Archive for July, 2014

NYC Geodatabase Updates: Spatialite Upgrade & ZIPs to ZCTAs

Wednesday, July 30th, 2014

I released the latest version of the NYC geodatabase (nyc_gdb) a few weeks ago. In addition to simply updating the data (for subway stations and ridership, city point features, and ZIP Code Business Patterns data) I had to make a couple of serious upgrades.

Spatialite Updates

The first was that is was time for me to update the version of Spatialite I was using, from 2.4 to 4.1, and to update my documentation and tutorial from the Spatialite GUI 1.4 to 1.7. I used the spatialite_convert tool (see the bottom of this page for info)to upgrade and had no problem. There were some major benefits to making the switch. For one, writing statements that utilize spatial indexes is much simpler – this was version 2.4, generating a neighbor list of census tracts:

SELECT tract1.tractid AS tract, tract2.tractid AS neighbor
FROM a_tracts AS tract1, a_tracts AS tract2
WHERE ST_Touches(tract1.geometry, tract2.geometry) AND tract2.ROWID IN (
SELECT pkid FROM idx_a_tracts_geometry
WHERE pkid MATCH RTreeIntersects (MbrMinX(tract1.geometry), MbrMinY(tract1.geometry),
MbrMaxX(tract1.geometry), MbrMaxY(tract1.geometry)))

And here’s the same statement in 4.1 (for zctas instead of tracts):

SELECT zcta1.zcta AS zcta, zcta2.zcta AS neighbor
FROM a_zctas AS zcta1, a_zctas AS zcta2
WHERE ST_Touches(zcta1.geometry, zcta2.geometry)
AND zcta1.rowid IN (
SELECT rowid FROM SpatialIndex
WHERE f_table_name=’a_zctas’ AND search_frame=zcta2.geometry)
ORDER BY zcta, neighbor

There are also a number of improvements in the GUI. Tables generated by the user are now grouped under one heading for user data, and the internal tables are grouped under subsequent headings, so that users don’t have to sift through all the objects in the database to see what they need. The import options have improved – with shapefiles and dbfs you can now designate your own primary keys on import. You also have the option of importing Excel spreadsheets of the 97-2003 variety (.xls). In practice, if you want the import to go smoothly you have to designate data types (format-cells) in the Excel sheet (including number of decimal places) prior to importing.

spatialite_gui_17

I was hesitant to make the leap, because version 2.4 was the last version where they made pre-compiled binaries for all operating systems; after that, the only binaries are for MS Windows and for Mac and Linux you have to compile from source – which is daunting for many Mac users that I am ill-equipped to help. But now that Spatialite seems to be more fully integrated with QGIS (you can create databases with Q and using the DB Manager you can export layers to an existing database) I can always steer folks there as an alternative. As for Linux, more distros are including updated version of the GUI in their repositories which makes installation simple.

One of the latest features in Spatialite 4.1.1 is the ability to import XML ISO metadata into the database, where it’s stored as an XML-type blob in a dedicated table. Now that I’m doing more work with metadata this is something I’ll explore for the future.

ZIPs to ZCTAs

The other big change was how the ZIP Code Business Patterns data is represented in the database. The ZBP data is reported for actual ZIP Codes that are taken from the addresses of the business establishments, while the boundaries in the nyc_gdb database are for ZIP Code Tabulation Areas (ZCTAs) from the Census. Previously, the ZBP data in the database only represented records for ZIP Codes that had a matching ZCTA number. As a result, ZIP Codes that lacked a corollary because they didn’t have any meaningful geographic area – the ZIP represented clusters of PO Boxes or large organizations that process a lot of mail – were omitted entirely.

In order to get a more accurate representation of business establishments in the City, I instituted a process to aggregate the ZIP Code data in the ZBP to the ZCTA level. I used the crosswalk table provided by the MCDC which assigns ZIPs to ZCTAs, so those PO Boxes and large institutions are assigned to the ZCTA where they are physically located. I used SQLite to import that crosswalk, imported the ZBP data, joined the two on the ZIP Code and did a group by on the ZCTA to sum employment, establishments, etc. For ZIPs that lacked data due to disclosure regulations, I added some note or flag columns that indicate how many businesses in a ZCTA are missing data. So now the data tables represent records for ZCTAs instead of ZIPs, and they can be joined to the ZCTA features and mapped.

The latest ZBP data in the new database is for 2012. I also went back and performed the same operation on the 2011 and 2010 ZBP data that was included in earlier databases, and have provided that data in CSV format for download in the archives page (in case anyone is using the old data and wants to go back and update it).

Some QGIS Odds and Ends

Thursday, July 3rd, 2014

My colleague Joe Paccione recently finished a QGIS tutorial on working with raster data. My introductory tutorial for the GIS Practicum gives only cursory treatment to rasters, so this project was initially conceived to give people additional opportunities to learn about working with them. It focuses on elevation modeling and uses DEMs and DRGs to introduce tiling and warping, and creating hillshades and contour lines.

topo_contour

The tutorial was written using QGIS 2.0 and was tested with version 2.4; thus it’s readily usable with any 2.x version of QGIS. With the rapid progression of QGIS my introductory tutorial for the workshop is becoming woefully outdated, having been written in version 1.8. It’s going to take me quite a while to update (among other things, the image for every darn button has changed) but I plan to have a new version out sometime in the fall, but probably not at the beginning of semester. Since I have a fair amount of work to do any way, I’m going to rethink all of the content and exercises. Meanwhile, Lex Berman at Harvard has updated his wonderfully clear and concise tutorial to Q version 2.x.

The workshops have been successful for turning people on to open source GIS on my campus, to the point were people are using it and coming back to teach me new things – especially when it comes to uncovering useful plugins:

  • I had a student who needed to geocode a bunch of addresses, but since many of them were international I couldn’t turn to my usual geocoding service at Texas A & M. While I’ve used the MMQGIS plugin for quite a while (it has an abundance of useful tools), I NEVER NOTICED that it includes a geocoding option that acts as a GUI for accessing both the Google and Open Streetmap API for geocoding. He discovered it, and it turned out quite well for him.
  • I was helping a prof who was working with a large point file of street signs, and we discovered a handy plugin called Points2One that allowed us to take those points and turn them into lines based on an attribute the points held in common. In this case every sign on a city block shared a common id that allowed us to create lines representing each side of the street on each block.
  • After doing some intersect and difference geoprocessing with shapefiles I was ending up with some dodgy results – orphaned nodes and lines that had duplicate attributes with good polygons. If I was in a database, an easy trick to find these duplicates would be to run a select query where you group by ID and count them, and anything with a count more than two are duplicates – but this was a shapefile. Luckily there’s a handy plugin called Group Stats that lets you create pivot tables, so I used that to do a summary count and identified the culprits. The plugin allowed me to select all the features that matched my criteria of having an id count of 2 or more, so I could eyeball them in the map view and the attribute table. I calculated the geometry for all the features and sorted the selected ones, revealing that all the duplicates had infinitesimally small areas. Then it was a simple matter of select and delete.

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