pyinterpolate-datasets 2023.0.0

Static datasets and data download scripts for pyinterpolate package

pyinterpolate-datasets

Datasets used throughout tutorials and examples in pyinterpolate Pyinterpolate Repository

Datasets

Point Kriging

csv

• pl_dem.csv : sample of DEM readings for region near the Polish city Gorzow Wielkopolski. Sample retrieved from Copernicus Land Monitoring Service, EU-DEM dataset.
• meuse/meuse.csv and meuse/meuse_grid.csv : from Pebesma, Edzer. (2009). The meuse data set: a tutorial for the gstat R package. URL: https://cran.r-project.org/web/packages/gstat/vignettes/gstat.pdf

numpy

• armstrong_data : data from book Basic Linear Geostatistics written by Margaret Armstrong with DOI: https://doi.org/10.1007/978-3-642-58727-6

txt

• pl_dem.txt : see pl_dem.csv,
• pl_dem_epsg2180.txt : the same dataset as pl_dem.txt but reprojected to metric system.

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Block Kriging

cancer_data.gpkg

Breast cancer rates are taken from the Incidence Rate Report for U.S. counties and were clipped to the counties of the Northeastern part of U.S. National Cancer Institute - Incidence Rates Table: Breast Cancer: Pennsylvania State. Observations are age-adjusted and multiplied by 100,000 for the period 2013-2017.

Population centroids are retrieved from the U.S. Census Blocks 2010 United States Census Bureau - Centers of Population for the 2010 Census. Breast cancer affects only females but for this example the whole population for an area was included. Raw and transformed datasets are available in a dedicated Github repository.

meta:

• block / polygon layer: areas,
• point support / population layer: points,
• point support value: POP10,
• block and point support geometry column: geometry,
• block index column: FIPS,
• block values column: rate.
• Raw data and transformation steps