Search Results (29)

A key barrier to achieving sustainability in 15 minute cities is the efficiency of supply-chain logistics, particularly in historic urban districts characterized by dense and heritage-protected urban forms. This article presents a data-driven urban methodology to optimize last-mile logistics in Ciutat Vella (Valencia, Spain). Within the ENACT 15 min cities project, a Spatial Decision Support System (SDSS) was developed, combining iterative geospatial adjustments to the logistics network under changing boundary conditions with a demand-estimation model derived from the Valencia open-data platform. Using cadastral and field-survey data, the workflow simulates and optimizes the selection of vacant commercial premises as urban logistics hubs. A genetic algorithm minimizes oversupply, maximizes demand coverage, and improves spatial balance. The methodology also estimates the resulting carbon footprint, demonstrating that the optimized configuration enhances sustainability and service efficiency in dense historic settings. The approach is generalized to other urban contexts. Full article

We live in an era where digitalization and omnichannel strategies significantly transform retail landscapes, and accurate spatial analytics from Geographic Information Systems (GIS) can deliver substantial competitive benefits. Nonetheless, despite evident practical advantages for specific targeting strategies and operational efficiency, the degree of GIS integration into academic marketing literature remains ambiguous. Clarifying this uncertainty is beneficial for advancing theoretical understanding and ensuring retail strategies fully leverage robust, data-driven spatial intelligence. To examine the intellectual development of the field, co-occurrence analysis, topic mapping, and citation structure visualization were performed on 4952 peer-reviewed articles using the Bibliometrix R package (version 4.3.3) within R software (version 4.4.1). The results demonstrate that although GIS-based methods have been effectively incorporated into fields like site selection and spatial segmentation, traditional marketing research has not yet entirely adopted them. One of the study’s key findings is the distinction between “author keywords” and “keywords plus,” where researchers concentrate on novel topics like omnichannel retail, artificial intelligence, and logistics. However, “Keywords plus” still refers to more traditional terms such as pricing, customer satisfaction, and consumer behavior. This discrepancy presents a misalignment between current research trends and indexed classification practices. Although the mainstream retail research lacks terminology connected to geomarketing, a theme evolution analysis reveals a growing focus on technology-driven and sustainability-related concepts associated with the Retail 4.0 and 5.0 paradigms. These findings underscore a conceptual and structural deficiency in the literature and indicate the necessity for enhanced integration of GIS and spatial decision support systems (SDSS) in retail marketing. Full article

Charging infrastructure is the backbone of electromobility. Due to new charging behaviors and power distribution and charging space constraints, the energy demand and supply patterns of electromobility and the locations of current refueling stations are misaligned. Infrastructure developers (charging point operators, fleet operators, grid operators, vehicle manufacturers, and real-estate developers) need new methodologies and tools that help reduce the cost and risk of investments. To this extent we propose a transport-energy-demand-centric, dynamic adaptive planning approach and a data-driven Spatial Decision Support System (SDSS). In the SDSS, with the help of a realistic digital twin of an electrified road transport system, infrastructure developers can quickly and accurately estimate key performance measures (e.g., charging demand, Battery Electric Vehicle (BEV) enablement) of a candidate charging location or a network of locations under user-specified transport electrification scenarios and constraints and interactively and continuously calibrate and/or expand their network plans as facts about the deep uncertainties about the supply side of transport electrification (i.e., access to grid capacity and real-estate and presence of competition) are gradually discovered/observed. This paper describes the components and the planning support of the SDSS and how these can be used in competitive and collaborative settings. Qualitative user evaluations of the SDSS with 33 stakeholder organizations in commercial discussions and pilots have shown that both transport-energy-demand-centric and dynamic adaptive planning of charging infrastructure planning are useful. Full article

To achieve net zero emissions from ships by 2050 and align with the IMO 2023 GHG strategy, the maritime industry must significantly increase zero-emission vessels by 2030. Transitioning to fully electric ferry lines requires enhanced energy supply through renewable energy sources (RES) for complete GHG mitigation and net-zero emissions. This study presents a GIS-based framework for optimally selecting offshore wind farm locations to meet the energy demands of electric ferry operations along coastal routes. The framework involves two stages: designing feasible zero-emission ferry routes between islands or to the mainland and identifying optimal offshore wind farm sites by evaluating technical, spatial, economic, social, and environmental criteria based on national legislation and the academic literature. The aim is to create a flexible framework to support decision making for establishing sustainable electric ferry operations at a regional level, backed by strategically located offshore wind farms. The study applies this framework to the Greek Coastal Shipping Network, focusing on areas with potential for future electrification. The findings can aid policymakers in utilizing spatial decision support systems (SDSS) to enhance efficient transportation and develop sustainable island communities. Full article

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. Full article

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. Full article

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. Full article

This research tests a sustainability assessment based on the 2030 Agenda’s Sustainable Development Goals (SDGs) through a process of their territorialization and implementation. This process enables the development of a spatial decision support system (SDSS) that can be integrated with strategic environmental assessments in urban planning. The assessment takes place on the transversality of the sustainability concept, considering the three dimensions (environmental, social, and economic) in a single assessment through the spatial sustainability assessment model (SSAM) by integrating geographic information systems (GIS) and multicriteria analyses. Economic development, social equity, and ecological integrity represent the three common visions for rethinking peri-urban edges. The choice of key indicators is due to the possibilities for action of urban plans and the vision of SDG 11a, which aims to support ‘positive economic, social, and environmental links among urban, peri-urban and rural areas by strengthening national and regional development planning’. In addition, they were selected to be representative of sustainable planning processes in the peri-urban area. In recognizing the limits of urban expansion processes, in the peri-urban area, it is necessary to promote a different growth based on agri-environmental values, the production of biodiversity reserves and corridors, new models of inhabiting open space, and the consolidation of civic and collective uses. The paper tests the assessment methodology in two urban plans of the Metropolitan City of Naples that address the development of the peri-urban area with different strategies. This provides insight into how to support decision-making processes so that economic development, social equity, and ecological integrity represent three common and integrated visions to enable development that is consistent with SDGs. The results show that it is possible to identify trade-offs among the three dimensions. In fact, where there are environmental subtractions necessary to accommodate peri-urban land-relation functions, these are offset by the social values of collective use and by the values of the current economy that aim to redistribute present resources. Full article

IWRM should include the integration of management instruments towards intersectoral efficient water allocation. A platform linking economywide and network-based models, available from a Spatial Decision Support System (SDSS), was used to analyze allocation decisions in 4-interlinked basins in Northeastern Brazil during a period of water scarcity. The SDSS can integrate water allocation issues considering hydrologic and socioeconomic aspects. In this study, we applied a normalized concentration index and exploratory spatial data analysis to socioeconomic data to identify job hotspots in economic sectors. Hydro-economic indicators were determined and used as economic weights of those hotspots and individual users for water allocation. This innovative method of allocation simulates the use of economic instruments. Removing the weights, the use of non-economic instruments is also simulated. The economic allocation transfers water from agriculture and industry to the services sector compared to the non-economic. This is justified given the low indicators of the main sectors of agriculture and industry in the region: sugarcane cultivation and the sugar–alcohol industry. Moreover, regional transfer results show that without using economic criteria and maintaining the current distribution network, there is a transfer of water stored in drier to humid regions. These results can support the decision-making process by defining effective management instruments. Full article

Spatial conservation prioritization does not necessarily lead to effective conservation plans, and good plans do not necessarily lead to action. These “science-action” gaps are pernicious and need to be narrowed, especially if the international goal of conserving 30% of the planet by 2030 is to be realized. We present the Earthwise Framework, a flexible and customizable spatial decision support system (SDSS) architecture and social process to address the challenges of these science-action gaps. Utilizing case study experience from regions within California, South Africa, and British Columbia, we outline the framework and provide the Little Karoo, South Africa SDSS data, code and results to illustrate five design strategies of the framework. The first is to employ an “open science” strategy for collaborative conservation planning and action. Another is that marginal value functions allow for the continuous accounting of element (e.g., habitat) representation in prioritization algorithms, allowing for an SDSS that is more automated and saves valuable time for stakeholders and scientists. Thirdly, we program connectivity modeling integrated within the SDSS, with an algorithm that not only automatically calculates all the least cost corridors of a region, but prioritizes among them and removes the ones that do not make ecological sense. Fourth, we highlight innovations in multi-criteria decision analysis that allow for both cost-efficient plan development, like representative solution sets, but also land-use planning requirements, like site specific valuation, in what appears to be a more transparent, understandable, and usable manner than traditional approaches. Finally, strategic attention to communicating uncertainty is also advocated. The Earthwise Framework is an open science endeavor that can be implemented via a variety of software tools and languages, has several frontiers for further research and development, and shows promise in finding a better way to meet the needs of both humans and biodiversity. Full article

Many spatial decision support systems suffer from user adoption issues in practice due to lack of trust, technical expertise, and resources. Automated machine learning has recently allowed non-experts to explore and apply machine-learning models in the industry without requiring abundant expert knowledge and resources. This paper reviews recent literature from 136 papers, and proposes a general framework for integrating spatial decision support systems with automated machine learning as an opportunity to lower major user adoption barriers. Challenges of data quality, model interpretability, and practical usefulness are discussed as general considerations for system implementation. Research opportunities related to spatially explicit models in AutoML, and resource-aware, collaborative/connected, and human-centered systems are also discussed to address these challenges. This paper argues that integrating automated machine learning into spatial decision support systems can not only potentially encourage user adoption, but also mutually benefit research in both fields—bridging human-related and technical advancements for fostering future developments in spatial decision support systems and automated machine learning. Full article

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. Full article

To achieve sustainable development in the road sector, the use of Electric Vehicles (EVs) appears as a positive response to transport emissions. Among the available technologies, dynamic charging seems to overcome the main weakness points of EVs, even if it requires that traditional roads (t-roads) be equipped with a system providing electricity for EVs. Thus, so-called electrified roads (e-roads) must be implemented into the urban road networks. Since it is not possible to electrify all roads simultaneously, and also to consider the demand needs of citizens, a selection criterion is essential. This research describes and develops a simple, self-explanatory, repeatable, and adaptable selection criterion aimed at helping city managers in prioritizing the roads of an urban network to be upgraded from t-road to e-road status. This method belongs to the so-called Multicriteria Spatial Decision Support Systems (MC-SDSS)—processes useful for solving spatial problems through the integration of multicriteria analysis (Fuzzy Analytic Hierarchy Process, F-AHP) with a geo-referenced data management and analysis tool (GIS). The developed algorithm is based on several criteria related to the infrastructure/transport, social and environmental areas. The result of the implemented method is a Feasibility Index (FI), able to prioritize the roads most eligible to be upgraded as e-roads, as also verified by its application on the urban area of Milan (Italy). Full article

Angola’s forests are abundant and highly productive with enormous potential to support local needs and exportation. The forests are well distributed across the country, but the existing road network is generally poor and, in some cases, inappropriate. Based on our previous work examining deforestation patterns and the modeling of primary tree attributes of vegetation types, we proposed forest management zones (MZ) for future planning in Huambo province in Angola. Herein, that same framework is applied for the detection of the existing road network in Huambo and the proposal of alternative routes inside the MZ. We used analytic hierarchy process (AHP) and geographic information systems (GIS) to optimize connectivity among the existing forest plantations and their distance to the closest major cities within the province. We developed road suitability maps based on AHP and GIS to ensure safer driving conditions and contribute to the forest planner’s access to the current plantations. According to the suitability map created, 59.51% of the total area is suitable for road development and is counted in classes 4 and 5 in automatic classification. Parameters such as geology, slope, distance from roads to the railway, soil types, elevation, flow accumulation, and aspect were used. We provide a completed assessment of the state of existing roads and evaluate the safety of the observed road sections based on the AHP method. The calculated weights of the factors were all consistent with the model used (consistency ratio was 0.09 < 0.1). Finally, we proposed the best alternative routes to the existing cities, MZ in miombo woodlands, and forest plantations inside the province. Our findings indicated that flow accumulation, soil type, and geology were the most significant factors impacting road construction. Overall, our framework is an important starting point for further research activities towards developing a spatial decision support system (SDSS) for planning road networks in Angola. Full article

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. Full article