The primary aim of this dissertation was to develop and validate a methodology for identifying spatial clusters (hotspots) of various paediatric cancers within South Africa by using GIS software. The Hotspot Analysis (Getis-Ord Gi*) Tool was used for this purpose. A series of spatial clusters (hotspots) were identified by the tool for each cancer type and these clusters were compared with the exiting literature regarding known environmental and other carcinogens. The quality of the cancer data used in the dissertation was however found to be questionable and significantly underreported. This caused the results of the tool to also be questionable. The dissertation therefore concluded that the tool could be successfully used to identify spatial clusters of cancer in principle. It was however found that the results of the tool needed to be viewed without caution in this dissertation due to the low quality of the cancer data used. / MSc (Geography and Environmental Management), North-West University, Potchefstroom Campus, 2014

The primary aim of this dissertation was to develop and validate a methodology for identifying spatial clusters (hotspots) of various paediatric cancers within South Africa by using GIS software. The Hotspot Analysis (Getis-Ord Gi*) Tool was used for this purpose. A series of spatial clusters (hotspots) were identified by the tool for each cancer type and these clusters were compared with the exiting literature regarding known environmental and other carcinogens. The quality of the cancer data used in the dissertation was however found to be questionable and significantly underreported. This caused the results of the tool to also be questionable. The dissertation therefore concluded that the tool could be successfully used to identify spatial clusters of cancer in principle. It was however found that the results of the tool needed to be viewed without caution in this dissertation due to the low quality of the cancer data used. / MSc (Geography and Environmental Management), North-West University, Potchefstroom Campus, 2014

Human impacts on ocean ecosystems are driving declines in marine biodiversity, including marine mammals. Comprehensive spatial data are vital for making informed management decisions that may aid species recovery and facilitate the sustainable use of ocean ecosystems. However, marine mammal studies are often data limited, thereby restricting possible research questions. Developing novel analytical approaches and incorporating unconventional datasets can expand the scope of analysis by increasing the information content of existing data sources. The goal of our research is to support conservation and management of marine mammals in British Columbia (BC), Canada, through the application of advanced spatial statistical methodology to characterize spatial distribution and density patterns and provide assessments of data uncertainty. Our first objective is to generate statistical models to map spatially continuous predictions of marine mammal distributions and densities within BC’s north coast and apply methodology from spatial statistics to identify hotspots of elevated use. We use species observations collected from systematic line transect surveys previously adjusted to generate estimates of density per nautical mile of transect. We predict the distribution and density patterns of nine marine mammal species by employing a species-habitat model to relate species densities to environmental covariates using a generalized additive model. We use spatial statistical hotspot analysis (Getis-Ord Gi*statistic) and an aspatial threshold approach to identify hotspots of high density. Our analysis reveals that hotspots selected using a top percentage threshold produced smaller and more conservative hotspots than those generated using the Gi*statistic. The Gi*statistic demonstrates a robust and objective technique for quantifying spatial hotspots and offers an alternative method to the commonly applied aspatial threshold measure. We find that maps show agreement with prior research and hotspots align with ecologically important areas previously identified by expert opinion. Our second objective is to apply map comparison techniques to compare cetacean density maps from disparate data collection methods (systematic surveys and citizen science) to evaluate the information content of each map product and quantify similarities and differences. Discrepancies are quantified by performing image differencing techniques on the rank order values of each map surface. We subsequently use the Gi*statistic to isolate regions where extreme differences occur. To assess similarities, a Gi*statistic is applied to both maps to locate spatially explicit areas of high cetacean density. Where clusters of high density values in both maps overlap we infer higher confidence that the datasets are representing a true ecological signal, while areas of difference we recommend as targeted locations for future sampling effort. We contextualize map similarities and differences using a dataset of human activity in the form of cumulative human effect scores. Overall, our analytical approach integrates novel spatial datasets from systematic surveys, citizen science, and remote sensing to provide updated information on cetacean distributions in BC. Our study generates geographic data products that fill knowledge gaps and results provide baseline information valuable for future decision-making. The methodology applied in this study can be generalized across species and locations to support spatial planning and conservation prioritization in both marine and terrestrial contexts. / Graduate / 2017-11-13

No description available.

The need for a safe and sustainable chemical industry has called for the development of emerging technologies with improved environmental performance. In this study, an emerging electrochemical hydrogenation process over Ni foam is being developed at the laboratory scale with an expectation of less environmental impacts than a conventional palladium on carbon hydrogenation process. To understand better the potential environmental performance of the process at the matured scale, a prospective life cycle assessment was conducted to identify environmental hotspots for early process improvement. There is no standardised method for prospective life cycle assessment, hence a methodological recommendation in conducting a prospective LCA was proposed through a literature review.  The proposed methodology consists of three steps which are a pre-inventory stage, an inventory stage, and a post-inventory stage. These steps have been connected to the ISO 14044 standard methodology for conducting an LCA where the pre-inventory stage relates to the goal and scope definition, the inventory stage to inventory analysis, and the post-inventory connected to both the inventory analysis, impact assessment, and interpretation stages of the ISO methodology. The proposed methodology was applied to the electrochemical hydrogenation process over nickel foam cathode where a three-case scenario (lab, worst- and best-case scenarios) was investigated to identify hotspots for early process improvement. The theoretical upscaled process had a better environmental performance compared to the lab process. The identified hotspots in the upscaled process (worst-case) include electricity process, evaporation process, and solvent recycling process for ecotoxicity (freshwater), human toxicity (cancer), human toxicity (non-cancer), climate change and resource use (minerals and metals) impact categories. The best-case scenario had its identified hotspots in the electricity process, solvent recycling process, and distillation process. This shows the importance of circularity, recycling, and lean manufacturing to the pillars of sustainability. Reducing resource consumption per unit product while increasing the recycling efficiency of process waste will be imperative towards ensuring a green chemical industry. Based on the results, a reduction of electricity demand for the process, utilisation of an alternative less energy-consuming processes, or cleaner energy sourcing could further improve the potential environmental performance of the process. Based on the quality of the data used, it is recommended that the outcome of the study be cautiously interpreted.

Urbanisation and a growing global population have caused our road networks to expand rapidly in the past decades. The consequences of transport infrastructure for wildlife include traffic mortality, habitat loss and habitat degradation and the negative impact of a road extends far beyond the road itself. In Ireland, there are mitigation measures for wildlife mortality in place on all major roads. Mitigation measures can help reduce wildlife-vehicle collisions and increase habitat connectivity but need to be properly monitored and maintained following implementation. This study was carried out in collaboration with the Environmental Policy & Compliance department at Transport Infrastructure Ireland (TII), a state agency in Ireland responsible for national road and public transport infrastructure. It applied various spatial and temporal analyses methods in order to ascertain how best to prioritise critical road sections and times for maintenance. The significance of the study is that recent site visits carried out in Ireland found that 66% of mitigation measures were of inadequate standard. The methods were applied to roadkill data taken over an eight year period on the M3 motorway in county Meath, Ireland. This case study was chosen as mitigation measures, such as underpasses and mammal underpasses, have been in operation since its’ opening in 2010. It was found that temporal analysis could provide an insight into whether roadkill was increasing or decreasing annually as well as what months were most recommendable to carry out maintenance. The spatial analysis began with using Ripley’s K-statistics to first determine whether or not clustering of roadkill was occurring along the study area. Four different methods of locating hotspots along a road network were then applied and compared; Malo’s method, 2D Hotspot Analysis using Siriema Road Mortality software, kernel density estimation using SANET and finally KDE+. The findings showed that, despite mitigation measures being in place, hotspots were still occurring indicating road sections experiencing higher numbers of roadkill than expected in a random situation. These sections could then be prioritised for maintenance. It was found that the KDE+ software in conjunction with the use of a roadkill data app was the most recommendable approach. It was also noted that that the app should be expanded to other road classes and rail. It is recommended that this be made a standard protocol, comparable on a national level, for the prioritisation of mitigation measures for maintenance. Finally, it was recommended that more public awareness about wildlife-vehicle collisions and mitigation measures be raised. In the future, the app could also be connected to GPS systems to warn drivers of critical road sections. If these methods and recommendations are applied to the Irish road network, a reduction in roadkill should be observed.

The Florida manatee (Trichechus manatus latirostris) is a protected species that is vulnerable to both anthropogenic and natural causes of mortality. The ability of wildlife managers to oversee regulation of this species is based on available abundance estimates and mortality data. Using existing manatee mortality data collected by Florida Fish and Wildlife Conservation Commission (FWC) from 1974-2012, this study focuses on identifying significant spatial clusters of high values or “hotspots” of manatee mortality and the temporal patterns of these hotspots using the novel “emerging hotspot analysis” ArcGIS tool. The categories of manatee mortality included in this analysis were watercraft-related, perinatal, cold-stress, and other natural (which includes red tide) and were classified into five hotspot pattern categories. Of interest were the locations where consecutive or new hotspot patterns were identified among the four categories of manatee mortality included in this analysis. Consecutive hotspot clusters were found near Tampa Bay (which includes parts of Pinellas, Hillsborough, and Manatee Counties) and in the counties of Hernando/Pasco, Monroe, Palm Beach/Broward/Miami-Dade, St. Johns/Flagler, and Citrus. New hotspot clusters were found in Tampa Bay (which includes parts of Pinellas, Hillsborough, and Manatee Counties) and in the counties of Nassau, Wakulla, Charlotte/Lee, St. Lucie/Martin, Levy, Duval, Dixie, Volusia/Seminole, and Citrus. These mortality hotspots frequently overlapped areas of higher manatee and human population densities. These hotspot clusters indicate emerging patterns that highlight areas to focus future research by wildlife managers; specifically, on the relationship between human population density and concentration of watercraft activities in coastal areas, as well as the influence coastal development has on the vital resources utilized by manatees.

不動產價格的影響因素可按影響範圍區分為三大類，分別為影響整體不動產市場的「總體環境因素」，對一定範圍內不動產產生價格影響的「區域環境因素」，及對於單一不動產價格有所影響的「房屋個體因素」。其中，區域環境因素為影響個別不動產價格之首要因素，不動產之價格會受到所屬區域之政治、經濟、自然、社會等因素影響，「公共建設因素」為重要之區域環境之一，包含公共設施水準及其配置狀態。影響個別不動產價格之次要因素為「房屋個體因素」，可再次細分為三大影響因素如下：房屋本身所具有的特徵因素，即建築物之內部結構；房屋的建築方式，住宅類型等與全棟房屋有關的因素；與房屋鄰近地區環境有關的因素。而集合式與獨立式住宅因分屬不同房屋類型，即上述房屋價格形成因素中「房屋之建築方式」。實際交易上，獨立式住宅多半以「整棟建物」作為交易計算單位，對於坐落之基地權利持分通常為全部，而集合式住宅係以「樓層」、「戶」作為交易之計算單位，所有之基地持分與其他住戶共同持有，基於上述差異，過去研究多將建築方式視為影響房屋價格的條件之一，並據此分類次市場，因此較少有研究同時探討二者在空間分布上所具有的區位差異，及購屋者對於環境的偏好是否有所不同。且過去文獻多半以使用傳統迴歸模型為主要分析方法。但傳統迴歸分析所使用最小平方法迴歸模型，經常會產生殘差項存在有空間自相關的問題，及空間本身所存在之空間異質性偏誤，即空間不穩定性。因此 本文以台中市都會區內之住家使用房屋為樣本，依特徵價格理論將獨立式住宅與集合式住宅視為差異化商品，其內外特徵納入變數，使用GeoDa軟體進行空間自相關分析，並使用ArcGIS軟體中的地理加權迴歸模組（GWR）進行迴歸分析，藉以探討不同類型房屋所偏好之外部特徵，瞭解不同空間環境對房屋價格之影響及台中市都會區空間發展型態，並驗證其於規劃建設產生的空間不穩定性。 研究結果顯示，台中市建立之重大市政建設及土地開發計畫會影響集合式住宅與獨立式住宅之地價熱點分布，其共同之房價熱點均座落於高地價市地重劃區及重大市政建設分布位置，而獨立式住宅之房價熱點，進一步分布於與高地價市重劃區鄰近之市地重劃區；在購屋者對周圍設施偏好方面，集合式住宅購屋者對於國中小學、大學、重大市政建設、市場、公園均有顯著偏好，惟獨立式住宅購屋者對於大學、重大市政建設、公園有顯著偏好，對於國中小學、市場有不偏好情形，顯示不同類型住宅對於公共設施之偏好不完全相同；集合式住宅與獨立式住宅之房屋特徵屬性呈現空間不穩定性，分析結果顯示，上述二種住宅類型，對於本研究所有公共設施距離特徵屬性均呈現空間不穩定、非均質性的結果，顯示不同類型住宅均會與彼此具有相依性，並形成各區域間的異質性。 / Locational characteristics are the determinants of house prices. While former research have examined the effects of proximity to resources and facilities have on residential property values, and the change of the importance as located regions or submarkets vary, the effects of different types of houses are rarely compared due to their dissimilarity in ways of building and ownership. Do house price effects of the same facility alter when properties are situated in different submarkets? Further, the issues of spatial non-stationarity are usually overlooked by previous studies. By using transaction data of two common types of residential houses in Taichung City, we found house price hot spots of both detached houses and condos in regions with major constructions and development plans. Apart from the mutual hot spots found in high land price redevelopment zones, we also discovery hot spots of detached houses in areas in proximity to these redevelopment zones. As for desirable facilities for home buyers, neighborhood schools, universities, major constructions, local markets and parks were found to have an notable price impact on condos, whereas only universities, major constructions and parks in vicinity of in detached houses can we found significant price effects, suggesting the differences in the preference of consumers in distinct regions. Also, spatial dependence and heterogeneity are verified in both types of houses, making the entire market area spatial non-stationary.