A new combination rule for Spatial Decision Support Systems for epidemiology.

BACKGROUND: Decision making in the health area usually involves several factors, options and data. In addition, it should take into account technological, social and spatial aspects, among others. Decision making methodologies need to address this set of information , and there is a small group of them with focus on epidemiological purposes, in particular Spatial Decision Support Systems (SDSS). METHODS: Makes uses a Multiple Criteria Decision Making (MCDM) method as a combining rule of results from a set of SDSS, where each one of them analyzes specific aspects of a complex problem. Specifically, each geo-object of the geographic region is processed, according to its own spatial information, by an SDSS using spatial and non-spatial data, inferential statistics and spatial and spatio-temporal analysis, which are then grouped together by a fuzzy rule-based system that will produce a georeferenced map. This means that, each SDSS provides an initial evaluation for each variable of the problem. The results are combined by the weighted linear combination (WLC) as a criterion in a MCDM problem, producing a final decision map about the priority levels for fight against a disease. In fact, the WLC works as a combining rule for those initial evaluations in a weighted manner, more than a MCDM, i.e., it combines those initial evaluations in order to build the final decision map. RESULTS: An example of using this new approach with real epidemiological data of tuberculosis in a Brazilian municipality is provided. As a result, the new approach provides a final map with four priority levels: "non-priority", "non-priority tendency", "priority tendency" and "priority", for the fight against diseases. CONCLUSION: The new approach may help public managers in the planning and direction of health actions, in the reorganization of public services, especially with regard to their levels of priorities.

Wind farm investments portfolio formation using GIS-based suitability analysis and simulation procedures.

The use of renewable energy sources instead of conventional energy sources is at the core of policy actions to reduce dependency on fossil fuels worldwide. As a result, especially during the last decade, the cost of renewable energy has significantly decreased, enriching renewable energy cost-competitiveness. Due to the spatial nature of renewable energy sector-related decisions, the synergy of geographical information systems (GIS) and Multiple Criteria Decision Analysis (MCDA) models can enrich the quality of the related decisions given their ability to effectively support land management considerations. Moreover, their implementation significantly enriches the performance of the traditional capital projects evaluation methods (CPEM) by providing physical data to the sizing process in a quick and accurate manner. Thus, decision-making frameworks that combine GIS-based suitability analysis with traditional financial evaluation techniques can significantly enrich the planning phase to achieve efficient installations in terms of required area reduction, power generation maximization and local characteristics examination. With respect to the realization of wind energy exploitation projects, the paper at hand proposes a framework capable of expanding the use of the traditional GIS-based derived suitability index to establishing portfolios. Moreover, the proposed framework is enriched by robust analysis using Monte Carlo Simulation (MCS), which provides significant insights regarding the stability of the derived portfolios and the projects that they comprise. The proposed framework is illustrated through a case study in the Thrace region in northeastern Greece.

A systematic review of spatial decision support systems in public health informatics supporting the identification of high risk areas for zoonotic disease outbreaks.

BACKGROUND: Zoonotic diseases account for a substantial portion of infectious disease outbreaks and burden on public health programs to maintain surveillance and preventative measures. Taking advantage of new modeling approaches and data sources have become necessary in an interconnected global community. To facilitate data collection, analysis, and decision-making, the number of spatial decision support systems reported in the last 10 years has increased. This systematic review aims to describe characteristics of spatial decision support systems developed to assist public health officials in the management of zoonotic disease outbreaks. METHODS: A systematic search of the Google Scholar database was undertaken for published articles written between 2008 and 2018, with no language restriction. A manual search of titles and abstracts using Boolean logic and keyword search terms was undertaken using predefined inclusion and exclusion criteria. Data extraction included items such as spatial database management, visualizations, and report generation. RESULTS: For this review we screened 34 full text articles. Design and reporting quality were assessed, resulting in a final set of 12 articles which were evaluated on proposed interventions and identifying characteristics were described. Multisource data integration, and user centered design were inconsistently applied, though indicated diverse utilization of modeling techniques. CONCLUSIONS: The characteristics, data sources, development and modeling techniques implemented in the design of recent SDSS that target zoonotic disease outbreak were described. There are still many challenges to address during the design process to effectively utilize the value of emerging data sources and modeling methods. In the future, development should adhere to comparable standards for functionality and system development such as user input for system requirements, and flexible interfaces to visualize data that exist on different scales. PROSPERO registration number: CRD42018110466.

Design of a GIS-based rating protocol to assess the potential for landfill closure using dredge material in post Hurricane Sandy New Jersey.

New Jersey is rapidly running out of capacity for storage of dredged material. A potential solution to this lack of storage space is to remove and reuse the dredged material for some beneficial use. Results from a Rutgers University project performed for the New Jersey Department of Transportation, Office of Maritime Resources, designed to assess the potential for closure of New Jersey landfills using dredge material from existing Confined Disposal Facilities (CDFs) are presented and discussed. The project included an update of the existing NJDEP landfill database, the development of a rating system to identify landfills with the highest potential to utilize dredged material for their closure, and the identification and preliminary investigation of the top candidate landfills based on this rating system.

Decision support systems for environmental management: a case study on wastewater from agriculture.

Dealing with spatial decision problems means combining and transforming geographical data (input) into a resultant decision (output), interfacing a Geographical Information System (GIS) with Multi-Criteria Decision Analysis (MCDA) methods. The conventional MCDA approach assumes the spatial homogeneity of alternatives within the case study area, although it is often unrealistic. On the other side, GIS provides excellent data acquisition, storage, manipulation and analysis capabilities, but in the case of a value structure analysis this capability is lower. For these reasons, several studies in the last twenty years have given attention to MCDA-GIS integration and to the development of Spatial Decision Support Systems (SDSS). Hitherto, most of these applications are based only on a formal integration between the two approaches. In this paper, we propose a complete MCDA-GIS integration with a plurality of MCDA methodologies, grouped in a suite. More precisely, we considered an open-source GIS (GRASS GIS 6.4) and a modular package including five MCDA modules based on five different methodologies. The methods included are: ELECTRE I, Fuzzy set, REGIME analysis, Analytic Hierarchy Process and Dominance-based Rough Set Approach. Thanks to the modular nature of the package, it is possible to add new methods without modifying the existing structure. To present the suite, we applied each module to the same case study, making comparisons. The strong points of the MCDA-GIS integration we developed are its open-source setting and the user friendly interface, both thanks to GRASS GIS, and the use of raster data. Moreover, our suite is a genuine case of perfect integration, where the spatial nature of criteria is always present.

Towards implementing precision conservation practices in agricultural watersheds: A review of the use and prospects of spatial decision support systems and tools.

Agricultural nonpoint source (NPS) pollution leads to water quality degradation. While agriculture is faced with the challenge of feeding a growing population in a changing climate, farmers must also strive to minimize adverse impacts of agriculture on the environment. As a result, policies, and agri-environmental programs to promote agricultural conservation practices for controlling NPS pollution have been emerging. Despite progress, reducing NPS is a complex challenge that requires ongoing innovation and investment. A major challenge is to achieve an optimal spatial trade-off between the economic costs and positive environmental outcomes of conservation practices on complex agricultural landscapes. Geospatial systems and tools can help to address this challenge and enhance the effectiveness and efficiency of conservation efforts. However, using these tools for precision conservation is underexamined. This review paper aims to address this gap through a critical exploration of spatial decision support systems and tools to provide synthesized knowledge for implementing precision conservation practices. This paper proposes a conceptual framework to guide the implementation of precision conservation and identifies areas for further development of geospatial systems and tools on planning and assessment of precision conservation efforts. All of which will be helpful for decision-makers and watershed managers in determining the most effective approaches for precision conservation. Furthermore, this review highlights the need for further research and development towards establishing an integrated spatial decision support system framework, which can improve socio-economic, environmental, and ecological outcomes.

Satellite Observations and Malaria: New Opportunities for Research and Applications.

Satellite remote sensing provides a wealth of information about environmental factors that influence malaria transmission cycles and human populations at risk. Long-term observations facilitate analysis of climate-malaria relationships, and high-resolution data can be used to assess the effects of agriculture, urbanization, deforestation, and water management on malaria. New sources of very-high-resolution satellite imagery and synthetic aperture radar data will increase the precision and frequency of observations. Cloud computing platforms for remote sensing data combined with analysis-ready datasets and high-level data products have made satellite remote sensing more accessible to nonspecialists. Further collaboration between the malaria and remote sensing communities is needed to develop and implement useful geospatial data products that will support global efforts toward malaria control, elimination, and eradication.

An Integrated GIS-Expert System Framework for Live Hazard Monitoring and Detection.

In the context of hazard monitoring, using sensor web technology to monitor anddetect hazardous conditions in near-real-time can result in large amounts of spatial data thatcan be used to drive analysis at an instrumented site. These data can be used for decisionmaking and problem solving, however as with any analysis problem the success ofanalyzing hazard potential is governed by many factors such as: the quality of the sensordata used as input; the meaning that can be derived from those data; the reliability of themodel used to describe the problem; the strength of the analysis methods; and the ability toeffectively communicate the end results of the analysis. For decision makers to make use ofsensor web data these issues must be dealt with to some degree. The work described in thispaper addresses all of these areas by showing how raw sensor data can be automaticallytransformed into a representation which matches a predefined model of the problem context.This model can be understood by analysis software that leverages rule-based logic andinference techniques to reason with, and draw conclusions about, spatial data. These toolsare integrated with a well known Geographic Information System (GIS) and existinggeospatial and sensor web infrastructure standards, providing expert users with the toolsneeded to thoroughly explore a problem site and investigate hazards in any domain.

Prevention of landfill pollution by multicriteria spatial decision support systems (MC-SDSS): development, implementation, and case study.

Landfilling of municipal solid waste (MSW) is one of the serious environmental concerns as improper location of MSW landfill site can release the pollutants into the surrounding environment. The process of selecting MSW landfill site is a complicated decision making problem since it is subjected to simultaneous assessment of several environmental criteria, rules, and restrictions besides sociocultural and economic ones. The current study suggests a framework based on Multicriteria spatial decision support systems (MC-SDSS) to select landfill site. The MC-SDSS is an advanced method to integrate multiple criteria decision analysis (MCDA) and geographical information systems (GIS) techniques. This approach enables the incorporation of several conflicting objectives and preferences into spatial decision models. In this study, 14 criteria were chosen and then divided into environmental, sociocultural, and economic categories. Finally, suitability maps were generated based on the MC-SDSS analysis. The developed method was implemented in a real case study in Arak city in northwestern region of Iran, which is environmentally sensitive area. The suitability maps of the case study in Arak showed that 10% (391 km2) is least suitable area, 23% (942 km2) is low suitable, 37% (1507 km2) is moderate suitable, 19% (783 km2) is suitable, and 11% (489 km2) is most suitable locations for landfill site, and finally, three best alternative sites were introduced for the final landfill site.