DOI: 10.5285/925ac4ec-2a9d-461a-bfaa-6314eb0888c8
title: BEDMAP1 - Ice thickness, bed and surface elevation for Antarctica - standardised shapefiles and geopackages

description: We present here the Bedmap1 ice thickness, bed and surface elevation aggregated points. The aggregated points consist of statistically-summarised shapefile points (centred on a continent-wide 500 m x 500 m grid) that reports the average values of Antarctic ice thickness, bed and surface elevation from the full-resolution survey data and information on their distribution. The points presented here correspond to the points used to grid Bedmap1.
The data comes from 127 individual surveys. They are available as geopackages and shapefiles.
The associated datasets consist of: 
- Bedmap2 statistically-summarised data points (shapefiles): https://doi.org/10.5285/0f90d926-99ce-43c9-b536-0c7791d1728b
- Bedmap3 statistically-summarised data points (shapefiles): https://doi.org/10.5285/a72a50c6-a829-4e12-9f9a-5a683a1acc4a
- Bedmap1 standardised CSV data points: https://doi.org/10.5285/f64815ec-4077-4432-9f55-0ce230f46029
This work is supported by the SCAR Bedmap project and the British Antarctic Survey's core programme: National Capability - Polar Expertise Supporting UK Research
description: The data consist of the Bedmap3 standardised CSV (doi: ) data converted to statistically-summarised points and lines. Lines were calculated automatically from the CSV data and split each time a gap of more than 5 km between two data points was found. 
The spatial distribution of the full-resolution survey CSV point data is extremely heterogeneous with, for example, dense, metre-scale sampling along modern flight-lines that are often separated across-track by kilometres to hundreds of kilometres, and this heterogeneity varies between campaigns and data providers. Such an uneven data distribution can cause gridding algorithms to be overly weighted to those areas with the highest sampling frequency, to the detriment of adjacent areas with valid data but sparser sampling. To reduce the impact of data density on gridding, the statistically-summarised shapefile point dataset (centred on a continent-wide 500 m x 500 m grid) reports the average values of the full-resolution survey data plus information on their distribution. These summary statistics enable assesment of the confidence in the averaged data values and the variability of the measurements within each cell (e.g., bed roughness). 
The variables consists of the following: 
Cnt_bed Number of points for bed elevation 
Cnt_surf Number of points for surface elevation 
Cnt_thick Number of points for ice thickness 
IQR_bed Interquartile range for bed elevation points (Meters)
IQR_surf Interquartile range for surface elevation points (Meters)
IQR_thick Interquartile range for ice thickness points (Meters)
Max_bed Maximum value of bed elevation (Meters)
Max_surf Maximum value of surface elevation (Meters)
Max_thick Maximum value of ice thickness (Meters)
Mean_bed Mean value of bed elevation (Meters)
Mean_dist Mean distance between cell centre and points (Meters)
Mean_surf Mean value of surface elevation (Meters)
Mean_thick Mean value of ice thickness (Meters)
Med_bed Median value of bed elevation (Meters)
Med_surf Median value of surface elevation (Meters)
Med_thick Median value of ice thickness (Meters)
Min_bed Minimum value of bed elevation (Meters)
Min_surf Minimum value of surface elevation (Meters)
Min_thick Minimum value of ice thickness (Meters)
SD_bed Standard deviation of bed elevation (Meters)
SD_surf Standard deviation of surface elevation (Meters)
SD_thick Standard deviation of ice thickness (Meters)
STE_bed Standard error of bed elevation (Meters)
STE_surf Standard error of surface elevation (Meters)
STE_thick Standard error of ice thickness (Meters)
description: The primary source data consist of survey point measurements of ice thickness, bed elevation and surface elevation, which principally comes from airborne radar surveys and seismic soundings, and to a smaller extent from ground-based radar surveys.
description: All non-value data were converted to -9999, including any negative ice thickness values and any points with clear outliers. We checked the minimum and maximum values of each field to ensure the data are in a reasonable range and calculated mean and standard deviation on each dataset to identify potential issues. For example, if longitude/latitude values did not fall within the expected -180 to 180 or -50 to -90 degrees range respectively, the entire row was removed. When no ice thickness values were provided but surface and bed elevation values existed, we simply calculated ice thickness by subtracting the surface value from the bed value. At times, bed elevation was higher than surface elevation, likely due to issues with the semi-automatic picker used to extract the surface and bed reflector or a lack of distinctive reflectors in areas of shallow ice. To prevent this affecting the gridded product, we converted these values to -9999 for both the surface and the bed. Finally, we also conducted routine checks on the ice thickness data by comparing the given ice thickness value with the inferred ice thickness calculated from subtracting surface with bed. If these did not match, we placed -9999 on the ice thickness values.