Spatial Decision Support System Research Articles

Assessment of Forest Transitions and Regions of Conservation Importance in Udupi district, Karnataka

The current study prioritizes regions of conservation importance at the disaggregated level in the Udupi district, Central Western Ghats, based on ecological, geo-climatic, land, and social aspects. Conservation importance regions (CIR) or Ecological Sensitive Regions (ESR) are the distinct ecological units with exceptional biotic and abiotic elements which need at most care and sustainable development. CIR prioritization at grid levels (5' × 5' grids or 9 × 9 km) acts as a spatial decision support system to better understand the forest landscape dynamics and planning. The analyses of forest landscape dynamics using the temporal remote sensing data in reveal an increase in built-up areas by 8.8% with a decline in forest cover, resulting in the rise in maximum temperature by 40C in Udupi district during 1990-2018. Multivariate statistical analysis is done to understand the role of landscape dynamics on the land surface temperature (LST). The correlation analysis shows an increasing trend of LST across the CIR region with r = 0.8 where CIR 1 indicates the lowest temperature and CIR 4 has the maximum temperature.

Integration of a Network-Based and an Economy-Wide Water Model to Support Decision Making on Water Resources Planning and Management in Northeastern Brazil

Allocation of Water Resources at the basin level is a critical issue for economic growth as well as for environmental sustainability. This study integrates network-based optimization with an Input-Output model, made available through a Spatial Decision Support System (HEAL), to support design and evaluation of water allocation policies. The innovative platform was applied to a case study using four-interlinked hydrographic basins in Northeastern of Brazil. The integrated modeling was able to measure broader socioeconomic impacts of decisions on reservoir volumes and water allocations at basin level, through indicators in a sectoral and regional scale, including ones associated with Sustainable Development Goals, such as the Water Use Efficiency (WUE) indicator. Results of the trade-offs between two scenarios, representative of the limits of performance of regulatory water instruments, were generated using the integrated modeling. They were compared with the Reference scenario for the base year (2011) and show that the economic sector most negatively affected by the regulatory instrument use is the industrial sector. Furthermore, the sugar and ethanol industry, main water users in the industrial sector (93.1% of the sector's water use) and less efficient (WUE 1.47 US$/m3 vs. 30.70 US$/m3 average of the sector) in the base year, maintain their percentage share of water use in the sector and even slightly expand it (93.2%), with slight efficiency gains (+2.3%). On the other hand, non-water-intensive industries, have their shares reduced (from 6.9 to 6.8%) and lose efficiency (−9.5%). Results of the same trade-offs by region showed that the largest proportional economic losses occur in the drier areas, damaging the economy, especially in the most industrialized municipalities with the highest GDPs. Integrated economic modeling can expand aspects involved in water security issues, assisting management by introducing socioeconomic impact measures, in a broader scale, associated with allocation decisions. Hydrological allocation criteria cannot distinguish between user efficiency and which economic sectors are using how much water. This results in economic and social losses. In water-scarce regions and with growing transfer needs, such as in the basins studied, adequate incentives, through management instruments designed based on economic theory, are essential to promote sustainable development.

An automated and optimized geo-computation approach for spatial fire risk modelling using geo-web service orchestration

Forest fire detection through space-based observations using thermal sensors is in operation since more than one decades. These active fire alerts are generated by using products from MODIS and SNPP–VIIRS satellites and are limited up to identification of active fire locations based on recorded temperature in satellite sensors. However, relation of fire location with forest type map, terrain profile and weather data are required for planning and monitoring of vulnerable areas. An attempt is made to develop online Spatial Decision Support System (SDSS) and spatial model for forest fire risk mapping. The developed SDSS is based on Geospatial Web Service Orchestration (GWSO) workflow which enables forest fire risk alerts and advisories as Web Map Service, Geo SMS and a Geoportal application. User can use this SDSS with minimum technical skills and limited computation resources to generate daily forest fire risk map for fire monitoring and management.

A Simple Method for Identifying Appropriate Areas for Onsite Wastewater Treatment

Onsite Domestic Wastewater Treatment Systems (ODWTS) are increasingly important for treating domestic wastewater in metropolitan contexts, especially in suburban sectors isolated from sewer networks and centralized treatment plants. When ODWTS are not correctly planned and located in suitable places, or are not properly designed, they can cause groundwater contamination and generate risks for human health. This work presents a Spatial Decision Support System (SDSS) to zone specific areas based on a few simple parameters. The proposed tool can be easily adapted to different contexts, even where institutional capacities are low. Results obtained in the metropolitan area of the Lerma Valley (Salta, Argentina) show strong contradictions between our zoning and current urbanization features in the study area. As a result, environmental impacts and health hazards are likely to manifest in the short or medium term. The sectors with the best receptivity conditions were found in the southern sector of the study area. We argue that ODWTS can be safely implemented in many areas as long as this concept is embedded in urban planning initiatives, which usually also require the consolidation or development of appropriate institutions and control systems.

Wind farms selection using geospatial technologies and energy generation capacity in Gwadar

Pakistan has been a victim of energy crisis since last few decades. This energy crisis has adversely affected country’s socio-economic development and continues to do so. The continuously increasing demand–supply gap has negatively impacted the economic stability of the country. With the recent awareness and development of alternate energy resources like wind and solar, the current energy crisis can be minimized. However, proper planning is essential for successful execution of these renewable energy projects. This study aims to identify the suitable sites for wind farms in District Gwadar, Balochistan using Geographical Information Systems (GIS) and Web-based Spatial Decision Support System (SDSS). In this study, multi-criteria decision making is applied which assists breaking down the site selection complexity. Multi-Criteria evaluation methods provides different set of procedures that facilitate decision making by analyzing different alternatives. The underlying geospatial and ICT technologies used in this analysis form the core component of the planning process. Gwadar is currently drawing investor’s attention due to its geographical location, deep seaport, and proposed China–Pakistan Economic Corridor (CPEC). This research is useful for stakeholders of Wind Energy to explore the wind potentials using GIS as an interactive decision-making tool during the pre-feasibility stage.Furthermore, this research has considered the environmental, social and economic aspects during the decision-making process of wind farm development. This is the strength of multi-criteria evaluation as differently weighted scenarios provide different output, depending on the factors considered of highest importance. A detailed analysis of the sites in terms of their wind potential and energy generation capacity has also been reported in this study. This long coastline of Balochistan with huge wind energy potential has not been explored yet and therefore this study will assist researchers to further explore this area and can have a positive impact on CPEC.

Towards NoSQL-based Data Warehouse Solution integrating ECDIS for Maritime Navigation Decision Support System

Maritime shipping is a complex process because it requires a continuous analysis of huge amounts of information. Much of this information has variable spatial and temporal properties. In addition, the analysis must be effective to help in making decisions in a timely manner. Generally the analysis is applied with Spatial On-Line Analytical Processing (SOLAP) basing on spatial data warehouses SDW. Data warehousing by current technologies with relational database management systems (RDBMS) cannot be applied effectively to manage the increasing complexity and volume of geo-referenced data. In order to increase the performance and availability of services, NoSQL databases technologies have appeared. This paper presents the modeling and the realization of a spatial decision support system (SDSS) for maritime navigation namely “Maritima”, basing on the integration of the ECDIS and OLAP systems. In fact, the spatio-multidimensional analysis of the stored data can benefit from the expressive power of the electronic navigation chart offered by the ECDIS system. In order to benefit from the NoSQL model advantages, we try to apply transformations for the conceptual model to create document NoSQL DW for maritime data. In addition to that, the multidimensional analysis at different dimensions and aggregation levels can generate beneficial indicators for the decision-aid and can be very useful in addition of other steering systems for secure maritime navigation. Finally, “Maritima” is tested with a set of spatial data related to two different harbors “Arzew” and “Oran” in western of Algeria.

Measuring Spatial Accessibility of Urban Medical Facilities: A Case Study in Changning District of Shanghai in China.

Medical facilities help to ensure a higher quality of life and improve social welfare. The spatial accessibility determines the allocation fairness and efficiency of medical facilities. It also provides information about medical services that residents can share. Although critical, scholars often overlooked the level of medical facilities, the composition of integrated transportation networks, and the size of service catchment in the literature on accessibility. This study aims to fill this research gap by considering the integrated transportation network, population scale, travel impedance between medical facilities and residential areas, and the impact of medical facilities’ levels on residents’ medical choices. An improved potential model was constructed to analyze the spatial accessibility of medical facilities in Changning District of Shanghai, China. Interpolation analysis was conducted to reveal the spatial accessibility pattern. Cluster and outlier analysis and Getis-Ord Gi* analysis were applied for the cluster analysis. Results show that the spatial accessibility of medical facilities is quite different in different residential areas of Changning District, Shanghai. Among them, the spatial accessibility of medical facilities is relatively high in Hongqiao subdistrict, Xinjing Town, and part of Xinhua Road subdistrict. In addition, residents have overall better access to secondary hospitals than to primary and tertiary hospitals in the study area. This study provides a spatial decision support system for urban planners and policymakers regarding improving the accessibility of healthcare facilities. It extends the literature on spatial planning of public facilities and could facilitate scientific decision making.

Land suitability determination for citrus cultivation using a GIS-based multi-criteria analysis in Mersin, Turkey

Determination of suitable locations for agricultural products is an important subject to study, as it is essential to identify optimal areas for crop productivity and sustainable farming activities. This study proposes a site selection assessment model for citrus cultivation using Geographic Information System (GIS) Based Multi-Criteria Decision Analysis (MCDA) in the Mersin province of Turkey. The purpose of the study is to create a Multi-Criteria Assessment-Based Spatial Decision Support System which takes into account the opinions of local citrus farmers as well as those of experts. 14 preference factors and 10 exclusion factors were determined for the analysis of land suitability for citrus farming. The weighting of preference factors for citrus cultivation was determined using the Analytic Hierarchy Process, and a land suitability map was generated using the weighted overlay method in the GIS environment. Based on the opinions of farmers and experts, higher weights were assigned to the average temperature in the flowering period, the average temperature in the winter season, and elevation, as compared to all other preference factors. The results also demonstrate that WorldClim, a global climate dataset, is an essential source of data in such studies, as climate conditions play a crucial role in potential citrus expansion. The final land suitability map shows that the most suitable and moderately suitable areas for citrus cultivation have surface areas of 85 824 ha and 85 430 ha, respectively. The Receiver Operating Characteristic (ROC) curve was used to evaluate validation of the land suitability for citrus and the Area Under the Curve (AUC) value was calculated at 0.87, indicating substantial consistency. This study proposes a methodology for the determination of suitable sites for citrus cultivation which relies on realistic data. The land suitability map produced in the study will be essential for the creation of sustainable agriculture strategies in the eastern Mediterranean region.

Multimodal travel‐time maps with formally correct and schematic isochrones

AbstractThe automatic generation of travel‐time maps is a prerequisite for many fields of application such as tourist assistance and spatial decision support systems, for example to analyze the accessibility of health and social facilities. The task is to determine outlines of zones that are reachable from a user’s location in a given amount of time. In this work we focus on travel‐time maps with a formally guaranteed SEPARATION PROPERTY in the sense that a zone exactly contains the part of the road network that is reachable within a pre‐defined time from a given starting point and start time. In contrast to other automated methods that create travel‐time maps, our approach generates schematized travel‐time maps that reduce the visual complexity by representing each zone by an octilinear polygon, that is, the edges of the polygons use only eight pre‐defined orientations. We aim for octilinear polygons with a small number of bends to further optimize the legibility of the map. The reachable parts of the road network are determined by the integration of timetable information for different modes of public transportation, for example buses, trains or ferries, and pedestrian walkways based on a multimodal time‐expanded network. Moreover, the travel‐time maps generated visualize multiple travel times using a map overlay of different time zones and taking natural barriers such as rivers into account. In experiments on real‐world data we compare our schematic visualizations to travel‐time maps created with other visualization techniques with respect to simple but robust quality measures such as the number of bends and the perimeter of the zones.

Stream power index for networks (SPIN) toolbox for decision support in urbanizing watersheds

Urbanization typically leads to erosion and instability in rivers, and many management and restoration strategies have been developed to dampen the worst impacts. Stream power, defined as the rate of energy expenditure in a river, is a promising metric for analyzing cumulative effects. In this paper we describe a spatial decision support system called the Stream Power Index for Networks (SPIN) toolbox that can be used to assess urban river stability at a watershed scale. The objectives of the paper are to: a) describe the toolbox algorithms and procedures and b) demonstrate the utility of the approach. SPIN is written in Python and packaged as an ArcGIS toolbox. The toolbox combines existing landscape analysis algorithms with new algorithms to model river confluences, channel sinuosity, and threshold sediment particle sizes. Data can also be ingested from a standard hydraulic model. Two case studies demonstrate use of the toolbox to: i) anticipate current morphology; ii) predict urban morphologic change; and iii) analyze the benefits for stormwater management and channel restoration scenarios on channel stability.

A methodology for managing public spaces to increase access to essential goods and services by vulnerable populations during the COVID-19 pandemic

PurposeThe purpose of this paper is to present a spatial decision support system (SDSS) to be used by the local authorities of a city in the planning and response phase of a disaster. The SDSS focuses on the management of public spaces as a resource to increase a vulnerable population’s accessibility to essential goods and services. Using a web-based platform, the SDSS would support data-driven decisions, especially for cases such as the COVID-19 pandemic which requires special care in quarantine situations (which imply walking access instead of by other means of transport).Design/methodology/approachThis paper proposes a methodology to create a web-SDSS to manage public spaces in the planning and response phase of a disaster to increase the access to essential goods and services. Using a regular polygon grid, a city is partitioned into spatial units that aggregate spatial data from open and proprietary sources. The polygon grid is then used to compute accessibility, vulnerability and population density indicators using spatial analysis. Finally, a facility location problem is formulated and solved to provide decision-makers with an adaptive selection of public spaces given their indicators of choice.FindingsThe design and implementation of the methodology resulted in a granular representation of the city of Lima, Peru, in terms of population density, accessibility and vulnerability. Using these indicators, the SDSS was deployed as a web application that allowed decision-makers to explore different solutions to a facility location model within their districts, as well as visualizing the indicators computed for the hexagons that covered the district’s area. By performing tests with different local authorities, improvements were suggested to support a more general set of decisions and the key indicators to use in the SDSS were determined.Originality/valueThis paper, following the literature gap, is the first of its kind that presents an SDSS focused on increasing access to essential goods and services using public spaces and has had a successful response from local authorities with different backgrounds regarding the integration into their decision-making process.

User experiences of using a spatial analysis tool in collaborative GIS for maritime spatial planning

AbstractMaritime spatial planning (MSP) is a decision‐making process for managing human activities at sea. Stakeholder participation is critical to MSP processes. Spatial decision support systems (SDSSs) can be effective tools for analyzing problems in MSP, for example, the impact of human activities on marine ecosystems. However, despite the fact that multiple SDSSs have been developed for MSP, they are rarely used in real‐world MSP processes. We aim to provide insight into stakeholders' understanding and perception of the appropriateness and completeness of SDSSs in an MSP stakeholder meeting. We studied whether SDSSs can benefit from being integrated into CGIS to support alternative methods for problem exploration and solving in groups. The results show that most, but not all, stakeholders understood well or fairly well what the tool does and how to use it, and agreed that the tool was appropriate and had the necessary requirements for problem solving. The results also show that problem exploration and solving with an SDSS in groups can benefit from the tool being integrated into a CGIS. Further research is needed to find effective solutions to overcome stakeholders' challenges in using GIS, and to develop flexible solutions that enable alternative problem‐solving methods.

Cyberinfrastructure and intelligent spatial decision support systems

Cyberinfrastructure and intelligent spatial decision support systems

A spatial decision support system for traffic accident prevention in different weather conditions

Natural conditions play an important role as determinants and cocreators of the spatiotemporal road traffic accident Hot Spot footprint; however, none of the modern commercial, or open-source, navigation systems currently provides it for the driver. Our findings, based on a spatiotemporal database recording 11 years of traffic accidents in Slovenia, proved that different weather conditions yield distinct spatial patterns of dangerous road segments. All potentially dangerous road segments were identified and incorporated into a mobile spatial decision support system (SLOCrashInfo), which raises awareness among drivers who are entering or leaving the predefined danger zones on the street network. It is expected that such systems could potentially increase road traffic safety in the future.

A Participatory and Spatial Multicriteria Decision Approach to Prioritize the Allocation of Ecosystem Services to Management Units

Forest management planning can be challenging when allocating multiple ecosystem services (ESs) to management units (MUs), given the potentially conflicting management priorities of actors. We developed a methodology to spatially allocate ESs to MUs, according to the objectives of four interest groups—civil society, forest owners, market agents, and public administration. We applied a Group Multicriteria Spatial Decision Support System approach, combining (a) Multicriteria Decision Analysis to weight the decision models; (b) a focus group and a multicriteria Pareto frontier method to negotiate a consensual solution for seven ESs; and (c) the Ecosystem Management Decision Support (EMDS) system to prioritize the allocation of ESs to MUs. We report findings from an application to a joint collaborative management area (ZIF of Vale do Sousa) in northwestern Portugal. The forest owners selected wood production as the first ES allocation priority, with lower priorities for other ESs. In opposition, the civil society assigned the highest allocation priorities to biodiversity, cork, and carbon stock, with the lowest priority being assigned to wood production. The civil society had the highest mean rank of allocation priority scores. We found significant differences in priority scores between the civil society and the other three groups, highlighting the civil society and market agents as the most discordant groups. We spatially evaluated potential for conflicts among group ESs allocation priorities. The findings suggest that this approach can be helpful to decision makers, increasing the effectiveness of forest management plan implementation.

SPATIAL DECISION SUPPORT SYSTEM FOR LIVELIHOOD PROGRAMMES IN NORTHERN NIGERIA

The paper examines the relevance of geospatial maps and technics in execution of livelihood support projects among rural poor. Previous attempts towards driving the poor along prosperity pathways in Nigeria have failed because of incorrect deployment of interventions in time and space. Global positioning systems and Geographical information systems were employed in this study to provide insight to the challenges faced by previous livelihood support projects in the country while also providing a robust spatial decision support system for geographical targeting of interventions to vulnerable households. The study selected 1,459 households from the 42,000 households adopted for the Feed the Future Project in 3 northern States namely; Sokoto, Kebbi States and the Federal Capital Territory. Acquired survey data were converted into GIS maps so as to expose hidden trends in the characteristics of target population and to suggest the best options for interventions. The data were subjected to spatial analysis such as simple distance analysis. The outcomes revealed 86% of sampled households to be largely below the poverty line. This is mainly due to poor access to facilities like health care, markets, good roads, agricultural inputs, agricultural information and advisory services. 10 % of children’s absolute score of Mid Upper Arm Circumference (MUAC) revealed acute malnutrition. 42 % of the household however now have stronger safety nets as a result of the interventions. The study found Spatial Information technology highly useful in social intervention project as the one by the Feed the Future project

ГЕОИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ В ГРАДОСТРОИТЕЛЬНОЙ ДЕЯТЕЛЬНОСТИ

The process of studying urban planning implies the involvement of land use and land surface studies. They are largely necessary on a range of topics and issues. Sustainable development depends on con-trolling the effects of change. GIS can achieve this through its various capabilities, which can be used by urban planners and decision makers. GIS can operate as a tool and as a system, or even as a group of systems that work simultaneously or sequentially. GIS is one of the most important applica-tions of urban planning. This study is devoted to the peculiarities of the use of geoinformation tech-nologies in urban planning. The problems of limiting the use of GIS in urban planning are identified. The main types of GIS in urban planning, which are database management, visualization, spatial analysis and spatial modeling, as well as the most commonly used functions (interpolation, map over-lay, buffering and connectivity measurement) and options for their use depending on different tasks and stages of urban development, as well as the advantages of raster and vector data. The article also reflects the emergence and development of spatial decision support system (SDSS) and planning sup-port system (PSS) in urban planning. The role of GIS in the analysis of spatial planning is formulated.

Quantifying soil carbon in temperate peatlands using a mid-IR soil spectral library

Abstract. Traditional laboratory methods for acquiring soil information remain important for assessing key soil properties, soil functions and ecosystem services over space and time. Infrared spectroscopic modeling can link and massively scale up these methods for many soil characteristics in a cost-effective and timely manner. In Switzerland, only 10 % to 15 % of agricultural soils have been mapped sufficiently to serve spatial decision support systems, presenting an urgent need for rapid quantitative soil characterization. The current Swiss soil spectral library (SSL; n = 4374) in the mid-infrared range includes soil samples from the Biodiversity Monitoring Program (BDM), arranged in a regularly spaced grid across Switzerland, and temporally resolved data from the Swiss Soil Monitoring Network (NABO). Given that less than 2 % of the samples in the SSL originate from organic soils, we aimed to develop both an efficient calibration sampling scheme and accurate modeling strategy to estimate the soil carbon (SC) contents of heterogeneous samples between 0 and 2 m depth from 26 locations within two drained peatland regions (School of Agricultural, Forest and Food Sciences (HAFL) data set; n = 116). The focus was on minimizing the need for new reference analyses by efficiently mining the spectral information of the SSL. We used partial least square regressions (PLSRs), together with five repetitions of a location-grouped, 10-fold cross-validation, to predict SC ranging from 1 % to 52 % in the local HAFL data set. We compared the validation performance of different calibration schemes involving local models (1), models using the entire SSL combined with local samples (2), commonly referred to as spiking, and subsets of local and SSL samples optimized for the peatland target sites using the resampling local (RS-LOCAL) algorithm (3). Using local and RS-LOCAL calibrations with at least five local samples, we achieved similar validation results for predictions of SC up to 52 % (R2 = 0.93 to 0.97; bias = -0.07 to 1.65; root mean square error (RMSE) = 2.71 % to 3.89 % total carbon; ratio of performance to deviation (RPD) = 3.38 to 4.86; and ratio of performance to interquartile range (RPIQ) = 4.93 to 7.09). However, calibrations using RS-LOCAL only required five or 10 local samples for very accurate models (RMSE = 3.16 % and 2.71 % total carbon, respectively), while purely local calibrations required 50 samples for similarly accurate results (RMSE < 3 % total carbon). Of the three approaches, the entire SSL spiked with local samples for model calibration led to validations with the lowest performance in terms of R2, bias, RMSE, RPD and RPIQ. Hence, we show that a simple and comprehensible modeling approach, using RS-LOCAL together with a SSL, is an efficient and accurate strategy when using infrared spectroscopy. It decreases field and laboratory work, the bias of SSL spiking approaches and the uncertainty of local models. If adequately mined, the information in the SSL is sufficient to predict SC in new and independent study regions, even if the local soil characteristics are very different from the ones in the SSL. This will help to efficiently scale up the acquisition of quantitative soil information over space and time.

Next-Generation Smart Response Web (NG-SRW): An Operational Spatial Decision Support System for Maritime Oil Spill Emergency Response in the Gulf of Finland (Baltic Sea)

The Baltic Sea is a unique and sensitive brackish-water ecosystem vulnerable to damage from shipping activities. Despite high levels of maritime safety in the area, there is a continued risk of oil spills and associated harmful environmental impacts. Achieving common situational awareness between oil spill response decision makers and other actors, such as merchant vessel and Vessel Traffic Service center operators, is an important step to minimizing detrimental effects. This paper presents the Next-Generation Smart Response Web (NG-SRW), a web-based application to aid decision making concerning oil spill response. This tool aims to provide, dynamically and interactively, relevant information on oil spills. By integrating the analysis and visualization of dynamic spill features with the sensitivity of environmental elements and value of human uses, the benefits of potential response actions can be compared, helping to develop an appropriate response strategy. The oil spill process simulation enables the response authorities to judge better the complexity and dynamic behavior of the systems and processes behind the potential environmental impact assessment and thereby better control the oil combat action.

Sensitivity analysis in multi-criteria evaluation of the suitability of urban green spaces for recreational activities

Abstract. Urban green spaces can have potential positive impacts on climate, biodiversity, health, and generally on the quality of life for urban residents and are of great importance for pursuing recreational activities. People as urban green space users should therefore be well informed about where they are located within a city and what activities they are suitable for. As people’s individual preferences play a significant role in the decision-making process for visiting urban green spaces, we present a method that enables to assess urban green spaces upon various activities in accordance with a range of criteria. A sensitivity analysis to investigate the influence of weighting the criteria in a multi-criteria evaluation of the suitability of urban green spaces for recreational activities depending on people’s preferences is in the focus. Initial results are shown based on an exemplary use case of playing Frisbee. Overall, the study is embedded in the development of a spatial decision support system implemented in the form of a web app to assist urban residents make decisions in the context of green space use.