Tag Archives: python

Summary of my off-the-cuff Maptime Presentation: Canadian Microwave Links

Screenshot from 2014-07-26 10:17:15@MaptimeTO asked me to summarize the brief talk I gave last week at Maptime Toronto on making maps from the Technical and Administrative Frequency List (TAFL) radio database. It was mostly taken from posts on this blog, but here goes:

  1. One of the many constraints in building wind farms is allowing for radio links. Both the radio and the wind industries have agreed on a process of buffering and consultation. Here’s how I handled it in Python: Making weird composite shapes with Shapely.
  2. The TAFL databases — which contains locations and technical data for all licensed transmitters — are now open data. You can find them here: Technical and Administrative Frequency List (TAFL).
  3. The format is a real delight for all legacy-data nerds: aka a horrible mess of conditional field widths and arcane numeric codes. I wrote a SpatiaLite SQL script to make sense of it all: scruss/taflmunge. This (kind of) explains what it does: TAFL — as a proper geodatabase.
  4. Here’s a raw dump (very little metadata, sorry) from 2013 in the wonderful uMap: Ontario Microwave Links.
  5. In a fabulous piece of #opendatafail, Industry Canada have migrated all the microwave data (so, all links ≥ 960 MHz) to a new system which doesn’t work yet, and also stripped out all of the microwave data from recent TAFL files. They claim to be fixing it, but don’t hold your breath. If you want data to play with, here’s Ontario’s data from October 2013 (nb: huge) — ltaf_ont_tafl-20131001.

QGIS: Getting your ClickFu back

Since QGIS 1.8 took the rather boneheaded move of hiding all the plugin sources except the official one, here’s how to get the other repositories back:

Now you can add all the plugins!

ClickFu is kind of magic. It allows you to choose an online map service, click anywhere on your GIS viewport, and the correct location link opens in your browser. Very handy for sharing locations with people who don’t have a GIS setup. It properly takes coordinate reference systems into account, too, so no messing about with datum shifts and the like.

Another plugin that hasn’t made it to the official repo is Luiz Motta’s Zip Layers. It still lurks at http://pyqgis.org/repo/contributed.

Click Fu runs just fun under QGIS 2.2 – as long as you remember to delete any pre-2.0 versions and then reinstall.

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:

#!/usr/bin/python
# -*- 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.