As the most destructive forest disturbance in British Columbia, wildfire becomes more worrisome for increasing uncertainty due to climate change. The current study investigates the potential to predict wildfire occurrence using climate indexes and quantify its marginal prices for property values at the municipal level, so as to provide a quantitative indicator for decision making in regard to influences of wildfire occurrence in the near future. First, significant correlations between monthly temperature and precipitation and large fire occurrence with distinctions in terms of distances to municipalities are proved by statistical analysis. Monthly wildfire occurrence are then statistically estimated with the four-month lags of the El Niño index and predicted using count models with regional differences. At last, the hedonic pricing model shows distance based positive impact of fire frequency and negative impact of fire size in neighbouring areas on property values. / Graduate / 0366 / 0478 / 0463 / zach_xu@hotmail.com

As the most destructive forest disturbance in British Columbia, wildfire becomes more worrisome for increasing uncertainty due to climate change. The current study investigates the potential to predict wildfire occurrence using climate indexes and quantify its marginal prices for property values at the municipal level, so as to provide a quantitative indicator for decision making in regard to influences of wildfire occurrence in the near future. First, significant correlations between monthly temperature and precipitation and large fire occurrence with distinctions in terms of distances to municipalities are proved by statistical analysis. Monthly wildfire occurrence are then statistically estimated with the four-month lags of the El Niño index and predicted using count models with regional differences. At last, the hedonic pricing model shows distance based positive impact of fire frequency and negative impact of fire size in neighbouring areas on property values. / Graduate / 0366 / 0478 / 0463 / zach_xu@hotmail.com

New district heating systems in Toronto have the potential for significant financial and environmental gains. Through the use of Geographical Information Systems (GIS) and the data required to estimate heating loads, heat maps were generated on a building-by-building basis for over 4400 buildings at nine different intersections in Toronto. School locations and planned construction maps were used to enhance the data and demonstrate the benefit of considering factors beyond finance and the environment. Out of the intersections studied, Yonge and Eglinton; Yonge and Sheppard; and Yonge and Empress held the largest heating loads. Individual building data allowed for plant locations to be suggested based on their proportional distance to the highest loads. GIS allowed for the visualization of the vast quantity of data. The opportunities for improvement include increasing the availability of location-based data and the application of the methodology to other areas of infrastructure planning and decision making.

New district heating systems in Toronto have the potential for significant financial and environmental gains. Through the use of Geographical Information Systems (GIS) and the data required to estimate heating loads, heat maps were generated on a building-by-building basis for over 4400 buildings at nine different intersections in Toronto. School locations and planned construction maps were used to enhance the data and demonstrate the benefit of considering factors beyond finance and the environment. Out of the intersections studied, Yonge and Eglinton; Yonge and Sheppard; and Yonge and Empress held the largest heating loads. Individual building data allowed for plant locations to be suggested based on their proportional distance to the highest loads. GIS allowed for the visualization of the vast quantity of data. The opportunities for improvement include increasing the availability of location-based data and the application of the methodology to other areas of infrastructure planning and decision making.

The objective of this work is to develop a Bayesian Network (BN) model to produce environmental risk maps for oil and gas site developments and to demonstrate the model’s scalability from a point to a collection of points. To reach this objective, a benchmark BN model was formulated as a “proof of concept” using Aquifers, Ecoregions and Land Use / Land Cover maps as local and independent input variables. This model was then used to evaluate the probabilistic geographical distribution of the Environmental Sensibility of Oil and Gas (O&G) developments for a given study area. A Risk index associated with the development of O&G operation activities based on the spatial environmental sensibility was also mapped. To facilitate the Risk assessment, these input variables (maps) were discretized into three hazard levels: high, moderate and low. A Geographical Information System (GIS) platform was used (ESRI ArcMap 10), to gather, modify and display the data for the analysis. Once the variables were defined and the hazard data was included on feature classes (layer shapefile format), Python 2.6 software was used as the computational platform to calculate the probabilistic state of all the Bayesian Network’s variables. This allowed to define Risk scenarios both on prognostic and diagnostic analysis and to measure the impact of changes or interventions in terms of uncertainty. The resulting Python – ESRI ArcMap computational script was called “BN+GIS, which populated maps describing the spatial variability of the states of the Environmental Sensibility and of the corresponding Risk index. The latter in particular, represents a tool for decision makers to choose the most suitable location for placing a drilling rig, since it integrates three fundamental environmental variables. Also, results show that is possible to back propagate the information from the Environmental Sensibility to define the inherent triggering scenarios (hazard variables). A case of study is presented to illustrate the applicability of the proposed methodology on a specific geographical setting. The Barnett Shale was chosen as a benchmark study area because sufficient information on this region was available, and the importance that it holds on the latest developments of unconventional plays in the country. The main contribution of this work relies in combining Bayesian Networks and GIS to define environmental Risk scenarios that can facilitate decision-making for O&G stakeholders such as land owners, industry operators, regulators and Non-Governmental Organizations (NGOs), before and during the development of a given site.

A combination of in situ sampling and real-time ocean observations was used to investigate the processes responsible for the formation and the areal extent of Texas coastal hypoxia from 2002 to 2011. In situ sampling, real-time mooring and buoy observations, and multivariate statistical modeling were used to investigate the physical processes driving hypoxia formation. Geostatistical interpolation (ordinary kriging) models were tested to compare the differences in annual hypoxia area on the Texas shelf. Results from these two sections were integrated into recommendations for improving federal hypoxia monitoring and mitigation strategies in the northwestern Gulf of Mexico. Winds, currents, temperature, salinity, and dissolved oxygen records revealed the annual, seasonal, and daily variability of hypoxia formation on the Texas coast from 2009 to 2011. Hypoxic events occurred from late May to late October lasting from hours to weeks. Hypoxia formation was either the result of salinity stratification, associated with the freshening of surface waters by the advection of Mississippi-Atchafalaya River freshwater westward or the wind- and current-driven upcoast or downcoast flow of Brazos River discharge. Records from 2010 and 2011 showed the variability and frequency of stratification development differs on the north and south Texas shelf. Multivariate linear model results showed contributing factors on the north Texas shelf vary annually and that primary factors for hypoxia development are near-surface current speeds and salinity-driven stratification. Interpolation models resulted in three size categories for hypoxia area: small (100 – 1,000 km^2), moderate (1,001 – 3,000 km^2), and large (3,001+ km^2). Moderate years include 2002, 2004, and 2007 and a large year was 2008. There was no increase in hypoxic area from years 2002 to 2011, but years 2007 and 2008 resulted in a hypoxic area over 5,000 km^2, which is the federally mandated hypoxia reduction target for the northwestern Gulf of Mexico. Geostatistical interpolators represent and predict the structure and spatial extent of the hypoxic area on the Texas shelf by accounting for the anisotropy of physical processes on the Texas shelf. Geostatistical interpolation models are preferred to deterministic models for developing and improving federal hypoxia monitoring and mitigation strategies on the northwestern Gulf of Mexico shelf.

In public safety, geography plays a significant role. One of the most important front-line elements of public safety is an efficient emergency transport and care system. The capacity to access and process information rapidly and organize resources where needed can be critically important in an emergency situation. Information about the locality of an event or a disaster is often vital in knowing how to respond. A significant operation in handling emergency situations is the routing of ambulances to incident sites and then to the closest appropriate hospitals. One of the important steps to survival in an emergency is quick response time. The aim of this thesis study is to build an immediate, rapid and efficient emergency medical transport system prototype, called Ambulance Routing Service Application Prototype (ARSAP), to be used in Middle East Technical University (METU) Emergency Service, Ankara, Turkey. In the study, geographical information systems (GIS) technology is used in assisting the development and implementation of an emergency medical service (EMS) response system. In this prototype, while choosing a proper facility, the available quantity of beds, respiratory equipments and doctors in a hospital&#039 / s intensive care room and the best traffic routes to the hospital in hand are also considered. The ARSAP is expected to shorten the commuting time and hence to reduce the damage to the patient to the lowest level and allow the ambulance staff to perform their task better. The results generated using the ARSAP are validated and analyzed by comparing with currently practiced emergency call paths data collected with the help of METU Emergency Service ambulance drivers.

Suicide has drawn much attention from both the scientific community and the public. Examining the impact of socio-environmental factors on suicide is essential in developing suicide prevention strategies and interventions, because it will provide health authorities with important information for their decision-making. However, previous studies did not examine the impact of socio-environmental factors on suicide using a spatial analysis approach. The purpose of this study was to identify the patterns of suicide and to examine how socio-environmental factors impact on suicide over time and space at the Local Governmental Area (LGA) level in Queensland. The suicide data between 1999 and 2003 were collected from the Australian Bureau of Statistics (ABS). Socio-environmental variables at the LGA level included climate (rainfall, maximum and minimum temperature), Socioeconomic Indexes for Areas (SEIFA) and demographic variables (proportion of Indigenous population, unemployment rate, proportion of population with low income and low education level). Climate data were obtained from Australian Bureau of Meteorology. SEIFA and demographic variables were acquired from ABS. A series of statistical and geographical information system (GIS) approaches were applied in the analysis. This study included two stages. The first stage used average annual data to view the spatial pattern of suicide and to examine the association between socio-environmental factors and suicide over space. The second stage examined the spatiotemporal pattern of suicide and assessed the socio-environmental determinants of suicide, using more detailed seasonal data. In this research, 2,445 suicide cases were included, with 1,957 males (80.0%) and 488 females (20.0%). In the first stage, we examined the spatial pattern and the determinants of suicide using 5-year aggregated data. Spearman correlations were used to assess associations between variables. Then a Poisson regression model was applied in the multivariable analysis, as the occurrence of suicide is a small probability event and this model fitted the data quite well. Suicide mortality varied across LGAs and was associated with a range of socio-environmental factors. The multivariable analysis showed that maximum temperature was significantly and positively associated with male suicide (relative risk [RR] = 1.03, 95% CI: 1.00 to 1.07). Higher proportion of Indigenous population was accompanied with more suicide in male population (male: RR = 1.02, 95% CI: 1.01 to 1.03). There was a positive association between unemployment rate and suicide in both genders (male: RR = 1.04, 95% CI: 1.02 to 1.06; female: RR = 1.07, 95% CI: 1.00 to 1.16). No significant association was observed for rainfall, minimum temperature, SEIFA, proportion of population with low individual income and low educational attainment. In the second stage of this study, we undertook a preliminary spatiotemporal analysis of suicide using seasonal data. Firstly, we assessed the interrelations between variables. Secondly, a generalised estimating equations (GEE) model was used to examine the socio-environmental impact on suicide over time and space, as this model is well suited to analyze repeated longitudinal data (e.g., seasonal suicide mortality in a certain LGA) and it fitted the data better than other models (e.g., Poisson model). The suicide pattern varied with season and LGA. The north of Queensland had the highest suicide mortality rate in all the seasons, while there was no suicide case occurred in the southwest. Northwest had consistently higher suicide mortality in spring, autumn and winter. In other areas, suicide mortality varied between seasons. This analysis showed that maximum temperature was positively associated with suicide among male population (RR = 1.24, 95% CI: 1.04 to 1.47) and total population (RR = 1.15, 95% CI: 1.00 to 1.32). Higher proportion of Indigenous population was accompanied with more suicide among total population (RR = 1.16, 95% CI: 1.13 to 1.19) and by gender (male: RR = 1.07, 95% CI: 1.01 to 1.13; female: RR = 1.23, 95% CI: 1.03 to 1.48). Unemployment rate was positively associated with total (RR = 1.40, 95% CI: 1.24 to 1.59) and female (RR=1.09, 95% CI: 1.01 to 1.18) suicide. There was also a positive association between proportion of population with low individual income and suicide in total (RR = 1.28, 95% CI: 1.10 to 1.48) and male (RR = 1.45, 95% CI: 1.23 to 1.72) population. Rainfall was only positively associated with suicide in total population (RR = 1.11, 95% CI: 1.04 to 1.19). There was no significant association for rainfall, minimum temperature, SEIFA, proportion of population with low educational attainment. The second stage is the extension of the first stage. Different spatial scales of dataset were used between the two stages (i.e., mean yearly data in the first stage, and seasonal data in the second stage), but the results are generally consistent with each other. Compared with other studies, this research explored the variety of the impact of a wide range of socio-environmental factors on suicide in different geographical units. Maximum temperature, proportion of Indigenous population, unemployment rate and proportion of population with low individual income were among the major determinants of suicide in Queensland. However, the influence from other factors (e.g. socio-culture background, alcohol and drug use) influencing suicide cannot be ignored. An in-depth understanding of these factors is vital in planning and implementing suicide prevention strategies. Five recommendations for future research are derived from this study: (1) It is vital to acquire detailed personal information on each suicide case and relevant information among the population in assessing the key socio-environmental determinants of suicide; (2) Bayesian model could be applied to compare mortality rates and their socio-environmental determinants across LGAs in future research; (3) In the LGAs with warm weather, high proportion of Indigenous population and/or unemployment rate, concerted efforts need to be made to control and prevent suicide and other mental health problems; (4) The current surveillance, forecasting and early warning system needs to be strengthened, to trace the climate and socioeconomic change over time and space and its impact on population health; (5) It is necessary to evaluate and improve the facilities of mental health care, psychological consultation, suicide prevention and control programs; especially in the areas with low socio-economic status, high unemployment rate, extreme weather events and natural disasters.

This thesis is structured around the analysis of a model that describes the Cantonment, the Civil Lines, the Sadr Bazar and part of the Native City of the Western Indian settlement of Poona in the late nineteenth and early twentieth centuries.

Diss. (sammanfattning) Alnarp : Sveriges lantbruksuniversitet, 2005. / Härtill 4 uppsatser.