Spatial Decision Support Research Articles

Conservation Planning for Action: End-User Engagement in the Development and Dual-Centric Weighting of a Spatial Decision Support System

The implementation of systematic conservation plans often involves collaboration among several organizations, so attaining consensus on the plan is an important driver of implementation success. We utilized participatory action research to explore an approach for developing consensus among organizations and facilitating implementation. A real-world challenge was addressed in the Little Karoo, South Africa by creating a spatial decision support system (SDSS) based primarily on weighted multi-criteria GIS overlays, but with an “incremental allocation algorithm” to allow efficient solution sets (i.e., systematic conservation planning). The end-users of the SDSS participated in the choice of criteria, in the selection of model parameter values, and in co-defining their group process. We co-designed a dual-centric weighting process in which the participants developed a set of weights and parameter values for attaining consensus among the organizations, and also a set for yielding the result that is best for biodiversity. The process yielded a consensus on the spatial priorities and, thus, a way forward. The estimated spatial priorities for each organization differed dramatically from each other and from the consensus priorities, indicating that a large discrepancy had been resolved. Our study indicates that the consensus-building and decision-support benefits of participatory multi-criteria overlay approaches merit closer attention in systematic conservation planning. The use of weighted overlays and an incremental allocation algorithm appear to be useful approaches for streamlining the participatory process while maintaining rigor and spatial complementarity. Finally, we uncovered theoretical psychological ramifications of the dual-centric weighting process that are promising and merit testing.

Engaging stakeholders in marine spatial planning for collaborative scoring of conflicts and synergies within a spatial tool environment

Collaborative spatial decision support tools can contribute with setups for including stakeholders into marine spatial planning (MSP) processes with the purpose of increasing trust in planning outcomes, facilitate knowledge co-creation and shared planning goals, and provide transparent, scientific, inclusive, and technical foundations for planning. A new collaborative spatial decision support setup based on the combination of functionalities from two spatial decision support tools called SEANERGY and Baltic Explorer was designed for and tested in a workshop in 2020 targeted local authorities, NGOs, and citizens in Denmark with an interest in MSP. While the setup needs further testing among a wider span of stakeholders to support a pluralistic approach, the findings illustrate promising potentials from ranking conflicts and synergies in collaborative settings to make marine activity interests spatially visible in MSP and gain an overview of opportunities for sea use multi-functionality in context-based, interactive, goal-oriented stakeholder processes. The use of a visual platform such as Baltic Explorer to systematically explore locations of marine uses was positively evaluated to facilitate the workshop conflict-synergy discussions. Challenges relate to how to deal with disagreements on conflict-synergy scores and the subjectivity of opinions, but the demonstrated flexible, quick, transparent way to test the sensitivity of spatial patterns to differences in input conflict-synergy scores is found to provide a promising setup for including stakeholder opinions through collaborative settings, a setup adjustable to supplementary large-scale, individual, more representative surveys as well.

A spatial analytics decision support system for analyzing the role of sea transport in public transportation

This study investigates the integration impact of passenger transport on the sea and public transport. A decision support model is proposed to plan the water route phases integrated with the public transportation system. The existing travel data and passenger travels between districts were analyzed and illustrated with Geographic Information Systems (GIS). A geographic database is developed for relevant phases, and their results reveal the spatial connection between location and passenger travel. The findings show that there is a significant relationship between distance and passenger travel choices in the case of maritime Istanbul.

Renaissance of villages for the revitalization of marginal areas

There is now a clear disparity between major cities and villages located in marginal areas of Italy. Progressive depopulation of inland areas and urban polarization such as consolidated territorial dynamics are difficult to dampen and adapt to the new paradigms of sustainable territorial development, although they have been abruptly redirected by the Covid 19 pandemic. The instability created by this pandemic offers the opportunity to redefine new parameters of intervention and new scenarios for the development of territories in relation to the new needs of decentralization and physical distancing. The project “Renaissance of villages for the revitalization of marginal areas” aims to create the conditions to repopulate and rebalance shrinking territories by establishing new centers of attractiveness. This project envisages the active involvement of municipalities and local authorities with the aims at implementing the multi-sectoral analysis of the tangible and intangible values of territories. It intends to develop an interactive web dashboard to be provided to municipalities in order to create both a learning environment and a spatial decision support system for future local policy actions towards a sustainable participatory local development. In this way, it is proposed a functional method with a place-based approach to managing the existing territorial complexity through innovative models of territorial governance and policymaking, among them the effective implementation of participatory and multi-actor visions of territorial development. Specifically, this paper provides the Italian villages’ archetypes through the quantitative spatial cluster multivariate analysis, which is the basis for the construction of the dashboard. To cluster the villages, the main variables have been identified, assesses, and mapped. The results are fundamental in order to define the future scenarios for each archetype assessing the Key Performance indicators (KPIs).

Land use suitability assessment for economic development at the provincial level: The case study of Yazd Province, Iran

• The concept of land use suitability (LUS) is intended to help stakeholders make better land-use decisions on a regional scale. • GIS application with a hybrid Multi-Criteria Decision Analysis (MCDA)-Rule-Based System (RBS) in LUS is recommended. • This study suggests the real-time Spatial Decision Support System (SDSS) in LUS analysis. • SDSS should be used in places where suitable geodatabases are not available. Iran's Yazd Province has expanded its economy during the past 40 years due to requests from investors and decisions made by the Provincial Planning and Development Council. This article aims to aid the council's decision-making process by presenting an analytical method for determining whether a region is suitable, unsuitable, or conditionally suitable for generating various economic activities in the form of individual suitability maps. Using a hybrid Multi-Criteria Decision Analysis (MCDA)-Rule Based System (RBS), the method is made up of the Analytic Hierarchy Process (AHP) and semi/non-compensatory decision rules in combination with GIS spatial analysis. The findings show that the province has exceeded its environmental carrying capabilities since a significant portion of its overall land area is unsuitable for most industrial and agricultural activities. The Provincial Planning and Development Council seeks to have even fewer options than to concentrate on high-tech and service sector development along major and dense corridors while relocating other economic activity to the periphery to address issues with water scarcity, air pollution, uneven infrastructure distribution, and environmental limitations. The proposed model of this study can be utilized as a real-time Spatial Decision Support System (SDSS) for the Yazd Provincial Planning and Development Council. In sequential decision-making processes, the model can examine the effects of previous decisions and adjust its findings/conclusions as needed.

Spatial decision support system for low-carbon sustainable cities development: An interactive storytelling dashboard for the city of Turin

The evaluation of city sustainability considering economic, social, and environmental subsystems is a fundamental field in urban planning. It directly affects the quality of urban economies toward efficiency and innovation with minimal resource consumption. This requires the use of appropriate SDSS (Spatial Decision Support Systems) which take into account inclusive approaches to engage local stakeholders. The study aims at developing an interactive dashboard to aid decision-makers in sustainable urban planning processes to co-create future transition strategies with the employment of spatialized outputs. The dashboard enables the evaluation of the sustainability performance of the city by the selection and assessment of KPIs (Key Performance Indicators) as a part of the MOLOC European Interreg Project. The study proposes a multidisciplinary GIS-based framework for the spatial evaluation of KPIs which can support scenario development by providing targeted suggestions for local development planning. Outcomes represent a useful interactive SDSS tool within a storytelling dashboard which is combinable with multi-criteria decision models at the city scale. The spatial distribution of results evidences the strengths and weaknesses of the city and facilitates the implementation of low-carbon action plans, avoiding unnecessary investments. The methodology illustrated was applied to the city of Turin as the case study, however, it can be implemented in other European urban contexts intending to achieve sustainable spatial planning since the methodological framework modulates to the territorial specificities of a place, always including all aspects of sustainability to be assessed.

Assessing the Spatial Accessibility of Urban Medical Facilities in Multi-Level and Multi-Period Scales Based on Web Mapping API and an Improved Potential Model

Urban medical facilities are an irreplaceable foundation for ensuring higher levels of public health and medical equity. Hospital accessibility has an extremely important impact on the allocation efficiency and fairness of medical facilities. Although critical, previous studies on accessibility have often overlooked the layout of medical facilities at different levels and the accurate measures of travel time to hospitals, which are both the most critical and fundamental indicators when assessing hospital accessibility. To avoid these pitfalls, this study considers the Shijingshan District, Beijing, China, as an empirical case and proposes an improved potential model based on Web Mapping API (Application Programming Interface) to assess the hospital accessibility of hospitals at different levels during different time periods. Results show that there are significant spatial and temporal differences in hospital accessibility in Shijingshan District, and traffic congestion and the layout of medical facilities are the two most important factors affecting hospital accessibility. This study further improves the hospital accessibility assessment method, with the findings provide a spatial decision support system for urban planners and policymakers regarding optimizing the spatial structure and layout of transportation systems and medical facilities.

Appraising Malaria Incidence with Spatial-Oriented Decision Support System in Parts of Zaria, Kaduna State-Nigeria

Prevalence of malaria in certain parts of the world without adequate planning and monitoring based on ill- informed decisions has wreaked havoc on the health system and economy of many developing nations. The purpose of this study is to create a functional Spatial Decision Support System (SDSS) for malaria monitoring in parts of Zaria, Kaduna State. A Geographic Information Systems (GIS) SDSS was created using ArcGIS 10.5 software for the 2019 Malaria data obtained from the Ministry of Health, Kaduna State Roll Back Malaria (RBM) Programme for collation and analysis across 13 political wards in Zaria Local Government Area (LGA) of Kaduna State. The results revealed that a total of 443,852 cases of malaria were reported across the 13 political wards of the study area. The reported cases had predominant occurrences at the Gyellesu ward, followed by the Kwarbai A ward. The spatial incidence was displayed via vector control maps, malaria burden maps, malaria prevalence in Zaria maps, and service area maps. The map of demography indicated a higher prevalence of malaria of 73.35% for the male gender compared to the female (26.65%). However, the independent t-test revealed no significant difference between males' and females' malaria prevalence. It was also revealed that the adult population had the highest malaria prevalence followed by children less than 5 years while pregnant women were the least infected. The study further indicated that 38.5% of the existing political wards recorded a shortfall of at least two (2) healthcare facilities. This study identified areas that required the establishment of health facilities to offset the existing shortfall. The SDSS was able to effectively monitor and appraise malaria incidence for proper surveillance and management that will be required for malaria prevention and elimination activities. The study recommends that policymakers invest in the deployment of an SDSS for better and robust planning, monitoring and execution of their malaria intervention programmes as this aligns with the sustainable development goal (SDG) target 3.

How to support cultural heritage-led development in Italian inner areas: a multi-methodological evaluation approach

ABSTRACT The marginal areas, in continuous depopulation, require policies addressed towards sustainable development for territories as well as for local communities. This work, inspired by the urgent need of inner area regeneration, proposes the application of a multidimensional spatial decision support system (MC-SDSS) aimed at investigating both the territorial attractiveness and vulnerability of fragile areas. Supported by a spatial analysis, the model exploits the potentials of the Multicriteria Decision Aiding techniques (MCDA) within the G.I.S. domain. The research discusses the results of a first application of the MC-SDSS with a focus on the development of the Attractiveness and Vulnerability value maps. The analysis has been developed for the Italian provinces of Alessandria and Vercelli, both crossed by the VENTO project, one of the longest Italian cycle routes and with a great potential to boost slow tourism. The paper is divided into several sections: an introduction on inner areas; the description of the MC-SDSS; the test of the evaluation model on a case study; the measurement of territorial attractiveness and vulnerability, and finally hints from the results and discussion of future research lines.

Effective medical center finding during COVID-19 pandemic using a spatial DSS centered on ontology engineering.

The global spread of the coronavirus has generated one of the most critical circumstances forcing healthcare systems to deal with it everywhere in the world. The complexity of crisis management, particularly in Iran, the unfamiliarity of the disease, and a lack of expertise, provided the foundation for researchers and implementers to propose innovative solutions. One of the most important obstacles in COVID-19 crisis management is the lack of information and the need for immediate and real-time data on the situation and appropriate solutions. Such complex problems fall into the category of semi-structured problems. In this respect, decision support systems use people’s mental resources with computer capabilities to improve the quality of decisions. In synergetic situations, for instance, healthcare domains cooperating with spatial solutions, coming to a decision needs logical reasoning and high-level analysis. Therefore, it is necessary to add rich semantics to different classes of involved data, find their relationships, and conceptualize the knowledge domain. For the COVID-19 case in this study, ontologies allow for querying over such established relationships to find related medical solutions based on description logic. Bringing such capabilities to a spatial decision support system (SDSS) can help with better control of the COVID-19 pandemic. Ontology-based SDSS solution has been developed in this study due to the complexity of information related to coronavirus and its geospatial aspect in the city of Tehran. According to the results, ontology can rationalize different classes and properties about the user’s clinical information, various medical centers, and users’ priority. Then, based on the user’s requests in a web-based SDSS, the system focuses on the inference made, advises the users on choosing the most related medical center, and navigates the user on a map. The ontology’s capacity for reasoning, overcoming knowledge gaps, and combining geographic and descriptive criteria to choose a medical center all contributed to promising outcomes and the satisfaction of the sample community of evaluators.

GENERATIVE DESIGN FOR PRECISION GEO-INTERVENTIONS

Abstract. The purpose of this research is to develop an approach for a Spatial Decision Support System (SDSS) that integrates Geographic Information Systems (GIS), Automated Machine Learning (AutoML), and Hyperparameter Optimization (HPO) to generate precision geo-interventions based on standardized geospatial data and user design constraints. The geo-intervention generation approach involves three steps: (1) Geo-binning, (2) AutoML, and (3) Prediction Optimization. Geo-binning is used to standardize geospatial data into regularized grids as inputs into AutoML models. Prediction optimization generates geo-interventions by applying user-design constraints and optimizing AutoML model output to find optimized input variables that form precise geo-interventions. An experiment in reducing road traffic collisions using infrastructural changes in Toronto, Ontario, Canada was done to evaluate the geo-intervention generation approach. The results of the experiment found that changing the number of schools, red light cameras, and transit shelters in high traffic areas could potentially halve the total number of traffic collisions according to a 80 by 80 geo-binned grid Auto-Sklearn model with a Mean Absolute Error (MAE) of 117.68. It was also found that user design constraints heavily affected the prediction optimization step as when the areas were altered to an alternative grid of cells with scarce infrastructure, the number of predicted collisions rose by 6127 collisions. Thus, limitations of this study included subjectivity in user design constraints, scalability, and interactivity. Future work involves improving modelling/optimization efficiency and developing an interactive interface for exploring generated precision geo-interventions.

A Spatial Decision Support System Based on a Hybrid AHP and TOPSIS Method for Fire Station Site Selection

A Spatial Decision Support System Based on a Hybrid AHP and TOPSIS Method for Fire Station Site Selection

Characterizing user needs for Beescape: A spatial decision support tool focused on pollinator health

Interactive maps can serve as powerful environmental decision-support tools. However, designing an interactive map that meets the needs of diverse constituencies is a challenge. In this article, we evaluate and characterize user needs for an interactive map and spatial decision-support tool called Beescape. Beescape is designed to visualize resources and environmental risks to bees and other pollinators (such as availability of nutritional resources from flowering plants and exposure to pesticides) in order to help users make informed decisions about managing bee populations and associated landscapes. We conducted a needs assessment workshop with twenty stakeholders from four user groups including beekeepers, growers, conservationists, and pollinator scientists to elicit their knowledge to guide future Beescape development. The results of the workshop identify current analytical gaps with the existing Beescape prototype, including the need for predictive and historical tools, more actionable data layers, finer-grain spatial data, and better explanations on what data represent and how they were created. Our findings on user's analytical, informational, and interface needs can be utilized to guide the future development of spatial decision support tools like Beescape, and our methodological approach may apply to other environmental informatics tools where it is important to design for multiple constituent user groups.

An informatics-driven intelligent system to improve healthcare accessibility for vulnerable populations

Broad disparities persist in access to healthcare and health outcomes in the United States, especially among vulnerable populations. An important step towards improving their healthcare access is to ensure “having access”, or “accessibility”. Drawing together the Behavioral Model of Health Services Use, social determinants of health (SDOH), and spatial healthcare accessibility literature, the research attempts to create an integrative accessibility measure to support the improvement of “having access” for vulnerable populations. A Vulnerable Population Healthcare Accessibility Framework (VPHAF) is proposed to systematically integrate vulnerable population characteristics, health behavior, customer satisfaction, and other SDOH factors to measure healthcare accessibility using advanced analytics. The qualitative and quantitative evaluation of VPHAF demonstrates that it enhances the existing spatial accessibility methods. An intelligent spatial decision support system (SDSS) instantiates the framework to support the decision making in improving healthcare accessibility for vulnerable populations in a regional healthcare plan. The system was evaluated by decision makers from a real-world organization as a useful tool with high usability. The intelligent SDSS enables the collection of SDOH data from multiple sources and uses analytics to measure healthcare accessibility focusing on characteristics of vulnerable populations. Policymakers and health plan providers can use the system to increase awareness of social risks and advocate for better policy decisions in creating equitable healthcare access and reducing health disparity.

Usability of Visiting Routes in Heritage: The Case Study of Mercati di Traiano.

Large museums as services to the community, can be considered strategic facilities needing exploitation after the health emergency. Through optimising usability, some methodologies and tools taken from other building uses (hospitals, airports) for analysing users and flows seem effective in testing new spaces' uses. The case study of Mercati Traianei-Museo dei Fori Imperiali was investigated in two different periods and methods for comparing the impacting factors on usability and orientation of visiting routes. A direct analysis in 2015 detected crucial issues for walking through museum routes and connections, while a more recent indirect analysis went deeper through configurational methodologies included in the Spatial Decision Support System (SDSS). This methodology empowers analysis of the space syntax, and the factors affecting the visitors' satisfaction. The two methods were found to be effective for a deeper knowledge of the museum and its priorities. Tools of direct on-site observation are essential to promptly identify obstacles for upgrading usability. Instead, the configurational analysis allows a much faster application and supports a more global and dynamic vision of more inclusive visiting routes, avoiding visitors' stigmatisation.

Alerting people prioritising territories over technologies. A design framework for local decision makers in France

Public alerting is a major challenge in a world where risks and communication modes are constantly evolving. Since a few years, new massive alerting tools geolocating the population in real time (Cell Broadcast or Location-Based SMS) have been deployed at national level in several countries. However, this evolution does not always reach with local needs. Decision-makers can use a wide multiplicity of tools and this can induce vulnerability differences from one territory to another. To help decision makers in their choice of equipment, this study proposes a Spatial Decision Support System and applied it in 40 various French municipalities in order to observe how 13 alerting tools are adapted (or not) to the characteristics of the municipalities. Early findings highlighted a great diversity of adapted tools between municipalities and within municipalities, in different alert zones. Alerting tool equipment must be considered at an infra-municipal scale to fully consider the diversity of alert zones. The greater the number of inhabitants, the more diverse the alerting tools needed. Also, few tools are suitable for municipalities with a low population and poor access to telecommunication networks. Finally, as tools geolocating individuals are suitable for highly populated municipalities, such national equipment shall not be used on small alert zones involving few individuals.

A comparison of tree planting prioritization frameworks: i-Tree Landscape versus spatial decision support tool

Different models as well as planting prioritization and optimization schemes based upon diverse ecological, social, and economic goals and preferences have been used to develop more efficient and effective tree planting schemes. We compare tree planting prioritization scenarios identified from i-Tree Landscape’s priority planting index to optimal scenarios identified from a spatially explicit multi-objective decision support framework at the census block group level in the Bronx, NY. We explore four scenarios with varying objectives considering populations below the poverty line, avoided runoff, and PM2.5 air pollutant removal monetary benefits. Results show that when prioritizing single objectives (e.g., PM2.5 air pollutant removal) using the same per area of tree canopy benefits from the spatially distributed modeling of ecosystem services, the two approaches recommend similar block groups for additional tree cover. Scenarios considering multiple objectives, however, result in different optimal solutions, with the decision support framework generally recommending more block groups for increased tree cover than i-Tree Landscape’s methodology. When the per area of tree canopy benefits from i-Tree Landscape are used as input in the i-Tree Landscape prioritization scenarios and the spatially distributed benefits used in the decision support framework scenarios, different optimal solutions are identified between the two approaches across all four scenarios, with i-Tree Landscape recommending fewer block groups for increased tree cover. Such a comparison will help inform the development of flexible multi-objective decision support tools to guide future greening initiatives towards prioritizing planting locations that maximize multiple objectives, as well as areas to preserve urban forests.

Sector arc routing-based spatial decision support system for waste collection in Brazil.

We present a sector arc routing problem-based spatial decision support system technology to develop tactical and operational plans and manage solid waste collection activities including household refuse collection and human and mechanical street sweeping. The proposed technology can also manage solid waste services through optimization (minimizing sector costs, distances and resources). The proposed technology is called optimized planning and integrated logistics management. It can be directly applied to Brazilian municipalities as it contains its major natural features. The system uses advanced technics from sector arc routing and sector scheduling, fleet and staff scheduling and daily plan management using mobile smartphone apps. The technology also allows for waste system inspections that are transparent to all participants in real time. We present the results of cases evaluated in the field for residential refuse collection and human and mechanical street sweeping in two Brazilian cities (Campo Grande and São Paulo). The plan implementations achieved from 12 to 28% actual fixed and variable cost savings for sectors (vehicles and workers) and routes (time and distances) for residential refuse collection and from 7 to 42% savings for human and mechanical street sweeping for the companies.

Spatial Group Decision Support System to Facilitate Site Selection Negotiation Process, Reduce Decision-Maker Conflict, and Augment System Performance

The selection of a suitable land requires careful thought and several criteria must be taken into account to ensure a good decision. Typically, selection processes are based on face-to-face meetings. But this strategy focuses on simple predefined searches and selections according to some basic criteria, such as land price, etc. They do not take into account spatially explicit criteria and spatial analyzes relevant to a site selection process (for example, proximity to hospital). To overcome these limitations, the proposed system relies on the integration of four key concepts: a multi-agent system, a multi-criteria method, geographic and ontological information systems. The results of the implementation provide manageability of large data storage. Despite the large number of approaches integrating geographic information systems and multicriteria analyzes, their use, in practice, is limited because they lack data semantics. As a result, the concept of ontology which introduces the semantics of the proposed approach offers many possibilities in terms of the type of terrain.

A decision support system to assess the feasibility of onshore renewable energy infrastructure

This article introduces a new web-based decision support system created for early-stage feasibility assessments of renewable energy technologies in England, UK. The article includes a review of energy policy and regulation in England and a critical evaluation of literature on similar decision support systems. Overall, it shows a novel solution for a repeatable, scalable digital evidence base for the policy compliant deployment of renewable energy technologies.Data4Sustain is a spatial decision support system developed to quickly identify the feasibility of seven renewable energy technologies across large areas including wind, solar, hydro, shallow and geothermal. A multi-actor approach was used to identify the key factors that influence the technical feasibility of these technologies to generate electricity or heat for local consumption or regional distribution. The research demonstrates opportunities to improve the links between policy and regulation with deployment of renewable energy technologies using novel approaches to digital planning.Deployed, resilient, cost-effective and societally accepted renewable energy generation infrastructure has a role to play in ensuring universal access to affordable, reliableand modern energy supply. This is central to supporting a concerted transition to a low-carbon future in order to address climate change. The selection and siting of renewable energy technology is driven by natural resource availability and physical and regulatory constraints. These factors inform early-stage feasibility of renewables, helping to focus investment of time and money. Understanding their relative importance and identifying the most suitable technologies is a highly complex task due to the disparate and often unconnected sources of data and information needed. Data4Sustain help to overcome these challenges.