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The effective and efficient planning of rural land-use changes and their impact on the environment is critical for land-use managers. Many land-use growth models have been proposed for forecasting growth patterns in the last few years. In this work; a cellular automata (CA)-based land-use model (Metronamica) was tested to simulate (1999–2007) and predict (2007–2035) land-use dynamics and land-use changes in Andalucía (Spain). The model was calibrated using temporal changes in land-use covers and was evaluated by the Kappa index. GIS-based maps were generated to study major rural land-use changes (agriculture and forests). The change matrix for 1999–2007 showed an overall area change of 674971 ha. The dominant land uses in 2007 were shrubs (30.7%), woody crops on dry land (17.3%), and herbaceous crops on dry land (12.7%). The comparison between the reference and the simulated land-use maps of 2007 showed a Kappa index of 0.91. The land-cover map for the projected PRELUDE scenarios provided the land-cover characteristics of 2035 in Andalusia; developed within the Metronamica model scenarios (Great Escape; Evolved Society; Clustered Network; Lettuce Surprise U; and Big Crisis). The greatest differences were found between Great Escape and Clustered Network and Lettuce Surprise U. The observed trend (1999–2007–2035) showed the greatest similarity with the Big Crisis scenario. Land-use projections facilitate the understanding of the future dynamics of land-use change in rural areas; and hence the development of more appropriate plans and policies

A customized geographical information system (GIS) has been developed to support focal indoor residual spraying (IRS) operations as part of a scaled-up campaign to progressively eliminate malaria in Vanuatu. The aims of the GISbased spatial decision support system (SDSS) were to guide the planning, implementation and assessment of IRS at the household level. Additional aims of this study were to evaluate the user acceptability of a SDSS guiding IRS interventions. IRS was conducted on Tanna Island, Republic of Vanuatu between 26 October and 5 December 2009. Geo-referenced household information provided a baseline within the SDSS. An interactive mapping interface was used to delineate operation areas, extract relevant data to support IRS field teams. In addition, it was used as a monitoring tool to assess overall intervention coverage. Surveys and group discussions were conducted during the operations to ascertain user acceptability. Twenty-one operation areas, comprising a total of 187 settlements and 3,422 households were identified and mapped. A total of 3,230 households and 12,156 household structures were sprayed, covering a population of 13,512 individuals, achieving coverage of 94.4% of the households and 95.7% of the population. Village status maps were produced to visualize the distribution of IRS at the sub-village level. One hundred percent of survey respondents declared the SDSS a useful and effective tool to support IRS. The GIS-based SDSS adopted in Tanna empowered programme managers at the provincial level to implement and asses the IRS intervention with the degree of detail required for malaria elimination. Since completion, SDSS applications have expanded to additional provinces in Vanuatu and the neighbouring Solomon Islands supporting not only specific malaria elimination and control interventions, but also the broader public health sector in general.

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.

Island territories and their coastal regions are subject to a wide variety of stresses, both natural and anthropogenic. With increasing pressures on these vulnerable environments, the need to improve our knowledge of these ecosystems increases as well. Unmanned Aerial Vehicles (UAVs) have recently shown their worth as a tool for data acquisition in coastal zones. This literature review explores the field of UAVs in the context of coastal monitoring on island territories by highlighting the types of platforms, sensors, software, and validation methods available for this relatively new data acquisition method. Reviewing the existing literature will assist data collectors, researchers, and risk managers in more efficiently monitoring their coastal zones on vulnerable island territories. The scientific literature reviewed was strictly analyzed in peer-reviewed articles ranging from 2016 to 2022. This review then focuses on the operationalization of the concept of resilience as a risk management technique. The aim is to identify a procedure from raw data acquisition to quantifying indicators for the evaluation of the resilience of a territory and finally linking the analyzed data to a spatial decision support system. This system could aid the decision-making process and uses the islands of French Polynesia and its Resilience Observatory as a case study.

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.

Background Zoonotic diseases account for a substantial portion of infectious disease outbreaks and burden on public health programs to maintain surveillance and preventative measures. Taking advantage of new modeling approaches and data sources have become necessary in an interconnected global community. To facilitate data collection, analysis, and decision-making, the number of spatial decision support systems reported in the last 10 years has increased. This systematic review aims to describe characteristics of spatial decision support systems developed to assist public health officials in the management of zoonotic disease outbreaks. Methods A systematic search of the Google Scholar database was undertaken for published articles written between 2008 and 2018, with no language restriction. A manual search of titles and abstracts using Boolean logic and keyword search terms was undertaken using predefined inclusion and exclusion criteria. Data extraction included items such as spatial database management, visualizations, and report generation. Results For this review we screened 34 full text articles. Design and reporting quality were assessed, resulting in a final set of 12 articles which were evaluated on proposed interventions and identifying characteristics were described. Multisource data integration, and user centered design were inconsistently applied, though indicated diverse utilization of modeling techniques. Conclusions The characteristics, data sources, development and modeling techniques implemented in the design of recent SDSS that target zoonotic disease outbreak were described. There are still many challenges to address during the design process to effectively utilize the value of emerging data sources and modeling methods. In the future, development should adhere to comparable standards for functionality and system development such as user input for system requirements, and flexible interfaces to visualize data that exist on different scales. PROSPERO registration number: CRD42018110466.

Significant recent advances in satellite remote sensing allow environmental managers to detect and monitor cyanobacterial harmful algal blooms (cyanoHAB), and these capabilities are being used more frequently in water quality management. A quantitative estimate of the socioeconomic benefits generated from these new capabilities, known as an impact assessment, was missing from the growing literature on cyanoHABs and remote sensing. In this paper, we present an impact assessment framework to characterize the socioeconomic benefits of satellite remote sensing for detecting cyanoHABs and managing recreational advisories at freshwater lakes. We then apply this framework to estimate the socioeconomic benefits of satellite data that were used to manage a 2017 cyanoHAB event in Utah Lake. CyanoHAB events on Utah Lake can pose health risks to people who interact with the blooms through recreation. We find that the availability of satellite data yielded socioeconomic benefits by improving human health outcomes valued at approximately$370,000, though a sensitivity analysis reveals that this central estimate can vary significantly ($ 55,000– $1,057,000 in benefits) as a result of different assumptions regarding the time delay in posting a recreational advisory, the number of people exposed to the cyanoHAB, the number of people who experience gastrointestinal symptoms, and the cost per case of illness. Key Points Cyanobacterial harmful algal blooms (cyanoHABs) are a human health risk that is typically mitigated by recreational advisories and warnings We present a framework to quantify the socioeconomic benefits of using remote sensing technology to test for the presence of cyanoHABs Satellite technology yielded between $ 55,000 and $1,057,000 in socioeconomic benefits associated with improved human health outcomes

Several modeling tools commonly used for supporting flood risk assessment and management are highly effective in representing physical phenomena, but provide a rather limited understanding of the multiple implications that flood risk and flood risk reduction measures have on highly complex systems such as urban areas. In fact, most of the available modeling tools do not fully account for this complexity—and related uncertainty—which heavily affects the interconnections between urban systems evolution and flood risk, ultimately resulting in an ineffective flood risk management. The present research proposes an innovative methodological framework to support decision‐makers involved in an urban regeneration process at a planning/strategic level, accounting for the multi‐dimensional implications of flood risk and of different flood risk management strategies. The adopted approach is based on the use of System Thinking principles and participatory System Dynamics modeling techniques, and pursues an integration between scientific and stakeholder knowledge. Reference is made to one of the case studies of the CUSSH and CAMELLIA projects, namely Thamesmead (London), a formerly inhospitable marshland currently undergoing a process of urban regeneration, and perceived as being increasingly vulnerable to flooding. It represents an interesting opportunity for building a replicable modeling approach to integrate urban development dynamics with flood risk, ultimately supporting policy and decision‐makers in identifying mitigation/prevention measures and understanding how they could help achieve multi‐dimensional benefits (e.g., environmental, social and economic). Plain Language Summary Flooding is one of the most harmful natural disasters that cities may encounter, particularly considering the impacts of climate change and the highly dynamic evolution of urban areas. As traditional modeling approaches tend to provide an effective yet simplified view of flood risk assessment and management in urban areas, decision‐makers need integrated approaches that can capture on the one hand the complex implications of flood events and flood risk management strategies in urban areas and, on the other hand, the impacts of urban development on flood risk. This work proposes an innovative approach based on qualitative modeling tools for urban flood risk analysis, which also incorporates the knowledge provided by local stakeholders. Although applied to a specific case study, the approach can be implemented in different contexts. The results highlight the importance of integrating urban dynamics and flood risk, especially in the preliminary identification of mitigation/prevention measures. However, further activities have already been started to support their operationalization. Key Points Stakeholders' knowledge can provide relevant insights into the urban dynamics related to flooding Causal Loop Diagrams support better integrating flood risk management in evolving urban systems Behavior Over Time graphs support scenario analysis including the impact of prevention/mitigation measures

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

The recent emergence of urban air mobility (UAM) is transforming urban landscape, with studies predicting its increasing role in the coming decades. This emergence of UAM calls for developing decision-support tools for planning of launch and landing sites (LLS) in constrained and complex urban environments. Such decision-support tools must be useful and usable in the context where planning processes related to this emerging technology are also emerging themselves. In practice, this means carefully designing the functionality of the tool itself according to the current and future user needs, as well as clarifying the roles and responsibilities of different actors in the planning process. This research presents the development of a geospatial decision-support tool for identifying the potential urban UAM LLS for drones with a diameter of up to 3 meters. In addition, this study presents the preliminary evaluation of this decision-support tool with a set of potential users, including urban planners from the cities of Helsinki (Finland), Stockholm (Sweden) and Hamburg (Germany).