. It’s massive population and fast urbanizing speed aside, China is also unique in terms of its socio-political system and historical-cultural context: it is a hybrid of government planning and market forces. Since it encompasses a large part of the global population and has had a vastly different urbanization experience than that of Western countries, around which most theories are based, studying China’s urbanization is an opportunity to contribute to the field of urban studies in an unprecedented manner. However, these differences also make it difficult to develop a comprehensive study of China’s urban system since the predominant theories in the field are best suited to Western cities. This research rises to this challenge by systematically studying the relationship between the socioeconomic and biophysical processes in the Chinese urban system to understand the interaction between human and physical factors, and the landscape patterns that result from these interactions. This complex urban system is examined using a hierarchical, top-down approach. At the highest level is a Macro-scale analysis of the national urban system, followed by a study of the regional urban system: the JingJinJi Metropolitan Area at the Meso-scale, and finally a Micro-scale examination with a focus on the city of Beijing. Since urban systems develop over both time and space, the urban system is analyzed spatio-temporally on all three levels. Research at the national scale is composed of two parts. First, the challenges and opportunities of China’s urban development since the foundation of the People’s Republic of China in 1949 are investigated in a general context. The institutional barriers that impede the management and continuation of China’s urban development are also discussed. Rank-size Analysis and satellite images are used to present the structural transitions of city scaling and urban clusters. These changes come with a series of challenges that are also iterated and discussed. This is followed by an analysis of the spatial distribution and transition patterns of China’s urban system using Centrographic Analysis, particularly since the post-1979 reforms. Second, the Macro-scale research focuses on a study of the urban hierarchy that is based on inter-city interactions as determined by the Synthesized Gravity Model (SGM). Under this model socioeconomic variables are synthesized and represented by the Influential Factor, while the Function Distance is derived from a Network Analysis that is based on multiple transportation methods. As an improvement on the conventional Gravity Model (GM), the SGM is used to accurately establish and represent the nodal structure of China’s urban system, the evolution of its hierarchical structure, and the relationships that exist between the nodal structure and socioeconomic factors. The results based on the SGM indicate that China’s national urban system is characterized by the emergence of urban clusters with stronger inter-city interactions since the 1990s. However, development among cities within certain urban clusters is not even, although the general pattern indicates a lessening inequality among cities. Spatially, while most cities at the top of the hierarchy are located in the east of China, cities in the middle and west of the country are also gaining higher positions in the hierarchy over time. On the Meso-scale, the applicability of the Cellular Automata (CA)-based SLEUTH model for regional urban growth pattern is studied through a focus on the JingJinJi Metropolitan Area (Beijing-Tianjin-Hebei). By integrating socioeconomic factors into a modified SLEUTH model, the urban growth dynamics and future development scenarios of the area are simulated and predicted. The results based on the CA model show that this region is characterized by a dynamic development pattern with high spreading and breeding growth rules that relies greatly on the growing transportation systems. It also allows for the projection of three possible future urban growth scenarios, each occurring under different environmental and development conditions, showing the future urban growth with or without further intervention. This research confirms that four factors play essential roles in the formulation of the urban growth mechanism of the JingJinJi Metropolitan Area: Urban policies, Industry restructuring, Rural-urban migration, and Reclassification of urban boundaries. The Micro-scale study of Beijing is conducted from two perspectives: the social and natural. The social aspect adopts the factorial ecology approach to identify the social landscape patterns and the factors that have shaped Beijing’s social space in 1990 and 2000. The social mosaic has experienced a significant change due to suburbanization, resulting in a more dynamic and complex internal structure since the 2000s. From a natural perspective, Beijing’s physical landscape patterns are extracted by processing remotely sensed images that have the same temporal span. The physical change through landscape metrics demonstrates that Beijing’s expansion has generated a more complex and fragmented land use/cover pattern. Meanwhile, transportation systems play a significant role in urban expansion, although the expansion across the space (zonal rings and directional sectors) is not even. Finally, the relationship between the social and physical landscapes is quantitatively defined by the Geographically Weighted Regression (GWR) technique, using physical landscape metrics as dependent variables and social areas as independent variables. The GWR is able to demonstrate the relationship between the social and physical landscapes at this level: as a city’s social mosaic becomes more varied over time it results in the fragmentation of that city’s physical space.

Geographically dispersed organizations are becoming increasingly common, however, the organizational culture is often weaker in this type of organization due to geographical distances. One important aspect of the organizational culture is the organizational values, and if shared by all organizational members they can benefit the company on many levels, for example by increasing motivation and communication. If employees, on the other hand, fail to interpret the organizational values it may lead to decreased working moral and overall dissatisfaction. In family businesses it is the values of the family that constitute the organizational values, and these may be difficult to communicate to non-family employees, because family businesses often rely on informal communication.   The purpose of this thesis is to describe how non-family employees interpret the organizational values of a family business, as well as to increase the understanding of whether geographical distance affects their interpretation. In this thesis we have conducted a qualitative case study, in which ten semi-structured interviews were made in a geographically dispersed family business. We have assumed an employee perspective and have, thus, interviewed non-family employees on the different geographical locations of this family business.   The findings from the empirical data showed that the case studied organization does not have any formally written organizational values and the non-family employees have as a result interpreted the values slightly differently. The employees state the organizational values in general terms, which would make them applicable to any organization, or even society at large.   In the analysis we compare the theoretical framework and the empirical findings in order to present conclusions for the organization being studied. The analysis will be divided into themes; organizational structure, organizational culture and values as well as the relation between the non-family employees and the family business.   We were able to conclude that informal communication is not sufficient in order to efficiently communicate the organizational values in a geographically dispersed organization. Nevertheless, the geographical distance for each workplace respectively could not be identified as the only influencing factor that affects the employees’ interpretation of the organizational values. The organizational structure emerged as a contributing factor. The non-family employees’ interpretations were broad and general in terms of the organizational values. One of our recommendations is for the organization to write down the organizational values and distribute them throughout the organization, which will enable the organizational values to be correctly interpreted by all organizational members.