Skip to content

johannesuhl/geoprocessing

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

12 Commits
 
 
 
 
 
 

Repository files navigation

Geoprocessing

Selected python scripts for geoprocessing using open source geospatial resources

java 8 and prio java 8  array review example

binned_statistics_2d_XYZ_csv_to_geotiff.py

-- Rasterization of 2d point data (x,y) with an attribute z into a grid of arbitrary cellsize and spatial reference system, using a user-specified summary statistic f(z) applied at the grid-cell level.

-- Input: CSV file holding (x,y,z) data

-- Output: A GeoTIFF containing a gridded surface, with f(z) in each grid cell.

-- f(z) can be the mean, median, count, diversity, etc.

This script generates a gridded surface in GeoTiff format, based on one or more CSV files holding marked points (i.e., [x,y,z] with x and y being geospatial coordinates and z being a variable of interest to be summarized), using scipy.stats.binned_statistic_2d() and GDAL.

Suitable for country and continental scale data processing. The output .tif will be LZW-compressed. Each CSV file may hold the data for a geographic sub-region within the total area of interest. However, the content of the CSV files may not overlap spatially, as f(z) is added cell-by-cell to a zero raster for each CSV file. This strategy is very efficient, but may produce inaccurate results at "overlapping" grid cells (e.g., at the edge between two adjacebt sub-regions). A template GeoTiff is required that dictates the target grid.

Johannes H. Uhl, University of Colorado Boulder, USA

About

Selected python scripts for geoprocessing using open source geospatial resources

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages