Collaborative Mapping Research Articles

Collaborative mapping to investigate the relationship between places and happiness

Collaborative mapping to investigate the relationship between places and happiness

Investigating the Strategic Role of Digital Transformation Path of SMEs in the Era of COVID-19: A Bibliometric Analysis Using R

The COVID-19 pandemic and the subsequent increased use of digital tools can be seen as an incentive for small and medium-sized enterprises (SMEs) to adapt to the digital age. SMEs, whose resilience and adaptability had already been tested during the previous period of the global financial crisis, were called upon to face a new emergency. The aim of this paper is twofold: (i) to investigate the evolution of digital transformation in small and medium enterprises during the pandemic of COVID-19 and (ii) to highlight the main research trends of digital transformation in the post-pandemic era. To approach these issues, a bibliometric analysis based on R package was conducted and examined 765 articles that were published in the timespan of 2014–2022. In the current bibliometric analysis, a range of indicators were applied, such as co-citation analysis of both sources and institutions, the annual scientific production, country collaboration map, world tree map and Multiple Correspondence Analysis. The bibliometric software of Biblioshiny and VOSviewer were used as the main tools to process the data and contributed to the visualization of the results. Findings of the research show that emerging technologies such as blockchain, artificial intelligence, machine learning and 3D printing have started integrating SMEs in their business models. In addition, the technology–organization–environment framework (TOE) has emerged as a niche theme in the research field of digital transformation of SMEs. The above reveals the willingness and the effort of SMEs to adapt to the new circumstances created by the pandemic of COVID-19, by transforming their business models from conventional to digital one. The social media model is also highlighted, as a new product development of SMEs during the crisis of pandemic of COVID-19. The intention to adopt both TOE business model and social media are significantly influenced by emerging technologies and can raise the awareness of government to support SMEs in this effort.

Global Trends in Nursing-Related Research on COVID-19: A Bibliometric Analysis.

BackgroundCoronavirus disease 2019 (COVID-19) has significantly impacted healthcare, especially the nursing field. This study aims to explore the current status and hot topics of nursing-related research on COVID-19 using bibliometric analysis.MethodsBetween 2019 and 2022, publications regarding nursing and COVID-19 were retrieved from the Web of Science core collection. We conducted an advanced search using the following search query string: TS = (“Novel coronavirus 2019” or “Coronavirus disease 2019” or “COVID 19” or “2019-nCOV” or “SARS-CoV-2” or “coronavirus-2”) and TS = (“nursing” or “nurse” or “nursing-care” or “midwife”). Bibliometric parameters were extracted, and Microsoft Excel 2010 and VOSviewer were utilized to identify the largest contributors, including prolific authors, institutions, countries, and journals. VOSviewer and CiteSpace were used to analyze the knowledge network, collaborative maps, hotspots, and trends in this field.ResultsA total of 5,267 papers were published between 2020 and 2022. The findings are as follows: the USA, China, and the UK are the top three prolific countries; the University of Toronto, the Harvard Medical School, the Johns Hopkins University, and the Huazhong University of Science & Technology are the top four most productive institutions; Gravenstein, Stefan, and White, Elizabeth M. from Brown University (USA) are the most prolific authors; The International Journal of Environmental Research and Public Health is the most productive journal; “COVID-19,” “SARS-CoV-2,” “nurse,” “mental health,” “nursing home,” “nursing education,” “telemedicine,” “vaccine-related issues” are the central topics in the past 2 years.ConclusionNursing-related research on COVID-19 has gained considerable attention worldwide. In 2020, the major hot topics included “SARS-CoV-2,” “knowledge,” “information teaching,” “mental health,” “psychological problems,” and “nursing home.” In 2021 and 2022, researchers were also interested in topics such as “nursing students,” “telemedicine,” and “vaccine-related issues,” which require further investigation.

Bibliometric Analysis: Symbolic Power Publication Trens in Scopus.com

The purpose of this study is to explore references to one source that can provide benefits for researchers about symbolic power, it is in the interest to use bibliometric methods to map the spification of research, namely 1) to reveal the number of articles produced by symbolic power research. 2) Conduct mapping to review symbolic power research that has been done, and 3) reveal the collaboration map. The method provides objective parameters in controlling literature and provides opportunities to encourage increased accuracy and minimize the bias of researchers in scientific literature reviews by paying attention to the research results of scholars working in the field research area. This bibliometric method provided citation analysis guidelines, co-citation analysis, coupling bibliography, co-author analysis, and co-word analysis, as well as work guidelines. Bibliometric methods would complement the meta-analysis and review of qualitatively structured literature as a method for exploring and evaluating scientific literature. To demonstrate bibliometric methods, it was done with citation analysis and co-citation to map the intellectual structure of symbolic power journals. The results showed that the investigation of human-related symbolic power was shown by 109 events widely published by the scopus.com database during 2012-2022. From the mapping carried out through bibliometrics shows that the trend of research is related to the term human keywords.

Visualization and Bibliometric Analysis of Research Trends on Hyperbaric Oxygen Therapy.

Hyperbaric oxygen therapy (HBOT) is a professional medical regimen with a wide range of clinical applications in various research fields. In addition to treating diving decompression sickness and air embolism, HBOT is used as an adjuvant in the management of various diseases. A large number of studies have been published to confirm its efficacy. Although HBOT has been clinically applied to the treatment of many diseases, the effectiveness of these treatments remains controversial. Exploring and evaluating HBOT will contribute to the future development of research in this field. Through a quantitative analysis of the literature, this paper explores the citation relevance and collaboration map and their impact on research outcomes. This study used bibliometric and cartographic techniques with VOSviewer to identify the most influential countries and scholars using this treatment, based on syndrome differentiation. It also provides continuous quality evaluation and lean management of the medical expenses associated with HBOT.

Simulation, modelling and classification of wiki contributors: Spotting the good, the bad, and the ugly

Data crowdsourcing is a data acquisition process where groups of voluntary contributors feed platforms with highly relevant data ranging from news, comments, and media to knowledge and classifications. It typically processes user-generated data streams to provide and refine popular services such as wikis, collaborative maps, e-commerce sites, and social networks. Nevertheless, this modus operandi raises severe concerns regarding ill-intentioned data manipulation in adversarial environments. This paper presents a simulation, modelling, and classification approach to automatically identify human and non-human (bots) as well as benign and malign contributors by using data fabrication to balance classes within experimental data sets, data stream modelling to build and update contributor profiles and, finally, autonomic data stream classification. By employing WikiVoyage – a free worldwide wiki travel guide open to contribution from the general public – as a testbed, our approach proves to significantly boost the confidence and quality of the classifier by using a class-balanced data stream, comprising both real and synthetic data. Our empirical results show that the proposed method distinguishes between benign and malign bots as well as human contributors with a classification accuracy of up to 92%.

Mapping strategy for health and wellbeing community assets in the city of Buenos Aires

implementing an asset model in large urban territories is a great challenge. The objective was to develop and evaluate community asset (CA) mapping strategies in a large city. mixed technique, in phases. At first, community resources (CR) were collected through interviews with key informants, search of information sources and field surveys with university students and community volunteers in some pilot neighborhoods. In the second phase, a mass communication campaign was carried out to disseminate the collaborative virtual map. Assets were validated according to eligibility criteria. Campaign evaluation was carried out through indicators of visualization, interaction and participation. In a third phase, community work in the field was reinforced in different neighborhoods. 1144 CR and CA were identified, all of them validated, classified and grouped into five categories. A month after launching the communication campaign "#MeHaceBienMiBarrio," it had been broadcast in the main media in the country. In just two weeks, 120,383 people had been reached with the news on Facebook, and 9,213 had interacted. By then, the map had more than 23,000 views and 87 new community assets were received. The participation rate fell as diffusion decreased. The first stage was positive in terms of dissemination and interaction with the map. Field work is essential to sustain and enhance results.

COLLABORATIVE MAPPING STRATEGY IN SUPPORT OF SLUM UPGRADING: THE CASE OF BOGOTÁ

Abstract. The article presents the development of a methodology for collaborating data gathering carried out remotely in a South American informal settlement for the purpose of a slum upgrading project. It was built in progress considering issues, limitations, and opportunities that arose during data collection. This text describes the creation process of the methodology as well as considerations and advice in retrospect, for those who may be interested in replicating or adapting it to similar contexts and needs.The methodology consists of the collaboration of various subjects, on the field and remotely to facilitate, speed up and expand the mapping possibilities of informal and/or hardly accessible context, taking into account that it has been applied in a period of severe limitations due to the pandemic. The entire workflow was developed having in mind a collaborative and open framework, involving communities at different scales and adopting Free and Open Source Softwares.In the study case, 702 Points of interest in a Bogota’s slum were mapped thanks to one field survey, carried out by a local NGO using Mapillary for street-level images collection, and a collaborative mapping that involved volunteers from various nations in the OpenStreetMap environment.It proved to have a further result in the widening of the knowledge of the involved participants and the creation of networks that could lead to new collaborations or facilitate future projects.

ANALYSIS OF MASSIVE IMPORTS OF OPEN DATA IN OPENSTREETMAP DATABASE: A STUDY CASE FOR FRANCE

Abstract. Importing spatial open data in OpenStreetMap (OSM) project, is a practice that has existed from the beginning of the project. The rapid development and multiplication of collaborative mapping tools and open data have led to the growth of the phenomenon of importing massive data into OSM. The goal of this paper is to study the evolution of the massive imports over time. We propose an approach in three steps: classification of the sources used to edit features in the OSM platform including those massively imported, classification of modifications, and identification of evolution patterns. The approach is mixing global analysis (i.e. sources and modifications are classified) and feature based analysis (i.e. imported features are analyzed with respect to their evolution over time). The approach is applied on three datasets coming from OSM considered for their heterogeneity in terms of complexity, imports, and spatial and temporal characteristics. The results show that there is a sustained activity of edition on imported features, with a ratio between geometry editions and semantic editions depending on the type of the features, with roads being the features concentrating the most activity.

Lean six-sigma (LSS) applications in hospitals: a decade (2011–2020) bibliometric analysis

PurposeThis study examines the research landscape of Lean Six-Sigma (LSS) applications in hospitals for the period of the last decade (2011–2020) to derive answers to the research questions RQ 1: What are the current publication trends for the application of LSS in hospitals concerning document type, Journal (Source), active authors and country-wise publications and their comparison in the two most reputed scientific databases, i.e. Scopus and Web of Science (WoS), RQ2: What are the clusters based on the authors and keywords? RQ3: What are the research trends and author's productivity in LSS applications in Hospitals? RQ4: What are the future research areas?Design/methodology/approachThis article compares these two databases (Scopus and WoS) based on publication pattern, document type, active authors and co-citation analysis. This article analyzes the core sources, author's productivity, globally cited articles, word growth analysis, thematic map and world collaboration map on the WoS and Scopus dataset. The software used are Vosviewer, Biblioshiny (R Package for Bibliometric) and M.S. Excel.FindingsThe application of LSS in hospitals is a niche theme. In the WoS database International Journal of Lean Six-Sigma and in Scopus database International Journal of Health Care Quality Assurance are the most relevant sources publishing research articles in this field. The USA has the highest scientific production in this field. Among the authors, Antony J is the most active author in this area, with the highest contribution over the years.Originality/valueThis study fills the literature gap by mapping the field of LSS in hospitals.

Machine Learning-Based Surgical State Perception and Collaborative Control for a Vascular Interventional Robot

In robot-assisted vascular interventional surg- ery (VIS), surgeons often need to operate outside the operating room to avoid exposure to X-ray. However, it greatly changes the operating ways of surgeons, which affects judgment and operation safety. In this paper, a novel VIS robot system was developed to predict guidewire insertion states and operate collaboratively. To assist the surgeons in perceiving the insertion state, an insertion multi-states prediction model based on softmax logistic regression was proposed. Combined with the prediction model, a human-machine collaborative control strategy was designed, which allows surgeons to perceive the insertion states based on not only the force feedback constructed by the master side but also the prediction results from the slave side. Moreover, a human-machine trust evaluation model and a master-slave collaborative mapping model were proposed for improving safety and efficiency of surgery. To verify the effectiveness of these models, the evaluation experiments in the blood vessel model were carried out. It was indicated by the experiment results that the guidewire insertion states can be predicted by the prediction model in different environments, and the overall accuracy is 93%. The master-slave mapping ratio can be adjusted by the collaborative control strategy automatically to adapt to different surgical conditions. The experimental results showed the usability of the robot-assisted VIS system with the novel force-based perception method.

Education on Sustainable Development Goals: Geographical Perspectives for Gender Equality in Sustainable Cities and Communities

Because of its interdisciplinary approach as a social science directly related to the natural sciences, geography is the academic discipline and school subject that equips students well with knowledge, skills, and values related to education on sustainable development goals. This study is part of the results of MyGEO, a project funded by the European Commission, and it is based on a collaborative mapping of streets named after women in a medium-sized city (Zaragoza, Spain), in connection with the international initiative GeoChicas (GeoGirls) on OpenStreetMap. Its main objective is to obtain evidence to emphasize, through public and digital space, gender equality and the empowerment of women in teacher education by means of their achievements. The learning methodology consists of (i) standardization and correction of alphanumeric information referring to street names contained in the OpenStreetMap spatial database that identify gender and (ii) linking and creating thematic information through Wikipedia editing. The results show that Zaragoza is at the top of the ranking of Spanish and Latin American cities mapped to date in the “Women’s Streets” viewer, with 18% of the streets named after women, compared to the average 15% in the rest of the 30 cities involved in GeoChicas. The direct participation of trainee primary and secondary schoolteachers in this project makes it possible to consolidate a narrative as well as a specific instructional design on education for sustainable development, particularly on SDG number 5 (gender balance) and SDG number 11 (sustainable cities and communities).

Mapping oysters on the Pacific coast of North America: A coast-wide collaboration to inform enhanced conservation.

To conserve coastal foundation species, it is essential to understand patterns of distribution and abundance and how they change over time. We synthesized oyster distribution data across the west coast of North America to develop conservation strategies for the native Olympia oyster (Ostrea lurida), and to characterize populations of the non-native Pacific oyster (Magallana gigas). We designed a user-friendly portal for data entry into ArcGIS Online and collected oyster records from unpublished data submitted by oyster experts and from the published literature. We used the resulting 2,000+ records to examine spatial and temporal patterns and made an interactive web-based map publicly available. Comparing records from pre-2000 vs. post-2000, we found that O. lurida significantly decreased in abundance and distribution, while M. gigas increased significantly. Currently the distribution and abundance of the two species are fairly similar, despite one species being endemic to this region since the Pleistocene, and the other a new introduction. We mapped the networks of sites occupied by oysters based on estimates of larval dispersal distance, and found that these networks were larger in Canada, Washington, and southern California than in other regions. We recommend restoration to enhance O. lurida, particularly within small networks, and to increase abundance where it declined. We also recommend restoring natural biogenic beds on mudflats and sandflats especially in the southern range, where native oysters are currently found most often on riprap and other anthropogenic structures. This project can serve as a model for collaborative mapping projects that inform conservation strategies for imperiled species or habitats.

The impact of user characteristics of smallholder farmers on user experiences with collaborative map applications.

In the future, farmers will have increasing opportunities to use collaborative smartphone applications for agricultural management. Geospatial information in combination with agricultural-relevant information is a great source of knowledge for farmers. Including maps in collaborative mobile agriculture applications benefits communication processes related to agricultural-relevant questions. Ensuring a positive user experience with map interfaces depends on their design. To develop design guidelines for map-oriented mobile agricultural applications, this study evaluates 24 different map design variations (varying in their elements and degrees of complexity) and characterizes their user experience with 72 coffee farmers as study participants. Our findings show that the most crucial factors for a positive user experience were restricted interactivity, simple tasks to conduct (selecting single point features), and a simplified base map style, highlighting relevant landmarks. Since our farmers consisted primarily of less-experienced smartphone and map users, our findings may also be helpful for users in general, sharing similar user characteristics. While empirical, in-situ studies pose many challenges, they provide relevant insights into the real use situation and user behavior of mobile map applications. Our findings help establish some basic principles for designing map adaptations, serving as a guideline for creating effective mapping applications, which adapt to the farmers’ contextual factors. Based on our study results, we suggest future research for continuing conceptualizing principles of map design adaptation and support this effort through empirical, in-situ studies for relating contextual user factors to the adaptation behavior of map applications.

Energy-Efficient Ground Traversability Mapping Based on UAV-UGV Collaborative System

With the development of science and technology, robots have been widely used in smart cities. The traversability mapping of environment perception is the prerequisite for robots to perform tasks. To save the energy consumption of traversability mapping for unmanned ground vehicle (UGV), we fusion a wide range of aerial images and a small amount of ground images to provide vision for UGV. Current map fusion methods are usually constrained by homogeneous model of robotic systems and lack of diverse sensors. As a result, they cannot work well in heterogeneous collaborative robotic systems that consist of aerial and ground robots. In this paper, we use heterogeneous robot systems, including UGV and unmanned aerial vehicles (UAV) to build an occupancy grid map that can be used for navigation. To fuse sensor data of different types, we propose a Collaborative Map Fusion algorithm based on Multi-task Gaussian Process Classification (MTGPC) using heterogeneous robotic systems. Besides, probabilistic model is exploited in traversability mapping, so the active perception can be used to build the map efficiently. Our system is tested in real scenes and can achieve an accuracy of more than 70%. The map fusion using active perception is better than map fusion using random strategy in terms of speed and accuracy. To our knowledge, this is the first work that can build the occupancy grid map using sparse data points sampled from aerial images and ground lidar map.

UMap: A Free Open-Source Alternative to Google My Maps

Since their release in 2005, Google Maps-based tools have become the de facto solutions for a variety of online cartographic projects. Their success has been accompanied by a range of critiques denouncing the individualistic market-based logic imposed by these mapping services. Alternative options to this dominant model have been released since then; uMap is one of them. uMap is a free, open-source online mapping platform that builds on OpenStreetMap to enable anyone to easily publish web maps individually or collaboratively. In this paper, we propose to reflect on the potential and limits of uMap based on our own experiences of deploying it in six different mapping projects. Through these experiences, uMap appears particularly well-suited for collaborative mapping projects, due to its ease in connecting to remote data and its high level of interoperability with a range of other applications. On the other hand, uMap seems less relevant for crowdmapping projects, due to its lack of built-in options to manage and control public contributions. Finally, the open-source philosophy of uMap, combined with its simplicity of use and its strong collaborative capacity, make it a great option for activist mapping projects as well as for pedagogical purposes to teach a range of topics including online collaborative cartography.

Digital Medical Education Empowered by Intelligent Fabric Space

Medical education plays an important role in promoting the development of global medical science. Nevertheless, the intrinsic gap existing between institutional medical teaching and practical clinical tasks causes low education efficiency and students’ weak initiative. Recent developments of sensing fabric and embedded computing, along with the advances in Artificial intelligence (AI) and digital twin technology are paving the way for the transformation of medical research towards digitization. In this work, we present an intelligent fabric space based on novel functional fabric materials and digital twin networking enabled by 5G and Internet of Things (IoT) technologies. In this space, medical students can learn knowledge with collaborative mapping of the digital and real world, cyber-physical interaction and real-time tactile feedback. And the proposed service system will evaluate and feedback students’ operational behaviors to improve their experimental skills. We provide four typical applications of intelligent fabric space for medical education, including medical education training, health and behavior tracking, operation playback and reproduction, as well as medical knowledge popularization. The proposed intelligent fabric space has the potential to promote innovative technologies for training cutting-edge medical students by effective and efficient ways.

Multi-criteria decision-making for coronavirus disease 2019 applications: a theoretical analysis review.

The influence of the ongoing COVID-19 pandemic that is being felt in all spheres of our lives and has a remarkable effect on global health care delivery occurs amongst the ongoing global health crisis of patients and the required services. From the time of the first detection of infection amongst the public, researchers investigated various applications in the fight against the COVID-19 outbreak and outlined the crucial roles of different research areas in this unprecedented battle. In the context of existing studies in the literature surrounding COVID-19, related to medical treatment decisions, the dimensions of context addressed in previous multidisciplinary studies reveal the lack of appropriate decision mechanisms during the COVID-19 outbreak. Multiple criteria decision making (MCDM) has been applied widely in our daily lives in various ways with numerous successful stories to help analyse complex decisions and provide an accurate decision process. The rise of MCDM in combating COVID-19 from a theoretical perspective view needs further investigation to meet the important characteristic points that match integrating MCDM and COVID-19. To this end, a comprehensive review and an analysis of these multidisciplinary fields, carried out by different MCDM theories concerning COVID19 in complex case studies, are provided. Research directions on exploring the potentials of MCDM and enhancing its capabilities and power through two directions (i.e. development and evaluation) in COVID-19 are thoroughly discussed. In addition, Bibliometrics has been analysed, visualization and interpretation based on the evaluation and development category using R-tool involves; annual scientific production, country scientific production, Wordcloud, factor analysis in bibliographic, and country collaboration map. Furthermore, 8 characteristic points that go through the analysis based on new tables of information are highlighted and discussed to cover several important facts and percentages associated with standardising the evaluation criteria, MCDM theory in ranking alternatives and weighting criteria, operators used with the MCDM methods, normalisation types for the data used, MCDM theory contexts, selected experts ways, validation scheme for effective MCDM theory and the challenges of MCDM theory used in COVID-19 studies. Accordingly, a recommended MCDM theory solution is presented through three distinct phases as a future direction in COVID19 studies. Key phases of this methodology include the Fuzzy Delphi method for unifying criteria and establishing importance level, Fuzzy weighted Zero Inconsistency for weighting to mitigate the shortcomings of the previous weighting techniques and the MCDM approach by the name Fuzzy Decision by Opinion Score method for prioritising alternatives and providing a unique ranking solution. This study will provide MCDM researchers and the wider community an overview of the current status of MCDM evaluation and development methods and motivate researchers in harnessing MCDM potentials in tackling an accurate decision for different fields against COVID-19.

Collaborative Mapping on Sustainable Development Goals in Latin America UNESCO Global Geoparks

Collaborative Mapping on Sustainable Development Goals in Latin America UNESCO Global Geoparks

A new method of multi-robot cooperative exploration

This paper aims at the task of the map fusion and environmental exploration method. The multi-robot collaborative map creation is based on the local map by each individual robot, and the nodes in the local map are matched to obtain the association between multiple maps. Multi-robot task allocation is also a process of maximising the benefits of resource utilisation. How to allocate resources reasonably so that each robot can quickly complete the corresponding tasks in coordination, and how to solve the problem of the heavy tasks for individual robots or few tasks for individual robots, multitask allocation based on market law can be a solution. The new topological scene map in this paper is used to represent the environment and carry out robot exploration. It can be seen that multi-robot cooperative exploration is more efficient efficiency.