Mapping Methodology Research Articles

Optimizing Solar Potential Analysis in Cuba: A Methodology for High-Resolution Regional Mapping

The development of solar energy at a regional scale necessitates a thorough understanding of available resources. Cuba, facing prolonged economic, environmental, and energy crises, urgently needs to enhance its sustainability through solar energy. Although solar resource mapping is widespread, Cuba lacks extensive field measurements, often relying on models that may not be ideally suited for large regions, like Matanzas province. Spanning over 12,000 km² with nearly 150 km between its northern and southern extremes, Matanzas presents challenges for high-resolution solar mapping. This study introduces a methodology that integrates various methods and databases to achieve the maximum resolution in the resulting solar map. This approach is designed for large areas, where conventional high-resolution models fall short. By optimizing calculation times and parameterizing the entire surface latitudinally, a high-resolution solar resource map for Matanzas has been developed. This map significantly enhances the understanding of solar resources in Cuba and enables the proposal of new methodologies for analyzing solar potential in similarly large regions.

Systematic Mapping of Climate Change Impacts on Human Security in Bangladesh

There is an increasing consensus that climate change undermines human security by exacerbating existing challenges, acting as a “threat multiplier”. Bangladesh is a climate hot spot due to its geographical location, dense population and vulnerable socio-economic infrastructure. Although there is an increasing number of studies on the impacts of climate change in Bangladesh, aggregated research that combines this evidence and provides a comprehensive overview is lacking. The aim of this research is to thoroughly investigate the characteristics of the academic literature on the complex pathways through which climate variability affects different components of human security in Bangladesh, allowing for determination of the trends and research gaps, as well as whether they lead to conflict or cooperation. We used a systematic mapping methodology to search and screen 1839 publications in Web of Science, including 320 publications for the final synthesis. We found a predominant research focus on rural areas, especially in the southwestern region, with declining crop yield, economic loss, migration, water shortage, food scarcity and health hazards being the highlighted impacts of climate change for Bangladesh. The impacts on food, economic, environmental, health and water security have been well studied, but we found significant research gaps in some human security components, especially energy security. Women and the economically disadvantaged are disproportionately affected, and the causal pathways between conflict or cooperation and the ever-changing climate lack research efforts, implying a dire need to focus on under-researched areas before they become more complex and difficult to address. Policies and interventions should prioritise climate-resilient agricultural practices and infrastructure in high-risk areas, building local capacities and integrating climate risk assessments into urban planning, considering the high influx of environmental migrants. This systematic map provides a foundation for future longitudinal studies, establishes a baseline for this era for future comparisons and serves as a reliable database for relevant stakeholders and policy makers.

Leveraging Crowdsourcing for Mapping Mobility Restrictions in Data-Limited Regions

This paper introduces a novel methodology for the real-time mapping of mobility restrictions, utilizing spatial crowdsourcing and Telegram as a traffic event data source. This approach is efficient in regions suffering from limitations in traditional data-capturing devices. The methodology employs ArcGIS Online (AGOL) for data collection, storage, and analysis, and develops a 3W (what, where, when) model for analyzing mined Arabic text from Telegram. Data quality validation methods, including spatial clustering, cross-referencing, and ground-truth methods, support the reliability of this approach. Applied to the Palestinian territory, the proposed methodology ensures the accurate, timely, and comprehensive mapping of traffic events, including checkpoints, road gates, settler violence, and traffic congestion. The validation results indicate that using spatial crowdsourcing to report restrictions yields promising validation rates ranging from 67% to 100%. Additionally, the developed methodology utilizing Telegram achieves a precision value of 73%. These results demonstrate that this methodology constitutes a promising solution, enhancing traffic management and informed decision-making, and providing a scalable model for regions with limited traditional data collection infrastructure.

Mapping Histories: An Environmental Perspective for Writing Local History in Pakistan

Local history in Pakistan has a complex relationship with national historical narratives that are derived from ideological debates on Muslim nationhood in South Asia. This article discusses how ‘local’ is understood in Pakistan’s historiography and puts forth an argument for the adoption of an environmental framework to enhance our understanding of local history. Mapping methodology used for a project on the Rachna Doab is discussed in order to demonstrate how local environmental history can give new insights into history by eschewing preconceived frames of reference and using a different concept of space and time defined by environmental change.

Generating high-resolution land use and land cover maps for the greater Mariño watershed in 2019 with machine learning

Land Use and Land Cover (LULC) maps are important tools for environmental planning and social-ecological modeling, as they provide critical information for evaluating risks, managing natural resources, and facilitating effective decision-making. This study aimed to generate a very high spatial resolution (0.5 m) and detailed (21 classes) LULC map for the greater Mariño watershed (Peru) in 2019, using the MORINGA processing chain. This new method for LULC mapping consisted in a supervised object-based LULC classification, using the random forest algorithm along with multi-sensor satellite imagery from which spectral and textural predictors were derived (a very high spatial resolution Pléiades image and a time serie of high spatial resolution Sentinel-2 images). The random forest classifier showed a very good performance and the LULC map was further improved through additional post-treatment steps that included cross-checking with external GIS data sources and manual correction using photointerpretation, resulting in a more accurate and reliable map. The final LULC provides new information for environmental management and monitoring in the greater Mariño watershed. With this study we contribute to the efforts to develop standardized and replicable methodologies for high-resolution and high-accuracy LULC mapping, which is crucial for informed decision-making and conservation strategies.

Geospatial Mapping and Detection of Ferrous, Iron Oxides, and Clay Minerals in District Mohmand, Pakistan

The study focuses on the utilization of remote sensing techniques to effectively identify and detect ferrous,iron oxides, and clay minerals in Mohmand district, Pakistan, using LANDSAT 8 multispectral images. This studyfocuses on the increasing demand and importance of these minerals in mining and commercial activities. Mineralexploration in Mohmand district, utilizing remote sensing technology to identify mineral compositions using the bandratio is done. This technique allows for a more focused and precise approach to exploration and extraction techniques.Cloud-free LANDSAT 8 images with minimal vegetation cover were utilized for the analysis. The band ratio approachwas utilized to identify areas exhibiting diverse mineral compositions. The study highlights the effectiveness of thesuggested methodology in mapping and detecting ferrous, iron oxides, and clay minerals, indicating the significantpotential of remote sensing for mineral exploration. The results highlight the importance of developing distributionmaps to support more efficient methods for mining and mineral exploration. The study contributes to a more focusedand efficient assessment of mineral resources and extraction techniques in the Mohmand district and similar geologicalterrains, offering stakeholders a valuable tool for informed decision-making in mineral exploration and exploitationefforts.

Development of a hybrid karst aquifer vulnerability map by using geospatial and statistical tools. The case study of Ziria aquifer in northern Peloponnese

Aquifer vulnerability assessment constitutes a crucial tool for the protection and sustainable management of groundwater resources, particularly in complex karst aquifers. This study conducted a detailed comparative analysis of two tailored and widely used vulnerability mapping methodologies, COP and PaPRIKa, to evaluate their performance within a Mediterranean karst groundwater system. The methods were employed for the development of a hybrid vulnerability map after geospatial and statistical analysis. Both COP and PaPRIKa were applied using a combination of geological, hydrological, and geomorphological aquifer characteristics that can potentially influence its vulnerability to contamination. While COP predominantly assigned Low and Very Low vulnerability values across the study area, PaPRIKa identified the Moderate class as dominant, suggesting a finer sensitivity to karst-specific features. To address potential biases in PaPRIKa, a single-parameter sensitivity analysis was performed, leading to adjusted weights and the development of a modified version, PaPRIKa-Mod. A quantitative comparison of all three methods highlighted the varying degrees of consensus and discord, with PaPRIKa and its modification showing the highest consistency, suggesting robust methodological integrity. Conversely, the comparison of COP with either version of PaPRIKa revealed a lesser yet notable concordance, underscoring their capacity to be integrated and their potential to complement each other in vulnerability assessments. Finally, a hybrid vulnerability map was developed from the integration of similar vulnerability classes, attributing the most vulnerable setting prevailing across all methods. The methodological approach that was followed is adaptable and can provide significant insights for vulnerability estimations across different regions and methodological concepts.

Adult perceptions of mental health access barriers facing youth in rural Washington State: A group concept mapping study.

To explore adult community members' perspectives concerning barriers to mental health care that confront rural-dwelling youth. Group concept mapping, a participatory community-engaged research method, was used. Adult community members brainstormed and sorted statements describing barriers rural youth encounter in accessing mental health services. Point and cluster maps were created to visualize conceptual similarities between statements. Statements were rated according to their commonness and importance (1: low commonness/importance, 5: high commonness/importance). Thirty-five adults sorted and/or rated 71 barriers facing rural youth in accessing mental health services. Seven conceptual clusters were identified: system-level barriers, knowledge and communication, youth concerns, parent/guardian concerns, parent/guardian barriers, costs and convenience, and school-level barriers. Within youth concerns, community members also identified a subcluster focused on stigma. Common and important statements related to limited after-school programs and community mental health support initiatives. Group concept mapping methodology provides structure for conceptualizing challenges facing rural youth in accessing mental health services. Policies should be informed by rural community concerns and priorities. After-school and support programs may align with mental health needs identified by rural communities.

Space is a latent sequence: A theory of the hippocampus.

Fascinating phenomena such as landmark vector cells and splitter cells are frequently discovered in the hippocampus. Without a unifying principle, each experiment seemingly uncovers new anomalies or coding types. Here, we provide a unifying principle that the mental representation of space is an emergent property of latent higher-order sequence learning. Treating space as a sequence resolves numerous phenomena and suggests that the place field mapping methodology that interprets sequential neuronal responses in Euclidean terms might itself be a source of anomalies. Our model, clone-structured causal graph (CSCG), employs higher-order graph scaffolding to learn latent representations by mapping aliased egocentric sensory inputs to unique contexts. Learning to compress sequential and episodic experiences using CSCGs yields allocentric cognitive maps that are suitable for planning, introspection, consolidation, and abstraction. By explicating the role of Euclidean place field mapping and demonstrating how latent sequential representations unify myriad observed phenomena, our work positions the hippocampus in a sequence-centric paradigm, challenging the prevailing space-centric view.

Estimates of Baroclinic Tidal Sea Level and Currents from Lagrangian Drifters and Satellite Altimetry

Abstract Internal waves generated by the interaction of the surface tides with topography are known to propagate long distances and lead to observable effects such as sea level variability, ocean currents, and mixing. In an effort to describe and predict these waves, the present work is concerned with using geographically distributed data from satellite altimeters and drifting buoys to estimate and map the baroclinic sea level associated with the M2, S2, N2, K1, and O1 tides. A new mapping methodology is developed, based on a mixed L1/L2-norm optimization, and compared with previously developed methods for tidal estimation from altimeter data. The altimeter and drifter data are considered separately in their roles for estimating tides and for cross-validating estimates obtained with independent data. Estimates obtained from altimetry and drifter data are found to agree remarkably well in regions where the drifter trajectories are spatially dense; however, heterogeneity of the drifter trajectories is a disadvantage when they are considered alone for tidal estimation. When the different data types are combined by using geodetic mission altimetry to cross validate estimates obtained with either exact-repeat altimetry or drifter data, and subsequently averaging the latter estimates, the estimates significantly improve on the previously published HRET8.1 model, as measured by their utility for predicting sea level and surface currents in the open ocean. The methodology has been applied to estimate the annual modulations of M2, which are found to have much smaller amplitudes compared to those reported in HRET8.1, and suggest that the latter estimates of these tides were not reliable. Significance Statement The mechanical and thermodynamic forcing of the ocean occurs primarily at very large scales associated with the gravitational perturbations of the sun and moon (tides), atmospheric wind stress, and solar insolation, but the frictional forces within the ocean act on very small scales. This research addresses the question of how the large-scale tidal forcing is transformed into the smaller-scale motion capable of being influenced by friction. The results show where internal waves are generated and how they transport energy across ocean basins to eventually be dissipated by friction. The results are useful to scientists interested in mapping the flows of mechanical energy in the ocean and predicting their influences on marine life, ocean temperature, and ocean currents.

Methodology for the data flow mapping and analysis in the European Member States

Methodology for the data flow mapping and analysis in the European Member States

Mapping COVID‐19 at home

AbstractHand‐drawn maps are an effective method for collecting information around sense of place. In this project, we have used a hand‐drawn mapping methodology as a tool for supporting children and young people in navigating and articulating their experiences of COVID‐19 at home. The nationwide exercise was conducted in partnership with the Royal Geographical Society with the Institute of British Geographers and National Museums Liverpool, and was part of the larger AHRC‐funded ‘Stayhome Stories’ project. The mapping initiative provided a platform for children and young people to visually convey their encounters with the pandemic and provides a lens through which to view how they understood and renegotiated meanings of home through the repeated lockdowns.

Enhance Productivity In The Garment Industry Through A Modified Value Stream Mapping Approach

The objective of this study is to overcome problems that occur in the garment industry, the company has tried to develop an agile system to increase productivity in this industry. The garment company that focusses on the production of woven bottoms and outerwear, noted that women's woven bottoms products have the highest demand throughout 2022. However, the company was unable to meet Brand demand of 3.5%. Production data shows defects in products, low quality raw materials, and unstable production processes. A modified Value Stream Mapping (VSM) methodology is use to consider all aspects of production. The results of traditional VSM and Value Stream Analysis Tools (VALSAT) analysis include current state and future state, identification of value added and non-value-added elements during the production process, as well as setting priorities to reduce waste in the production process, especially in inventory (19%), defective parts (17%), and transportation (15%). Based on the VALSAT matrix, Process Activity Mapping (33.5%), Supply Chain Response Matrix (21.6%), and Demand Amplification Mapping (15.3%) were obtained. The findings of this research are Pilot Run, which succeeded in becoming an agile and reliable system during the production process. Through the application of the methodology combined with VSM the results have improved compared to the traditional VSM method. This proves that this method is effective in overcoming takt time, cycle time, man power, and value-added ratio. Limitations of this research were also discussed, including that this research only focused on women's woven bottom products only on the production workflow. Kata Kunci: Agile System, Enhance Productivity, Garment Manufacturing, Lean Manufacturing, Value Stream Mapping.

Improving Lead Time through Lean Manufacturing: A Case Study

Lean manufacturing is a well-established methodology aimed at optimizing production by eliminating waste, enabling industries to thrive in a globally competitive environment. This paper presents a case study of a well-known automotive manufacturing industry on the axle process. This article aims the Value Stream Mapping (VSM) methodology was used in the axle process to reduce lead time by producing quality products with a reduction in non-value-added activities. The current state of the axle process was created using industry data from the past six months. Significant improvements were realized following the successful implementation of VSM, including a reduction in the lead time from 89.50 hours to 50.55 hours. The new state map was also created after implementing the improvements. The results illustrated that the VSM increased the effectiveness by 56.48% in the axle process.

Remote Sensing Technologies for Mapping Ecosystem Services: An Analytical Approach for Urban Green Infrastructure

Urban Green Infrastructures (UGIs) have gained increasing relevance in the field of climate adaptive design because of their capacity to provide regulating ecosystem services apt to respond to the impacts of global warming with short-term strategies. The effectiveness of UGIs in reducing climate risks depends on both the state of natural resources and the understanding of urban ecosystem processes over time. The implementation of analytic methods to better understand urban ecosystem dynamics, as well as the local effective potential of ESs, is crucial for addressing climate impacts in cities. The advances in remote sensing methodologies for mapping and monitoring urban ecosystems represent a key opportunity to deepen the ecological features of existing urban green areas as a potential planning asset to respond to climate impacts. Indeed, remote sensing technologies implement a new data-driven planning approach that enables models and simulations of different project scenarios by supporting planning decisions and reducing the risk of failures. According to these assumptions, this paper discusses the results of a literature review aimed at providing the current state of the art in applying remote sensing technologies for mapping and monitoring ecosystem services, focusing on operational opportunities in urban environments. It examines how remote sensing can depict ESs and ensure data quality and reliability for UGI design. The emphasis is on the potential of ESs to mitigate and adapt to heat wave risks which will be more frequent in the next decade, particularly in cities, as highlighted by the IPCC Report 2023. Therefore, UGIs are strategic tools for addressing heat wave impacts, necessitating a shift from empirical approaches to analytical, data-driven planning methods.

Inverse mapping of quantum properties to structures for chemical space of small organic molecules

Computer-driven molecular design combines the principles of chemistry, physics, and artificial intelligence to identify chemical compounds with tailored properties. While quantum-mechanical (QM) methods, coupled with machine learning, already offer a direct mapping from 3D molecular structures to their properties, effective methodologies for the inverse mapping in chemical space remain elusive. We address this challenge by demonstrating the possibility of parametrizing a chemical space with a finite set of QM properties. Our proof-of-concept implementation achieves an approximate property-to-structure mapping, the QIM model (which stands for “Quantum Inverse Mapping”), by forcing a variational auto-encoder with a property encoder to obtain a common internal representation for both structures and properties. After validating this mapping for small drug-like molecules, we illustrate its capabilities with an explainability study as well as by the generation of de novo molecular structures with targeted properties and transition pathways between conformational isomers. Our findings thus provide a proof-of-principle demonstration aiming to enable the inverse property-to-structure design in diverse chemical spaces.

A Rigorous Academic Inquiry: Leveraging Concept Mapping Methodologies for Amplifying Learning Efficacy in Inaugural BHMS 2 CBDC Cohorts

A Rigorous Academic Inquiry: Leveraging Concept Mapping Methodologies for Amplifying Learning Efficacy in Inaugural BHMS 2 CBDC Cohorts

Aligning technical knowledge to an industry domain in global software development: A systematic mapping

AbstractFinding software developers with expertise in specific technologies that align with industry domains is an increasingly critical requirement. However, due to the ever‐changing nature of the technology industry, locating these professionals has become a significant challenge for companies and institutions. This research presents a comprehensive overview of studies exploring suitable recommendation systems that can assist companies in addressing this pressing need. To conduct this study, we employ a hybrid systematic mapping approach with an initial number of 1,251 studies and a final selection of 21 studies. Our work focuses on collecting data on key technologies, methodologies, and data sets utilized in proposed recommendation systems, to design a new recommendation system that can effectively identify specialists capable of aligning specific technical knowledge with industry domains. The outcomes of this study include insights into the current research trends in this field, alongside a practical overview of considerations necessary for developing a recommendation system that successfully meets the criteria for aligning technical skills with industry domains. By following a hybrid systematic mapping methodology and presenting the outcomes in the form of insights, this research addresses the challenge of finding software developers with domain‐specific expertise in a rapidly changing technology industry, laying the groundwork for aligning technical skills with industry domains.

Profiling residential energy vulnerability: Bayesian-based spatial mapping of occupancy and building characteristics

With global trends in electrification, communities highly dependent on electricity are facing increasing challenges, particularly due to more frequent weather events, which lead to significant surges in electricity demand or result in blackouts. However, the impact is disproportionate across space and time, which raises an urgent need to comprehensively assess residential energy vulnerabilities to ensure energy security for households. Existing methodologies, whether large-scale regional assessments or community-based surveys, predominantly rely on static data. This reliance significantly limits their ability to capture the spatial and temporal dynamics of energy vulnerability, which is particularly important in the face of climate change. This study analyses residential energy vulnerability in the dimensions of social vulnerability, building resilience, and energy burden by innovatively linking occupancy patterns with the built environment through a Bayesian-based spatial mapping methodology. We applied this method through a case study in a Philadelphia census tract area identified for its high vulnerability based on FEMA's National Risk Index. By mapping occupancy with residential buildings, we simulated the energy consumption patterns of approximately 900 residential buildings. The simulations utilized DOE single-family energy prototypes, tailored to account for empirical occupancy and building features, to predict hourly energy consumption and assess indoor thermal comfort during power outages on days with extreme temperatures. The findings reveal disparities across different dimensions of energy vulnerability and illustrate the dynamic nature of energy vulnerability that fluctuates with daily activities. The study also highlights areas with low thermal resilience, suggesting a need for retrofitting older buildings to improve energy efficiency. The implications of this research extend beyond the built environment to the socioeconomic aspects of energy use, advocating for adaptive community planning and the development of resilient energy infrastructures. The results serve as a foundation for stakeholders to implement targeted interventions, prioritize retrofitting efforts, and support equitable access to energy resources, thereby enhancing urban sustainability and resilience.

Scoping review and evidence mapping of interventions aimed at improving reproducible and replicable science: Protocol.

Many interventions, especially those linked to open science, have been proposed to improve reproducibility in science. To what extent these propositions are based on scientific evidence from empirical evaluations is not clear. The primary objective is to identify Open Science interventions that have been formally investigated regarding their influence on reproducibility and replicability. A secondary objective is to list any facilitators or barriers reported and to identify gaps in the evidence. We will search broadly by using electronic bibliographic databases, broad internet search, and contacting experts in the field of reproducibility, replicability, and open science. Any study investigating interventions for their influence on the reproducibility and replicability of research will be selected, including those studies additionally investigating drivers and barriers to the implementation and effectiveness of interventions. Studies will first be selected by title and abstract (if available) and then by reading the full text by at least two independent reviewers. We will analyze existing scientific evidence using scoping review and evidence gap mapping methodologies. The results will be presented in interactive evidence maps, summarized in a narrative synthesis, and serve as input for subsequent research. This protocol has been pre-registered on OSF under doi https://doi.org/10.17605/OSF.IO/D65YS.