Volunteered geographic information use in crisis, emergency and disaster management: a scoping review and a web atlas

ABSTRACT

Nowadays, an increasing number of crises worldwide, triggered by climate extremes, natural and human-made hazards, the coronavirus pandemic, and more, pose a high pressure on crisis, emergency, and disaster management. Spatial data and Volunteered Geographic Information (VGI) are key issues in the successful and immediate response to crises. This paper aims to explore the use of VGI in crisis management, including emergency and disaster management, based on a scoping review of existing literature in English for five years (2016–2020). Specifically, the research intends to answer Scoping Review Questions (SRQ) regarding the use of VGI in crisis, emergency, and disaster management, and the verified cases’ spatial distribution, the VGI sources utilized (e.g. OpenStreetMap – OSM, Crowdsourcing, Twitter), the types of hazards (e.g. natural and human-made hazards, pandemic), the specific tasks in crisis, emergency or disaster management and VGI use in the management of actual crisis events, e.g. COVID-19 pandemic, Hurricane Katrina, etc. Eligible papers on VGI use in crisis, emergency, and disaster management are geolocated based on first-author affiliation, and as a result, a spatial bibliography is provided. Thus, the term Spatial Scoping Review is introduced. Scoping Review Questions are answered, and the results are analyzed and discussed. Finally, implementing the “VGICED Atlas”, a web atlas, permits the publication of the research results to a broad audience and the visualization of the analysis with several interactive maps.

KEYWORDS:

• Volunteered geographic information (VGI)
• crisis management
• emergency management
• disaster management
• social media
• web atlas
• spatial bibliography
• scoping review

1. Introduction

In recent years, we have witnessed an increasing number of crises worldwide, triggered by climate extremes, natural and human-made hazards, the coronavirus pandemic, and more. Those crises pose a high pressure globally for integrative emergency, crisis, and disaster management, empowered by using spatial information from various sources. Volunteered Geographic Information (VGI) is a valuable source as it contains multiple, up-to-date crisis-related information.

The need for fast and effective decision-making has driven the development of spatial technologies (Mansor et al. 2004). The use of spatial data and technologies in crisis management is crucial. Spatial data is used to assess the consequences of an incident, identify the population at risk and inform them promptly, and identify the best ways and routes of escape. Decision-makers have used technology to make numerous tasks more manageable, and recently it has also been used in helping cities become more informed, more prepared, and thus more resilient (GAR 2015). Advances in information communication technologies can lead to advanced systems’ efficiency of emergency management and accuracy by using modern data processing techniques (Xu, Sugumaran, and Zhang 2015) and new data sources such as VGI provided from, for example, vulnerable groups or people at risk (Tzavella, Fekete, and Fiedrich 2017). Emergency managers incorporate vital spatial components to assess the potential hazard impact or identify the best evacuation routes during disasters (Cova 1999). Generally, with the help of Geographic Information Systems (GIS), spatial indicators are applied to calibrate and manage phenomena such as climate change, natural hazards, deforestation, land-use change, fires, floods, earthquakes, tsunamis (Fekete et al. 2015) in most cases using data from official agencies and data banks (local, national and international).

However, VGI offers the possibility of exploiting real-time information that can be used in the early stages of assessing a crisis, assisting the response phase, and later determining the damage in considerable detail. This information can be helpful in emergencies, resulting in improved situational awareness, response time, and enhanced resilience. For example, people experiencing disasters could still have the ability to share messages and spatial information via different platforms, voluntarily supplying information regarding the affected areas (Goodchild and Glennon 2010) to cope with uncertainty (Palen and Anderson 2016). Such platforms are social media (e.g. Facebook, Instagram, Twitter), mobile applications (e.g. WhatsApp), collaborative platforms, or in situ and mobile sensors (Palen and Anderson 2016; Goodchild and Glennon 2010; Tzavella, Fekete, and Fiedrich 2017). The current tasks of social media-based research include disaster identification and spatiotemporal analysis, assessment of disaster intensity, sentiment analysis, fake news identification, communication node analysis, and information display (Tang and Wang 2020).

In general, in the last decade, there has been a trend shift in disaster management toward Information and Communication Technologies (ICTs) based approaches (Sood and Sood 2021), where big data, information, and communications technology in disasters see an increasing interest (Freeman et al. 2019). An integrative review by (Freeman et al. 2019) revealed that social media and GIS are two of the four most frequently mentioned tools within disaster management and communications technology, including patient health information during and after a disaster and disaster modeling.

Since its introduction, VGI has attracted massive research interest (Juhász, Adam, and Jamal 2016; Connors, Lei, and Kelly 2012; Klonner et al. 2016). Researchers have verified evidence of VGI use in crisis, emergency and disaster management frameworks through systematic literature reviews and meta-analyses covering up to fifteen years of research and knowledge (Reuter and Kaufhold 2018). This paper aims to explore the use of VGI in crisis management, including emergency and disaster management, based on a scoping review of existing literature for several years. In this article, verified cases of VGI use in crisis, emergency and disaster management are geolocated, and thus a spatial bibliography is provided. Finally, an interactive and viable (i.e. updatable) web atlas created further permits the publication of the research results to a broad audience and their visualization with several interactive maps. The paper is organized as follows: Section 2 provides additional background about VGI and crisis, emergency, and disaster management, presents the existing literature reviews on the subjects, and introduces the research plan; Section 3 describes the methods applied; Section 4 presents the analysis and the results, Section 5 presents the web map application, that is, the VGICED Atlas, where various interactive layers of information are provided to the users, and Section 6 discusses the results and suggests plans for future research with recommendations for the scientific community and policymakers.

2. Background

2.1. VGI for crisis, emergency and disaster management

VGI is a relatively new source of spatial information utilized in social media-based research, crisis monitoring, and decision-making. It features instant information and specific content (Tang and Wang 2020) in unstructured and structured forms. Specifically, VGI is the use of digital tools to collect, analyze, and disseminate geographic information provided by individuals (Ferster et al. 2018); or else crowdsourced. VGI was introduced as a term in 2010 (Goodchild and Glennon 2010) as crowdsourcing information, and it has become one of the top priority research topics in GIScience for the decade from 2007 to 2017 (Yan et al. 2020). Specifically to their utilization in crisis, emergency and disaster management, this kind of information produced, extracted from, or disseminated by social media was also introduced as “crisis informatics” (Hagar 2007).

Further, the term was expanded (Hughes and Leysia 2009) and has gained increasing interest since then (Reuter, Amanda, and Kaufhold 2018). Crisis informatics is the interconnectedness of people (or a crowd), organizations, technology, and information during crises, examining the intersecting directions of social, technical, and information perspectives in various crises (Hagar 2007). The term views emergency response as an expanded sociotechnical system where information is disseminated between official media, public media, and multiple entities. Under the term VGI, researchers study the use of information and technology in the mitigation, preparation, response, and recovery phases of crises, disasters, and other emergencies (Palen et al. 2010), where timely information retrieval and exchange are critical. In this article, the term VGI used is the one adopted as introduced in Tzavella, Fekete, and Fiedrich (2017). Therefore, VGI is “the voluntarily exchangeable information that includes any geographic and spatial information provided by the population (before, during and after a crisis) and is available through different VGI platforms. i.e. OpenStreetMap, social media platforms, mobile applications, and more”. Therefore, when discussing VGI in this article, collected, analyzed, and exchanged structured and unstructured information is taken into consideration.

A single dimension cannot define crisis management because of the multiplicity and complexity of humanitarian crises, including human-made and natural disasters (Jeong and Yeo 2017). Examples of humanitarian crises include natural and climate-induced disasters (such as floods, flash floods, earthquakes, volcanic eruptions, tsunamis, hurricanes, droughts, and wildfires), human-made disasters (such as wars, oil spills, terrorist attacks), compound impacts (such as forced migration, decreased food security, famines) and other emergencies (such as epidemics, pandemics). Crisis management is implemented in a specific context through broad risk analysis, incident identification, prevention, enhanced preparedness, mitigation, response, and recovery. Therefore, there is high interconnectedness and, on many occasions, overlap in the broad aspects of a crisis, emergency and disaster management cycle. Internationally accepted definitions and terms of crisis, emergency, and disaster are found in Appendix A in Table A1. The terms crisis and disaster are closely related and share more features than the term emergency, depending on the situation (Al-dahash, Thayaparan, and Kulatunga 2016). Despite the sudden and, most times, unpredictable nature of crises, emergencies, and disasters, there are attempts to understand the nature and different character of such terms. The reason is to be correctly used in the primary literature stream. It is observed in the literature that many relevant studies are context and term-specific, making literature reviews challenging to collect relevant to the subject research studies (see section 2.2). This article uses the crisis as the general umbrella of crisis, emergency, and disaster. Mainly, crisis management aspects are analyzed with an awareness of the often overlapping terms of disaster and emergency management.

2.2. Existing reviews on VGI in crisis management

Various literature reviews have been conducted over the years to investigate VGI use in crisis management. Due to the vast number of studies, longitudinal research can be performed, even if it phases challenges such as the rapid evolvement of social media platforms and a change in the context in which the platforms are used. Therefore, the researchers must distinguish between generalizable findings and those related to a specific social media platform or crisis context (Reuter, Amanda, and Kaufhold 2018). Longitudinal studies make observation changes more accurate and can answer questions for specific periods regarding the type of crisis/event that VGI is used, the phase of the crisis management cycle, the platform on that VGI is produced and disseminated, and more. Most studies are term and topic-directional restricted. For example, the researchers focus on one term, i.e. “crisis” (De Longueville et al. 2010; Luokkala and Kirsi 2014; Palen et al. 2007; Reuter, Amanda, and Kaufhold 2018; Saroj and Pal 2020; Stieglitz et al. 2018), or “disaster” (Gao, Barbier, and Goolsby 2011; Haworth and Eleanor 2015; Tang and Wang 2020; Kankanamge et al. 2019; McDougall 2012; Nilupaer 2019; Scholz et al. 2018; Zook et al. 2010). Additionally, the researchers use the terms “social media” or “crowdsourcing” or focus on one or more management phases of such overlapping management cycles, with most focusing on “situational awareness/preparedness” (Luokkala and Kirsi 2014; Vieweg et al. 2010; Seppänen and Virrantaus 2015; Vongkusolkit and Huang 2021; Horita and João 2013), “response phase” (Goodchild and Glennon 2010; Meesters, van Beek, and Van de Walle 2016) and “disaster relief” (Gao, Barbier, and Goolsby 2011; Poiani et al. 2016; Zook et al. 2010). Further, VGI-related literature reviews focus on specific VGI sources with a preference for social media platforms such as Twitter, which is the most popular due to the accessibility of data (Hughes and Leysia 2009; Vieweg et al. 2010; Shanley et al. 2013). A systematic literature review shows that the relatively more diverse VGI research topics investigated over a more extended period in North America and Europe, compared with other regions of the globe, highlight the significant problems studied across the VGI research network (Yan et al. 2020).

2.3. Research plan

This paper is based on a scoping literature review. A systematic review typically tries to answer a focused research question with narrow parameters and usually fits into the PICO (Population, Intervention, Comparison, Outcome) question format. On the other hand, a scoping review tries to answer a broad question with more complex, exploratory research questions and often does not fit into the PICO question format.¹ A critical difference between scoping and systematic reviews is that in terms of a review question, a scoping review will have a broader “scope” than traditional systematic reviews with correspondingly more expansive inclusion criteria (Munn et al. 2018). Additionally, the general goal of conducting scoping reviews is to identify and map the available evidence (Arksey and Lisa 2005; Anderson et al. 2008).

Literature reviews’ results can be coupled with geolocation to introduce the spatial character of bibliography research. Spatial bibliographies, i.e. online collections of georeferenced context-specific literature bodies, assist the research on such multidisciplinary and multidirectional studies. The benefits of researching georeferenced media are outlined in the literature (Howell et al. 2019), including accessibility benefits (Karl, Gillan, and Herrick 2013), reduced redundancy, decreased bias toward underrepresented areas, and providing solid ground on which meta-analysis can be based (Karl 2018). Information on location in published studies represents an untapped resource for discovering literature and applies to various domains (Karl 2018). For example, location knowledge can promote collaborations among authors and researchers in geographically separate areas worldwide, advance science, and address sustainability challenges. However, search tools’ thematic and not geographic nature makes location-based searches challenging and inefficient (Page 2009).

Building on the experience of a previous spatial scoping study, some of the authors of this article (Skopeliti and Katerina 2018) have conducted a geolocated context-specific longitudinal study through a literature review on VGI use in disaster management in 2018, where the geolocated results were presented in maps to assist further place-based research.

In the present research framework, a scoping review of spatial character is combined with the geolocation of the results, as in a spatial bibliography. Thus the term “Spatial Scoping Review” is introduced. Specifically, in this article, crisis, emergency and disaster are used as the main terms of our literature search to analyze the use of VGI in crisis, emergency and disaster management among global, multidirectional and multidisciplinary studies identified. Further, the authors do not proceed to a quality assessment of the studies, as it is a practice in systematic literature reviews (Arksey and Lisa 2005). The aim is to review all types of research for the last five years to a) assist further meta-analyses, b) avoid redundancies in the researched subject, c) promote international collaborations, and d) advance policymaking by providing information on additional information sources, mining methods, and tools.

To the authors’ knowledge, no scoping review has been conducted to identify, collect, and visualize the vast multidisciplinary literature on VGI use in crisis, emergency and disaster management in a multi-term, global and multidirectional context with an interactive web atlas. Precisely, the authors, after the definition of broad scoping review questions, a) assess the extent of the available evidence in this more general context-specific research, b) organize it into groups utilizing research-specific criteria after selecting keywords with various methods and tools, and c) visualize it, providing a spatial georeferencing citation tool and further recommendations for future research.

The research aims to answer the following Scoping Review Questions (SRQ):

SRQ1:Is VGI used in crisis, emergency, or disaster management, and what is the spatial distribution of scientific interest (e.g. international)?

SRQ2:Which VGI sources are utilized (e.g. OpenStreetMap – OSM, Crowdsourcing, Twitter, USHAHIDI)?

SRQ3:For which hazard types (e.g. natural and human-made hazards, pandemic) and for which task in crisis, emergency, or disaster management is VGI utilized (e.g. situational awareness, preparedness, response, recovery)?

SQR4:Has VGI been used to manage actual crisis events, e.g. the COVID-19 pandemic, and Hurricane Katrina?

Finally, the authors aim to investigate the geovisualization of the findings of this spatial scoping review with several interactive maps in the framework of a web atlas (see section 6) to provide a viable and updatable spatial georeferencing tool disseminating the results of the analysis according to the SRQs mentioned above available to the scientific community and policymakers.

3. Methods

The scoping literature review, in a condensed form relevant for a literature review paper, was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and precisely according to the extension for Scoping Reviews (PRISMA-ScR) as suggested by (Tricco et al. 2018). A checklist for the specific scoping review (PRISMA-ScR) is provided as supplementary material in Appendix B (Table B1). The present scoping review has no published protocol.

3.1. Information sources and search methods

An organized literature search for scientific articles that satisfy specific criteria was performed in Scopus. Scopus² is the world’s largest abstract and citation database of peer-reviewed literature: scientific journals, books, and conference proceedings (Schotten et al. 2017). It was selected since it delivers a comprehensive overview of the global research output in science, technology, medicine, social sciences, and arts and humanities.³ Additionally, it features tools to track, analyze and visualize research that helps in a preliminary study of the results.

A key element for a literature search is the formation of the query. It constitutes an electronic search strategy that can be repeated upon request. The query (Figure 1) includes keywords related to VGI (e.g. vgi*, “volunteered geographic*”, “crowdsourc*”, “geotag*”, “social media”, “blog”, “message”, “SMS”, “Citizen Science”, “social network”, “mobile app*”) and to crisis management (e.g. “disaster management”, “emergency management”, “cris* management”, “humanit*”). Using the wildcard “*“*” permits the retrieval of multiple records with similar wording. Keyword combinations are built using Boolean operators such as “AND“*” and “OR“*”. Keywords are searched in the “Abstract“*”, “Title“*”, or “Keywords“*” fields in the Scopus database.

Figure 1. Scopus search query utilized for the scoping review.

For each research paper that satisfied the query, the following fields are extracted from Scopus: “Authors”, “Title”, “Year”, “Source title”, “Volume”, “Issue”, “Cited by”, “DOI”, “Link”, “Affiliations”, “Abstract”, “Author Keywords”, “Index Keywords” and “Document Type”. This information was downloaded from SCOPUS, saved as an ASCII file (CSV), and imported to Excel for further screening, processing, data, and information mining and analysis.

3.2. Eligibility criteria

Several eligibility criteria were applied. The present scoping review aims to study articles from all sources published during 2016–2020 in English regarding the inclusion criteria. Five recent years are selected to give an overview of the latest developments in the usage of VGI avoiding trends that might appear in a single year only. In addition, the English language was chosen to acquire publications from international literature. Various filters in the query restricted the results in these five years of research and language with research outside this period and in other languages to be excluded.

3.3. Screening – selection of sources of evidence

Papers missing critical information such as “Abstract”, “Authors”, and “Affiliations” were excluded. It is essential to exclude false articles or duplications. To accelerate the process, screening automation is proposed to exploit a list of representative keywords. This keyword list is formed from the papers per se to assure objectivity.

For this reason, Natural Language Processing (NLP) using artificial intelligence was conducted. “Author keywords” extracted from Scopus for all papers were processed in TextRazor,⁴ an NLP tool. NLP tools are valuable since they aid and streamline the identification of keywords and small phrases consisting of two words, i.e. “disaster management”, that appear more frequently in “Author keywords”. In this framework, NLP tools further assist with identifying the inclusion and exclusion criteria; thus, papers’ classification for qualitative and quantitative meta-analyses is possible. In the second stage, reviewing the abstracts of the articles during the screening led to identifying additional keywords that ensure the inclusion of the maximum number of relevant to the researched subject articles.

Finally, keywords are classified by the authors (Table 1) in categories related to and addressing the Scoping Research Questions (SQRs):

• Use: use in crisis, emergency or disaster management (4 keywords), e.g. disaster management

• VGI sources: sources of VGI (23 keywords), e.g. Twitter

• Hazard: type of hazard (52 keywords), e.g. flood

• Task: task in crisis management (53 keywords), e.g. alert

• Crisis events: specific events with geographic reference at the country level and time (year), e.g. 2017 Hurricane Harvey

Table 1. Keywords utilized for the inclusion and screening of the papers for each SQR.

Scoping Review Question (SRQ)	Keywords
SRQ1: Use	Disaster management, Emergency management, Crisis management, Humanitarian Mapping/Humanitarian aid
SRQ2: VGI sources	Big Data, Citizen Science, Collaborative Mapping, Crowdsourcing, Ehealth, E-participation, Facebook, Geotagging, Internet Of Things (IoT), Mhealth, Mobile app, OSM, Participatory GIS, SMS, Social Media, Social Networks, Text messaging, Twitter, Ushahidi, VGI, Volunteer, Wearable Technology, WeChat
SRQ3: Hazard	Air Crush, Buildings Indoor/Outdoor Mapping, Chemical Accident, Climate, COVID-19, Crime, Crisis, Crowd Emergencies, Cyclone, Disaster, Disease, Earthquake, Ebola, Emergency, Epidemic, Events/Happenings, Explosion, Famine, Fire, Flood, Food, Hazard, Haze, Heatwave, Hostage, Hurricane, Ice Jam, Ice Storm, Influenza, Landslide, Mapping, Missile, Monsoon, Plague, Politics, Rain Storm, Rain Storm, Rainfall, Refugee Crisis, Shooting, Storm, Terrorism, Tornado, Tsunami, Tuberculosis, Typhoon, Volcano, War, Water Crisis, Weather, Weather Forecasting, Wildfire
SRQ3: Task	Adaptation, Alarming, Alert, Awareness, Building Damage, Care Ethics, Community Resilience, Crisis Communication, Crisis Detection, Crisis Informatics, Crisis Management, Crisis Mapping, Cultural Heritage Protection, Data Analysis, Decision Making, Decision Support, Disaster Assessment, Disaster Impacts, Disaster Informatics, Disaster Management, Disaster Recovery, Disaster Relief, Disaster Resilience, Disaster Response, Disaster Risk, Early Warning, Emergency Evacuation, Emergency Management, Emergency Response, Emergency Service, Governance Management, Humanitarian aid, Humanitarian Crisis, Humanitarian Response, Location Estimation, Logistics, Mapping, Misinformation, Post-Crisis, Prediction, Preparedness, Prevention, Recovery, Rescue, Resource Allocation, Risk Assessment, Risk Management, Sentiment Analysis, Situation Awareness, Spatial Analysis, Spatiotemporal Analysis
SRQ4: Crisis events	See Table A7 in Appendix A

The numbers indicate the number of keywords used.

The screening was applied with a semi-automated process that used queries and extracted information about the papers’ content based on the abovementioned keywords (see Table 1) in Excel. In this way, only the papers that meet specific conditions were selected as sources of evidence. For a decision on the articles’ inclusion for further analyses, keywords from the “Use” and “VGI” columns in Table 1 were searched in the “Author Keywords”, “Index Keywords”, and “Title” fields. The search is applied sequentially. Initially, the “Author Keywords” fields are checked for “Use” keywords; if no keywords exist, then the “Index Keywords” fields are reviewed, and finally, if no keywords exist, then “Title” is checked. The process is repeated for the “VGI” keywords only for eligible papers based on the “Use” keywords. Only papers that satisfy keywords from both categories are further studied. As a result, a subset of the initially proposed papers by Scopus is selected. Finally, “Abstracts” are screened by two reviewers, and papers are excluded accordingly. Moreover, NLP results contributed to identifying the technology and geomatics tools appearing in the research papers. (Table A2 in Appendix A).

3.4. Synthesis of the results

To answer SRQ 2 and SQR 3 (see section 3.3), additional information was extracted for the eligible records based on keywords for “Hazard” and “Task” (see Table 1). Keywords are searched in the “Author Keywords”, ‘Index Keywords”, and “Title” fields sequentially. This step is only a classification phase, and no exclusion is performed.

In the analysis framework, keywords addressing each SRQ in Table 1 are further classified (see Table 2) for meta-analyses. Keywords for “VGI sources” are classified into four groups, i.e. Big Data/IoT, Crowdsourcing, Map/GIS, Social media, and Special Apps. Keywords for “Hazards” are classified based on the Emergency Events Database⁵ (EM-DAT)⁵ in six groups, i.e. General, Meteorological, Hydrological, Natural Hazard, Human-made, and Disease/Pandemic. Keywords for “Tasks” are classified into four groups related to the disaster management phase, e.g. General (e.g. mapping, information classification, big data computing, information mining, and more), Pre-event, During the event, and Post-event. Further, to investigate the VGI use in world-known crises events, e.g. Hurricane Katrina, the “Abstract” field is searched based on keywords extracted with NLP tools and updated by the authors. The final list of keywords for “Crises Events” can be found in Table A7 in Appendix A. The classification represents the information extracted from the articles’ content; therefore, the keywords are classified accordingly.

Table 2. Classification of keywords into groups.

Scoping Review Questions (SRQs)	Groups	Keywords
SRQ2: VGI sources	Big Data/IoT	Big data, the Internet of Things, Wearable technology
	Crowdsourcing	Citizen science, Crowdsourcing, Volunteer
	Map/GIS	Collaborative Mapping, Geotagging, OSM, Participatory GIS, VGI, Volunteered Geographic Information
	Social media	Social Media, Facebook, Social Network, Twitter
	Apps	EHealth, E-participation, MHealth, Mobile app, SMS, Text Messaging, Ushahidi, WeChat
SRQ3: Hazard	General	Disaster, Emergency, Hazard, Events/Happenings
	Human-made	Buildings Indoor/Outdoor Mapping, Chemical Accident, Crime, Crowd Emergencies, Explosion, Famine, Food, Hostage, Mapping, Missile, Politics, Refugee Crisis, Shooting, Terrorism, War
	Hydrological	Flood, Tsunami, Water Crisis
	Meteorological	Climate, Cyclone, Heatwave, Hurricane, Ice Jam, Ice Storm, Monsoon, Rain Storm, Rainfall, Storm, Tornado, Typhoon, Weather, Weather Forecasting
	Disease	COVID-19, Disease, Ebola, Epidemic, Influenza, Plague, Tuberculosis
	Natural	Earthquake, Fire, Haze, Hurricane, Landslide, Volcano, Wildfire
SRQ3: Task and Management Phase	General	Adaptation, Care ethics, Crisis management, Crisis mapping, Cultural heritage protection, Data Analysis, Decision making, Decision support, Disaster assessment, Disaster impacts, Disaster informatics, Disaster management, Disaster risk, Emergency management, Emergency service, Governance management, Humanitarian crisis, Location estimation, Logistics, Mapping, Misinformation, Risk assessment, Risk management, Sentiment analysis, Spatial analysis, Spatiotemporal analysis
	Pre-event	Alarming, Alert, Awareness, Early Warning, Prediction, Preparedness, Prevention
	During the event	Crisis Communication, Crisis detection, Crisis informatics, Rescue, Resource allocation
	Post-event	Building damage, Community resilience, Disaster recovery, Disaster relief, Disaster resilience, Disaster response, Emergency evacuation, Emergency response, Humanitarian aid, Humanitarian response, Post-crisis, Recovery, Situation awareness

3.5. Geolocation of eligible cases

Charting the geographic scope of the included studies is a common practice in scoping review studies (Carrillo et al. 2021; Dol et al. 2019; Audate et al. 2019; Molina-Maturano, Speelman, and De Steur 2020). The geographic distribution of the studies at the region (Audate et al. 2019; Molina-Maturano, Speelman, and De Steur 2020) or country level is presented in tabular form (Dol et al. 2019) or portrayed on a map (Audate et al. 2019).

This study aims to geolocate the eligible papers at the city level to form a spatial bibliography and visualize the results with interactive maps in a web atlas framework. Research paper geolocation is based on data in “Affiliation” and, more precisely, on the first-mentioned author in case of more than one, which is a common practice for visualizing the spatial distribution of scientific articles (Yan et al. 2020). A detailed list of cities and countries was utilized to identify cities and countries in “Affiliation”. Based on data extracted for City and Country, geographical coordinates were assigned. Each eligible paper is transformed to a spatial point based on the geographical coordinates in the ArcGIS environment, keeping all info, e.g. “Title” and “Abstract”, as non-spatial attributes. This innovation regarding the geolocation of the scoping review results permits the introduction of the spatial scoping review term. Due to the existence of URL addresses in the “DOI” and “Link” fields for each geolocated paper, the data set can be considered Linked Data.⁶ A constraint of this type of geolocating articles by current first-author affiliation is that cities and countries of the other authors’ affiliations are often not recorded.

4. Results

In this section, analysis results are presented in two sub-sections. First, a presentation of the study characteristics follows a PRISMA flow diagram describing the articles’ selection process. Later, sub-sections follow where the results of the conducted data analyses are presented, aiming to answer the main scoping review questions (SRQs) introduced in section 2.3.

4.1. Study characteristics

The total number of records satisfying the SCOPUS query (Figure 1) as of the 4^th of January 2021 was 2147. The search was limited to peer-reviewed articles in English for 2016–2020. From this group, 107 were excluded since the “Abstract”, “Authors”, and “Affiliations” fields were empty (Table 3).

Table 3. Scopus paper screening resulted in an eligible subset further utilized.

Year	2016	2017	2019	2020	Total
Scopus results	327	310	520	610	2147
With author, affiliation and abstract	301	288	496	591	2040
Eligible	188	162	218	213	969

From the detailed screening based on keywords for “Use” and “VGI”, 1071 papers were excluded because they did not meet the eligibility requirements, and 969 were identified for the final review as eligible for further analysis. Eligible articles include articles (531), book chapters (29), conference papers (367), editorials (2), and reviews (40). From the number of articles published every year, it is concluded that the number of articles published on the research subject has increased significantly over time (Table 3). The process described above follows a PRISMA flow suggested (Tricco et al. 2018), and its diagram is presented in Figure 2.

Figure 2. PRISMA flow diagram of new systematic reviews for eligible articles to include in the review process (Page et al. 2021).

4.2. Data analysis

VGI use (Table 1) is verified for “Disaster Management”, “Emergency Management”, “Crisis Management”, and “Humanitarian Aid/Mapping”. Based on the pie chart in Figure 3, “Disaster Management” (49%) covers almost half of the data, whereas “Emergency Management” (29%) and “Crisis Management” (20%) cover the other half. “Humanitarian Aid/Mapping” exhibits a small percentage of 1%.

Figure 3. Pie chart of VGI use in crisis, emergency, disaster management, and humanitarian aid/mapping.

4.2.1. VGI sources

At first, VGI sources are examined (Figure 4(a), Table A3). Based on the classification of VGI sources into groups (Table 2), it is observed (Figure 4(b)) that more than three-quarters of VGI is extracted from “Social Media” (76%). The other sources, i.e. “Special (to event/subject) designed apps”, “Big Data/IoT”, and “Map/GIS”, contribute each by 5%, whereas the general term “Crowdsourcing” reaches 10%. A histogram is utilized for a detailed presentation of each VGI source, covering at least 0.5% of cases (Figure 4(b)). In this classification, a general reference to “Social Media” and “Social Networking” exhibits a 60%. Specific references to programs/applications include Twitter, Facebook, OSM, Ushahidi, MHealth, EHealth, and E-participation. OSM, Collaborative Mapping, and Participatory GIS, which have a pure geographic character cover of only 2%, suggest that citizens contribute to VGI mainly passively (e.g. social media) and less actively (e.g. OSM). New technologies like IoT and Big Data emerge, and older technology such as SMS exhibit a small percentage.

Figure 4. Word cloud of sources utilized in crisis management showcasing the frequency of use (a), pie chart with VGI sources groups, and histogram with predominant VGI sources (at least 1%) (b).

4.2.2. Hazard

Regarding VGI use for managing different hazards, it is evident that VGI is used for various crises (Figure 4(a), Table A4). Based on the classification of hazards into broader categories (Table 3), the “General” category covers almost 60% of the cases. Percentages of around 10% appear for “Hydrological”, “Natural”, and “Meteorological”, and smaller percentages around 5% for “Disease/Pandemic” and “Human-made” hazards (Figure 5(b)). A detailed presentation with a histogram for each hazard with more than 0.5% of cases can be found in Figure 5(b). In this graph, general keywords such as “Disaster”, “Crisis”, and “Emergency” that cover 60% were excluded to focus on specific hazards. Most cases refer to “Flood” (10%), but “Hurricane” (8%) and “Earthquake” (7%) are also significant. One should comment on the presence of “COVID-19” in the graph with almost the same cases as “Fire” (2%), although it is present only in two, i.e. 2019 and 2020, of the five years covered by this work. Two human-made hazards appear in this list, “Terrorism” and “Refugee crisis”, whereas two hazards “, COVID-19” and “Disease”, are related to health.

Figure 5. Word cloud of hazards appearing in the use of VGI in crisis management showcasing the frequency of use (a), pie chart with hazard groups, and histogram with predominant hazards (at least 1%).

4.2.3. Crisis, emergency, disaster management task or phase that utilizes VGI

VGI use in crisis management is observed in different tasks and phases in crisis, emergency, and disaster management (Table 2). Figure 6(a) and Table A7 provide evidence for the use of VGI for different tasks and phases of the crisis, emergency and disaster management cycles. Usually, this information is not provided in the articles studied, and “General use” is reported. General descriptions, i.e. “Disaster Management”, “Crisis Management”, and “Emergency management”, exhibit the most significant number of cases (68%). Apart from the “General” group, tasks are classified according to the event (pre, during, post). These groups’ percentage is portrayed in a pie chart (Figure 6(b)). A slight lead is noted for the “Pre-event” and “During the event” use. A detailed presentation with a histogram for each actual use with more than 1% of cases can be found in Figure 6(b). However, this graph does not depict general descriptors to focus on particular tasks. Most VGI use cases refer to “Crisis communication” (7%), “Awareness” (5%), and “Alarming”, proving the VGI merit in crisis notification.

Figure 6. Word cloud of VGI use in different management tasks showcasing the frequency of use (a), pie chart with management phase percentages, and histogram with tasks appearing to more than 1% of eligible articles (b).

4.2.4. Geographical scope

International participation is observed at the continent level based on the first author’s affiliations in the eligible papers (Figure 7). Significant VGI use in crisis management (30%) is observed in Europe, Asia, and America. Eligible articles come from 61 countries (Table A6). Countries that cover at least 1.5% of cases are depicted in a histogram (Figure 7). Most papers come from the USA (25%), India (9%), and China’s mainland (8%) are critical players, whereas the champions in Europe are Germany (7%), the UK (6%), and Italy (4%). Additionally, Australia exhibits a 4%.

Figure 7. Articles’ geographical scope showcasing - pie chart with percentages at the continent level and histogram of countries with more than 1.5% of eligible papers.

4.2.5. VGI use in specific crises around the globe

An interesting aspect of this research is the verification of the VGI contribution to disaster management by capturing the use in specific real crises, e.g. the 2017 Hurricane Harvey in the USA. For this reason, the “Abstracts” of the eligible papers are screened to find the above information. If such information is not present in the “Abstract” and appears in the full text, it cannot be extracted by this type of screening. About 32% of the papers (310) refer to actual crisis events in specific countries (Table A7).

Crises are observed in 81 countries (Table A8) and nine geographic regions (e.g. Africa, Atlantic Region, Balkans, Europe/European Union, Arab World, Asia, International, and West Africa). Crises distribution at the continent level is portrayed in Figure 8(a). Crises in Asia (38%) hold the most significant percentage whereas America (30%) and Europe (22%) follow. This observation comes in contrast with the researchers’ distribution at the continent level (Figure 8(b)), where Europe exhibits a significant percentage (35%) with Asia (31%) and America (30%) to follow. It contrasts with the continent disaster risk reported in the 2021 World Risk Report.⁷ Oceania has the highest disaster risk, mainly due to its high exposure to extreme natural events, with Africa, America, Asia, and Europe following in descending order of disaster risk.

Figure 8. Specific crises - crises events with at least 3% of eligible articles (a), crises percentages by continent, and top 10 countries with the most eligible articles (b).

Regarding the spatial distribution of these crises at the country level, a histogram (Figure 8(b)) portrays the top 10 countries where these crisis events occur. The USA is the first one with more than 100 mentions. Most of these countries, i.e. China, India, Nepal, Indonesia, Philippines, are situated in Asia, and Australia is in the fourth position. Finally, the three last countries, i.e. Germany, Italy, United Kingdom, are in Europe.

For several actual crisis events identified from the eligible article’s analysis (165 in number) and have captured the world’s interest, the use of VGI is verified (Table A7). Crisis events with at least 3% of mentions are depicted in a histogram (Figure 8(a)), with the oldest real crisis event to be recorded and analyzed in 2003 and the most recent in 2020.

For all crises that appear in the list of “10 Disasters that Changed the World”,⁸ VGI use is verified by this study, i.e. Haiti Earthquake (2010), Tōhoku Earthquake and Tsunami, Fukushima Daiichi Nuclear Disaster (2011), Hurricane Sandy (2012), Typhoon Haiyan (2013), West Africa Ebola Outbreak (2014–2016), Nepal Earthquake (2015), Hurricane Harvey (2017), Hurricane Maria (2017), Cyclone Idai (2019), Global Wildfires (2019). Additionally, some papers are written in the framework of projects that research the use of VGI in disaster management, such as COSMIC, DRIVER, E2mC, EmerGent, ENSURE, I-REACT, Slandail, SOTERIA.

5. The interactive VGICED atlas: VGI in crisis, emergency, and disaster management atlas

This study suggests the geolocation of eligible studies at the city level by introducing the spatial scoping review method. Geolocated context-specific articles are best explored and visualized through an atlas (Pigeon, Fekete, and Hufschmidt 2017) since several maps can present different aspects in a condensed yet detailed form of the analyses of the results. Implementing a web atlas permits disseminating the results to a broader audience, e.g. the scientific community and policymakers. Additionally, its interactive and updatable character, serving the goals of the spatial scoping review, permits quick identification of context-specific and place-based literature and viability, further offering a powerful citation tool. Interactive maps portray environment-specific findings, and data can be queried and investigated based on data stored in the non-spatial attributes (Table 4). Although modern scientific search engines may provide a citation map (e.g. WOS), the functionality and cartographic excellence provided by the tailor-made web atlas are missing.

Table 4. Non-spatial attributes for geolocated articles.

Fields from Scopus	Fields resulting from the Analysis
“Authors”, “Title”, “Year”, “Source title”, “Volume”, “Issue”, “DOI”, “Link”, “Affiliations”, “Abstract”, “Author Keywords”, “Index Keywords” and “Document Type”	Use, VGI Source, VGI Source Type, Hazard, Hazard Type, Task, Data Management Phase, City Affiliation, Country Affiliation, Crisis Event, Event country

In the ArcGIS Online platform of ESRI, a web map application (Figure 9), “VGICED Atlas” (VGI in Crisis, Emergency, and Disaster Management Atlas), was created for the presentation of the analysis results.⁹ The standard web map tools such as “Zoom in/out”, “Pan”, “Search Address or Place”, “Default extent”, and “My location” are provided for map exploration. Additionally, the user can select one of the offered Base maps from the gallery, such as OSM, Image Hybrid, etc. Data retrieval regarding attributes (Table 4) for each paper is also possible (Figure 9).

Figure 9. Screenshot of the VGICED atlas (VGI in crisis, emergency, and disaster management atlas) hosted on ArcGIS online.

5.1. Geographic scope and spatial distribution

In the VGICED Atlas framework (Figure 10), the general spatial overview of worldwide scientific interest in the VGI exploitation for crisis, emergency and disaster management purposes is portrayed with three maps that address SRQ1.

Figure 10. Spatial distribution of eligible articles portrayed with three maps: eligible articles – geolocation (a), eligible articles – aggregation (b), and research density (c).

The users can select one of each layer from the “Layer List” according to the results they are interested to see in different scales and forms and further explore:

• Eligible papers – Geolocation (Figure 10(a)): This dot map portrays the geolocation of every eligible paper with a black point symbol. Data retrieval for each article is possible. This map visualizes the articles at the city level of the first name appearing in affiliations. It portrays the geographic distribution and density of the research subject based on discretely distributed papers. This method is better than any other method of showing distribution because of its accuracy, and it is readable by the layman.

• Eligible papers – Aggregation (Figure 10(b)): This map uses proportional symbols (e.g. circles) to portray papers aggregated in groups for larger geographic areas; thus, the spatial distribution can be summarized and displayed at smaller scales. When the users zoom in, circles are de-clustered into smaller aggregated circles up to the paper level. Data retrieval for each group at the paper level is still possible.

• Research Density (Figure 10(c)): This indicator portrays the research density for the specific topic. It is computed from the eligible papers’ geolocation data and illustrates the research distribution with zones. The Colour Value is used to symbolize this indicator, where two main concentrations in Europe and West USA are observed after visualization.

5.2. Geovisualization of the results

Additional maps that portray the analysis results are available to the user. One can select from the Layer List the maps entitled: Publication Year (Figure 11(a)), VGI Source (Figure 11(b)), Hazard Type (Figure 11(c)), and Disaster Management Phase (Figure 11(d)). In these maps, papers are visualized based on keywords applied for meta-analysis and address time distribution, SRQ2, SRQ3, and SRQ4.

Figure 11. Additional maps portray the analysis results: publication year (a), VGI source (b), hazard type (c), and disaster management phase (d).

The last three maps utilize pictorial symbols easily understood even by the layman. A legend also appears. Finally, the “Crisis Event” map portrays VGI use for crisis events (Figure 12). Data retrieval for each paper is possible on every map.

Figure 12. VGI use in crisis events (i.e. COVID-19 pandemic), as analyzed in this article, around the globe.

5.3. Queries and filters of the atlas

Queries and Filters allow the user to search for or limit the visibility of papers on a map. Only papers that meet the expression criteria will be visible on the map or highlighted. Filters restrict papers’ visibility in the corresponding layer based on predefined keywords for VGI, Hazard, and Disaster Management Task (see Tables 1 and 2) utilized for answering the Scoping Review Questions and the Publication Year (Figure 13).

Figure 13. Filters allow the user to limit the visibility of papers in a layer according to, e.g. task/phase searched, i.e. disaster relief.

Additionally, Queries can be applied to find papers satisfying specific conditions for one field (Figure 14), i.e. Author, Title, Affiliation, and Keywords based on free text inserted by the user.

Figure 14. Examples of queries that can be applied to find eligible articles that satisfy specific conditions for one field, i.e. affiliations in Germany.

6. Discussion, conclusions and future plans

Spatial analysis, visualization and dissemination provided by the coupling of VGI, handling technologies, and methods for geographic information systems, and remote sensing are valuable for filling potential information and gaps of sources for crisis management globally. This paper aims to explore the use of VGI in crisis management, including emergency and disaster management, based on a scoping review of existing literature in English for five years (2016–2020). The research intends to answer four Scoping Review Questions (SRQ). The visualization of the spatial scoping review results follows an atlas framework based on spatial bibliographical approaches. A web atlas is created, enabling easy access to the research results with maps and data exploration with queries and visualization.

The authors’ Scoping Review Questions (SRQs) are successfully answered. Regarding SRQ1, VGI use in crisis, emergency, or disaster management is verified. “Humanitarian Aid/Mapping” has a small percentage of less than 1%, indicating that VGI is not utilized substantially for humanitarian aid even though it has proven valuable for prioritizing resource allocation. Concerning the geographical context of VGI use, the international distribution of scientific interest is verified. Asia appears as engaged as America and Europe; an observation that contrasts the results of a generalized multidirectional literature review (Yan et al. 2020) revealed that from 2007 to 2017, most research was conducted in Europe and America. Although Oceania has a higher world disaster risk, it lags behind other areas in VGI use. Almost identical percentages of 30% for those three continents argue that the inclusion of articles only in the English language has not spoiled the international aim of this research. The international character of VGI involvement in crisis, emergency, or disaster management is also visualized in the VGICED Atlas maps.

Additionally, it is revealed that developing countries (as globally observed and cited in the literature as the “Global South”) lack the scientific attention that should be given due to high climate-related risks that they phase and are expected to phase in the future with an increasing amount of impacts. Precisely, the World Risk Index of 2021 (WRI2021¹⁰) and the Global Climate Risk Index (GCRI¹¹) place the Global South in the highest risk areas, with a) the most vulnerable population, b) the highest risk for hazard-related impacts on a societal and infrastructural level and c) the highest risk from climate- and extreme weather-induced impacts. Further, as mentioned in the WRI2021, countries with low economic capacities and income tend to be more vulnerable. Thus, insufficient capability to prevent disasters and extreme weather events often reduces existing capacities. VGI is proven valuable for the timely provision of crucial information before, during, and after a crisis, emergency or disaster. Therefore, it is suggested that funding should be directed toward countries of high vulnerability. Since there is a global consensus that climate adaptation measures need acceleration through financing actions helping developing countries, we suggest that part of the funding should be streamed toward VGI-related projects (e.g. update or expansion of telecommunication infrastructure, technology update of research institutes, and higher education organizations).

Further, answering the SRQ2 addressing VGI sources, the majority is collected through “Social media” (76%). Only a small percentage has a pure geographic character from VGI that has been contributed actively, e.g. OSM. In contrast, passive collection through social media, i.e. Twitter, is predominant. Citizens should be encouraged to actively participate in the VGI collection with initiatives such as YouthMappers (https://www.youthmappers.org/) and Humanitarian OSM (https://www.hotosm.org/). In the HOT framework, volunteers can join Open Mapping Hubs for Eastern and Southern Africa and West and Northern Africa to help developing countries lag behind other geographical areas in VGI use in crisis management. Incentives such as those applied in Geo-Wiki, e.g. coauthor on a scientific paper, an Amazon gift voucher (Laso et al. 2017), and gamification can encourage citizens to participate actively in VGI collection (Antoniou and Christoph 2018). Based on the analysis of active social media users as a percentage of the total population for 2022, percentages for Europe, Asia, and America range from 70 to 85%, but it falls to 27% for Africa.¹² This small percentage of social media users is related to the low usage of VGI for crisis, emergency and disaster management in Africa since social media is the primary source. For citizens in developing countries where smartphones are a luxury, simple applications for VGI collection in SMS format may be more appropriate. However, in Oceania, where 84% of the total population uses social media, the VGI usage is too few.

According to the analysis, new technologies like IoT and Big Data emerge as VGI sources, and older technology such as SMS back away. Specifically, one of the meta-analyses (see Table A2 in Appendix A) regarding the technologies used for VGI extraction and handling shows that big data computing methods and technologies, including high-performance computing, machine learning, deep learning, and multi-source data fusion, have increasingly become critical components of using social sensing to understand the impact of and response to disaster events promptly (Howell et al. 2019; Li, Huang, and Christopher 2019). More specifically, the analysis conducted in this paper shows an increasing interest from 2016 to 2020, and for deep learning, the research interest is observed to be more recent for 2019 and 2020. This growing interest in deep learning methodologies and technologies is expected to be higher since such techniques have started to be supported through software that provides deep learning tools (e.g. ArcGIS Pro, QGIS, Orfeo Toolbox, TensorFlow). Deep learning combined with remote sensing and VGI seems promising for mapping and mining information purposes of underrepresented areas in developing countries where VGI creation and dissemination infrastructure is scarce. Such data handling processes can be valuable, for example, for settlement change identification, real-time forecasting, and timely humanitarian aid, which are globally recognized as practical methods for enhancing and strengthening the preparedness for response and recovery (mitigation phase).

Further, machine learning methodologies can accelerate the classification of VGI and big data handling, providing the opportunity for prompt topic identification, fake news identification, mining of geolocated information regarding the progress of extreme weather-related impacts, and decreasing mapping inequality gap observed globally, despite the advances of mapping. IoT collects data related to crisis, emergency, and disaster management are considered big data due to volume, velocity, variety, veracity, and value. The Cloud is the only appropriate environment for processing, storing, and visualizing these data.

Concerning the SRQ3, the analysis showed that it is evident that VGI is exploited in the management of a variety of hazards, with the “General” category capturing the majority and smaller percentages for “Hydrological”, “Natural”, and “Meteorological”. Regarding the specific type of hazards, “Flood”, “Hurricane”, and “Earthquake” are dominant. One should comment on “COVID-19” (2%), although it is present only in two, i.e. 2019 and 2020, of the five years covered by this work due to its new existence. The results have also shown that 69% of the research is not “phase-related”. This relatively high percentage can be an indication that VGI exploitation in various phases of an event is rather complex to a) retrieve due to lack of technologies, b) identify, c) cleaned – providing phase-related information, d) classify, as was also a complex process for the scoping review conducted in this paper.

Finally, for the SRQ4, VGI use in actual crisis events (165) with international influence in 81 countries, specifically for the 10 Disasters that changed the world (Meyers 2019), is documented. This argues for the VGI verified contribution to disaster management. Certified VGI use in disaster management for world-known crises can be exploited to recruit volunteers worldwide and persuade those who question the VGI value.

The presented research has several strengths and presents several innovations:

• The Scoping method for literature review is combined with a Spatial Bibliography, and a new method called Spatial Scoping Review is proposed and tested.

• Conducting a Spatial Scoping Review on VGI use in crisis, emergency and disaster for five years (2016-2020), including 969 eligible articles, created the larger dataset existing so far, covering a comprehensive perspective of this article’s subject.

• Compared to existing reviews, this paper argues that using only abbreviated terms, as used in ten-year (multi-year) reviews, is already a significant limitation for the literature search and creates high biases in the research outcomes regarding the spatial distribution of scientific interest. For example, the generalized multidirectional literature review conducted by (Yan et al. 2020) used only the abbreviated term of the volunteered geographic information, i.e. VGI, limiting its search to articles using only the abbreviation and excluding valuable information from the review process.

• The methodological approach suggested in the article is enriched with NLP tools. Artificial intelligence provided by available NLP tools is proven valuable for analysis purposes such as keyword extraction, which further assists classification and drawing of results. Specifically for a scoping review or even a systematic review, NLP tools allow for additional identification of the inclusion and exclusion criteria and papers’ classification for qualitative and quantitative meta-analyses purposes. Further, NLP tools are valuable since they aid and streamline the identification of keywords and small phrases (i.e. the pairing of keywords), and therefore, it is recommended that they are included in literature reviews for a comprehensive collection of relevant records. Of course, a final check by a researcher is indispensable for assure consistency.

• The eligibility process of the methodology presented does not exclude many articles; instead, it includes relevant to the subject records providing a comprehensive and relevant subject database. Taxonomies of keywords shown in Table 1 and sub-grouped as in Table 2 constitute synoptic documentation of current research in VGI use for crisis, emergency and disaster management based on the SRQs. They can be used to screen papers from other search engines, e.g. Web of Science, or published after the period covered. In addition, by comparing taxonomies extracted by articles in the coming years, research innovations can be spotted, and research directions can be proposed.

• The creation of an interactive web atlas as a viable and updatable visualization and dissemination tool enables further information mining for disaster, crisis, and emergency-related purposes.

• The ability to publish and visualize research results on the web makes them available to a broader crowd, policymakers, and the scientific community.

Several limitations of the present research are also present, such as the following:

• The exclusive use of the Scopus database

• Extracted results and process success of a literature review depend on the choice of authors’ keywords. If the authors of the research articles investigated have not utilized one of the three main keywords, “crisis”, “emergency”, and “disasters”, in the title, author keywords, index keywords, and abstract, then the articles are excluded from the selection process. For example, authors have utilized hazard-related keywords, e.g. floods. In such hazard-related articles, the authors mainly use the broad term “flood management”, and the keyword pairing “disaster management” was omitted

• Based on SCOPUS data, it is observed that journals or conferences accept and publish articles that miss crucial information, such as abstract and formal affiliation. By formal affiliation, the full name of the education or research institute that conducts the published research and a complete address is meant. Such missing or incomplete information must be a priority before publication. In this way, the quality of research can be further validated, and further research and collaborations on the subjects published can be promoted

• Even though this research investigated VGI use in crisis, emergency and disaster management based on publications from the academic field in the framework of a literature review, the VGI use may be wider as cases from the public and private sectors are not usually captured in scientific publications

• Developing a spatial bibliography can be time-consuming due to the lack of location documentation for affiliations in the publication process or the lack of information on geolocated case studies. Moreover, research on geolocation was solely based on the first author resulting in a biased result where international collaboration is not captured. Thus, the geolocated cities are not guaranteed to reflect where the reported research was conducted (Bornmann et al. 2011). For this reason, in this article we also present statistical analyses on smaller geographical scales too (i.e., country and continent level).

Therefore, the methodology can be enriched with searches in abstract and text for case-study geolocation. Additionally, this research methodology can be further expanded in the future by including additional research databases such as the Web of Science. Additionally, it can be extended in time, e.g. the remaining of 2021, and periodically updated at the end of each year to stay temporarily up to date. The geolocation of eligible papers should be multiple: based on all affiliations of the first author and the location of the study area. This will also enhance the number of possible visualizations, information mining, and classification.

Additionally, to reduce the need to rely heavily on complicated search methods and article parsing to continually find new research, a collection, and dissemination of the results in an interactive Web Atlas can be paired with an existing database managed by a library. Article parsing, as outlined by (Karl 2018), can play an important role in automating the identification of coordinates from newly published works to update a spatial bibliography (Howell et al. 2019). Generally, the proposed method can be adopted for literature reviews in other sectors. Combining a Spatial Scoping Review with a Web Atlas constitutes a valuable research tool for data mining, knowledge extraction, and quick dissemination for the crowd, the scientists, and policymakers.

Author Contributions

Initial Vision and Conceptualization: Katerina Tzavella; Writing Original Draft: Katerina Tzavella and Andriani Skopeliti; Final Form: Katerina Tzavella and Andriani Skopeliti; Methodology and Analysis: Katerina Tzavella† and Andriani Skopeliti†; Web Atlas Development and Map Design: Andriani Skopeliti; Reviewing of Drafts: Katerina Tzavella, Andriani Skopeliti, Alexander Fekete; Editing of Drafts: Katerina Tzavella and Andriani Skopeliti; Final Version: Katerina Tzavella† and Andriani Skopeliti†. All authors have read and agreed to the published version of the manuscript. †The authors contributed equally to this work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Part of the research has been funded by the interdisciplinary project “BigWa – Civil protection and security research in social and technological change”, funded by the NRW Ministry of Innovation, Science and Research and the MIWF-Funding Program FH-STRUKTUR 2016/08 [Grant Number 322-8.03.04.02]. The partial funding concerns only the first author Katerina Tzavella. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Notes on contributors

Katerina Tzavella

Katerina Tzavella is a postdoctoral researcher at Vrije Universiteit Amsterdam. She is looking into improving crisis management and risk communication through social media and crowdsourcing for the LINKS EU project. In 2021, she obtained her PhD in Engineering (Safety Engineering) from the Bergische University of Wuppertal. Since 2011 she has been a project researcher and work package leader on EU and national-funded projects (Germany). Her research interests include applied geoinformatics, spatial analysis, crisis, emergency, and disaster management, as well as societal, emergency response, critical infrastructure, and systems of systems resilience.

Andriani Skopeliti

Andriani Skopeliti is currently an Assistant Professor at the National Technical University of Athens (Greece) in the School of Rural, Surveying and Geoinformatics Engineering in Analytical Cartography and Geovisualization. She holds a Dipl. Eng. (1994) in Rural and Surveying Engineering and a PhD (2001) in Cartography, both from the National Technical University of Athens. Her main research interests focus on web cartography, cartographic generalization, VGI use in mapping and quality.

Alexander Fekete

Alexander Fekete is a full professor at TH Köln - University of Applied Sciences in Germany since 2012. He received his PhD degree in 2010 from the University of Bonn.

Notes

1. https://libraryguides.mcgill.ca/knowledge-syntheses/question.

2. https://www.scopus.com/home.uri.

3. https://service.elsevier.com/app/answers/detail/a_id/15100/supporthub/scopus/.

4. https://www.textrazor.com/.

5. Universite Catholique de Louvain (UCL) -CRED, D. Guha-Sapir - www.emdat.be, Brussels, Belgium.

6. https://en.wikipedia.org/wiki/Linked_data.

7. https://reliefweb.int/sites/reliefweb.int/files/resources/2021-world-risk-report.pdf.

8. https://www.directrelief.org/2019/12/10-disasters-that-changed-the-world/.

9. https://ntua-gis.maps.arcgis.com/apps/webappviewer/index.html?id=876e9083304f4f59ace29fcabbbc6cd2.

10. https://www.weforum.org/reports/the-global-risks-report-2021/.

11. https://www.germanwatch.org/en/cri.

12. https://www.smartinsights.com/social-media-marketing/social-media-strategy/new-global-social-media-research/.

Appendix A

Table A1. Definitions of crisis, emergency and disaster.

Term	Definition/-s	Source
Crisis	Unstable condition involving an impending abrupt or significant change that requires urgent attention and action to protect life, assets, property, or the environment.	ISO – Online Browsing Platform (OBP) - http://creativecommons.org/licenses/by/4.0/
Disaster	A serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability, and capacity, leading to human, material, economic and environmental losses, and impacts.	United Nations Office for Disaster Risk Reduction – http://creativecommons.org/licenses/by/4.0/
Emergency	Sudden, urgent, usually unexpected occurrence or event requiring immediate action.	ISO – Online Browsing Platform (OBP) - http://creativecommons.org/licenses/by/4.0/
	An unexpected situation may cause injury, loss of life, destruction of property, or cause the interference, loss, or disruption of an organization’s normal business operations to such an extent that it poses a threat.	Disaster Recovery Institute International (DRI) - http://creativecommons.org/licenses/by/4.0/
	… .is sometimes used interchangeably with the term disaster, for example, in the context of biological and technological hazards or health emergencies, which, however, can also relate to hazardous events that do not result in the serious disruption of the functioning of a community or society.	United Nations Office for Disaster Risk Reduction – http://creativecommons.org/licenses/by/4.0/

Table A2. Technology and geomatics tools utilized in crisis, emergency and disaster management in combination with VGI.

Technology

Geomatics

3D printing 5 G Algorithm Artificial intelligence Augmented reality Computational intelligence Computational linguistics Computer network Computer simulation Computer vision Data analysis Data management Database Decision support system Deep learning Eye-tracking Gamification Internet Interoperability Machine learning Multimedia Natural language processing Neural network Ontology (information science) Parallel computing Serious game Service-oriented architecture Software Supervised learning Text mining Virtual reality Virtual world Wearable computer Web 2.0 World Wide Web

Cartography Critical cartography CyberGIS Data visualization Digital image processing Digitization Environmental monitoring Geocoding Geographic information system Geography Geolocation Geomatics Geosocial networking Geospatial metadata Geotagging Geovisualization Geoweb Global Positioning System Location-based service Machine learning Map Neogeography Remote sensing Spatial analysis Visualization (graphics)

Table A3. VGI sources analyzed in this article.

	Source of VGI	Count	Percentage
1	Social Media	448	46.23%
2	Twitter	132	13.62%
3	Social Network	129	13.31%
4	Crowdsourcing	76	7.84%
5	VGI	38	3.92%
6	Big Data	27	2.79%
7	Facebook	27	2.79%
8	Mobile App	27	2.79%
9	OSM	14	1.44%
10	Internet of Things	13	1.34%
11	Volunteer	9	0.93%
12	SMS	9	0.93%
13	Citizen Science	5	0.52%
14	Ushahidi	3	0.31%
15	EHealth	2	0.21%
16	Collaborative Mapping	2	0.21%
17	WeChat	2	0.21%
18	Geotagging	1	0.10%
19	Participatory GIS	1	0.10%
20	Wearable Technology	1	0.10%
21	MHealth	1	0.10%
22	E-participation	1	0.10%
23	Text Messaging	1	0.10%

Table A4. Hazards analyzed in this article.

	Hazard Type	Count	Percentage
1	Disaster	291	30.03%
2	Crisis	138	14.24%
3	Emergency	136	14.04%
4	Flood	98	10.11%
5	Hurricane	75	7.74%
6	Earthquake	70	7.22%
7	Fire	18	1.86%
8	COVID-19	16	1.65%
9	Typhoon	14	1.44%
10	Terrorism	11	1.14%
11	Disease	11	1.14%
12	Refugee Crisis	8	0.83%
13	Epidemic	6	0.62%
14	Mapping	6	0.62%
15	Wildfire	5	0.52%
16	Haze	4	0.41%
17	Cyclone	4	0.41%
18	Tsunami	4	0.41%
19	Explosion	4	0.41%
20	Buildings Indoor/Outdoor Mapping	4	0.41%
21	Crowd Emergencies	4	0.41%
22	Storm	3	0.31%
23	Food	3	0.31%
24	Rain Storm	3	0.31%
25	War	2	0.21%
26	Crime	2	0.21%
27	Politics	2	0.21%
28	Ebola	2	0.21%
29	Weather	2	0.21%
30	Tuberculosis	1	0.10%
31	Air Crush	1	0.10%
32	Landslide	1	0.10%
33	Hazard	1	0.10%
34	Tornado	1	0.10%
35	Water Crisis	1	0.10%
36	Volcano	1	0.10%
37	Events/Happenings	1	0.10%
38	Shooting	1	0.10%
39	Plague	1	0.10%
40	Climate	1	0.10%
41	Famine	1	0.10%
42	Ice Jam	1	0.10%
43	Heatwave	1	0.10%
44	Weather Forecasting	1	0.10%
45	Rain Storm	1	0.10%
46	Chemical Accident	1	0.10%
47	Rainfall	1	0.10%
48	Hostage	1	0.10%
49	Missile	1	0.10%
50	Monsoon	1	0.10%
51	Ice Storm	1	0.10%
52	Influenza	1	0.10%

Table A5. Disaster management tasks analyzed in this article.

	Disaster Management Task e	Count	Percentage
1	Disaster Management	241	24.87%
2	Emergency Management	149	15.38%
3	Crisis Management	127	13.11%
4	Crisis Communication	69	7.12%
5	Awareness	48	4.95%
6	Disaster Response	40	4.13%
7	Data Analysis	34	3.51%
8	Crisis Informatics	30	3.10%
9	Prevention	25	2.58%
10	Disaster Risk	18	1.86%
11	Disaster Relief	13	1.34%
12	Emergency Service	12	1.24%
13	Alarming	10	1.03%
14	Preparedness	9	0.93%
15	Disaster Informatics	9	0.93%
16	Disaster Recovery	9	0.93%
17	Decision Making	8	0.83%
18	Prediction	7	0.72%
19	Community Resilience	7	0.72%
20	Risk Management	7	0.72%
21	Mapping	6	0.62%
22	Decision Support	6	0.62%
23	Crisis Mapping	6	0.62%
24	Adaptation	6	0.62%
25	Humanitarian Aid	5	0.52%
26	Misinformation	5	0.52%
27	Alert	4	0.41%
28	Emergency Response	4	0.41%
29	Logistics	4	0.41%
30	Early Warning	4	0.41%
31	Humanitarian Crisis	4	0.41%
32	Resource Allocation	3	0.31%
33	Emergency Evacuation	3	0.31%
34	Location Estimation	3	0.31%
35	Building Damage	3	0.31%
36	Rescue	3	0.31%
37	Situation Awareness	2	0.21%
38	Cultural Heritage Protection	2	0.21%
39	Post-Crisis	2	0.21%
40	Risk Assessment	2	0.21%
41	Crisis Detection	2	0.21%
42	Sentiment Analysis	2	0.21%
43	Spatiotemporal Analysis	2	0.21%
44	Spatial Analysis	2	0.21%
45	Humanitarian Response	2	0.21%
46	Recovery	2	0.21%
47	Governance Management	2	0.21%
48	Care Ethics	1	0.10%
49	Disaster Recovery	1	0.10%
50	Disaster Assessment	1	0.10%
51	Disaster Resilience	1	0.10%
52	Disaster Impacts	1	0.10%
53	Decision Support	1	0.10%

Table A6. Researchers’ geographical scope analyzed in this article.

	Country	Count	Percentage
1	United States	249	25.70%
2	China’s mainland	83	8.94%
3	India	76	7.84%
4	Germany	72	7.43%
5	United Kingdom	62	6.40%
6	Australia	41	4.23%
7	Italy	34	3.51%
8	Spain	24	2.48%
9	Austria	21	2.17%
10	Qatar	19	1.96%
11	Canada	16	1.65%
12	Indonesia	16	1.65%
13	Norway	15	1.55%
14	France	15	1.55%
15	Netherlands	13	1.34%
16	Brazil	13	1.34%
17	Malaysia	12	1.24%
18	Japan	11	1.14%
19	Portugal	11	1.14%
20	Turkey	10	1.03%
21	Philippines	9	0.93%
22	Iran	8	0.83%
23	Switzerland	7	0.72%
24	Sweden	7	0.72%
25	Denmark	7	0.72%
26	Ireland	6	0.62%
27	South Korea	6	0.62%
28	Israel	6	0.62%
29	Greece	6	0.62%
30	Algeria	5	0.52%
31	United Arab Emirates	5	0.52%
32	Tunisia	5	0.52%
33	Pakistan	4	0.41%
34	South Africa	4	0.41%
35	Singapore	4	0.41%
36	Chile	4	0.41%
37	Jordan	4	0.41%
38	Thailand	4	0.41%
39	Sri Lanka	3	0.31%
40	Poland	3	0.31%
43	Belgium	3	0.31%
44	Ecuador	3	0.31%
45	Finland	3	0.31%
46	Saudi Arabia	3	0.31%
47	Bangladesh	2	0.21%
48	Mexico	2	0.21%
49	Czech Republic	2	0.21%
50	Russia	2	0.21%
51	Romania	2	0.21%
52	Cyprus	2	0.21%
53	Egypt	2	0.21%
54	Estonia	2	0.21%
55	Lebanon	2	0.21%
56	Serbia	2	0.21%
57	Cameroon	1	0.10%
58	Slovenia	1	0.10%
59	Nigeria	1	0.10%
60	Vietnam	1	0.10%
61	Kuwait	1	0.10%
62	Luxembourg	1	0.10%
63	Puerto Rico	1	0.10%

Table A7. Specific crises where VGI is used, as analyzed in this article.

	Crisis Event	Percentage
1	2017 Hurricane Harvey	17%
2	2012 Hurricane Sandy	12%
3	2015 Nepal earthquake	11%
4	2013 Typhoon Haiyan	6%
5	2015 Chennai floods	6%
6	2017 Hurricane Irma	6%
7	2016 Italy Earthquake	5%
8	2019–2020 COVID-19	5%
9	2010 Haiti Earthquake	4%
10	2015 Tianjin Port Explosion	3%
11	2016 Hurricane Mathew	3%
12	2013 Colorado floods	2%
13	2015 South Carolina Floods	2%
14	2017 Manchester Arena bombing	2%
15	2018 Hurricane Florence	2%
16	2018 Hurricane Michael	2%
17	2018 Kerala Floods	2%
18	2005 Hurricane Katrina	2%
19	2012 Beijing Rainstorm	2%
20	2013 Ya’an earthquake	2%
21	2015–2016 Zika Virus Crisis	2%
22	2015 Hurricane Joaquin	2%
23	2015 Paris Terror Attack	2%
24	2015 Refugee Crisis	2%
25	2016 Ecuador Earthquake	2%
26	2016 Fort McMurray Wildfire	2%
27	2016 Louisiana Floods	2%
28	2016 Munich shooting	2%
29	2017 Hurricane Maria	2%
30	2017 Mexico earthquake	2%
31	2008 Wenchuan	1%
32	2010–11 Australia floods	1%
33	2011 Great East Japan Disaster (3.11)	1%
34	2011 Thailand Great flood	1%
35	2012 Emilia Earthquake	1%
36	2013 Boston Marathon bombing	1%
37	2013 Czech floods	1%
38	2013 Dresden floods	1%
39	2013 European floods	1%
40	2013 Germany floods	1%
41	2013 Horse Meat Scandal	1%
42	2013 Seoul floods	1%
43	2014 FIFA World Cup	1%
44	2014 The Lindt Cafe siege	1%
45	2015 Germanwings air crush	1%
46	2015 MERS	1%
47	2016 Beijing Hotel Incident	1%
48	2016 Brussels Terror Attacks	1%
49	2016 Ecuador Earthquake	1%
50	2016 Kaikoura Earthquake	1%
51	2017 Atlantic hurricane	1%
52	2017 California fire	1%
53	2018 Hawaii false ballistic missile alert	1%
54	2018 Shouguang flood	1%
55	2019 Cyclone Idai	1%
56	2003 Typhoon Nepartak	1%
57	2009–2011 H1N1	1%
58	2009 and 2011 Jeddah floods	1%
59	2010–2011 New Zealand Earthquakes	1%
60	2010 Bedbug crisis	1%
61	2010 UK December Heavy Snow	1%
62	2010s Arab Spring	1%
63	2011 Bangkok flood	1%
64	2011 Bangkok Thailand food relief	1%
65	2011 EHEC Food contamination	1%
66	2011 Penn State child sex abuse scandal	1%
67	2011 Somalia Famine	1%
68	2011 Tohuku Earthquake	1%
69	2011 Typhoon Washi	1%
70	2011 UK August Riots	1%
71	2011–13 Australia Floods	1%
72	2012–2013 Colorado wildfires	1%
73	2012 Italy Earthquakes	1%
74	2012 Kony – Invisible Children	1%
75	2012 Madrid Arena tragedy	1%
76	2012 Yiliang Earthquake	1%
77	2013–2014 Australian Bushfire	1%
78	2013–2016 Ebola Crisis	1%
79	2013–2014 Winter Floods	1%
80	2013 Alaska ice jam	1%
81	2013 Bohol Earthquake	1%
82	2013 Colorado Fires	1%
83	2013 Sudan floods	1%
84	2013 Weather	1%
85	2013 Wenchuan Earthquakes	1%
86	2013 World Police and Fire Games	1%
87	2014 Australia Bushfire	1%
88	2014 California Earthquake	1%
89	2014 Ebola outbreak	1%
90	2014 Flint Michigan Water Crisis	1%
91	2014 Genoa Flood	1%
92	2014 Ice storms in Atlanta	1%
93	2014 Kelantan massive flood	1%
94	2014 Kunming terrorist attack	1%
95	2014 Lundian Earthquake	1%
96	2014 Malaysia Airlines Flight 370 missing	1%
97	2014 Napa valley earthquake	1%
98	2014 Sumatra Haze	1%
99	2014–2015 2015–2016 Monsoonal Season	1%
100	2015–2016 Refugee Crisis	1%
101	2015–2016 Winter Storms	1%
102	2015 Amtrak derailment in Philadelphia	1%
103	2015 Baltimore protests after Freddie Grey’s death	1%
104	2015 Cyclone Pam, Cyclone Winston	1%
105	2015 Disasters	1%
106	2015 Gorkha Earthquake	1%
107	2015 Hurricane Harvey	1%
108	2015 Manilla Floods	1%
109	2015 Texas Floods	1%
110	2015 Typhoon Chan-hom	1%
111	2015 Wacken Open Air	1%
112	2015 World Cup of Cricket	1%
113	2015 Zika Crisis	1%
114	2015–2016 Survey of emergency responders	1%
115	2016–2020 Yemen hunger and cholera crisis	1%
116	2016 Berlin Terror Attack	1%
117	2016 El Nino	1%
118	2016 H5N1 Avian Influenza	1%
119	2016 Kalamata Floods	1%
120	2016 Loiret	1%
121	2016 Meranti Typhoon	1%
122	2016 Orleans flood	1%
123	2016 Turkish coup attempt	1%
124	2016 Wuhan rainstorm and flood	1%
125	2016 Zika Virus	1%
126	2017 “High Ten” Concert Cancelation	1%
127	2017 Ariana Grande concert bombing	1%
128	2017 Barcelona and Cambrils terrorist attack	1%
129	2017 Different Disasters	1%
130	2017 Emergencies	1%
131	2017 F1 Tornado Central Pennsylvania	1%
132	2017 Hurricanes	1%
133	2017 Jiuzhaigou earthquake	1%
134	2017 Kermanshah earthquake	1%
135	2017 Montoro post-fire flood	1%
136	2017 Sri Lanka mass-flooding	1%
137	2017 Storm Cindy	1%
138	2017 Tour de France	1%
139	2017 United Airlines incident	1%
140	2017 Zuizhaigou Earthquake	1%
141	2018–2019 Kerala floods	1%
142	2018 Antwerp gas explosion	1%
143	2018 Cardassar (Majorca) flood	1%
144	2018 Chipotle’s E. coli crisis	1%
145	2018 Cyclone Gaja	1%
146	2018 East Attica Fire	1%
147	2018 Eastern Australian bushfires	1%
148	2018 Indonesia earthquake	1%
149	2018 Majorca Island flash floods	1%
150	2018 Mendocino Complex	1%
151	2018 Typhoon Mangkhut	1%
152	2018 Woolsey Fire	1%
153	2019 Chennai water crisis	1%
154	2019 Cyclone Kenneth	1%
155	2019 Ebola in Africa	1%
156	2019 Harper winter storm	1%
157	2019 Istanbul Earthquake	1%
158	2019 Louisiana and Texas coastal floods and hurricane	1%
159	2019 Nebraska floods	1%
160	2019 Tuberculosis outbreak	1%
161	2019–2020 Australian bushfires	1%
162	2020 Elazig earthquake	1%
163	2020 Jakarta flood	1%
164	2020 Kenya floods	1%
165	2020 Nepal landslides	1%

Table A8. Countries of specific crises where VGI is used, as analyzed in this article.

	Country	Count	Percentage
1	USA	123	25.36%
2	China’s mainland	56	11.55%
3	India	26	5.36%
4	Australia	23	4.74%
5	Nepal	18	3.71%
6	Indonesia	17	3.51%
7	Philippines	17	3.51%
8	Germany	17	3.51%
9	Italy	17	3.51%
10	United Kingdom	13	2.68%
11	Haiti	8	1.65%
12	South Korea	7	1.44%
13	France	7	1.44%
14	Canada	6	1.24%
15	Spain	6	1.24%
16	Brazil	6	1.24%
17	Thailand	6	1.24%
18	Japan	5	1.03%
19	Malaysia	5	1.03%
20	Norway	5	1.03%
21	New Zealand	4	0.82%
22	Turkey	4	0.82%
23	Sweden	4	0.82%
24	UK	3	0.62%
25	Saudi Arabia	3	0.62%
26	Ecuador	3	0.62%
27	Greece	3	0.62%
28	Belgium	3	0.62%
29	Mexico	3	0.62%
30	Democratic Republic of the Congo	2	0.41%
31	Switzerland	2	0.41%
32	South Asia	2	0.41%
33	Jordan	2	0.41%
34	Netherlands	2	0.41%
35	Somalia	2	0.41%
36	Chile	2	0.41%
37	Pakistan	2	0.41%
40	Syria	2	0.41%
41	Israel	2	0.41%
42	Czech Republic	2	0.41%
43	Denmark	2	0.41%
44	Uganda	2	0.41%
45	Singapore	2	0.41%
46	Puerto Rico	2	0.41%
47	Ecuador	1	0.21%
48	Bangladesh	1	0.21%
49	Sri Lanka	1	0.21%
50	Kuwait	1	0.21%
51	Kenya	1	0.21%
52	Guatemala	1	0.21%
53	Himalaya	1	0.21%
54	Poland	1	0.21%
55	Afghanistan	1	0.21%
56	Portugal	1	0.21%
57	Austria	1	0.21%
58	Iran	1	0.21%
59	Algeria	1	0.21%
60	Iraq	1	0.21%
61	Cote D’Ivoire	1	0.21%
62	Lao	1	0.21%
63	Sri Lanka	1	0.21%
64	Laos	1	0.21%
65	Zimbabwe	1	0.21%
66	Samoa	1	0.21%
67	Namibia	1	0.21%
68	Guinea	1	0.21%
69	Tasmania	1	0.21%
70	Sierra Leone	1	0.21%
71	Tunisia	1	0.21%
72	Malawi	1	0.21%
73	Northern Ireland	1	0.21%
74	Malawi	1	0.21%
75	Soudan	1	0.21%
76	United States	1	0.21%
77	Romania	1	0.21%
78	Yemen	1	0.21%
79	Rome	1	0.21%
80	Russia	1	0.21%
81	Mozambique	1	0.21%

Appendix B

Table B1 Checklist (as downloaded) for preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) checklist.

SECTION	ITEM	PRISMA-ScR CHECKLIST ITEM	REPORTED ON PAGE
TITLE
Title	1	Identify the report as a scoping review.
ABSTRACT
Structured summary	2	Provide a structured summary that includes (as applicable): the background, objectives, eligibility criteria, sources of evidence, charting methods, results, and conclusions that relate to the review questions and objectives.
INTRODUCTION
Rationale	3	Describe the rationale for the review in the context of what is already known. Explain why the review questions/objectives lend themselves to a scoping review approach.
Objectives	4	Provide an explicit statement of the questions and objectives being addressed concerning their key elements (e.g. population or participants, concepts, and context) or other relevant key elements used to conceptualize the review questions and/or objectives.
METHODS
Protocol and registration	5	Indicate whether a review protocol exists; state if and where it can be accessed (e.g. a Web address); and if available, provide registration information, including the registration number.	-
Eligibility criteria	6	Specify characteristics of the sources of evidence used as eligibility criteria (e.g. years considered, language, and publication status), and provide a rationale.	5-6
Information sources*	7	Describe all information sources in the search (e.g. databases with dates of coverage and contact with authors to identify additional sources), as well as the date the most recent search was executed.	5-6
Search	8	Present the full electronic search strategy for at least 1 database, including any limits used, such that it could be repeated.	5
Selection of sources of evidence†	9	State the process for selecting sources of evidence (i.e. screening and eligibility) included in the scoping review.	6-7
Data charting process‡	10	Describe the methods of charting data from the included sources of evidence (e.g. calibrated forms or forms that have been tested by the team before their use, and whether data charting was done independently or in duplicate) and any processes for obtaining and confirming data from investigators.	8-9
Data items	11	List and define all variables for which data were sought and any assumptions and simplifications made.	6-7
Critical appraisal of individual sources of evidence§	12	If done, provide a rationale for conducting a critical appraisal of included sources of evidence; describe the methods used and how this information was used in any data synthesis (if appropriate).	-
Synthesis of results	13	Describe the methods of handling and summarizing the data that were charted.	7-9
RESULTS
Selection of sources of evidence	14	Give number of sources of evidence screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally using a flow diagram.	10-11
Characteristics of sources of evidence	15	For each source of evidence, present characteristics for which data were charted and provide the citations.	11-15
Critical appraisal within sources of evidence	16	If done, present data on critical appraisal of included sources of evidence (see item 12).	-
Results of individual sources of evidence	17	For each included source of evidence, present the relevant data that was charted that relate to the review questions and objectives.	15-18
Synthesis of results	18	Summarize and/or present the charting results as they relate to the review questions and objectives.	18-19
DISCUSSION
Summary of evidence	19	Summarize the main results (including an overview of concepts, themes, and types of evidence available), link to the review questions and objectives, and consider the relevance to key groups.	19-22
Limitations	20	Discuss the limitations of the scoping review process.	21
Conclusions	21	Provide a general interpretation of the results concerning the review questions and objectives, as well as potential implications and/or next steps.	21
FUNDING
Funding	22	Describe sources of funding for the included sources of evidence, as well as sources of funding for the scoping review. Describe the role of the funders of the scoping review.	23

‡The frameworks by Arksey and O’Malley (6) and Levac and colleagues (7) and the JBI guidance (4, 5) refer to the process of data extraction in a scoping review as data charting. § The process of systematically examining research evidence to assess its validity, results, and relevance before using it to inform a decision. This term is used for items 12 and 19 instead of “risk of bias” (which is more applicable to systematic reviews of interventions) to include and acknowledge the various sources of evidence that may be used in a scoping review (e.g. quantitative and qualitative research, expert opinion, and policy document).

From: Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMAScR): Checklist and Explanation. Ann Intern Med. 2018;169:467–473. DOI: 10.7326/M18-0850