As of 21 Sep 2016, various files (e.g. SQlite, CSV) derived from FracFocus.org’s September 2016 FracFocusRegistry have been downloaded, extracted, transformed, loaded, archived, and uploaded to the frackingdata.info/downloads site and their respective links also posted to FrackingData’s FracFocus Data Page .

This time, FracFocus posted their SQL Server backup on 21 Sep 2016, about 5 weeks later than its previous posting of 15 Aug 2016.

Once again, of significance this time was that the download of the files from the FracFocus.org website and their subsequent extract, transform, load, archiving, and exporting to CSV, SQLite, and PostgreSQL files was performed by a Windows batch script without human intervention. This automated method shaved hours from the extract, transform, load, archive, and export process.  In addition, the batch script now uses WinSCP to automatically upload the files in question to the http://frackingdata.info/downloads page.

When this Windows batch file is sufficiently stable, and I’ve soft-coded the data-cleansing views into the script itself,  I’ll post a link to it in the Source Code section of this blog.  Soft-coding of the data-cleansing views is the last hurdle to publishing this script.

Khepry Quixote 2016-09-21

As I promised earlier, I’ve downloaded earthquakes from NCEDC’s web site (1898 to date), reverse-geocoded them via GeoNames and K-D Trees (thereby obtaining their country, state, county, and city/village values), archived the resulting files via 7-ZIP and uploaded both the CSV and SQLite datasets to:

• NCEDC_earthquakes_18980101_thru_20160911_reverse_geocoded.csv.7z
  • pipe-separated values, approximately 67 MB in size
• NCEDC_earthquakes_18980101_thru_20160911_reverse_geocoded.sqlite.7z
  • SQLite 3 database, approximately 208 MB in size

I have authored a program in Python 3 that reverse-geocodes (via GeoNames and K-D Trees) the lat/longs into their respective countries, states, counties, and cities/villages.  This is the link to the open-source Python 3 reverse-geocoder project.  The program processes nearly 3 million rows in approximately 240 seconds.

Khepry Quixote
11 Sep 2016