FOSS4G SotM Oceania, 2018: Lightning talks
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Lead, Stephen
Shorter, Cameron
Woodrow, Nathan
Dawson, Nyall
Harvey, Andrew
Hill, Ewen
Bashfield, Andrew
O'Grady, Julian
Campbell, Hamish
Kastl, Daniel
Roberts, Rebecca
Saalmans, Hugh
Tomko, Martin
Bennett, Steve
Todic, Nikola
Lightning talks by: Stephen Lead Cameron Shorter Nathan Woodrow Nyall Dawson Andrew Harvey Ewen Hill Andrew Bashfield Julian O'Grady Hamish Campbell Daniel Kastl Rebecca Roberts Hugh Saalmans Martin Tomko Steve Bennett Nikola Todic
 Thank you. Thank you. Good afternoon, everybody. How are we doing? Welcome to the lightning talk session. My name is Steven Leigh. I'm from full extent in Sydney. Thank you, Alex. We've got a very exciting session for you this afternoon. We've got 15 presentations from a range of experts and practitioners in open source, open data, and everyone that I've spoken to said they're more excited and more nervous about their five minute talk here today than they were about their big talks during the week. Now, we're going to be very strict about the five minute time limit. So we've got a little timer on the table here. We're very strict about the time limit. Now, when you guys hear the timer go off, I need your help, could you please do a slow clap for me until the presenter stops? Can we just do a little test now? Slow clap. Yep. Getting louder and louder and louder until they finally shut up. Exactly. That's what we're going to do. So I'm going to start my timer and Alex and John are very carefully, very considerably put me first in the talks. So I'm going to start the timer and I'm going to do my talk now. Okay. So here's my talk on a map-centric travel blog that I read. So this is me. This is my wife Gloria or Glow and she's from England. I'm from Australia. We met in Canada. We've been traveling the world together ever since we met back in 2001. Now, I've always loved travel photography. So long before there was Flickr or long before there was Facebook or Instagram, I put together a travel blog using Tumblr and that's what I've been using ever since. It's getting a bit old and a bit long in the tooth but that's what I've been using. Now, the beautiful thing about being a developer these days is that everything I need is on my laptop. I've got VPN to connect to work. I've got GitHub for the code. Most of the stuff's in the cloud anyway so I could work from anywhere. So I decided last year to take my family on a round-the-world trip. Now, being a good geek, my first thought was I better do a new blog. The blog's getting a bit old so I looked around at some of the best blogs that are out there and even if they're beautifully designed, they've all got the same floor as you're seeing here, they treat location as an alphabetical list. Australia is next to Austria and I knew I could do better than that. So looking at my old blog, I could see that the hint of what I wanted to do was already in there in the blog post. Every post knew where it was. It had the title, Hanmer Springs, Berlin, when in Dublin. So the location was there already in the blog. It seems to do something with it. What I decided I wanted to do with my blog is make it geo-aware. It had to know about the places. I wanted it to be map-centric but I didn't want the map to take away from the photos and the text so it had to be unobtrusive. So those were my requirements. Like many optimists and fools before me, I decided to write my own blog and I'm a Rails developer so I decided to use Rails. Now, Rails is written on the back end in Ruby and it allows you to use the front end JavaScript code. What I love about Rails is it's got a bunch of gems which are functions written by other people which you can just insert easily. One of those is the geo-coder gem. As you'd expect, you give it a place name, Paris and it returns lat long. What's lovely about this gem though is with one line of code, it just binds into the application. So I set or change the place name and it automatically goes into the right spot as a latitude-longitude column. So here I've got a geo-enabled table, basically a geo-coded table. So let's take a look at what I did once I had a geo-coded table of all of the blog posts. So I put together this blog and I wanted it to be photo-centric. So the first thing you see is a header across the top with a photo. Then there are photos further down as you scroll. You can also click on one of the photos and then get a full-screen photo which you can then zoom through. So they're pretty cute, aren't they? Just like a normal blog, you can go previous or next blog post. So here I'm going to the previous post and you can see it's zooming into Basel. Here's where we're starting to get the hints of the geo-enablement. The post knows it is in Basel, Switzerland and there is a map. Now that map is interactable. So you can use the map to find other blog posts. So now we jump across to London. And now when we click on that, we're going to the London post and now the whole thing changes to be about London. And so the photos are about London. If we now jump to the previous post, we're now going to jump from London to Tokyo and looking at the map here, I can go into full-screen mode. So the map isn't just a little map, it's a full-screen map as well. And now I can start to interact with all of the posts that I've written over the years. What's nice about this is it gives you a totally different view of the same content rather than having to go next previous. You're seeing it in a map. So we jump from over there across into Central America and we jump down into Panama. And so we've managed to go across space and time. This is like six months later from where I was in Tokyo, I ended up in Panama. Now we go back into the full-screen mode. You notice that in full-screen mode, we keep the same extent as before. And now we jump across to Guatemala. So you can see it's a really easy and intuitive way to view the same content rather than being just on a, the text, we're seeing it in a map. Now not everyone here is a JavaScript programmer. So for you guys on the left-hand side, there's some nice photos to look at. For everyone else, let's look at the code and how it works. What's nice about Rails is you can add.json to a.jax request and it returns the posts as, the timer hasn't gone off. The timer hasn't gone off. Give me a chance, guys. Yeah, wait for the timer. So here we're seeing the marker clusters. So basically, return it as.json, convert to.json and then see it as markers, as clustered markers. The way that the bookmarks work, when you open up the map, it knows that there was a cookie set for the last lat long. So it opens up with that lat long. And then so I set the cookies every time the map pans or zooms. I set those cookies. I'm going to have to jump forward because I'm running out of time. Okay, so to summarize, the idea was that it's, do you aware that it has a map front and center, but it's not, the map is not obtrusive. So there's no table contents. You can start clapping now. So I'll just finish up by saying, I'll finish up by saying that if you wanted to have a look at the map, it's at StevenGlo.com and you can download the code from there. Thank you. Okay, so next I'm going to bring up Cameron Shorter. Now, one of the advantages of using commercial software is that you get a use, usually a nice setup.exe to run it. With open source software, it's often a bit more difficult. And Cameron's going to tell us about ways to make that easier. So please welcome Cameron Shorter. Now, I'm just going to talk. Okay, Osageo Live is a project I've been involved in. It's a Linux distribution that has been pre-installed with over 50 Geospatial open source applications along with sample datasets, overviews and quick starts. You can boot it up from a USB or run it in a virtual machine and you can immediately start playing with the best Osageo has to offer. It is used for training, training new applications and outreach. We launched it at the International Phosphogy event here in Australia in 2009 and it has grown and attracted hundreds of contributors since then. In this cut down lightning talk, I'll introduce its more popular packages which effectively compresses a Phosphogy conference into a five minute elevator pitch. So our focus with Osageo Live is identifying and promoting robust and sustainable projects. Our metrics page, our metrics web page, attracts, aggregates the dashboards from Open Hub and you can use it to gain insights and meet into each project's health and developer engagement. So let's have a look at the popular applications installed on Osageo Live starting with the desktop. This is where we find the heavy lifting applications. QGIS allows you to visualize, manage, edit, analyze data and then compose printable maps. It supports numerous vector, raster, database formats as well as tools and external plug-ins. Such as the GRAS Analytics module. GRAS provides powerful raster vector processing. It includes hundreds of tools for spatial modeling, visualization and analytics. And moving now into the browser, we've got browser clients are used to publish maps and increasingly are also delivering functionality which previously was only available in the desktop. Open layers and leaflets are JavaScript libraries for putting maps into web pages. Open layers aims to be comprehensive. Leaflet is more lightweight targeting simplicity. Cesium is a JavaScript library for creating 3D globes and 2D maps in a browser. It uses WebGL for hardware accelerator graphics and is tuned for dynamic data visualization. GNode is a content management system. It supports loading, editing, publishing, searching and commenting on maps and associated metadata. And then moving on to web services. These are accessed via a URL and return maps in various formats usually based upon OGC standards. GServer and Mapserver are the leading open source applications for publishing maps to the internet. GServer is Java based while Mapserver is written in C. And while not as performant, QGIS server is handy for publishing your QGIS project directly as a web map service. GNode and PyCSW provide catalog services for maintaining metadata about datasets. GNetwork is an older Java based project and PyCSW is lightweight and quick to set up. OGLive includes two sensor observation service projects and three web processing services. These are web processing services that allows you to move complex processing to a central server. And this is used for accessing large dynamic datasets such as climate models. And at the bottom of the stack of the databases, we've got a few of those. PostGIS especially enables a popular Postgres database. It's stable, fast, standards-compliant, includes hundreds of spatial functions and is currently the most widely used open source spatial database. Next time we recover the spatial tools, GDAL and OGR provide command line tools to translate between spatial formats and projections. R provides a command line interface to powerful statistical computing and graphic algorithms and exceeds at analyzing spatial datasets. And of course, all these tools become much more useful with access to free data. OpenStreetMap is crowdsourced map data of the world which has grown to become one of the most detailed sources of local scale maps. OGLive includes a city-size extract of OpenStreetMap and we also have some sample datasets in other formats as well to demonstrate some of the examples. So this has just been a taster. Check out our project overviews and quick starts on our website for more details and then go and boot a USB or a virtual machine and get some real-world experience in OZGI applications. Thank you. Thank you Kamishota. You guys are very, very impatient up the back there. All right. So next up we have Nathan Woodrow, one of the key QGIS contributors and he's going to talk about generating JavaScript code from QGIS expressions for fun in inverted commas. Please welcome Nathan. So exactly generating JavaScript from QGIS expressions. Exactly about why would you want to do that. Indeed. Crazy indeed. Mainly for fun and learning because it's the same thing in the end. Sometimes it's Saturday night, nothing else to do. But also for QGIS web. So if you haven't used QGIS web before, Tom Chadwick, I think his name is, he developed QGIS web which takes a QGIS project done to open layers or leaflet. He had a problem trying to get the rule-based expressions to work in QGIS web but he was trying to use regex to parse the whole expression and I almost died when I saw that. You can news read decks but friends don't let friends do that. So we take a QGIS expression and then we generate a JavaScript function which actually has the actual expression value generated inside it and then the result is just calling it function. And then the parsed int function, func to int function is actually just a wrapper on that as well. So why the hell's going on? So QGIS expressions are actually, they generate abstract syntax tree underneath. That's not that kind of tree, it's that kind of tree. We take the program and take the expression, break it down to all individual nodes and how they connect to each other, which means we can take that abstract syntax tree and generate pretty much anything we want from it. So we take the input node which is the root level, we walk the tree, we say what type of node it is. In this case we're gonna call handle binary, so if it's binary node which is a plus or minus and equals all that sort of stuff. So the handle binary function is like that, takes another node and walks each left and right side. And if the left side, the operator is a like, then we generate some like syntax that's equivalent for JavaScript. Same for not like, same for not incase sensitive like. Handle function is exactly the same, we walk the expression again, get the function that you're trying to call, generate a stubbed function that would do the actual logic and then walk all the arguments in that internal do the same thing again, so that way you can have functions inside functions and functions. So basically the steps are take the Q just expression, walk tree, generate expression and profit. Having quite work ahead of the profit step yet. That's why we're all here. But can we go down deeper than that? Can we go to something like a log code? We haven't seen log code before. Yeah, you can look it up. But I didn't do that because I couldn't get it to fill. But you could, but I'm not going to. That's it. Wow. That's very nice. Thank you. You got a couple minutes left, if you want to just keep on talking. I think I scared. It's ruthless man, it's ruthless. I scared everyone with the whole like slow clapping thing. I think, yeah. All right, so next up we've got Niall Dawson who's going to tell us about adventures in reverse engineering, isry style and layer files. Please welcome Niall Dawson. I don't know if I should go for like a minute and a half to beat Nathan. This is me in another life. I'm here to talk about Slayer. Slayer is a little tool that was developed around to get these layer files, which are basically like a symbol set. So it's a way of styling layers and embedding all the information about how they should be symbolized inside that one file. Like this. So they usually have thousands of symbols and stuff in there. They tend to be distributed along with official data sets. That might be like you go to your government part of portal, you get your data and it also has a layer file sitting next to it that's got the official symbology for that layer. Unfortunately, they're locked up in purgatory. What I mean by that is a layer file is actually an arctis format. Like I said, they're locked up in purgatory. So this Slayer story starts at SMEC. In SMEC, there was an official government data for GeoTech, which came with basically like thousands and thousands of symbols for all these different kind of GeoTech categories. And they all had certain colors that you had to respect. We needed to get those from the Ezra world into the cutest world, but because there was thousands and thousands, no one's gonna sit there and copy all those colors across. And basically there's every way you could think of to get them out, because there's been blocked in some way by people. So the mission was to get them out and save them and take them into the promise queue just like this. But like I said, unfortunately, layer files, like they're a binary format, there's no specifications for them. They're all locked up and they're encrypted. Locked up. So after much of this, Slayer was born. And Slayer is basically a Python kind of library tool, something, parser, which could kind of work at what those little binary bits in the layer formats were and turn them back into something usable, like the colors for those symbols. So the very first version of Slayer was really, really primitive, but it did let us get these couple of thousand colors and symbols for that GeoTech library layer back to the wonderful Qtas Promise land and there was much rejoicing. Like a really initial roadblock we hit was with colors. So basically there's a certain format of color that Esri used to store color values inside these layer files. It's not RGB, like you'd hope, where it's just three numbers plugged in there. It's this side lab LAB format. But all the color formulas and conversion formulas that are standard for getting between these two color systems, none of them quite matched up. It would kind of get close, but maybe it'd be like 30 or 40 difference in that RGB channel. So good enough, but not enough to make me happy. So that's kind of a roadblock and Slayer kind of just sat dormant for about a year or so after that initial task. A tool along came, this company, opengis.ch. Cool Swiss company contacted me. They had a customer who had, again, a library of a couple of thousand symbols that they developed over multiple years in these layer files. They were moving to an open source adoption in that enterprise and they needed to get these symbols out and they didn't want to do it by hand. So opengis threw a bunch of funds at Slayer and Slayer got powered up by this funding thing and we threw it at these symbols and basically got them all across and got them all converted into Qtas symbols. So today, this is kind of what it looks like. There's two parts to it. One is the part that reads those layer files and works out all the little bits and stuff and converts them into something usable. The second part is like a Qtas plugin which just put the front end on this and offers tools like converting the Esri style files to GPR color pallets and to Qtas style XMLs. Looks a bit like this. You pick your style file and you choose a bunch of settings and stuff and it gives those symbols out at the end. This is a bit of the current status of Slayer. So currently, only Esri style databases are supported. So unfortunately, you still need an ArcGIS license to get your layer file to convert that to a style database and then you convert that but that's gonna change. RGB colors are now like passed to plus or minus one for the RGB channels. So we broke that. Fill symbols, pretty much complete support. Line symbols, pretty much complete support. Markers more or less complete support as well. Color ramps supported too. So it's got really good conversion. And fortunately, there's a massive overlap between Qtas symbology and ArcGIS symbology. So almost everything we can translate and get looking nearly identical to the original. In the future. So Slayer, at the moment it's basically made for Qtas conversion but there's no reason why the same thing couldn't be used and basically for our SLD or CARDOS, CSS or whatever other kind of thing there. So you could directly take your SRI symbology and get out an SLD equivalent of that. In the future again, so it's kind of a lie. At the moment you can't get a layer file and convert that. You have to go right at the style file. So the number one person need for Slayer is to actually directly support layers. So you don't need SRI, you don't need all these kind of other dependencies and you'll be able to run it on any platform. Also in the future is we've looked into MXD support and it should be 100% possible. So the holy grail goal here is basically to get your MXD, your ArcGIS map documents, put them into Slayer and get a Qtas document out. The end which has got all those layer sets in the same symbology, you print layouts and all that sort of stuff converted just with a kind of click of the button. That's the goal and I'm sure we're gonna get there. At the moment that's how you get it. So you have.com slash Nile Dawson, nang slash Slayer and you can download the kind of beta version of it. Convert all your style files into Qtas files. Oh! Thank you! Thank you! Thank you! Thank you! Thank you! Thank you! Thank you! Thank you! Thank you! Thank you! Thank you! Thank you! Thank you! Thank you! Thank you! Thank you! Open Street Map Schools with Open Data. Please welcome Andrew Harvey. Thank you! Thank you! Thank you! Hi, so I wanna talk about a project. I went through to update schools in Open Street Map using Open Data. So the first step was to identify the data source. So in New South Wales, Department of Education have this great Open Data portal. It's really good, I can just go and search and quite quickly I found this master dataset for New South Wales Government Schools. It was in a CSV format, so quite easy to use. Done. Step two, license check. It was Creative Commons licensed. So we need a waiver. Open Street Map Foundation published this great waiver. So it's very easy, sent it off. I think it was like two months later or so, they got back to me. But the good news is they agreed to it. They said it's fine to use this in Open Street Map under the open ODBL. So next step was to develop and document a plan. So the first step as part of that was to consult the community. So I sent this email to the talk AU which is the Australian Open Street Map Community mailing list. And I said, hey, I'm investigating the possibility of importing this into Open Street Map. Your feedback, like what do people think about this? Should we do it? Shouldn't we? So as part of that, I put together a plan about mapping it into the OSM schema because they use kind of their own schema and there was a bit of work to then get it in. So I said, here are the tags like I think we should use. And a few people kind of chunned in and provided some really good feedback to make it better. So it's like this is good. The Open Street Map has kind of a process to doing imports because if I just kind of went in and did it, I would have made a lot of mistakes. So that actually worked really well. So yeah, the import plan, sorry, so here's the example of the different attributes that were in the open data. All the contact details, address, when the school was built, the grades to the school levels that it offers, if it's like a high school, primary schools at year, seven to 10 or seven to 12, things about specialties, does it offer like distance education, really detailed level of data. So yeah, and that was basically the import plan. But the next part of that was to actually compare it with what's in Open Street Map already, because there was actually a lot of schools mapped out by the whole Open Street Map community. So I did this comparison and found out that there's about almost 500 features from this government open data set that were missing in Open Street Map. And another almost 2000 features, so they're schools with new or different tags. So either we can improve what's in Open Street Map or there's maybe something that we need to look into further. I can't tell you the other way, like how many schools Open Street Map had that weren't in the Open Government data set because in Open Street Map, public and private schools are all mixed in together most of the time, unless someone's actually added the tags to that, whereas this is a data set that only had public schools. So, yeah, I put together, like this is a script, to actually go in and compare that, and it produced this big table of results which actually matched up the Open Data and the Open Street Map data. And you can see here all these tags are added in, and you that we can import, and then it's found the difference in the name. So in this case, there was quite a lot of differences in the name, but most of them are quite different. They're quite like public school versus primary school, and so what we did was to just add, so leave what's in Open Street Map at the moment, but just add that as an alternative name to trust like what people have actually surveyed over this. So the next step was to actually do the import. That was quite straightforward, but it took a while because it is the process of going through all this manually to make sure that we don't mess things up. But then there's a remote control plugin for Jossum that makes it really easy that you kind of say, yep, this is okay, I'll go apply in Jossum, and it actually goes ahead and makes that. So in Jossum, we can just hit upload. So lessons, agencies with Open Data Portals are usually very happy for you to use their data if you talk to them. They're usually quite happy about that. In this case, the cell starter was very high quality, but it's not always the case. So you need to use a lot of caution. So that's why comparing it to what's in Open Street Map, getting some local knowledge is good. Some diversity issues in Open Street Map data were revealed. Like a lot of, there are schools inside hospitals and they were basically non-existent in Open Street Map, so we were able to bring them in. And also there are some specialist schools for children with disabilities that were also quite missing in Open Street Map that were able to add in through this process. So in the future, better software could also help actually run these imports. And this was just a one-off, but in terms of keeping it updated as new government data comes in. So just quickly, I also want to talk about Open Addresses, global data set of, sorry, global collection of address points. So in Australia and New Zealand, we have 17 sources. That's all of them, like GNF, LYNS. We process them, we bring them in into a common schema, so it's a global thing. And we process this mostly automatic. So yeah, I hope in the future it could one day compete with GNF because we could bring in state data, which is often published like weekly or more, and have a more faster than quarterly GNF competitor. So check out Open Addresses. Thank you. Thank you. Are you enjoying this? Are you enjoying this? No, we've got Ewan Hill actually. Is Ewan Hill here? Ewan? Cool. So Ewan's going to be telling us, I know your comments about Open Street Map comments. So you're on this one here. I don't know how you know you're getting those slides. I don't know, but why? Oh, okay. Can we use the arrows? Yeah. Ewan's chosen not to use PowerPoint and to use something different. It's a Mac. Okay, go back to Chrome. Click on it. Click on this one. Yeah. All right. How do you start the show? Should be the LA. I can take it out of. Okay, you need to press that. Okay. Should we get the next bit of going? Oh, it's okay. It's pretty good. Okay, here we go. Please welcome Ewan Hill. Thank you. Thank you to Andrew, who's done a huge amount of work in the OSM area and a lot of the Australian stuff that is there now, courtesy of him. So thank you. The note system and the comments within OSM is a small little subset. There's not much to it. You can add a comment to say this is wrong, that you can add a comment to say coming to my great online shop, unfortunately. There's a whole lot of different things you can use comments for. There are now over 1.5 million comments. I tried to download the XML file, which was 64 gig of comments, and it was a little bit difficult. So I did try to do a little bit of a snapshot of the Australian stats along with New Zealand. All the other Oceania areas were fairly low. So currently we've got 2,950 odd comments that need clarification, rectification. And you can see that as time goes on, more and more comments are coming in. And they're not all being rectified. So what I'd really like is for everyone to go out and just find 10 comments and try and rectify them today. No. The overall comments, Germany is right up there, but if you have a look, Australia and New Zealand, down there a little bit, but comments is a hugely popular subsystem of OSM. You can have a look at the bottom graph and you can see that comments are only increased. And so it is something that we do need to have a look at if we're going to give a consistent approach to OSM. A quick look at the states and territories. And you can see how their northern territory is pretty good. South Australia, New South Wales and New Zealand. So all of those Kiwis out there, any Jaffas, please help and fix up one or two of those. The top 11 locations, North Adelaide, is probably one of the highest density of issues that I've got. So go in, click on one of those. You should be able to find it fairly quickly. I'll go through this very quickly. Christchurch, New Zealand, Tewong in southern Queensland, Auckland and Napier, Armadale in country, New South Wales. And this is one that I sadly didn't take a screenshot when I did, but there was one user who actually commented on every path track. However... However, they were all fairly correct. OK. So we've got the south of Perth, Newcastle, north Perth, and who do you reckon is going to win? So, yeah, there's a... Right around the whole of Yosiania, we need to probably get... Some of these are a year old, two years old, and we really need to try and work out how to actually improve the process. Finally, here's a map of somewhere called... LAUGHTER Thank you. Thank you very much, you and Hill. APPLAUSE All right, next we've got Andrew Bashfield from RMIT. Yusien, tell us about using QGIS for teaching land development. Please welcome Andrew Bashfield. APPLAUSE Thank you, Stephen. OK, so my presentation is about an exercise that we carried out. It was advanced diploma of surveying students, and it was a TAFE-level students at RMIT University School of Vocational Engineering. And the exercise was all about getting students to conduct an opportunity analysis for their clients, in this case, a property developer, and by identifying a top 10 list of potential properties for multi-lot development in the student's own locality. So every student did their own area. The students were responsible for assembling the spatial data sets, conducting the analysis, producing A3 maps, and a basic written report. And finally, they published the data on the web in a way that the developer client could see and access interactively. So who are the players in the property development example? Here are some property developers. This is what they look like. They want to win, and they want to get rich. We've got to remember that when we're talking about these guys as clients. And the next group are the students, and here they are conducting an asset data capture exercise in Argole Square in Carlton. And finally, there's quite an age range from those recently out of high school to others having worked for a few years to people making a late career change. And here is the tool we use, QGIS. So the first task was actually to gather data, to create a base data for analysis, and the two key data sets were VicMaps, cadastral boundaries, and the Urban Development Program, Broad Hecht Air Residential Land. And here's what it looks like. So you can see that Broad Hecht Air Land is basically that land on the outskirts of Melbourne. Now, we also had students coming from the country where this sort of data is not actually mapped. So we had to come up with a definition of Broad Hecht Air, and we actually said anything over 10,000 square metres in size. The next data set was Features of Interest. Once again, coming from the state government. And the cognitive features of interest were interested in transportation facilities, parks, schools, shops, hospitals, and those kind of services that are of interest to property buyers. This example is Features of Interest in Richmond. So this is a map produced by a student. Here's another example. This is an enlargement of a map produced of North Geelong. Once again, showing that students chose a symbology. There are 60 students involved in this program, and they can choose something different. So the next step was the identification of dual occupancy and multi-lite development opportunities. We call a dual occupancy a lot opportunity as something where it's defined as a land parcel that's a size between 1,000 square metres and 2,000 square metres. And a dual occupancy is between 2,000 and 10,000. We were looking for consistency between the councillors, and most of the time it was there. The yellow ones are multi-lite subdivision opportunities. The yellow ones just a single parcel chopped into half. So the student had to produce a list of their top 10 development opportunities within their chosen neighbourhood. A couple of key criteria. The first one, we were asking them to try and choose corner blocks with dual road frontage to lessen the amount of land that was used up by driveways and rights of access. The second was to choose unoccupied blocks or blocks with only single dwellings. These were more attractive for the purchase and construction of medium-hence density housing. And here's the kind of example of the top 10. So in this example, which is Richmond, the students were asked to show on Google Earth whereabouts it was and also on QGIS with the planning map. A few other examples. Now, what came up a lot was car parks because there was no one occupying them. And then the next thing was to report on proximity because we were looking for parcels that were as close as possible to schools and transport. So in this location, which is Balaclava in Melbourne, you'll see that yellow parcel has been calculated as five minutes walking distance from Balaclava Rauway Station. The final task was web publishing. The students were asked to provide that interactive method of delivering the data to their clients, not just the paper format. And the assumption was the client wouldn't have GIS so they'd need a web tool. So we used QGIS Cloud because it was free. And the students were doing all of this on their own laptops, not on RMIT computers, so it was important that they had their own licenses. Here's an example. This is a published web page, and you can see... The student shows funny little houses rather than colouring up the parcels in this case, but you can see that there's attribution attached to a particular lot. This one is a little better. This is West Meadows next to Melbourne Airport. So the outcomes for the students, they produced a spatial analysis portfolio and website that they could use to help them get their jobs with employers. And what we do is all about producing or creating employability for them. They got a more focused appreciation of the developer's interests and what makes one lamphouse or more attractive for buyers than the other. And they got hands-on experience with spatial tools, QGIS, QGIS Cloud, and they got licenses that they could take home and actually possibly create a business from them. So thanks for your attention. APPLAUSE Thank you, Andrew Bashfield. That is fantastic. It's so different from back when I was at uni. All right, next up we've got Julie Nograde. He's been telling us about exploring total water levels around Australia using R Shiny and Leaflet. Please welcome Julian. APPLAUSE All right, so at CSIRO we're interested in how high the total water level is for coastal inundation and coastal erosion. And we're looking to provide this information to coastal stakeholders, coastal councils, for example, through web-based portals. So we're using R Shiny and Leaflet. So I'd like to acknowledge my co-authors on the work, which Tim Irwin, who put me onto the Shiny work, and the fellow coastal scientists, Claire, Kathy and Ron, and we're all based here in Melbourne, down in Aspindale. So how high the water level depends on where you are or where you're measuring it. So in the open ocean, satellites measure the water surface and changes from temperature and density. So that determines how high the water is. In protected coastal estuaries, protected from large waves that you experience on the beach, you can measure coastal increases in storm... in water level from storm surge, which is wind and atmosphere piling up water onto the coast and also tide. So you can determine how high the water level is there. And on open beaches, there are techniques with cameras that measure the effect of waves. As waves, large waves, move into the coast, they break, they pile up onto the coast and push water up into the coast, and that determines how high the water level is on the coast from the surge of waves running up the beach. So it's important for coastal assets right near the coastal boundary. So what we wanted to do is develop a nationwide tool where we could access this data. So we're using the Shiny sort of web tool. So the way it's explained to me how Shiny works is that a group of RGO science ninjas got together with some HTML ninjas, coding ninjas, and they produced Shiny. That's my understanding. So we're using the R-statistical... Well, I use the R-statistical software, libraries for Shiny and Leaflet to generate this web page, which has on the top left some radio buttons to choose which locations to plot on the map, and you can zoom in, zoom out of the map and click on the different locations. Switch locations to be the tide gauges or to be coastal points. You can zoom into locations. So here I've zoomed into Sydney Harbour and there's a tide gauge located there. So we can click on that point and see what the coastal information is, or we can show the beaches, the different beaches. So here we've got Bondi Beach highlighted, we click on it, and we can produce these plots of return level height. So these are plots that engineers use to determine what a 100-year storm height might be. Now, the effect of waves on beaches depends on local features, like the beach slope. So with this tool, we're able to modify the local beach slope and change these return level periods on the fly to get the local information. So in conclusion, I guess this isn't the only shiny web app that's been developed in CSIRO, CSIRO is a big company, and there's many in-house shiny apps being developed. So the team's building in-house shiny servers to house or to host all the apps. And from my experience with the coding, I had a little bit of issues getting labels on the names in the leaflet sort of maps. And by trial and error, down at the end of the code here, I've got the labels in that add circles function equals the tilde space tilde as character name, which I don't know how I got to that, but it seemed to work, so I was pretty happy. So if you're interested in the website that we're developing, or this bug, please contact me. There's my... Well, it's not a bug, it's a workaround. There's my contact. APPLAUSE Thank you, Julian. Again, you've got two minutes left, Julian. All right. Next up on this computer over here, we have Hemish, and he's going to tell us about how he was famous for a day in New Zealand. So please welcome Hemish. APPLAUSE Let's be honest, it's not that hard. LAUGHTER Just really quickly, shout out to the spatial community Slack. There's a really cool growing community, international Slack community, thespatialcommunity.org. It's a really cool go there sign up. So I just want to talk really quickly about an interesting experience I had this year. So I was working from home in the evening on a beautiful Auckland July, so dark and rainy and horrible. And this week came along, and it was... Someone at Denmark had done a little bit of analysis on what's the most heroic location defined as being the furthest away from any structure. And they found this point that was 4.4 kilometres and it's next to a road. And I thought, you know, David Hood was like, I wonder what this looks like for New Zealand. And I thought, well, if there's one thing we've got out in New Zealand, it's being far away from other places. So let's have a look. And it happens to know that in New Zealand, if you want to be far away from a place, then you're probably going to be on Doc, Department of Conversation Reserve land. And land information in New Zealand happens to publish the Hutt information as points. So there's a good bet that the furthest point away from any location in New Zealand is going to be far away from a Doc Hutt. So over the next... Over that evening and into the next morning, I guess, I thought, well, hey, look, let's pull that data down and see if I can mangle my way through this kind of analysis. And I want to say I'm a software engineer and I mostly deal with sort of SAS Web Apps. I use Qtis now and then, but I'm not an analyst. So there are better ways of doing this, but this is just sort of my little process. And also Qtis has 3D views now, which are really cool. So I thought, hey, the first thing I'll do is put on a 3D map because that's always cool. You can just like tilt it and now it looks like you... Yeah, it's cool. Yeah, that's really neat. It's a bit buggy though. I think 3.4, hopefully, has improved that a little bit. I haven't used it since then. Yeah, and so I sort of... mangle my way through. The first thing I tried to do was kind of... was I broke it up into squares and looked at different areas and, you know, predictably. But sure, off the bat, you know, you kind of know if you know anything about the geography of New Zealand, you're going to suspect that it's going to be in Fjordland because it's massive and almost nothing is there, except for some moose maybe. And so... So I thought I'd identify the likely location and then, using the hot points, produce the sort of... for any polygons, I'm going to mangle the words here. And sort of a whole lot of flailing around later managed to get something that looked like it was going to produce some kind of result. And I think that's the first thing I tried to do was to look at the different areas of the map and understand that it's going to be a little bit different. And... Unsatisfactorily, I ended up sort of hand-moving things around a little bit, and I found this location. It's the yellow dot up there. The blue dots are the centroids of the relative polygons. And because I couldn't figure out how to treat the coastline as not being an edge, I had to manually figure out what was the closest point to that blue dot on the left. And it turns out to be this little point that is on the edge of this unnamed cove deep in Fiordland in the south of New Zealand. Which is pretty cool. I figured it was...it's about...what do I say? Calling it to plus or minus a kilometre, the south end of the bay, of the Coal River Mouth, which is not an official name, some 29.8 kilometres northwest of the suburb of Cove, Hutt, as the crow flies. So 4.4 kilometres is nothing. And that's right next to a road as well. This thing is a... It's a 40 kilometre minimum walk from the nearest hut. That's over steep mountainous rugged terrain, forest. The hut itself is a four-day walk to the end of a lake. The lake itself, you have to get a boat across from the other end of the lake. So, you know, if you really want to get there, it's a pretty big journey, you know, it's serious work. The next day I had this PM on Twitter from a local journalist saying, hey, you know, he'd been following along with us. It got a little bit of, you know, a bit shared around the very well-known and enormous New Zealand GIS community. And so, hey, you know, can I talk about it? We had a little chat on the phone that morning. Nick, minute. Yeah, so straight into the New Zealand Herald. Yeah, it was in the paper that I got in the morning, so I'm a strange person. Actually, it's a physical paper still, and it had this story in it, which was pretty cool. Got to show the kids. And possibly, my highlight is a dad, biggest dad moment in the car after school pickup, sitting in the car, and Radio New Zealand's on the radio, and the Jim Morris show in the afternoon, where they talk about mostly an ain rubbish. The ain rubbish of the day was my story, so... So that was really cool. So, yeah, for one day, you know, I got to be on the New Zealand upbeat pages, which was pretty rad. And then, you know, someone who actually knows what they're doing, Richard Law, who unfortunately can't be here today, he did, oh, let's just do the ruster version in GDAL, and within, like, a minute, confirmed that, indeed... LAUGHTER AND APPLAUSE So, I got coding late at night. You know, sometimes just have a sleep, think about it. You know, things will be much easier in the morning. So, yeah, but it was really cool to see, you know, how would someone who really knows what they're doing actually solve this problem really quickly and simply, and he had ruster analysis with GDAL was the way to go, which is really cool. And then, really cool, a couple of days later, this fellow, Tomon, you know, replied to me saying, hey, you know, he actually sailed past here on an expedition trip, took a photo, and so you can see a photo of the place up there. So, yeah, thank you very much. APPLAUSE Thank you very much, Hamish. We'd love to see you, Nain rubbish. That's what we're all about here. All right, so the other day, I had to go to my local council page, and they had a half-page of instructions on how to find my address. I live in Australia, I thought it was a bit crazy. So Daniel Castel is going to tell us about a place where it's a lot more difficult to geocode. So, where the streets have no name, please welcome Daniel Castel. APPLAUSE Hello. So this is, yeah, I'm not Australian. I'm German, and I'm living quite a long time now in Japan. And we're just far away, and you know, Germany is very well mapped in terms of open street map, and some people say you either import Germans or data. And so, they chose to import data, actually. But when I started mapping in 2007, Japan looked like this, very gray, a lot to do. And it's not easy. So, big cities, lots of people in some areas, super-dense cities, like, you don't know where to start. Also, the mapping doesn't really look very beautiful, so this kind of, the thickness of streets does not work in Japan of the default rendering. And you have, yeah, you also have to map highways, what an UK highway is in Japanese, whatever, past type. You don't know what it is, actually. And you also have to deal with these kind of things. So, real vertical, so GPS signals are not so good at the bottom. And how to draw it, actually. Yeah, and then you have also buildings that look like this, and a street goes through, that's in Osaka. So, yeah, one advantage is there are no road names, usually. So, there are few places that have road names, but so you don't have to just take the GPS truck and load this data, upload this data, and you don't have to name it, because there's no street name. But then you have something, like, when you're looking for a place, you have these kind of district maps you can find here and there. So, in this case, on the left side, this is like a district, like a local area, and the name of each owner of this property is actually written on this map. And if you, after some time, in Japan, actually figured out that the kind of the district where you are is always written on the poles of lights, for example. So, when you can read this, you know actually where you are, otherwise you have to ask someone to find the address. And the postman actually, I don't know how they know it, but address systems, first, if you don't know them, you don't know really how it works. And the problem happens when you attend the Phosphor G conference. Because you go abroad, and you have to fill in the correct address to get, to send the invoice to your company. And the address of the form does not match with your address. So, this is a typical problem where I have to actually bend my address that it looks later on the printout form as I want to have it. And usually, wherever you go, you have these kind of house numbering systems, like the European scheme, the clockwise scheme or whatever. So, there are many, many ways how to, yeah, describe a street address, but I don't have a street address. And so, I want to tell everyone who makes geocoders, for example, that there are other addresses than street addresses. A Japanese address addressing system is based on areas and subdivided from big to small. So, you also write the address from big to small. And, yeah, usually you start with the country, then there's only one. Then you have prefectures and then cities. Sometimes you have wards. And then, when you are at this level about, then you have districts, small areas, and they are numbered. And within this place, you are numbering the houses. And the houses are numbered depending on the city, either the time when the house was built or they have some sort of rule. You can easily squeeze a small building inside because you just give it the next highest number. It's actually not so bad. You might think it's inconvenient, but it's for geocoding, it's not so bad. Because if you know the district, you're actually not so far away. The problem is when you are writing addresses, for example, to receive a Christmas present, then the left side is the Japanese writing, starting with the postal code and ending with the name. So in the middle, you can see how you would write this in Romanization. And on the right side is how you have to turn this around that the actual present reaches you. Because Japanese post is actually quite tolerant, so they can read many things, but the German post office would not send this middle address to Japan. And so Japanese addresses are actually also... OSM is also very good to deal with any kind of tagging. So I think the Japanese community made the way through the tagging scheme. The problem is that then the applications often don't support what Japanese mappers map. And that's not enough because you have actually even more exceptions to street addresses. South Korea is different, Taiwan is different, Venice has a different address system, and even in Japan there's yet another one. And that's Kyoto, it's quite famous in Japan. And I took these images from a Japanese friend who has a geocoder for this on Geodos, and it actually is a descriptive way how to reach an address. It says, go two streets north and then right. And yeah, so it describes the way to the address, and people really use it. And the postman knows how to find that place, so yeah. This is...just remember if you write some software with addresses, or if you make a form for your website, there are other countries that don't have western street addresses. And that's my message. APPLAUSE Thank you very much, Daniel. Next up we have Rebecca. Rebecca Roberts, and she's going to talk about calculating livability across Australia using open data and tools. Please welcome Rebecca. APPLAUSE Hi, I'm a GIS analyst at RMIT University, and I'm currently involved in a project calculating livability across Australia using open data and open tools. So what exactly is livability? At RMIT we define a liveable neighbourhood as a neighbourhood which is safe, socially cohesive and inclusive and environmentally sustainable. Additionally, a liveable neighbourhood must also have affordable and diverse housing which is linked via public transport, walking and or cycling infrastructure to employment, education, public open space, local shops, health and community services, and leisure and cultural opportunities. So the following maps collectively titled Mapping Access to Frequent Public Transport are an example of one of the many indicators we have created using open data. These maps show the percentage of residences by suburb which are within 400 metres of a public transport stop with a service frequency of 30 minutes or less. We created this indicator for each of the eight capital cities across Australia to enable comparison both within and between cities. These maps were published in a report called Creating Livable Cities in Australia which you may also freely download and view. Now I'd like to explain how we built this particular transport indicator. It's easiest to work backward in the flowchart so I'll start with our near end product which is called the information output. In this case it spatially identifies which dwellings are within a 400 metre walking distance of a public transport stop which meets our service frequency specifications of 30 minutes or less. In this example I'll also refer to Melbourne because slightly different data sets were used to create different indicators for each city. So the information output was derived using the following input building blocks which are pedestrian road network dwellings and frequently service stops. We built the road network through Open Street Map but we put this network through a cleaning process which remove roads such as freeways and busways. This was done to ensure we had modelled a road network which is accurately as possible reflected one on which a pedestrian could legally and safely walk. Dwellings were extracted from the open source gene F address which file which is located in the Federal Australian Government Spatial Data Repository and we got the Melbourne Public Transport data from the Victorian Government Spatial Repository. So after we calculated which dwellings were within a 400 metre walking distance of a frequently service public transport stop we then downloaded more open source data which were the ABS geographic boundaries and we used these spatial boundaries to aggregate the information up into our final indicators. The final indicators were actually the percentage of dwellings which were within a 400 metre walking distance of a frequently service public transport stop calculated at different scales. All of this data was stored and analysed in a post-gis database and we used QGIS to produce the maps for publication. So the creation of this transport indicator is just an example of one of the many different indicators that we've been creating to model liveability. And some of the other indicators that we have done rely on additional open source tools such as KNIME and Python which we use in combination with post-gis. All of these open data sources and tools have enabled us to successfully calculate and publish these national liveability measures across Australia. Thank you. Thank you, Rebecca Roberts. Alright, next up we have Hugh Summons from IG Insurance. He's going to talk to us about big data, open source and ethics. Please welcome Hugh Summons. Thank you Steve. I've given myself the challenge of talking about three topics in five minutes so here we go. Okay, I'd like to talk to you today about movement data. What is movement data? Movement data is basically location data from smartphones and GPS devices. It is data that tells you where people have been, where they are going, where they are going to. It tells us where they live, where their home is, where their work is, where their kids go to daycare, where they like to shock, which pub do they like to go to, what team do they support, what football fields do they go to, you name it. What can we use this data for? This data is good for customer segmentation. It is really good for transportation and retail analysis. It is also good for reducing the number of questions required in censuses. Since I am not sure about that one. It is also really good in the insurance world for risk modeling. So let's have a look at some examples. So we're looking here at 1% of Melbourneians on July 25. This is a 24-hour period. Red trips are long. Green trips are short. Another example. Some risk data. Here we have someone who doesn't drive very well. So we can see as they go around corners, they are getting higher than usual G-forces, which means they are driving like a Wally. OK. Is this big data? Yes, it is. This is not Google Facebook grade big data, but it is still big data. What you see on the screen there is 150 bytes or thereabouts. If we are capturing that every five seconds, which could be one second, let's say five seconds for the fun of it, and people are moving around at least four hours of the day, and we are looking at 1% of Australia, you are looking at 100 gig of data per day. So we are looking at about 36 terabytes per year. It adds up pretty quickly. How the heck are we going to manage and process all that data? Well, unless you have a supercomputer, you are going to need a cloud provider to give you some infrastructure to run this lot on. How are we going to process the data in a cost-effective and high-performance manner? You are going to, of course, need some open source. So let's have a look at a couple of contenders of tools that can do this sort of work. GeoMesa and PostGIS. I am not sure how many people have heard of GeoMesa. It basically allows you to store index query and transform spatiotemporal data at scale. I am quite proud of the fact that I got to say spatiotemporal in front of a large audience. So what is it? It is simple. It is basically a bunch of spatial data type, spatial functions, and indexing for some big data platforms, such as Apache Spark, Kafka, Cassandra, Acumulo, HBase and a few others. Very powerful tools. These are the kind of tools that are being used by the Facebooks and the Ubers and the Netflixes of the world to manage their data. It allows you to store your data in Hadoop or HDFS, cloud storage systems like AWS S3, as well as local files if you have got a really whopping big hard drive. We have put together some sample Python code which allows you to run GeoMesa in Elastic Map Produce in AWS or on your stand-alone Mac or PC. If you go to the IAG Geo GitHub page, you will see it there. You can just some geo-enabled news data called GDELT and then do a quick filter just to prove it works. Okay, let's compare events. I've got an old friend PostGIS which allows you to do exactly the same thing, but in a relational database. Some comparisons. Obviously, it's a bunch of spatial data types, spatial functions and indexing as well. However, it has more types and functions than GeoMesa. It also has the simple SQL syntax. Now, for those of you who haven't done a lot of SQL, you might say simple, you've got to be kidding. But with GeoMesa, you have to code in Python, Java or Scala. So there's a little bit more of a learning curve to automate and manage your data in GeoMesa. PostGIS, generally speaking, is harder to scale for big data than package cloud services. So I want to say package, things like Elastic Map Produce which allow you to spin up Spark and Hadoop. It's reasonably straightforward. If you want to go from 10 servers to 100 servers in Elastic Map Produce, you add a zero onto one parameter. So AWS made that quite simple. It's a bit more work with PostGIS. Okay, PostGIS has some neat things. So you're dealing with billions of records. They're very hard to manage and continue using. What you can do though is you can distill that down into some polygons and some lines. So whenever someone stops and they've got little GPS points bouncing around a certain spot, you can do things like DB sand clustering. You can do point in polygon queries inside property boundaries, for example, to cluster those points and replace hundreds of points with one record. Similarly with journeys. So all the points that make up someone's journey, you can create an ordered line string. So it's a line string ordered by time, which is a really neat little trick in PostGIS. Okay, last but not least, governance and ethics. As I mentioned, we know where your children go to daycare. So we've got data, and I say we generally, people working with moving data, we've got data of say hundreds of thousands of people. This is highly sensitive data, but we want to get lots of juicy insights out of it. That's not going to happen without governance and ethics. First things first, secure your data. If you have a data breach, you may have just seen the end of your organization, either financially, reputationally, or both. You may no longer exist. If anyone said of Cambridge Analytica, a little company in London, what they did with Facebook was the end of them. They're gone now. Last but not least, you need to look at your use cases. Are you going to have ethical usage of your data? Are you going to create algorithms which are unfair to certain groups? Are you going to upset or harm people? Because they're going to ruin you as well, financially or reputationally. Thank you. Thank you. Hugh Salmons. Next, we have Dr. Martin Tomko. He's going to tell us about teaching GIS science using Sosphogy. Is there an alternative? A rhetorical question. I like it. Please welcome Martin Tomko. Thank you. Thank you everyone for being here. I'm very pleased to be here. I didn't feel like I can talk about my research. I'll talk about my day job, which is really teaching. What I'm trying to do is to teach people that can then be in this room. I'm very proud of that. The question is, teaching GIS science, Sosphogy, is there an alternative? Maybe you think, sure. Or maybe you think, surely not. And that depends on how we understand the title. Because I want to talk about GIS science, not about GIS. Sure, you can teach GIS. You can teach people to go and do GIS analysis. But what we are interested to do here is to teach people that can sit in this conference in the future. So let's go through the scientific process a little bit. You have to start with some questions that you are curious to answer. And that should hold. And the answer to which should contribute to human knowledge for posterity or until proven incorrect. So you have a hypothesis that you want to test. And you're going to design an experiment around it. You're going to collect some data in a careful manner. You're going to process them. You're going to analyze them. You're going to interpret your results and evaluate and verify those results. And at the end, you can say yes or no. Did you confirm or reject the hypothesis? That doesn't stop there. There's one more step. Your process should be forever, or ideally, verifiable by someone else, not you, by your peers, or whoever else is interested in this. Now, how does this apply to spatial? In the GIS process, you do not have that external verification, not necessarily, or you do not depend on it. Anyone can go and do GIS, can do it for a local government, but it doesn't have to be somewhere published, doesn't have to be verifiable, might not hold next year. And it's fine for many things. But not if you want to contribute to a scientific process where the GIS process is part of a scientific process, or, what I'm talking about here, if we want to develop new methods for data collection, new methods for their analysis, or for their processing. Right? So I'm talking about developing the methods and functions you find in your tools that you're using here. Someone has done research on it. And our tree has been researched and proven to be more performant than whatever was before, or perform better than Quotree, let's say. And that's what we're interested in doing here. So it is the difference about going and getting take away from knowing how to cook your meal. Okay? And we want to teach, if we teach GIS scientists or technologies that will be implementing results of GIS science, to be able to cook something new and understand how it was cooked. So this is something that some of my students see in their exams. We looked, let's say, at a shapely code, and they need to understand it. So I'm not talking about commercial, not commercial here. I'm just talking about whether the source is examinable, which is important for us when we teach. So, you know, in some ways, Coca-Cola is open source because it has been shown to someone and they have been able to say, yes, it's kosher. In that sense, we can say QGIS is kosher or any of the other things. Thank you. Thank you, Martin. Next up we have Steve Bennett. And he's been telling us a couple of little tricks to use with MacBox GL. So please welcome Steve Bennett. Thank you. I know what you're all thinking is the last talk. Thank God we're having a nice, soft, fluffy one with no technical details at all. Too bad. Five tricks. Trick number one, animated interactions. So this is just a cute little thing you can do in MacBox GL. For those who don't know what it is, it's like an open source web JavaScript library better than leaflet. When you click on points, the way we make this work is you just create a new layer with a radius of zero and then just immediately set it to something other than zero. So in this case, I'm setting it to a radius of 20 pixels and MacBox GL.js just can't help itself. It transitions the hell out of anything that changes. Next up, this is a least compelling demonstration possible of a really, really cool ability. Basically, MacBox GL.js lets you symbolize stuff using a couple of options like circles and icons, boring things like letters. But we can actually link anything we want to it using a marker. So you can use the full power of the HTML DOM. So in this case, I've done something really unimpressive. I've put some rounded corners on a box. And the way we do that is I'm actually tying it directly to the places in the underlying map data. So in this case, it's a map box streets layer. And what we do is we basically, I keep a record of which things I've already touched markers to. I check that the market hasn't already been created. And if it hasn't, then I'm just going to create one and I create an HTML element. And in this case, I just apply the class name of rounded label and I get my Oso sexy rounded corners on my label. So you've got to do so much more with this. For instance, you could do, you could tie like D3 to it so you could have like little pie charts on your map. And they would just like be completely attached to the underlying objects in the map box data. It doesn't even have to be your own data. Quickly look down that really long URL and you can see for yourself. A coro pletha kind of standard thing you see in QGIS in every other platform, but there's no native support for them in MapBox. I had to implement them like three times for three different projects and it's quite a lot of work to implement map box, sorry, implement coro plets. So I've packaged up all that work in a plugin. I've really creatively entitled map box hyphen coro pletha, which you can find on NPM. So I recommend you use that just pass through, you know, a few dozen friendly parameters like the, the geometry that you're using for your coro pletha, the CSV file that you're getting the data from, what color scheme you want, it supports color brewer names. And then obviously details like what are the names of the fields in both the geometry and the CSV file that you're going to tie together. And lo and behold, you get an ugly coro pletha. Fourth one, this is not specific to MapBox by any means, but it's just a technique you don't really see a lot of visualizing two variables at once using a single point. So you can actually obviously change the color of the outside of the circle separately from the inside of a circle. So in this case, this is the city of Melbourne's dynamic parking map. If you didn't know that existed, well, now you do. You can see which spaces in the city are being occupied right now based on real time sensors. And you can actually access that data directly and build your own app that uses it. So here I'm just highlighting the outside of each parking sense with a color indicating the time restriction. So red is like really short and blue is much longer. And then the inside of the circle tells you whether or not there's a car currently there. I'm surprised how easy that is to sort of mentally process often by variant maps. I find it really hard, but I think that actually works quite well. I don't have any code because it's kind of self-explanatory. And then the last one is this kind of visualization where you take a... Typically you'd be using like building heights or something as your Z-axis if you're doing that. But we can also just use a statistical variable. So in this case, it's the total amount of employment within each of these. I think they're called clue blocks where clue is the census of land use and employment. So this is data collected by the city of Melbourne by knocking on an awful lot of doors on a revolving basis. And let's just see if this... I tried to sneak in an extra... This is not the live version. Okay, I tried to sneak in the actual video where you can see this action. But I guess that isn't going to work. Which means I've got a minute and a half for questions. Thank you. See you in a minute. Cool. I'm not from T-shirts. Do I like our T-shirts? Yeah. Thank you, Steve. I'll use my last minute for my own story of fame a couple of years ago. I can totally relate to the... They calculate the remoteness area. We created a stupid thing called the hipster map of Melbourne. Happened to know a guy who worked at the age. A week later, it was page three of the age. Stupidest website ever. I will never get over the fact that my biggest moment of impact is a website with no practical value whatsoever. But anyway, cool. Thank you, Steve Bennett. All right. Next up, we have Nikola Todek from Bing Maps or Microsoft. He's going to tell us how Bing Maps Australia is now using open data. So please welcome Nikola. Thanks everyone for having me here. My name is Nikola Todek. I'm actually living in Serbia and I've been building the Australian map from Serbia for Microsoft for the past year. So at a high level, this July Microsoft Bing Maps has Australia continent being served to a portion of our users. It's entirely based on open data. Open street maps, ABS, open addresses. So we provide the whole set of services on top of this, the routing rendering, geocoding, etc. So we're doing this, I mean, we're just starting with Australia. We want to surface the best maps data to our customers and want to empower the open community, essentially for an outreach. So they can have much more impact by going through Microsoft products. The challenges is OSM and other open data, good enough. And I guess it's going to take time to persuade people that that's the case. So while Australia, it's an important market for Microsoft, obviously. It's an isolated continent which we can easily stitch and the big plus for that was that the government mandated the government data to be open. So we had to fork our entire service stack in order to ensure that the open data does not in any way get mixed up with the proprietary data. So OSM Australia, we had a belief is great, but we kind of wanted to see what's good about it, what's not. The Cardo feature is absolutely amazing and the data is fresh and up to date. What we found that the data lacked was the coverage in administrative areas, term restrictions, address point data, and don't pay the attributes on the road. So we had to do something about it. Yeah, so as I said, the map is up to date, really up to date. Like what I'm showing here is some road development that still doesn't exist in the satellite imagery, but we have it in OSM. And this is what we routinely see when we look at all geographies across the world. The community is incredibly active in updating recent road development. And so this is also common with OSM. The places that are less populated really get less attention. So the administrative polygon coverage, the red dots are representing the points from our test sets where we don't have coverage in OSM. Address points, I mean we have only 10,000 address points inserted in OSM and big thanks for open addresses for aggregating all of this data for us to use it. On the term restriction front, we have analyzed the data. The big important number here is that we have half a percent of the routes that we plot to our users that are essentially taking you to a turn where the turn is forbidden. So we had to do something about this as well. So what we did is we employed some people. I'm a big fan of our editorial team. Here they are on the OSM list, like at the top then. They've, across the last year, they've imported and actually drew more than 100,000 kilometers of road in OSM added more than 20,000 turn restrictions. This really helped us out in building a solid routing engine. And finally for the paved on paved attributes, we have more than 300,000 kilometers of road in OSM which is, which doesn't have the tag whether it's paved or not paved. So we had to build machine learning algorithm using essentially computer vision deep learning and their infrastructure to process all of the Australian tiles and essentially do the tagging ourselves. Unfortunately, we cannot do the import to OSM, but that's something we're definitely considering just pulling on GitHub. And finally, we need your help. So, I mean, this is the first time we're doing this. We have on the order of 100,000 user queries hitting the Australian map every day. We need feedback on our services on the data and we need you to keep editing the map. I think it's incredible what the Australian community has done thus far. And then together we can really make the best map in the world. Thank you.
 Thank you.  Thank you.  Good afternoon, everybody. How are we doing? Welcome to the lightning talk session. My  name is Steven Leigh. I'm from full extent in Sydney. Thank you, Alex. We've got a very  exciting session for you this afternoon. We've got 15 presentations from a range of experts  and practitioners in open source, open data, and everyone that I've spoken to said they're  more excited and more nervous about their five minute talk here today than they were  about their big talks during the week. Now, we're going to be very strict about the five  minute time limit. So we've got a little timer on the table here. We're very strict about  the time limit. Now, when you guys hear the timer go off, I need your help, could you  please do a slow clap for me until the presenter stops? Can we just do a little test now? Slow  clap. Yep. Getting louder and louder and louder until they finally shut up. Exactly.  That's what we're going to do. So I'm going to start my timer and Alex and John are very  carefully, very considerably put me first in the talks. So I'm going to start the timer  and I'm going to do my talk now. Okay. So here's my talk on a map-centric travel blog  that I read. So this is me. This is my wife Gloria or Glow and she's from England. I'm  from Australia. We met in Canada. We've been traveling the world together ever since we  met back in 2001. Now, I've always loved travel photography. So long before there was Flickr  or long before there was Facebook or Instagram, I put together a travel blog using Tumblr  and that's what I've been using ever since. It's getting a bit old and a bit long in the  tooth but that's what I've been using. Now, the beautiful thing about being a developer  these days is that everything I need is on my laptop. I've got VPN to connect to work.  I've got GitHub for the code. Most of the stuff's in the cloud anyway so I could work  from anywhere. So I decided last year to take my family on a round-the-world trip. Now,  being a good geek, my first thought was I better do a new blog. The blog's getting  a bit old so I looked around at some of the best blogs that are out there and even if  they're beautifully designed, they've all got the same floor as you're seeing here,  they treat location as an alphabetical list. Australia is next to Austria and I knew I  could do better than that. So looking at my old blog, I could see that the hint of what  I wanted to do was already in there in the blog post. Every post knew where it was. It  had the title, Hanmer Springs, Berlin, when in Dublin. So the location was there already  in the blog. It seems to do something with it. What I decided I wanted to do with my  blog is make it geo-aware. It had to know about the places. I wanted it to be map-centric  but I didn't want the map to take away from the photos and the text so it had to be unobtrusive.  So those were my requirements. Like many optimists and fools before me, I decided to write my  own blog and I'm a Rails developer so I decided to use Rails. Now, Rails is written on the  back end in Ruby and it allows you to use the front end JavaScript code. What I love  about Rails is it's got a bunch of gems which are functions written by other people which  you can just insert easily. One of those is the geo-coder gem. As you'd expect, you give  it a place name, Paris and it returns lat long. What's lovely about this gem though is with  one line of code, it just binds into the application. So I set or change the place name and it  automatically goes into the right spot as a latitude-longitude column. So here I've  got a geo-enabled table, basically a geo-coded table. So let's take a look at what I did  once I had a geo-coded table of all of the blog posts. So I put together this blog and  I wanted it to be photo-centric. So the first thing you see is a header across the top with  a photo. Then there are photos further down as you scroll. You can also click on one of  the photos and then get a full-screen photo which you can then zoom through. So they're  pretty cute, aren't they? Just like a normal blog, you can go previous or next blog post.  So here I'm going to the previous post and you can see it's zooming into Basel. Here's  where we're starting to get the hints of the geo-enablement. The post knows it is in Basel,  Switzerland and there is a map. Now that map is interactable. So you can use the map to  find other blog posts. So now we jump across to London. And now when we click on that,  we're going to the London post and now the whole thing changes to be about London. And  so the photos are about London. If we now jump to the previous post, we're now going  to jump from London to Tokyo and looking at the map here, I can go into full-screen mode.  So the map isn't just a little map, it's a full-screen map as well. And now I can start  to interact with all of the posts that I've written over the years. What's nice about  this is it gives you a totally different view of the same content rather than having to  go next previous. You're seeing it in a map. So we jump from over there across into Central  America and we jump down into Panama. And so we've managed to go across space and time.  This is like six months later from where I was in Tokyo, I ended up in Panama. Now we  go back into the full-screen mode. You notice that in full-screen mode, we keep the same  extent as before. And now we jump across to Guatemala. So you can see it's a really easy  and intuitive way to view the same content rather than being just on a, the text, we're  seeing it in a map. Now not everyone here is a JavaScript programmer. So for you guys  on the left-hand side, there's some nice photos to look at. For everyone else, let's  look at the code and how it works. What's nice about Rails is you can add.json to a .jax request and it returns the posts as, the timer hasn't gone off. The timer hasn't  gone off. Give me a chance, guys. Yeah, wait for the timer. So here we're seeing the marker  clusters. So basically, return it as.json, convert to.json and then see it as markers,  as clustered markers. The way that the bookmarks work, when you open up the map, it knows that  there was a cookie set for the last lat long. So it opens up with that lat long. And then  so I set the cookies every time the map pans or zooms. I set those cookies. I'm going to  have to jump forward because I'm running out of time. Okay, so to summarize, the idea was  that it's, do you aware that it has a map front and center, but it's not, the map is  not obtrusive. So there's no table contents. You can start clapping now. So I'll just finish  up by saying, I'll finish up by saying that if you wanted to have a look at the map, it's  at StevenGlo.com and you can download the code from there. Thank you.  Okay, so next I'm going to bring up Cameron Shorter. Now, one of the advantages of using  commercial software is that you get a use, usually a nice setup.exe to run it. With open  source software, it's often a bit more difficult. And Cameron's going to tell us about ways  to make that easier. So please welcome Cameron Shorter.  Now, I'm just going to talk. Okay, Osageo Live is a project I've been involved in. It's  a Linux distribution that has been pre-installed with over 50 Geospatial open source applications  along with sample datasets, overviews and quick starts. You can boot it up from a USB  or run it in a virtual machine and you can immediately start playing with the best Osageo  has to offer. It is used for training, training new applications and outreach. We launched  it at the International Phosphogy event here in Australia in 2009 and it has grown and  attracted hundreds of contributors since then. In this cut down lightning talk, I'll  introduce its more popular packages which effectively compresses a Phosphogy conference  into a five minute elevator pitch. So our focus with Osageo Live is identifying and promoting  robust and sustainable projects. Our metrics page, our metrics web page, attracts, aggregates  the dashboards from Open Hub and you can use it to gain insights and meet into each project's  health and developer engagement. So let's have a look at the popular applications installed  on Osageo Live starting with the desktop. This is where we find the heavy lifting applications.  QGIS allows you to visualize, manage, edit, analyze data and then compose printable maps.  It supports numerous vector, raster, database formats as well as tools and external plug-ins.  Such as the GRAS Analytics module. GRAS provides powerful raster vector processing. It includes  hundreds of tools for spatial modeling, visualization and analytics. And moving now into the browser,  we've got browser clients are used to publish maps and increasingly are also delivering  functionality which previously was only available in the desktop. Open layers and leaflets are  JavaScript libraries for putting maps into web pages. Open layers aims to be comprehensive.  Leaflet is more lightweight targeting simplicity. Cesium is a JavaScript library for creating  3D globes and 2D maps in a browser. It uses WebGL for hardware accelerator graphics and  is tuned for dynamic data visualization. GNode is a content management system. It supports  loading, editing, publishing, searching and commenting on maps and associated metadata.  And then moving on to web services. These are accessed via a URL and return maps in various  formats usually based upon OGC standards. GServer and Mapserver are the leading open  source applications for publishing maps to the internet. GServer is Java based while Mapserver  is written in C. And while not as performant, QGIS server is handy for publishing your  QGIS project directly as a web map service. GNode and PyCSW provide catalog services for  maintaining metadata about datasets. GNetwork is an older Java based project and PyCSW is  lightweight and quick to set up. OGLive includes two sensor observation service projects and  three web processing services. These are web processing services that allows you to move  complex processing to a central server. And this is used for accessing large dynamic  datasets such as climate models. And at the bottom of the stack of the databases, we've  got a few of those. PostGIS especially enables a popular Postgres database. It's stable,  fast, standards-compliant, includes hundreds of spatial functions and is currently the most  widely used open source spatial database. Next time we recover the spatial tools, GDAL  and OGR provide command line tools to translate between spatial formats and projections. R  provides a command line interface to powerful statistical computing and graphic algorithms  and exceeds at analyzing spatial datasets. And of course, all these tools become much  more useful with access to free data. OpenStreetMap is crowdsourced map data of the world which  has grown to become one of the most detailed sources of local scale maps. OGLive includes  a city-size extract of OpenStreetMap and we also have some sample datasets in other formats  as well to demonstrate some of the examples. So this has just been a taster. Check out our  project overviews and quick starts on our website for more details and then go and boot  a USB or a virtual machine and get some real-world experience in OZGI applications.  Thank you. Thank you Kamishota. You guys are very, very impatient up the back there. All  right. So next up we have Nathan Woodrow, one of the key QGIS contributors and he's going  to talk about generating JavaScript code from QGIS expressions for fun in inverted commas.  Please welcome Nathan. So exactly generating JavaScript from QGIS expressions. Exactly  about why would you want to do that. Indeed. Crazy indeed. Mainly for fun and learning because  it's the same thing in the end. Sometimes it's Saturday night, nothing else to do. But also  for QGIS web. So if you haven't used QGIS web before, Tom Chadwick, I think his name  is, he developed QGIS web which takes a QGIS project done to open layers or leaflet. He  had a problem trying to get the rule-based expressions to work in QGIS web but he was  trying to use regex to parse the whole expression and I almost died when I saw that. You can  news read decks but friends don't let friends do that.  So we take a QGIS expression and then we generate  a JavaScript function which actually has the actual  expression value generated inside it and then the result  is just calling it function.  And then the parsed int function,  func to int function is actually just a wrapper on that  as well.  So why the hell's going on?  So QGIS expressions are actually,  they generate abstract syntax tree underneath.  That's not that kind of tree, it's that kind of tree.  We take the program and take the expression,  break it down to all individual nodes  and how they connect to each other,  which means we can take that abstract syntax tree  and generate pretty much anything we want from it.  So we take the input node which is the root level,  we walk the tree, we say what type of node it is.  In this case we're gonna call handle binary,  so if it's binary node which is a plus or minus  and equals all that sort of stuff.  So the handle binary function is like that,  takes another node and walks each left and right side.  And if the left side, the operator is a like,  then we generate some like syntax  that's equivalent for JavaScript.  Same for not like, same for not incase sensitive like.  Handle function is exactly the same,  we walk the expression again,  get the function that you're trying to call,  generate a stubbed function that would do the actual logic  and then walk all the arguments in that internal  do the same thing again,  so that way you can have functions inside functions  and functions.  So basically the steps are take the Q just expression,  walk tree, generate expression and profit.  Having quite work ahead of the profit step yet.  That's why we're all here.  But can we go down deeper than that?  Can we go to something like a log code?  We haven't seen log code before.  Yeah, you can look it up.  But I didn't do that because I couldn't get it to fill.  But you could, but I'm not going to.  That's it.  Wow.  That's very nice.  Thank you.  You got a couple minutes left,  if you want to just keep on talking.  I think I scared.  It's ruthless man, it's ruthless.  I scared everyone with the whole like slow clapping thing.  I think, yeah.  All right, so next up we've got Niall Dawson  who's going to tell us about adventures in reverse engineering,  isry style and layer files.  Please welcome Niall Dawson.  I don't know if I should go for like a minute and a half  to beat Nathan.  This is me in another life.  I'm here to talk about Slayer.  Slayer is a little tool that was developed  around to get these layer files,  which are basically like a symbol set.  So it's a way of styling layers  and embedding all the information  about how they should be symbolized inside that one file.  Like this.  So they usually have thousands of symbols and stuff in there.  They tend to be distributed along with official data sets.  That might be like you go to your government part of portal,  you get your data and it also has a layer file sitting next to it  that's got the official symbology for that layer.  Unfortunately, they're locked up in purgatory.  What I mean by that is a layer file  is actually an arctis format.  Like I said, they're locked up in purgatory.  So this Slayer story starts at SMEC.  In SMEC, there was an official government data for GeoTech,  which came with basically like thousands and thousands  of symbols for all these different kind of GeoTech categories.  And they all had certain colors that you had to respect.  We needed to get those from the Ezra world  into the cutest world, but because there was thousands  and thousands, no one's gonna sit there  and copy all those colors across.  And basically there's every way  you could think of to get them out,  because there's been blocked in some way by people.  So the mission was to get them out and save them  and take them into the promise queue just like this.  But like I said, unfortunately,  layer files, like they're a binary format,  there's no specifications for them.  They're all locked up and they're encrypted.  Locked up.  So after much of this, Slayer was born.  And Slayer is basically a Python kind of library tool,  something, parser, which could kind of work  at what those little binary bits in the layer formats were  and turn them back into something usable,  like the colors for those symbols.  So the very first version of Slayer  was really, really primitive,  but it did let us get these couple of thousand colors  and symbols for that GeoTech library layer  back to the wonderful Qtas Promise land  and there was much rejoicing.  Like a really initial roadblock we hit was with colors.  So basically there's a certain format of color  that Esri used to store color values inside these layer files.  It's not RGB, like you'd hope,  where it's just three numbers plugged in there.  It's this side lab LAB format.  But all the color formulas and conversion formulas  that are standard for getting between these two color systems,  none of them quite matched up.  It would kind of get close,  but maybe it'd be like 30 or 40 difference in that RGB channel.  So good enough, but not enough to make me happy.  So that's kind of a roadblock  and Slayer kind of just sat dormant for about a year or so  after that initial task.  A tool along came, this company, opengis.ch.  Cool Swiss company contacted me.  They had a customer who had, again,  a library of a couple of thousand symbols  that they developed over multiple years in these layer files.  They were moving to an open source adoption  in that enterprise and they needed to get these symbols out  and they didn't want to do it by hand.  So opengis threw a bunch of funds at Slayer  and Slayer got powered up by this funding thing  and we threw it at these symbols  and basically got them all across  and got them all converted into Qtas symbols.  So today, this is kind of what it looks like.  There's two parts to it.  One is the part that reads those layer files  and works out all the little bits and stuff  and converts them into something usable.  The second part is like a Qtas plugin  which just put the front end on this  and offers tools like converting the Esri style files  to GPR color pallets and to Qtas style XMLs.  Looks a bit like this.  You pick your style file  and you choose a bunch of settings and stuff  and it gives those symbols out at the end.  This is a bit of the current status of Slayer.  So currently, only Esri style databases are supported.  So unfortunately, you still need an ArcGIS license  to get your layer file to convert that  to a style database and then you convert that  but that's gonna change.  RGB colors are now like passed to plus or minus one  for the RGB channels.  So we broke that.  Fill symbols, pretty much complete support.  Line symbols, pretty much complete support.  Markers more or less complete support as well.  Color ramps supported too.  So it's got really good conversion.  And fortunately, there's a massive overlap  between Qtas symbology and ArcGIS symbology.  So almost everything we can translate  and get looking nearly identical to the original.  In the future.  So Slayer, at the moment it's basically made  for Qtas conversion but there's no reason  why the same thing couldn't be used  and basically for our SLD or CARDOS, CSS  or whatever other kind of thing there.  So you could directly take your SRI symbology  and get out an SLD equivalent of that.  In the future again, so it's kind of a lie.  At the moment you can't get a layer file and convert that.  You have to go right at the style file.  So the number one person need for Slayer  is to actually directly support layers.  So you don't need SRI, you don't need all these  kind of other dependencies and you'll be able  to run it on any platform.  Also in the future is we've looked into MXD support  and it should be 100% possible.  So the holy grail goal here is basically to get your MXD,  your ArcGIS map documents, put them into Slayer  and get a Qtas document out.  The end which has got all those layer sets  in the same symbology, you print layouts  and all that sort of stuff converted  just with a kind of click of the button.  That's the goal and I'm sure we're gonna get there.  At the moment that's how you get it.  So you have.com slash Nile Dawson,  nang slash Slayer and you can download  the kind of beta version of it.  Convert all your style files into Qtas files.  Oh!  Thank you!  Thank you!  Thank you!  Thank you!  Thank you!  Thank you!  Thank you!  Thank you!  Thank you!  Thank you!  Thank you!  Thank you!  Thank you!  Thank you!  Thank you!  Thank you!  Open Street Map Schools with Open Data.  Please welcome Andrew Harvey.  Thank you!  Thank you!  Thank you!  Hi, so I wanna talk about a project.  I went through to update schools in Open Street Map  using Open Data.  So the first step was to identify the data source.  So in New South Wales, Department of Education  have this great Open Data portal.  It's really good, I can just go and search  and quite quickly I found this master dataset  for New South Wales Government Schools.  It was in a CSV format, so quite easy to use.  Done.  Step two, license check.  It was Creative Commons licensed.  So we need a waiver.  Open Street Map Foundation published this great waiver.  So it's very easy, sent it off.  I think it was like two months later or so,  they got back to me.  But the good news is they agreed to it.  They said it's fine to use this in Open Street Map  under the open ODBL.  So next step was to develop and document a plan.  So the first step as part of that  was to consult the community.  So I sent this email to the talk AU  which is the Australian Open Street Map Community mailing list.  And I said, hey, I'm investigating the possibility  of importing this into Open Street Map.  Your feedback, like what do people think about this?  Should we do it?  Shouldn't we?  So as part of that, I put together a plan  about mapping it into the OSM schema  because they use kind of their own schema  and there was a bit of work to then get it in.  So I said, here are the tags like I think we should use.  And a few people kind of chunned in  and provided some really good feedback to make it better.  So it's like this is good.  The Open Street Map has kind of a process to doing imports  because if I just kind of went in and did it,  I would have made a lot of mistakes.  So that actually worked really well.  So yeah, the import plan, sorry,  so here's the example of the different attributes  that were in the open data.  All the contact details, address,  when the school was built,  the grades to the school levels that it offers,  if it's like a high school, primary schools at year,  seven to 10 or seven to 12,  things about specialties,  does it offer like distance education,  really detailed level of data.  So yeah, and that was basically the import plan.  But the next part of that was to actually compare it  with what's in Open Street Map already,  because there was actually a lot of schools mapped out  by the whole Open Street Map community.  So I did this comparison and found out  that there's about almost 500 features  from this government open data set  that were missing in Open Street Map.  And another almost 2000 features,  so they're schools with new or different tags.  So either we can improve what's in Open Street Map  or there's maybe something that we need to look into further.  I can't tell you the other way,  like how many schools Open Street Map had  that weren't in the Open Government data set  because in Open Street Map,  public and private schools are all mixed in together  most of the time, unless someone's actually added  the tags to that,  whereas this is a data set that only had public schools.  So, yeah, I put together, like this is a script,  to actually go in and compare that,  and it produced this big table of results  which actually matched up the Open Data  and the Open Street Map data.  And you can see here all these tags are added in,  and you that we can import,  and then it's found the difference in the name.  So in this case, there was quite a lot of differences  in the name, but most of them are quite different.  They're quite like public school versus primary school,  and so what we did was to just add,  so leave what's in Open Street Map at the moment,  but just add that as an alternative name  to trust like what people have actually surveyed over this.  So the next step was to actually do the import.  That was quite straightforward,  but it took a while because it is the process  of going through all this manually  to make sure that we don't mess things up.  But then there's a remote control plugin for Jossum  that makes it really easy that you kind of say,  yep, this is okay, I'll go apply in Jossum,  and it actually goes ahead and makes that.  So in Jossum, we can just hit upload.  So lessons, agencies with Open Data Portals  are usually very happy for you to use their data  if you talk to them.  They're usually quite happy about that.  In this case, the cell starter was very high quality,  but it's not always the case.  So you need to use a lot of caution.  So that's why comparing it to what's in Open Street Map,  getting some local knowledge is good.  Some diversity issues in Open Street Map data were revealed.  Like a lot of, there are schools inside hospitals  and they were basically non-existent in Open Street Map,  so we were able to bring them in.  And also there are some specialist schools  for children with disabilities  that were also quite missing in Open Street Map  that were able to add in through this process.  So in the future, better software could also help  actually run these imports.  And this was just a one-off,  but in terms of keeping it updated  as new government data comes in.  So just quickly, I also want to talk about Open Addresses,  global data set of, sorry, global collection of address points.  So in Australia and New Zealand, we have 17 sources.  That's all of them, like GNF, LYNS.  We process them, we bring them in into a common schema,  so it's a global thing.  And we process this mostly automatic.  So yeah, I hope in the future it could one day compete with GNF  because we could bring in state data,  which is often published like weekly or more,  and have a more faster than quarterly GNF competitor.  So check out Open Addresses.  Thank you.  Thank you.  Are you enjoying this?  Are you enjoying this?  No, we've got Ewan Hill actually.  Is Ewan Hill here?  Ewan?  Cool.  So Ewan's going to be telling us,  I know your comments about Open Street Map comments.  So you're on this one here.  I don't know how you know you're getting those slides.  I don't know, but why?  Oh, okay.  Can we use the arrows?  Yeah.  Ewan's chosen not to use PowerPoint  and to use something different.  It's a Mac.  Okay, go back to Chrome.  Click on it.  Click on this one.  Yeah.  All right.  How do you start the show?  Should be the LA.  I can take it out of.  Okay, you need to press that.  Okay.  Should we get the next bit of going?  Oh, it's okay.  It's pretty good.  Okay, here we go.  Please welcome Ewan Hill.  Thank you.  Thank you to Andrew, who's done a huge amount of work  in the OSM area and a lot of the Australian stuff  that is there now, courtesy of him.  So thank you.  The note system and the comments within OSM  is a small little subset.  There's not much to it.  You can add a comment to say this is wrong,  that you can add a comment to say  coming to my great online shop, unfortunately.  There's a whole lot of different things  you can use comments for.  There are now over 1.5 million comments.  I tried to download the XML file,  which was 64 gig of comments,  and it was a little bit difficult.  So I did try to do a little bit of a snapshot  of the Australian stats along with New Zealand.  All the other Oceania areas were fairly low.  So currently we've got 2,950 odd comments  that need clarification, rectification.  And you can see that as time goes on,  more and more comments are coming in.  And they're not all being rectified.  So what I'd really like is for everyone to go out  and just find 10 comments and try and rectify them today.  No.  The overall comments, Germany is right up there,  but if you have a look, Australia and New Zealand,  down there a little bit,  but comments is a hugely popular subsystem of OSM.  You can have a look at the bottom graph  and you can see that comments are only increased.  And so it is something that we do need to have a look at  if we're going to give a consistent approach to OSM.  A quick look at the states and territories.  And you can see how their northern territory is pretty good.  South Australia, New South Wales and New Zealand.  So all of those Kiwis out there,  any Jaffas, please help and fix up one or two of those.  The top 11 locations, North Adelaide,  is probably one of the highest density of issues that I've got.  So go in, click on one of those.  You should be able to find it fairly quickly.  I'll go through this very quickly.  Christchurch, New Zealand, Tewong in southern Queensland,  Auckland and Napier,  Armadale in country, New South Wales.  And this is one that I sadly didn't take a screenshot when I did,  but there was one user who actually commented on every path track.  However...  However, they were all fairly correct.  OK.  So we've got the south of Perth, Newcastle, north Perth,  and who do you reckon is going to win?  So, yeah, there's a...  Right around the whole of Yosiania,  we need to probably get...  Some of these are a year old, two years old,  and we really need to try and work out how to actually improve the process.  Finally, here's a map of somewhere called...  LAUGHTER  Thank you.  Thank you very much, you and Hill.  APPLAUSE  All right, next we've got Andrew Bashfield from RMIT.  Yusien, tell us about using QGIS for teaching land development.  Please welcome Andrew Bashfield.  APPLAUSE  Thank you, Stephen.  OK, so my presentation is about an exercise that we carried out.  It was advanced diploma of surveying students,  and it was a TAFE-level students at RMIT University School of Vocational Engineering.  And the exercise was all about getting students to conduct an opportunity analysis  for their clients, in this case, a property developer,  and by identifying a top 10 list of potential properties  for multi-lot development in the student's own locality.  So every student did their own area.  The students were responsible for assembling the spatial data sets,  conducting the analysis, producing A3 maps, and a basic written report.  And finally, they published the data on the web  in a way that the developer client could see and access interactively.  So who are the players in the property development example?  Here are some property developers.  This is what they look like.  They want to win, and they want to get rich.  We've got to remember that when we're talking about these guys as clients.  And the next group are the students,  and here they are conducting an asset data capture exercise  in Argole Square in Carlton.  And finally, there's quite an age range from those recently out of high school  to others having worked for a few years to people making a late career change.  And here is the tool we use, QGIS.  So the first task was actually to gather data,  to create a base data for analysis,  and the two key data sets were VicMaps,  cadastral boundaries, and the Urban Development Program,  Broad Hecht Air Residential Land.  And here's what it looks like.  So you can see that Broad Hecht Air Land  is basically that land on the outskirts of Melbourne.  Now, we also had students coming from the country  where this sort of data is not actually mapped.  So we had to come up with a definition of Broad Hecht Air,  and we actually said anything over 10,000 square metres in size.  The next data set was Features of Interest.  Once again, coming from the state government.  And the cognitive features of interest were interested in  transportation facilities, parks, schools, shops, hospitals,  and those kind of services that are of interest to property buyers.  This example is Features of Interest in Richmond.  So this is a map produced by a student.  Here's another example.  This is an enlargement of a map produced of North Geelong.  Once again, showing that students chose a symbology.  There are 60 students involved in this program,  and they can choose something different.  So the next step was the identification of dual occupancy  and multi-lite development opportunities.  We call a dual occupancy a lot opportunity  as something where it's defined as a land parcel  that's a size between 1,000 square metres and 2,000 square metres.  And a dual occupancy is between 2,000 and 10,000.  We were looking for consistency between the councillors,  and most of the time it was there.  The yellow ones are multi-lite subdivision opportunities.  The yellow ones just a single parcel chopped into half.  So the student had to produce a list of their top 10 development  opportunities within their chosen neighbourhood.  A couple of key criteria.  The first one, we were asking them to try and choose corner blocks  with dual road frontage to lessen the amount of land  that was used up by driveways and rights of access.  The second was to choose unoccupied blocks  or blocks with only single dwellings.  These were more attractive for the purchase and construction  of medium-hence density housing.  And here's the kind of example of the top 10.  So in this example, which is Richmond,  the students were asked to show on Google Earth whereabouts it was  and also on QGIS with the planning map.  A few other examples.  Now, what came up a lot was car parks  because there was no one occupying them.  And then the next thing was to report on proximity  because we were looking for parcels that were as close as possible  to schools and transport.  So in this location, which is Balaclava in Melbourne,  you'll see that yellow parcel has been calculated  as five minutes walking distance from Balaclava Rauway Station.  The final task was web publishing.  The students were asked to provide that interactive method  of delivering the data to their clients,  not just the paper format.  And the assumption was the client wouldn't have GIS  so they'd need a web tool.  So we used QGIS Cloud because it was free.  And the students were doing all of this on their own laptops,  not on RMIT computers, so it was important  that they had their own licenses.  Here's an example.  This is a published web page, and you can see...  The student shows funny little houses  rather than colouring up the parcels in this case,  but you can see that there's attribution attached  to a particular lot.  This one is a little better.  This is West Meadows next to Melbourne Airport.  So the outcomes for the students,  they produced a spatial analysis portfolio and website  that they could use to help them get their jobs with employers.  And what we do is all about producing  or creating employability for them.  They got a more focused appreciation of the developer's interests  and what makes one lamphouse or more attractive for buyers  than the other.  And they got hands-on experience with spatial tools,  QGIS, QGIS Cloud,  and they got licenses that they could take home  and actually possibly create a business from them.  So thanks for your attention.  APPLAUSE  Thank you, Andrew Bashfield.  That is fantastic.  It's so different from back when I was at uni.  All right, next up we've got Julie Nograde.  He's been telling us about exploring total water levels  around Australia using R Shiny and Leaflet.  Please welcome Julian.  APPLAUSE  All right, so at CSIRO we're interested  in how high the total water level is for coastal inundation  and coastal erosion.  And we're looking to provide this information  to coastal stakeholders, coastal councils, for example,  through web-based portals.  So we're using R Shiny and Leaflet.  So I'd like to acknowledge my co-authors on the work,  which Tim Irwin, who put me onto the Shiny work,  and the fellow coastal scientists, Claire, Kathy and Ron,  and we're all based here in Melbourne, down in Aspindale.  So how high the water level depends on where you are  or where you're measuring it.  So in the open ocean, satellites measure the water surface  and changes from temperature and density.  So that determines how high the water is.  In protected coastal estuaries, protected from large waves  that you experience on the beach,  you can measure coastal increases in storm...  in water level from storm surge,  which is wind and atmosphere piling up water onto the coast  and also tide.  So you can determine how high the water level is there.  And on open beaches, there are techniques with cameras  that measure the effect of waves.  As waves, large waves, move into the coast, they break,  they pile up onto the coast and push water up into the coast,  and that determines how high the water level is on the coast  from the surge of waves running up the beach.  So it's important for coastal assets right near the coastal boundary.  So what we wanted to do is develop a nationwide tool  where we could access this data.  So we're using the Shiny sort of web tool.  So the way it's explained to me how Shiny works  is that a group of RGO science ninjas got together  with some HTML ninjas, coding ninjas,  and they produced Shiny.  That's my understanding.  So we're using the R-statistical...  Well, I use the R-statistical software,  libraries for Shiny and Leaflet to generate this web page,  which has on the top left some radio buttons  to choose which locations to plot on the map,  and you can zoom in, zoom out of the map  and click on the different locations.  Switch locations to be the tide gauges or to be coastal points.  You can zoom into locations.  So here I've zoomed into Sydney Harbour  and there's a tide gauge located there.  So we can click on that point and see what the coastal information is,  or we can show the beaches, the different beaches.  So here we've got Bondi Beach highlighted, we click on it,  and we can produce these plots of return level height.  So these are plots that engineers use to determine  what a 100-year storm height might be.  Now, the effect of waves on beaches depends on local features,  like the beach slope.  So with this tool, we're able to modify the local beach slope  and change these return level periods on the fly  to get the local information.  So in conclusion, I guess this isn't the only shiny web app  that's been developed in CSIRO, CSIRO is a big company,  and there's many in-house shiny apps being developed.  So the team's building in-house shiny servers to house  or to host all the apps.  And from my experience with the coding,  I had a little bit of issues getting labels  on the names in the leaflet sort of maps.  And by trial and error, down at the end of the code here,  I've got the labels in that add circles function  equals the tilde space tilde as character name,  which I don't know how I got to that,  but it seemed to work, so I was pretty happy.  So if you're interested in the website that we're developing,  or this bug, please contact me.  There's my... Well, it's not a bug, it's a workaround.  There's my contact.  APPLAUSE  Thank you, Julian.  Again, you've got two minutes left, Julian.  All right. Next up on this computer over here,  we have Hemish, and he's going to tell us about  how he was famous for a day in New Zealand.  So please welcome Hemish.  APPLAUSE  Let's be honest, it's not that hard.  LAUGHTER  Just really quickly, shout out to the spatial community Slack.  There's a really cool growing community, international Slack community,  thespatialcommunity.org.  It's a really cool go there sign up.  So I just want to talk really quickly about  an interesting experience I had this year.  So I was working from home in the evening  on a beautiful Auckland July, so dark and rainy and horrible.  And this week came along, and it was...  Someone at Denmark had done a little bit of analysis  on what's the most heroic location defined  as being the furthest away from any structure.  And they found this point that was 4.4 kilometres  and it's next to a road.  And I thought, you know, David Hood was like,  I wonder what this looks like for New Zealand.  And I thought, well, if there's one thing we've got out in New Zealand,  it's being far away from other places.  So let's have a look.  And it happens to know that in New Zealand,  if you want to be far away from a place,  then you're probably going to be on Doc,  Department of Conversation Reserve land.  And land information in New Zealand happens to publish  the Hutt information as points.  So there's a good bet that the furthest point away  from any location in New Zealand is going to be far away  from a Doc Hutt.  So over the next...  Over that evening and into the next morning, I guess,  I thought, well, hey, look,  let's pull that data down and see if I can  mangle my way through this kind of analysis.  And I want to say I'm a software engineer  and I mostly deal with sort of SAS Web Apps.  I use Qtis now and then, but I'm not an analyst.  So there are better ways of doing this,  but this is just sort of my little process.  And also Qtis has 3D views now, which are really cool.  So I thought, hey, the first thing I'll do is put on a 3D map  because that's always cool.  You can just like tilt it and now it looks like you...  Yeah, it's cool.  Yeah, that's really neat.  It's a bit buggy though.  I think 3.4, hopefully, has improved that a little bit.  I haven't used it since then.  Yeah, and so I sort of...  mangle my way through.  The first thing I tried to do was kind of...  was I broke it up into squares  and looked at different areas and, you know, predictably.  But sure, off the bat, you know, you kind of know  if you know anything about the geography of New Zealand,  you're going to suspect that it's going to be in Fjordland  because it's massive and almost nothing is there,  except for some moose maybe.  And so...  So I thought I'd identify the likely location  and then, using the hot points,  produce the sort of...  for any polygons,  I'm going to mangle the words here.  And sort of a whole lot of flailing around later  managed to get something that looked like  it was going to produce some kind of result.  And I think that's the first thing I tried to do  was to look at the different areas of the map  and understand that it's going to be a little bit different.  And...  Unsatisfactorily, I ended up sort of hand-moving things around  a little bit, and I found this location.  It's the yellow dot up there.  The blue dots are the centroids of the relative polygons.  And because I couldn't figure out how to treat the coastline  as not being an edge, I had to manually figure out  what was the closest point to that blue dot on the left.  And it turns out to be this little point  that is on the edge of this unnamed cove  deep in Fiordland in the south of New Zealand.  Which is pretty cool.  I figured it was...it's about...what do I say?  Calling it to plus or minus a kilometre,  the south end of the bay, of the Coal River Mouth,  which is not an official name,  some 29.8 kilometres northwest of the suburb of Cove,  Hutt, as the crow flies.  So 4.4 kilometres is nothing.  And that's right next to a road as well.  This thing is a...  It's a 40 kilometre minimum walk from the nearest hut.  That's over steep mountainous rugged terrain, forest.  The hut itself is a four-day walk to the end of a lake.  The lake itself, you have to get a boat across  from the other end of the lake.  So, you know, if you really want to get there,  it's a pretty big journey, you know, it's serious work.  The next day I had this PM on Twitter from a local journalist  saying, hey, you know, he'd been following along with us.  It got a little bit of, you know,  a bit shared around the very well-known  and enormous New Zealand GIS community.  And so, hey, you know, can I talk about it?  We had a little chat on the phone that morning.  Nick, minute.  Yeah, so straight into the New Zealand Herald.  Yeah, it was in the paper that I got in the morning,  so I'm a strange person.  Actually, it's a physical paper still,  and it had this story in it, which was pretty cool.  Got to show the kids.  And possibly, my highlight is a dad,  biggest dad moment in the car after school pickup,  sitting in the car, and Radio New Zealand's on the radio,  and the Jim Morris show in the afternoon,  where they talk about mostly an ain rubbish.  The ain rubbish of the day was my story, so...  So that was really cool.  So, yeah, for one day, you know,  I got to be on the New Zealand upbeat pages,  which was pretty rad.  And then, you know, someone who actually knows what they're doing,  Richard Law, who unfortunately can't be here today,  he did, oh, let's just do the ruster version in GDAL,  and within, like, a minute, confirmed that, indeed...  LAUGHTER AND APPLAUSE  So, I got coding late at night.  You know, sometimes just have a sleep, think about it.  You know, things will be much easier in the morning.  So, yeah, but it was really cool to see, you know,  how would someone who really knows what they're doing  actually solve this problem really quickly and simply,  and he had ruster analysis with GDAL was the way to go,  which is really cool.  And then, really cool, a couple of days later,  this fellow, Tomon, you know,  replied to me saying, hey, you know,  he actually sailed past here on an expedition trip,  took a photo, and so you can see a photo of the place up there.  So, yeah, thank you very much.  APPLAUSE  Thank you very much, Hamish.  We'd love to see you, Nain rubbish.  That's what we're all about here.  All right, so the other day, I had to go to my local council page,  and they had a half-page of instructions on how to find my address.  I live in Australia, I thought it was a bit crazy.  So Daniel Castel is going to tell us about a place  where it's a lot more difficult to geocode.  So, where the streets have no name, please welcome Daniel Castel.  APPLAUSE  Hello. So this is, yeah, I'm not Australian.  I'm German, and I'm living quite a long time now in Japan.  And we're just far away, and you know,  Germany is very well mapped in terms of open street map,  and some people say you either import Germans  or data.  And so, they chose to import data, actually.  But when I started mapping in 2007, Japan looked like this,  very gray, a lot to do.  And it's not easy.  So, big cities, lots of people in some areas,  super-dense cities, like, you don't know where to start.  Also, the mapping doesn't really look very beautiful,  so this kind of, the thickness of streets does not work  in Japan of the default rendering.  And you have, yeah, you also have to map highways,  what an UK highway is in Japanese, whatever, past type.  You don't know what it is, actually.  And you also have to deal with these kind of things.  So, real vertical, so GPS signals are not so good at the bottom.  And how to draw it, actually.  Yeah, and then you have also buildings that look like this,  and a street goes through, that's in Osaka.  So, yeah, one advantage is there are no road names, usually.  So, there are few places that have road names,  but so you don't have to just take the GPS truck  and load this data, upload this data,  and you don't have to name it, because there's no street name.  But then you have something, like, when you're looking for a place,  you have these kind of district maps you can find here and there.  So, in this case, on the left side, this is like a district,  like a local area, and the name of each owner of this property  is actually written on this map.  And if you, after some time, in Japan, actually figured out  that the kind of the district where you are  is always written on the poles of lights, for example.  So, when you can read this, you know actually where you are,  otherwise you have to ask someone to find the address.  And the postman actually, I don't know how they know it,  but address systems, first, if you don't know them,  you don't know really how it works.  And the problem happens when you attend the Phosphor G conference.  Because you go abroad,  and you have to fill in the correct address to get,  to send the invoice to your company.  And the address of the form does not match with your address.  So, this is a typical problem where I have to actually bend my address  that it looks later on the printout form as I want to have it.  And usually, wherever you go, you have these kind of house numbering systems,  like the European scheme, the clockwise scheme or whatever.  So, there are many, many ways how to, yeah, describe a street address,  but I don't have a street address.  And so, I want to tell everyone who makes geocoders, for example,  that there are other addresses than street addresses.  A Japanese address addressing system is based on areas  and subdivided from big to small.  So, you also write the address from big to small.  And, yeah, usually you start with the country, then there's only one.  Then you have prefectures and then cities.  Sometimes you have wards.  And then, when you are at this level about, then you have districts,  small areas, and they are numbered.  And within this place, you are numbering the houses.  And the houses are numbered depending on the city,  either the time when the house was built or they have some sort of rule.  You can easily squeeze a small building inside  because you just give it the next highest number.  It's actually not so bad.  You might think it's inconvenient, but it's for geocoding, it's not so bad.  Because if you know the district, you're actually not so far away.  The problem is when you are writing addresses,  for example, to receive a Christmas present,  then the left side is the Japanese writing,  starting with the postal code and ending with the name.  So in the middle, you can see how you would write this in Romanization.  And on the right side is how you have to turn this around  that the actual present reaches you.  Because Japanese post is actually quite tolerant,  so they can read many things,  but the German post office would not send this middle address to Japan.  And so Japanese addresses are actually also...  OSM is also very good to deal with any kind of tagging.  So I think the Japanese community made the way through the tagging scheme.  The problem is that then the applications often don't support what Japanese mappers map.  And that's not enough because you have actually even more exceptions to street addresses.  South Korea is different, Taiwan is different,  Venice has a different address system, and even in Japan there's yet another one.  And that's Kyoto, it's quite famous in Japan.  And I took these images from a Japanese friend who has a geocoder for this on Geodos,  and it actually is a descriptive way how to reach an address.  It says, go two streets north and then right.  And yeah, so it describes the way to the address, and people really use it.  And the postman knows how to find that place, so yeah.  This is...just remember if you write some software with addresses,  or if you make a form for your website,  there are other countries that don't have western street addresses.  And that's my message.  APPLAUSE  Thank you very much, Daniel.  Next up we have Rebecca.  Rebecca Roberts, and she's going to talk about calculating livability across Australia  using open data and tools.  Please welcome Rebecca.  APPLAUSE  Hi, I'm a GIS analyst at RMIT University,  and I'm currently involved in a project calculating livability across Australia  using open data and open tools.  So what exactly is livability?  At RMIT we define a liveable neighbourhood as a neighbourhood which is safe,  socially cohesive and inclusive and environmentally sustainable.  Additionally, a liveable neighbourhood must also have affordable and diverse housing  which is linked via public transport, walking and or cycling infrastructure  to employment, education, public open space, local shops,  health and community services, and leisure and cultural opportunities.  So the following maps collectively titled Mapping Access to Frequent Public Transport  are an example of one of the many indicators we have created using open data.  These maps show the percentage of residences by suburb  which are within 400 metres of a public transport stop  with a service frequency of 30 minutes or less.  We created this indicator for each of the eight capital cities across Australia  to enable comparison both within and between cities.  These maps were published in a report called  Creating Livable Cities in Australia which you may also freely download and view.  Now I'd like to explain how we built this particular transport indicator.  It's easiest to work backward in the flowchart  so I'll start with our near end product which is called the information output.  In this case it spatially identifies which dwellings are within a 400 metre walking distance  of a public transport stop which meets our service frequency specifications of 30 minutes or less.  In this example I'll also refer to Melbourne  because slightly different data sets were used to create different indicators for each city.  So the information output was derived using the following input building blocks  which are pedestrian road network dwellings and frequently service stops.  We built the road network through Open Street Map  but we put this network through a cleaning process which remove roads such as freeways and busways.  This was done to ensure we had modelled a road network which is accurately as possible  reflected one on which a pedestrian could legally and safely walk.  Dwellings were extracted from the open source gene F address  which file which is located in the Federal Australian Government Spatial Data Repository  and we got the Melbourne Public Transport data from the Victorian Government Spatial Repository.  So after we calculated which dwellings were within a 400 metre walking distance  of a frequently service public transport stop  we then downloaded more open source data which were the ABS geographic boundaries  and we used these spatial boundaries to aggregate the information up into our final indicators.  The final indicators were actually the percentage of dwellings  which were within a 400 metre walking distance of a frequently service public transport stop  calculated at different scales.  All of this data was stored and analysed in a post-gis database  and we used QGIS to produce the maps for publication.  So the creation of this transport indicator is just an example of one of the many different indicators  that we've been creating to model liveability.  And some of the other indicators that we have done rely on additional open source tools  such as KNIME and Python which we use in combination with post-gis.  All of these open data sources and tools have enabled us to successfully calculate  and publish these national liveability measures across Australia.  Thank you.  Thank you, Rebecca Roberts.  Alright, next up we have Hugh Summons from IG Insurance.  He's going to talk to us about big data, open source and ethics.  Please welcome Hugh Summons.  Thank you Steve.  I've given myself the challenge of talking about three topics in five minutes so here we go.  Okay, I'd like to talk to you today about movement data.  What is movement data?  Movement data is basically location data from smartphones and GPS devices.  It is data that tells you where people have been, where they are going, where they are going to.  It tells us where they live, where their home is, where their work is, where their kids go to daycare,  where they like to shock, which pub do they like to go to, what team do they support,  what football fields do they go to, you name it.  What can we use this data for?  This data is good for customer segmentation.  It is really good for transportation and retail analysis.  It is also good for reducing the number of questions required in censuses.  Since I am not sure about that one.  It is also really good in the insurance world for risk modeling.  So let's have a look at some examples.  So we're looking here at 1% of Melbourneians on July 25.  This is a 24-hour period.  Red trips are long. Green trips are short.  Another example.  Some risk data.  Here we have someone who doesn't drive very well.  So we can see as they go around corners, they are getting higher than usual G-forces,  which means they are driving like a Wally.  OK. Is this big data?  Yes, it is.  This is not Google Facebook grade big data, but it is still big data.  What you see on the screen there is 150 bytes or thereabouts.  If we are capturing that every five seconds, which could be one second,  let's say five seconds for the fun of it,  and people are moving around at least four hours of the day,  and we are looking at 1% of Australia,  you are looking at 100 gig of data per day.  So we are looking at about 36 terabytes per year.  It adds up pretty quickly.  How the heck are we going to manage and process all that data?  Well, unless you have a supercomputer,  you are going to need a cloud provider to give you some infrastructure to run this lot on.  How are we going to process the data in a cost-effective and high-performance manner?  You are going to, of course, need some open source.  So let's have a look at a couple of contenders of tools that can do this sort of work.  GeoMesa and PostGIS.  I am not sure how many people have heard of GeoMesa.  It basically allows you to store index query and transform spatiotemporal data at scale.  I am quite proud of the fact that I got to say spatiotemporal in front of a large audience.  So what is it?  It is simple.  It is basically a bunch of spatial data type, spatial functions,  and indexing for some big data platforms, such as Apache Spark, Kafka, Cassandra,  Acumulo, HBase and a few others.  Very powerful tools.  These are the kind of tools that are being used by the Facebooks  and the Ubers and the Netflixes of the world to manage their data.  It allows you to store your data in Hadoop or HDFS, cloud storage systems like AWS S3,  as well as local files if you have got a really whopping big hard drive.  We have put together some sample Python code which allows you to run GeoMesa in Elastic Map  Produce in AWS or on your stand-alone Mac or PC.  If you go to the IAG Geo GitHub page, you will see it there.  You can just some geo-enabled news data called GDELT and then do a quick filter just to prove it works.  Okay, let's compare events.  I've got an old friend PostGIS which allows you to do exactly the same thing,  but in a relational database.  Some comparisons.  Obviously, it's a bunch of spatial data types, spatial functions and indexing as well.  However, it has more types and functions than GeoMesa.  It also has the simple SQL syntax.  Now, for those of you who haven't done a lot of SQL, you might say simple, you've got to be kidding.  But with GeoMesa, you have to code in Python, Java or Scala.  So there's a little bit more of a learning curve to automate and manage your data in GeoMesa.  PostGIS, generally speaking, is harder to scale for big data than package cloud services.  So I want to say package, things like Elastic Map Produce which allow you to spin up Spark and Hadoop.  It's reasonably straightforward.  If you want to go from 10 servers to 100 servers in Elastic Map Produce, you add a zero onto one parameter.  So AWS made that quite simple.  It's a bit more work with PostGIS.  Okay, PostGIS has some neat things.  So you're dealing with billions of records.  They're very hard to manage and continue using.  What you can do though is you can distill that down into some polygons and some lines.  So whenever someone stops and they've got little GPS points bouncing around a certain spot,  you can do things like DB sand clustering.  You can do point in polygon queries inside property boundaries, for example,  to cluster those points and replace hundreds of points with one record.  Similarly with journeys.  So all the points that make up someone's journey, you can create an ordered line string.  So it's a line string ordered by time, which is a really neat little trick in PostGIS.  Okay, last but not least, governance and ethics.  As I mentioned, we know where your children go to daycare.  So we've got data, and I say we generally, people working with moving data,  we've got data of say hundreds of thousands of people.  This is highly sensitive data, but we want to get lots of juicy insights out of it.  That's not going to happen without governance and ethics.  First things first, secure your data.  If you have a data breach, you may have just seen the end of your organization,  either financially, reputationally, or both.  You may no longer exist.  If anyone said of Cambridge Analytica, a little company in London,  what they did with Facebook was the end of them.  They're gone now.  Last but not least, you need to look at your use cases.  Are you going to have ethical usage of your data?  Are you going to create algorithms which are unfair to certain groups?  Are you going to upset or harm people?  Because they're going to ruin you as well, financially or reputationally.  Thank you.  Thank you.  Hugh Salmons.  Next, we have Dr. Martin Tomko.  He's going to tell us about teaching GIS science using Sosphogy.  Is there an alternative?  A rhetorical question. I like it.  Please welcome Martin Tomko.  Thank you.  Thank you everyone for being here.  I'm very pleased to be here.  I didn't feel like I can talk about my research.  I'll talk about my day job, which is really teaching.  What I'm trying to do is to teach people that can then be in this room.  I'm very proud of that.  The question is, teaching GIS science, Sosphogy, is there an alternative?  Maybe you think, sure.  Or maybe you think, surely not.  And that depends on how we understand the title.  Because I want to talk about GIS science, not about GIS.  Sure, you can teach GIS.  You can teach people to go and do GIS analysis.  But what we are interested to do here is to teach people that can sit in this conference in the future.  So let's go through the scientific process a little bit.  You have to start with some questions that you are curious to answer.  And that should hold.  And the answer to which should contribute to human knowledge for posterity or until proven incorrect.  So you have a hypothesis that you want to test.  And you're going to design an experiment around it.  You're going to collect some data in a careful manner.  You're going to process them.  You're going to analyze them.  You're going to interpret your results and evaluate and verify those results.  And at the end, you can say yes or no. Did you confirm or reject the hypothesis?  That doesn't stop there.  There's one more step.  Your process should be forever, or ideally, verifiable by someone else, not you, by your peers, or whoever else is interested in this.  Now, how does this apply to spatial?  In the GIS process, you do not have that external verification, not necessarily, or you do not depend on it.  Anyone can go and do GIS, can do it for a local government, but it doesn't have to be somewhere published, doesn't have to be verifiable, might not hold next year.  And it's fine for many things.  But not if you want to contribute to a scientific process where the GIS process is part of a scientific process,  or, what I'm talking about here, if we want to develop new methods for data collection, new methods for their analysis, or for their processing.  Right?  So I'm talking about developing the methods and functions you find in your tools that you're using here.  Someone has done research on it.  And our tree has been researched and proven to be more performant than whatever was before, or perform better than Quotree, let's say.  And that's what we're interested in doing here.  So it is the difference about going and getting take away from knowing how to cook your meal.  Okay? And we want to teach, if we teach GIS scientists or technologies that will be implementing results of GIS science, to be able to cook something new and understand how it was cooked.  So this is something that some of my students see in their exams.  We looked, let's say, at a shapely code, and they need to understand it.  So I'm not talking about commercial, not commercial here.  I'm just talking about whether the source is examinable, which is important for us when we teach.  So, you know, in some ways, Coca-Cola is open source because it has been shown to someone and they have been able to say, yes, it's kosher.  In that sense, we can say QGIS is kosher or any of the other things.  Thank you.  Thank you, Martin.  Next up we have Steve Bennett.  And he's been telling us a couple of little tricks to use with MacBox GL.  So please welcome Steve Bennett.  Thank you.  I know what you're all thinking is the last talk.  Thank God we're having a nice, soft, fluffy one with no technical details at all.  Too bad.  Five tricks.  Trick number one, animated interactions.  So this is just a cute little thing you can do in MacBox GL.  For those who don't know what it is, it's like an open source web JavaScript library better than leaflet.  When you click on points, the way we make this work is you just create a new layer with a radius of zero and then just immediately set it to something other than zero.  So in this case, I'm setting it to a radius of 20 pixels and MacBox GL.js just can't help itself.  It transitions the hell out of anything that changes.  Next up, this is a least compelling demonstration possible of a really, really cool ability.  Basically, MacBox GL.js lets you symbolize stuff using a couple of options like circles and icons, boring things like letters.  But we can actually link anything we want to it using a marker.  So you can use the full power of the HTML DOM.  So in this case, I've done something really unimpressive.  I've put some rounded corners on a box.  And the way we do that is I'm actually tying it directly to the places in the underlying map data.  So in this case, it's a map box streets layer.  And what we do is we basically, I keep a record of which things I've already touched markers to.  I check that the market hasn't already been created.  And if it hasn't, then I'm just going to create one and I create an HTML element.  And in this case, I just apply the class name of rounded label and I get my Oso sexy rounded corners on my label.  So you've got to do so much more with this. For instance, you could do, you could tie like D3 to it so you could have like little pie charts on your map.  And they would just like be completely attached to the underlying objects in the map box data.  It doesn't even have to be your own data.  Quickly look down that really long URL and you can see for yourself.  A coro pletha kind of standard thing you see in QGIS in every other platform, but there's no native support for them in MapBox.  I had to implement them like three times for three different projects and it's quite a lot of work to implement map box, sorry, implement coro plets.  So I've packaged up all that work in a plugin.  I've really creatively entitled map box hyphen coro pletha, which you can find on NPM.  So I recommend you use that just pass through, you know, a few dozen friendly parameters like the, the geometry that you're using for your coro pletha, the CSV file that you're getting the data from,  what color scheme you want, it supports color brewer names.  And then obviously details like what are the names of the fields in both the geometry and the CSV file that you're going to tie together.  And lo and behold, you get an ugly coro pletha.  Fourth one, this is not specific to MapBox by any means, but it's just a technique you don't really see a lot of visualizing two variables at once using a single point.  So you can actually obviously change the color of the outside of the circle separately from the inside of a circle.  So in this case, this is the city of Melbourne's dynamic parking map.  If you didn't know that existed, well, now you do.  You can see which spaces in the city are being occupied right now based on real time sensors.  And you can actually access that data directly and build your own app that uses it.  So here I'm just highlighting the outside of each parking sense with a color indicating the time restriction.  So red is like really short and blue is much longer.  And then the inside of the circle tells you whether or not there's a car currently there.  I'm surprised how easy that is to sort of mentally process often by variant maps.  I find it really hard, but I think that actually works quite well.  I don't have any code because it's kind of self-explanatory.  And then the last one is this kind of visualization where you take a...  Typically you'd be using like building heights or something as your Z-axis if you're doing that.  But we can also just use a statistical variable.  So in this case, it's the total amount of employment within each of these.  I think they're called clue blocks where clue is the census of land use and employment.  So this is data collected by the city of Melbourne by knocking on an awful lot of doors on a revolving basis.  And let's just see if this...  I tried to sneak in an extra...  This is not the live version.  Okay, I tried to sneak in the actual video where you can see this action.  But I guess that isn't going to work.  Which means I've got a minute and a half for questions.  Thank you. See you in a minute.  Cool. I'm not from T-shirts. Do I like our T-shirts?  Yeah.  Thank you, Steve.  I'll use my last minute for my own story of fame a couple of years ago.  I can totally relate to the...  They calculate the remoteness area.  We created a stupid thing called the hipster map of Melbourne.  Happened to know a guy who worked at the age.  A week later, it was page three of the age.  Stupidest website ever.  I will never get over the fact that my biggest moment of impact is a website with no practical value whatsoever.  But anyway, cool.  Thank you, Steve Bennett.  All right. Next up, we have Nikola Todek from Bing Maps or Microsoft.  He's going to tell us how Bing Maps Australia is now using open data.  So please welcome Nikola.  Thanks everyone for having me here.  My name is Nikola Todek.  I'm actually living in Serbia and I've been building the Australian map from Serbia for Microsoft for the past year.  So at a high level, this July Microsoft Bing Maps has Australia continent being served to a portion of our users.  It's entirely based on open data.  Open street maps, ABS, open addresses.  So we provide the whole set of services on top of this, the routing rendering, geocoding, etc.  So we're doing this, I mean, we're just starting with Australia.  We want to surface the best maps data to our customers and want to empower the open community, essentially for an outreach.  So they can have much more impact by going through Microsoft products.  The challenges is OSM and other open data, good enough.  And I guess it's going to take time to persuade people that that's the case.  So while Australia, it's an important market for Microsoft, obviously.  It's an isolated continent which we can easily stitch and the big plus for that was that the government mandated the government data to be open.  So we had to fork our entire service stack in order to ensure that the open data does not in any way get mixed up with the proprietary data.  So OSM Australia, we had a belief is great, but we kind of wanted to see what's good about it, what's not.  The Cardo feature is absolutely amazing and the data is fresh and up to date.  What we found that the data lacked was the coverage in administrative areas, term restrictions, address point data, and don't pay the attributes on the road.  So we had to do something about it.  Yeah, so as I said, the map is up to date, really up to date.  Like what I'm showing here is some road development that still doesn't exist in the satellite imagery, but we have it in OSM.  And this is what we routinely see when we look at all geographies across the world.  The community is incredibly active in updating recent road development.  And so this is also common with OSM.  The places that are less populated really get less attention.  So the administrative polygon coverage, the red dots are representing the points from our test sets where we don't have coverage in OSM.  Address points, I mean we have only 10,000 address points inserted in OSM and big thanks for open addresses for aggregating all of this data for us to use it.  On the term restriction front, we have analyzed the data. The big important number here is that we have half a percent of the routes that we plot to our users that are essentially taking you to a turn where the turn is forbidden.  So we had to do something about this as well. So what we did is we employed some people. I'm a big fan of our editorial team.  Here they are on the OSM list, like at the top then.  They've, across the last year, they've imported and actually drew more than 100,000 kilometers of road in OSM added more than 20,000 turn restrictions.  This really helped us out in building a solid routing engine.  And finally for the paved on paved attributes, we have more than 300,000 kilometers of road in OSM which is, which doesn't have the tag whether it's paved or not paved.  So we had to build machine learning algorithm using essentially computer vision deep learning and their infrastructure to process all of the Australian tiles and essentially do the tagging ourselves.  Unfortunately, we cannot do the import to OSM, but that's something we're definitely considering just pulling on GitHub.  And finally, we need your help. So, I mean, this is the first time we're doing this.  We have on the order of 100,000 user queries hitting the Australian map every day.  We need feedback on our services on the data and we need you to keep editing the map.  I think it's incredible what the Australian community has done thus far.  And then together we can really make the best map in the world.  Thank you.
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