Beekeepers in Appalachia market varietal honeys derived from particular species of deciduous trees; however, finding places in a mountainous landscape to locate new beeyards is difficult. Site selection is hindered by the high up-front costs of negotiating access to remote areas with limited knowledge of the available forage. Remotely sensed data and species distribution modeling (SDM) of trees important to beekeepers could aid in locating apiary sites at the landscape scale. The objectives of this study are i) using publicly available forest inventory data, to model the spatial distribution of three native tree species that are important to honey producers in eastern Kentucky: American Basswood, Sourwood and Tulip Poplar, and to assess the accuracy of the models, ii) to incorporate a method for discounting the value of a nectar resource as a function of distance based on an energetic model of honeybee foraging, and iii) to provide an example by ranking potential apiary locations around the perimeter of a mine site in the study area based on their proximity to probable species habitat using a GIS model. Logistic regression models were trained using presence-absence records from 1,059 USFS Forest Inventory and Analysis (FIA) sub-plots distributed throughout a 9,000 km2 portion of the Kentucky River watershed. The models were evaluated by applying them to a separate dataset, 950 forest inventory sub-plots distributed over a 40.5 km2 research forest maintained by the University of Kentucky. Weights derived from an energic model of honeybee foraging were then applied to the probabilities of tree species occurrence predicted by the SDM. As an example, 24 potential apiary locations around the perimeter of a reclaimed mine site were selected and then ranked according to a site suitability index. Three tributary areas corresponding to different honeybee flight ranges were considered: 500m, 700m, and 1,200m. Results confirm that rankings are dependent on the foraging range considered, suggesting that the number of colonies at an apiary location would be an important factor to consider when choosing a site. However, the methodology makes assumptions that are only anecdotally supported, notably i) that colonies will forage preferentially at the target species when it is in bloom and, ii) that foragers will exhaust resources closest to the hive first, regardless of patch size. Additional study of how bees deplete the nectar resources surrounding an apiary is needed to verify the usefulness of SDM in site selection for varietal honey production.

近年來由於政府實施無紙化及金融業者推行電子帳單的成效卓越，使得國內郵件的收寄量逐年下滑，郵局如何與民營業者競爭國內物流市場並達成盈餘目標，成為營運中不可忽視之因素。 傳統的郵局設點多依據公司規定與配合政府政策需求，甚少採用涉及複雜因素之區位分析進行選址。因此，如何有效且公正地評選郵局新設據點以提高收益，成為亟待解決之問題。 本研究目的在於提供高收益之郵局設點建議，我們提出一種評估中華郵政公司設點效益的方法，以國內郵局實際設點位置與相關空間資料來建置實驗模型。研究結果顯示，以本研究方法建立之預測模型可成功的提供中華郵政公司建議於何處新增據點可收最大功效。 我們首先蒐集中華郵政公司設點之鄰近區域資料，如競爭者設點數、人口因素、重要交通路口、郵件收寄量等。其次導入資料探勘技術分析影響郵件收寄量之因素，建立中華郵政公司設點收寄量預測模型。然後依照建立預測模型時所得到之區辨力分數，判斷採用何種資料探勘技術建立預測模型較適當。最後將所選定的預測模型套用於台北縣市各村里建物重心，透過環域資料分析以計算預估之收寄量，再整合各資料探勘技術之預測結果後推論出最佳設點建議。 實作中，以台北縣市資料來測試我們的方法。實驗數據顯示，我們的方法成功地找出十一個建議設點的村里，可提供給中華郵政公司作為高收益的設點建議。 / The amount of postal mail declines in recent years due to the efforts of paper-reduce policies implemented by the government, the industries, and the general publics. It becomes one of the important issues of the Chunghwa Post Company, to compete with other companies in domestic freight and mail services and to achieve the desired profits. Traditionally, the location of post offices were decided according to the government policies as well as the company regulations. The issues involved in the site selection analysis were seldom considered. Hence, developing an effective and fair mechanism to find the new post office locations that could improve the company’s surplus becomes an important problem to be solved. The purpose of this thesis is to provide recommendations to the post office site selection which will yield high profit to the company. We proposed a method to evaluate the effective profits that could be produced by a particular post office through the data mining techniques and the related GIS information. We first collect various data, such as neighborhood population, traffic flow, postal mail received at particular post office, competitor’s information, etc., and analyze these data using data mining techniques in order to establish prediction models. The most appropriate model was chosen to find the new post office sites. The Metropolitan Taipei area was chosen to illustrate our idea. The best sites for new post offices were selected through the buffering analysis as well as the data mining techniques. The experimental results show that our method can successfully find eleven locations which could generate most profit to Chunghwa Post Company if the new post offices were located in these places.

近年來台灣連鎖咖啡店消費人口的穩定成長，提升了連鎖咖啡店的市場規模與消費產值，傳統利潤導向的市場經營方式，使得連鎖咖啡店的競爭更趨激烈，如何訂定正確的選址與經營策略，成為在高度競爭市場中存活的重要關鍵。 傳統的選址問題需要投入大量的人力與時間進行相關資訊的蒐集、訪查與評估，故而在新設營業點時，較少運用複雜的因素進行區位選址的分析與評估。因此能透過較多的因素，從區位選址與營利效應等觀點進行分析，協助投資者獲得更好的利潤，提高決策成功的機率，是極為重要的問題。 本論文的目的，在於為連鎖咖啡店之選址決策，提出能增加成功機率之設點建議。我們依據連鎖咖啡市場雙雄在訂定選址決策的成功經驗，透過相關係數進行人口與經濟活動因素之統計分析，以找出其成功選址之關鍵因素。同時運用資料探勘的分類技術，建構成功選址之分類模型，並經由地理資訊系統提供的圖層資料，對連鎖咖啡市場雙雄之競爭關係進行分析與評估，以提供正確選址及設點之建議。 實作中我們採用台北市出租店面之空間資料，以探討並評估本研究建議模型之實際效益。實驗結果顯示，透過本研究之選址分類模型進行設點類型之預測，有七成以上之達成率，顯示本研究提出之模型能有效增加選址的成功機率，同時經由競爭對手設點空間關係之分析，亦能提供有利選址決策之建議。 / The number of customers of coffee shop chains has grown steadily in recent years that cause the market size as well as the total consumption value increase rapidly and continuously. The competition among the chain coffee stores get even worse under the traditional profit oriented management style. In such case, it is crucial to make the correct decisions when selecting the coffee shop locations as well as making operation strategies in opening new coffee shops. Traditionally, it takes a great amount of time and human resources in collecting relevant information, conducting field visits as well as site evaluations when making coffee shop site selections. One seldom considers complex factors of site evaluation or field analyzing in selecting the location of new coffee shop. Hence, it will be one of the major contributions if one can find a mechanism in analyzing the site selection as well as profit evaluation to help the investors to produce better profit and to improve the chance of success. The goal of this thesis is to provide recommendations to improve the success rate of chain coffee shop site selection strategy. Based on the coffee market leaders’ success experiences in formulating the site selection strategies, we analyzed the correlation coefficients of the population as well as economy activities in order to identify the key factors in successful site selection strategies. We also used data mining techniques to construct the classification models of successful site selection. In addition, we analyzed and evaluated competition relations between the two leading chain coffee brands using the geographic information systems to obtain appropriate recommendations in new site selections. The shop rental information of Taipei City was used to explore and to evaluate the models recommended in our mechanism. The experimental results showed that the prediction through the classification models for site selections can achieve 70% of success rate. This indicates our mechanism effectively improve the successful rate of site selections. Moreover, the experimental results also show that the spatial analysis of site selections between the competitors is helpful in providing appropriate site selection strategies.

Our country is a risky position in terms of natural disasters. In the long run, preferentially settlement areas were selected to ensure maximum benefits in terms of both economic and security aspects, other criteria is not taken account when selection of sites. The main purpose of this study is to examine and compare the properties of settlement location based on natural hazard and environmental constraints to be able to understand the interaction between the settlements and natural conditions at the regional scale of YeSilirmak Basin. A MCDA was set up with 10 different data layers in two data domains (environmental and natural hazards domains), are evaluated. The results of the MCDA scores are then transferred to settlement databases in order to evaluate the number of existing settlements in different environmental and natural hazard related suitability classes. It is found that almost 29% of YeSilirmak catchment is environmentally favorable for settlement, and in coherence with that 41% of all existing settlements are located in this zone, indicating a clear preference among the perception of environmentally better places to be settled in. On the other hand with respect to the natural hazards dataset, the locations of the settlements fail to create any preference, as 73,32% of the area is used by 73,50% of existing settlements, which indicates that the perception of natural hazards are low and do not effect settlement criteria, while the acceptable risk of community is high.

Protected area site selection is generally carried out using biodiversity data as surrogates. However, reliable and complete biodiversity data is rarely available due to limited resources, time and equipment. Instead of drawing on inadequate biodiversity data, an alternative is to use environmental diversity (ED) as a surrogate in conservation planning. However, there are few studies that use environmental diversity for site selection or that evaluates its efficiency / unfortunately, no such example exists for Turkey, where biodiversity is high but our knowledge about it is unsatisfactory. Hence, this study was carried out to investigate the efficiency of environmental surrogates and the utility of different biological taxa in conservation planning. The objective was to find out the most efficient surrogates, either environmental or biological, for conservation planning, so that limited resources can be used more efficiently to establish an effective protected areas network. The study was carried out in northeastern Turkey, within the Lesser Caucasus ecoregion. The taxonomic groups considered include large mammals, breeding birds, globally threatened reptiles and amphibians, butterflies, highly threatened plants, and ecological communities. The distribution data was taken from a previous study, while climate and topographical data were obtained from various sources and produced through spatio-statistical techniques. Complementarity-based site selection was carried out with Marxan software, where the planning unit was the 100 sq.km. UTM grid square. Various statistical methods, including geographically weighted regression, principal components analysis, and p-median algorithm, were used to determine ED across the units. Performance of different approaches and different sets of surrogates were tested by comparing them to a random null model as well as representation success. Results indicate that endemic or non-endemic highly threatened plant species, butterfly species and ecological communities represent biodiversity better than other taxa in the study area. As such, they can be used on their own as efficient biodiversity surrogates in conservation area planning. Another finding is that highly threatened plant species are required to be used in the site selection process if they need to be represented well / in other words, they are their own surrogates. It was demonstrated that while ED alone can be used as a surrogate to represent biodiversity of an area, they are not as good as biodiversity surrogates themselves. It is also suggested that using species taxa with smaller distributional ranges or taxa that complement each other due to ecological differences as surrogates provide better results. On the other hand, ED might be a more suitable surrogate if resources are very limited or field work is impossible. In such cases, using ED in conjunction with one of the better biodiversity surrogates is probably the best solution.

In urbanized areas, building and transportation systems generally comprise the majority of greenhouse gas (GHG) emissions and energy consumption. Realization of global environmental sustainability depends upon efficiency improvements of building and transportation systems in the built environment. The selection of efficient buildings and locations can help to improve the efficient utilization of transportation and building systems. Green building design and rating frameworks provide some guidance and incentive for the development of more efficient building and transportation systems. However, current frameworks are based primarily on prescriptive, component standards, rather than performance-based, whole-building evaluations. This research develops a commercial building/site evaluation framework for the minimization of GHG emissions and energy consumption of transportation and building systems through building/site selection. The framework examines, under uncertainty, multiple dimensions of building/site operation efficiencies: transportation access to/from a building site; heating, ventilation, air conditioning, and domestic hot water; interior and exterior lighting; occupant conveyances; and energy supply. With respect to transportation systems, the framework leverages regional travel demand model data to estimate the activity associated with home-based work and non-home-based work trips. A Monte Carlo simulation approach is used to quantify the dispersion in the estimated trip distances, travel times, and mode choice. The travel activity estimates are linked with a variety of existing calculation resources for quantifying energy consumption and GHG emissions. With respect to building systems, the framework utilizes a building energy simulation approach to estimate energy consumption and GHG emissions. The building system calculation procedures include a sensitivity analysis and Monte Carlo analysis to account for the impacts of input parameter uncertainty on estimated building performance. The framework incorporates a life cycle approach to performance evaluation, thereby incorporating functional units of building/site performance (e.g energy use intensity). The evaluation framework is applied to four case studies of commercial office development in the Atlanta, GA metropolitan region that represent a potential range of building/site alternatives for a 100-employee firm in an urbanized area. The research results indicate that whole-building energy and GHG emissions are sensitive to building/site location, and that site-related transportation is the major determinant of performance. The framework and findings may be used to support the development of quantitative performance evaluations for building/site selection in green building rating systems and other efficiency incentive programs designed to encourage more efficient utilization and development of the built environment.

A forced landing for an Unmanned Aerial Vehicle (UAV) is required if there is an emergency on board that requires the aircraft to land immediately. Piloted aircraft in the same scenario have a human on board that is able to engage in the complex decision making process involved in the choice of a suitable landing location. If UAVs are to ever fly routinely in civilian airspace, then it is argued that the problem of finding a safe landing location for a forced landing is an important unresolved problem that must be addressed. This thesis presents the results of an investigation into the feasibility of using machine vision techniques to locate candidate landing sites for an autonomous UAV forced landing. The approach taken involves the segmentation of the image into areas that are large enough and free of obstacles; classification of the surface types of these areas; incorporating slope information from readily available digital terrain databases; and finally fusing these maps together using a high level set of simple linguistic fuzzy rules to create a final candidate landing site map. All techniques were evaluated on actual flight data collected from a Cessna 172 flying in South East Queensland. It was shown that the use of existing segmentation approaches from the literature did not provide the outputs required for this problem in the airborne images encountered in the gathered dataset. A simple method was then developed and tested that provided suitably sized landing areas that were free of obstacles and large enough to land. The advantage of this novel approach was that these areas could be extracted from the image directly without solving the difficult task of segmenting the entire image into the individual homogenous objects. A number of neural network classification approaches were tested with the surface types of candidate landing site regions extracted from the aerial images. A number of novel techniques were developed through experimentation with the classifiers that greatly improved upon the classification accuracy of the standard approaches considered. These novel techniques included: automatic generation of suitable output subclasses based on generic output classes of the classifier; an optimisation process for generating the best set of input features for the classifier based on an automated analysis of the feature space; the use of a multi-stage classification approach; and the generation of confidence measures based on the outputs of the neural network classifiers. The final classification result of the system performs significantly better than a human test pilot's classification interpretation of the dataset samples. In summary, the algorithms were able to locate candidate landing site areas that were free of obstacles 92.3 ±2.6% (99% confidence in the result) of the time, with free obstacle candidate landing site areas that were large enough to land in missed only 5.3 ±2.2% (99% confidence in the result) of the time. The neural network classification networks developed were able to classify the surface type of the candidate landing site areas to an accuracy of 93.9 ±3.7% (99% confidence in the result) for areas labelled as Very Certain. The overall surface type classification accuracy for the system (includes all candidate landing sites) was 91.95 ±4.2% (99% confidence in the result). These results were considered to be an excellent result as a human test pilot subject was only able to classify the same data set to an accuracy of 77.24 %. The thesis concludes that the techniques developed showed considerable promise and could be used immediately to enhance the safety of UAV operations. Recommendations include the testing of algorithms over a wider range of datasets and improvements to the surface type classification approach that incorporates contextual information in the image to further improve the classification accuracy.

No description available.

The lack of safe drinking water worldwide has drawn the attention of decision makers to riverbank filtration (RBF) for its many advantages in purifying surface water. This study provides an overview of the hydrogeologic, fluvial, and environmental influences on the performance of RBF systems and aims to develop a model for RBF site selection. Using multi-attribute utility theory (MAUT), this study structured the RBF siting problem and assessed a multiplicative utility function for the decision maker. In a case study, geostatistical methods were used to acquire the necessary data and geographic information systems (GIS) were used to screen sites suitable for RBF implementation. Those suitable sites were then evaluated and ranked using the multi-attribute utility model. The result showed that sites can be identified as most preferred among the selected suitable sites based on their expected utility values. This study definitively answers the question regarding the capability of MAUT in RBF site selection. Further studies are needed to verify the influences of the attributes on the performance of RBF systems.:Abstract iii Zusammenfassung iv Acknowledgments v Table of Contents vi List of Tables viii List of Figures x Definition of terms xiii 1. Abbreviations xiii 2. Symbols xiii Part I Introduction 1 1. Introduction 2 2. Statement of purpose 2 3. Research questions 3 4. Overview of methodology 3 5. Organization of the dissertation 3 Part II Fundamentals and Literature Review 5 1. The definition of bank filtration 6 2. The Significance of RBF 7 2.1 RBF in drinking water supply 7 2.2 Benefits of RBF for China 14 3. RBF Site Selection 19 3.1 RBF site selection model 20 3.2 Definition of successful RBF sites 24 4. Factors Affecting RBF Site Selection 26 4.1 River hydrology/hydraulics 27 4.2 Geology 28 4.3 Land cover 36 4.4 Well field location 36 4.5 Water quality 37 4.6 Aquifer properties 38 4.7 Distance to river 41 4.8 Riverbed characteristics 43 5. Effect of Clogging on Yield 46 6. Summary 51 Part III Developing a Multi-attribute Utility Model for RBF Site Selection 53 1. Introduction 54 2. Objectives and Attributes 54 3. Assessment of the Utility Function 57 3.1 Investigation of the qualitative preference structure 58 3.2 Assessment of component utility function 62 3.3 Assessment of the scaling constants 63 4. Results 67 5. Discussion 69 6. Summary 74 Part IV Case Study 75 1. Introduction 76 2. Materials and Methods 78 2.1 GIS data collection 78 2.1.1 Geologic data 79 2.1.2 Land cover data 79 2.1.3 Groundwater quality data 80 2.1.4 Aquifer properties data 80 2.1.5 Surface water area data 80 2.1.6 Surface water quality data 81 2.1.7 Streambed material data 81 2.2 Kriging the saturated thickness 91 2.3 Aggregation of all constraint maps 103 3. Results 105 3.1 Kriging 105 3.2 Suitable sites 105 4. Discussion 109 4.1 A discussion of the kriging results 109 4.2 A discussion of the multi-attribute utility model results 117 5. Summary 122 Part V Conclusions and Recommendations 123 1. Conclusion and Recommendation 124 Appendix 1 Environmental quality standards for surface water (GB 3838-2002) 125 Appendix 2 Quality standard for groundwater (GB14848-93) 127 Appendix 3 Explanation to Germany’s RBF site location data 130 Appendix 4 Layer information of drillings 133 Appendix 5 Streambed materials used by Schälchli (1993) 141 Appendix 6 Interview and questionnaires 143 Appendix 7 Surface water area of Jilin City 150 Bibliography 152

Insular fauna face disproportionate risks of extinction owing to direct human perturbation and intrinsic factors that are enhanced at small population sizes. Currently, our understanding of the processes that promote long-term persistence of naturally small populations and the cryptic processes that may contribute to accelerating their decline is limited by lack of empirical investigations across the range of natural conditions. Implementing effective protections for rare and understudied taxa requires the identification and examination of factors that limit recruitment at critical life stages. Predicting population health outcomes of future perturbations further necessitates an understanding a taxon’s behavioral ecology. Finally, cryptic threats to viability, such as inbreeding depression, must be investigated with an appreciation for taxon-specific life history, as these attributes can alter the context in which severe fitness reductions are expressed. In this project I enlist integrative and cross-disciplinary approaches to study the behavioral ecology and conservation genetics of a critically endangered West Indian Rock Iguana, Cyclura nubila caymanensis, on Little Cayman Island. I demonstrate how coastal communal nesting areas, a critical limiting resource on the island, serve a diverse population demographic and contribute to significantly enhanced nesting outcomes. These data emphasize the importance of expanding protections for major sites, as aggregative nesting appears to be perpetuated by both habitat suitability and adaptive fitness benefits. I next evaluate the possibility of evolved inbreeding avoidance strategies, including natal dispersal, non-assortative mate choice, and genetic bet-hedging. I conclude that the contribution of pre-reproductive dispersal to inbreeding avoidance likely outweighs that of active mate choice. Importantly, observed patterns of siring success imply constrained female choice and sexual conflict over genetic mating outcomes – a pattern that may extend to many territorial, male-driven mating systems and therefore should be an important consideration in genetic management. Finally, I investigate age-dependent inbreeding effects and the degree to which inbreeding depression may limit recruitment to the breeding population. I fail to reveal significant correlations of multi-locus heterozygosity with hatchling fitness; however, negative effects of parental inbreeding on fecundity and hatching success imply fitness consequences of inbreeding depression could be felt at other life stages.