e-mail from Jim on 2012-11-16:

As a quick but hopefully sufficient way of transferring the geoscrub results back to you, I dumped my geoscrub output table out to CSV and stuck it on vegbiendev at /tmp/public.2012-11-04-07-34-10.r5984.geoscrub_output.csv.

Here is the essential schema info:

 decimallatitude double precision

 decimallongitude double precision

 country text

 stateprovince text

 county text

 countrystd text

 stateprovincestd text

 countystd text

 latlonvalidity integer

 countryvalidity integer

 stateprovincevalidity integer

 countyvalidity integer

The first 6 columns are identical to what you provided me as input, and if you do a projection over them, you should be able to recover the geoscrub_input table exactly. I confirmed this is the case in my database, but after importing on your end you should double check to make sure nothing screwy happened during the dump to CSV.

The added countrystd, stateprovincestd, and countystd columns contain the corresponding GADM place names in cases where the scrubbing procedure yielded a match to GADM. And the four *validity columns contain scores as described in my email to the bien-db list a few minutes ago.

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e-mail from Jim on 2012-11-16:

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To produce these scores, I first passed the data through a geoscrubbing pipeline that attempts to translate asserted names into GADM (http://gadm.org) names with the help of geonames.org data, some custom mappings, and a few other tricks. Then I pushed them through a geovalidation pipeline that assesses the proximity of asserted lat/lon coordinates to their putative administrative areas in cases where scrubbing was successful. All operations happen in a Postgis database, and the full procedure ran for me in ~2 hours on a virtual server similar to vegbiendev. (This doesn't include the time it takes to set things up by importing GADM and geonames data and building appropriate indexes, but that's a one-time cost anyway.)

I still need to do a detailed writeup of the geoscrubbing and geovalidation procedures, but I think you have all the context you need for this email.

My validity codes are defined as follows, with the general rule that bigger numbers are better:

For latlonvalidity:

-1: Latitude and/or longitude is null

 0: Coordinate is not a valid geographic location

 1: Coordinate is a valid geographic location

For countryvalidity/stateprovincevalidity/countyvalidity:

-1: Name is null at this or some higher level

 0: Complete name provided, but couldn't be scrubbed to GADM

 1: Point is >5km from putative GADM polygon

 2: Point is <=5km from putative GADM polygon, but still outside it

 3: Point is in (or on border of) putative GADM polygon

Importantly, note that validity at each administrative level below country is constrained by the validity at higher levels. For example, if a stateprovince name is given but the country name is null, then the stateprovincevalidity score is -1. And of course, if a point doesn't fall within the scrubbed country, it certainly can't fall within the scrubbed stateprovince. To put it another way, the integer validity code at a lower level can never be larger than that of higher levels.

You could generate this yourself from the attached data, but for convenience, here is a tabulation of the lat/lon validity scores by themselves:

 latlonvalidity   count

             -1       0

              0    4981

              1 1702989

... and here are separate tabulations of the scores for each administrative level, in each case considering only locations with a valid coordinate (i.e., where latlonvalidity is 1):

 countryvalidity   count

              -1  222078

               0    6521

               1   49137

               2   23444

               3 1401809

 stateprovincevalidity   count

                    -1  298969

                     0   19282

                     1  107985

                     2   34634

                     3 1242119

 countyvalidity   count

             -1 1429935

              0   61266

              1   24842

              2   12477

              3  174469

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e-mail from Jim on 2013-1-16:

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Back in Nov 2012 I created a local Git repo to manage my geovalidation

code as I was developing it and messing around. Technically I'm pretty

sure there is a way to graft it into the BIEN SVN repo you've been

using, but I don't have time to deal with that now, and anyway there may

be advantages to keeping this separate (much as the TNRS development is

independent). I'm happy to give you access so you can clone the Git repo

yourself if you'd like, but in any event, I just created a 'BIEN Geo'

subproject of the main BIEN project in Redmine, and exposed my repo

through the browser therein. It's currently set as a private project,

but you should be able to see this when logged in:

https://projects.nceas.ucsb.edu/nceas/projects/biengeo/repository

So now you should at least be able to view and download the scripts, and

peruse the history. (If you do want to be able to clone the repo, send

me a public key and I'll send you further instructions.)

I just spent some time improving comments in the various files, as well

as writing up a README.txt file that I hope will get you started. Let me

know if (when) you have questions. As I mention in the README, the

scripts are probably not 100% runnable end-to-end without some

intervention here and there. Pretty close, but not quite. That was

certainly my goal, but given that this was all done in a flurry of a few

days with continual changes, it's not quite there.

Also, the shell scripts do have the specific wget commands to grab GADM2

and geonames.org data that need to be imported into the database to

geoscrub/geovalidate-enable it. But if you want (of for that matter if

you have trouble with downloading anything), I can put the files I got

back in November somewhere on vegbiendev. In fact, although the GADM2

data should be unchanged (it's versioned at 2.0), the geonames.org

downloads come from their daily database dumps IIRC, so what you'd get

now might not be the same as what I got. Probably unlikely to be many,

if any, changes in the high-level admin units we're using, but who

knows.

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e-mail from Jim on 2013-1-18:

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Regarding calculation of precision estimates for counties, I just resuscitated something I had started working up (with Bob's inspiration) back on the final Friday afternoon of the Nov 2012 BIEN WG meeting at NCEAS. Using GADM2 administrative polygons data (http://www.gadm.org), this calculation works by first reprojecting a given county into whatever UTM zone contains the county centroid, then (just as Bob suggested in his email) computing the distance from the centroid to the most distance point on the county border.

Have a look at the attached CSV of results for US counties, and mull over whether this seems useful. Maybe even sanity-check against your favorite county or two! For those who care, the SQL statement is pasted below my sig, along with a 'utmzone' function that I borrowed from the PostGIS wiki.

Note that I did the whole UTM reprojection bit because the GADM2 data are in a geographic projection, for which planar distance calculations aren't in meaningful units and aren't consistent across latitudes. Although postgis provides some functions that know how to work directly on the WGS 84 ellipsoid and don't require a projection, the exact functions we need for this aren't available.

Some misc things that were in my head as I did this:

1. This precision measure is conservative insofar as it basically describes a circle of radius equal to the precision value, and most counties will only actually occupy a fraction of the circle depending on their shape -- think of long skinny counties, or those which have an offshore island under their jurisdiction. Totally reasonable! But this _is_ just one conception of what "precision" means for county data.

2. In GADM2, counties are the lowest admin level for the US, making things easy. But other countries have additional levels below county/parish, meaning that we'd need to add a step that first unions those smaller polygons into a single polygon for the county/parish.

3. I was a little concerned about the, um, precision of these UTM-based precision estimates. Although UTM is convenient insofar as a local zone can be applied when looking at a given smallish area (like a county), distance is not perfectly preserved in this projection, which suffers from distortion as you approach the edges of a given zone. However, I just did some testing of the UTM distortion using points at roughly opposite east-west ends of Santa Barbara County. SB makes for a decent test case because it's a fairly large county that lies across two UTM zones. Distance between the test points I used is 100833 meters in UTM, and 100782 meters using the more accurate ellipsoid calculation. So I'm pretty confident we could say that our county precision estimates are themselves precise to 1 km (and probably usually much better than that), which I assume is fine?

As an aside, I was curious to see Franklin County, VA, showing up as having one of the worst precision values. Turns out GADM2 counts Franklin County as including the independent city of Franklin located a few hundred km east (http://en.wikipedia.org/wiki/Franklin,_Virginia). What can I say, anything we do is only as good as our inputs.

Cheers,

Jim

-- generate precision estimates for all US counties

-- (requires utmzone function, defined below)

SELECT name_1 AS state, name_2 AS county,

       ROUND(ST_Length(

         ST_Longestline(

           ST_Transform(geom, utmzone(ST_Centroid(geom))),

           ST_Centroid(ST_Transform(geom, utmzone(ST_Centroid(geom))))

         )

       )::numeric/1000, 3) AS error_km

  FROM gadm2

  WHERE name_0='United States'

  ORDER BY state, county;

-- function taken from postgis users wiki

CREATE OR REPLACE FUNCTION utmzone(geometry)

   RETURNS integer AS

 $BODY$

 DECLARE

     geomgeog geometry;

     zone int;

     pref int;

 BEGIN

     geomgeog:= ST_Transform($1,4326);

     IF (ST_Y(geomgeog))>0 THEN

        pref:=32600;

     ELSE

        pref:=32700;

     END IF;

     zone:=floor((ST_X(geomgeog)+180)/6)+1;

     RETURN zone+pref;

 END;

 $BODY$ LANGUAGE 'plpgsql' IMMUTABLE

   COST 100;

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e-mail from Jim on 2013-1-18:

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See attached, same as before but now with centroid coordinates (after

back-transforming from the local UTM zone to WGS84), along with their

calculated precision in km. Note that these centroids may differ from

what other authorities claim, because of differences in the method

and/or differences in the input polygons.

Revised CSV attached, revised SQL statement below.

Cheers,

Jim

SELECT name_1 AS state, name_2 AS county,

       ST_Y(ST_Transform(ST_Centroid(geom), 4326)) AS latitude,

       ST_X(ST_Transform(ST_Centroid(geom), 4326)) AS longitude,

       ROUND(

         ST_Length(ST_Longestline(geom, ST_Centroid(geom))

       )::numeric/1000, 3) AS error_km

  FROM (

    SELECT name_0, name_1, name_2,

           ST_Transform(geom, utmzone(ST_Centroid(geom))) AS geom

      FROM gadm2

      WHERE name_0='United States') AS gadm2_utm

  ORDER BY state, county

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