Spatial Decision Support Research Articles

A Spatial Decision-Support System for Wind Farm Site Selection in Djibouti

The escalating energy demand in Djibouti requires the investigation of renewable energy sources, with wind energy emerging as a promising solution. To ensure the long-term efficiency and sustainability of wind energy projects, it is imperative to determine suitable sites for wind farm construction. When selecting a suitable site for a wind farm, there are multiple criteria to consider, such as wind velocity, ground slope, and distance to urban areas. Nevertheless, the current body of the literature reveals that no previous research has been conducted to explore an approach which involves multiple criteria to determine suitable sites for wind farms in Djibouti, as opposed to solely considering wind energy potential. This study proposes a spatial decision-support system to address the research gap in the selection of wind farm sites. Seven criteria are simultaneously evaluated in this system, including wind velocity, changes in wind direction, ground slope, distance to urban areas, distance to road network, distance to energy transmission networks, and land use. The CRITIC (Criteria Importance Through Intercriteria Correlation) method is used to objectively calculate the weights of the criteria. According to the results of performing the CRITIC method, wind velocity and distance to energy transmission networks were determined to be the most important criteria, while ground slope and land use were determined to be the least important criteria in comparison to others. A final suitability map showing the possible locations of wind farms in Djibouti was generated by considering the said criteria and their respective weights. The final suitability map reveals that the most suitable sites for the development of wind farms in Djibouti are located in the northeastern area between Obock and Khor-Angor, the southeastern area encompassing Lakes Ghoubet and Bara, and the southwestern area stretching from Lake Abbe to the Hanlé region. Using the proposed spatial decision-support system, decision makers would be empowered to make strategic and well-informed decisions when selecting the most suitable site for a wind farm in Djibouti.

Where to plan shared streets: Development and application of a multicriteria spatial decision support tool

In response to the growing recognition of the vital role played by streets as public spaces in enhancing the vibrancy of urban life, various concepts aiming at creating greener and more inclusive streets have gained popularity in recent years, especially in North America. Shared streets are one example of such concepts that have attracted the attention of citizens and of urban and transportation planning professionals alike. This was the case in the city of Sherbrooke (Quebec, Canada) where, in response to numerous citizens’ requests, a need was identified to develop decision aid tools to help evaluate and rank street segments based on their potential to become shared streets. To achieve this, an action-research project was initiated in which we conducted a socio-technical process based on MACBETH, a multicriteria evaluation method. The project led to the development of a spatial decision support tool, operationally used today by the city professionals. This tool ensures a more informed and transparent decision-making process and supports shared streets planning policy. The methods developed are generalizable and can be adapted to other cities facing similar planning problems.

Web-based Land Suitability Analysis for Horticulture Crop (Kiwi) in Champawat districts, Uttrakhand using MCDM and Geospatial Analysis

Identifying the suitability of a horticulture fruit with a defined area is an active research area, it has been attempted through different approaches, surveying and geographic information system are the two most popular techniques utilized extensively for this activity across the globe. GIS has its proven advantage over the field surveys based approach and modern GIS tools offers automation of the entire work flow for this activity. This study presents a web-based land suitability analysis for kiwi fruit cultivation in the Champawat District of Uttarakhand, using AHP, TOPSIS, and Ensemble modeling in conjunction with Multi-Criteria Decision Making (MCDM) and Geospatial Analysis. Land suitability was evaluated across various classes based on nine criteria viz. elevation, chilling hours, land use/land cover (LULC), soil texture, soil depth, temperature, rainfall, slope, and aspect. The AHP method identified 26.09 hectares as highly suitable, 14,606.19 hectares as suitable, 31,899.77 hectares as moderately suitable, 8,807.99 hectares as less suitable, and 192.23 hectares as not suitable. TOPSIS results showed 4,294.32 hectares as highly suitable, 14,193.54 hectares as suitable, 11,782.52 hectares as moderately suitable, and 2,399.21 hectares as less suitable. The ensemble method indicated 24.15 hectares as highly suitable, 8,763.51 hectares as suitable, 13,785.13 hectares as moderately suitable, 2,472.13 hectares as less suitable, and 191.94 hectares as not suitable. Integrating these findings revealed that the ensemble approach closely aligns with AHP in identifying highly suitable and less suitable areas, while TOPSIS emphasizes moderate suitability. This synthesis provides valuable insights for land use planning for kiwi cultivation. Additionally, an online spatial decision support system (SDSS) was developed using the Django framework, offering a modular and secure environment for informed decision-making.

Dynamic Adaptive Charging Network Planning Under Deep Uncertainties

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.

A New Framework for Integrating DNN-Based Geographic Simulation Models within GISystems

As a crucial spatial decision support tool, Geographic Information Systems (GISystems) are widely used in fields such as digital watersheds, resource management, environmental assessment, and regional governance, with their core strength lying in the integration of geographic simulation models from various disciplines, enabling the analysis of complex geographical phenomena and the resolution of comprehensive spatial problems. With the rapid advancement of artificial intelligence, deep neural network-based geographic simulation models (DNN-GSMs) have increasingly replaced traditional models, offering significant advantages in simulation accuracy and inference speed, and have become indispensable components in GISystems. However, existing integration methods do not adequately account for the specific characteristics of DNN-GSMs, such as their formats and input/output data types. To address this gap, we propose a novel tight integration framework for DNN-GSMs, comprising four key interfaces: the data representation interface, the model representation interface, the data conversion interface, and the model application interface. These interfaces are designed to describe spatial data, the simulation model, the adaptation between spatial data and the model, and the model’s application process within the GISystem, respectively. To validate the proposed method, we construct a spatial morphology simulation model based on CNN-LSTM, integrate it into a GISystem using the proposed interfaces, and conduct a series of predictive experiments on island morphology evolution. The results demonstrate the effectiveness of the proposed integration framework for DNN-GSMs.

Quantification of construction and demolition waste disposal behaviors during COVID-19 using satellite imagery

The COVID-19 pandemic disrupted conventional municipal solid waste (MSW) management practices and affected waste generation rates. While MSW streams have been extensively studied and reported, the impact on construction and demolition (C&D) waste remains overlooked. This research develops an innovative analytical framework utilizing satellite imagery to quantify C&D waste disposal rates during COVID-19 restrictions in a mid-sized Canadian city. Supervised classification of Landsat-8 images is conducted to derive the settlement area over a period of 8.8 years (2014–2022). The C&D disposal rates and settlement area relationship is evaluated using regression analysis. Results reveal a 73.4% reduction in mean weekly C&D disposal in 2020 compared to pre-pandemic years, reflecting diminished construction activity. The settlement area exhibits a strong positive correlation (R2 = 0.812) with per capita C&D disposal rate, providing spatial evidence of urbanization patterns affecting C&D waste generation. Among socioeconomic factors examined, the value of building permits issued most influences C&D quantities (R2 = 0.934). The satellite imagery-based approach allows indirect estimation of disrupted C&D waste streams when on-site auditing is restricted during pandemics. The framework offers municipal authorities spatial decision support to formulate data-driven C&D waste management policies that are essential to smart cities and resilient to future public health emergencies.

Multi-pressure based environmental vulnerability assessment in a coastal area of Bangladesh: A case study on Cox’s Bazar

Bangladesh ranks among the top 10 countries globally in terms of climate change impacts and faces numerous anthropogenic and natural pressures. Cox’s Bazar, its primary tourist district, is experiencing severe degradation of its physical and ecological environments due to anthropogenic disturbances and climate change. To improve its environmental quality and preserve its ecological resources effectively, it is essential to develop a spatial decision support instrument addressing multi-pressures and cumulative environmental vulnerability (EV). This study presents an expert opinion-independent, scalable, and customizable spatial methodological framework, integrating multi-sourced geospatial data with GIS-based Fuzzy Logic to assess spatial distributions of five pressure groups and their resulting EV in Cox’s Bazar. 18 criteria were chosen based on a structured literature review to evaluate the five pressure groups. Results revealed that 17 % to 27 % of the study area is exposed to high to very high hydro-meteorological, topographic, land resource, anthropogenic, and natural hazard pressures. The EV results indicated that one-third of the study area, majorly covering Kutubdia, Pekua, Cox’s Bazar Sadar, Teknaf, and Ukhia upazilas, is highly environmentally vulnerable. For enhanced environmental protection, this study improved the existing method of environmental protection zoning by introducing a novel zoning approach that integrates in-situ biodiversity data with EV data. This new zoning method delineated 24 % (555 sq. km.) as strict, 45 % (1047 sq. km.) as medium, and 31 % (725 sq. km.) as soft protection zones in the study area. The sensitivity analysis identified land resource pressure as the most influential component of EV. With a correlation coefficient of 0.91, the accuracy assessment confirms a high level of reliability in the EV results. This study provides valuable insights into environmental pressures and vulnerability in Cox’s Bazar, which are crucial for informing policies at various levels, including international and national frameworks, emphasizing terrestrial ecosystem protection, coastal vulnerability mitigation, climate change impact reduction, biodiversity preservation, and sustainable land resource management.

GIS-Based Optimal Siting of Offshore Wind Farms to Support Zero-Emission Ferry Routes

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.

Conceptualization of territorial resilience potential indicators to coastal hazards through the use of unmanned aerial vehicles

Major natural hazards in recent decades have led to a revisiting of the concept of resilience, particularly in order to analyze traditional models of response to an unforeseen event and post-crisis management mechanisms. For resilience to be an applicable/operational concept to guide management and inform decisions, it must ultimately be characterized for its assessment. This assessment can be done through the identification of indicators specifically contextualized to the study site and object of study. However, today, operationalizing of the concept of resilience tends to draw on annual censuses or aggregated data, providing a generalized large scale view of the territorial resilience potential. The objective of this study is to identify a table of Territorial Resilience Potential (TRP) indicators to coastal hazards applied to coastal island territories, supplied by data from Unmanned Aerial Vehicles (UAVs) to allow for rapid and site specific data acquisition for repetitive surveys. This acquisition method makes it possible to calculate a number of relevant indicators for assessing the resilience potential of coastal areas. In particular, it allows rapid updating of data following major meteorological events and identification of hot and cold spots of resilience potential of a specific study site. To demonstrate the applicability of this method to island territories, the island of Bora Bora in French Polynesia was used as a case study. Finally, these kinds of results can be fed through a spatial decision support system to help decision-makers choose an adaptation and protection strategy in order to move towards resilient territories, over a long period of time.

Review on Potential Urban Development Site Selection Using Geospatial-Based Multi-Criteria Decision Analysis (MCDA) Techniques in the Context of Ethiopia

This review paper provides a comprehensive assessment of the opportunities and challenges in leveraging geospatial-based technologies, particularly Multi-Criteria Decision Analysis (MCDA) techniques, to enhance the effectiveness of urban development site selection in Ethiopia. The overarching findings highlight several critical focus areas for improvement. Firstly, the review emphasizes the need to strengthen the availability, quality, and accessibility of geospatial data through the development of robust data infrastructures and innovative data collection methods. Secondly, it recommends adopting a more inclusive and collaborative approach to stakeholder engagement, coupled with targeted capacity-building initiatives to enhance the technical skills and knowledge of urban planners, GIS experts, and local stakeholders. Thirdly, the paper suggests incorporating advanced spatial modeling and simulation techniques to capture the dynamic nature of urban growth patterns, as well as integrating climate change impact assessments and disaster risk analyses into the decision-making framework. Fourthly, it emphasizes the importance of strengthening institutional and organizational frameworks to promote cross-sectoral collaboration, data sharing, and integrated decision-making processes. Finally, the review underscores the significance of designing user-friendly and customizable Spatial Decision Support Systems (SDSS) that cater to the specific needs and constraints of the Ethiopian urban development context. The review concludes that the use of geospatial-based MCDA approaches holds immense potential in addressing Ethiopia's complex urban development challenges, as it enables a comprehensive evaluation of suitability factors and the exploration of alternative urban development strategies.

A spatial decision support framework for equitable sensor network distribution in the smart city

AbstractThis paper introduces a proof‐of‐concept spatial decision support system (SDSS) that assists decision‐makers to generate equitably distributed sensor networks and evaluate their placement with reference to specific population‐based coverage criteria. Our approach centres equity in infrastructure distribution; we focus on the decision‐making process required to achieve the best possible sensor coverage of the geographical area for selected vulnerable populations and visualise trade‐offs in coverage inherent in infrastructure distribution. The development of the tool brings together expertise from quantitative geography, urban planning, data science and software engineering, and its technical development is underpinned and shaped by interviews with decision‐makers and their iterative feedback. Through this, we ask: how can decision support tools help with the work of building equitable infrastructure? As well as a technical application, our approach develops a conceptual framework for evaluating sensor network purpose and distribution before actual placement. The unique combination of distribution algorithms, user interface and decision‐maker input, developed by an interdisciplinary team, offers a novel approach to sensor network conceptualisation and generation. Our research contributes to the understanding of the distribution of essential infrastructure and can be repurposed for any sensor type and geographical location to promote equity in infrastructure distribution.

Equitable Prioritization of Bridge Rehabilitation Projects Using a Spatial Multi-Criteria Decision Support Framework

Equitable Prioritization of Bridge Rehabilitation Projects Using a Spatial Multi-Criteria Decision Support Framework

Integrating in-situ data and spatial decision support systems (SDSS) to identify groundwater potential sites in the Esan plateau, Nigeria

Establishing suitable groundwater resource areas is a challenging endeavour across the globe. However, novel spatial technologies have emerged as valuable tools for the effective strategy, management, and assessment of groundwater resources, especially in data-scarce emerging economies. The current study used spatial decision support systems (SDSS) for evaluating and defining groundwater potential sites (GPSs) in Nigeria's Edo central region to promote sustainable governance of groundwater. By merging multiple groundwater contributing theme layers, a leading-edge information-based multiparametric analytical hierarchy process (AHP) was applied to define the groundwater prospective areas. By systematically assigning weights to every subject-specific layer and feature, the subject matter layers of geology, geomorphology, drainage density, slope, soil properties, landuse/landcover, rainfall distribution, hydraulic conductivity, transmissivity, curvature, proximity to surface water bodies, and elevation were generated and used for groundwater potential map generation. Each thematic layer's weights were allocated and adjusted depending on their qualities and relevance to groundwater recharge. The multicollinearity (MC) analysis was used to evaluate the model's predictive capacity. Finally, groundwater potential sites were created by integrating the theme-specific maps with the weighted total overlay computation tool. The study area contained three separate groundwater potential sites: low, moderate, and high. According to the regional geographic distribution, the largest portion of the area (65%) fell within the moderately significant groundwater potential geographical area. The high and low GPSs, which both have a low curvature and a valley plain characteristic, account for 25% and 10%, respectively, of the entire area. The outcomes were contrasted with the yield of groundwater from boreholes gathered in the study region. The validation analysis found an acceptable 88.89% similarity. This highlights the potential for the groundwater map's significant prediction. Therefore, the applied approach is a viable choice for the advancement of groundwater in the central Edo region and with comparable geology all over the globe.

Development of a Spatial Decision Support System (SDSS) for Grape Cultivation by Using a Geographical Information System (GIS): Çatak (Turkey) Example

Development of a Spatial Decision Support System (SDSS) for Grape Cultivation by Using a Geographical Information System (GIS): Çatak (Turkey) Example

Ten steps towards integrated decision making for ecological restoration in cities: Rewilding the European beaver (Castor fiber) in Berlin, Germany

Ensuring a livable city for all within the more-than-human discourse, restoration of urban ecosystems requires careful consideration of both human and non-human needs. However, traditional assessments and therefore most management plans usually fail to include the latter as a core planning requirement. This article presents and explains a 10-step method which simultaneously and actively considers both to identify potential restoration areas within urban ecosystems. To do so, a Strengths-Weaknesses-Opportunities-Threats (SWOT) analysis for the multispecies needs identification is combined with a Multicriteria Spatial Decision Support System (MCSDSS) for the spatial assessment. To validate this method, a case study of Berlin, Germany, an explicitly urban case, is presented. The aim of the study was to evaluate the ecosystem restoration (rewilding) potential of the city's riparian and riverine ecosystems through the enhancement of Eurasian beaver habitats.•Method combining SWOT analysis with MCSDSS for an integrated spatial assessment•Well-suited for multispecies (human and non-human) perspective on urban nature restoration

Enhancing Carbon Sequestration: A Systematic Literature Review of Spatial Decision Support Tools

Human activities impact greenhouse gas emissions through changes in land cover, land use, and land management. Conservation, restoration, and improved land use and land management are increasingly recognized as mitigation solutions. Policy instruments are crucial for addressing environmental challenges and supporting governance actors in enhancing carbon sequestration and reducing emissions in the land sector. The aim of this study was to evaluate the existing spatial decision support systems (SDSSs) for assessing land-based mitigation options and to help policymakers choose the best way to use and manage land. In order to search for tools, a systematic literature review was conducted, where 187 articles suitable for the specified criteria were found, from which 68 tools were selected. Additionally, following the application of the exclusion criteria, 18 tools were chosen for the final analysis. The tools were classified and analyzed based on various features such as type of land-use management, land use, country of application, information on carbon pools, and the number of published articles associated with each tool. Five SDSSs were found to be most suitable for policymakers seeking to implement the most effective land use and land management in order to enhance carbon sequestration.

Datacubes as enabler for advanced decision support systems in land management

AbstractInformed, wise, land management has been a challenge since a long time, but has gained extra importance through the need for responding to climate change effects. The European Green Deal is one example of a concertation effort in a world where data availability, sharing options, and just‐in‐time exploitation are not entirely solved. The LandSupport service responds to this challenge through a combination of a Spatial Decision Support System on top of a datacube management and analytics engine. In this paper, we specifically focus on this datacube engine and how it can contribute to a better decision making through an ordered multidimensional data management paired with strictly standards‐compliant interfaces. Further, the approach described shows a remedy against the current fragmentation of services: through a location‐transparent federation a single common pool of information is established, including distributed fusion allowing an unlimited mix‐and‐match of data regardless of their location.

Utilizing remote sensing and aerogeophysical data for spatial decision support system for optimal gold mineralization site identification in North-Central Nigeria

Utilizing remote sensing and aerogeophysical data for spatial decision support system for optimal gold mineralization site identification in North-Central Nigeria

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.