gis research topics 2020

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Prev Chronic Dis

Peer Reviewed

Incorporating geographic information science and technology in response to the covid-19 pandemic, charlotte d. smith.

1 University of California, Berkeley, School of Public Health, Berkeley, California

Jeremy Mennis

2 Temple University, Philadelphia, Pennsylvania

Incorporating geographic information science and technology (GIS&T) into COVID-19 pandemic surveillance, modeling, and response enhances understanding and control of the disease. Applications of GIS&T include 1) developing spatial data infrastructures for surveillance and data sharing, 2) incorporating mobility data in infectious disease forecasting, 3) using geospatial technologies for digital contact tracing, 4) integrating geographic data in COVID-19 modeling, 5) investigating geographic social vulnerabilities and health disparities, and 6) communicating the status of the disease or status of facilities for return-to-normal operations. Locations and availability of personal protective equipment, ventilators, hospital beds, and other items can be optimized with the use of GIS&T. Challenges include protection of individual privacy and civil liberties and closer collaboration among the fields of geography, medicine, public health, and public policy.

What is already known about this topic?

Incorporating geographic information science and technology (GIS&T) into COVID-19 pandemic surveillance, modeling, and response enhances understanding and control of the disease.

What is added by this report?

Applications of GIS&T include developing spatial data infrastructures for surveillance and data sharing, incorporating mobility data in infectious disease forecasting, using geospatial technologies for digital contact tracing, integrating geographic data in COVID-19 modeling, investigating geographic social vulnerabilities and health disparities, and communicating the status of the disease or status of facilities for return-to-normal operations.

What are the implications for public health practice?

Protections for individual privacy and close collaboration among the fields of geography, medicine, public health, and public policy to use GIS&T are imperative.

Introduction

The spread of infectious disease is inherently a spatial process; therefore, geospatial data, technologies, and analytical methods play a critical role in understanding and responding to the coronavirus disease 2019 (COVID-19) pandemic. Geographic information science and technology (GIS&T) is the academic field centered on geospatial data and analysis. The field encompasses geographic information systems (GIS), spatial statistics and visualization, and location-based data derived from global navigation satellite systems (GNSS, eg, global positioning systems [GPS]) and remotely sensed imagery. Opportunities for incorporating GIS&T into COVID-19 pandemic surveillance, modeling, and response include 1) developing spatial data infrastructures (SDI) for surveillance and data sharing, 2) incorporating mobility data in infectious disease forecasting, 3) using geospatial technologies for digital contact tracing, 4) integrating geographic data in COVID-19 modeling, 5) investigating geographic health disparities and social vulnerabilities, and 6) communicating the status of the disease or status of facilities for return-to-normal operations. Locations and availability of personal protective equipment, ventilators, hospital beds, and other items can be optimized with the use of GIS&T.

Developing Spatial Data Infrastructures for COVID-19 Surveillance and Data Sharing

Current surveillance of COVID-19 at the national and global levels is built on lessons learned from maintaining previously developed databases of contamination and disease, such as FluNet ( 1 ). Disease surveillance systems have been enhanced by the use of GIS for monitoring disease outbreaks, facilitating contact tracing, and evaluating the efficacy of interventions. For example, Zenilman et al described the application of GIS to a surveillance system for sexually transmitted diseases at the Fort Bragg military base ( 2 ). The assessment of various potential risk factors indicated that geography was the only variable positively associated with gonorrhea among study participants. The Connect to Protect program is an example of how researcher–community collaborations (or community-based participatory research) can assist program planners to efficiently use limited resources ( 3 ). Connect to Protect, a nongovernmental organization, uses GIS and community involvement to prioritize resources and outreach activities. The research team uses GIS to evaluate the geographic epidemiology of sexually transmitted diseases and HIV among adolescents in 15 US cities and Puerto Rico. Their work led to a shift from traditional evaluations of condom use, number of sex partners, and demographic characteristics, to identification of sociophysical environments. The observation of clusters of cases in geographic areas informed research teams on where to apply interventions. The use of GIS supports the investigation of the social and environmental correlates of disease clusters, thereby facilitating targeted interventions and researcher–community collaborations to assist program planners to efficiently use limited resources ( 3 ).

An important aspect of monitoring the spread of infectious disease is spatial data infrastructure (SDI), composed of the human resources and institutions that create and maintain the foundation to which additional spatial data can be attached and used. Key components of an SDI include geospatial culture and awareness, resources for information and communications technology, common standards for data integration and interoperability, a legal framework for data security and privacy, a common lexicon, the use of robust statistical and epidemiological methods, and interdisciplinary collaboration and partnerships ( 4 ). Along with the SDI, the concepts of open data, crowd sourcing, and data sharing for georeferenced health data are important components of real-time infectious disease surveillance, particularly in under-resourced settings ( 5 ).

Maps play a key role in communicating the risks and spread of COVID-19 ( 6 ). Interactive web-based maps and dashboards present near–real-time data on morbidity, mortality, and recovery, as well as pandemic-related factors such as supply-chain quantities of personal protective equipment or therapeutics. A dashboard developed by Johns Hopkins University in collaboration with ESRI (Redlands, California), which originally showed the number of COVID-19 cases, deaths, and recoveries, was updated to show smaller geographic areas (ie, counties) and detailed infographics ( 7 ). This type of infographic has been useful for tracking COVID-19 cases globally and for allocating resources and planning for “return-to-normal” conditions. Location-enabled infographics also allow for dissemination of knowledge on, for example, the readiness of facilities such as retail outlets to accept customers, or schools and campuses to reopen. An interactive dashboard (ESRI, Redlands, California), developed for faculty, staff, students, and administrators at the University of California, Berkeley, shows the status of custodians’ efforts to disinfect university buildings ( Figure ). The dashboard is populated in real time as custodial staff members complete disinfection of rooms. The room number and type (eg, classroom, laboratory, bathroom), the date and time completed, and the product used for disinfection appear in a pop-up on the dashboard when the user selects a building.

An interactive dashboard for showing the status of disinfection of buildings during the coronavirus disease 2019 (COVID-19) pandemic on the campus of the University of California, Berkeley.

The GIS&T community has long worked toward development of the National Spatial Data Infrastructure (NSDI) for the United States ( 8 ), an effort managed by the US Federal Geographic Data Committee (FGDC); facilitated by spatial data interoperability standards, such as those developed by the Open Geospatial Consortium (OGC); and recently bolstered by the Geospatial Data Act of 2018, a component of H.R.302, the FAA Reauthorization Act of 2018. The US NSDI is typically considered an infrastructure for geospatial framework data (eg, cadastral and transportation) and not necessarily health data; however, just as the events of September 11, 2001, catalyzed the development of enhanced spatial data sharing to support disaster response in the United States, the COVID-19 pandemic has the potential to spark the improvement of health data infrastructures to facilitate spatial data sharing and interoperability for health crisis response. A particular challenge is that SDIs for responding to a crisis like COVID-19 require sharing data not only among various national and international governments but, as with the US NSDI, also among various levels of government, including the federal, state, and county levels. Corporate partners also play a pivotal role in the development of SDI for pandemic response, because they have large sets of spatial data on the mobility, purchasing, and web browsing behaviors of individuals and other relevant place-based and georeferenced data that may be useful in understanding disease dynamics. In addition, responding to a rapidly evolving health crisis such as the COVID-19 pandemic requires pipelines for supplying health and related data in near real time, which presents challenges. Finally, privacy protection for individuals is paramount in developing useful SDIs for pandemic response. As with the US NSDI, initiative and management at the federal level is likely necessary to develop an SDI for pandemic response.

Incorporating Population and Mobility Data in COVID-19 Forecasting

Along with handwashing and social distancing, perhaps the foremost mitigation strategy for reducing person-to-person contact and transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the absence of pharmaceutical intervention is regulation restricting mobility (ie, human movement and travel behavior). Consequently, one key role for geospatial technologies in responding to the COVID-19 pandemic is monitoring population distribution and mobility through the use of social media and location-tracking applications embedded in mobile telephones that employ GNSS, cell phone tower connections, and/or wireless connections ( 9 ). Several corporate location-data collectors and vendors have released spatially aggregated COVID-19 pandemic-related data on population mobility. These data have been widely used by the popular media to report on the effects of jurisdictional stay-at-home orders on population mobility and by researchers to analyze the efficacy of population mobility change for altering disease dynamics ( 10 ).

Modeling population distribution and mobility has a long history in GIS&T and focuses on fine-scale estimations of population distribution and mobility ( 11 , 12 ), most recently by using mobile telephone–based location data ( 13 , 14 ). The scholarly response to the pandemic marks a major advance in the incorporation of fine-resolution data on population and individual mobility from geospatial technologies to understand disease dynamics and formulate effective intervention strategies. Because questions remain about the best way to measure and collect data on individual mobility, provide such data to researchers, and incorporate such mobility measures into infectious disease models, the COVID-19 pandemic provides an opportunity for testing methods for using such data to evaluate and forecast the effects of nonpharmaceutical interventions that restrict mobility. However, current legal frameworks and practices for preserving the privacy of individuals are obstacles to widespread adoption.

Using Geospatial Technologies for Digital Contact Tracing

Monitoring mobility at the individual level, in addition to the population level, has also emerged as an important use of geospatial technologies, particularly in its application to digital contact tracing. Conventional contact tracing, involving identifying, contacting, and encouraging quarantine for the people with whom an infected person has had close contact to mitigate disease transmission, is labor intensive. The process can be made more efficient and scaled up to large populations by exploiting individual digital mobility data, as well as data indicating proximity among mobile telephones using Bluetooth or related technologies, to computationally show close proximity among individuals ( 15 ). Such location data can be combined with health and other data that might indicate vulnerability to infection or disease. Individuals can then be contacted and given quarantine instructions automatically through mobile telephone text messages, or their future behavior may even be monitored to encourage or enforce quarantine. Such procedures have been used to some degree, in combination with population mobility restrictions, in an attempt to reduce SARS-CoV-2 transmission in China, Israel, Singapore, and South Korea, among other nations, and developments for digital contact tracing technologies by the largest international technology companies continue ( 16 ).

Advances in GIS&T have been made in modeling the geographic trajectories of individuals throughout their daily lives, their interactions with other people, and their immediate environment using geographic and computational constructs such as activity space and space–time prisms ( 17 – 20 ). However, to leverage this body of research for digital contact tracing, progress needs to be made in developing, testing, and implementing digital contact tracing applications, including evaluations of behavioral compliance, efficacy, and scaling. Additionally, this approach raises concerns about confidentiality and civil liberties that need to be addressed before widespread adoption ( 21 ).

Integrating Geographic Data in COVID-19 Modeling

A strength of GIS is the ability to integrate diverse spatial data sets based on georeferencing, facilitating the integration of health data with contextual characteristics. Descriptive modeling research that leverages this capability has examined the spatial associations of COVID-19 with socioeconomic and environmental characteristics. This research found, for example, that lower income and income inequality ( 22 ), higher temperature and humidity ( 23 ), exposure to fine particulate air pollution ( 24 ), and mobility and transportation networks ( 25 , 26 ) were associated with a higher prevalence of COVID-19 cases or mortality. GIS&T also offers approaches to investigating statistical spatial effects and spatial heterogeneity, such as spatial autoregressive models and geographically weighted regression, to account for modeling geographic processes such as spatial diffusion and the variation in relationships among variables over space ( 27 , 28 ). Recent research leveraged these approaches in demonstrating the spatial heterogeneity in the relationships among observed COVID-19 cases and mortality with georeferenced socioeconomic and environmental variables ( 22 , 29 , 30 ) and found that the influence of area-based socioeconomic status, pre-existing health conditions, and environmental characteristics on disease transmission may vary from place to place.

Computational infectious disease models are widely used to predict or forecast the spread of COVID-19 disease and the effects of intervention strategies. Predictive modeling approaches can be generally categorized as SEIR/SIR (susceptible, exposed, infected, and removed/recovered) ( 31 ), agent-based ( 32 ), or statistical modeling ( 33 ). Such modeling approaches are inherently geographic in the sense that they make predictions for certain areas or regions, although only some models contain an explicit spatial interaction component or forecast the spatial variation in disease incidence over small areas. Explicitly incorporating a spatial component into infectious disease models attempts to account for 1) place-based contextual mechanisms of infection or disease related to the socioeconomic, built, or natural environments, such as air pollution or type of employment, 2) spatial heterogeneity in the drivers of disease transmission, for example, where certain socioeconomic characteristics may be associated with disease prevalence in one region but not in another as a result of regional differences in culture or behavioral norms, and 3) transportation networks or patterns of human mobility to better account for disease transmission dynamics ( 34 , 35 ). Such approaches have been extended to modeling the spread of COVID-19, providing evidence that restrictions on mobility have mitigated the spread of COVID-19 in different parts of the world and aided in forecasts of disease diffusion under various scenarios of mobility restriction ( 36 , 37 ).

Spatial transportation and mobility data can play an important role in forecasting disease prevalence, where, for example, the effect of nonpharmaceutical interventions (eg, restrictions on mobility) on city-level transmission of COVID-19 in China was analyzed using mobility data harvested from mobile telephone location-based services. This method allows one to parameterize the local contact rate and forecast the geographic distribution of disease prevalence under different intervention timing scenarios ( 37 ). Related approaches to modeling the spread of COVID-19 also incorporated airline transportation networks ( 38 ) and were extended to other countries with extensive COVID-19 outbreaks, such as Italy ( 36 ), providing substantial evidence that restrictions on mobility have mitigated the spread of COVID-19 in different parts of the world.

Investigating Geographic Health Disparities of the COVID-19 Pandemic

Indices of social vulnerability are place-based variables that incorporate factors such as race/ethnicity and socioeconomic status to encode the vulnerability to adverse health outcomes and other types of hazards ( 39 ). Community social vulnerability, along with health care resources, plays an important role in predicting health care capacity in responding to the COVID-19 pandemic ( 40 ). Social vulnerability can interact with pre-existing medical conditions and access to medical resources, such as prescription drugs, to produce inequities in COVID-19 outcomes ( 41 ). People with underlying medical conditions, such as asthma, obesity, and diabetes, as well as people who are immunocompromised or aged 65 or older are at higher risk of serious consequences from SARS-CoV-2 infection than their healthier or younger counterparts. Because such medical conditions are often concentrated geographically and among certain demographic groups, understanding the spatial and demographic distribution of these conditions is critical to investigating health disparities associated with COVID-19. For example, COVID-19 morbidity and mortality are higher among African American and Hispanic people than among non-Hispanic white people ( 42 ). Such racial/ethnic disparities highlight the importance of efficient collection of socioeconomic, demographic, and other data among people with COVID-19.

Resources for investigating COVID-19-related social disparities include publicly available data on COVID-19 cases by small areas, such as zip codes ( 43 ), although such data are not widely available at a national level. The same issue exists for fine spatial resolution data on social vulnerability. The Public Health Disparities Geocoding Project at the Harvard T.H. Chan School of Public Health seeks to address this latter shortcoming ( 44 ). Researchers should understand the geographic and historical background of discrimination and resource deprivation that may produce place-based social vulnerabilities, to avoid stigmatizing or placing blame on certain communities. An understanding of the social determinants and structural forces, such as food insecurity, housing insecurity, and disparities in educational or health care infrastructure, that can influence health outcomes such as obesity, hypertension, and certain types of cancer, is important.

The multidimensional social, economic, and health consequences of the COVID-19 pandemic are geographically inequitable: some places and populations have greater social, economic, health and other effects than other places and populations. Beyond the need to identify such factors as lack of access to resources or the prevalence of pre-existing health conditions is the need to recognize and understand the mechanisms of vulnerability that have been in place and led to the exacerbation of the COVID-19 crisis in some communities. Community recovery from the COVID-19 pandemic requires incorporation of social, economic, and health components and an emphasis on investigating how place shapes the uneven effect of COVID-19.

Implications for Public Health

We have outlined how GIS&T can be used for understanding and responding to the COVID-19 pandemic and future infectious disease epidemics and pandemics. Central to this understanding and response is a commitment for the use of GIS and geospatial technologies as the platform for collecting, integrating, and analyzing georeferenced data on the locations and characteristics of individuals and the spatial distribution of socioeconomic, health, and built and natural environmental characteristics. Geospatial resources for COVID-19 response are available through several organizations, including the University Consortium for Geographic Information Science ( www.ucgis.org/covid-19-resources ), the OGC ( www.ogc.org/resources-for-COVID-19-from-ogc ), and the National Alliance for Public Safety GIS Foundation ( www.napsgfoundation.org/resources/covid-19 ).

Leveraging GIS&T for responding to the COVID-19 pandemic requires a close and extensive collaboration between researchers in the fields of geography, medicine, public health, and public policy. The field of GIS&T has a long history of research in data synthesis, statistical modeling, and computational simulation for spatial data and applications. Recognizing that GIS&T is a theoretical and scientific approach rather than simply a set of analytical tools will facilitate transdisciplinary collaboration. Advances in preserving individual privacy and civil liberties in the age of big spatial data, where geospatial technologies generate massive repositories of individual-level data on movement, health, and behavior widely available, are also necessary. These advances will likely require enhanced government regulations, corporate policies, and technological innovations in data sharing and privacy protection.

The COVID-19 pandemic is still in the beginning phase, and the research community is continuing to learn and revise the best way to respond to this global public health crisis. Geospatial data, methods, and technologies have a crucial role to play in understanding and responding to the pandemic, and the lessons learned on the use of GIS&T for pandemic response at this time should enhance preparedness and response for future public health crises.

Acknowledgments

No copyrighted materials were used in the preparation of this article.

The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions.

Suggested citation for this article: Smith CD, Mennis J. Incorporating Geographic Information Science and Technology in Response to the COVID-19 Pandemic. Prev Chronic Dis 2020;17:200246. DOI: https://doi.org/10.5888/pcd17.200246 .

Advertisement

A bibliometric and content analysis of research trends on GIS-based landslide susceptibility from 2001 to 2020

• Review Article
• Published: 24 October 2022
• Volume 29 , pages 86954–86993, ( 2022 )

Cite this article

• Junpeng Huang   ORCID: orcid.org/0000-0003-4785-7506 1 ,
• Xiyong Wu 2   nAff1 ,
• Sixiang Ling   ORCID: orcid.org/0000-0001-9697-1212 1 , 2 ,
• Xiaoning Li 3 ,
• Yuxin Wu 1 ,
• Lei Peng 1 &
• Zhiyi He 1  

1963 Accesses

13 Citations

5 Altmetric

Explore all metrics

To assess the status of hotspots and research trends on geographic information system (GIS)–based landslide susceptibility (LS), we analysed 1142 articles from the Thomas Reuters Web of Science Core Collection database published during 2001–2020 by combining bibliometric and content analysis. The paper number, authors, institutions, corporations, publication sources, citations, and keywords are noted as sub/categories for the bibliometric analysis. Thematic LS data, including the study site, landslide inventory, conditioning factors, mapping unit, susceptibility models, and mode fit/prediction performance evaluation, are presented in the content analysis. Then, we reveal the advantages and limitations of the common approaches used in thematic LS data and summarise the development trends. The results indicate that the distribution of articles shows clear clusters of authors, institutions, and countries with high academic activity. The application of remote sensing technology for interpreting landslides provides a more convenient and efficient landslide inventory. In the landslide inventory, most of the sample strategies representing the landslides are point and polygon, and the most frequently used sample subdividing strategy is random sampling. The scale effects, lack of geographic consistency, and no standard are key problems in landslide conditioning factors. Feature selection is used to choose the factors that can improve the model’s accuracy. With advances in computing technology and artificial intelligence, LS models are changing from simple qualitative and statistical models to complex machine learning and hybrid models. Finally, five future research opportunities are revealed. This study will help investigators clarify the status of LS research and provide guidance for future research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility

Review of landslide susceptibility assessment based on knowledge mapping

Analysis and evaluation of landslide susceptibility: a review on articles published during 2005–2016 (periods of 2005–2012 and 2013–2016), data availability.

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abdulwahid WM, Pradhan B (2017) Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR). Landslides 14:1057–1076. https://doi.org/10.1007/s10346-016-0744-0

Article   Google Scholar  

Aditian A, Kubota T, Shinohara Y (2018) Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology 318:101–111. https://doi.org/10.1016/j.geomorph.2018.06.006

Aghdam IN, Varzandeh MHM, Pradhan B (2016) Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran). Environ Earth Sci 75:553. https://doi.org/10.1007/s12665-015-5233-6

Akgun A, Erkan O (2016) Landslide susceptibility mapping by geographical information system-based multivariate statistical and deterministic models: in an artificial reservoir area at Northern Turkey. Arabian J Geosci 9:165. https://doi.org/10.1007/s12517-015-2142-7

Akinci H, Kilicoglu C, Dogan S (2020) Random forest-based landslide susceptibility mapping in coastal regions of Artvin. Turkey ISPRS Int J Geo-Inf 9:553. https://doi.org/10.3390/ijgi9090553

Althuwaynee OF, Pradhan B, Park H-J, Lee JH (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena 114:21–36. https://doi.org/10.1016/j.catena.2013.10.011

Arabameri A, Pradhan B, Rezaei K, Lee C-W (2019) Assessment of landslide susceptibility using statistical- and artificial intelligence-based FR-RF integrated model and multiresolution DEMs. Remote Sens 11:999. https://doi.org/10.3390/rs11090999

Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31. https://doi.org/10.1016/j.geomorph.2004.06.010

Ba Q, Chen Y, Deng S, Yang J, Li H (2018) A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment. Earth Sci Inf 11:373–388. https://doi.org/10.1007/s12145-018-0335-9

Begueria S (2006) Validation and evaluation of predictive models in hazard assessment and risk management. Nat Hazards 37:315–329. https://doi.org/10.1007/s11069-005-5182-6

Bordoni M, Galanti Y, Bartelletti C, Persichillo MG, Barsanti M, Giannecchini R, Avanzi GD, Cevasco A, Brandolini P, Galve JP, Meisina C (2020) The influence of the inventory on the determination of the rainfall-induced shallow landslides susceptibility using generalized additive models. Catena 193:104630. https://doi.org/10.1016/j.catena.2020.104630

Brabb EE (1984) Innovative approaches to landslide hazard mapping. Proceedings of the 4th International Symposium of Landslides, Toronto, Canada. pp 307–324

Brenning A (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Hazards Earth Syst Sci 5:853–862. https://doi.org/10.5194/nhess-5-853-2005

Broeckx J, Vanmaercke M, Duchateau R, Poesen J (2018) A data-based landslide susceptibility map of Africa. Earth Sci Rev 185:102–121. https://doi.org/10.1016/j.earscirev.2018.05.002

Budimir MEA, Atkinson PM, Lewis HG (2015) A systematic review of landslide probability mapping using logistic regression. Landslides 12:419–436. https://doi.org/10.1007/s10346-014-0550-5

Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96:28–40. https://doi.org/10.1016/j.catena.2012.04.001

Bui DT, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13:361–378. https://doi.org/10.1007/s10346-015-0557-6

Bui DT, Shahabi H, Shirzadi A, Chapi K, Alizadeh M, Chen W, Mohammadi A, Bin Ahmad B, Panahi M, Hong H, Tian Y (2018) Landslide detection and susceptibility mapping by AIRSAR data using support vector machine and index of entropy models in Cameron Highlands. Malaysia Remote Sens 10:1527. https://doi.org/10.3390/rs10101527

Bui DT, Tsangaratos P, Nguyen VT, Liem NV, Trinh PT (2020) Comparing the prediction performance of a deep learning neural network model with conventional machine learning models in landslide susceptibility assessment. Catena 188:104426. https://doi.org/10.1016/j.catena.2019.104426

Cama M, Conoscenti C, Lombardo L, Rotigliano E (2016) Exploring relationships between grid cell size and accuracy for debris-flow susceptibility models: a test in the Giampilieri catchment (Sicily, Italy). Environ Earth Sci 75:238. https://doi.org/10.1007/s12665-015-5047-6

Camargo JMR, Silva MVB, Ferreira AV, Araujo TCM (2019) Marine geohazards: a bibliometric-based review. Geosciences 9:100. https://doi.org/10.3390/geosciences9020100

Canavesi V, Segoni S, Rosi A, Ting X, Nery T, Catani F, Casagli N (2020) Different approaches to use morphometric attributes in landslide susceptibility mapping based on meso-scale spatial units: a case study in Rio de Janeiro (Brazil). Remote Sens 12:1826. https://doi.org/10.3390/rs12111826

Carvalho DV, Pereira EM, Cardoso JS (2019) Machine learning interpretability: a survey on methods and metrics. Electronics 8:832. https://doi.org/10.3390/electronics8080832

Chacon J, Irigaray C, Fernandez T, El Hamdouni R (2006) Engineering geology maps: landslides and geographical information systems. Bull Eng Geol Environ 65:341–411. https://doi.org/10.1007/s10064-006-0064-z

Chang KT, Merghadi A, Yunus AP, Pham BT, Dou J (2019) Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques. Sci Rep 9:12296. https://doi.org/10.1038/s41598-019-48773-2

Article   CAS   Google Scholar  

Chang ZL, Du Z, Zhang F, Huang FM, Chen JW, Li WB, Guo ZZ (2020) Landslide susceptibility prediction based on remote sensing images and GIS: comparisons of supervised and unsupervised machine learning models. Remote Sens 12:502. https://doi.org/10.3390/rs12030502

Chen CM (2017) Science mapping: a systematic review of the literature. J Data Info Sci 2:1–40. https://doi.org/10.1515/jdis-2017-0006

Chen W, Li X, Wang Y, Liu S (2013) Landslide susceptibility mapping using LiDAR and DMC data: a case study in the Three Gorges area, China. Environ Earth Sci 70:673–685. https://doi.org/10.1007/s12665-012-2151-8

Chen W, Peng JB, Hong HY, Shahabi H, Pradhan B, Liu JZ, Zhu AX, Pei XJ, Duan Z (2018a) Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Sci Total Environ 626:1121–1135. https://doi.org/10.1016/j.scitotenv.2018.01.124

Chen W, Shahabi H, Zhang S, Khosravi K, Shirzadi A, Chapi K, Binh Thai P, Zhang T, Zhang L, Chai H, Ma J, Chen Y, Wang X, Li R, Bin Ahmad B (2018b) Landslide susceptibility modeling based on GIS and novel bagging-based kernel logistic regression. Applied Sciences-Basel 8:2540. https://doi.org/10.3390/app8122540

Chen W, Xie XS, Peng JB, Shahabi H, Hong HY, Dieu Tien B, Duan Z, Li SJ, Zhu AX (2018c) GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method. Catena 164:135–149. https://doi.org/10.1016/j.catena.2018.01.012

Chen W, Chen YZ, Tsangaratos P, Ilia I, Wang XJ (2020) Combining evolutionary algorithms and machine learning models in landslide susceptibility assessments. Remote Sens 12:3854. https://doi.org/10.3390/rs12233854

Choi J, Oh H-J, Lee H-J, Lee C, Lee S (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol 124:12–23. https://doi.org/10.1016/j.enggeo.2011.09.011

Ciampalini A, Raspini F, Lagomarsino D, Catani F, Casagli N (2016) Landslide susceptibility map refinement using PSInSAR data. Remote Sens Environ 184:302–315. https://doi.org/10.1016/j.rse.2016.07.018

Clerici A, Perego S, Tellini C, Vescovi P (2006) A GIS-based automated procedure for landslide susceptibility mapping by the conditional analysis method: the Baganza valley case study (Italian Northern Apennines). Environ Geol 50:941–961. https://doi.org/10.1007/s00254-006-0264-7

Clerici A, Perego S, Tellini C, Vescovi P (2010) Landslide failure and runout susceptibility in the upper T. Ceno valley (Northern Apennines, Italy). Nat Hazards 52:1–29. https://doi.org/10.1007/s11069-009-9349-4

Conoscenti C, Di Maggio C, Rotigliano E (2008) GIS analysis to assess landslide susceptibility in a fluvial basin of NW Sicily (Italy). Geomorphology 94:325–339. https://doi.org/10.1016/j.geomorph.2006.10.039

Dai FC, Lee CF (2001) Terrain-based mapping of landslide susceptibility using a geographical information system: a case study. Can Geotech J 38:911–923. https://doi.org/10.1139/t01-021

Dai FC, Lee CF, Li J, Xu ZW (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40:381–391. https://doi.org/10.1007/s002540000163

Das I, Stein A, Kerle N, Dadhwal VK (2011) Probabilistic landslide hazard assessment using homogeneous susceptible units (HSU) along a national highway corridor in the northern Himalayas, India. Landslides 8:293–308. https://doi.org/10.1007/s10346-011-0257-9

Das I, Stein A, Kerle N, Dadhwal VK (2012) Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models. Geomorphology 179:116–125. https://doi.org/10.1016/j.geomorph.2012.08.004

Dou J, Yunus AP, Bui DT, Sahana M, Chen C-W, Zhu Z, Wang W, Pham BT (2019) Evaluating GIS-based multiple statistical models and data mining for earthquake and rainfall-induced landslide susceptibility using the LiDAR DEM. Remote Sens 11:638. https://doi.org/10.3390/rs11060638

Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu ZF, Chen CW, Han Z, Pham BT (2020) Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan. Landslides 17:641–658. https://doi.org/10.1007/s10346-019-01286-5

Dumperth C, Rohn J, Fleer A, Wang JG, Xiang W, Zimmermann K (2016) An easy approach to assess the susceptibility of a landslide by utilizing simple raster overlay methods: a case study on Huangtupo landslide (PR China). J Mt Sci 13:1701–1710. https://doi.org/10.1007/s11629-016-4011-6

Eker AM, Dikmen M, Cambazoglu S, Duzgun SHB, Akgun H (2015) Evaluation and comparison of landslide susceptibility mapping methods: a case study for the Ulus district, Bartin, northern Turkey. Int J Geogr Inf Sci 29:132–158. https://doi.org/10.1080/13658816.2014.953164

Ercanoglu M, Temiz FA (2011) Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu, Turkey). Environ Earth Sci 64:949–964. https://doi.org/10.1007/s12665-011-0912-4

Fan JL, Shen S, Wang JD, Wei SJ, Zhang X, Zhong P, Wang H (2020) Scientific and technological power and international cooperation in the field of natural hazards: a bibliometric analysis. Nat Hazards 102:807–827. https://doi.org/10.1007/s11069-020-03919-8

Federici PR, Puccinelli A, Cantarelli E, Casarosa N, Avanzi GDA, Falaschi F, Giannecchini R, Pochini A, Ribolini A, Bottai M, Salvati N, Testi C (2007) Multidisciplinary investigations in evaluating landslide susceptibility - an example in the Serchio River valley (Italy). Quat Int 171–72:52–63. https://doi.org/10.1016/j.quaint.2006.10.018

Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZ, Eng J-JTCL (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning Commentary. Eng Geol 102:99–111. https://doi.org/10.1016/j.enggeo.2008.03.014

Garfield E, Pudovkin AI, Istomin VS (2003) Why do we need algorithmic historiography? J Am Soc Inf Sci Tech 54:400–412. https://doi.org/10.1002/asi.10226

Ghasemain B, Asle DT, Pham BT, Avand M, Nguyen HD, Janizadeh S (2020) Shallow landslide susceptibility mapping: a comparison between classification and regression tree and reduced error pruning tree algorithms Vietnam J. Earth Sci 42:208–227. https://doi.org/10.15625/0866-7187/42/3/14952

Goetz JN, Guthrie RH, Brenning A (2011) Integrating physical and empirical landslide susceptibility models using generalized additive models. Geomorphology 129:376–386. https://doi.org/10.1016/j.geomorph.2011.03.001

Gorsevski PV, Brown MK, Panter K, Onasch CM, Simic A, Snyder J (2016) Landslide detection and susceptibility mapping using LiDAR and an artificial neural network approach: a case study in the Cuyahoga Valley National Park, Ohio. Landslides 13:467–484. https://doi.org/10.1007/s10346-015-0587-0

Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31:181–216. https://doi.org/10.1016/s0169-555x(99)00078-1

Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81:166–184. https://doi.org/10.1016/j.geomorph.2006.04.007

Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang K-T (2012) Landslide inventory maps: new tools for an old problem. Earth Sci Rev 112:42–66. https://doi.org/10.1016/j.earscirev.2012.02.001

Heckmann T, Gegg K, Gegg A, Becht M (2014) Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows. Nat Hazards Earth Syst Sci 14:259–278. https://doi.org/10.5194/nhess-14-259-2014

Hess DM, Leshchinsky BA, Bunn M, Mason HB, Olsen MJ (2017) A simplified three-dimensional shallow landslide susceptibility framework considering topography and seismicity. Landslides 14:1677–1697. https://doi.org/10.1007/s10346-017-0810-2

Hoang N, Mehrabi M, Kalantar B, Moayedi H, MaM A (2019) Potential of hybrid evolutionary approaches for assessment of geo-hazard landslide susceptibility mapping. Geomat Nat Haz Risk 10:1667–1693. https://doi.org/10.1080/19475705.2019.1607782

Hong Y, Adler RF (2008) Predicting global landslide spatiotemporal distribution: Integrating landslide susceptibility zoning techniques and real-time satellite rainfall estimates. Int J Sediment Res 23:249–257. https://doi.org/10.1016/s1001-6279(08)60022-0

Hong Y, Adler R, Huffman G (2007a) Use of satellite remote sensing data in the mapping of global landslide susceptibility. Nat Hazards 43:245–256. https://doi.org/10.1007/s11069-006-9104-z

Hong Y, Adler RF, Huffman G (2007b) An experimental global prediction system for rainfall-triggered landslides using satellite remote sensing and geospatial datasets. IEEE Trans Geosci Remote Sens 45:1671–1680. https://doi.org/10.1109/tgrs.2006.888436

Hong HY, Pradhan B, Sameen MI, Chen W, Xu C (2017) Spatial prediction of rotational landslide using geographically weighted regression, logistic regression, and support vector machine models in Xing Guo area (China). Geomat Nat Haz Risk 8:1997–2022. https://doi.org/10.1080/19475705.2017.1403974

Hong HY, Liu JY, Bui DT, Pradhan B, Acharya TD, Binh Thai P, Zhu AX, Chen W, Bin Ahmad B (2018) Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China). Catena 163:399–413. https://doi.org/10.1016/j.catena.2018.01.005

Huang Y, Zhao L (2018) Review on landslide susceptibility mapping using support vector machines. Catena 165:520–529. https://doi.org/10.1016/j.catena.2018.03.003

Huang HP, Yang KC, Lin BW (2013) Statistical evaluation of the effect of earthquake with other related factors on landslide susceptibility: using the watershed area of Shihmen reservoir in Taiwan as a case study. Environ Earth Sci 69:2151–2166. https://doi.org/10.1007/s12665-012-2044-x

Huang JP, Sun CW, Wu XY, Ling SX, Wang S, Deng R (2020a) Stability assessment of tunnel slopes along the Dujiangyan City to Siguniang Mountain Railway, China. Bull Eng Geol Environ 79:5309–5327. https://doi.org/10.1007/s10064-020-01913-9

Huang JX, Xie MW, Atkinson PM (2020b) Dynamic susceptibility mapping of slow-moving landslides using PSInSAR. Int J Remote Sens 41:7509–7529. https://doi.org/10.1080/01431161.2020.1760398

Huang FM, Tao SY, Chang ZL, Huang JS, Fan XM, Jiang SH, Li WB (2021) Efficient and automatic extraction of slope units based on multi-scale segmentation method for landslide assessments. Landslides 18:3715–3731. https://doi.org/10.1007/s10346-021-01756-9

Huang JP, Ling SX, Wu XY, Deng R (2022a) GIS-based comparative study of the Bayesian network, decision table, radial basis function network and stochastic gradient descent for the spatial prediction of landslide susceptibility. Land 11:436. https://doi.org/10.3390/land11030436

Huang JP Ma N Ling SX, Wu XY (2022b) Comparing the prediction performance of logistic model tree with different ensemble techniques in susceptibility assessments of different landslide types. Geocarto Int 1-31. https://doi.org/10.1080/10106049.2022.2087751

Hungr O, Leroueil S, Picarelli L (2014) The Varnes classification of landslide types, an update. Landslides 11:167–194. https://doi.org/10.1007/s10346-013-0436-y

Hussin HY, Zumpano V, Reichenbach P, Sterlacchini S, Micu M, van Westen C, Balteanu D (2016) Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model. Geomorphology 253:508–523. https://doi.org/10.1016/j.geomorph.2015.10.030

Jaafari A (2018) LiDAR-supported prediction of slope failures using an integrated ensemble weights-of-evidence and analytical hierarchy process. Environ Earth Sci 77:42. https://doi.org/10.1007/s12665-017-7207-3

Jaafari A, Najafi A, Rezaeian J, Sattarian A, Ghajar I (2015) Planning road networks in landslide-prone areas: a case study from the northern forests of Iran. Land Use Policy 47:198–208. https://doi.org/10.1016/j.landusepol.2015.04.010

Jaafari A, Panahi M, Pham BT, Shahabi H, Bui DT, Rezaie F, Lee S (2019) Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility. Catena 175:430–445. https://doi.org/10.1016/j.catena.2018.12.033

Jaboyedoff M, Oppikofer T, Abellan A, Derron MH, Loye A, Metzger R, Pedrazzini A (2012) Use of LIDAR in landslide investigations: a review. Nat Hazards 61:5–28. https://doi.org/10.1007/s11069-010-9634-2

Jebur MN, Pradhan B, Tehrany MS (2014) Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sens Environ 152:150–165. https://doi.org/10.1016/j.rse.2014.05.013

Ji SP, Yu DW, Shen CY, Li WL, Xu Q (2020) Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks. Landslides 17:1337–1352. https://doi.org/10.1007/s10346-020-01353-2

Jimenez-Peralvarez JD, El Hamdouni R, Palenzuela JA, Irigaray C, Chacon J (2017) Landslide-hazard mapping through multi-technique activity assessment: an example from the Betic Cordillera (southern Spain). Landslides 14:1975–1991. https://doi.org/10.1007/s10346-017-0851-6

Kakavas MP, Nikolakopoulos KG (2021) Digital elevation models of rockfalls and landslides: a review and meta-analysis. Geosciences 11:256. https://doi.org/10.3390/geosciences11060256

Kaminski M (2020) The impact of quality of digital elevation models on the result of landslide susceptibility modeling using the method of weights of evidence. Geosciences 10:488. https://doi.org/10.3390/geosciences10120488

Kamp U, Growley BJ, Khattak GA, Owen LA (2008) GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region. Geomorphology 101:631–642. https://doi.org/10.1016/j.geomorph.2008.03.003

Kanungo DP (2015) Landslide susceptibility zonation (LSZ) mapping — a review. Landslides 12:631–640. https://doi.org/10.1007/s10346-015-0586-1

Kayastha P, Dhital MR, De Smedt F (2013) Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal. Comput Geosci 52:398–408. https://doi.org/10.1016/j.cageo.2012.11.003

Kim J, Lee K, Jeong S, Kim G (2014) GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model. Eng Geol 182:63–78. https://doi.org/10.1016/j.enggeo.2014.09.001

Kornejady A, Ownegh M, Bahremand A (2017) Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena 152:144–162. https://doi.org/10.1016/j.catena.2017.01.010

Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data journals. Int J Remote Sens 26:1477–1491. https://doi.org/10.1080/01431160412331331012

Lee S (2019) Current and future status of GIS-based landslide susceptibility mapping: a literature review. Korean J Remote Sens 35:179–193. https://doi.org/10.7780/kjrs.2019.35.1.12

Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40:1095–1113. https://doi.org/10.1007/s002540100310

Lee S, Pradhan B (2006) Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. J Earth Syst Sci 115:661–672. https://doi.org/10.1007/s12040-006-0004-0

Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41. https://doi.org/10.1007/s10346-006-0047-y

Lee S, Ryu JH, Lee MJ, Won JS (2003) Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea. Environ Geol 44:820–833. https://doi.org/10.1007/s00254-003-0825-y

Lee S, Ryu JH, Lee MJ, Won JS (2006) The application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea. Math Geol 38:199–220. https://doi.org/10.1007/s11004-005-9012-x

Lee S, Ryu J-H, Kim I-S (2007) Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea. Landslides 4:327–338. https://doi.org/10.1007/s10346-007-0088-x

Lee J-H, Sameen MI, Pradhan B, Park H-J (2018) Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods. Geomorphology 303:284–298. https://doi.org/10.1016/j.geomorph.2017.12.007

Lee S, Jang J, Kim Y, Cho N, Lee M-J (2020) Susceptibility analysis of the Mt. Umyeon landslide area using a physical slope model and probabilistic method. Remote Sens 12:2663. https://doi.org/10.3390/rs12162663

Leydesdorff L, Rafols I (2012) Interactive overlays: a new method for generating global journal maps from Web-of-Science data. J Informetr 6:318–332. https://doi.org/10.1016/j.joi.2011.11.003

Li Y, Xu ZS, Wang XX, Wang XZ (2020) A bibliometric analysis on deep learning during 2007–2019. Int J Mach Learn Cybern 11:2807–2826. https://doi.org/10.1007/s13042-020-01152-0

Liang Z, Wang CM, Duan ZJ, Liu HL, Liu XY, Khan KUJ (2021) A hybrid model consisting of supervised and unsupervised learning for landslide susceptibility mapping. Remote Sens 13:1464. https://doi.org/10.3390/rs13081464

Lin L, Lin QG, Wang Y (2017) Landslide susceptibility mapping on a global scale using the method of logistic regression. Nat Hazards Earth Syst Sci 17:1411–1424. https://doi.org/10.5194/nhess-17-1411-2017

Ling SX, Chigira M (2020) Characteristics and triggers of earthquake induced landslides of pyroclastic fall deposits: an example from Hachinohe during the 1968 M7.9 tokachi-Oki earthquake, Japan. Eng Geol 264:105301. https://doi.org/10.1016/j.enggeo.2019.105301

Ling SX, Sun CW, Li XN, Ren Y, Xu JX, Huang T (2021) Characterizing the distribution pattern and geologic and geomorphic controls on earthquake-triggered landslide occurrence during the 2017 Ms 7.0 Jiuzhaigou earthquake, Sichuan. China Landslides 18:1275–1291. https://doi.org/10.1007/s10346-020-01549-6

Ling SX, Zhao SY, Huang JP, Zhang XT (2022) Landslide susceptibility assessment using statistical and machine learning techniques: a case study in the upper reaches of the Minjiang River, southwestern China. Front Earth Sci 10:986172. https://doi.org/10.3389/feart.2022.986172

Liu HH, Li XG, Meng T, Liu YY (2020) Susceptibility mapping of damming landslide based on slope unit using frequency ratio model. Arabian J Geosci 13:790. https://doi.org/10.1007/s12517-020-05689-w

Luo X, Lin F, Chen Y, Zhu S, Xu Z, Huo Z, Yu M, Peng J (2019) Coupling logistic model tree and random subspace to predict the landslide susceptibility areas with considering the uncertainty of environmental features. Sci Rep 9:15369. https://doi.org/10.1038/s41598-019-51941-z

Ma ZJ, Mei G, Piccialli F (2021) Machine learning for landslides prevention: a survey. Neural Comput Appl 33:10881–10907. https://doi.org/10.1007/s00521-020-05529-8

Mahalingam R, Olsen MJ (2016) Evaluation of the influence of source and spatial resolution of DEMs on derivative products used in landslide mapping. Geomat Nat Haz Risk 7:1835–1855. https://doi.org/10.1080/19475705.2015.1115431

Mahalingam R, Olsen MJ, O’Banion MS (2016) Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study). Geomat Nat Haz Risk 7:1884–1907. https://doi.org/10.1080/19475705.2016.1172520

Mahdadi F, Boumezbeur A, Hadji R, Kanungo DP, Zahri F (2018) GIS-based landslide susceptibility assessment using statistical models: a case study from Souk Ahras province, N-E Algeria. Arabian J Geosci 11:476. https://doi.org/10.1007/s12517-018-3770-5

Mancini F, Ceppi C, Ritrovato G (2010) GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy. Nat Hazards Earth Syst Sci 10:1851–1864. https://doi.org/10.5194/nhess-10-1851-2010

Maxwell AE, Sharma M, Kite JS, Donaldson KA, Thompson JA, Bell ML, Maynard SM (2020) Slope failure prediction using random forest machine learning and LiDAR in an eroded folded mountain belt. Remote Sens 12:486. https://doi.org/10.3390/rs12030486

Merghadi A, Yunus AP, Dou J, Whiteley J, ThaiPham B, Bui DT, Avtar R, Abderrahmane B (2020) Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance. Earth Sci Rev 207:103225. https://doi.org/10.1016/j.earscirev.2020.103225

Mohan A, Singh AK, Kumar B, Dwivedi R (2021) Review on remote sensing methods for landslide detection using machine and deep learning. Trans Emerging Telecommun Technol 32:e3998. https://doi.org/10.1002/ett.3998

Neuland H (1976) A prediction model of landslips. Catena 3:215–230. https://doi.org/10.1016/0341-8162(76)90011-4

Nhu VH, Shirzadi A, Shahabi H, Chen W, Clague JJ, Geertsema M, Jaafari A, Avand M, Miraki S, Asl DT, Pham BT, Bin Ahmad B, Lee S (2020) Shallow landslide susceptibility mapping by random forest base classifier and its ensembles in a semi-arid region of Iran. Forests 11:421. https://doi.org/10.3390/f11040421

Niu RQ, Wu XL, Yao DK, Peng L, Ai L, Peng JH (2014) Susceptibility assessment of landslides triggered by the Lushan earthquake, April 20, 2013, China. IEEE J Sel Top Appl Earth Obs Remote Sens 7:3979–3992. https://doi.org/10.1109/jstars.2014.2308553

Pandey VK, Pourghasemi HR, Sharma MC (2020) Landslide susceptibility mapping using maximum entropy and support vector machine models along the highway corridor, Garhwal Himalaya. Geocarto Int 35:168–187. https://doi.org/10.1080/10106049.2018.1510038

Pardeshi SD, Autade SE, Pardeshi SS (2013) Landslide hazard assessment: recent trends and techniques. Springerplus 2:523. https://doi.org/10.1186/2193-1801-2-523

Pareek N, Pal S, Sharma ML, Arora MK (2013) Study of effect of seismic displacements on landslide susceptibility zonation (LSZ) in Garhwal Himalayan region of India using GIS and remote sensing techniques. Comput Geosci 61:50–63. https://doi.org/10.1016/j.cageo.2013.07.018

Park NW, Chi KH (2008) Quantitative assessment of landslide susceptibility using high-resolution remote sensing data and a generalized additive model. Int J Remote Sens 29:247–264. https://doi.org/10.1080/01431160701227661

Paulin GL, Bursik M, Lugo-Hubp J, Orozco JJZ (2010) Effect of pixel size on cartographic representation of shallow and deep-seated. landslide, and its collateral effects on the forecasting of landslides by SINMAP and Multiple Logistic Regression landslide models. Phys Chem Earth 35:137–148. https://doi.org/10.1016/j.pce.2010.04.008

Pham BT, Bui DT, Prakash I (2018) Bagging based support vector machines for spatial prediction of landslides. Environ Earth Sci 77:146. https://doi.org/10.1007/s12665-018-7268-y

Pham BT, Phong TV, Nguyen-Thoi T, Trinh PT, Tran QC, Ho LS, Singh SK, Duyen TTT, Nguyen LT, Le HQ, Le HV, Hanh NTB, Quoc NK, Prakash I (2020) GIS-based ensemble soft computing models for landslide susceptibility mapping. Adv Space Res 66:1303–1320. https://doi.org/10.1016/j.asr.2020.05.016

Pourghasemi HR, Rahmati O (2018) Prediction of the landslide susceptibility: which algorithm, which precision? Catena 162:177–192. https://doi.org/10.1016/j.catena.2017.11.022

Pourghasemi HR, Yansari ZT, Panagos P, Pradhan B (2018) Analysis and evaluation of landslide susceptibility: a review on articles published during 2005–2016 (periods of 2005–2012 and 2013–2016). Arabian J Geosci 11:193. https://doi.org/10.1007/s12517-018-3531-5

Pourghasemi HR, Kornejady A, Kerle N, Shabani F (2020) Investigating the effects of different landslide positioning techniques, landslide partitioning approaches, and presence-absence balances on landslide susceptibility mapping. Catena 187:104364. https://doi.org/10.1016/j.catena.2019.104364

Pradhan B (2011) Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environ Earth Sci 63:329–349. https://doi.org/10.1007/s12665-010-0705-1

Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365. https://doi.org/10.1016/j.cageo.2012.08.023

Pradhan B, Buchroithner MF (2010) Comparison and validation of landslide susceptibility maps using an artificial neural network model for three test areas in Malaysia. Environ Eng Geosci 16:107–126. https://doi.org/10.2113/gseegeosci.16.2.107

Pradhan AMS, Kim YT (2016) Evaluation of a combined spatial multi-criteria evaluation model and deterministic model for landslide susceptibility mapping. Catena 140:125–139. https://doi.org/10.1016/j.catena.2016.01.022

Pradhan B, Lee S (2010a) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci 60:1037–1054. https://doi.org/10.1007/s12665-009-0245-8

Pradhan B, Lee S (2010b) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ ModeLl Softw 25:747–759. https://doi.org/10.1016/j.envsoft.2009.10.016

Pradhan B, Singh RP, Buchroithner MF (2006) Estimation of stress and its use in evaluation of landslide prone regions using remote sensing data. Adv Space Res 37:698–709. https://doi.org/10.1016/j.asr.2005.03.137

Pradhan B, Lee S, Mansor S, Buchroithner M, Jamaluddin N, Khujaimah Z (2008) Utilization of optical remote sensing data and geographic information system tools for regional landslide hazard analysis by using binomial logistic regression model. J Appl Remote Sens 2:023542. https://doi.org/10.1117/1.3026536

Qin SW, Lv JF, Cao C, Ma ZJ, Hu XY, Liu F, Qiao SS, Dou Q (2019) Mapping debris flow susceptibility based on watershed unit and grid cell unit: a comparison study. Geomat Nat Haz Risk 10:1648–1666. https://doi.org/10.1080/19475705.2019.1604572

Qin Z, Zhao Z, Xia L, Ohore OE (2022) Research trends and hotspots of aquatic biofilms in freshwater environment during the last three decades: a critical review and bibliometric analysis. Environ Sci Pollut Res 29:47915–47930. https://doi.org/10.1007/s11356-022-20238-6

Regmi NR, Giardino JR, McDonald EV, Vitek JD (2014) A comparison of logistic regression-based models of susceptibility to landslides in western Colorado, USA. Landslides 11:247–262. https://doi.org/10.1007/s10346-012-0380-2

Reichenbach P, Busca C, Mondini AC, Rossi M (2014) The influence of land use change on landslide susceptibility zonation: the Briga catchment test site (Messina, Italy). Environ Manage 54:1372–1384. https://doi.org/10.1007/s00267-014-0357-0

Reichenbach P, Rossi M, Malamud BD, Mihir M, Guzzetti F (2018) A review of statistically-based landslide susceptibility models. Earth Sci Rev 180:60–91. https://doi.org/10.1016/j.earscirev.2018.03.001

Rosi A, Tofani V, Tanteri L, Stefanelli CT, Agostini A, Catani F, Casagli N (2018) The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: geomorphological features and landslide distribution. Landslides 15:5–19. https://doi.org/10.1007/s10346-017-0861-4

Saadatkhah N, Kassim A, Lee LM (2015) Susceptibility assessment of shallow landslides in Hulu Kelang Area, Kuala Lumpur, Malaysia using analytical hierarchy process and frequency ratio. Geotech Geol Eng 33:43–57. https://doi.org/10.1007/s10706-014-9818-8

Sahin EK, Colkesen I, Kavzoglu T (2020) A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping. Geocarto Int 35:341–363. https://doi.org/10.1080/10106049.2018.1516248

Saito T, Rehmsmeier M (2015) The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE 10:e0118432. https://doi.org/10.1371/journal.pone.0118432

Saleem N, Huq ME, Twumasi NYD, Javed A, Sajjad A (2019) Parameters derived from and/or used with digital elevation models (DEMs) for landslide susceptibility mapping and landslide risk assessment: a review. ISPRS Int J Geo-Inf 8:545. https://doi.org/10.3390/ijgi8120545

Sameen MI, Pradhan B, Bui DT, Alamri AM (2020a) Systematic sample subdividing strategy for training landslide susceptibility models. CATENA 187:104358. https://doi.org/10.1016/j.catena.2019.104358

Sameen MI, Pradhan B, Lee S (2020b) Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment. CATENA 186:104249. https://doi.org/10.1016/j.catena.2019.104249

Sassa K (2019) Registered speakers of the Fifth World Landslide Forum-Implementing and Monitoring the Sendai Landslide Partnerships 2015–2025-Voluntary contribution to the Sendai Framework 2015–2030 and the Agenda 2030-Sustainable Development Goals. Landslides 16:1423–1424. https://doi.org/10.1007/s10346-019-01213-8

Scaioni M, Longoni L, Melillo V, Papini M (2014) Remote sensing for landslide investigations: an overview of recent achievements and perspectives. Remote Sens 6:9600–9652. https://doi.org/10.3390/rs6109600

Shahabi H, Ahmad BB, Khezri S (2013) Evaluation and comparison of bivariate and multivariate statistical methods for landslide susceptibility mapping (case study: Zab basin). Arabian J Geosci 6:3885–3907. https://doi.org/10.1007/s12517-012-0650-2

Shahabi H, Khezri S, Bin Ahmad B, Hashim M (2014) Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models. CATENA 115:55–70. https://doi.org/10.1016/j.catena.2013.11.014

Shano L, Raghuvanshi TK, Meten M (2020) Landslide susceptibility evaluation and hazard zonation techniques - a review. Geoenvironmental Disasters 7:18. https://doi.org/10.1186/s40677-020-00152-0

Sharma LP, Patel N, Ghose MK, Debnath P (2011) Landslide vulnerability assessment and zonation through ranking of causative parameters based on landslide density-derived statistical indicators. Geocarto Int 26:491–504. https://doi.org/10.1080/10106049.2011.598951

Shirzadi A, Soliamani K, Habibnejhad M, Kavian A, Chapi K, Shahabi H, Chen W, Khosravi K, Binh Thai P, Pradhan B, Ahmad A, Bin Ahmad B, Dieu Tien B (2018) Novel GIS based machine learning algorithms for shallow landslide susceptibility mapping. Sensors 18:3777. https://doi.org/10.3390/s18113777

Shirzadi A, Solaimani K, Roshan MH, Kavian A, Chapi K, Shahabi H, Keesstra S, Bin Ahmad B, Dieu Tien B (2019) Uncertainties of prediction accuracy in shallow landslide modeling: Sample size and raster resolution. Catena 178:172–188. https://doi.org/10.1016/j.catena.2019.03.017

Shou K-J, Yang C-M (2015) Predictive analysis of landslide susceptibility under climate change conditions - a study on the Chingshui River Watershed of Taiwan. Eng Geol 192:46–62. https://doi.org/10.1016/j.enggeo.2015.03.012

Simon N, De Roiste M, Crozier M, Rafek AG (2017) Representing landslides as polygon (areal) or points? How different data types influence the accuracy of landslide susceptibility maps. Sains Malays 46:27–34. https://doi.org/10.17576/jsm-2017-4601-04

Stanley T, Kirschbaum DB (2017) A heuristic approach to global landslide susceptibility mapping. Nat Hazards 87:145–164. https://doi.org/10.1007/s11069-017-2757-y

Stehman SV, Hansen MC, Broich M, Potapov PV (2011) Adapting a global stratified random sample for regional estimation of forest cover change derived from satellite imagery. Remote Sens Environ 115:650–658. https://doi.org/10.1016/j.rse.2010.10.009

Sujatha ER, Kumaravel P, Rajamanickam VG (2012) Landslide susceptibility mapping using remotely sensed data through conditional probability analysis using seed cell and point sampling techniques. J Indian Soc Remote Sens 40:669–678. https://doi.org/10.1007/s12524-011-0192-1

Suzen ML, Doyuran V (2004) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ Geol 45:665–679. https://doi.org/10.1007/s00254-003-0917-8

Tang YM, Feng F, Guo ZZ, Feng W, Li ZG, Wang JY, Sun QY, Ma HN, Li YN (2020) Integrating principal component analysis with statistically-based models for analysis of causal factors and landslide susceptibility mapping: a comparative study from the loess plateau area in Shanxi (China). J Cleaner Prod 277:124159. https://doi.org/10.1016/j.jclepro.2020.124159

Tanoli JI, Chen N, Regmi AD, Jun L (2017) Spatial distribution analysis and susceptibility mapping of landslides triggered before and after Mw7.8 Gorkha earthquake along Upper Bhote Koshi, Nepal. Arabian J Geosci 10:277. https://doi.org/10.1007/s12517-017-3026-9

Tao J, Qiu DY, Yang FQ, Duan ZP (2020) A bibliometric analysis of human reliability research. J Cleaner Prod 260:121041. https://doi.org/10.1016/j.jclepro.2020.121041

Vakhshoori V, Zare M (2018) Is the ROC curve a reliable tool to compare the validity of landslide susceptibility maps? Geomat Nat Haz Risk 9:249–266. https://doi.org/10.1080/19475705.2018.1424043

van Eck NJ, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84:523–538. https://doi.org/10.1007/s11192-009-0146-3

van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102:112–131. https://doi.org/10.1016/j.enggeo.2008.03.010

Waltman L, van Eck NJ, Noyons ECM (2010) A unified approach to mapping and clustering of bibliometric networks. J Informetr 4:629–635. https://doi.org/10.1016/j.joi.2010.07.002

Wan S (2009) A spatial decision support system for extracting the core factors and thresholds for landslide susceptibililot map. Eng Geol 108:237–251. https://doi.org/10.1016/j.enggeo.2009.06.014

Wang HB, Liu GJ, Xu WY, Wang GH (2005) GIS-based landslide hazard assessment: an overview. Prog Phy Geog 29:548–567. https://doi.org/10.1191/0309133305pp462ra

Wang X, Zhang L, Wang S, Lari S (2014) Regional landslide susceptibility zoning with considering the aggregation of landslide points and the weights of factors. Landslides 11:399–409. https://doi.org/10.1007/s10346-013-0392-6

Wu XL, Chen XY, Zhan FB, Hong S (2015) Global research trends in landslides during 1991–2014: a bibliometric analysis. Landslides 12:1215–1226. https://doi.org/10.1007/s10346-015-0624-z

Wu YL, Ke YT, Chen Z, Liang SY, Zhao HL, Hong HY (2020) Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping. Catena 187:104396. https://doi.org/10.1016/j.catena.2019.104396

Xiao PF, Wu DD, Wang JQ (2022) Bibliometric analysis of global research on white rot fungi biotechnology for environmental application. Environ Sci Pollut Res 29:1491–1507. https://doi.org/10.1007/s11356-021-15787-1

Xie Z, Chen G, Meng X, Zhang Y, Qiao L, Tan L (2017) A comparative study of landslide susceptibility mapping using weight of evidence, logistic regression and support vector machine and evaluated by SBAS-InSAR monitoring: Zhouqu to Wudu segment in Bailong River Basin. China Environ Earth Sci 76:313. https://doi.org/10.1007/s12665-017-6640-7

Xiong YB, Zhou Y, Wang FT, Wang SX, Wang JM, Ji JW, Wang ZQ (2021) Landslide susceptibility mapping using ant colony optimization strategy and deep belief network in Jiuzhaigou Region. IEEE J Sel Top Appl Earth Obs Remote Sens 14:11042–11057. https://doi.org/10.1109/Jstars.2021.3122825

Xu C, Dai FC, Xu XW, Lee YH (2012) GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology 145:70–80. https://doi.org/10.1016/j.geomorph.2011.12.040

Xu C, Xu XW, Yu GH (2013) Landslides triggered by slipping-fault-generated earthquake on a plateau: an example of the 14 April 2010, Ms 7.1, Yushu. China Earthquake Landslides 10:421–431. https://doi.org/10.1007/s10346-012-0340-x

Yalcin A, Bulut F (2007) Landslide susceptibility mapping using GIS and digital photogrammetric techniques: a case study from Ardesen (NE-Turkey). Nat Hazards 41:201–226. https://doi.org/10.1007/s11069-006-9030-0

Yang JG, Cheng CX, Song CQ, Shen S, Ning LX (2019) Visual analysis of the evolution and focus in landslide research field. J Mt Sci 16:991–1004. https://doi.org/10.1007/s11629-018-5280-z

Yang YF, Chen GH, Reniers G, Goerlandt F (2020) A bibliometric analysis of process safety research in China: understanding safety research progress as a basis for making China’s chemical industry more sustainable. J Cleaner Prod 263:121433. https://doi.org/10.1016/j.jclepro.2020.121433

Yi YN, Zhang ZJ, Zhang WC, Xu Q, Deng C, Li QL (2019) GIS-based earthquake-triggered-landslide susceptibility mapping with an integrated weighted index model in Jiuzhaigou region of Sichuan Province, China. Nat Hazards Earth Syst Sci 19:1973–1988. https://doi.org/10.5194/nhess-19-1973-2019

Yi YN, Zhang ZJ, Zhang WC, Jia HH, Zhang JQ (2020) Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: a case study in Jiuzhaigou region. Catena 195:104851. https://doi.org/10.1016/j.catena.2020.104851

Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat-Turkey). Comput Geosci 35:1125–1138. https://doi.org/10.1016/j.cageo.2008.08.007

Yilmaz I (2010) The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks. Environ Earth Sci 60:505–519. https://doi.org/10.1007/s12665-009-0191-5

Yilmaz I, Keskin I (2009) GIS based statistical and physical approaches to landslide susceptibility mapping (Sebinkarahisar, Turkey). Bull Eng Geol Environ 68:459–471. https://doi.org/10.1007/s10064-009-0188-z

Youssef AM, Pradhan B, Jebur MN, El-Harbi HM (2015) Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia. Environ Earth Sci 73:3745–3761. https://doi.org/10.1007/s12665-014-3661-3

Yu CL, Chen JP (2020) Application of a GIS-based slope unit method for landslide susceptibility mapping in Helong city: comparative assessment of ICM, AHP, and RF model. Symmetry 12:1848. https://doi.org/10.3390/sym12111848

Yu X, Wang Y, Niu R, Hu Y (2016) A combination of geographically weighted regression, particle swarm optimization and support vector machine for landslide susceptibility mapping: a case study at Wanzhou in the Three Gorges Area, China. Int J Environ Res Public Health 13:487. https://doi.org/10.3390/ijerph13050487

Zezere JL, Pereira S, Melo R, Oliveira SC, Garcia RAC (2017) Mapping landslide susceptibility using data-driven methods. Sci Total Environ 589:250–267. https://doi.org/10.1016/j.scitotenv.2017.02.188

Zhang Y, Chen YP (2020) Research trends and areas of focus on the Chinese Loess Plateau: a bibliometric analysis during 1991–2018. CATENA 194:104798. https://doi.org/10.1016/j.catena.2020.104798

Zhao Y, Wang R, Jiang YJ, Liu HJ, Wei ZL (2019) GIS-based logistic regression for rainfall-induced landslide susceptibility mapping under different grid sizes in Yueqing, Southeastern China. Eng Geol 259:105147. https://doi.org/10.1016/j.enggeo.2019.105147

Zhou CB, Song WY (2021) Digitalization as a way forward: a bibliometric analysis of 20 Years of servitization research. J Cleaner Prod 300:126943. https://doi.org/10.1016/j.jclepro.2021.126943

Zhou C, Yin KL, Cao Y, Ahmed B, Li YY, Catani F, Pourghasemi HR (2018) Landslide susceptibility modeling applying machine learning methods: a case study from Longju in the Three Gorges Reservoir area, China. Comput Geosci 112:23–37. https://doi.org/10.1016/j.cageo.2017.11.019

Zhou XZ, Wen HJ, Li ZW, Zhang H, Zhang WG (2022) An interpretable model for the susceptibility of rainfall-induced shallow landslides based on SHAP and XGBoost. Geocarto Int 1–32. https://doi.org/10.1080/10106049.2022.2076928

Zhuang JQ, Peng JB, Wang GH, Iqbal J, Wang Y, Li W, Xu Q, Zhu XH (2017) Prediction of rainfall-induced shallow landslides in the Loess Plateau, Yan’an, China, using the TRIGRS model. Earth Surf Processes Landforms 42:915–927. https://doi.org/10.1002/esp.4050

Zou Y, Zheng C (2022) A scientometric analysis of predicting methods for identifying the environmental risks caused by landslides. Appl Sci 12:4333. https://doi.org/10.3390/app12094333

Zulhaidi H, Shafri M, Zahidi IM, Abu Bakar S (2010) Development of landslide susceptibility map utilizing remote sensing and Geographic Information Systems (GIS). Disaster Prev Manag 19:59–69. https://doi.org/10.1108/09653561011022144

Download references

Acknowledgements

The authors thank editor-in-chief Dr. Philippe Garrigues, editorial assistants Fanny Creusot and Giulia Marinaccio, and three reviewers for their critical comments and valuable suggestions.

This work was supported by the National Natural Science Foundation of China (No. 41907228), Chengdu Science and Technology Program (2022-YF05-00340-SN), Sichuan Science and Technology Program, China (No. 2020YFS0297), and the Fundamental Research Funds for the Central Universities (No. 2682020CX11).

Author information

Present address: Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, 611756, Chengdu, China

Authors and Affiliations

Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, 611756, Chengdu, China

Junpeng Huang, Sixiang Ling, Yuxin Wu, Lei Peng & Zhiyi He

Ministry of Education, Key Laboratory of High-Speed Railway Engineering, Southwest Jiaotong University, Chengdu, 610031, China

Xiyong Wu & Sixiang Ling

School of Emergency Management, Xihua University, Chengdu, 610039, China

Xiaoning Li

You can also search for this author in PubMed   Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Junpeng Huang, Yuxin Wu, Lei Peng, and Zhiyi He. The first draft of the manuscript was written by Junpeng Huang and reviewed by Sixiang Ling and Xiaoning Li. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Sixiang Ling .

Ethics declarations

Ethics approval.

Not applicable.

Consent to participate

Consent for publication, competing interests.

The authors declare no competing interests.

Additional information

Responsible Editor: Philippe Garrigues

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (XLSX 81 KB)

Rights and permissions.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Huang, J., Wu, X., Ling, S. et al. A bibliometric and content analysis of research trends on GIS-based landslide susceptibility from 2001 to 2020. Environ Sci Pollut Res 29 , 86954–86993 (2022). https://doi.org/10.1007/s11356-022-23732-z

Download citation

Received : 28 January 2022

Accepted : 16 October 2022

Published : 24 October 2022

Issue Date : December 2022

DOI : https://doi.org/10.1007/s11356-022-23732-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Bibliometric analysis
• Content analysis
• Landslide susceptibility
• Research trends
• Machine learning
• Find a journal
• Publish with us
• Track your research

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

A review of GIS methodologies to analyze the dynamics of COVID-19 in the second half of 2020

Affiliations.

• 1 GIS Laboratory Escuela Nacional de Estudios Superiores Morelia Universidad Nacional Autónoma de México Michoacán Mexico.
• 2 Department of Epidemiology Spatial Science for Public Health Center Johns Hopkins Bloomberg School of Public Health Baltimore MD USA.
• 3 Soil Erosion and Degradation Research Group Department of Geography Valencia University Valencia Spain.
• PMID: 34512103
• PMCID: PMC8420105
• DOI: 10.1111/tgis.12792

COVID-19 has infected over 163 million people and has resulted in over 3.9 million deaths. Regarding the tools and strategies to research the ongoing pandemic, spatial analysis has been increasingly utilized to study the impacts of COVID-19. This article provides a review of 221 scientific articles that used spatial science to study the pandemic published from June 2020 to December 2020. The main objectives are: to identify the tools and techniques used by the authors; to review the subjects addressed and their disciplines; and to classify the studies based on their applications. This contribution will facilitate comparisons with the body of work published during the first half of 2020, revealing the evolution of the COVID-19 phenomenon through the lens of spatial analysis. Our results show that there was an increase in the use of both spatial statistical tools (e.g., geographically weighted regression, Bayesian models, spatial regression) applied to socioeconomic variables and analysis at finer spatial and temporal scales. We found an increase in remote sensing approaches, which are now widely applied in studies around the world. Lockdowns and associated changes in human mobility have been extensively examined using spatiotemporal techniques. Another dominant topic studied has been the relationship between pollution and COVID-19 dynamics, which enhance the impact of human activities on the pandemic's evolution. This represents a shift from the first half of 2020, when the research focused on climatic and weather factors. Overall, we have seen a vast increase in spatial tools and techniques to study COVID-19 transmission and the associated risk factors.

© 2021 John Wiley & Sons Ltd.

PubMed Disclaimer

Graphical summary of the GIS…

Graphical summary of the GIS tools, thematic variables used, and objectives categorized in…

GIS tools and (a) variables…

GIS tools and (a) variables used, (b) topics covered

Similar articles

• Mapping the pandemic: a review of Geographical Information Systems-based spatial modeling of Covid-19. Aboalyem MS, Ismail MT. Aboalyem MS, et al. J Public Health Afr. 2023 Dec 1;14(12):2767. doi: 10.4081/jphia.2023.2767. eCollection 2023 Dec 27. J Public Health Afr. 2023. PMID: 38204808 Free PMC article.
• Assessment and statistical modeling of the relationship between remotely sensed aerosol optical depth and PM2.5 in the eastern United States. Paciorek CJ, Liu Y; HEI Health Review Committee. Paciorek CJ, et al. Res Rep Health Eff Inst. 2012 May;(167):5-83; discussion 85-91. Res Rep Health Eff Inst. 2012. PMID: 22838153
• Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review. Nazia N, Butt ZA, Bedard ML, Tang WC, Sehar H, Law J. Nazia N, et al. Int J Environ Res Public Health. 2022 Jul 6;19(14):8267. doi: 10.3390/ijerph19148267. Int J Environ Res Public Health. 2022. PMID: 35886114 Free PMC article.
• Ten GIS-Based Solutions for Managing and Controlling COVID-19 Pandemic Outbreak. Samany NN, Liu H, Aghataher R, Bayat M. Samany NN, et al. SN Comput Sci. 2022;3(4):269. doi: 10.1007/s42979-022-01150-9. Epub 2022 May 5. SN Comput Sci. 2022. PMID: 35531569 Free PMC article. Review.
• The Global Genome Question: Microbes as the Key to Understanding Evolution and Ecology: This report is based on a colloquium, “The Global Genome Question: Microbes as the Key to Understanding Evolution and Ecology,” sponsored by the American Academy of Microbiology and held October 11-13, 2002, in Longboat Key, Florida. [No authors listed] [No authors listed] Washington (DC): American Society for Microbiology; 2004. Washington (DC): American Society for Microbiology; 2004. PMID: 33119236 Free Books & Documents. Review.
• Spatiotemporal hierarchical Bayesian analysis to identify factors associated with COVID-19 in suburban areas in Colombia. Cortes-Ramirez J, Wilches-Vega JD, Caicedo-Velasquez B, Paris-Pineda OM, Sly PD. Cortes-Ramirez J, et al. Heliyon. 2024 Apr 24;10(9):e30182. doi: 10.1016/j.heliyon.2024.e30182. eCollection 2024 May 15. Heliyon. 2024. PMID: 38707376 Free PMC article.
• Conditioning factors in the spreading of Covid-19 - Does geography matter? Vandelli V, Palandri L, Coratza P, Rizzi C, Ghinoi A, Righi E, Soldati M. Vandelli V, et al. Heliyon. 2024 Feb 3;10(3):e25810. doi: 10.1016/j.heliyon.2024.e25810. eCollection 2024 Feb 15. Heliyon. 2024. PMID: 38356610 Free PMC article. Review.
• Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: A functional data analysis from August 2020 to March 2022. Ribeiro M, Azevedo L, Santos AP, Pinto Leite P, Pereira MJ. Ribeiro M, et al. PLoS One. 2024 Feb 1;19(2):e0297772. doi: 10.1371/journal.pone.0297772. eCollection 2024. PLoS One. 2024. PMID: 38300912 Free PMC article.
• Key risk factors associated with fractal dimension based geographical clustering of COVID-19 data in the Flemish and Brussels region, Belgium. Natalia YA, Faes C, Neyens T, Hammami N, Molenberghs G. Natalia YA, et al. Front Public Health. 2023 Nov 3;11:1249141. doi: 10.3389/fpubh.2023.1249141. eCollection 2023. Front Public Health. 2023. PMID: 38026374 Free PMC article.
• Housing situations and local COVID-19 infection dynamics using small-area data. Freise D, Schiele V, Schmitz H. Freise D, et al. Sci Rep. 2023 Aug 31;13(1):14301. doi: 10.1038/s41598-023-40734-0. Sci Rep. 2023. PMID: 37652980 Free PMC article.
• Abdallah, H. S. , Khafagy, M. H. , & Omara, F. A. (2020). Case study: Spark GPU‐enabled framework to control COVID‐19 spread using cell‐phone spatio‐temporal data. Computers, Materials & Continua, 65(2), 1303–1320.
• Adler, P. , Florida, R. , & Hartt, M. (2020). Mega regions and pandemics. Tijdschrift voor Economische en Sociale Geografie, 111(3), 465–481. 10.1111/tesg.12449 - DOI - PMC - PubMed
• Ahasan, R. , Alam, M. S. , Chakraborty, T. , & Hossain, M. M. (2020). Applications of GIS and geospatial analyses in COVID‐19 research: A systematic review. F1000Research, 9, 1379. 10.12688/f1000research.27544.1 - DOI - PMC - PubMed
• Alasadi, H. A. A. , Aziz, M. T. , Dhiya, M. , & Abdulmajed, A. (2020). A network analysis for finding the shortest path in hospital information system with GIS and GPS. Journal of Network Computing and Applications, 5(1), 10–23. 10.23977/jnca.2020.050103 - DOI
• Albahri, O. S. , Zaidan, A. A. , Albahri, A. S. , Zaidan, B. B. , Abdulkareem, K. H. , Al‐qaysi, Z. T. , … Rashid, N. A. (2020). Systematic review of artificial intelligence techniques in the detection and classification of COVID‐19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects. Journal of Infection and Public Health, 13(10), 1381–1396. 10.1016/j.jiph.2020.06.028 - DOI - PMC - PubMed

Publication types

• Search in MeSH

Related information

Linkout - more resources, full text sources.

• Europe PubMed Central
• PubMed Central
• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

remote sensing and gis Recently Published Documents

Total documents.

• Latest Documents
• Most Cited Documents
• Contributed Authors
• Related Sources
• Related Keywords

Expected spatio-temporal variation of groundwater deficit by integrating groundwater modeling, remote sensing, and GIS techniques

Relative shannon’s entropy approach for quantifying urban growth using remote sensing and gis: a case study of cuttack city, odisha, india, review of conceptual models of estimating the spatio-temporal variations of water depth using remote sensing and gis for the management of dams and reservoirs, temporal assessment of sedimentation in siruvani reservoir using remote sensing and gis, evaluating the groundwater potential of wadi al-jizi, sultanate of oman, by integrating remote sensing and gis techniques, diachronic study of land cover of the medjerda watershed and estimation of rusle-c factor using ndvi-based equation, remote sensing, and gis, slum categorization for efficient development plan—a case study of udhampur city, jammu and kashmir using remote sensing and gis, morphometric analysis of damodar river sub-watershed, jharkhand, india, using remote sensing and gis techniques, land use/land cover change detection and validation of swat model on vishow sub-basin using remote sensing and gis techniques, zoning groundwater potential recharge using remote sensing and gis technique in the red river delta plain.

Abstract The Red River delta plain is the second largest delta in Vietnam and is located in the North of the country with an area of 14,860 km2 and residing more than 22.5 million inhabitants. Groundwater is mainly exploited in Quaternary sedimentary aquifers with a total discharge of about 3 million m3/day. Some localities have shown signs of over-exploitation such as in Hanoi and in Nam Dinh, which may lead to related problems such as depletion, subsidence, saltwater intrusion, and water pollution. In order to be able to sustainably exploit groundwater, the groundwater potential recharge needs to be estimated. There have been many studies using different methods to estimate the groundwater recharge and to zone potential recharge. In the study area, there are several studies for groundwater recharge, but some are still uncertain because of using indirect methods, some are locally estimated in specific areas. Therefore, the objective of this study is to apply remote sensing and GIS to zone the groundwater potential recharge and its verification by using radioactive isotope 3H analysis in the Red River delta plain. Various types of satellite images have been used and interpreted to detect the different thematic layers which concern the groundwater potential recharge. GIS has been applied as a platform for analysis and integration of thematic layers for zonation, finally. Field trip and water sampling for chemical and radioactive 3H analysis were also conducted. Zones with low, moderate, and high groundwater potential recharge have been delineated with good agreement from the direct estimation of groundwater recharge by radioactive isotopes 3H.

Export Citation Format

Share document.

• Brandeis Library
• Research Guides

Health Data

• Getting Started
• What is Geospatial Data?
• Administrative Boundaries
• Archaeology & Classics Data
• Census & Demographic Data
• Elevation Data
• Energy, Environment & Climate Data
• Humanitarian Data
• Transportation & Infrastructure Data
• Aerial Photos & Satellite Imagery Guide >>
• European Environment and Epidemiology (E3) Network Geoportal Geospatial data on infectious disease epidemiology in Europe.
• Malaria Atlas Project Global database on malaria risk and intervention coverage produced by a global network of researchers with expertise in public health, mathematics, geography, epidemiology, etc.
• U.S. AID DHS Program: Spatial Data Repository Provides geographically-linked health and demographic data from the DHS Program and the U.S. Census Bureau. Includes data on survey boundaries, modeled surfaces, indicators, population estimates, and geospatial covariates.

U.S. Health Data

• 500 Cities & PLACES Data Portal (CDC) Includes city- and census tract-level small area estimates for chronic disease risk factors, health outcomes, and clinical preventive service use for the largest 500 cities in the United States. Data sets include GIS-friendly formats.
• CDC GIS Resources Compilation of some publicly available, federally produced, geospatial datasets and resources. Follow the links provided to find resources available for download, covering a variety of geospatial and health related topics.
• Dartmouth Atlas of Health Care Focused on Medicare, find data by hospital, region and medical issues.
• National Cancer Institute: GIS for Cancer Control Includes resources for mapping and downloading geographically-based cancer-related information.
• United States Diabetes Surveillance System (CDC) Access data on diabetes prevalence and incidents, social determinants of health, preventative care practices, and more.

Food Access and Food Security Data

Food access and food security data sources are listed under humanitarian data .

More Library Guides

Make sure to check out the research guides our librarians have created for different programs on campus. These guides will include additional helpful data and statistical sources that you can integrate into your GIS projects.

• Global Health Policy and Management (MS GHPM) by Maric Kramer Last Updated May 17, 2024 72 views this year
• << Previous: Energy, Environment & Climate Data
• Next: Humanitarian Data >>
• Last Updated: Jun 6, 2024 12:51 PM
• URL: https://guides.library.brandeis.edu/GISdata

Advances in GIS and Remote Sensing the Landscape Pattern of Land Cover on Urban Climate and Urban Ecology

Loading... Editorial 01 November 2023 Editorial: Advances in GIS and remote sensing the landscape pattern of land cover on urban climate and urban ecology Pedzisai Kowe , Cletah Shoko  and  Steven Jerie 860 views 0 citations

Original Research 25 August 2023 Study on the spatial variability of thermal landscape in Xi’an based on OSM road network and POI data Jiang Wu ,  2 more  and  Qun Hui 1,784 views 1 citations

Original Research 28 July 2023 The evolution of spatiotemporal patterns and influencing factors of high-level tourist attractions in the Yellow River Basin Rentian Shu ,  2 more  and  Xiangdan Kong 1,112 views 0 citations

Original Research 15 June 2023 Relationships between microplastic pollution and land use in the Chongqing section of the Yangtze River Sheng Ye  and  Desheng Pei 2,027 views 3 citations

Original Research 11 May 2023 Landscape ecological risk assessment and influencing factor analysis of basins in suburban areas of large cities – A case study of the Fuchunjiang River Basin, China Xiaomeng Cheng ,  5 more  and  Bin Xu 1,557 views 2 citations

Original Research 05 May 2023 Large-scale measurement of urban streets’ space health based on the spatial disorder theory—A case study on the old urban area of Daoli District of Harbin City Ting Wan  and  Mingxue Wang 1,432 views 1 citations

Loading... Original Research 28 March 2023 Evaluating land use/cover change associations with urban surface temperature via machine learning and spatial modeling: Past trends and future simulations in Dera Ghazi Khan, Pakistan Muhammad Sajid Mehmood ,  5 more  and  Qin Yaochen 3,672 views 2 citations

Loading... Original Research 21 March 2023 Urban expansion impacts on agricultural land and thermal environment in Larkana, Pakistan Ghani Rahman ,  2 more  and  Nadhir Al Ansari 4,234 views 7 citations

Loading... Original Research 03 March 2023 GIS integrated RUSLE model-based soil loss estimation and watershed prioritization for land and water conservation aspects Mahesh Chand Singh ,  4 more  and  Anurag Malik 2,641 views 15 citations

Loading... Original Research 19 January 2023 Characterizing urban growth and land surface temperature in the western himalayan cities of India using remote sensing and spatial metrics Rajman Gupta ,  2 more  and  Rajendra Kr Joshi 3,706 views 4 citations

Loading... Original Research 28 November 2022 Landscape ecological risk assessment and driving mechanism of coastal estuarine tidal flats—A case study of the liaohe estuary wetlands Haifu Li ,  5 more  and  Yunlong Zheng 1,985 views 7 citations

Discover your power with ArcGIS

Capabilities

ArcGIS offers unique capabilities and flexible licensing for applying location-based analytics to your business practices. Gain greater insights using contextual tools to visualize and analyze your data. Collaborate and share via maps, apps, dashboards and reports.

Spatial Analysis & Data Science

Connect the seemingly disconnected with the most comprehensive set of analytical methods and spatial algorithms available. Use location as the connective thread to uncover hidden patterns, improve predictive modeling, and create a competitive edge. Leverage the power of spatial analysis and data science on demand and at scale.

Field Operations

Location is at the heart of field activities. Focused ArcGIS applications can be used stand-alone or in combination to support field workflows and enable office and field personnel to work in unison, using the same authoritative data.

Maps help you spot spatial patterns in your data so you can make better decisions and take action. Maps also break down barriers and facilitate collaboration. ArcGIS gives you the ability to create, use, and share maps on any device.

3D GIS brings real-world context to your maps and data. Instantly transform your data into smart 3D models and visualizations that help you analyze and solve problems and share ideas and concepts with your team and customer.

Imagery & Remote Sensing

ArcGIS gives you everything you need to manage and extract answers from imagery and remotely sensed data. It includes imagery tools and workflows for visualization and analysis, and access to the world’s largest imagery collection.

Data Collection & Management

With ArcGIS, you can easily collect, crowdsource, store, access, and share your data efficiently and securely. You can integrate data stored in your business systems and geo-enable any data from any source.

Port of Rotterdam

The Port Authority in Rotterdam uses ArcGIS to keep operations running smoothly and efficiently

Seneca Resources

Seneca Resources in Pittsburgh is using ArcGIS to enable vital business process workflows.

City of Seattle

Seattle Police and Fire Departments use ArcGIS as a common operating picture for public safety.

Walgreens uses ArcGIS in a strategic geo-centric approach to provide better customer service.

What is included:

ArcGIS is your first step toward better, smarter decision making and a more efficient organization. Just about every problem and situation has a location aspect. Unlock the power of location with one of the best technology investments you can make.

Data and maps

Developer tools, esri user conference, flexible deployment.

Bring ArcGIS into your organization quickly and easily. Esri offers flexible deployment options to meet your business requirements.

Esri's cloud infrastructure

Available 24/7 and highly scalable, ArcGIS software-as-a-service (SaaS) make managing IT infrastructure easy. Just sign in and start working.

Your cloud infrastructure

ArcGIS runs in your own infrastructure, secure on your local network or on cloud platforms such as AWS and Microsoft Azure. Deploy on a single machine or a distributed system.

Published: 27 October 2023

Cybersecurity refers to any technology, measure or practice for preventing cyberattacks or mitigating their impact. 

Cybersecurity aims to protect individuals’ and organizations’ systems, applications, computing devices, sensitive data and financial assets against computer viruses, sophisticated and costly ransomware attacks, and more.

Cyberattacks have the power to disrupt, damage or destroy businesses, and the cost to victims keeps rising. For example, according to IBM's Cost of a Data Breach 2023 report, 

• The average cost of a data breach in 2023 was USD 4.45 million, up 15% over the last three years;
• The average cost of a ransomware-related data breach in 2023 was even higher, at USD 5.13 million. This number does not include the cost of the ransom payment, which averaged an extra USD 1,542,333, up 89% from the previous year. 

By one estimate, cybercrime might cost the world economy  USD 10.5 trillion per year by 2025  (link resides outside ibm.com). 1

The expanding information technology (IT) trends of the past few years include:

• a rise in cloud computing adoption,
• network complexity,
• remote work and work from home,
• bring your own device (BYOD) programs,
• and connected devices and sensors in everything from doorbells to cars to assembly lines.

All these trends create tremendous business advantages and human progress, but also provide exponentially more opportunities for cybercriminals to attack.

Not surprisingly, a recent study found that the global cybersecurity worker gap—the gap between existing cybersecurity workers and cybersecurity jobs that need to be filled—was 3.4 million workers worldwide. 2 Resource-strained security teams are focusing on developing comprehensive cybersecurity strategies that use advanced analytics, artificial intelligence and automation to fight cyberthreats more effectively and minimize the impact of cyberattacks.

The X-Force Threat Intelligence Index offers new insights into top threats to help you prepare and respond faster to cyberattacks, extortion and more.

Register for the Cost of a Data Breach report

A strong cybersecurity strategy protects all relevant IT infrastructure layers or domains against cyberthreats and cybercrime.

Critical infrastructure security protects the computer systems, applications, networks, data and digital assets that a society depends on for national security, economic health and public safety. In the United States, the National Institute of Standards and Technology (NIST) developed a cybersecurity framework to help IT providers in this area. The US Department of Homeland Security’ Cybersecurity and Infrastructure Security Agency (CISA) provides extra guidance.

Network security prevents unauthorized access to network resources, and detects and stops cyberattacks and network security breaches in progress. At the same time, network security helps ensure that authorized users have secure and timely access to the network resources they need.

Endpoints—servers, desktops, laptops, mobile devices—remain the primary entry point for cyberattacks. Endpoint security protects these devices and their users against attacks, and also protects the network against adversaries who use endpoints to launch attacks.

Application security protects applications running on-premises and in the cloud, preventing unauthorized access to and use of applications and related data. It also prevents flaws or vulnerabilities in application design that hackers can use to infiltrate the network. Modern application development methods—such as  DevOps and DevSecOps —build security and security testing into the development process.

Cloud security secures an organization’s cloud-based services and assets—applications, data, storage, development tools, virtual servers and cloud infrastructure. Generally speaking, cloud security operates on the shared responsibility model where the cloud provider is responsible for securing the services that they deliver and the infrastructure that is used to deliver them. The customer is responsible for protecting their data, code and other assets they store or run in the cloud. The details vary depending on the cloud services used.

Information security (InfoSec) pertains to protection of all an organization's important information—digital files and data, paper documents, physical media, even human speech—against unauthorized access, disclosure, use or alteration. Data security, the protection of digital information, is a subset of information security and the focus of most cybersecurity-related InfoSec measures.

Mobile security encompasses various disciplines and technologies specific to smartphones and mobile devices, including mobile application management (MAM) and enterprise mobility management (EMM). More recently, mobile security is available as part of unified endpoint management (UEM) solutions that enable configuration and security management for multiple endpoints—mobile devices, desktops, laptops, and more—from a single console.

Malware—short for "malicious software"—is any software code or computer program that is written intentionally to harm a computer system or its users. Almost every modern  cyberattack  involves some type of malware.

Hackers and cybercriminals create and use malware to gain unauthorized access to computer systems and sensitive data, hijack computer systems and operate them remotely, disrupt or damage computer systems, or hold data or systems hostage for large sums of money (see Ransomware).

Ransomware is a type of  malware  that encrypts a victim’s data or device and threatens to keep it encrypted—or worse—unless the victim pays a ransom to the attacker. According to the  IBM Security X-Force Threat Intelligence Index 2023 , ransomware attacks represented 17 percent of all  cyberattacks  in 2022.

“Or worse” is what distinguishes today's ransomware from its predecessors. The earliest ransomware attacks demanded a single ransom in exchange for the encryption key. Today, most ransomware attacks are double extortion attacks, demanding a second ransom to prevent sharing or publication of the victims data. Some are triple extortion attacks that threaten to launch a distributed denial of service attack if ransoms aren’t paid.

Phishing attacks are email, text or voice messages that trick users into downloading malware, sharing sensitive information or sending funds to the wrong people. Most users are familiar with bulk phishing scams—mass-mailed fraudulent messages that appear to be from a large and trusted brand, asking recipients to reset their passwords or reenter credit card information. But more sophisticated phishing scams, such as spear phishing and business email compromise (BEC) , target specific individuals or groups to steal especially valuable data or large sums of money.

Phishing is just one type of social engineering —a class of ‘human hacking’ tactics and attacks that use psychological manipulation to tempt or pressure people into taking unwise actions.

Insider threats are threats that originate with authorized users—employees, contractors, business partners—who intentionally or accidentally misuse their legitimate access, or have their accounts hijacked by cybercriminals. Insider threats can be harder to detect than external threats because they have the earmarks of authorized activity, and are invisible to antivirus software, firewalls and other security solutions that block external attacks.

One of the more persistent cybersecurity myths is that all cybercrime comes from external threats. In fact, according to a recent study, 44% of insider threats are caused by malicious actors, and the average cost per incident for malicious insider incidents in 2022 was USD 648,062. 3 Another study found that while the average external threat compromises about 200 million records, incidents involving an inside threat actor resulted in exposure of one billion records or more. 4

A DDoS attack attempts to crash a server, website or network by overloading it with traffic, usually from a botnet—a network of multiple distributed systems that a cybercriminal hijacks by using malware and remote-controlled operations.

The global volume of DDoS attacks spiked during the COVID-19 pandemic. Increasingly, attackers are combining DDoS attacks with ransomware attacks, or simply threatening to launch DDoS attacks unless the target pays a ransom.

Despite an ever-increasing volume of cybersecurity incidents worldwide and ever-increasing volumes of learnings that are gleaned from them, some dangerous misconceptions persist.

Strong passwords alone are adequate protection . Strong passwords make a difference. For example, a 12-character password takes 62 trillion times longer to crack than a 6-character password. But because cybercriminals can steal passwords (or pay disgruntled employees or other insiders to steal them), they can’t be an organization’s or individual’s only security measure.  

The major cybersecurity risks are well known . In fact, the risk surface is constantly expanding. Thousands of new vulnerabilities are reported in old and new applications and devices every year. Opportunities for human error—specifically by negligent employees or contractors who unintentionally cause a data breach—keep increasing.  

All cyberattack vectors are contained . Cybercriminals are finding new attack vectors all the time—including Linux systems, operational technology (OT), Internet of Things (IoT) devices and cloud environments.  

‘My industry is safe.’ Every industry has its share of cybersecurity risks, with cyber adversaries exploiting the necessities of communication networks within almost every government and private-sector organization. For example, ransomware attacks are targeting more sectors than ever, including local governments, non-profits and healthcare providers. Threats on supply chains, ".gov" websites, and critical infrastructure have also increased.  

Cybercriminals don’t attack small businesses . Yes, they do. For example, in 2021, 82 percent of ransomware attacks targeted companies with fewer than 1,000 employees; 37 percent of companies attacked with ransomware had fewer than 100 employees. 5

The following best practices and technologies can help your organization implement strong cybersecurity that reduces your vulnerability to cyberattacks and protects your critical information systems without intruding on the user or customer experience.

Security awareness training helps users understand how seemingly harmless actions—from using the same simple password for multiple log-ins, to oversharing on social media—increases their own or their organization’s risk of attack. Security awareness training combined with thought-out data security  policies can help employees protect sensitive personal and organizational data. It can also help them recognize and avoid phishing and malware attacks.

Identity and access management (IAM) defines the roles and access privileges for each user, and the conditions under which they are granted or denied their privileges. IAM technologies include  multi-factor authentication , which requires at least one credential in addition to a username and password, and adaptive authentication, which requires more credentials depending on context. 

Attack surface management (ASM) is the continuous discovery, analysis, remediation and monitoring of the cybersecurity vulnerabilities and potential attack vectors that make up an organization’s attack surface . Unlike other cyberdefense disciplines, ASM is conducted entirely from a hacker’s perspective, rather than the perspective of the defender. It identifies targets and assesses risks based on the opportunities they present to a malicious attacker.

Organizations rely on analytics- and AI-driven technologies to identify and respond to potential or actual attacks in progress because it's impossible to stop all cyberattacks. These technologies can include (but are not limited to) security information and event management (SIEM) , security orchestration, automation and response (SOAR) , and endpoint detection and response (EDR) . Typically, these technologies are used as part of a formal incident response plan.

Disaster recovery capabilities often play a key role in maintaining business continuity in the event of a cyberattack. For example, the ability to fail over to a backup that is hosted in a remote location can enable a business to resume operations quickly following a ransomware attack (and sometimes without paying a ransom).

Outsmart attacks with a connected, modernized security suite. The QRadar portfolio is embedded with enterprise-grade AI and offers integrated products for endpoint security, log management, SIEM and SOAR—all with a common user interface, shared insights and connected workflows.

Proactive threat hunting, continuous monitoring and a deep investigation of threats are just a few of the priorities facing an already busy IT department. Having a trusted incident response team on standby can reduce your response time, minimize the impact of a cyberattack, and help you recover faster.

AI-driven unified endpoint management (UEM) protects your devices, apps, content and data. This protection means you can rapidly scale your remote workforce and bring-your-own-device (BYOD) initiatives while building a zero trust security strategy. 

Implemented on premises or in a hybrid cloud, IBM data security solutions help you investigate and remediate cyberthreats, enforce real-time controls and manage regulatory compliance.

Proactively protect your organization’s primary and secondary storage systems against ransomware, human error, natural disasters, sabotage, hardware failures and other data loss risks.

Be better prepared for breaches by understanding their causes and the factors that increase or reduce costs. Learn from the experiences of more than 550 organizations that were hit by a data breach.

SIEM (security information and event management) is software that helps organizations recognize and address potential security threats and vulnerabilities before they can disrupt business operations.

Know the threat to beat the threat—get actionable insights that help you understand how threat actors are waging attacks, and how to proactively protect your organization.

Understand your cybersecurity landscape and prioritize initiatives together with senior IBM security architects and consultants in a no-cost, virtual or in-person, 3-hour design thinking session.

Threat management is a process used by cybersecurity professionals to prevent cyberattacks, detect cyber threats and respond to security incidents.

Find insights for rethinking your ransomware defenses and building your ability to remediate an evolving ransomware situation more rapidly.

Cybersecurity threats are becoming more advanced, more persistent and are demanding more effort by security analysts to sift through countless alerts and incidents. IBM Security QRadar SIEM helps you remediate threats faster while maintaining your bottom line. QRadar SIEM prioritizes high-fidelity alerts to help you catch threats that others miss.

1  Cybercrime threatens business growth. Take these steps to mitigate your risk.  (link resides outside ibm.com)

2  Bridging the 3.4 million workforce gap in cybersecurity (link resides outside ibm.com)

3  2022 Ponemon Cost of Insider Threats Global Report  (link resides outside ibm.com)

4  Verizon 2023 Data Breach Investigations Report  (link resides outside ibm.com)

5   82% of Ransomware Attacks Target Small Businesses, Report Reveals  (link resides outside ibm.com)