Startup hacks and engineering miracles from your exhausted friends at Faraday

How to crunch lots of geodata in parallel

Bill Morris on

This post is part of our data science and practical cartography series.

GNU parallel + ogr2ogr = happy data scientists

These power tools in combination make it very easy to process lots of geodata at once, in as many parallel operations as your local machine or server can support.

Reprojecting in bulk

Here's an example, assuming you have a folder full of shapefiles you want to reproject into Geographic coordinates. Make a directory for the output, then pipe every shapefile through ogr2ogr in parallel:

mkdir wgs84  
ls *.shp | parallel ogr2ogr -t_srs 'EPSG:4326' wgs84/{} {}  

Running a sequence of commands on many files

In order to build whole data workflows, you can wrap your sequence of commands in a bash function. Here's an example, where we:

  1. Download each state landmarks file from the census FTP
  2. Extract each file
  3. Create a new file for each consisting of only airport landmarks, projected to WGS84
# grab this handy list of all state FIPS codes
wget -c

# define the function
get_airports() {  
  # grab the data from the census server
  wget -c$
  unzip tl_2016_$
  # extract just airports (code K2451) and reproject to WGS84
  ogr2ogr -t_srs "EPSG:4326" -where "MTFCC = 'K2451'" tl_2016_$1_airports.shp tl_2016_$1_pointlm.shp
  echo "done with state $1"
export -f get_airports

# kick off the parallel processing!
cat state_fips_codes.txt | parallel get_airports {}

This crunches through 52 states and territories in 21.8 seconds on a small ec2 server, limited only by network speed.


Install the tools

  • GNU parallel
    • OSX: brew install parallel
    • Ubuntu: apt-get install parallel
  • ogr2ogr
    • OSX: brew install gdal --HEAD
    • Ubuntu: sudo apt-get install gdal-bin

Bonus toolkit: From Derek Watkins, here are a few dozen examples of the awesome geoprocessing you can you with GDAL/OGR.

Happy mapping!

How to convert a fixed-width file into CSV

Seamus Abshere on

This is part of our data science series. How predictive!

(The more valuable and massive a data set is, the less likely it's in a format you can just parse. Has anybody else noticed that?)

Here's how to convert a fixed-width file to CSV with the standard GNU unix tool gawk:


Thanks to stackoverflow: (reproducing verbatim)

gawk '$1=$1' OFS=, FIELDWIDTHS='4 2 5 1 1' infile > outfile.csv  

Where FIELDWIDTHS is a list of field widths and OFS is the output file separator.

Real life

In real life, fixed width files contain commas and double quotes.

# put this in a file called fixed2csv.awk
  for (i=1;i<=NF;i++) {
    printf "\"%s\"%s", $i, (i<NF?OFS:ORS)

Then run it on your data:

gawk -f fixed2csv.awk OFS=, FIELDWIDTHS='4 2 5 1 1' infile > outfile.csv  

Thanks to Ed Morton on Stackoverflow for inspiration!