Making weird composite shapes with Shapely

Let’s say you operate microwave radio links, and you want to see if you could build a link between, say, the huge comms tower at Clear Creek, ON and your tower just south of Aylmer. You know that Erie Shores Wind Farm is in the middle of the route. Do the 77m diameter turbines attenuate your signal to nothing?

This is a real issue I have to deal with every day, except in reverse: when I’m planning wind farms, I don’t want to be blocking existing licensed links. I use Spectrum Direct to find these links (and precisely two years ago, I showed you how to write Spectrum Direct data as CSV), but up until now I worked out affected zones (called “consultation zones”) by hand.

The Radio Advisory Board of Canada and the Canadian Wind Energy Association developed a joint protocol on sizing consultation zone for wind turbines, documented in the RABC CANWEA Guidelines. The one for a microwave link comprises:

  • A 1km buffer around the start and end points of the link;
  • A distance-, frequency- and wind-turbine diameter-specific path width between the points.

Here I’m only going to consider the 2D geometry of the path. Lots of people in the radio industry get paid money to do line-of-sight terrain analysis, and I’m ignoring this for now. All I’m doing here is using Python’s Shapely library to make the dumb-bell shaped buffer around the path and endpoints. I learned of Shapely from Erik Westra’s book Python Geospatial Development. Here’s some code that outputs pipe-separated well-known text that imports nicely into QGIS:

# -*- coding: utf-8 -*-
# Calculate RABC/CanWEA Microwave Link Consultation Zone
# scruss - 2012-10-29

import pyproj
import shapely.geometry
from shapely.wkt import dumps

# hard-coded details (sorry)
startlat = 42.582892  # Clear Creek
startlong = -80.603875
endlat = 42.731776  # South Aylmer
endlong = -80.98768
frequency = 6e9  # 6 GHz
rotor = 77.0  # wtg rotor diameter, m

# calculate distance between points
g = pyproj.Geod(ellps='WGS84')
(az12, az21, dist) = g.inv(startlong, startlat, endlong, endlat)

# Width of microwave link fresnel zone
Lc = rotor + 52 * ((dist / 1000) / (frequency / 1e9)) ** 0.5

# calculate line string along path with segments <= 1 km
lonlats = g.npts(startlong, startlat, endlong, endlat,
                 1 + int(dist / 1000))

# npts doesn't include start/end points, so prepend/append them
lonlats.insert(0, (startlong, startlat))
lonlats.append((endlong, endlat))

# translate line string into projected coordinates
utms = []
srcp = pyproj.Proj(init='epsg:4326')  # WGS84
destp = pyproj.Proj(init='epsg:32617')  # WGS84 UTM Zone 17N
for (lon, lat) in lonlats:
    (utmx, utmy) = pyproj.transform(srcp, destp, lon, lat)
    utms.append((utmx, utmy))

# turn start and end points into Shapely point objects
startpt = shapely.geometry.Point(utms[0])
endpt = shapely.geometry.Point(utms[-1])

# draw a 1km buffer around start/end points
startb = startpt.buffer(1000)
endb = endpt.buffer(1000)

# convert microwave path to a Shapely LineString
path = shapely.geometry.LineString(utms)
pathb = path.buffer(Lc / 2) # buffer it to fresnel zone width

# join the buffers into one shape
zone = startb.union(pathb).union(endb)

# output the shape as well known text
print 'id|wkt'
print '|'.join(('1', dumps(zone)))

That little snippet creates the buffer in projected coordinates from two geographic coordinates as inputs. Grabbing the Erie Shores coordinates from OpenStreetMap and the USGS WMS, let’s see if the beam will pass:

Uh, no. That’s three turbines in the path in that little section alone. Back to the drawing board.

The Invisible City

Now you see it …

There’s not a whole lot north of North Bay. Highway 11 winds through some extensive geometry, but few habitations. Until something wonderful (and quite a bit wrong) happened at 46° 31′ 21″ N, 79° 33′ 9″ W on OpenStreetMap. A whole new town about 25 minutes out of North Bay sprung up overnight — and was as quickly deleted as mappers caught and reverted the vandalism.

Now you don’t.

The misguided mapper put quite a lot of work into this ephemeral town. It’s got urban rail, parklands, residential areas and more. You can see the detail on this large image of the town (650 KB PNG). If you want to play with the data, here’s a zipped OSM XML file of the area before it was reverted. But please, don’t re-upload it; OpenStreetMap should be for real features on the ground, and not for confusing map data consumers.

Thanks to Bootprint for noticing this, and to rw__ for reverting the edits.

Awesome USGS orthoimagery … in Canada?

Detail of Erie Shores Wind Farm showing USGS orthophotos from WMS

I was trying to do some OpenStreetMap edits in rural southern Ontario, but the default Bing background aerial imagery is very poor. Clicking around in semi-plokta mode to find better images, I happened upon “OSM US USGS Large Scale Aerial Imagery”, which loaded up some beautiful and recent pictures. I guess that since we’re close to the border, the USGS doesn’t bother to trim its images too close.

Ian Dees, who admins the particular OSM server, confirmed that these are from The National Map‘s TNM_Large_Scale_Imagery service; thanks, Ian! These images are at least as good for my purposes as the SWOOP data — except not $50/km2, and without elaborate usage restrictions.

Mephisto’s Oriental Mystery — solved!

I bought a pair of Mephisto shoes today, and to go with their wayfaring imagery, the insoles are decked out with map-like symbols. Prominent is a map coordinate: N 38° 51.343′ E 94° 47.963′.

If you look at a map there, it’s a fairly empty mountainous place near the border of Qinghai and Gansu. It’s definitely not where the shoes are made, unless Mephisto have a stealth factory there.

There was something familiar about the coordinate, through. I thought I’d try flipping it to the western hemisphere:

It’s the Garmin headquarters in Olathe, KS! Every Garmin GPS I’ve ever owned has had the HQ as a default waypoint. I’m guessing a designer at Mephisto was looking to add something legitimately mappy to the graphics, and picked up their satnav to pull out a coordinate. Guessing that Olathe isn’t a very mysterious, rugged destination, they flipped the hemisphere to give it more eastern promise.’s excellent database provides a crowdsourced postal code geocoder under the ODbL. You can download the database as CSV directly. Here’s a bash script to convert that text file into a (very large) point shapefile:

# - convert CSV to a shape file
# NB: input CSV is UTF-8; it is passed through unchanged
# Needs >= v1.7 of GDAL
# scruss - 2012/04/15

if [ ! -f Canada.csv.gz ]
    echo ""
    echo " " Download \"Canada.csv.gz\" into the current directory from
    echo "  "
    echo " " and try again.
    echo ""
    exit 1

# make input file with header
echo PostalCode,Latitude,Longitude,City,Province > Canada2.csv
gunzip -c Canada.csv.gz >> Canada2.csv

# create GDAL VRT file
cat > Canada2.vrt <<EOF
  <!-- note that OGRVRTLayer name must be basename of source file -->
  <OGRVRTLayer name="Canada2">
    <GeometryField encoding="PointFromColumns" x="Longitude" y="Latitude"/>
    <Field name="PostalCode" type="String" width="6" />
    <Field name="Latitude" type="Real" />
    <Field name="Longitude" type="Real" />
    <Field name="City" type="String" width="60" />
    <Field name="Province" type="String" width="2" />

# create shapefile
ogr2ogr PostalCodes.shp Canada2.vrt

# clean up
rm -f Canada2.csv	Canada2.vrt

Though the script is a bit Unix-centric, it’s just a simple list of instructions which could be run on any command line. What it does is add some headers to the file, then sets up an OGR Virtual Format to convert the text into a fairly well-defined shapefile. When you use this shapefile, you should credit as the ODbL requires.

Eek! has been sued by Canada Post! (News responses: Michael Geist, Boing Boing, CBC) I’ve donated to defend this useful service.

Georeferencing QGIS output rasters

Some of the design packages I rely on use very crude GIS facilities. In fact, all they can support is a georeferenced raster as a background image, so it’s more of a rough map than GIS. It helps if these rasters are at a decent resolution, typically 1m/pixel or better.

A while back, I asked on the QGIS forum if the package could output high resolution georeferenced rasters. I received a rather terse response that no, it couldn’t (and I inferred from the tone that the poster thought that it shouldn’t, and I was wrong to want such a thing). I shelved the idea at the time.

After having to fix a lot of paths in a QGIS project file I’d moved to a new system, I noticed that all the map composer attributes are rather neatly defined in the XML file structure. Some messing around with Perl, XML::Simple and Data::Dumper::Simple, and I had a little script that would spit out an ESRI World File for the map composer raster defined in the project.

To run this, you have to create a project with just one Print Composer page, save the composed map as an image, save the project, then run the script like this:

./ project.qgs > image.pngw

There are some caveats:

  • This probably won’t work for projects with multiple print composers
  • It doesn’t quite get the scale right, but it’s within a pixel or so. I may not have corrected for image borders.

Though there’s some fairly hideous XML-mungeing in the code, what the script does is entirely trivial. If you feel you can use it, good; if you feel you can improve it, be my guest.

#!/usr/bin/perl -w
# geoprint - georef a QGIS output image by creating a world file
# one arg: qgis project file (xml)
# $Id:,v 1.3 2012/04/06 03:32:01 scruss Exp $

use strict;
use XML::Simple;
use constant MM_PER_INCH => 25.4;

my $qgis = $ARGV[0];
die "$qgis must exist\n" unless ( -f $qgis );

my $q = XMLin($qgis) or die "$!\n";
my %composer = %{ $q->{Composer} };
my $image_width =
  int( $composer{Composition}->{paperWidth} *
    $composer{Composition}->{printResolution} /
    MM_PER_INCH );
my $image_height =
  int( $composer{Composition}->{paperHeight} *
    $composer{Composition}->{printResolution} /
    MM_PER_INCH );

# we need xpixelsize, ypixelsize, ulx and uly
my $xpixelsize =
  ( $composer{ComposerMap}->{Extent}->{xmax} -
    $composer{ComposerMap}->{Extent}->{xmin} ) /
  int( $composer{ComposerMap}->{ComposerItem}->{width} *
    $composer{Composition}->{printResolution} /
    MM_PER_INCH );
my $ypixelsize =
  -1.0 *
  ( $composer{ComposerMap}->{Extent}->{ymax} -
    $composer{ComposerMap}->{Extent}->{ymin} ) /
  int( $composer{ComposerMap}->{ComposerItem}->{height} *
    $composer{Composition}->{printResolution} /
    MM_PER_INCH );
my $ulx =
  $composer{ComposerMap}->{Extent}->{xmin} -
  $xpixelsize *
  int( $composer{ComposerMap}->{ComposerItem}->{x} *
    $composer{Composition}->{printResolution} /
    MM_PER_INCH ) - $xpixelsize;
my $uly =
  $composer{ComposerMap}->{Extent}->{ymax} -
  $ypixelsize *
  int( $composer{ComposerMap}->{ComposerItem}->{y} *
    $composer{Composition}->{printResolution} /
    MM_PER_INCH ) - $ypixelsize;

printf( "%.12f\n%.12f\n%.12f\n%.12f\n%.12f\n%.12f\n",
  $xpixelsize, 0.0, 0.0, $ypixelsize, $ulx, $uly );

# FIXME? pixel scale seems a tiny bit off - allow for border?

Updating maps … to outdated data

I caved, and bought a Garmin nüvi 1490. Not that my GPSMap 60CSx wasn’t great at routing, but remembering to update maps before travel and its fiddly mounting requirements were a pain.

So, two hours of downloading map updates, and I fire it up … to find that the three year old hotel in Dartmouth, NS we were staying in was in the wrong place on the map:

Compare with OpenStreetMap:

So now, back near home, I ask it to find a post office. Here’s me parked outside one; do you see it on the screenshots?

Again, compare with OpenStreetMap:

So I thought I’d help Garmin out, but their Report a Map Error page needs me to know the type of my GPS, its serial number, the type and revision of my map, my name and e-mail address, and the coordinates of the error. OSM has me spoiled: at best, I can go in and edit; at second best, I can drop markers on OpenStreetBugs to flag errors for others to fix.

Garmin already has my name, e-mail address, GPS type, serial number and map revision through myGarmin™. The company could just as easily have a web-based map correction system that would be point-and-click. Follow the Leader is one of Garmin’s mottoes. In terms of user correctability of maps, however, they’re only the leader because they don’t know they’ve been lapped.

Toronto Data really open

Looks like the data sets at might finally actually be open; that is, usable in a way that doesn’t bind subsequent users to impossible terms. The new licence (which unfortunately is behind a squirrelly link) basically just requires you to put a reference to this paragraph somewhere near your data/application/whatever:

Contains public sector Datasets made available under the City of Toronto’s Open Data Licence v2.0.

and a link to the licence, where possible.

Gone are the revocation clauses, which really prevented any open use before, because they would require you to track down all the subsequent users of the data and get them to stop. Good. I think we can now use the data in OpenStreetMap.

While commenting on the licence’s squirrelly URL — I mean, could you remember — I stumbled upon the comedy gold that is the City of Toronto Comments Wall log. There goes my planned reading for the day.