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4 results for “hazard identification”
Supplementary Data for "Identification of Neighborhood Hotspots via the Cumulative Hazard Index: Results from a Community-Partnered Low-cost Sensor Deployment"
<p>These are the underlying data sets needed to build the kriging maps and calculate dissemination block cumulative hazard indices described in the paper. There are three data sets:</p> <ol> <li><strong>"Sampling location names and coordinates.csv"</strong>: locations and IDs of the low-cost sensors and the regulatory monitoring stations used in this work.<strong> [NOTE: </strong>latitudes and longitudes for the sensor deployments have been intentionally rounded to protect the location of volunteer sensor hosts.]</li> <li><strong>"Dissemination Block Populations.csv"</strong>: These are the relevant dissemination blocks in the study domain and their associated populations. This information was originally extracted from: https://censusmapper.ca/#13/49.2430/-123.1252</li> <li><strong>"Daily average concentrations by site and pollutant.csv"</strong>: This contains the PM2.5, NO2 and O3 daily averages for the entire study period across all low-cost sensor sites and regulatory monitoring stations. Refer to "Sampling location names and coordinates.csv" to parse the labels in this data set.</li> </ol> <p>There is also a sample code in Python to construct the kriging maps provided in 2 formats. <strong>[NOTE: </strong>we have intentionally excluded uploading the exact data sets imported by this code; our original data contains exact locations of sensor host volunteers and thus cannot be shared.]</p> <ol> <li><strong>"Jain et al - GeoHealth - Kriging Script.ipynb"</strong>: A Jupyter notebook script to import the data, build kriging maps, calculate CHIs, and export the data.</li> <li><strong>" Jain et al - GeoHealth - Kriging Script.pdf"</strong>: A PDF export of the Jupyter notebook so that you can read the Python scripts even if you are not a Jupyter notebooks user.</li> </ol>
Wind and Precipitation Extremes in Great Britain (1979-2019) to apply the methodology for Spatiotemporal Identification of Compound Hazards
<p>The data used in this study is extracted from ERA5. ERA5 is a climate reanalysis product which was released in 2019 by ECMWF and benefits from the latest improvements in the field (Hersbach et al., 2020). ERA5 data (ECMWF, 2020) is available 1979 to present (we use up to September 2019), with a spatial resolution of 0.25deg x 0.25deg and an hourly temporal resolution. The data resolves the atmosphere using 137 levels from the surface up to a height of 80 km (ECMWF, 2020). ERA5 data are generated with a short forecast of 18 h twice a day (06:00 and 18:00 UTC) and assimilated with observed data (ECMWF, 2020). more information about ERA5 can be found <a href="https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation">here</a>.</p> <p>The two following variables are extracted from the product:</p> <ul> <li> <p>Extreme precipitation (p): accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth’s in one hour (mm). This value is averaged over a grid cell.</p> </li> <li> <p>Extreme wind (w): hourly maximum wind gust at a height of 10 m above the surface of the Earth (m s-1). The WMO (2021) defines a wind gust as the maximum of the wind averaged over 3 s intervals. As this duration is shorter than a model time step, this value is deduced from other parameters such as surface stress, surface friction, wind shear and stability. This value is averaged over a grid cell.</p> </li> </ul> <p>Importation of the raw data</p> <p>Input data is divided into 4 files for each variables representing 4 periods:</p> <ol> <li>1979-1986</li> <li>1987-1997</li> <li>1998-2008</li> <li>2009-2019</li> </ol> <pre>library(ncdf4) filer=c(paste0(getwd(),"/data/in/raindat_7986.nc"), paste0(getwd(),"/data/in/raindat_8797.nc"), paste0(getwd(),"/data/in/raindat_9808.nc"), paste0(getwd(),"/data/in/raindat_0919.nc")) filew=c(paste0(getwd(),"/data/in/windat_7986.nc"), paste0(getwd(),"/data/in/windat_8797.nc"), paste0(getwd(),"/data/in/windat_9808.nc"), paste0(getwd(),"/data/in/windat_0919.nc")) Startdate=as.POSIXct("1979-01-01 10:00:00") Enddate=as.POSIXct("1986-12-31 23:00:00") # ncr = nc_open(filer) # ncw = nc_open(filew)</pre> <p>Intermediary data</p> <p>Intermediary data are stored in the “data/interdat” folder which contains the following files in Rdata format:</p> <pre><code>## [1] "allraininclusters1.Rdata" "allraininclusters2.Rdata" ## [3] "allraininclusters3.Rdata" "allraininclusters4.Rdata" ## [5] "extremEventsWind.Rdata" "interclustRain.Rdata" ## [7] "interclustWind.Rdata" "metaclustRain.Rdata" ## [9] "metaclustWind.Rdata" "Rain_99_AllP.Rdata" ## [11] "rainP1.Rdata" "rainP2.Rdata" ## [13] "rainP3.Rdata" "rainP4.Rdata" ## [15] "rawclustRain.Rdata" "rawclustWind.Rdata" ## [17] "timeP1.Rdata" "timeP2.Rdata" ## [19] "timeP3.Rdata" "timeP4.Rdata" ## [21] "windP1.Rdata" "windP2.Rdata" ## [23] "windP3.Rdata" "windP4.Rdata" ## [25] "Wnd_99_AllP.Rdata" </code></pre> <ul> <li> <p>allraininclustersX: [data.frame] files are used to assess more accurately the accumulated precipitation during events by collecting precipitations from timesteps in which precipitation is above and below the threshold for every grid cell and the whole duration of the cluster.</p> </li> <li> <p>99_allp: [matrix] value of extreme precipitation and extreme wind gust threshold over the whole domain (one value per grid cell)</p> </li> <li> <p>interclust: [list] files contain a list of data from wind and precipitation clusters divided in the 4 periods aggregated over space and clusters (1 value per grid cell per cluster). These files are uses to create the files “RainEv_ldat” and “Windev_ldat”.</p> </li> <li> <p>metaclust: [list] files contain a list of metadata from wind and precipitation clusters divided in the 4 periods . These files are uses to create the files “RainEv_meta” and “Windev_meta”.</p> </li> <li> <p>rainPX: [matrix] files contain a 3D matrix of dimension long<em>lat</em>time containing precipitation data for the period X.</p> </li> <li> <p>rawclust: [list] files contain a list of data.frame from wind and precipitation clusters divided in the 4 periods. These files are uses to create the files “RainEv_hdat” and “Windev_hdat”.</p> </li> <li> <p>timePX: [vector] contain vectors of time for the 4 periods.</p> </li> <li> <p>windPX: [matrix] files contain a 3D matrix of dimension long<em>lat</em>time containing wind gust data for the period X.</p> </li> </ul> <p>Output data</p> <p>Output data contains metadata and raw data of single and compound hazard clusters are stored in the “data/out” folder which contains the following files in Rdata format:</p> <pre><code>## [1] "compoundclusters.csv" "CompoundRW_79-19.v3x.Rdata" ## [3] "extremEvents_Rain.Rdata" "extremEvents_Wind.Rdata" ## [5] "Rain_stfprint.Rdata" "rainclusters.csv" ## [7] "RainEv_hdat_1979-2019.Rdata" "RainEv_ldat_1979-2019.Rdata" ## [9] "Rainev_ldatp_1979-2019.Rdata" "RainEv_meta_1979-2019.Rdata" ## [11] "RainEv_metap_1979-2019.Rdata" "Wind_stfprint.Rdata" ## [13] "windcluster.csv" "WindEv_hdat_1979-2019.Rdata" ## [15] "WindEv_ldat_1979-2019.Rdata" "WindEv_meta_1979-2019.Rdata" </code></pre> <ul> <li> <p>CompoundRW: [data.frame] contains metadata for the compound hazard clusters identified</p> </li> <li> <p>_hdat: [data.frame] hourly data of precipitation and wind gust clusters.</p> </li> <li> <p>_ldat: [data.frame] aggregated data over space and clusters (1 value per grid cell per cluster) for wind gust and precipitation clusters.Rain_ldatp contains aggregated values including non-extreme timesteps. Created from allraininclustersX.</p> </li> <li> <p>_meta:[data.frame] metadata for wind gust and precipitation clusters</p> </li> <li> <p>stfprint: [data.frame] files containing duration*footprint of each hazard clusters during all clusters</p> </li> <li> <p>sptdf: [data.frame] data.frame containing spatial, temporal, cluster and intensity information</p> </li> </ul> <p>Codes assiciated to the method are availaible here: https://github.com/Alowis/SI-CH</p>
LATEst: Efficient computation of hazard ratios and identification of causal relationships using 'natural experiments' in medical informatics databases
Open the record for dataset details and reuse information.
Informing Hazard Identification and Risk Characterization of Environmental Chemicals by Combining Transcriptomic and Functional Data from Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes
GEO Series GSE262419. Homo sapiens. 4558 samples. Type: Expression profiling by high throughput sequencing.
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