Spatial Decision Support System Research Articles

Walkability index for world heritage cities in developing countries

Walkability principles are an important part in the planning process of cities that face urban problems such as gentrification, pollution, and decay of their built heritage. The proposed factors – connectivity, proximity, land use mix, and retail density – form a comprehensive framework for evaluating walkability that transcends the boundaries of historical cities. These factors, while initially identified within historical contexts, possess inherent qualities that render them universally adaptable to various urban landscapes. By leveraging these factors, urban planners gain insights into the intricate fabric of pedestrian experiences in cities. They serve as universal evaluative tools, applicable not only to historical cities but also to burgeoning metropolises and smaller urban centres. This work introduces a novel approach to assessing the Walkability Index for World Heritage Cities, utilizing a Multiple Criteria Spatial Decision Support System (GIS-MCDA) structured in four stages. The approached methodology is particularly valuable for governments and decision-makers in developing countries of the Global South, where limitations in data and available tools are common challenges. The insights gained from this study can guide the improvement of policies, enable more precise implementation of sustainable mobility infrastructure, and motivate the pursuit or maintenance of UNESCO World Heritage nominations. The case study focused on the Historical Centre of Arequipa, Peru, a city designated as a UNESCO World Heritage site. The results of this study demonstrate the effectiveness of the proposed approach in such contexts, owing to its specificity and the integration of both objective and subjective elements.

Spatio-temporal spread of artemisinin resistance in Southeast Asia.

Current malaria elimination targets must withstand a colossal challenge-resistance to the current gold standard antimalarial drug, namely artemisinin derivatives. If artemisinin resistance significantly expands to Africa or India, cases and malaria-related deaths are set to increase substantially. Spatial information on the changing levels of artemisinin resistance in Southeast Asia is therefore critical for health organisations to prioritise malaria control measures, but available data on artemisinin resistance are sparse. We use a comprehensive database from the WorldWide Antimalarial Resistance Network on the prevalence of non-synonymous mutations in the Kelch 13 (K13) gene, which are known to be associated with artemisinin resistance, and a Bayesian geostatistical model to produce spatio-temporal predictions of artemisinin resistance. Our maps of estimated prevalence show an expansion of the K13 mutation across the Greater Mekong Subregion from 2000 to 2022. Moreover, the period between 2010 and 2015 demonstrated the most spatial change across the region. Our model and maps provide important insights into the spatial and temporal trends of artemisinin resistance in a way that is not possible using data alone, thereby enabling improved spatial decision support systems on an unprecedented fine-scale spatial resolution. By predicting for the first time spatio-temporal patterns and extents of artemisinin resistance at the subcontinent level, this study provides critical information for supporting malaria elimination goals in Southeast Asia.

Designing user-centered decision support systems for climate disasters: What information do communities and rescue responders need during floods?

Flooding events are the most common natural hazard globally, resulting in vast destruction and loss of life. An effective flood emergency response is necessary to lessen the negative impacts of flood disasters. However, disaster management and response efforts face a complex scenario. Simultaneously, regular citizens attempt to navigate the various sources of information being distributed and determine their best course of action. One thing is evident across all disaster scenarios: having accurate information and clear communication between citizens and rescue personnel is critical. This research aims to identify the diverse needs of two groups, rescue operators and citizens, during flood disaster events by investigating the sources and types of information they rely on and information that would improve their responses in the future. This information can improve the design and implementation of existing and future spatial decision support systems (SDSSs) during flooding events. This research identifies information characteristics crucial for rescue operators and everyday citizens' response and possible evacuation to flooding events by qualitatively coding survey responses from rescue responders and the public. The results show that including local input in SDSS development is crucial for improving higher-resolution flood risk quantification models. Doing so democratizes data collection and analysis, creates transparency and trust between people and governments, and leads to transformative solutions for the broader scientific community.

Proposed spatial decision support system for delineating ecological corridors in green infrastructure planning constrained by lack of data: a case study in Galicia, Spain

AbstractIn response to the constant loss of biodiversity in European ecosystems, which is partly due to the impacts of climate change, the European Commission urges member states to include Green Infrastructure (GI) in their land-use plans. However, although the European Commission establishes the fundamental principles to be applied, the ambiguity of some terms generates a certain degree of complexity regarding the delineation of GI elements, especially Ecological Corridors (ECs). Thus, a straightforward methodology for delineating GI elements is required. Here, we propose a Spatial Decision Support System (SDSS) that could help non-expert planners identify areas with a high potential to function as ECs and that could thus facilitate the inclusion of these areas in regional GI plans. Probability distribution maps were constructed by fitting a maximum entropy model (MaxEnt) to publicly available data on selected focal species. The maps were combined with other variables that negatively affect species mobility and later inserted in a graph theory tool to determine the least-cost path that would serve as the basis for delineating ECs. The method was applied to the design of an EC network in Galicia (NW Spain), and use of the system as a tool to help spatial decision-making was evaluated. Despite some limitations, the method yielded promising results that could help non-expert planners to establish the basis for delineating EC networks and other GI elements.

Web-based spatial decision support system for precision agriculture: A tool for delineating dynamic management unit zones (MUZs)

Precision agriculture (PA) is a farming management concept that aims to provide agronomic, economic, and environmental benefits. One of the fields of research in PA is the delineation of Management Unit Zones (MUZs). MUZs are the sub-division of fields featuring an inter-zonal variation delineated by agronomists for on-field PA operations. To develop MUZs, three factors typically need to be considered: input multi-dimensional data, procedures to process the information, and the optimal number of zones a field should be divided into. PA uses digital technologies to collect and analyze a large amount of data, outline MUZs, monitor crops, and carry out site-specific crop management. Web-based spatial decision support systems (WB-SDSS) can provide users with tools that ease the complex procedures for PA. The objective of this study is twofold: on the one hand, we developed a free and open source (FOSS) WB-SDSS to facilitate the implementation and use of such tools for delineating MUZs and monitoring crops; on the other, a MUZs outline procedure was developed based on Sentinel-2 and Planetscope time series data, and spatio-temporal dynamic clustering model using fuzzy c-means. Our study highlighted that the WB-SDSS might be a helpful solution for harmonizing data collected from different sources, easing the implementation and use of complex geospatial procedures for PA, and delineating MUZs. We tested the system on a particularly representative farm in the Emilia Romagna region (Northern Italy), with 512 hectares of durum wheat crops. Using the WB-SDSS, we quickly delineated homogeneous zones for 27 fields in the study area during the phenological cycle of durum wheat (November 2018-June 2019).

A spatial decision support system for multi-dimensional sustainability assessment of river basin districts: the case study of Sarno river, Italy

Effective management of the complex Socio-ecological systems (SES) of the river basin, influenced by a range of social, economic, and environmental variables, requires multi-purpose Spatial Decision Support Systems (SDSSs) to identify and select balanced and sustainable regeneration scenarios. In many cases, the lack of cooperation between administrative institutions and the low level of participation of the stakeholders involved are the main obstacles to effective planning and regeneration practices and call for new forms of governance. However, to address these critical issues, few studies use quantitative approaches to assess the multidimensional resources of river basins in an integrated perspective in terms of their capacity to activate cooperation networks between river-crossed cities. This study aims to suggest a methodological approach combining Multi-Criteria Decision Analysis and Geographic Information Systems to achieve integration in pursuing sustainable regeneration pathways for a river basin district, analysing the case study of the Sarno River, composed of 20 administrative units, in Southern Italy. The specific purpose is to provide decision-makers with an SDSS to evaluate sustainability scenarios based on the multi-dimensional performances of River Basin District Units (RBDU). The results delivered four partial rankings of RBDU related to social, cultural, ecological, and economic scenarios and one overall ranking for an integrated sustainability scenario. Each ranking includes a clustering of municipalities according to three classes: Leader, Link and Follower. The outcome is twofold: to provide decision-makers with recommendations for supporting governance models based on sustainability pathways and to suggest valuable methods and tools for dealing with the spatial misfits, considered as the mismatch between the physical boundaries of a natural system and the management area of an organization.

Analysis of the suitability for ecotourism in Beira Baixa region using a spatial decision support system based on a geographical information system

Ecotourism can contribute to the conservation of environmental values, as well as to the development of positive synergies between agents in the tourism sector, tourists and the local population. The growing interest in tourism offers with low environmental impact, which support the local economy, translates into a competitive advantage for low‐density territories. On the basis of this principle, the scope of the present study is intended to evaluate the potential of the existing natural and rural features in the Beira Baixa region, located in the central interior of Portugal, which can anchor the creation of sustainable tourism products. Despite the undeniable interest in carrying out studies to determine an aggregate suitability index for the development of ecotourism using spatial multicriteria decision analysis (MCDA), there are still no studies carried out in Portugal. The identification of areas suitable for the development of ecotourism, and their constraints, was carried out on the basis of the integration of nine criteria using the analytic hierarchy process (AHP) in a geographical information system (GIS) environment. The results obtained reveal that the region has high suitability for the practice of ecotourism activities, given its important natural heritage. This study was innovative as it shows the ecotourism potential based on a diversity of criteria as an aid to public administration agents with functions in planning, territorial management and management of support mechanisms for the tourist sector. The use of the landscape diversity criteria, which had not yet been tested in previous studies, proved to be important in determining the ecotourism potential of the region, together with the landscape value based in the importance of the Montado agrosystem, which supports a variety of sustainable tourist activities. The information produced in the study may be useful for the design of new policies for valuing natural resources through tourism.

Spatial Decision Support System Model for Economic Resilience in East Java Province

Economic resilience has many dimensions and aspects to consider. The complexity of studying resilience ultimately requires simplification in the form of a model that can be applied in spatial decision-making. This study aims to find a model and simulation of economic resilience policy priorities for East Java Province in the form of a Spatial Decision Support System. The calculation is done by applying the TOPSIS algorithm on the vectorMCDA plugin in a geographic information system and qualitative descriptive analysis. The results of the calculation indicate that the focus of economic resilience policy is on the aspects of recovery and transformational innovation, while the ideal resilience policy alternative is the most appropriate priority policy alternative.

Conceptual design of a nationwide spatial decision support system for forest fire prevention and fighting

Conceptual design of a nationwide spatial decision support system for forest fire prevention and fighting

Advancing Urban Healthcare Equity Analysis: Integrating Public Participation GIS with Fuzzy Best–Worst Decision-Making

This study provides an innovative collaborative spatial decision support system (SDSS) that aims to ensure an equitable spatial distribution of healthcare services. Evaluating the equality of access to health services across different geographical areas is important, as it requires the analysis of various criteria such as the proximity of health centres and hospitals (HCHs), the quality of services offered, connectivity to primary roads, the availability of public transportation hubs, and the density and distribution patterns of HCHs. This purpose is accomplished via the use of geographic information systems (GIS) and multi-criteria decision analysis (MCDA) methods. The proposed model includes the weights of the criteria, which are determined through the ordered weighted average (OWA) and evaluated based on their ORness, which ranges from 0 to 1. Furthermore, this model is improved by the best–worst fuzzy method (F-BWM). This approach produces a spatial map that clearly shows the equity of healthcare systems in urban environments. The findings show that the maximum score observed in this study was 0.38% (with an ORness value of 1), whilst the minimum score recorded was 0.28%. In the most severe scenario (ORness = 0), over 70% of the region shows different degrees of fairness, ranging from moderate to suitable and very suitable conditions. Governments and health authorities can use this information strategically to allocate resources and address inequities in access to healthcare facilities.

Utilizing Comprehensive Criteria and Indicators for Post-Fire Forest Restoration in Spatial Decision Support Systems (SDSS)

Amidst the increasing frequency and severity of forest fires globally, the imperative of effective post-fire forest restoration has gained unprecedented significance. This study outlines a comprehensive approach to post-fire forest restoration and discusses its implementation through spatial decision-making systems. The methodology involves utilizing multi-criteria analysis (MCA) to identify and prioritize criteria based on their relative importance. This allows for the creation of easily assessable alternatives and their application to spatial maps, providing local officials with valuable information. To achieve optimal decision-making, the study utilized the Analytic Hierarchy Process (AHP) and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods along with Spatial Decision Support Systems (SDSS) to generate a suitability map. The results highlight that 28% of the study area is well-suited for post-fire forest restoration, with 44% moderately appropriate, while 3% is deemed unsuitable for restoration until the end of 2023 due to severe soil loss or inherent geographical challenges.

An Online Multicriteria—Spatial Decision Support System for Public Services Planning

Dynamic processes in society and demographics require a rapid response and adaptation of the existing public service infrastructure. Responsible planners must be able to analyse the situation, identify gaps in provision and derive decisions. The paper presents a web application that supports finding answers to various questions and planning decisions in the context of public service infrastructures (e.g., schools, pharmacies, supermarkets). Due to the high complexity, the combination of multi-criteria decision analysis and geographical information systems, especially in the web context, is still rarely implemented. With this spatial decision support system, we are pursuing an approach that makes multi-criteria analyses with spatially explicit methods available to the planning domain. By using this application, the user can select and adjust input parameters independently and thus strongly influence the analysis process and results—without a deep knowledge of decision-making methods or software skills. With such a centrally provided online multi-criteria spatial decision support system, the transparency and the quality of the analysis increase, while the workload for the planners is reduced, since they no longer have to spend time on preliminary data search and integration or on building complex methods and models themselves.

Quo vadis, smallholder forest landscape? An introduction to the LPB-RAP model.

The impacts of the Anthropocene on climate and biodiversity pose societal and ecological problems that may only be solved by ecosystem restoration. Local to regional actions are required, which need to consider the prevailing present and future conditions of a certain landscape extent. Modeling approaches can be of help to support management efforts and to provide advice to policy making. We present stage one of the LaForeT-PLUC-BE model (Landscape Forestry in the Tropics-PCRaster Land Use Change-Biogeographic & Economic model; in short: LPB) and its thematic expansion module RAP (Restoration Areas Potentials). LPB-RAP is a high-resolution pixel-based scenario tool that relies on a range of explicit land use types (LUTs) to describe various forest types and the environment. It simulates and analyzes future landscape configurations under consideration of climate, population and land use change long-term. Simulated Land Use Land Cover Change (LULCC) builds on dynamic, probabilistic modeling incorporating climatic and anthropogenic determinants as well as restriction parameters to depict a sub-national regional smallholder-dominated forest landscape. The model delivers results for contrasting scenario settings by simulating without and with potential Forest and Landscape Restoration (FLR) measures. FLR potentials are depicted by up to five RAP-LUTs. The model builds on user-defined scenario inputs, such as the Shared Socioeconomic Pathways (SSP) and Representative Concentration Pathways (RCP). Model application is here exemplified for the SSP2-RCP4.5 scenario in the time frame 2018-2100 on the hectare scale in annual resolution using Esmeraldas province, Ecuador, as a case study area. The LPB-RAP model is a novel, heuristic Spatial Decision Support System (SDSS) tool for smallholder-dominated forest landscapes, supporting near-time top-down planning measures with long-term bottom-up modeling. Its application should be followed up by FLR on-site investigations and stakeholder participation across all involved scales.

Algiers master plan, land use and forced relocation: Monitoring change with a spatial decision support system

Over the past two decades, Algerian authorities have made significant investments in the housing sector, leading to substantial changes in Algiers' urban landscape. The implementation of forced housing relocation programs, such as the Program for the Resorption of Precarious Housing (RHP), has been pivotal in transforming Algiers into the first African city without slums, recognized by the United Nations in 2016. However, the selection of receiving sites has presented challenges for the authorities, as the actual implementation deviated from the initial plans outlined in the PDAU master plan. This study focuses on examining the site selection process for relocation in Algiers and delineating the disparities between planned and implemented land mobilization. Using an integrated approach, the analysis targets an in-depth study of the PDAU documents. It draws on a comprehensive data collection process, complemented by interviews with all stakeholders which have been engaged in either in the relocation operations in Algiers or in the elaboration of the PDAU. Furthermore, the study is supported with by a large cartographic overlay of the PDAU investigated sites, which allows a better understanding of the subject. Similar studies conducted worldwide have shown that failures in master planning often result from difficulties in execution and compliance, which are common issues in developing countries. In Algiers, the slow process of land mobilization has compelled authorities to adopt unplanned alternative solutions, making land management a blind spot in the planning practice and hindering the implementation of the PDAU as well as limiting the monitoring of urban expansion and development. Additionally, the study shows that despite attempts to incorporate a Spatial Decision Support System (SDSS) within the PDAU, its execution was to a large extent clearly ineffective. The paper contends that failures in urban land management and data collection constitute weak links impeding spatial development. This has shed light on deficiencies in the cadastral system in Algiers, with ambiguities in the legal framework of land management. The study also shows that the strict adherence to master plans can be challenging when dealing with relocation operations due to various factors. Addressing these issues is crucial for the elaboration of more effective relocation programs. By learning from the gaps and weaknesses in the selection process and implementing improved strategies, Algerian authorities can optimize the outcomes of relocation projects and promote more sustainable urban development.

A Geospatial Decision Support System for Supporting the Assessment of Land Degradation in Europe

Nowadays, Land Degradation Neutrality (LDN) is on the political agenda as one of the main objectives in order to respond to the increasing degradation processes affecting soils and territories. Nevertheless, proper implementation of environmental policies is very difficult due to a lack of the operational, reliable and easily usable tools necessary to support political decisions when identifying problems, defining the causes of degradation and helping to find possible solutions. It is within this framework that this paper attempts to demonstrate a new valuable web-based operational LDN tool as a component of an already running Spatial Decision Support System (S-DSS) developed on a Geospatial Cyberinfrastructure (GCI). The tool could be offered to EU administrative units (e.g., municipalities) so that they may better evaluate the state and the impact of land degradation in their territories. The S-DSS supports the acquisition, management and processing of both static and dynamic data, together with data visualization and on-the-fly computing, in order to perform modelling, all of which is potentially accessible via the Web. The land degradation data utilized to develop the LDN tool refer to the SDG 15.3.1 indicator and were obtained from a platform named Trends.Earth, designed to monitor land change by using earth observations, and post-processed to correct some of the major artefacts relating to urban areas. The tool is designed to support land planning and management by producing data, statistics, reports and maps for any EU area of interest. The tool will be demonstrated through a short selection of practical case studies, where data, tables and stats are provided to challenge land degradation at different spatial extents. Currently, there are WEBGIS systems to visualize land degradation maps but—to our knowledge—this is the first S-DSS tool enabling customized LDN reporting at any NUTS (nomenclature of territorial units for statistics) level for the entire EU territory.

Toward Precision Agriculture in Outdoor Vertical Greenery Systems (VGS): Monitoring and Early Detection of Stress Events

Vertical greenery systems (VGS) have been proposed as a nature-based solution to mitigate the adverse effects of urban heat islands and climate change in cities. However, large-scale VGS are costly and require ongoing maintenance, typically carried out manually through trial and error based on professional experience. Advanced management is essential for the sustainability of VGS due to its limited accessibility and associated costs. To address these challenges, we examined the use of remote sensing methods for outdoor VGS monitoring as a basis for a precision agriculture approach for VGS management and maintenance. This study presents the first ongoing monitoring of real-scale VGS using thermal, hyperspectral, and RGB vegetation indices. These indices were employed for the early detection of vegetation stress, focusing on two case studies exhibiting visible yellowing symptoms. Through the application of unsupervised classification techniques, stressed pixels were successfully detected 14–35 days before visual yellowing, achieving an accuracy of 0.85–0.91. Additionally, the thermal index provided valuable information regarding the spatial distribution of watering along the VGS. Stress maps based on noninvasive methods were demonstrated, forming the basis of a spatial decision support system capable of detecting issues related to plant vitality and VGS irrigation management.

A framework for co‐designing decision‐support systems for policy implementation: The LANDSUPPORT experience

AbstractThis work proposes a framework for co‐designing decision‐support tools for sustainable land management and soil protection at multiple scales. Geospatial dashboards, due to their key capabilities in the use of spatial or geospatial information, are quickly gaining traction for planning and policymaking. Developing the decision‐support system (DSS) as a transversal system capable of capturing trends in land and soil properties at the local, regional, national, and EU levels has been co‐designed with policy stakeholders. This work seeks to link (i) the main goal of the Soil Mission and the UN Sustainable Development Goals (SDGs), to raise awareness and knowledge on soil conditions (ii) and the LANDSUPPORT (LS) project cross‐evaluation on how the spatial decision‐support system (SDSS) can support policy‐related stakeholders and help them to take evidence‐based decisions. To achieve this objective, we present the user engagement process to ensure broad testing and evaluation of the LS SDSS's ability to support selected EU policies and soil‐related SDGs by testing the LS platform's European scale tools, including an analysis and conformity check of the data delivered by the LS tools and a critical review of results. The indicators were assessed via direct contact with end users, such as semi‐structured interviews (SSI) and 184 questionnaires. Results of the test series have been analyzed by the spatial scale per respective tool and performance indicators. We present a unique, integrated, science‐based approach to co‐create data‐driven decision‐making with the stakeholders to promote sustainable land management practices. This methodology strives to involve many stakeholders in scientific research, empowering them to participate in the decisions on topics that directly affect them. Public bodies responsible for land policy implementation, environmental stakeholders, spatial planners, and other users have engaged in the process to ensure broad testing of the LS platform from 2020 to 2022. A strengths, weaknesses, opportunities, and threats (SWOT) analysis provided a synthesis of the performance of the LS tools. The testing phase proved the utmost importance of usability, underlining that the mixed method of testing allowing quantitative and qualitative analyses based on the same key indicators proved essential for co‐designing SDSS tools to be used by a wide range of stakeholders.

Spatial Modeling of the Relationship Between the Social Map and the Level of Educational Development in the Municipalities of the Luján River Basin, Argentina

This study aimed to determine the degree of association between the social map (Synthetic Economic-Housing Index—ISEH) and the level of educational development (Synthetic Educational Index—ISE) modeled for the municipalities of the Lujan River basin. The indices were constructed by applying multivariate analysis methods based on Geographic Information Systems (GIS), and the Exploratory Analysis of Spatial Data determined their links using a spatial decision support system (SDSS). The result facilitated the contrast of the proposed hypothesis. In this sense, a theoretical statement and a methodological tool are built from the cartography. The analysis highlights the importance of contextual variables and their incidence on educational development indicators since the spatial perspective provides the bases to support spatial decision-making at the municipal management level.

Remote sensing enabled essential biodiversity variables for invasive alien species management: towards the development of spatial decision support system

Remote sensing enabled essential biodiversity variables for invasive alien species management: towards the development of spatial decision support system

Exploring the impact of urban development on mountain view visualization using a GIS-based landscape assessment model: a case study in Lishui, China

Amid China’s high-speed urbanization, the lack of urban landscape and homogenization of urban image are becoming increasingly serious, prompting discussion on how to preserve the natural urban landscape. This study extends the existing landscape assessment model to evaluate the urban hillscape of the Lishui urban area of Nanjing over the past 50 years. First, a landscape quality analysis is conducted based on raster images, and a landscape quality map of the study area is drawn. Then, a visibility analysis of the site is conducted, key observation points (KOPs) are screened using a digital surface model, and the visual magnitude of each KOP is quantitatively calculated. Finally, the visual impact scores of the mountains in the site are synthesized. The results show that the model can accurately quantify the visual impact of historical mountain views. The following conclusions are drawn (1) The visual quality of the landscape in the area has improved as the urban area has spreads out. (2) The viewshed and magnitude of the area are trending downward over time. (3) The Chengnan area’s visual impact score increased by 25.7%, but that of the Tuanshan and Chengzhong areas decreased by 48% and 26.9%, respectively. This study presents a spatial decision support system that can obtain better landscape assessment results with more accessible open-source data, and can be used for historical landscape evaluation. By comparing the characteristics and trends of urban hillscapes in different historical periods, this study’s landscape assessment model can help managers better understand the impact of development on urban intentions and make better decisions.