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Since earlier in the year, I’ve been reporting off and on about digital privacy<\/strong> – there’s more than enough material. Most recently I looked at the OKCupid data scraped in May, and how exposed it left the people who had signed up for that dating service.<\/p>\n For a series on real estate, I mapped <\/a><\/strong>gentrification using tax data based on postal codes, comparing 2004 to 2013. The theory was: if low-income tax filers were vanishing from a postal area, it would indicate a pattern of gentrification in the period we were looking at. The Toronto map made intuitive sense (we looked at Toronto and Vancouver). The map shows the historic mixed-income neighbourhoods of the city\u2019s east and west ends getting much less mixed-income over the decade. For a story on the public health crisis in native communities in northern Ontario, we used maps showing causes of death and premature death to visualize the problem from several different angles.<\/p>\n <\/p>\n Ontario’s controversial ban on pit bulls, enacted more than a decade ago, was supposed to reduce the number of dog bites. It didn’t work out that way, City of Toronto bite statistics show.<\/p>\n I illustrated our regional setup stories for the 2015 federal election with poll-level interactive maps of the 2011 election. For the Ontario one, which depicts about 24,000 polls, it was challenging to create a map that loaded smoothly. In the end, I created a map which drew on four different Fusion Tables maps on the back end.<\/p>\n <\/p>\n The leak of the Ashley Madison user data in August, 2015 created technical challenges because of the size of the databases, and also ethical challenges in how it could be used. We used the leaked data for several exclusive stories<\/a><\/strong>.The data included geotags created by users’ mobile devices which showed, often contrary to their claims, that over 1,200 were men seeking men in conservative countries where same-sex activities are harshly punished.<\/p>\n Investigation: Slowly, Toronto firefighters become more diverse<\/a><\/strong> Chequed out: Inside the payday loan cycle<\/a><\/strong> CBSA learned of its own detainee\u2019s death by accident \u2013 three weeks later<\/a><\/strong> Toronto taxi licence prices are plummeting. Is Uber to blame?<\/a><\/strong> Ford Nation 2014: 14 things demographics tell us about Toronto voters<\/a><\/strong> What 24,903 polls taught us about Ontario\u2019s shifting political landscape<\/a><\/strong> Mapping Canada\u2019s D-Day dead<\/a><\/strong> Here\u2019s the sex offender map Ontario didn\u2019t want you to see<\/a><\/strong> Toronto\u2019s vanishing grow-ops fall victim to pot economics<\/a><\/strong> Sun in your eyes: \u2018Torontohenge\u2019 sees accidents spike<\/a><\/strong><\/p>\n \u201cTorontohenge,\u201d when the setting sun aligns with Toronto\u2019s east-west street grid and forces drivers to squint through salt-crusted windshields, coincides with the third-worst day of the year for car accidents, data shows.<\/p>\n Exclusive: Newer cars almost never fail Drive Clean. Why keep testing them?<\/a><\/strong><\/p>\n Almost no one fails Drive Clean. But it\u2019s needed anyway, Wynne argues<\/a><\/strong><\/p>\n Nearly all recent car models pass their Drive Clean tests, Ontario government data shows. The story called the point of the mandatory testing program into question. The government defended it, but later dropped the $30 fee. The story won an RTDNA award<\/strong><\/a>.<\/p>\n\n
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\nLow- and middle-income families vanish as urban neighbourhoods gentrify<\/strong><\/a><\/p>\n<\/p>\n![\"attawapiskat\"](\"http:\/\/www.patrickcain.ca\/wp-content\/uploads\/2016\/05\/attawapiskat.jpg\")
<\/a><\/p>\nAttawapiskat\u2019s suicide emergency is no surprise to anyone paying attention<\/a><\/h1>\n
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\nToronto\u2019s pit bulls are almost gone. So why are there more dog bites than ever?<\/a><\/strong><\/p>\n<\/p>\n![\"election\"](\"http:\/\/www.patrickcain.ca\/wp-content\/uploads\/2016\/05\/election.jpg\")
<\/a><\/p>\nInteractive maps: In Ontario, 20 unpredictable races to watch<\/a><\/h1>\n
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\nToronto’s population is 49 per cent visible minorities, but its fire department is over 96 per cent white, something that only recently attracted controversy. We used data on the postal codes of successful fire department applicants to show that Toronto firefighters have mostly been hired from areas outside the city with few immigrants and visible minorities. In the process, we produced the first really thorough look at race and firefighting in Toronto.<\/p>\n<\/p>\n![\"\"](\"http:\/\/static.globalnews.ca\/content\/interactives\/images\/general_news_images\/150331_gen\/9.jpg\")
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\nMaps show how cheque-cashing outlets are placed in census tracts with low income and high welfare rates. Once the maps made the connection impossible to deny, we could move on to a discussion of why it exists.<\/p>\n<\/p>\n![\"\"](\"http:\/\/static.globalnews.ca\/content\/interactives\/images\/general_news_images\/150331_gen\/8.jpg\")
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\nThis is not obviously a data story, but is in the background: it started life with a database related to immigration detention that we requested under access-to-information laws, and followed up with further access requests about specific custody deaths. The end result was a story about how border officials lost track of a detainee to the point of not knowing about his death, in a local jail, until three weeks after it had happened. It is part of a larger series on immigration detention in Canada.<\/p>\n<\/p>\n![\"\"](\"http:\/\/static.globalnews.ca\/content\/interactives\/images\/general_news_images\/150331_gen\/7.jpg\")
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\nWe used a database of Toronto taxi licence prices obtained under access-to-information laws to show how the value of taxi plates has collapsed since Uber entered the local taxi market. In the process, we ventured into Toronto’s tangled, thorny taxi politics.<\/p>\n<\/p>\n![\"\"](\"http:\/\/static.globalnews.ca\/content\/interactives\/images\/general_news_images\/150331_gen\/6.jpg\")
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\n Poll-level maps show Toronto\u2019s election in extreme detail<\/a><\/strong>
\nLocal elections in Toronto in October ended Rob Ford’s long, troubled tenure as Toronto’s mayor. After the vote, we produced detailed poll-level maps looking at the plurality winner by poll, and a series of scatterplot graphs mashing up demographic data with the election results:
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\nWe used Toronto traffic charge data and an accident database obtained under access-to-information laws to look at ‘dooring’ accidents involving cyclists: where the city’s worst areas are (and why) and questioning why many incidents didn’t result in charges.
\nINTERACTIVE: Why College Street is Toronto cyclists\u2019 \u2018dooring zone\u2019<\/a><\/strong>
\nThen, we looked at similar data for Vancouver, which shows that police there seldom use British Columbia laws on the books to charge drivers after cyclists are doored.
\nPeople seldom charged when cyclists get \u2018doored,\u2019 Vancouver data shows<\/a><\/strong><\/p>\n<\/p>\n![\"\"](\"http:\/\/static.globalnews.ca\/content\/interactives\/images\/general_news_images\/150331_gen\/4.jpg\")
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\n Your guide to Ontario\u2019s political polarization (have fun governing that)<\/a><\/strong>
\nWe plugged demographic data from the census into the results of a provincial election in Ontario, creating a series of scatterplot graphs looking at political choice in relation to a number of factors including education, commuting mode (car vs. transit), immigration and income. We also used Fusion Tables to map results from the province’s 24,903 polling stations on a one-stop-shop map, with a similarly detailed map of the 2011 election for contrast. The maps also showed each party\u2019s support broken out separately.
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\nWe mapped Canada’s D-Day casualties down to the household\/community level, tracing addresses through contemporary newspaper reports, historians, previous overlapping research, reader contributions and online resources. Of 388 Canadians and Newfoundlanders who died on the first day of the landings, we were able to trace at least a partial next-of-kin address for all but 15.<\/p>\n<\/p>\n![\"\"](\"http:\/\/static.globalnews.ca\/content\/interactives\/images\/general_news_images\/150331_gen\/2.jpg\")
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\nRelated stories:<\/b>
\nUnanimous Supreme Court decision ends six-year FOI ordeal<\/a><\/strong>
\n Sex offender case a \u2018colossal waste of money’: Tory jail critic<\/a><\/strong>
\nFollowing a six-year legal battle that ended<\/b><\/a> in the Canadian Supreme Court, we published a map of Ontario’s registered sex offenders broken down by partial postal codes. By then, the process of getting the data had long since eclipsed the data itself as a story.
\nCanadian three-character postal ‘forward sortation areas’ have populations in the tens of thousands, making them large enough to protect the anonymity of a few individuals. The map struck a balance between showing geographic patterns and not endangering individuals. For registered sex offenders, which have been attacked in other jurisdictions, this was a particular concern. The Supreme Court decision<\/a><\/strong>, which was unanimous, changed an aspect of Canadian freedom-of-information law which has to do with the burden of proof that has to be met by government bodies wanting to use a law-enforcement exemption. The story won an RTDNA award<\/strong><\/a>.<\/p>\n<\/p>\n![\"\"](\"http:\/\/static.globalnews.ca\/content\/interactives\/images\/general_news_images\/150331_gen\/1.jpg\")
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\nWe looked at police data obtained under access-to-information laws showing a sharp drop in marijuana grow operation busts in Toronto, and explained how it was connected to the fall in marijuana prices in the United States. Behind the data were addresses of about 1,400 grow ops requested from Toronto police under access-to-information laws. The story featured a hex map<\/a><\/strong>, one way around the legal and practical problems of displaying all 1,400 points.
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\nMy first cartogram map <\/a><\/strong>was made for the last papal election. It shows the disparity between voting cardinals and the distribution of Catholics.<\/p>\n![\"pope\"](\"http:\/\/www.patrickcain.ca\/wp-content\/uploads\/2016\/04\/pope.jpg\")
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\nThis visualization of voting patterns<\/a><\/strong> in Toronto’s city council in the early Rob Ford years using NodeXL was modeled to an extent on this relational diagram<\/a><\/strong> of U.S. Senate voting patterns. Parliamentary government doesn’t lend itself to very interesting voting diagrams, but municipal councils can. The diagrams, which show the likelihood of any given councillor voting with any other given councillor, show the breakup of the once-tight group of councillors supporting Rob Ford. Click on the image for an interactive version.<\/p>\n
![\"ford\"](\"http:\/\/www.patrickcain.ca\/wp-content\/uploads\/2016\/04\/ford.jpg\")
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