Spatial Decision Research Articles

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.

Enhancing water balance assessment in urban areas through high-resolution land cover mapping: Case study of Debrecen, Hungary

Land cover (LC) mapping in urban areas is one of the core applications in remote sensing (RS), and it plays an important role in modern urban planning and management. In order to support up-to-date simplified water balance calculation, LC modelling needs to be developed for precise and high-resolution operations to monitor rapidly expanding urban areas. This study provides a spatial decision support tool for urban water balance calculations considering hydrological parameters at pixel scale. The study is aimed to create a high-resolution LC map for more precise water balance calculations to identify and link the most important parameters, such as crop coefficient, for estimating crop evapotranspiration, runoff coefficients, and infiltration rate. Meteorological data from 2016 to 2019 were involved in evapotranspiration estimation. The study site is Debrecen, a city in northeast Hungary with a population of about 200,000. By integrating Landsat 8 imagery, Google Earth (GE), and Corine Land Cover (CLC), a LC map of 30 m resolution was created with 81.2 % overall accuracy (OA) and a coefficient of kappa greater than 0.78. For the classification of Landsat 8, seven classes were assigned (forests, sealed surfaces, areas with crop cover, grassland, semi-sealed surfaces, bare ground and surface water bodies). Classification results were validated by 101 ground truth samples using other satellite images and aerial photographs. Water balance parameters (i.e. surface runoff, infiltration and evapotranspiration) were then defined and calculated at a pixel scale and for larger areas. The technique developed in this study can also be utilized in other urban areas.

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

In 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.

Spatial Governance and Analysis of Rural Health Infrastructure: The Case of Ondo State, Nigeria

The governance of rural health infrastructure depends on the spatial decisions of local and health policymakers during the process of their provision. This study analysed fifty-one public rural health infrastructures in three Local Government Areas (LGAs) in Ondo State such as Akoko North West, Ondo East, and Irele. These LGAs were sampled randomly from the three Senatorial Districts in Ondo State using the Nearest Neighbour Analysis to determine if the existing locations were effectively governed. The findings show variations in the spatial patterns of the rural health infrastructures. The study suggested the provision of additional infrastructure in each LGA to address unequal access. It also recommended the designing of an infrastructure plan to guide future spatial decisions.

Spatial multi-criteria decision analysis for site selection of wind power plants: a case study

ABSTRACT Site selection for wind power plant (WPP) installation is a complex process that includes not only technical requirements but also physical, economic, social, and environmental sanctions. This study was carried out to determine optimal sites for WPP installation within the borders of Çanakkale province using spatial multi-criteria decision analysis (MCDA). Within the scope of the study, sites with a high level of suitability where a WPP can be established were determined by the geographic information system (GIS) and the analytical hierarchy process (AHP). The results were examined in two ways: by excluding and not excluding forest areas. After creating the two separate suitability maps, the annual electrical power generation potential (GP) map of the study area was generated. The results show that for the case where the forest areas were excluded, 67.26% of the study area is restricted and 29.94% is highly suitable, and for the case where the forest areas were not excluded, 58.94% is restricted and 36.25% is highly suitable. The total GP value of the highly suitable sites is 104,061.61 GWh/year. When the locations of existing WPPs were examined for control purposes, it was seen that the majority of them were located inside the areas with high suitability and outside the non-installable areas, but some of them were established in forested areas.

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.

Protected area development examination based on land use curve number value changes in Nusantara National Capital Region

This study examines the impact of land use changes on the intended extent of protected areas in the Nusantara National Capital Region (IKN). This study’s land use changes result from land use projections based on the IKN’s spatial plan (RTRW IKN) and land use projections considering the potential flood inundation areas for the Q100 recurrence interval, as determined by Clue analysis. Regression analysis with SPSS is used to determine the relationship between one or more independent variables (predictors) and a dependent variable (response variable) to analyze the mathematical equation used to calculate the size of protected areas. The analysis results reveal alterations in the size of protected areas within a watershed due to variations in the Curve Number (CN) values. A more excellent CN value reduces the extent of protected areas in a watershed and vice versa. This study is anticipated to provide helpful information for spatial planning decisions in the IKN, particularly in the Government Core Centre (KIPP) region.

Green Neighbourhood Sustainability Index – A measure of the balance between anthropogenic pressure and ecological relevance

Peri-urban landscapes grow more urbanised due to suburbanisation. This, in turn, makes them environmentally and culturally susceptible to changes. Keen demand for residential areas threatens the sustainability of the peri-urban landscape. Green spaces are at particular risk of conversion. Recently, researchers focused on the function of green sites in urban environments. What is more, research on green spaces involves mainly their mapping and analysis of spatiotemporal changes. Nevertheless, there is a research gap in the multi-criteria research on the hypothetical sustainability of green spaces and the identification of areas that are the most at risk of conversion. Therefore, the present study is an attempt to develop a simple technique tool for objective measurement of the sustainability of green spaces in peri-urban areas. To this end, we developed the Green Neighbourhood Sustainability Index (GNSI), with which one can assess the sustainability of the landscape and green spaces using spatial data and GIS technology. The GNSI was then put to the test in the peri-urban zone of Kraków (Poland). The research shows that as the area of agricultural land increases, the sustainability of nearby green spaces decreases. Forest land, on the other hand, improves sustainability. The structure of land use types in the peri-urban zone of Kraków is well balanced. It is both desirable and good for landscape sustainability and harmony. The GNSI can be among the aids used by decision-makers to take science-informed strategic spatial planning decisions to ensure the sustainability of green spaces and the proper functioning of the ecosystem in peri-urban areas, especially in post-socialist transformation countries.

Quantitative methods for integrating climate adaptation strategies into spatial decision support models

With the onset of rapid climate change and the legacy of past forest management and fire suppression policies, the capacity for forested landscapes to maintain core functionality and processes is being challenged. As such, managers are tasked with increasing the pace and scale of management to mitigate negative impacts of future large disturbances and improve resilience and climate adaptation of large landscapes. Such efforts require consensus building, with partners and stakeholders to determine where to allocate scarce resources. We present a methodology to identify strategic (where to go) and tactical (what to do) priorities across large landscapes to assist in project level planning. The model integrates a spatial assessment of current ecosystem resource conditions and spatial outputs from a landscape succession and disturbance simulation model (LANDIS-II) to assess the potential to achieve desired conditions under climate change with ongoing disturbances. Based on the expected trajectory of landscape conditions over time, the model applies fuzzy logic modeling to provide quantitative support for four management strategies (Monitor, Protect, Adapt, and Transform) across the landscape. We provide an example application of these methods targeting sustainable carbon loads across a 970,000 ha landscape in the central Sierras in California. By including future landscape conditions in the model, decisions made at the stand-level are inherently tied to and influenced by larger landscape-level processes that are likely to have the greatest impact on future landscape dynamics. The methods outlined here are able to incorporate multiple metrics to capture the many resources targeted by management. Model outputs could also be used as inputs into spatial optimization models to assess tradeoffs and synergies among treatment options and to aid in long-term planning.

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.

GIS based spatial decision-making approach for solar energy site selection, Ardabil, Iran

Fossil fuel emissions have caused immense harm to the environment, making renewable energy sources like solar power essential. However, finding the optimal location for a solar power plant requires multi-criteria decision analysis (MCDA) due to various factors influencing the selection process. This study used the AHP method to weigh criteria such as GHI, Temperature, Elevation, Slope, Land cover, Distance from city, and Distance from road. The layers created from satellite imagery were combined using algebraic sums to produce a final map with 9 classes The analysis showed that class 9 has the most desirable values for each criterion, indicating the most suitable regions for a solar power plant. The results of the study have identified the southern and some central regions of Ardabil province as being the most suitable location for the construction of a solar power plant. These regions have been found to have favorable values for the criteria studied, indicating a higher potential for solar energy generation. Based on the criteria assigned to class 9, the best lands have been identified, occupying a total area of 3085 hectares. This area represents approximately 0.17% of the total area of Ardabil province. These findings highlight the importance of careful site selection for solar power plants to ensure maximum efficiency and sustainability.

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.

Geometrical Structure of Bifurcations during Spatial Decision-Making

Animals must constantly make decisions on the move, such as when choosing among multiple options, or “targets,” in space. Recent evidence suggests that this results from a recursive feedback between the (vectorial) neural representation of the targets and the resulting motion defined by this consensus, which then changes the egocentric neural representation of the options, and so on. Here we employ a simple model of this process both to explore how its dynamics accounts for the experimentally observed abruptly branching trajectories exhibited by animals during spatial decision-making, and to provide new insights into spatiotemporal computation. Essential neural dynamics, notably local excitation and long-range inhibition, are captured in our model via spin-system dynamics, with groups of Ising-spins representing neural “activity bumps” corresponding to target directions (as in a neural ring-attractor network, for example). Analysis, employing a novel “mean-field trajectory” approach, reveals the nature of the spontaneous symmetry breaking bifurcations in the model that result in literal bifurcations in trajectory space—and how it results in new geometric principles for spatiotemporal decision-making. We find that all bifurcation points, beyond the very first, fall on a small number of “bifurcation curves.” It is the spatial organization of these curves that is shown to be key to determining the shape of the trajectories, such as self-similar or space filling, exhibited during decision-making, irrespective of the trajectory's starting point. Furthermore, we find that a non-Euclidean (neural) representation of space (effectively an elliptic geometry) considerably reduces the number of bifurcation points in many geometrical configurations (including from an infinite number to only three), preventing endless indecision and promoting effective spatial decision-making. This suggests that a non-Euclidean neural representation of space may be expected to have evolved across species in order to facilitate spatial decision-making. Published by the American Physical Society 2024

Linking geodiversity and geosystem services to human well-being for the sustainable utilization of the subsurface and the urban environment.

Because the functions of the subsurface are hidden from view, its important role in society is often ignored or taken for granted. The subsurface is, however, an essential part of the global ecosystem with important contributions to human well-being. Geodiversity is an important characteristic in this respect. Material supply is the more obvious role of the subsurface with projections of a doubling of global material use in 2060 as compared to 2017. Moreover, creating underground spaces and infrastructure are gaining importance in the urban environment. The main reason for the inadequate protection of geodiversity is the lack of a comprehensive and integrative framework. Linking socio-economic activities to biophysical system characteristics of the subsurface is facilitated by the geosystem services approach. Sustainable urban development strategies require including geodiversity in decision-making on human well-being and setting conditions for land use change. Spatial plans and decisions on the use of natural endowments should look at processes over much longer timeframes. In this paper, we explore the links between human well-being and the subsurface with an emphasis on the role of geodiversity. We set out a methodological framework and describe possible long term three-dimensional land use planning consequences for sustainable utilization of the subsurface. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.

Agricultural land suitability classification and crop suggestion using machine learning and spatial multicriteria decision analysis in semi-arid ecosystem

Agricultural land suitability classification and crop suggestion using machine learning and spatial multicriteria decision analysis in semi-arid ecosystem

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.