13:01:47 So for everyone listening, we are recording. So.
13:01:50 We're now recording.
13:01:57 So welcome.
13:01:58 This is our first seminar collab between canoe
13:02:02 and the geo health network.
13:02:05 My name is Lauren, and I'm the chair of geo health network.
13:02:08 We've also got Naomi and Megan here on the line from geo health.
13:02:14 So we are a graduate student run group.
13:02:17 Although some of us have not graduated.
13:02:29 I'm based out of the university of Toronto.
13:02:32 And we originally started this group because we
13:02:35 wanted to provide opportunities and create more space for
13:02:39 people looking to.
13:02:40 Bridge the gap between their health.
13:02:42 Skills and knowledge and their geography skills and knowledge.
13:02:45 And we found that there's so many people across Canada and around the
13:02:48 world who want to learn and share with each other in this
13:02:52 space. But typically.
13:02:53 Each group has one health geographer I'm scattered between
13:02:58 different disciplines.
13:03:05 So it's less common that there's a lot of us working together in one
13:03:09 space.
13:03:10 So we thought it would also be a nice opportunity to come together
13:03:13 more and learn from each other and share. So.
13:03:15 We got together with canoe.
13:03:17 Because canoe is a fabulous data consortium and they hold an
13:03:20 incredible amount of data and support several different research
13:03:24 initiatives.
13:03:30 And this is a great opportunity for everyone to learn about data
13:03:33 that's available, how it's used,
13:03:35 and maybe how you can start to integrate some canoe data in your
13:03:38 research. So,
13:03:44 We also, before I introduce our two speakers today,
13:03:47 we have a wonderful prize donated by Springer text
13:03:51 and Dr. Valerie crooks at Simon Fraser.
13:03:55 So we have one person is going to win two
13:03:58 textbooks.
13:03:59 From a new health geography series edited by Valerie crooks.
13:04:03 So in order to win that you just need to be a student college,
13:04:06 undergrad master's PhD.
13:04:08 I'm going to drop a Google form in the chat.
13:04:10 And just fill that out anytime today or tomorrow.
13:04:16 And then next week,
13:04:17 we're going to randomly draw the winner and we'll get in touch with
13:04:21 you and Springer text.
13:04:22 We'll let you pick from a list. I'll also put the list in the chat.
13:04:25 You can pick any two awesome health geography books that you'd like.
13:04:32 And, and a Springer texts will mail those to you.
13:04:34 So thank you so much to Valerie crooks and Springer texts.
13:04:41 And we're also supported by university of Toronto school of cities and
13:04:44 department of geography and planning.
13:04:45 So we can not do so much without their help.
13:04:54 So now I will pass it over.
13:04:57 So happy to welcome our speakers today, we have Dr.
13:05:00 Eleanor satin and Dr. Danny [unknown] from canoe.
13:05:03 So I'm going to stop sharing my screen and allow Dr.
13:05:06 Satiny to go ahead.
13:05:10 Hi, everyone. Just give me one second to get this screen share going.
13:05:28 All right. Well,
13:05:29 thanks everyone for taking time to join us here today.
13:05:32 And it's really nice to see people from all over the world joined.
13:05:35 This is really cool,
13:05:36 and we're really proud of what we're doing here at canoe. And.
13:05:39 Canoe is actually quite unique in the world.
13:05:41 There's nobody really doing it like we're doing it.
13:05:43 And I hope to show, show you today how simple it is to.
13:05:46 Get into the canoe data and start using it.
13:05:49 And Danny's going to show you some of.
13:05:51 How we've been doing it, we're using it ourselves.
13:05:52 So, of course it's not just Danny and I I'm the managing director for
13:05:55 canoe.
13:05:56 Oops. Hang on.
13:06:01 And my background is in environmental exposure. I'm the GIS.
13:06:05 Geek in the group. Right.
13:06:06 It's me.
13:06:08 And I'm going to give you sort of the overview and tour.
13:06:18 And Danny of course is our link between, you know,
13:06:21 the canoe data and health data,
13:06:22 but we've also have data scientists on, on team and.
13:06:26 Group of really interesting principal investigators that include sort
13:06:29 of the leading researchers in Canada, around environment and health.
13:06:32 And our scientific director is Jeff Brooke.
13:06:34 Who's at the university of Toronto.
13:06:37 Done a little bit on the school of public health.
13:06:39 So just visit the new website and you see more about who our team is.
13:06:45 So I'm just going to start and give you a just it's really a high
13:06:47 level tour. Nothing you can't do on your own,
13:06:49 but it's probably worth me showing you a few of the ropes here.
13:06:51 And I'm going to focus.
13:06:52 Oops. Sorry.
13:06:53 That's right. You asked us to talk about our career journeys.
13:06:56 Didn't you.
13:06:57 All right. So here's where I guess I embarrassed myself.
13:06:59 I got a BA in environmental geography from UBC.
13:07:04 And when I'm talking to students, I, I laugh and say on my final exam.
13:07:14 I got the question wrong. What is GPS stamp for?
13:07:17 And it's not because I was a bad student.
13:07:18 It's just that it was that long ago that GPS.
13:07:21 Wouldn't be wasn't commonly being used anywhere.
13:07:33 So you can guess that's like the 1990s way back when,
13:07:36 so there was just one course in geography in G I S when I was at UBC
13:07:40 and it was all command-line arc GIS was just starting.
13:07:43 [unknown].
13:07:45 Greasy was around sort of rosters.
13:07:46 But yeah, it was, it was pretty early days for that.
13:07:49 And so basically you could just take one undergrad course in GIS.
13:07:53 But I was really captivated by it. I thought this is really gonna,
13:07:57 you know,
13:07:58 give me a great technical skill that will take me in lots of places.
13:08:01 And that was absolutely true. So I'm happy that I.
13:08:03 I landed there.
13:08:04 I immediately followed on with the MSC at the university of Victoria.
13:08:24 Looking at some, you know,
13:08:25 mundane statistical stuff and then jumped right under that.
13:08:28 And pretty much into contracting and consulting in resource
13:08:31 management, impact assessment,
13:08:32 using all my GIS skills for mapping and doing spatial analysis.
13:08:35 So it was a lot of fun, but my heart was really more green than that.
13:08:39 And I wanted to do something that made more impact on the environment
13:08:42 and I thought environment and health.
13:08:43 You know, I had seen a lot of the work I'd done being about wildlife.
13:08:47 And while that is compelling, I thought, you know,
13:08:48 if you can show people that they're being impacted by their
13:08:51 environment or their kids are getting sick from it,
13:08:52 then maybe that will be a stronger message for us to make a change.
13:08:55 And so that's why.
13:08:56 I went back and did a PhD. I'm specifically thinking about.
13:08:59 How do we look at environmental exposures and connect those with
13:09:02 people? So I hadn't any,
13:09:04 hadn't had any epidemiology background at all and, and.
13:09:07 And I'm picked some of that up,
13:09:08 but I work closely with epidemiologists, right? So I still,
13:09:11 my real expertise is still more in the spatial GIS data side of
13:09:14 things.
13:09:15 So once I finished my PhD,
13:09:16 I pretty much stayed at Ubik and became an adjunct.
13:09:19 Around the spatial sciences research lab worked for Careks Canada,
13:09:22 which is a carcinogen exposure.
13:09:24 Project. And then for canoe, where I'm the managing director.
13:09:26 So GIS has been good to me for sure.
13:09:28 So just sort of to set the stage.
13:09:30 When we think about exposure, when we talk about exposure,
13:09:33 It's about things that vary over space and time.
13:09:37 Air pollution, noise and greenness climate walkability.
13:09:39 They're not all bad.
13:09:40 Right. Some are goods.
13:09:50 And I think when I talk about what I do,
13:09:52 people are often surprised when you say, you know,
13:09:54 do you think Canada has good air quality? Most people would say, yeah,
13:09:56 we've got great air quality in Vancouver.
13:10:05 You know, super fresh air there,
13:10:06 but the reality is it's very different across space and time for
13:10:10 people living in cities. If you live close to a busy road,
13:10:12 your air quality can be as bad as it is in a third world country.
13:10:15 So even though we like to think Canada.
13:10:18 You know, is a super country is surprising that we can actually find
13:10:22 the impacts on population health,
13:10:24 of all kinds of different exposures like this even here,
13:10:26 where we feel like we're well-developed and.
13:10:28 In our environments. Good.
13:10:29 So we take these exposures and essentially from canoe,
13:10:31 we support epidemiological studies.
13:10:33 So looking for, is there a relationship with a health outcome? If so,
13:10:37 how strong is it?
13:10:38 We also use these same data for what we call exposure surveillance.
13:10:49 You know, so where is exposure high? You know,
13:10:52 is this where a lot of people are living? Is this changing over time?
13:10:54 And both of those kinds of studies really support a policy.
13:10:58 So if we change this.
13:10:59 You know, what's going to happen to health.
13:11:01 What's going to happen to equity.
13:11:05 You know,
13:11:06 are we reducing or increasing exposures in a good way over time as we
13:11:09 change urban form.
13:11:10 So that's really the basic driving force for canoe is to try and
13:11:13 tackle these questions.
13:11:14 With lots of data.
13:11:15 So before canoe was around essentially most research teams in Canada
13:11:19 sort of found their own person.
13:11:25 Maybe borrowed some data from somewhere. If they could,
13:11:27 if they were all looking at air pollution,
13:11:28 they might all come up with their own different kinds of metrics from
13:11:31 different sources.
13:11:32 So a lot of burden on each individual team to,
13:11:34 to come up with good exposure data.
13:11:45 And then for health data that has to go into a secure system, right?
13:11:49 We never actually get people's confidential health data.
13:11:51 It has to go into their secure servers where it gets linked.
13:11:54 And essentially that gets done with postal codes.
13:11:55 But before canoe,
13:11:57 all these teams would be hitting all these different health databases,
13:11:59 you know?
13:12:00 So the people running the health databases would be getting requests.
13:12:11 You know,
13:12:12 maybe to even really link the same data for different projects,
13:12:15 not very efficient. And then, you know, pushing out papers from that,
13:12:18 which is great,
13:12:20 but it essentially makes for studies that might be difficult to
13:12:23 compare because they're not quite using the same metrics.
13:12:25 All of the results might not coincide. Exactly.
13:12:28 Difficult to reproduce the study.
13:12:29 If you're five years later trying to, you know, do it again,
13:12:32 where did that exposure data go?
13:12:33 Difficult for people to reuse it. So sharing between as well.
13:12:37 And super time consuming and redundant for those,
13:12:39 those health databases.
13:12:40 They've they've got lots on their plate.
13:12:46 So canoe was established in 2016 to kind of take care of these kinds
13:12:50 of problems. So what we do is we take,
13:12:52 we take in all the data from and create data from a wide variety of
13:12:55 different sources.
13:12:56 We standardize it. We index it street to postal codes.
13:13:01 And this means that the data that most,
13:13:03 most of our data now is for academic use.
13:13:05 The postal codes are actually a licensed issue.
13:13:08 So you should have an academic.
13:13:13 Affiliation in Canada or someone on your team with that to use the
13:13:16 data, but essentially we've become, you know,
13:13:18 the data aggregator where we create high quality,
13:13:21 standardized documented data.
13:13:23 We can send it to the secure health data places they can just link at
13:13:26 once.
13:13:27 And then they can make it available anytime they want to.
13:13:29 Any number of studies.
13:13:31 So we've really cleaned up this issue of just kind of having one-off
13:13:35 studies all over the place. Right.
13:13:36 So people now can get standard metrics.
13:13:39 I can use exactly the same data that somebody else used on their
13:13:41 papers, you know, just by.
13:13:43 You know, we're using the canoe data.
13:13:48 Anybody can get to it.
13:13:49 And the health data holders just need to bring in the data and link at
13:13:51 once and they can make it available to everyone.
13:13:56 So that's been a real big change and there isn't really anybody else
13:13:59 in the world that's working like this.
13:14:00 There's a few big cohort studies who are.
13:14:03 You know,
13:14:04 that have cohorts in different parts of Europe and they have a central
13:14:07 place, but there's nobody doing this really at a national scale.
13:14:10 I think it's been really successful. This is sort of our one pager,
13:14:13 but.
13:14:14 Essentially, what you can see is, you know, on this side,
13:14:24 What we do is we bring in a wide variety of data from all kinds of
13:14:27 sources, from satellites,
13:14:28 from monitors models that researchers have built,
13:14:31 and we convert it all into.
13:14:33 Index at all to post the code locations. Right?
13:14:35 So for every single data set,
13:14:37 we assign values to every postal code in Canada.
13:14:42 So that's about over 800,000 locations and we're doing this sometimes
13:14:45 as far back as 1983, 1984. So you can get a big time series.
13:14:49 That data goes into the confidential health.
13:14:53 Spots. And so far we've in a few years,
13:14:56 had more than 200 separate data projects go through.
13:15:16 I'm serving, you know, almost 300 students and researchers,
13:15:19 the papers are starting to come out more than 40 peer reviewed papers
13:15:22 using canoe data and students are starting to complete their
13:15:26 thesis doing it. So in, you know, a fairly short amount of time,
13:15:29 you can see that people really wanted to use the canoe data and it's
13:15:32 being super valuable.
13:15:33 Now, if you want to use canoe data,
13:15:35 basically of the canoe data portal,
13:15:36 you can go and look at this anytime it's up@canoedata.ca.
13:15:39 We're always adding new data to it.
13:15:41 Right now we've got quite a lot of data on satellite greenness.
13:15:47 And each one of these is a data set, which has its own variables.
13:15:49 So for example, in this landset and you all greenness,
13:15:53 that's in a measure of, of greenness from.
13:15:55 At 30 meters.
13:16:00 They'll be different metrics of that.
13:16:01 So what's the average grievance within a hundred meters of this postal
13:16:04 code within 500 meters of the postal code and so on.
13:16:07 So these data set.
13:16:08 Represent hundreds and hundreds of different variables.
13:16:11 In the neighborhoods.
13:16:12 We've got everything from active living through to socioeconomic.
13:16:16 Indicators of a deprivation marginalization.
13:16:19 Interesting.
13:16:20 How close are these postal codes to roads to water?
13:16:24 Even stuff about urban farm, you know, is it gentrifying?
13:16:31 How green are the roads, right? How much urban sprawl is there?
13:16:34 Lots of great air quality data.
13:16:36 As well as climate, so maximum temperatures, precipitation, et cetera.
13:16:51 So there's a lot of information there in every single one of these
13:16:53 data sets is index to the exact same set of postal codes and
13:16:56 locations year after year after year.
13:16:58 So it's really easy to cut and paste them together.
13:17:01 Integrate them into your studies.
13:17:02 It's. Yeah, so great dataset.
13:17:04 If you want to get the data, basically just make a data request.
13:17:07 It's pretty simple.
13:17:08 It's not what they call, clip, zip and ship. Right.
13:17:11 It's not automatic where you just go select some data and get it.
13:17:14 Cause you do have to show that you've got this academic affiliation.
13:17:20 But it's pretty straight forward. When you do a data request,
13:17:22 you'll see this first scene.
13:17:23 Basically you have to fill out this form online.
13:17:26 Download and sign the sharing agreement.
13:17:28 Oh, it's pretty straight forward. Your project.
13:17:32 A password, a basic summary.
13:17:33 So we're not looking for two page pages, just the basic idea.
13:17:37 Your email.
13:17:42 Any team members that you want to have and access to
13:17:46 your team data.
13:17:47 And that's it basically, then you'll download.
13:17:49 This basic agreement that gives the sharing.
13:17:52 Talks about not being able to.
13:17:59 Share it outside of academic uses.
13:18:01 So you're agreeing that you have an academic affiliation.
13:18:04 That you're going to abide by the data sharing you sign it.
13:18:08 And you're awake to the races and then we activate your project
13:18:11 immediately.
13:18:13 For data download, basically, you'll just come back in.
13:18:15 You'll put in your project.
13:18:17 And then you'll be able to select any of the data sets that I showed
13:18:20 you.
13:18:25 Any particular variable that goes in that dataset any
13:18:28 particular year or all years. And you can select it.
13:18:30 By Canada or by province.
13:18:31 And once you've done that, you basically get a quick CSV file.
13:18:36 A download and there you go. So you'll get a zip file.
13:18:43 When you unzip it, you'll have whatever you picked.
13:18:45 I just picked an annual greenness for 2019, just for BC.
13:18:53 And it'll just opens up into a CSV file that you can open an Excel.
13:18:56 They are about 800,000 rows long.
13:18:59 So you need to have a current version of Excel.
13:19:01 That'll give you up to a million, but basically it comes in.
13:19:03 You can see it's a postal code.
13:19:04 An X, Y, and the value.
13:19:20 And the metadata will tell you that comes along with it will tell you
13:19:23 what each variable is.
13:19:24 So this postal code is important because right now our main audience
13:19:28 is health researchers.
13:19:29 And when they want to link these variables to people who they have
13:19:33 health information for.
13:19:34 They've got their address and they get this one-to-one link on the
13:19:36 postal code. Right?
13:19:38 So that's why we do this is complete it's to facilitate health data
13:19:41 research. And I mean, this is a, a huge.
13:19:44 Facilitation, you know, from going from the exposure data.
13:19:47 Into the health databases to have it formatted like this,
13:19:50 just with the six digit postal code,
13:19:51 it's just a snap for health data researchers to just make this quick
13:19:54 one-to-one link.
13:19:55 Nice and easy.
13:19:57 And then for us GIS people, it's also easy to pull that CSV file into.
13:20:06 Your favorite software,
13:20:07 turn it into a map that you can use.
13:20:10 And this is the variable I downloaded. This is the, you know,
13:20:12 greenness values in the Vancouver area.
13:20:18 So you can do lots, but it is interesting to see it is by postal code.
13:20:21 This is not a flat roster surface. If you're interested in that,
13:20:24 hopefully in the next six months to a year, we'll start.
13:20:31 Banking,
13:20:32 some of these things that are not connected to two postal codes so
13:20:35 that people can use them without the DMT I licensed,
13:20:38 but that's kind of a next phase for us.
13:20:39 Our first phase was really focusing on this, this health piece.
13:20:42 And supporting health research and I think that's going great.
13:20:44 So looking forward before I turn it over to date.
13:20:47 Danny.
13:20:48 We've got always got new data coming. Right.
13:20:50 So we've been starting to work with more open source stuff.
13:20:52 So OSM derived layers.
13:20:53 We've been using the building footprints.
13:20:55 Which is pretty cool.
13:20:56 That's a North America set with every boat building footprint.
13:20:59 I'm in Canada in the us and thinking how we can use those data.
13:21:08 And we've been looking at higher resolution satellite stuff,
13:21:10 or like the three meter planet data,
13:21:12 and we've got noise transportation and some other data sets come in
13:21:15 for select cities.
13:21:20 One thing we're interested in promoting is the idea of a hackathon.
13:21:23 We're interested to hear how interested you guys are.
13:21:26 Maybe in creating data that could come into the canoe portal.
13:21:29 What new metrics could you think of that?
13:21:31 Maybe use some of our input data or your own new input data.
13:21:41 And if we pick a winner, I mean,
13:21:42 those data will go straight into the hands of the leading researchers
13:21:45 to do work. And I think that'd be really cool.
13:21:47 I think there's some real smart people out there on the GIS side,
13:21:51 we think creatively about.
13:21:52 How we make the data, it would be great.
13:21:54 A great way to connect with the health researchers.
13:21:56 And then just finally, we're going to have a virtual,
13:21:58 a general meeting May 18th with some cool speakers.
13:22:01 And options for feedback.
13:22:03 And so we really hope that you'll come and attend and give us your
13:22:06 ideas on what you think canoe can do for you.
13:22:08 Over the next five years and, and what we should be doing.
13:22:10 So with that, I'm going to pass it over.
13:22:12 To Danny.
13:22:20 Elena. Thank you so much.
13:22:21 We had two questions come up. Maybe before we hear,
13:22:25 take a deep dive into the paper.
13:22:26 So the first one is from Tate in Calgary.
13:22:31 So tapes asking if you can speak a bit about what the
13:22:34 transportation.
13:22:42 Data set is that you have at the moment and maybe if
13:22:46 there's any talk of what other transportation metrics that can you is
13:22:50 talking about at the moment.
13:22:51 Sure.
13:22:52 So the transportation data that will be coming in the next few months
13:22:55 was developed.
13:22:56 By Marianne house blue at university of Toronto.
13:22:59 It's gonna be met Del housey dance.
13:23:02 I got three. I can't remember he's from,
13:23:03 so these are people that are transportation engineers.
13:23:06 Essentially. And so for, okay.
13:23:08 I'm sorry. I'd have to remember all the cities, but essentially.
13:23:27 I think it's Vancouver, maybe not Vancouver, Montreal, Toronto,
13:23:30 Halifax, Winnipeg,
13:23:32 the outputs of really detailed trans transportation modeling.
13:23:35 So I don't know if you know, the M M E M M E the Emmy models.
13:23:38 So this is actual traffic flows per segment for time of day.
13:23:41 Broken out into a fleet mix.
13:23:43 So I think that's going to be really interesting for people that are
13:23:46 interested at looking at noise and air quality in those specific
13:23:49 cities, you can do some really detailed work.
13:23:51 Was that not only just connecting it to health,
13:23:53 but even just thinking about the equity, you know,
13:23:55 where are the noisiest places? Where are the.
13:23:57 Highest emissions. And how does that intersect with low income?
13:24:04 You know, communities, perhaps.
13:24:05 So it's the kind of thing that you can't really do nationally.
13:24:08 Every city is there's so intensive inputs to that. It's,
13:24:11 it's tough to do them for every city,
13:24:12 but we will have them for a number of big ones.
13:24:14 Otherwise on the sort of higher end looking at open street map,
13:24:17 just pulling out, you know, intersection density.
13:24:20 A transit stop density, those kinds of things that we're doing now.
13:24:23 So it's kind of like,
13:24:24 Big scale.
13:24:25 Small scale.
13:24:26 That's great. Darren Scott is at McMaster.
13:24:29 Yeah.
13:24:30 He taught me in.
13:24:43 So we have a few more questions and
13:24:47 maybe we'll just take one or two more that are the higher data level.
13:24:51 And that way we can save the rest for the admin.
13:24:54 We will have time.
13:24:55 So I can answer quickly that there's a question.
13:24:57 Does canoe have any water?
13:24:59 Quality and air quality data. They, they certainly do.
13:25:06 So that is on the canoe website and there is excellent metadata
13:25:10 that you can read through,
13:25:11 like Eleanor mentioned going back to the 80s, so you can really see.
13:25:15 How that's changed over time.
13:25:16 And water quality, not water quality, water quality.
13:25:19 Just air quality.
13:25:20 And.
13:25:24 There's a question here from Malcolm,
13:25:25 our polygon shape files of aerial extent. Ready-made for download.
13:25:39 No. Yeah, we're not doing that right now. I mean, eventually,
13:25:42 eventually we might have like, like I said, this idea of the clip,
13:25:45 zip and ship idea where you have a shape file that you're interested
13:25:48 in, that you can upload and extract for that,
13:25:49 but we're not doing it at this point.
13:25:50 Right now it's essentially, you know, pick a province.
13:25:53 If you're interested and then you would clip that yourself to whatever
13:25:56 you're interested in.
13:25:57 We do special requests for sure.
13:25:58 And so people often do send us shape files and we'll extract.
13:26:01 Data.
13:26:02 You know, specifically to that, but we're a pretty small team.
13:26:04 So we can't do that all the, all the time.
13:26:07 We do our back for sure.
13:26:08 And just two more questions that I, I feel like.
13:26:18 Are a bit quicker, maybe not. We'll see.
13:26:20 There's a question here are data sets only available for academic
13:26:23 researchers or could public like government agencies.
13:26:33 Get licensed. So right now it's just for academic use.
13:26:36 Some government agencies have a license for the DMT postal codes.
13:26:40 So if you're an academic,
13:26:41 you can get all the DMT I data through your library.
13:26:44 And so that's what we're using.
13:26:45 But we are definitely in this next phase,
13:26:47 going to start producing more open use data. We can.
13:26:51 Certainly use a non OSA.
13:26:53 The code index data.
13:26:54 So some people might want to just use the input rosters.
13:26:57 We might be able to aggregate it.
13:27:06 Right, but we're not there yet. It's coming. And then finally,
13:27:08 what about international collaborations? For example,
13:27:10 if someone who was academically affiliated in the United States wanted
13:27:14 to use, can you data in Canada or.
13:27:17 Do you have international collaborations or expanding international
13:27:20 partnerships on the horizon?
13:27:22 We do certainly welcome all,
13:27:25 but there has to be a Canadian researcher in the team.
13:27:27 Okay. That's helpful.
13:27:30 At this point, once we have more open data, that's,
13:27:33 that's not connected to this DMT I licensed,
13:27:35 then your shirt would be wide open.
13:27:36 Okay.
13:27:37 And then there's just one more.
13:27:39 Can the data overlay on census geographies?
13:27:55 And I would say yes. And that's a great GIS scale for you to practice.
13:27:59 I'd say that's probably, that's the first thing I'd be doing.
13:28:01 Cause I'm, I'm a little obsessed with the Canadian census.
13:28:05 So that's my favorite thing to do is see how these data intersect with
13:28:09 the census.
13:28:10 Okay, so we're going to pass it over to Danny.
13:28:14 And I'll just ask to the,
13:28:15 the easiest way for us free to ask questions is if you use the Q and a
13:28:18 tool, which is at the bottom.
13:28:19 Of your screen. Cause the chat can be a bit hard for us to navigate.
13:28:23 So feel free to use the Q and a tool.
13:28:25 And at the end of Danny's presentation,
13:28:28 we'll have a chance to get to your questions again.
13:28:30 Thanks, Laurie.
13:28:31 So hi everyone. And thanks for joining the webinar today.
13:28:41 I'll also start by giving a bit of background about myself.
13:28:44 So I have a BA with concentration in geography and biology
13:28:48 from Concordia university.
13:28:50 And after my bachelor's I enrolled in a master's in
13:28:54 public policy at Simon Fraser university.
13:28:57 Where I.
13:28:59 I looked at the intersections between environmental policy,
13:29:02 public health and your urban planning.
13:29:03 And, and through an internship that I did.
13:29:10 Within this program,
13:29:11 I got access to data from British Columbia as a provincial health
13:29:15 services authority.
13:29:16 To look at relationships between built environment features.
13:29:19 And active transportation in the lower mainland.
13:29:23 And after completing my masters in Vancouver.
13:29:28 I moved back to Montreal and started working as a research associate.
13:29:31 Assistant at a maelstrom research.
13:29:34 Which was originally based at invested, not here.
13:29:46 But eventually moved to Miguel.
13:29:47 And so my work at Malmstrom focused on,
13:29:49 on developing solutions for harmonizing and combining data from large
13:29:53 epidemiological cohort studies.
13:29:54 Across Europe and North America.
13:29:58 And contacts that I made through these projects led me to embark
13:30:02 on a PhD in epidemiology at the university of Basel in Switzerland.
13:30:11 Which they completed in 2019. And my,
13:30:15 my thesis work as a PhD student focused on chronic
13:30:19 respiratory health effects of ambient air pollution exposure.
13:30:22 Since completing my PhD.
13:30:24 Dedicate half of my time at the respiratory epidemiology and clinical
13:30:27 research unit at McGill.
13:30:29 And the other half as a canoes data linkage lead.
13:30:37 So in my part of the presentation today, I'll, I'll,
13:30:39 I'll outline findings from a recently published study that shows.
13:30:43 How data sets held and distributed by canoe can be used to explore
13:30:46 environmental equity issues.
13:30:48 And intersections of multiple urban environmental exposures and
13:30:52 Canadian cities.
13:30:53 You can move to the next slide, please.
13:30:54 Thanks.
13:31:03 So we had three specific objectives for this project. First,
13:31:06 we wanted to explore the distribution and,
13:31:09 and intersections of walkability.
13:31:12 Nitrogen dioxide, air pollution and greenness within Canada.
13:31:15 Three largest cities.
13:31:17 Secondly,
13:31:19 we wanted to assess how environmental factors are distributed across
13:31:23 a neighborhood socioeconomic gradients.
13:31:30 And last we wanted to identify potentially favorable parts of each
13:31:34 city. So, so areas that are correct realized by high walkability,
13:31:37 high greediness and lawyer air pollution.
13:31:39 Which what we referred to as sweet spots.
13:31:41 And areas characterized by low walkability,
13:31:43 low greenness and high air pollution.
13:31:45 What we called sour spots in the paper.
13:31:46 To achieve these objectives. We use nationally standardized data.
13:31:49 Index to six digit postal codes.
13:31:51 So to assess neighborhood walkability.
13:31:58 Within your city,
13:31:59 we use the Canadian active living environments index,
13:32:02 or Ken ale dataset.
13:32:04 For greenness, we use satellite derive in DVI data.
13:32:15 And our third environmental exposure was modeled.
13:32:19 I know to air pollution, which,
13:32:20 which largely estimates air pollution from traffic sources. And again,
13:32:23 all these data sets are available via canoe.
13:32:25 Next slide, please, Eleanor.
13:32:36 So to examine environmental equity,
13:32:39 we looked at how extreme exposures are distributed across material
13:32:43 deprivation in each city.
13:32:44 And so the material deprivation data that we used.
13:32:47 Is an index of, of education,
13:32:51 employment, and income, which we derive from a Canadian census.
13:32:55 And index the postal codes.
13:32:56 And so this, this work involved.
13:32:58 A few steps. So first we split exposure to her towels.
13:33:05 Based on within city exposure, distributions,
13:33:08 and then we divided the material deprivation index into city specific
13:33:11 Quintiles.
13:33:16 We then calculated prevalence rates or proportions of postal codes
13:33:20 falling in low and high exposure to hotels within each deprivation.
13:33:24 Quintile.
13:33:25 And this.
13:33:39 This proportion was then divided by the overall proportion of low and
13:33:42 high exposure in each city. So, so, so in the end,
13:33:45 this calculation essentially allowed us to identify the
13:33:48 prevalence of high and low exposure in each deprivation, quintile,
13:33:52 relative to citywide prevalence of, of that exposure.
13:33:55 Secondly to,
13:33:57 to explore a spatial distribution of favorable and unfavorable areas,
13:34:01 we simply overlaid favorable turtles of exposures.
13:34:04 So low air pollution, high greenness, and high walkability.
13:34:06 It's a nine to five where they intersected and did the same for four
13:34:10 unfavorable to hotels. And this then allowed us to map.
13:34:12 Sweet and sour spots in your city.
13:34:14 This slide summarizes the main findings from our
13:34:18 environmental equity analysis.
13:34:26 So for walkability,
13:34:27 a results showed that in the most deprived areas of Toronto,
13:34:30 Montreal and Vancouver high walkability postal codes were about half
13:34:34 as, as prevalent.
13:34:35 Compared to citywide prevalence of, of high walkability.
13:34:40 And conversely the least deprived or,
13:34:43 or more well off areas and new city had a 68% to
13:34:46 114.
13:34:47 Percent higher prevalence of, of highly walkable postal codes.
13:34:50 In terms of [unknown].
13:34:52 Air pollution.
13:34:54 Highly deprived areas were between 18 and 80%,
13:34:57 less likely to experience low levels of air pollution.
13:35:00 In each city.
13:35:04 And a 38% in 23% more likely to experience high air
13:35:08 pollution in Toronto and Vancouver, respectively.
13:35:22 Finally for greenness. So people living in Toronto,
13:35:25 Montreal and Vancouver, neighborhoods of high deprivation,
13:35:27 we're about half as likely to be surrounded by a high
13:35:30 vegetation index relative to citywide high greenness prevalence.
13:35:34 And individuals in high deprivation areas were also between 23 and
13:35:38 44% more likely to be surrounded by low levels of vegetation.
13:35:42 So essentially.
13:35:43 Our results suggested that environmental inequity is occurring in all
13:35:46 three cities. And for all three exposures that we, that we examined.
13:35:54 So now I'm going to show you some maps of each city that
13:35:58 illustrate how exposures are distributed in space and how.
13:36:01 They intersect spatially with material deprivation.
13:36:07 So the first three maps also illustrate the overlap of high material
13:36:11 deprivation with high air pollution and low greenness.
13:36:22 On this map here of Toronto,
13:36:25 you see postal codes in the highest quintile of material
13:36:28 deprivation and high air pollution in yellow,
13:36:31 high deprivation in low greenness and orange.
13:36:34 And what we might call a triple burden areas where we're high
13:36:37 deprivation, high air pollution, and low greenness coincide.
13:36:41 You can move on to the next slide. So this is the same,
13:36:44 same map for, for Montreal.
13:36:46 And next slide.
13:36:51 And for Vancouver. So as you can see in all three cities,
13:36:53 we really do see clusters of unfavorable, postal codes.
13:36:57 In in, in each city.
13:37:04 So the next three maps that I'll I'll show here illustrate the
13:37:07 distribution of sweet and sour spots in each city.
13:37:09 In this map of Toronto dark red or the sour spots.
13:37:12 So again, areas where [unknown].
13:37:14 Two concentrations are in the top turtle.
13:37:16 And greenness and walkability.
13:37:17 Are in the bottom, a turtle values.
13:37:27 And the green triangles here, the big sweet spot. So postal codes of,
13:37:31 of low air pollution and high greenness and high walkability.
13:37:41 And on the right of these, these maps,
13:37:43 I randomly selected Google street view and satellite images to
13:37:47 illustrate what the built environment looks like for sweet and sour
13:37:50 spots in each city.
13:37:51 Side yet. So this is the same for Montreal.
13:37:55 And next slide.
13:37:56 And for Vancouver.
13:37:58 So next slide.
13:38:01 Thanks.
13:38:05 So the environmental equity analysis.
13:38:10 And maps that I presented highlight the multiple exposure burdens.
13:38:17 Experienced by different subgroups of, of the populations.
13:38:20 And Canada's three largest cities.
13:38:22 And in terms of policy implications.
13:38:26 These, these types of analysis could,
13:38:28 could be used by urban planners or public health professionals.
13:38:35 When developing interventions, such as there've been tree planting,
13:38:38 it's finding products or natural areas, traffic calming,
13:38:41 or diversion measures.
13:38:42 Increasing nine, the us mix or three.
13:38:44 In connectivity.
13:38:48 Secondly, the nationally standardized environmental data,
13:38:51 such as the ones held in distributed by canoe.
13:38:57 Can also also be used to benchmark cities in terms of urban
13:39:02 exposures and for tracking urban environmental risks.
13:39:06 And finally these data sets can also facilitate comparisons of
13:39:10 environmental risks across different populations in Canada.
13:39:17 Related to this. I wanted to,
13:39:19 to highlight a real life example where we're analyses such as the ones
13:39:22 that I just presented can result in policy.
13:39:27 So the valid now program was recently launched in Montreal.
13:39:30 And this is an initiative that is funded by the Quebec ministry of
13:39:33 health.
13:39:34 And it's to start this coming spring, spring.
13:39:37 2021.
13:39:38 And the program will focus on reducing heat islands.
13:39:42 In Montreal, neighborhoods of high deprivation.
13:39:44 So over the next two years.
13:39:49 Over $1 million will be invested in urban tree planting to
13:39:53 reduce extreme temperatures.
13:40:01 In three neighborhoods of low socioeconomic status,
13:40:04 which often bear, you know, this disproportionate burden of,
13:40:07 of summer heat waves in the city.
13:40:14 So, as I mentioned,
13:40:16 a similar method to what I presented today was used by Valley now to
13:40:19 identify neighborhoods that that would most benefit from urban
13:40:23 greening efforts.
13:40:24 Next slide.
13:40:36 So lastly,
13:40:37 I want to highlight a mobile web application that we're developing at
13:40:41 canoe called good score.
13:40:43 And essentially this app will allow the general public to explore
13:40:47 spatial patterns of urban environmental exposures and how they
13:40:50 intersect with each other in Canadian cities.
13:40:52 So I encourage all of you to access the better version of this app and
13:40:57 you can access it via our canoe.ca website.
13:41:00 By clicking the goods, core.city,
13:41:03 a link that you see on the screen.
13:41:04 And we've also set up a survey for you to give us feedback
13:41:08 on how, how we could improve the application.
13:41:11 Before we, we release it.
13:41:13 So try it out and let us know what you think of via our survey.
13:41:17 I think we can probably start here and, and pause for some,
13:41:21 some questions.
13:41:26 Okay. Thanks, Danny. That was excellent. And so interesting.
13:41:29 And I also really appreciate your policy analysis.
13:41:40 I think that's an important part of this,
13:41:41 that when you're learning GIS skills,
13:41:44 that's not a course that that's offered. Right.
13:41:47 So that's a really important piece of the puzzle.
13:41:49 So for questions for Danny.
13:41:52 Please use this Q and a tool.
13:41:54 Does anyone have any.
13:41:55 And then we can take some more general canoe data questions.
13:41:59 I think now we're waiting.
13:42:09 I just want to reiterate, you know, that the analysis that Danny did,
13:42:12 they're not difficult, right? It's basically over laying.
13:42:15 Using GIS.
13:42:17 The postal code data attached data and the census data. Right?
13:42:20 So this is the kind of thing that, you know, most.
13:42:27 Most undergraduates who have one or two GIS courses under their belt
13:42:31 can do these analyses. Right? Of course, there's a lot of work in,
13:42:33 okay. Now that we've seen these patterns, what.
13:42:35 Did they mean and all of those, but you know, this is the cry.
13:42:38 You know, you shouldn't be afraid of.
13:42:51 I'm getting into this because it's extremely easy to use the data
13:42:55 there's so much you can do with it, you know,
13:42:57 without needing necessarily to confidential health data. And,
13:43:00 you know, that's a great publication, really interesting stuff.
13:43:03 So I hope that you're inspired in that way to think about all the many
13:43:06 things that you could be doing.
13:43:07 Even if you don't have access to the confidential health data.
13:43:09 The confidential health data is definitely, you know,
13:43:12 something that you would want to be in a team with an experienced
13:43:15 epidemiologist.
13:43:24 You get the canoe data that way by making a special application to the
13:43:28 health data holder, a cohort specifically.
13:43:30 So if any of you are super interested in that Avenue,
13:43:32 just drop us a note and you know,
13:43:34 we can help you understand what the process is for getting the pre
13:43:37 linked data with confidential health data. And.
13:43:40 And maybe connecting with,
13:43:41 with teams that are already doing that kind of thing.
13:43:43 Most of those studies are grant funded, right? So it's unusual.
13:43:46 It typically for someone to have their own confidential health data.
13:43:49 Outside of one of these big secure facilities.
13:43:51 Yeah.
13:43:52 Yeah. Thank you.
13:43:53 I think also too, like, I really appreciate,
13:43:55 appreciate you saying that Eleanor,
13:43:56 like to just sort of get started and go for it. There's a lot.
13:44:01 That you can do without going for private access to
13:44:04 data. So health data is.
13:44:13 Secure and requires, you know,
13:44:15 a supervisor who's in that space already.
13:44:17 And like Eleanor said already has a funded work and, you know,
13:44:20 an approved project.
13:44:22 Most of the time.
13:44:23 And I learned a lot of my GIS skills through open data
13:44:27 or data that was licensed to me as a student at an academic.
13:44:43 Institution. And there's so much you can do in Canada.
13:44:46 There's been so much work on aggregating data so we can use
13:44:50 it like the census or the Canadian community health survey.
13:44:53 There's a lot general social survey.
13:44:55 There's a lot of data that you can use to get started to practice your
13:44:58 skills.
13:45:01 You don't necessarily need the individual health data to do quite a
13:45:05 bit of area level regional
13:45:07 looking.
13:45:08 Cause there's different patterns that exist at the individual level
13:45:11 and then at every scale of geography. Right?
13:45:13 So there's so much that you can.
13:45:15 Look into, and then I'll also say that like,
13:45:17 you're the expert on where you live in your own communities.
13:45:20 So if you have a research question and you see something happening in
13:45:24 your own community,
13:45:29 That's a great place to start. And, you know,
13:45:32 maybe think about what research questions you can answer for where
13:45:35 you're,
13:45:36 you're living using either open data or data that's available to you
13:45:40 through your institutional license.
13:45:42 So we have a question for Danny.
13:45:44 So in terms of the sweet and sour areas,
13:45:50 What about the neighborhoods with high air pollution and high
13:45:52 walkability? So the downtown core,
13:45:54 is it also important to recognize these areas and inform policy makers
13:45:58 for interventions?
13:46:06 Yeah, that's a good question. So I mean, the, the objective of our,
13:46:09 our analysis was really to look at unfavorable areas.
13:46:13 So combining high air pollution and, and, and.
13:46:21 Low low walkability and low greenness.
13:46:24 But yeah, I mean, with the, with these data, you can really,
13:46:27 we can, you can really,
13:46:28 it's four different avenues depending on what your objective is.
13:46:38 And I'd also add to,
13:46:40 to Eleanor's last point that the deprivation data that was
13:46:44 included in this, in this paper is also available via canoe.
13:46:47 So this is the, the Pamphilon index based on census data,
13:46:50 but we also recently added a second deprivation.
13:46:59 A dataset called con called Ken Marge. So we,
13:47:03 we kind of define urban exposure pretty,
13:47:05 pretty broadly by, you know,
13:47:07 having a physical environment or physical built environment features,
13:47:11 but also socioeconomic neighborhood level of features that we make
13:47:14 available to, to researchers.
13:47:16 Thank you.
13:47:17 The okay. We have a question from Jeremy.
13:47:19 Jeremy's curious how granular the application of these data to
13:47:23 neighborhoods.
13:47:24 And streets and park design could be.
13:47:26 So could you imagine your results informing the
13:47:30 design?
13:47:31 In a more fine scale fashion than choosing which areas need an
13:47:34 intervention. For example, the heat Island policy example.
13:47:37 Could you imagine these data informing the best way to design a
13:47:40 walkable street?
13:47:43 So that's a great question. Like how do you,
13:47:46 when you find the results of your study,
13:47:47 based on this scale of analysis and the scale of data.
13:47:52 And Jeremy correct me if I'm wrong about your question,
13:47:55 how do you then interpret the scale of your results and the scale of
13:47:57 your policy recommendations?
13:47:59 So, I mean, our data, I don't think would be.
13:48:06 Best to inform urban design features
13:48:10 really small scale urban design.
13:48:12 Features such as, you know, how do you design walkable streets?
13:48:15 But they do, like I showed in, in the, in the paper.
13:48:20 They do allow, you know, pinpointing areas.
13:48:25 That would be a ripe for, for interventions.
13:48:29 We currently don't have a urban heat Island.
13:48:31 Dataset.
13:48:37 But we're looking at developing one and making it available to
13:48:41 researchers.
13:48:51 But I think having, you know,
13:48:54 an idea of the Vedic vegetation cover is,
13:48:58 is a good proxy for what urban heat Island distribution would be
13:49:01 within a city.
13:49:02 Yeah. And there could be other like building index.
13:49:05 How dense are the buildings?
13:49:06 There are different ways to get at the same feature for sure.
13:49:13 You know what I'm just playing with the good score app. And these,
13:49:15 this is all open source data.
13:49:17 The underlying data is all rasters at a hundred meter resolution.
13:49:20 So for every hundred meter pixel, we're counting up.
13:49:27 In this case,
13:49:28 how many bus stops are within a kilometer of that 100 meter pixel?
13:49:31 So in some ways, I mean, if you were creative as a planner,
13:49:34 you could say, okay, I can see these areas require more transit,
13:49:37 you know, so, but.
13:49:39 To come down to the actual nuts and bolts of designing a park.
13:49:41 I think you're right. Downey,
13:49:42 that sort of lives in the world of landscape architecture.
13:49:46 Urban planning,
13:49:47 but that doesn't mean that you can't zone in on the areas where those
13:49:50 things should be happening.
13:49:51 And get creative with what's on the ground. There.
13:49:53 Yeah.
13:49:58 We're hoping with good score may be that we just actually got a large
13:50:02 grant.
13:50:04 We have actually $1 million over the next three and a half years to
13:50:07 develop good score.
13:50:08 More fully as well as build another similar tool that looks at equity.
13:50:11 Right. So how the equitable, equitable distribution.
13:50:14 And we think that this tool.
13:50:24 I might be really fun for advocacy groups or,
13:50:27 or planners to use as an outreach to the community and say,
13:50:30 upload your photos. You know, tell us what you think about this area.
13:50:33 Increase the data, all of those things. So.
13:50:35 For sure.
13:50:36 Take a look at the survey and give us your ideas about what would be
13:50:40 cool to add here. And of course, adding additional layers.
13:50:45 But yeah, I love that question. I think it's,
13:50:48 I think it's going to be really the next, like five,
13:50:51 10 or more years of,
13:50:52 of sort of health geography research in Canada is understanding.
13:50:55 You know, the scale of the data.
13:50:56 We have the scale of the analysis tools we have and then the
13:51:00 scale of the authority to make.
13:51:02 Decisions with public money.
13:51:04 And how do these intersect,
13:51:05 where do they line up and where do they not?
13:51:17 And it's something I'm really interested in too.
13:51:20 I think that's a really excellent question. And I, and I'll,
13:51:22 I also want to add here, like,
13:51:24 when it comes down to making decisions around a park too,
13:51:26 like that's a really beautiful opportunity to take your project and
13:51:29 now.
13:51:39 You know, maybe at this point you want to make it qualitative.
13:51:42 Like maybe this is where you want to interview the community members
13:51:45 and show them what you found using data and say,
13:51:48 how do you feel about this? Does this,
13:51:50 is this your experience in this park? You know,
13:51:51 like I think there's a lot of great opportunity here for mixed methods
13:51:55 and collaborating with the communities and.
13:51:57 So I like what Eleanor said about it points to an opportunity to
13:52:01 like target your resources more instead of, you know,
13:52:03 you're looking at the whole country or the whole province. So.
13:52:06 I think that's so super interesting.
13:52:10 So I'm just trying to stay on topic and then we can move to
13:52:15 some more general questions.
13:52:16 How does the universal design for those with movement challenges,
13:52:19 factor into the good scores, five factors.
13:52:22 Hmm. Well, right now we're just, you know,
13:52:23 this is totally prototype we're playing with using open data.
13:52:26 What kinds of things we can get out to.
13:52:39 We don't really have, I guess what's the, I mean,
13:52:42 people are working hard on trying to understand how we can map those
13:52:45 micro scale urban features, if that's what you mean, like where,
13:52:48 where are their curb cuts? Where are their barriers,
13:52:50 where things like that, that increase or decrease mobility.
13:52:53 And that's a layer that may come right.
13:52:55 Maybe I'm missing the point.
13:52:57 When are you thinking about mobility as for disabled people on
13:53:01 scooters, wheelchairs, you know, with mobility?
13:53:03 Or is it more.
13:53:04 Just physical activity.
13:53:05 Yeah, this was Malcolm.
13:53:06 Malcolm would you, you're also welcome to come on video.
13:53:08 If anyone wants to ask or answer their question on, on.
13:53:10 Video. You're welcome to as well.
13:53:14 So, is it, what type of mobility are we talking about,
13:53:16 I guess is my clarification question.
13:53:18 Now it comes as exactly.
13:53:19 Okay.
13:53:22 Ankle mobility. People who have mobility issues.
13:53:43 So, I mean, that would be cool.
13:53:45 I think one of the things we're working on that we haven't talked
13:53:47 about is how we can use computer vision and machine learning to do
13:53:50 things like,
13:53:51 and Danny and I have been working a lot in the last six months on
13:53:54 using machine learning to see things in Google street, view images.
13:53:58 So counting up the number of cars.
13:53:59 Trees, things like that and,
13:54:01 and seeing how we can predict walkability.
13:54:03 I'm you.
13:54:04 You know,
13:54:05 involved with a few other groups that are super interested in,
13:54:07 like I said, the micro scale piece of it.
13:54:09 So where are the sidewalks are their trip hazard?
13:54:11 Can we get that from Google street view and we,
13:54:13 and if not from Google because of proprietary and we drive our own
13:54:16 and.
13:54:18 Yeah.
13:54:19 UDL next way for all of these, how we measure cities,
13:54:21 it's going to come from high resolution satellites where you're know
13:54:24 you're at half past meter, right? Like sub.
13:54:26 Sub-meters satellite imagery.
13:54:28 The speed level stuff, the videos, and you can apply computer vision,
13:54:31 just Spanish. You're happy. We can do a whole webinar about that.
13:54:34 That's cool.
13:54:35 That's totally where the next wave of,
13:54:37 of geographic data on cities is coming from and environment.
13:54:40 Super cool field.
13:54:41 So, no, we don't have it yet,
13:54:43 but you can imagine it's the kind of micro scale.
13:54:45 Bears that are important for mobility.
13:54:47 But how do we capture that?
13:54:48 At the micro scale.
13:54:52 For large regions.
13:54:53 That's the kind of stuff we like to struggle with that canoe.
13:54:55 Maybe that's a good hackathon.
13:54:56 On challenge.
13:55:07 Yeah.
13:55:08 I feel like the problem I worked for a lighter accompany in my masters
13:55:11 in Calgary called Northwest geomatics. And yeah,
13:55:14 we were doing three centimeter resolution imagery and it's expensive.
13:55:17 So that's the problem. So it's like,
13:55:19 I feel like the project would be flying UAV.
13:55:23 With LIDAR to capture for small areas. Maybe. I don't know.
13:55:26 I was just on a call with the USC.
13:55:28 DC has a physical activity group that I listed.
13:55:31 We have a quarterly call and.
13:55:43 We were just had a presentation from a grad student and they were
13:55:45 using Google street view and actually manually labeling, you know,
13:55:49 where,
13:55:50 where the trip hazards were to high amount of grass growing
13:55:54 between the sidewalk cracks,
13:55:55 like all kinds of stuff that really speaks to what you're talking
13:55:57 about. So people are really thinking about it.
13:55:59 And like I said, other than.
13:56:01 Doing a physical survey where someone goes to the city and walks along
13:56:04 and, and writes it all down the old style way.
13:56:07 People are really trying hard to automate this using imagery and
13:56:10 computer vision. So.
13:56:11 Yeah.
13:56:12 It's not there yet, but it's coming and it won't be long.
13:56:14 Yeah.
13:56:15 That's it.
13:56:16 Really interesting.
13:56:18 So there's a few other questions.
13:56:21 But just in the interest of time, I'm going to.
13:56:24 Share my screen here.
13:56:27 Let me un-share mine. Hang on.
13:56:28 Oh, you've got it. Okay. Good.
13:56:29 Yeah. So while we take a few other questions,
13:56:34 Just wanted to, can everyone see my screen here?
13:56:36 Yeah.
13:56:38 Okay. So.
13:56:43 We were so glad to have all you here.
13:56:58 There's a few other questions that have to do with general canoe data
13:57:02 access. So the best way to,
13:57:04 I think if you have specific questions about accessing data for your
13:57:07 own project is to get in touch with canoe.
13:57:10 And next month, we're going to continue this seminar series.
13:57:13 With canoe and we're going to be featuring grad students and
13:57:17 early career researchers who have used canoe data.
13:57:19 So next month,
13:57:20 we're going to have me on the show who is going to be talking about
13:57:23 space Yeo.
13:57:25 Temporal relationships and using canoe data.
13:57:28 And that will be on March woops on March 26.
13:57:33 So I'm going to find the link for that.
13:57:36 And I will put that in the chat.
13:57:39 And perhaps while I leave this up, we have a few more minutes.
13:57:43 If anyone wants to hang out and chat.
13:57:45 But otherwise, thank you so much.
13:57:47 We encourage you to apply for the lottery to win.
13:57:49 Those textbooks donated by Springer and.
13:57:51 Dr. Valerie crooks at Simon Fraser.
13:57:58 And otherwise, thank you so much, Danny and Eleanor,
13:58:00 it was really excellent to learn about canoe.
13:58:13 And I think there's lots of students on the line who you're going to
13:58:16 hear from,
13:58:17 and we hope everyone has a chance to integrate some canoe data into
13:58:20 their thesis, especially it's it's so ready to go and easy to use.
13:58:24 So.
13:58:27 Yeah. So I think we have a few more minutes.
13:58:29 I don't know if anyone has any more pressing questions,
13:58:32 otherwise we can end here,
13:58:35 or if there's any final words that either of you wanted to say.
13:58:42 Oh, just thanks everyone.
13:58:44 And we certainly welcome all day to users and don't feel intimidated.
13:58:47 We're here to help for sure. Just by all means email.
13:58:50 If you have questions about how to even do your study,
13:58:52 we've got lots of expertise on the.
13:58:54 Spatial side and on the health side and on the analysis side. So yeah,
13:58:57 we're, we're here to help.
13:58:58 Okay.
13:58:59 In terms of accessing data, that's pre linked to health data. You can,
13:59:03 you can access it via the Ken path.
13:59:05 Cohort.
13:59:07 The Canadian longitudinal study on aging.
13:59:13 And the child study. So those three cohorts are,
13:59:17 or some of Canada's biggest observational cohorts,
13:59:19 and they have pre linked canoe data ready for you to.
13:59:22 To access and use.
13:59:24 Plus also the administrative data, which is another huge,
13:59:26 all of our health records.
13:59:48 Provincially are held by research institutions.
13:59:52 So I'm in BC and population data.
13:59:54 BC holds all of our data and they can link just a vast array of say,
13:59:58 hospital information with education outcomes,
14:00:01 with a pharmacy use. And they've got all the canoe data there.
14:00:05 Manitoba has it. New Brunswick has it.
14:00:07 And we're working on what's the last one.
14:00:09 In Ontario is.