17 datasets found
  1. 4

    ImProDiReT Land Subsidence Household Survey

    • data.4tu.nl
    • figshare.com
    • +1more
    zip
    Updated Oct 22, 2020
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    Abby Muricho Onencan (2020). ImProDiReT Land Subsidence Household Survey [Dataset]. http://doi.org/10.4121/uuid:b6dc1ed5-5543-4def-9596-ad38f5ab86a3
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    zipAvailable download formats
    Dataset updated
    Oct 22, 2020
    Dataset provided by
    4TU.ResearchData
    Authors
    Abby Muricho Onencan
    License

    Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
    License information was derived automatically

    Area covered
    Solotvyno municipality
    Description

    This file contains 1424 household responses to two scales. First, the demographic scale results for the Solotvyno household survey. The demographic household survey contained the following variables:1) Age; 2)Sex; 3) Family type; 4) Religion; 5) Ethnic Origin; 6) Education; 7) Housing; 8) Annual income (in USD); 9) Years of stay; 10) Housing Type; 11) Living Situation and 12) disability.

    Second, the results from the Solotvyno Municipality Land Subsidence scale. The first land subsidence risk evaluation sub-scale seeks to answer the following two questions:1) Do you have the following ready in case the land subsides? Please check to each item either 'yes','unsure' or 'no.' and 2) Please rate the difficulty of preparing for each item, by your household, on a five-point scale ranging from 'not difficult at all' to 'extremely difficult.' The second land subsidence sub-scale seeks to answer the following two questions: 1) Please indicate the extent of disaster risk preparedness by your household to each item, by checking either 'yes', 'unsure' or 'no.' 2) Please rate the difficulty of preparing for each item, by your household, on a five-point scale ranging from 'not difficult at all' to 'extremely difficult.'

  2. g

    Property Subsidence Assessment dataset 2022

    • gimi9.com
    • hosted-metadata.bgs.ac.uk
    • +2more
    Updated Jul 9, 2025
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    (2025). Property Subsidence Assessment dataset 2022 [Dataset]. https://gimi9.com/dataset/uk_property-subsidence-assessment-dataset-2022/
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    Dataset updated
    Jul 9, 2025
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    The BGS Property Subsidence Assessment (PSA) dataset provides insurers and homeowners access to a better understanding of the shrink-swell hazard at both the individual property and/or postcode level for England and Wales. It builds upon the GeoSure shrink-swell data by mapping the hazard to the individual building polygon and considering the other susceptibility factors of building type, foundation depth, and drainage and tree proximity. The user receives GIS building polygons with an overall susceptibility to subsidence score between 1-100. Scores are also classified from non-plastic to very high. Each building polygon is also scored from 1-10 for each subsidence factor (geology, foundation, drainage, building type, building storey and tree proximity). Postcode data is also available as a table showing the ‘average’ PSA score for all buildings within the postcode. The identification of shrink-swell related subsidence prone areas, alongside the inclusion of potential sources to exacerbate this phenomena, can better inform insurers and homeowners and form the basis to make decisions concerning prevention and remediation. The product enhances geological information obtained from GIP and GeoSure via the inclusion of the crucial shrink-swell susceptibility factors (proximity to trees and foundation depth). This therefore allows the derivation of a risk element for the housing stock at Building level, which is then generalised to Postcode level.

  3. v

    Dataset associated with: Land subsidence risk to infrastructure in US...

    • data.lib.vt.edu
    tiff
    Updated May 9, 2025
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    Leonard Ohenhen; Manoochehr Shirzaei; Guang Zhai; Jonathan Lucy; Susanna Werth; Grace Carlson; Mohammad Khorrami; Florence Onyike; Nitheshnirmal Sadhasivam; Ashutosh Tiwari; Khosro Ghobadi Far; Sonam Futi Sherpa; Jui-Chi Lee; Sonia Zehsaz (2025). Dataset associated with: Land subsidence risk to infrastructure in US metropolises [Dataset]. http://doi.org/10.7294/27606942.v3
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    tiffAvailable download formats
    Dataset updated
    May 9, 2025
    Dataset provided by
    University Libraries, Virginia Tech
    Authors
    Leonard Ohenhen; Manoochehr Shirzaei; Guang Zhai; Jonathan Lucy; Susanna Werth; Grace Carlson; Mohammad Khorrami; Florence Onyike; Nitheshnirmal Sadhasivam; Ashutosh Tiwari; Khosro Ghobadi Far; Sonam Futi Sherpa; Jui-Chi Lee; Sonia Zehsaz
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    United States
    Description

    Vertical land motion (VLM), angular distortion, and building risks for 28 urban cities in the United States. The file also contains supplementary tables 1 to 8.Abstract: Land subsidence is a slow-moving hazard with adverse environmental and socioeconomic consequences worldwide. However, spatially dense subsidence rates to capture granular variations at high spatial density are often lacking, hindering assessment of associated infrastructure risk. We use space geodetic measurements from 2015 to 2021 to create high resolution maps of subsidence rates for 28 most populous US cities. We estimate that at least 20% of the urban area is sinking in all cities, mainly due to groundwater extraction, affecting ~34 million people. Additionally, more than 29,000 buildings are located in high and very high damage risk areas, indicating a greater likelihood of infrastructure damage. These datasets and information are crucial for developing ad hoc policies to adapt urban centers to these complex environmental challenges.

  4. a

    DP Mine Subsidence Risk Area

    • data-waikatolass.opendata.arcgis.com
    • hub.arcgis.com
    Updated Jul 27, 2020
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    WaikatoDistrictCouncil (2020). DP Mine Subsidence Risk Area [Dataset]. https://data-waikatolass.opendata.arcgis.com/items/403cdc9dc03b4f5a9fa04628e3004421
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    Dataset updated
    Jul 27, 2020
    Dataset authored and provided by
    WaikatoDistrictCouncil
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    Waikato District Council - Proposed District Plan (Stage 2 Natural Hazards), Notified 27 July 2020. This layer is a spatial representation of an overlay in the Proposed District Plan and indicates where land use will be regulated by various associated rules. It will be used as a guide in the regulatory process of implementing the Proposed District Plan and managing land use, subdivision, the environment and economy. This dataset is subject to changes undertaken through the Resource Management act. Note individual Proposed Plan rules can have different statuses, some may have current legal effect and others will not until the Proposed Plan becomes operative. This data is provided for use in the District Plan only. The Mine Subsidence Risk Area identifies land in Huntly East that is currently at risk of subsidence due to historic underground coal mining activities and the subsequent closure and refilling of the Huntly East underground mine. An assessment has been carried out to confirm the likelihood of ongoing mine subsidence and methane gas migration from mine workings to the ground surface above the Huntly East mine and the South Headings as a result of the closure of the Huntly East Mine and subsequent flooding of the underground mine workings (see Appendix 5(c) of Section 32 report Natural Hazards and Climate Change). This belongs to the series of data relating to Natural Hazards which includes the following groups - coastal erosion, coastal inundation, inland flooding, and land subsidence. This layer belongs to the land subsidence group (this is the only layer in this group).

  5. M

    Mine Subsidence Insurance Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated May 18, 2025
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    Data Insights Market (2025). Mine Subsidence Insurance Report [Dataset]. https://www.datainsightsmarket.com/reports/mine-subsidence-insurance-1935477
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    ppt, pdf, docAvailable download formats
    Dataset updated
    May 18, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global mine subsidence insurance market is experiencing robust growth, driven by increasing mining activities worldwide and stricter regulations concerning environmental protection and liability. The market's expansion is fueled by a rising awareness among mining companies of the potential financial risks associated with subsidence, including damage to property, infrastructure, and the environment. The substantial costs associated with remediation and legal liabilities following subsidence events are compelling insurers to develop more comprehensive and tailored insurance products. This demand is further amplified by the increasing complexity of mining operations, particularly in challenging geological terrains, where the risk of subsidence is magnified. The market is segmented by application (surface and underground mining) and purchase type (personal and collective buying), with significant growth anticipated in both segments. While underground mining currently represents a larger market share due to the higher inherent risks, surface mining insurance is growing rapidly as mining operations expand into more sensitive areas. Collective buying schemes, particularly prevalent among smaller mining operations, offer significant cost advantages while providing crucial risk mitigation. Key players in the market are strategically expanding their product offerings and forging partnerships to capture this growing demand. North America and Europe currently dominate the market share, but Asia-Pacific is projected to witness substantial growth driven by the expansion of mining activities in countries like China and India. The market's growth, however, is tempered by certain restraints. The inherent complexities in assessing and quantifying subsidence risk can make underwriting challenging and potentially lead to higher premiums. Moreover, fluctuations in commodity prices and overall economic conditions can impact the demand for mine subsidence insurance. Despite these challenges, the long-term outlook remains positive, with consistent growth projected throughout the forecast period. The ongoing development of advanced risk assessment technologies, coupled with innovative insurance products, will play a crucial role in shaping the future of this market. Strategic partnerships between insurers and mining companies, aimed at facilitating risk mitigation and early detection of potential subsidence, will be key to sustaining this growth trajectory. The market is poised for further consolidation as larger insurance companies expand their presence and smaller players seek strategic alliances or acquisitions.

  6. D

    Mine Subsidence Insurance Market Report | Global Forecast From 2025 To 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Oct 16, 2024
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    Dataintelo (2024). Mine Subsidence Insurance Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/mine-subsidence-insurance-market
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    csv, pptx, pdfAvailable download formats
    Dataset updated
    Oct 16, 2024
    Authors
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Mine Subsidence Insurance Market Outlook



    The global Mine Subsidence Insurance market size is projected to grow significantly from $1.2 billion in 2023 to $2.5 billion by 2032, representing a compound annual growth rate (CAGR) of 8.2%. The primary growth factor fueling this market is the increasing awareness of the risks associated with mine subsidence, coupled with the growing number of mining activities worldwide.



    One of the major growth drivers of the Mine Subsidence Insurance market is the heightened awareness and understanding of the risks posed by mine subsidence. As mining activities increase globally, the potential for subsidence incidents also rises. This has led to a greater emphasis on preventive measures and mitigation strategies, thereby driving the demand for insurance products that can cover the financial repercussions of such events. Governments and private entities alike are recognizing the importance of protecting assets and infrastructure from potential subsidence-related damages.



    Moreover, technological advancements in risk assessment and predictive modeling are significantly contributing to the market's growth. These technologies enable insurers to better understand and evaluate the risks associated with mine subsidence, leading to more accurate pricing of insurance policies. Enhanced risk assessment capabilities help in designing more tailored and comprehensive insurance products, which in turn attract more customers. In addition, the integration of Geographic Information Systems (GIS) and remote sensing technologies has improved the monitoring and management of mine subsidence risks, further propelling market growth.



    The rising trend of urbanization and industrialization is another critical factor driving the Mine Subsidence Insurance market. As more residential, commercial, and industrial structures are built near mining areas, the need for effective insurance solutions becomes imperative. Urban expansion often encroaches on previously abandoned mining sites, which are prone to subsidence. Consequently, property owners and businesses are increasingly investing in mine subsidence insurance to safeguard their investments and ensure financial stability in the event of subsidence-related damages.



    Regional analysis reveals that the market exhibits varied growth dynamics across different geographies. North America holds a substantial share of the market due to its extensive mining activities and well-established insurance infrastructure. Europe also demonstrates significant growth, driven by stringent regulations and high awareness levels. The Asia Pacific region is anticipated to witness the highest CAGR, attributed to rapid industrialization and increasing mining activities in countries like China and India. Latin America and the Middle East & Africa, while smaller in market share, are expected to show steady growth due to emerging mining sectors and rising awareness about mine subsidence risks.



    Coverage Type Analysis



    The Mine Subsidence Insurance market can be segmented by coverage type into Residential, Commercial, and Industrial. Each of these segments caters to different types of properties and stakeholders, and their growth dynamics vary accordingly. The Residential segment, which covers individual homeowners, is experiencing robust growth due to increasing awareness among property owners about the risks of mine subsidence. Homeowners are becoming more proactive in protecting their most significant asset — their home — from potential subsidence-related damages. This segment is also driven by government initiatives and regulations mandating mine subsidence insurance in certain high-risk areas.



    The Commercial segment encompasses businesses and commercial properties, which are often located in or near mining areas. The growth of this segment is propelled by the burgeoning commercial real estate market and the increasing number of businesses operating in proximity to mining sites. Commercial property owners are investing in mine subsidence insurance to safeguard their investments and ensure business continuity in the event of subsidence-related incidents. This segment is characterized by high-value policies and comprehensive coverage options, reflecting the significant financial stakes involved.



    The Industrial segment includes large-scale industrial facilities such as factories, warehouses, and manufacturing plants. This segment is expected to witness considerable growth, driven by the expansion of industrial activities in regions with active or historical mining operations. Industrial prop

  7. 4

    Community-based Risk Evaluation Assessment (CREA)

    • data.4tu.nl
    zip
    Updated Oct 22, 2020
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    Abby Muricho Onencan (2020). Community-based Risk Evaluation Assessment (CREA) [Dataset]. http://doi.org/10.4121/uuid:69a64473-4d22-401f-9ea7-75bca50bddd4
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    zipAvailable download formats
    Dataset updated
    Oct 22, 2020
    Dataset provided by
    4TU.ResearchData
    Authors
    Abby Muricho Onencan
    License

    Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
    License information was derived automatically

    Area covered
    Solotvyno municipality, Ukraine
    Description

    This file contains 1424 household responses to two scales. First, the demographic scale results for the Solotvyno household survey. The demographic household survey contained the following variables:1) Age; 2)Sex; 3) Family type; 4) Religion; 5) Ethnic Origin; 6) Education; 7) Housing; 8) Annual income (in USD); 9) Years of stay; 10) Housing Type; 11) Living Situation and 12) disability.

    Second, the results from the Solotvyno Municipality Risk Evaluation scale. Risk assessment and ranking is part of WP1 work, however, the task of WP3 is to conduct an evaluation of the risk assessment and incorporate the community values.Based on the community values and the evidence gathered under WP1, the risk rating matrix can be colour-coded to visualise risk rankings and designate the high, medium and low-risk zones. This would enable the Solotvyno community at-a-glance to view which risks require to be prioritised. In addition, there is geo-referenced data collected on the street where the communities reside to map the community exposure to the land subsidence risk.

  8. f

    Table1_Property Risk Assessment for Expansive Soils in Louisiana.docx

    • frontiersin.figshare.com
    docx
    Updated Jun 4, 2023
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    Rubayet Bin Mostafiz; Carol J. Friedland; Robert V. Rohli; Nazla Bushra; Chad L. Held (2023). Table1_Property Risk Assessment for Expansive Soils in Louisiana.docx [Dataset]. http://doi.org/10.3389/fbuil.2021.754761.s001
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    docxAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    Frontiers
    Authors
    Rubayet Bin Mostafiz; Carol J. Friedland; Robert V. Rohli; Nazla Bushra; Chad L. Held
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Louisiana
    Description

    The physical properties of soil can affect the stability of construction. In particular, soil swelling potential (a term which includes swelling/shrinking) is often overlooked as a natural hazard. Similar to risk assessment for other hazards, assessing risk for soil swelling can be defined as the product of the probability of the hazard and the value of property subjected to the hazard. This research utilizes past engineering and geological assessments of soil swelling potential, along with economic data from the U.S. Census, to assess the risk for soil swelling at the census-block level in Louisiana, a U.S. state with a relatively dense population that is vulnerable to expansive soils. Results suggest that the coastal parts of the state face the highest risk, particularly in the areas of greater population concentrations, but that all developed parts of the state have some risk. The annual historical property loss, per capita property loss, and per building property loss are all concentrated in southeastern Louisiana and extreme southwestern Louisiana, but the concentration of wealth in cities increases the historical property loss in most of the urban areas. Projections of loss by 2050 show a similar pattern, but with increased per building loss in and around a swath of cities across southwestern and south-central Louisiana. These results may assist engineers, architects, and developers as they strive to enhance the resilience of buildings and infrastructure to the multitude of environmental hazards in Louisiana.

  9. g

    Deltas at Risk | gimi9.com

    • gimi9.com
    Updated Mar 23, 2025
    + more versions
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    (2025). Deltas at Risk | gimi9.com [Dataset]. https://gimi9.com/dataset/mekong_deltas-at-risk/
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    Dataset updated
    Mar 23, 2025
    Description

    The Delta at Risk study is a global, systematic assessment of how delta risk is increasing due to sea-level rise and human drivers of delta land subsidence. Risk is expected to increase greatly due to relative sea-level rise in deltas. Relative sea-level rise (the combination of offshore sea-level rise and coastal land subsidence) will have a large impact on coastal communities due to coastal flooding. Wealthy countries however have a greater social capacity and wealth to mitigate against such hazards but the economic costs is high. The study utilizes decade-to-century economic trend forecasts to estimate the varying impact on delta risk across each delta system. Efforts to address the root causes of land subsidence in the near-term are critical for long-term sustainability.

  10. Ground subsidence in Mekong delta, Vietnam (2018-10-26)

    • data.europa.eu
    Updated Feb 7, 2019
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    Joint Research Centre (2019). Ground subsidence in Mekong delta, Vietnam (2018-10-26) [Dataset]. https://data.europa.eu/data/datasets/0f97c8d8-6470-400b-bea3-54ea8fac4294?locale=bg
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    esri file geodatabaseAvailable download formats
    Dataset updated
    Feb 7, 2019
    Dataset authored and provided by
    Joint Research Centrehttps://joint-research-centre.ec.europa.eu/index_en
    License

    http://data.europa.eu/eli/dec/2011/833/ojhttp://data.europa.eu/eli/dec/2011/833/oj

    Area covered
    Mekong River, Vietnam, Mekong River Delta
    Description


    Activation date: 2018-10-26
    Event type: Other

    Activation reason:
    The EMSN057 service provides geospatial information facilitating assessment of drivers of ground subsidence and to supporting analysis of the relation between detected ground subsidence and land use changes in Ca Mau, Long Xuyen and Rach Gia areas in the Mekong delta, Vietnam. The primary objective of the service is provision of spatially and temporally consistent, dense and synoptic results giving insight on the distribution and variance of subsidence phenomena in space and its dynamics in time. The persistent scatterers interferometry (PSI) technique, measuring ground deformations from stacks of archive SAR imagery (Sentinel-1 and TerraSAR-X), was utilized to estimate displacements. Products should complement ground based measurements, information from previous InSAR studies and contribute to evidence-based risk assessment.Annual Ground Subsidence Displacement The raster product, with 10 x 10m resolution, shows annual ground subsidence displacements in the vertical direction. The product was interpolated from displacement values of persistent scatterer points detected by the PSI technique from a stack of archived satellite SAR images.Annual Ground Subsidence Displacement ChangeThe change product shows differences of annual ground subsidence displacements in the vertical direction. It was obtained by deduction of previous from subsequent annual versions of Displacement products.In addition, average annual subsidence displacement velocity and displacement trend were evaluated from the PSI results, as demonstrated below.

  11. T

    Land subsidence database for coastal cities in China (2015,2020)

    • data.tpdc.ac.cn
    • tpdc.ac.cn
    zip
    Updated Mar 30, 2023
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    Jiayi FANG (2023). Land subsidence database for coastal cities in China (2015,2020) [Dataset]. http://doi.org/10.1038/s41467-022-34525-w
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    zipAvailable download formats
    Dataset updated
    Mar 30, 2023
    Dataset provided by
    TPDC
    Authors
    Jiayi FANG
    Area covered
    Description

    https://doi.org/10.1038/s41467-022-34525-w is data sources. Because of a lack of detailed open ground lift data along the coastal area of China, through a systematic literature review, we got the lifting of the surface of China's coastal regions in detail and established China's coastal cities land subsidence database, including China's coastal cities sedimentation rate in the different periods and measurements. The literature mainly comes from CNKI database, Web of Science, and the grey literature, such as reporting, planning, etc. The data can be used in the study of relative sea level change, coastal extreme water level and coastal flooding risk research, and can provide the reference and basis for adaptation measures for coastal land subsidence prevention and control and planning, etc.

  12. o

    Solotvyno Community-based Risk Evaluation Approach (CREA) Hungarian...

    • explore.openaire.eu
    Updated May 12, 2020
    + more versions
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    Abby Muricho Onencan (2020). Solotvyno Community-based Risk Evaluation Approach (CREA) Hungarian Questionnaire [Dataset]. http://doi.org/10.5281/zenodo.3822448
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    Dataset updated
    May 12, 2020
    Authors
    Abby Muricho Onencan
    Area covered
    Solotvyno
    Description

    This study is part of the larger European Union ImProDiReT project. More information on this project can be found on http://www.improdiret.eu/. The purpose of the household survey was to evaluate the level of acceptance, tolerability or intolerability to the land subsidence risk in the Solotvyno municipality. The survey was conducted through the major schools in Solotvyno and coordinated by the English-speaking teachers. Other teachers and students supported the household survey data collection process in their respective homes and neighbourhoods. The household survey was conducted from 15 September 2019 to 22 December 2019. In addition, a consent form was completed. The questionnaire was translated into the three main locally spoken languages (Ukrainian, Hungarian and Romanian).

  13. T

    Risk assessment data of factors causing freezing-thawing disasters in the...

    • data.tpdc.ac.cn
    • tpdc.ac.cn
    zip
    Updated Jun 3, 2022
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    Guoming ZHANG (2022). Risk assessment data of factors causing freezing-thawing disasters in the Himalayan Region and Water tower region of Asia (2021) [Dataset]. http://doi.org/10.11888/HumanNat.tpdc.271994
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    zipAvailable download formats
    Dataset updated
    Jun 3, 2022
    Dataset provided by
    TPDC
    Authors
    Guoming ZHANG
    Area covered
    Description

    Freezing-thawing disaster is the frost heaving and thawing settling caused by the change of thermal and mechanical stability of frozen soil, as well as the geological disasters caused by it, such as frost heaving hillock, ice cone, thermal thawing slump, thermal thawing subsidence, thawing mud flow, etc. In order to reveal the regional risk characteristics of freezing-thawing disasters around The Himalayas and in Asia's water tower region, it is very important to carry out the risk assessment of the factors causing the freezing-thawing disasters around the Himalayas and Asia's water tower region.The risk assessment of the risk factors of freezing-thawing disaster is mainly based on the climate, geography, environment and other factors of the evaluation area, and the geological conditions of the area are considered as the main factors of the risk assessment, and the risk assessment of the risk factors is graded.

  14. L

    Land Displacement Monitoring Report

    • archivemarketresearch.com
    doc, pdf, ppt
    Updated Mar 8, 2025
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    Archive Market Research (2025). Land Displacement Monitoring Report [Dataset]. https://www.archivemarketresearch.com/reports/land-displacement-monitoring-54137
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    pdf, doc, pptAvailable download formats
    Dataset updated
    Mar 8, 2025
    Dataset authored and provided by
    Archive Market Research
    License

    https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global land displacement monitoring market is experiencing robust growth, projected to reach $87 million in 2025 and maintain a compound annual growth rate (CAGR) of 4.3% from 2025 to 2033. This expansion is driven by several key factors. Increasing urbanization and infrastructure development necessitate precise land displacement monitoring to minimize environmental impact and ensure efficient resource management. Furthermore, the growing adoption of advanced technologies like satellite imagery, LiDAR, and AI-powered analytics is significantly enhancing the accuracy and speed of monitoring processes, making them more accessible and cost-effective. Government regulations aimed at protecting natural habitats and mitigating the effects of climate change are also fueling market demand. The construction, mining, and scientific research sectors are significant contributors to market growth, demanding accurate and timely data on land displacement for project planning and risk assessment. Different geographic segments show varying levels of market penetration, with North America and Europe currently leading due to higher adoption rates of advanced technologies and stringent environmental regulations. However, developing economies in Asia-Pacific are emerging as significant growth opportunities, fueled by increasing infrastructure projects and economic development. The market segmentation highlights the diverse applications of land displacement monitoring. The mountain, urban and suburban, and infrastructure segments reflect the varied geographical contexts where monitoring is crucial. Applications range from government initiatives focused on land use planning and disaster management to private sector activities in construction, mining, and scientific research. Key players in this market, such as Hexagon, Synspective, and Land Portal, are continuously innovating to offer comprehensive solutions integrating advanced technologies with user-friendly interfaces. The future growth trajectory will likely be influenced by further technological advancements, expanding regulatory frameworks, and increasing awareness of environmental sustainability. The integration of IoT and cloud-based platforms will contribute to making this technology more accessible and affordable globally.

  15. GeoSure Shrink Swell Deposits

    • find.data.gov.scot
    • finddatagovscot.dtechtive.com
    • +6more
    html
    Updated Jul 8, 2020
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    British Geological Survey (2020). GeoSure Shrink Swell Deposits [Dataset]. https://find.data.gov.scot/datasets/40737
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    html(null MB)Available download formats
    Dataset updated
    Jul 8, 2020
    Dataset provided by
    British Geological Surveyhttps://www.bgs.ac.uk/
    Area covered
    Scotland
    Description

    The GeoSure data sets and reports from the British Geological Survey provide information about potential ground movement or subsidence in a helpful and user-friendly format. The reports can help inform planning decisions and indicate causes of subsidence. The methodology is based on BGS DiGMap (Digital Map) and expert knowledge of the behaviour of the formations so defined. This dataset provides an assessment of the potential for a geological deposit to shrink and swell. Many soils contain clay minerals that absorb water when wet (making them swell), and lose water as they dry (making them shrink). This shrink-swell behaviour is controlled by the type and amount of clay in the soil, and by seasonal changes in the soil moisture content (related to rainfall and local drainage). The rock formations most susceptible to shrink-swell behaviour are found mainly in the south-east of Britain. Clay rocks elsewhere in the country are older and have been hardened by burial deep in the earth and are less able to absorb water. The BGS has carried out detailed geotechnical and mineralogical investigations into rock types known to shrink, and are modelling their properties across the near surface. This research underpins guidance contained in the national GeoSure dataset, and is the basis for our responses to local authorities, companies and members of the public who require specific information on the hazard in their areas. The BGS is undertaking a wide-ranging research programme to investigate this phenomenon by identifying those areas most at risk and developing sustainable management solutions. Complete Great Britain national coverage is available. The storage formats of the data are ESRI and MapInfo but other formats can be supplied.

  16. V

    CRMP_Impacts

    • data.virginia.gov
    • opendata.winchesterva.gov
    png, xlsx
    Updated Sep 26, 2024
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    Virginia Department of Conservation and Recreation (2024). CRMP_Impacts [Dataset]. https://data.virginia.gov/dataset/crmp_impacts
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    xlsx(183826), png(613454)Available download formats
    Dataset updated
    Sep 26, 2024
    Dataset authored and provided by
    Virginia Department of Conservation and Recreation
    Description

    The purpose of this document is to provide a technical overview of the impact assessment approach and methods used to identify and evaluate strategies for coastal resilience in the Virginia Coastal Resilience Master Plan (CRMP). The impact assessment produces quantitative data that characterizes how Virginia’s people and landscape will be affected by coastal hazards, now and into the future, accounting for sea level rise (SLR). The CRMP includes eight coastal Planning District Commissions and Regional Commissions. The impact assessment incorporates the coastal flood hazard modeling from the Coastal Hazard Framework, data gathering results, and informs risk summarization and resilience project evaluation. While the study area is subject to other flood hazards such as rainfall-driven (pluvial) flooding, riverine flooding, and other geomorphic hazards such as shoreline erosion and subsidence, these processes, while drivers of risk in all, or portions of the study area, were not included in the first iteration of the Virginia CRMP. These limitations are acknowledged and accepted. Future iterations of the CRMP will expand the hazards considered, including evaluation of cascading impacts from joint occurrence of these complicated natural processes.

  17. Data from: Nairobi and Istanbul Multi-Hazard Interrelationships Database

    • zenodo.org
    • data.niaid.nih.gov
    bin, pdf
    Updated Aug 20, 2024
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    Robert Šakić Trogrlić; Robert Šakić Trogrlić; Harriet E Thompson; Harriet E Thompson; Emin Yahya Menteşe; Emin Yahya Menteşe; Ekbal Hussain; Ekbal Hussain; Joel C Gill; Joel C Gill; Faith E Taylor; Faith E Taylor; Emmah Mwangi; Emmah Mwangi; Emine Öner; Vera G Bukachi; Vera G Bukachi; Bruce D Malamud; Bruce D Malamud; Emine Öner (2024). Nairobi and Istanbul Multi-Hazard Interrelationships Database [Dataset]. http://doi.org/10.5281/zenodo.13220740
    Explore at:
    pdf, binAvailable download formats
    Dataset updated
    Aug 20, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Robert Šakić Trogrlić; Robert Šakić Trogrlić; Harriet E Thompson; Harriet E Thompson; Emin Yahya Menteşe; Emin Yahya Menteşe; Ekbal Hussain; Ekbal Hussain; Joel C Gill; Joel C Gill; Faith E Taylor; Faith E Taylor; Emmah Mwangi; Emmah Mwangi; Emine Öner; Vera G Bukachi; Vera G Bukachi; Bruce D Malamud; Bruce D Malamud; Emine Öner
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Aug 20, 2024
    Area covered
    Nairobi, Istanbul
    Description

    Nairobi and Istanbul Multi-Hazard Interrelationships Database

    10.5281/zenodo.13220740

    This Nairobi and Istanbul Multi-Hazard Interrelationships Database uses a critical review of 135 sources (academic and grey literature, databases, online and social media), to identify the breadth of natural hazard types that might influence Nairobi (19 possible natural hazard types) and Istanbul (23 hazard types). We further identified hazard interrelationship pairs (e.g., an earthquake triggering landslides) in Nairobi (88 potential hazard interrelationship pairs) and Istanbul (105 hazard pairs) out of a possible 576 interrelationships. This extensive Excel (140 kb) database accompanies the paper Šakić Trogrlić et al. (2024).

    The Nairobi and Istanbul Multi-Hazard Interrelationships Database consists of the following eight tabs (in brackets the number of rows [R] × columns [C] of information):

    • Excel Tab A. Single Hazard Evidence Nairobi (87R×16C)
    • Excel Tab B. Single Hazard Evidence Istanbul (68R×11C)
    • Excel Tab C. Hazard Interrelationships Nairobi (118R×14C)
    • Excel Tab D. Hazard Interrelationships Istanbul (122R×13C)
    • Excel Tab E. Definitions (Evidence Types) (7 definitions)
    • Excel Tab F. Definitions (Hazards) (31R×5C)
    • Excel Tab G. Definitions (Hazard Relations) (3 definitions)
    • Excel Tab H. References

    For Nairobi (Tab A) and Istanbul (Tab B), each row in the database presents a source of evidence of a single hazard type influencing Nairobi or Istanbul. We compiled multiple evidence sources for many of the hazard types, each on its own row. In columns, we describe the evidence through various qualifiers, including the following:

    • identifying the hazard type (24 possible hazard types)
    • source information and URL link
    • source content
    • hazard interrelationships
    • anthropogenic influences
    • video evidence
    • source reflections

    For Nairobi (Tab C) and Istanbul (Tab D), each row in the databases presents a source of evidence of a hazard interrelationship in Nairobi or Istanbul. In columns, we describe the evidence through various qualifiers, including the following:

    • primary hazard (24 hazard types)
    • secondary hazard (where applicable, the same 24 hazards as for the primary hazard)
    • the generic description of hazard interrelationship mechanisms
    • whether the relationship is triggered or increased probability or both
    • source information and URL link
    • source content (e.g., interrelationship type, description, and hazard sequence)

    The reader is referred to Šakić Trogrlić et al. (2024) for a detailed description of the methodology by which this database was constructed.

    References

    Šakić Trogrlić, R., Thompson, H. E., Yahya Menteşe, E., Hussain, E., Gill, J. C., Taylor, F. E., Mwangi, E., Öner, E., Bukachi, V. G., & Malamud, B. D. (2024). Multi-hazard interrelationships and risk scenarios in urban areas: A case of Nairobi and Istanbul. Earth’s Future. 12, e2023EF004413. https://doi.org/10.1029/2023EF004413

  18. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Abby Muricho Onencan (2020). ImProDiReT Land Subsidence Household Survey [Dataset]. http://doi.org/10.4121/uuid:b6dc1ed5-5543-4def-9596-ad38f5ab86a3

ImProDiReT Land Subsidence Household Survey

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zipAvailable download formats
Dataset updated
Oct 22, 2020
Dataset provided by
4TU.ResearchData
Authors
Abby Muricho Onencan
License

Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
License information was derived automatically

Area covered
Solotvyno municipality
Description

This file contains 1424 household responses to two scales. First, the demographic scale results for the Solotvyno household survey. The demographic household survey contained the following variables:1) Age; 2)Sex; 3) Family type; 4) Religion; 5) Ethnic Origin; 6) Education; 7) Housing; 8) Annual income (in USD); 9) Years of stay; 10) Housing Type; 11) Living Situation and 12) disability.

Second, the results from the Solotvyno Municipality Land Subsidence scale. The first land subsidence risk evaluation sub-scale seeks to answer the following two questions:1) Do you have the following ready in case the land subsides? Please check to each item either 'yes','unsure' or 'no.' and 2) Please rate the difficulty of preparing for each item, by your household, on a five-point scale ranging from 'not difficult at all' to 'extremely difficult.' The second land subsidence sub-scale seeks to answer the following two questions: 1) Please indicate the extent of disaster risk preparedness by your household to each item, by checking either 'yes', 'unsure' or 'no.' 2) Please rate the difficulty of preparing for each item, by your household, on a five-point scale ranging from 'not difficult at all' to 'extremely difficult.'

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