Seems like something to strive for these days:
it is wrong always, everywhere, and for anyone, to believe anything upon insufficient evidence
Interested in analytics and transit planning? I’m looking for an Analyst to help Metrolinx generate and communicate evidence for project evaluation ca.linkedin.com/jobs/view…
4th Axe Pancreatic Cancer fundraiser
Tuesday, November 6, 2018
Thanks to generous support, the 4th Axe Pancreatic Cancer fundraiser was a great success. We raised over $32K this year and all funds support the PancOne Network. So far, we’ve raised close to $120K in honour of my Mom. Thanks to everyone that has supported this important cause!
Raising funds for pancreatic cancer research by throwing axes. Great fun!
Fascinating to think of creating cognitive scaffolding for elephants aeon.co/essays/if…
We’ve completely retooled our approach to predicting elections to use an agent-based model. Looking forward to comparing our predictions to the actual results tonight for the Toronto election!
Found the exact spot we first met! Who knew it would lead to this
Cold medication
Life 3.0 by Max Tegmark is a fascinating book on an important topic. AI’s implications for society are profound and require significant debate. This book is a start. 📚
I appreciate innovation in beer, but this one went too far for me.
Saturday, September 29, 2018 →
Great fun at the #fivepeaks Earth Run in the Rouge
Hiking Balsam Lake Provincial Park
Sunday, September 23, 2018
Saturday, September 22, 2018 →
Warming up at Balsam Lake Provincial Park
Thursday, September 13, 2018 →
We had fun meeting the author Dav Pilkey and getting a signed copy of Dog Man. I really appreciated his patience with spending several hours talking to kids and signing books. 📚
Enjoying great sour beers at #sourpalooza
Ready for Pentatonix
Wednesday, September 5, 2018 →
Axe Pancreatic Cancer tickets are available now! This is a very fun event for a great cause. Early Bird tickets are selling quickly. So, if you’re interested, sign up now at pancreaticcancercanada.ca/axe-pancr…
Ready for school
Fixing a hack finds a better solution
Sunday, September 2, 2018
In my Elections Ontario official results post, I had to use an ugly hack to match Electoral District names and numbers by extracting data from a drop down list on the Find My Electoral District website. Although it was mildly clever, like any hack, I shouldn’t have relied on this one for long, as proven by Elections Ontario shutting down the website.
So, a more robust solution was required, which led to using one of Election Ontario’s shapefiles. The shapefile contains the data we need, it’s just in a tricky format to deal with. But, the sf package makes this mostly straightforward.
We start by downloading and importing the Elections Ontario shape file. Then, since we’re only interested in the City of Toronto boundaries, we download the city’s shapefile too and intersect it with the provincial one to get a subset:
download.file("[www.elections.on.ca/content/d...](https://www.elections.on.ca/content/dam/NGW/sitecontent/2016/preo/shapefiles/Polling%20Division%20Shapefile%20-%202014%20General%20Election.zip)",
destfile = "data-raw/Polling%20Division%20Shapefile%20-%202014%20General%20Election.zip")
unzip("data-raw/Polling%20Division%20Shapefile%20-%202014%20General%20Election.zip",
exdir = "data-raw/Polling%20Division%20Shapefile%20-%202014%20General%20Election")
prov_geo <- sf::st_read(“data-raw/Polling%20Division%20Shapefile%20-%202014%20General%20Election”,
layer = “PDs_Ontario”) %>%
sf::st_transform(crs = “+init=epsg:4326”)
download.file("opendata.toronto.ca/gcc/votin…",
destfile = “data-raw/voting_location_2014_wgs84.zip”)
unzip(“data-raw/voting_location_2014_wgs84.zip”, exdir=“data-raw/voting_location_2014_wgs84”)
toronto_wards <- sf::st_read(“data-raw/voting_location_2014_wgs84”, layer = “VOTING_LOCATION_2014_WGS84”) %>%
sf::st_transform(crs = “+init=epsg:4326”)
to_prov_geo <- prov_geo %>%
sf::st_intersection(toronto_wards)
Now we just need to extract a couple of columns from the data frame associated with the shapefile. Then we process the values a bit so that they match the format of other data sets. This includes converting them to UTF-8, formatting as title case, and replacing dashes with spaces:
electoral_districts <- to_prov_geo %>%
dplyr::transmute(electoral_district = as.character(DATA_COMPI),
electoral_district_name = stringr::str_to_title(KPI04)) %>%
dplyr::group_by(electoral_district, electoral_district_name) %>%
dplyr::count() %>%
dplyr::ungroup() %>%
dplyr::mutate(electoral_district_name = stringr::str_replace_all(utf8::as_utf8(electoral_district_name), "\u0097", " ")) %>%
dplyr::select(electoral_district, electoral_district_name)
electoral_districts## Simple feature collection with 23 features and 2 fields
## geometry type: MULTIPOINT
## dimension: XY
## bbox: xmin: -79.61919 ymin: 43.59068 xmax: -79.12511 ymax: 43.83057
## epsg (SRID): 4326
## proj4string: +proj=longlat +datum=WGS84 +no_defs
## # A tibble: 23 x 3
## electoral_distri… electoral_distric… geometry
##
## 1 005 Beaches East York (-79.32736 43.69452, -79.32495 43…
## 2 015 Davenport (-79.4605 43.68283, -79.46003 43.…
## 3 016 Don Valley East (-79.35985 43.78844, -79.3595 43.…
## 4 017 Don Valley West (-79.40592 43.75026, -79.40524 43…
## 5 020 Eglinton Lawrence (-79.46787 43.70595, -79.46376 43…
## 6 023 Etobicoke Centre (-79.58697 43.6442, -79.58561 43.…
## 7 024 Etobicoke Lakesho… (-79.56213 43.61001, -79.5594 43.…
## 8 025 Etobicoke North (-79.61919 43.72889, -79.61739 43…
## 9 068 Parkdale High Park (-79.49944 43.66285, -79.4988 43.…
## 10 072 Pickering Scarbor… (-79.18898 43.80374, -79.17927 43…
## # ... with 13 more rows In the end, this is a much more reliable solution, though it seems a bit extreme to use GIS techniques just to get a listing of Electoral District names and numbers.
