Spatial decision support systems (SDSS) integrate the state of the art technology, such as GIS, database and distributed models into decision support systems to support geospatial analysis that is particularly useful for watershed management, such as TMDL development on watersheds required by the Clean Water Act. This dissertation focuses on the development of a SDSS to assess the economic and environmental impacts from various best management practices (BMPs) in reducing sediment yield on rangeland watersheds.The SDSS included three major parts: the models, database and web-based interfaces. The model part is the core of the SDSS that provides the functionality of watershed economic analysis. The model maximized the profit of a representative ranch assumed to cover the whole watershed with the constraints of production technology, resource, sediment control objectives and sustainable utilization. A watershed was spatially segmented into basic units, each unit with similar plant growth and forage utilization. There are two major types of models, static and dynamic. Each model type supported variations in plant growth, grazing and ranch operations. Upland erosion was estimated through RUSLE2 and the sediment yield of a watershed was estimated from upland erosion and sediment delivery ratios for each basic unit. GAMS programs were used to solve the optimization models. The SDSS provides a platform to automatically implement the models. The database was the major tool in managing spatial and non-spatial data. A series of customized web pages were developed to support users' inputs, watershed analysis and result visualization. The embedded procedures were integrated into the SDSS to support analytical functionality, including geospatial analysis, model parameterization and web page generation.The SDSS was used to assess sediment control on the Walnut Gulch Experimental Watershed. The SDSS was parameterized primarily using publicly available data and a preliminary validation was made. The SDSS functionality was illustrated through eight applications. The results showed that given recent prices, new infrastructure practices would cause a financial burden to ranches. Better grazing management may provide an economic alternative to meet the sediment control objective and cost sharing could provide ranchers the incentives to participate in conservation plans.

Landscape Character Assessment (LCA) provides a structured approach to identifying the character and distinctiveness about the landscape. It is a tool used to identify what makes a location unique, a set of techniques and procedures used to map differences between landscapes based on their physical, cultural and historical characteristics. Although the UK has committed to assessing all of its landscapes by signing the European Landscape Convention in 2006, only 60% of coverage has been achieved. The majority of LCAs are carried out by professional environment or landscape consultancies rather than ‘in-house’. Geographical Information Systems are increasingly being used to collate and analyse data and produce character maps. This research presents a Spatial Decision Support System (LCA-SDSS) based in ArcGIS 9.3 that can be used to support decision makers in conducting a LCA. The LCA-SDSS provides a method for storing data, a model base for the assessment of Landform, Ground Type, Land Cover & Cultural attributes and a method for the user to interact with the resulting maps. Using the Tamar Valley Area of Outstanding Natural Beauty (AONB) as a study area the SDSS was developed and tested, resulting in character maps for each stage of the modelling and a final characterisation map. These maps were compared to a LCA conducted by a professional environmental consultant and were found to have produced a good quality assessment as verified by the end user at the Tamar Valley AONB Partnership.

Improved strategic location of an Emergency Medical Service (EMS) facility can significantly increase EMS efficiency. Urban planners need to consider a location that satisfies multiple criteria in order to make an informed decision about a future EMS facility site. Apart from basic criteria such as parcel value and size, decision-makers need to consider area and population coverage from potential parcels. Geographic Information Systems (GIS) provide an adequate analysis environment for EMS facility siting as many considered criteria are of a spatial nature. However, urban planners making decisions about an EMS facility site often lack the necessary expertise to make full use of challenging GIS tools. In order to help urban planners in the analysis process, this research developed a Spatial Decision Support System (SDSS) for EMS facility siting. The system was developed in ESRI ArcGIS (9.3) using the Visual Basic for Applications (VBA) programming environment. The objective of the system was to integrate spatial data, analysis, and visualization in a single system to help users evaluate a facility siting problem. The system's performance was tested using data for the Town of Blacksburg, VA. In addition, the system was evaluated by local planners and GIS staff with experience in EMS facility siting. Planners agreed the system enables more comprehensive and straightforward use of GIS for EMS facility siting analysis than other available siting tools. Potential improvements include a simpler user interface, synthesis of geoprocessing techniques, reduction of analysis time through automation, and better decision-making by improved visualization of results. / Master of Science

Over the last two decades, wireless technology has become ubiquitous in the United States and other developed countries. Consumer devices such as AM/FM radios, cordless and cellular telephones, pagers, satellite televisions, garage door openers, and television channel changers are just some of the applications of wireless technology. More recently, wireless computer networking has seen increasing employment. A few reasons for this move toward wireless networking are improved electronics transmitters and receivers, reduced costs, simplified installation, and enhanced network expandability. The objective of the study is to generate understanding of the planning inherent in a broadband, fixed wireless telecommunication network and to implement that knowledge into an SDSS. Intermediate steps toward this goal include solutions to both fixed wireless point-to-multipoint (PMP) and fixed wireless mesh networks, which are developed and incorporated into the SDSS. This study explores the use of a Spatial Decision Support System (SDSS) for broadband fixed wireless connectivity to solve the wireless network planning problem. The spatial component of the DSS is a Geographic Information System (GIS), which displays visibility for specific tower locations. The SDSS proposed here incorporates cost, revenue, and performance capabilities of a wireless technology applied to a given area. It encompasses cost and range capabilities of wireless equipment, the customers' propensity to pay, the market penetration of a given service offering, the topology of the area in which the wireless service is proffered, and signal obstructions due to local geography. This research is both quantitative and qualitative in nature. Quantitatively, the wireless network planning problem may be formulated as integer programming problems (IP). The line-of-sight restriction imposed by several extant wireless technologies necessitates the incorporation of a GIS and the development of an SDSS to facilitate the symbiosis of the mathematics and geography. The qualitative aspect of this research involves the consideration of planning guidelines for the general wireless planning problem. Methodologically, this requires a synthesis of the literature and insights gathered from using the SDSS above in a what-if mode. / Ph. D.

This thesis studies the use of a collaborative spatial Multi-Criteria Analysis tool in site evaluation with multiple participants. The approach is situated within the context of three concepts of space, choice and participation, and is informed by fields as diverse as Decision-Making, Participatory Planning, Geographical Information Systems, Decision Support Systems, Voting, and Group Collaboration. A collaborative spatial Multi-Criteria Analysis software tool called MapChoice was designed for this thesis, built upon open source components and featuring easy-to-use decision support functionality in both single-user and collaborative modes. MapChoice was then evaluated in a real-world site selection situation with a case study on the location of much-needed affordable housing in the Town of Collingwood, Ontario. Based on previous discussions and workshops on the project, a workshop was held with a group of community housing advocates to compare a set of possible sites for an affordable housing project according to a set of spatial and aspatial criteria. The study indicates that a collaborative spatial MCA approach can be used in dealing with complex planning problems, and that it has the potential to contribute to improved consensus between participants.

This thesis studies the use of a collaborative spatial Multi-Criteria Analysis tool in site evaluation with multiple participants. The approach is situated within the context of three concepts of space, choice and participation, and is informed by fields as diverse as Decision-Making, Participatory Planning, Geographical Information Systems, Decision Support Systems, Voting, and Group Collaboration. A collaborative spatial Multi-Criteria Analysis software tool called MapChoice was designed for this thesis, built upon open source components and featuring easy-to-use decision support functionality in both single-user and collaborative modes. MapChoice was then evaluated in a real-world site selection situation with a case study on the location of much-needed affordable housing in the Town of Collingwood, Ontario. Based on previous discussions and workshops on the project, a workshop was held with a group of community housing advocates to compare a set of possible sites for an affordable housing project according to a set of spatial and aspatial criteria. The study indicates that a collaborative spatial MCA approach can be used in dealing with complex planning problems, and that it has the potential to contribute to improved consensus between participants.

No description available.

With increased degradation of natural resources due to land use decisions and the subsequent loss of biodiversity across large spatial scales, there is a need for a Spatial Decision Support System (SDSS) which showcases the impacts of developments on terrestrial and aquatic ecosystems. The Gap Analysis Program (GAP) and a Bayesian Belief Network (BBN) were used to assess the impacts of an impoundment in the Bienville National Forest, Smith County, Mississippi on landcovers, threatened and endangered species, species richness and fish populations. A test impoundment site was chosen on Ichusa Creek and using GAP data, landcovers, species and species richness were compared with those of Bienville National Forest, Smith County, Mississippi. For the aquatic analysis, a BBN model was developed for each fish so that population probabilities could be calculated using a given configuration of available habitats and compared to current fish population.

<p>Human use of the oceans is increasingly in conflict with conservation of endangered species. Methods for managing the spatial and temporal placement of industries such as military, fishing, transportation and offshore energy, have historically been post hoc; i.e. the time and place of human activity is often already determined before assessment of environmental impacts. In this dissertation, I build robust species distribution models in two case study areas, US Atlantic (Best et al. 2012) and British Columbia (Best et al. 2015), predicting presence and abundance respectively, from scientific surveys. These models are then applied to novel decision frameworks for preemptively suggesting optimal placement of human activities in space and time to minimize ecological impacts: siting for offshore wind energy development, and routing ships to minimize risk of striking whales. Both decision frameworks relate the tradeoff between conservation risk and industry profit with synchronized variable and map views as online spatial decision support systems.</p><p>For siting offshore wind energy development (OWED) in the U.S. Atlantic (chapter 4), bird density maps are combined across species with weights of OWED sensitivity to collision and displacement and 10 km2 sites are compared against OWED profitability based on average annual wind speed at 90m hub heights and distance to transmission grid. A spatial decision support system enables toggling between the map and tradeoff plot views by site. A selected site can be inspected for sensitivity to a cetaceans throughout the year, so as to capture months of the year which minimize episodic impacts of pre-operational activities such as seismic airgun surveying and pile driving.</p><p>Routing ships to avoid whale strikes (chapter 5) can be similarly viewed as a tradeoff, but is a different problem spatially. A cumulative cost surface is generated from density surface maps and conservation status of cetaceans, before applying as a resistance surface to calculate least-cost routes between start and end locations, i.e. ports and entrance locations to study areas. Varying a multiplier to the cost surface enables calculation of multiple routes with different costs to conservation of cetaceans versus cost to transportation industry, measured as distance. Similar to the siting chapter, a spatial decisions support system enables toggling between the map and tradeoff plot view of proposed routes. The user can also input arbitrary start and end locations to calculate the tradeoff on the fly.</p><p>Essential to the input of these decision frameworks are distributions of the species. The two preceding chapters comprise species distribution models from two case study areas, U.S. Atlantic (chapter 2) and British Columbia (chapter 3), predicting presence and density, respectively. Although density is preferred to estimate potential biological removal, per Marine Mammal Protection Act requirements in the U.S., all the necessary parameters, especially distance and angle of observation, are less readily available across publicly mined datasets.</p><p>In the case of predicting cetacean presence in the U.S. Atlantic (chapter 2), I extracted datasets from the online OBIS-SEAMAP geo-database, and integrated scientific surveys conducted by ship (n=36) and aircraft (n=16), weighting a Generalized Additive Model by minutes surveyed within space-time grid cells to harmonize effort between the two survey platforms. For each of 16 cetacean species guilds, I predicted the probability of occurrence from static environmental variables (water depth, distance to shore, distance to continental shelf break) and time-varying conditions (monthly sea-surface temperature). To generate maps of presence vs. absence, Receiver Operator Characteristic (ROC) curves were used to define the optimal threshold that minimizes false positive and false negative error rates. I integrated model outputs, including tables (species in guilds, input surveys) and plots (fit of environmental variables, ROC curve), into an online spatial decision support system, allowing for easy navigation of models by taxon, region, season, and data provider.</p><p>For predicting cetacean density within the inner waters of British Columbia (chapter 3), I calculated density from systematic, line-transect marine mammal surveys over multiple years and seasons (summer 2004, 2005, 2008, and spring/autumn 2007) conducted by Raincoast Conservation Foundation. Abundance estimates were calculated using two different methods: Conventional Distance Sampling (CDS) and Density Surface Modelling (DSM). CDS generates a single density estimate for each stratum, whereas DSM explicitly models spatial variation and offers potential for greater precision by incorporating environmental predictors. Although DSM yields a more relevant product for the purposes of marine spatial planning, CDS has proven to be useful in cases where there are fewer observations available for seasonal and inter-annual comparison, particularly for the scarcely observed elephant seal. Abundance estimates are provided on a stratum-specific basis. Steller sea lions and harbour seals are further differentiated by ‘hauled out’ and ‘in water’. This analysis updates previous estimates (Williams & Thomas 2007) by including additional years of effort, providing greater spatial precision with the DSM method over CDS, novel reporting for spring and autumn seasons (rather than summer alone), and providing new abundance estimates for Steller sea lion and northern elephant seal. In addition to providing a baseline of marine mammal abundance and distribution, against which future changes can be compared, this information offers the opportunity to assess the risks posed to marine mammals by existing and emerging threats, such as fisheries bycatch, ship strikes, and increased oil spill and ocean noise issues associated with increases of container ship and oil tanker traffic in British Columbia’s continental shelf waters.</p><p>Starting with marine animal observations at specific coordinates and times, I combine these data with environmental data, often satellite derived, to produce seascape predictions generalizable in space and time. These habitat-based models enable prediction of encounter rates and, in the case of density surface models, abundance that can then be applied to management scenarios. Specific human activities, OWED and shipping, are then compared within a tradeoff decision support framework, enabling interchangeable map and tradeoff plot views. These products make complex processes transparent for gaming conservation, industry and stakeholders towards optimal marine spatial management, fundamental to the tenets of marine spatial planning, ecosystem-based management and dynamic ocean management.</p> / Dissertation

Environmental Health and Safety (EHS) departments in many organizations are faced with two interrelated problems which limit their ability to make accurate decisions based on quality data. First, many EHS departments follow a reactive business management model and need to work towards a proactive continuous improvement model to better manage EHS. The second is a lack of data integration and interoperability between the numerous different EHS data sources and systems. EHS departments are challenged with managing large quantities of data generated through tracking and monitoring programs to continuously improve EHS performance. EHS data can be in many forms paper, digital files, spreadsheets, images, relational databases and proprietary software applications. EHS data have strong spatial relationships, which makes the use of Geographical Information Systems (GIS) a very cost effective and feasible solution for integrating and managing EHS data. This thesis will outline how GIS brings to EHS the advantages of traditional IT methods with the added benefit of spatial analytical operations such as map overlay, relationships and querying, and informative visual presentation through maps, floor plans, and imagery through the implementation of a GIS database for EHS called GeoSpatial Environmental Health and Safety (GEO-EHS).