Spatial Decision Support System Research Articles

The Assessment and Mapping of Urban Visual Pollution through an Assembly of Open Source Geospatial Tools

Urban surroundings and spaces are losing their identity due to the flooded visual pollution in the urban panorama of already densely populated cities in developing countries. Quantitative assessment of visual pollution and its spatial mapping; both are very recent and relatively un-explored branches of urban studies. The diversity of visual pollution objects (VPOs) and their traits, the subjectivity of observers, the scale of urban space and dependency on subjective variables has been key challenges for quantification during visual pollution assessment. The researcher has previously developed a paper-based score-card type visual pollution assessment (VPA) tool using Analytical Hierarchy Process (under publication) to address these issues. However, considering the challenges associated with the deployment of paper-based tool for VPA (inability to handle variety of data types i.e. text, numeric, geolocation, images etc.), the natural progression has been the development of a mobile-based solution which matches the fast-growing mobile penetration rate of urban centers and provides a turn-key solution to achieve efficiency and effectiveness in primary data collection. On the other hand, academic research on the spatial mapping of visual pollution has slightly progressed to explore its cartographic dimension. This research presents a spatial decision support system comprising of a combination of open source tools to collect, store and present visual pollution assessment data for any urban space of any scale. The system employs Open Data Kit (ODK) to build its mobilebased VPA tool which can be used to collect VPO attributes using any android device. The collected data is streamed to a web-based data management module of the system in real time which is built upon ODK Aggregate and PostgreSQL. Furthermore, the web-based visualization module of the system is built upon some other major open source tools including OpenGeo Suite and PHP. The visualization module presents the results of visual pollution index (VPI) in the form of a web-based dashboard containing real-time choropleth maps which can be filtered for any specific VPO.This research demonstrates the strengths of open geospatial tools to solve challenges of primary data collection on a diverse range of VPOs along with the systematic capturing of their spatial location and visual images. Furthermore, it proves the ability of open source web mapping tools to display visual pollution assessments in most appropriate cartographic representation.

Toward building a 3D Web-based spatial decision framework for apartment selection

In the last few decades, there has been a growing interest in effectively incorporating the analytic modeling capabilities of decision support systems and the spatial modeling capabilities of geospatial information systems to solve complex spatial decision-making problems in various fields. Spatial decision support systems assist decision makers in exploring, structuring, and generating solutions for complicated spatial decision problems such as apartment selection. The selection of an apartment is a decision which plays an important role in human life. The good location is the critical factor that affects the value and potential of a real estate. This emphasizes the significance of spatial factors in decision making in real estate business. The spatial accessibility value of each apartment to different service categories can be used while choosing the most suitable apartment. Hence, the study covers not only non-spatial aspects, for example, unit price, house size, and number of rooms, but also spatial aspects, such as spatial accessibility, of the apartment selection. To sum up, this study proposes a spatial decision framework, called EMEKLI, to facilitate the decision-making process for the selection of an apartment in the presence of different priorities and uncertainties among the decision criteria. Furthermore, the recommendations obtained from the decision-making process are shared with the decision makers in the 3D environment through a virtual globe.

Classification of the general viability of agricultural land in Aqrah district using the Agricultural Spatial Decision Support System (DSS Microlies)

Aqrah district is one of the districts of Dohuk governorate, and astronomically it is located on the coordinates of the two circles of latitude {36:31:56} and {37:02:56} north, and longitude {43:54:56} and {44:18:65} to the east. The study area consists of 261 villages, distributed in four sub-districts: Al Markaz district (Aqrah city), Dinarata district, Bijil sub-district and Kordasin sub-district, as agricultural activity is the most prominent profession among the vast majority of the residents of the district villages that own about (261256) dunums of arable land, of which approximately (35733) dunums are designated for irrigated agriculture, and (20 056) dunums are cultivated with winter grain crops, especially (wheat and barley), depending on the amounts of rain that fall annually, so Aqrah district is classified as a guaranteed area. The rain falls quantities ranged between (400 - 900) mm, while the rest of the agricultural lands, which amounted to (23467) dunums, were exploited in the cultivation of fruit orchards such as (figs, pomegranates, walnuts, peaches, apricots, grapes) which Aqrah district is famous for. While the area of land that is not suitable for agriculture is about (49695), It is distributed over rocky lands, pastures, natural and artificial forests, but the application of the Microlies system methodology has revealed the presence of new levels of productive capacity for soil in Aqrah district.

Establishing a benchmark for comparing relative cost-effectiveness of an alternative policy instrument for controlling non-point source pollution

Knowledge about economic environmental tradeoffs is a prerequisite to evaluate the relative cost-effectiveness of alternative policy instruments to control non-point source pollution (NPS). Failure to explicitly account for the impact of the spatial combination of alternative management practices on pollution contribution rates while quantifying these tradeoffs may cause relative comparisons between alternative policy instruments to control NPS pollution inappropriate. The main objective of this research is to develop a spatial decision support system able to quantify cost-effective economic environmental tradeoffs of maintaining alternative pollution standards taking the interrelated linkages between various agricultural management practices into account. The system is used to establish a benchmark tradeoff curve by means of which alternative policy instruments to control NPS pollution can be compared to determine their relative cost-effectiveness. Tradeoff analyses indicated that the pollution contribution factor plays a cardinal role in determining pollution abatement cost and therefore the benchmark. The importance of the contribution factor highlights the necessity of taking the interdependencies between management units into account when modelling economic environmental tradeoffs. Catchment level tradeoffs showed that the nitrate water quality indicator can be improved with little cost due to both positive and negative tradeoffs at the sub-catchment level. Significant variability exists at the sub-catchment level when nitrate pollution abatement and abatement cost are concerned. The conclusion is that care should be taken not to compare the relative cost-effectiveness of alternative policy instruments to control NPS pollution based on catchment level tradeoffs alone.

Spatial Rice Decision Support System for Effective Rice Crop Management

Spatial Rice Decision Support System for Effective Rice Crop Management

Introducing a group spatial decision support system for use in strategic environmental assessment of onshore wind farm development in Mexico

In this paper we introduce a method for potential use in strategic environmental assessment (SEA) which integrates knowledge and tools of Collective Intelligence, Complexity Theory and Geoprospective, via the implementation of a technological Group-Spatial Decision Support System (GSDSS), usable for scenario building for infrastructure project planning. It operates on the basis of interdisciplinary consensus of a multidisciplinary group of experts, without strict dependency on a spatial analysis based on a single cognitive stance, using existing historical data. The method is used in a study case on planning of onshore wind energy in Mexico, which has been developed through a collaborative Geoweb application, and is functioning in a distributed and asynchronous real-time way, so-called Geospatial System of Collective Intelligence (SIGIC). The significance of this research lies in demonstrating the feasibility of a collective intelligence analysis aimed at proposing spatial locations in an energy transition context within an SEA framework.

Efficacy of Rainfall-Runoff Models in Loose Coupling Spacial Decision Support Systems Modelbase

The need for a prognosis for water resources management in Brazil means that studies with hydrological models are required, as part of a Spatial Decision Support System (SDSS). Choosing the model in a modelbase requires knowledge of the performance of the existing models in the specific situations in which they are to be applied. This paper evaluated the performance of two physically-based models and two numerical-based models in their ability to represent the rainfall-runoff process. The study occurred in a watershed in southern Brazil. Historical data series from the same periods were taken to calibrate and train the models, using two different periods to validate their efficacy. The results show that the SWAT and TOPMODEL models presented an inferior performance compared to the numerical-based models (RT and ANN). However, all the models presented satisfactory levels of efficacy and the potential for use in different management situations.

A cyberGIS-enabled multi-criteria spatial decision support system: A case study on flood emergency management

ABSTRACTWith the increased frequency of natural hazards and disasters and consequent losses, it is imperative to develop efficient and timely strategies for emergency response and relief operations. In this paper, we propose a cyberGIS-enabled multi-criteria spatial decision support system for supporting rapid decision making during emergency management. It combines a high-performance computing environment (cyberGIS-Jupyter) and multi-criteria decision analysis models (Weighted Sum Model (WSM) and Technique for Order Preference by Similarity to Ideal Solution Model (TOPSIS)) with various types of social vulnerability indicators to solve decision problems that contain conflicting evaluation criteria in a flood emergency situation. Social media data (e.g. Twitter data) was used as an additional tool to support the decision-making process. Our case study involves two decision goals generated based on a past flood event in the city of Austin, Texas, U.S.A. As our result shows, WSM produces more diverse values and higher output category estimations than the TOPSIS model. Finally, the model was validated using an innovative questionnaire. This cyberGIS- enabled spatial decision support system allows collaborative problem solving and efficient knowledge transformation between decision makers, where different emergency responders can formulate their decision objectives, select relevant evaluation criteria, and perform interactive weighting and sensitivity analyses.

Spatial decision support system for land use management of newly reclaimed areas in arid regions

The current work is an attempt to establish a spatial decision support system (SDSS) for the newly reclaimed areas, especially saline soils of the arid regions. The proposed system is based on database of ArcGIS environment and includes land capability and suitability models. A base map of the study area, remotely sensed data, soil and water analytical data, meteorological data and socio-economic information formulate the essential database for the SDSS implementation. Based on processing the proposed SDSS, the agricultural and fish farming land uses would be increased from about 48% to 71% of the total area where almost 60% would be devoted to field crop cultivation, 11% can be used as permanent fish farms and 29% could be assigned to residential and industrial purposes or any other recreational uses. The soils capabilities would be improved through good management policy and land reclamation processes.

The use of ecological integrity indicators within the natural capital index framework: The ecological and economic value of the remnant natural capital of México

In this paper, the ecological integrity hierarchy framework (EIHF) and the natural capital index framework (NCI) are integrated as decision-making tools for evaluating the natural capital of Mexico. Two hierarchy-levels of ecological integrity indicators are used to estimate the quality and quantity of the natural capital, the amount of ecological degradation and ecological sustainability. After human transformation, the extent still considered as “natural” in the country is ∼67%; while the amount of human transformed areas is ∼33%, which gives a total estimate of NCI = 0.334; i.e., only ∼33.4% of the national capital remains available, while ∼33% is ecologically degraded. Furthermore, the critical natural capital; i.e., the legacy for future generations that remains in the country is only ∼12%. The total estimated value of the current natural capital in Mexico is ∼$457.1 billion/yr, which is ∼435 times greater than the national GDP ($1.051 billion in 2010). The cost of maintaining the degradation of the natural capital is ∼$144.6 billion/yr (∼138 times greater than national GDP in 2010). The potential value of the natural capital after restoration would be ∼$602 billion/yr. Valuing the natural capital can be helpful for strategic environmental evaluations and useful for spatial decision support systems that evaluate natural capital as a decision-making tool.

Ecosystem Health Assessment Using a Fuzzy Spatial Decision Support System in Taleghan Watershed Before and After Dam Construction

Developing new and practical methodologies in order to assess ecosystem health based on physical, ecological and socio-economic indicators is an essential field of environmental studies. Nowadays, because of the considerable importance of the spatiotemporal dynamics of ecosystem variables, scientists utilize technologies such as geospatial information systems and remote sensing to achieve different indicators. In this paper, the Taleghan watershed, in Alborz province in Iran was selected as a study area, which has been exposed to many stresses including the construction of a dam in 2007. First, an indicator system based on Driving force-Pressure-State-Impact (DPSI) model was established. Indicators were quantified before and after construction of the dam. At the end, the assessment was carried out by development of a spatial decision support system based on fuzzy analytical hierarchy process method. This system was made for calculating the weights of indicators, compositing the maps of various indicators, producing and displaying maps of DPSI indicators and regional ecosystem health, and identifying critical areas in terms of ecosystem health. The results show that ecosystem health values in the eastern (especially northeast) parts of the watershed (upstream of the dam) and the areas adjacent to the river have been lower in comparison with other areas before dam construction. However, after the dam construction, critical areas in terms of ecosystem health shifted to the downstream region in the western parts. 29.79% of the region in the first period and 23.37% in the second period had a very low and low level of ecosystem health.

Multi-criteria spatial decision support system for identifying strategic roads in disaster situations

A road transportation network has an important role in the management of disaster situations. Despite its' vulnerability to disasters, it helps to provide emergency responses to disaster management practices. Therefore it is required to identify the most important roads in the network in order to support decision-makers to make appropriate decisions about the roads.This paper introduces an integrated methodology to evaluate the transportation network performance (TNP) in disaster situations by developing a multi-criteria spatial decision support system (MC-SDSS). The developed MC-SDSS is a fully integrated system of Geospatial Information System (GIS) and Multi-Criteria Decision Making (MCDM) methods. On the one hand, GIS functionalities are used for storing the data, performing the analyses in order to produce the required criteria and displaying the results. On the other hand, AHP as a well-known MCDM method is used to receive priorities and preferences of decision-makers about the criteria.Based on the decision-making model (intelligent, design, and choice), four criteria are selected as indicators for evaluating the TNP in disaster situations: capacity, accessibility, vulnerability, and importance criteria. In this regard, criteria maps are generated by GIS tools, the experts' preferences about the criteria are acquired by AHP comparison matrix, and a ranking of the roads are prepared and visualized on the MC-SDSS. Finally, by utilizing the One-At-Time approach as the sensitivity analysis method, MC-SDSS tries to determine the robustness of the results due to the variation or uncertainty resulting from changing the important scales of the criteria in the AHP pairwise comparison matrix.The results show that about 9, 33, 20, and 38% of the roads are very high, high, moderate, and low strategic in the case study (Mazandaran province, Iran) respectively. It also shows that capacity/accessibility pairwise comparison is the most sensitive comparison in the AHP comparison matrix.

Spatial Decision Support Systems: Three decades on

This paper uses a bibliometric approach to examine the growth of and changes in the Spatial Decision Support Systems (SDSS) field over the past three decades. Bibliographic databases such as Web of Science (WOS) and Scopus provide valuable information on academic disciplines as they contain both the articles published and the articles cited. The articles published, and the disciplinary categorization of where they are published, are indicative of the changing disciplinary balance in SDSS, while the citation links of these papers illustrate the intellectual structure of the field. The analysis shows that despite conceptual links rooted in DSS, the field of SDSS developed largely independently from DSS, with little interaction between both. This is surprising, given the growing importance of spatial applications in DSS and an overlapping interest in business analytics and big data space-time analytics. The paper argues for greater interest in SDSS developments in the DSS field, including emergency response SDSS and public participation SDSS, as two forms of SDSS which extend DSS.

The qualitative risk assessment model of riskote decision support system

Sendai Framework for Disaster Risk Reduction 2015-2030 identify among other principles that disaster risk reduction requires a multi-hazard approach and inclusive risk-informed decision-making. The development of a harmonized decision support tool to ensure that planning decisions are informed about the hazards affecting a given territory, the vulnerability of different land uses, exposed elements and options available to mitigate risks is crucial in the context of spatial planning. The objective of this paper is to describe the qualitative risk assessment model used in the development of the RiskOTe decision support system (DSS). RiskOTe is a spatial decision support system to assist spatial planning integrating the risk management component at municipal level. The qualitative risk assessment model used in RiskOTe is based on risk matrix that relates consequences and likelihood. Oeiras municipality was used as case study to demonstrate the applicability of the spatial decision support system. The scenarios obtained from the case study show that decision-making integrating risk analysis can be supported on a solid basis of information obtained from the system.

Spatial multicriteria decision support for robust land-use suitability: The case of landfill site selection in Northeastern Greece

Multicriteria spatial decision support systems (MC-SDSS) have emerged as an integration of geographical information systems (GIS) and multiple criteria decision aid (MCDA) methods for incorporating conflicting objectives and decision makers’ preferences into spatial decision models. In this paper, we present spatial UTASTAR (S-UTASTAR), a raster-based MC-SDSS for land-use suitability analysis. The multicriteria component of the system is based on the UTA-type disaggregation-aggregation approach. S-UTASTAR is applied in a raster-based case study concerning land-use suitability analysis to identify appropriate municipal solid waste landfill (MSW) sites in Northeast Greece. Moreover, robustness analysis tools are implemented to guarantee robust decision support results. More specifically, during the aggregation phase, the Stochastic Multiobjective Acceptability Analysis (SMAA) is used to indicate the frequency at which a site achieves the best ranking positions within a large set of alternative landfill sites.

Spatial decision support systems: Exploring differences in pilot-testing with students vs. professionals

This study explores the implications of engaging students, versus professionals/experts, in pilot-testing of SDSS, and discusses likely differences in terms of expected outcomes for the given pilot-test.To this end we use data collected during two pilot tests of a novel SDSS that was developed by members of our project team. The pilot-tests were done with two different groups; one made of 13 doctoral students, while the other of 12 professionals/stakeholders. The pilot-test served to gather feedback on SDSS usability and other aspects of interest to the development team. On the basis of the outcomes obtained we develop an analytical framework meant to summarise what we come to notice as key aspects distinguishing how different types of testers will engage in an SDSS pilot-test, and the type of feedback these will consequently provide. These key aspects include expertise, stage of life, and institutional context (ESI). This framework could offer some help to other teams in planning, organizing, and delivering pilot-test, and processing the assessments received.

Spatial OLAP and Multicriteria Integrated Approach for Decision Support System

This article describes how Spatial On-Line Analytical Processing, such a decisional technology, offers the possibilities of spatial and multidimensional analysis of data stored in multidimensional structure namely spatial data warehouse. However, this technology is limited in the quality aspect of the decision related to the multicriteria consideration. In the current article, the objective is to propose a Spatial Decision Support System namely “Silvicultura” for facilitating decision making in complex situations. This approach is based on integrating multicriteria analysis with SOLAP in order to enrich the spatial and multidimensional analysis with the contribution of MCA tools for mitigating conflict situations. The authors have based their proposal modeling on Unified Modeling language, since it is a well-known standard modeling language and can be easily extended for multidimensional modeling. Finally, in order to validate their proposal, the authors present a case study to show how to use it in the agroforestry management.

A Review of Applying Spatial Modelling and GIS in Residential Water Use

Growing studies indicate that social geography characteristics and spatial interactions contribute to water sustainability management in residential water use. Recently, researches are being developed spatial decision support system by integrating spatial modelling with GIS, which clearly display the determined factors information of water consumption by the changing weights methods. Policy makers will use the tool to manipulate and evaluate the effective of water management strategies, and it is beneficial to analyse a range of alternative water conservation policies. Such studies and model applying are of great significance for reference in sustainable water management in China. Thus, this paper reviews the previous researches of spatial modelling and application in residential water use.

Assessing the visual impacts of new urban features: coupling visibility analysis with 3D city modelling

Land use changes associated with urbanization often have negative impacts on scenic beauty. In this paper, we explore and compare the visual impact of two different scenarios of urban growth including historical urban growth (HUG) and aesthetically sound urban growth (AUG) with two different categories of height for buildings in the city of Gorgan, Iran. This was done by viewshed and landscape metric analysis of different viewpoints and 3D representation of each scenario. The results show that with the AUG scenario, viewsheds are less impacted by new developments than the HUG scenario in all the viewpoints. It can be concluded that building locations can considerably affect the landscape visibility while building height does not impact to the same degree as location. The results of this research, as a Spatial Decision Support System, would help the managers for better understanding of different patterns of urbanization and its effect on landscape view.

Conceptual Framework for SDSS Development with an Application in the Retail Industry

Spatial information is becoming crucial for strategic decision making, but accessing and understanding this information is not easy. Dedicated tools can support the decision process in many ways, such as visualization interfaces or data analyses. Numerous Decision Support System (DSS) development methodologies exist along with dedicated Spatial Decision Support System (SDSS). Unfortunately, for multiple reasons, these tools and methodologies are not easily adaptable for the development of another SDSS. This paper proposes a framework for the development of a flexible SDSS that is built on open source software, allowing for low cost implementation. To support the efficiency of our approach, the design of a specific SDSS that is currently in use will be presented. This SDSS was developed for a company that distributes products through various retail networks. The multiple capabilities of the resulting SDSS will be revealed through an explanation of the different development steps. The complete framework is applied to a real data set that will be detailed in a demonstration.