Earthquakes: Reverse-Geocoded Files Posted to frackingdata.info/downloads

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:

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.  I will post a link to the open-source project shortly once I’ve vetted its license and repository.  The program processes nearly 3 million rows in approximately 240 seconds.

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Status Update 2016-05-02: FrackingData_FracFocusRegistry 2016-04 Files Uploaded

As of 02 May 2016, various files (e.g. SQlite, CSV, and PgSQL) derived from FracFocus.org’s March 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 .

The substantial delay between the last posting of the transformed FracFocusRegistry download in early March and this one in May was mostly due to FracFocus NOT posting anything until 26 April 2016.  This tardiness on FracFocus’s part is becoming a pattern.

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 2016-05-02