Integrating open into geo-education – Transcription
Is Kazeem here? Kazeem, are you in the room? Hello, everybody. We are a little in disarray because we seem to be missing the first speaker of this session. But the timing is such that we have plenty of room if he does come to have him go after Vaclav who is ready to talk you about integrating open into geo education. And I’m very excited to hear what he has to say because I work in a library and I’m very interested in trying to find out about getting this into the education system. So please. Thank you. So thank you for hanging around. This is on. Okay. Make sure you talk right into it. So hello, again. So this is the educational session which seems to be now the forgotten session as well. And I will talk about different things I have encountered in teaching Open in the geospatial context. My name is Vaclav Petras. I usually go with Vaclav. And I am a GIS developer. I have several other role like NC State student. And the disclaimer there is I’m actually not speaking for NC State. I’m speaking for myself. So just keep that in mind. And I’m also NCSU geo For life member. And I will tell you what that means. GeoForAll, who has heard of it? Some have. Great. So for those who didn’t, GeoForAll is a network of researchers and educators who are organized around open source geospatial foundation and several other organizations like SPRS. And the mission of this network is to make geospatial opportunities accessible to all. That is the mission statement. And what that means is that you have open everything. So you have open data. And to process this open data you, of course, need open source software. So you actually have something which is accessible. But at the same time, this open data, it’s in the simple. They have to be in open format, which is standardized and so on and so on. It’s important to realize there are different parts of it. With open source it seems to be straightforward at least in some areas. In many industry areas it seems that open source now is the norm. And we have even some companies like Redhead which are really leading the way and are working towards things like open organization and teaching about it. Which connects to what we heard this morning in the keynote. The collaborative leadership. In open science, that’s the area I’m interested in a lot. It seems that there is a big consensus that everything must be open. At least what is possible for privacy reasons, of course. But we don’t have the adoption there. But we are working towards that anyway. But what it comes down to is teaching. So we have to teach the students. We have to learn ourselves. And very often we have some requirements through it. And what I have seen of the requirements, they are often that students need to know some enterprise software. And then at the same time we want to minimize what the students actually have to learn to become experts in the field. And the result of these two requirements is the students are taught a single proprietary software in whatever field they are. Of course, there is this jump, this assumption, that enterprise and proprietary are the same thing. If you want to teach enterprise software, you need to teach proprietary software. That’s not a correct assumption. But that’s among the assumptions as well. In North Carolina State University, what we if you go to the Website, in 2016, what you would see is that in the center for geospatial analytics, which I’m part of, you are offering 12 courses. And more than half of those would explicitly mention proprietary software in their description. And from those courses, these would be actually all the courses which somehow mention some software. And then some of those also mention some open something. Not necessarily software. And one single course would mention open source advantages specific to the field. So not some general open source software like Linux, but something had that is specific to our geospatial field. In this case, it was Grass GS. And now in 2017 when you look at the web site, it improved. We still have the same courses offering, but the descriptions are now changed in the way that they actually don’t specifically mention software except in two cases. And in one case it’s some proprietary software is still mentioned, but also open is mentioned. And that’s actually an improvement in the sense that the focus now is on what is what are the skills which you learn in the class. Not necessarily the specific software. That’s at least for descriptions. Not necessarily for the content of the course itself. So with the content of the course, or having some open in the course, what I have also seen is that we would say, well, we are teaching open because we are teaching something like Python. But Python is, of course, open source, but it’s not specific to our geospatial field. And also all programming languages are open source. Most of them are open source. So teaching a programming language that’s open source, does it really count as open source? Maybe. Maybe not. Same thing for teaching about our GC standards. Are they they have open in their name, but, of course, they are standards. Everybody should use standards. The proprietary software, the opensource software. So that’s also not really such a big deal. So maybe what we should ask and say explicitly is why we are including “Open” into the classes. We have we can see explicit including of open in the classes in at University of Kentucky. Maybe you have heard about New Maps Plus graduate program. I know some people talked about it here. So that that specifically teaches some open source software and that’s part of the description of the courses and it’s really based around it. But at the same time it’s focused just on development technologies. But there is more than just sharing the data on the web or maybe interacting with the data on the web. There is also analysis. So we need to if we want to actually have students which know something about open source, open analysis, we need them to also learn about open source which can do analysis. Not only visualization. And that goes even when we zoom go OpenStreetMap where OpenStreetMap sometimes are used as an alternative to proprietary, analytical software. But it’s not really the case, right? OpenStreetMap is something else than software for analyzing data. How I would see OpenStreetMap in educational research in this context this, for example, this paper from the research team which was a comparison of OpenStreetMap data with the authoritative data sets. And it was combining both strong open source software like Grass GS for the analytical part, and then we have, of course, this strong part of OpenStreetMap. At North Carolina State University, where I am, we have now opened a new Ph.D. program which actually mentions specifically in some course descriptions, again, open source software. Or open options. But there there is maybe the last thing in this way that we don’t really have it as part of the vision. So it’s not clear why the open source is suddenly part of these courses, because is because it’s a cool buzzword? Or maybe we are on a mission for something like open science. We don’t know. So the small part for geospatial analytics at GSU, we have this idea of how to teach these courses which are mostly graduatelevel courses. And the idea is that we have lectures which teach the theory and concepts and the general things. And they are softwareindependent. And then we have labs and assignments for the students. And they actually use some software. And that’s learned in the lectures. However, like I said, it doesn’t really work well. We have students who are becoming only software users, not really experts, in their field. We can see that maybe examples like when they want to talk about vector data, they would say, “Save file.” So if OpenStreetMaps are open data, then so vector data, then they are shaped that doesn’t really work. But that’s what happens when you are using one single software and maybe one single software vendor. You get aligned with a specific technology, specific terminologies. And at the end for the science it’s also important that if you are tied just to one software, you are just tied to this selection of algorithms and you are not really ready to explore beyond that. So how we address this problem is that we require students to actually use two different software packages. Because if they would use one open source proprietary they would be just tied to this one. But we give them to work on two different two different software packages. And they do their assignments in that. And the hope is that they will generalize the concepts. They find in one software in another software, generalize this on a higher level and that can be connected to what they actually know from the lectures. We hope that this gives them the opportunity to actually choose in the future. And we know that some students who are leaving us and giving us feedback after years, they have this flexibility. This, of course, costs more time for students to actually work on it. But we believe it’s worth it. So there is nothing to we can do about it. The course which we teach this way is geospatial analytics and modeling. And we have great experience in teaching this course since 2008. It’s on campus and distance. It works. This seems to be a pattern which you can which you can use. In this course it’s focused on data analysis and modeling. And for that Grass GS is a great modeling. And we are using that, and from the proprietary side we have GS. And in another course, multidimensional geospatial modeling, that is an experimental course. We don’t teach it every semester. And in that course, we actually go a step further and we use more experimental technologies. So the students use different software packages based on the product they are working on. We worked with Grass GS and some temporal data management tools. And we used tangible landscape technology, which is a cool technology. You can learn about it on Sunday here in the morning. So that’s an advertisement for another workshop. In another course about UAV and Lidar data analytics, this is in development. We are in two semesters of it. When we were starting, we weren’t ready to use OpenDroneMap or other open things to process drone imagery. So we were using edges of photoscan. So at this point we are hoping that we will be able to work with both photoscan and OpenDroneMap with the two software packages. And OpenDroneMap has been recently improved with Map ODM interface, and I believe you will be able to learn about it more in a talk by Dakota Benjamin this time on Saturday. Again, in this room. And then last course, the latest one we have developed is a course which is specifically focusing on tools for open science. And it’s going through several different open science topics. And specifically focusing on tools and software tools like Jupyter Notebooks or Docker for recomputability and reusability and many other tools related to that. The course is already online. All these resources. This course and actually all the other courses, we share all the teaching materials we are using online under creative commons license. So you are free to use it. The source code for that is on GitHub. And we are also trying to share it as much as possible. So we registered all the courses in the OSGO educational content inventory which is now being redone for the new open source geospatial foundation Website. But in any case, you can find it on our GitHub. And feel free to use it and give us feedback. If you want to really dig deep into it, or maybe just read again what I was saying, there is a paper about some parts from what I was talking about. It’s open access. So you can have it very easily even if you are not part of some academic institution. And the last thing I want to talk about is data. So for the when we talk about data, let’s talk first about OpenStreetMaps. So our lab, what we are teaching, is graduatelevel courses. And the students, when they come to us, they usually already know OpenStreetMap. But very often they would think it’s a name for Esri base layer. So that’s very sad. And we unfortunately don’t really have power to work on work with these students when they actually encounter OpenStreetMap for the first time. When we work with them, it’s for specific projects. And at that time we have the opportunity to actually explain what OpenStreetMap is. And what we don’t have is a way how to systemically introduce it to all graduate students. So we are seeking ideas for introducing OpenStreetMap for graduatelevel courses. So, like, think master’s students and Ph.D. students and their research projects. And the other part of the data we are working with is in terms of teaching is a dataset which we are using for teaching all these different geospatial analytics classes. Our workshops on conferences, actually. And we had this idea that we create a general dataset which can be used for all these different classes and workshops. And also software documentation. So one thing was that we were trying to create something which is general. So very often when we create some simple dataset, we just name our digital elevation model DM, sometimes elevation, sometimes SRTM because there’s a source of the data. So we just said, okay, let’s simply have a name. Elevation for all this data. And whether it is from this source or this source, this simple dataset or this simple dataset, it will be the same name. So we have all these standardized names. And then what you can see on the slide, there are some comments for Grass GS. We don’t have to understand them now. This could be in Python or command line. And this will work on any dataset which has these names. So the idea is that for each area you are in, you can create special datasets. So for our students we are creating one from North Carolina. But there is one dataset for Czech Republic as well. One data set for Italy. And one dataset for Puerto Rico. They have the same names, and you can run the same same analysis on it as long as the names are the same. The challenge, of course, is the language. So as long as it is in English it works. But you can have some ideas there, maybe. The data sources for this are clear in terms of buildings and roads and things like that, we use OpenStreetMap. Or in the U.S. we can also use government data. With photo and digital elevation models I would like to use OpenAerialMap and open topography. Didn’t work out for our case. But maybe in the future. And with that I would just tell you that domain ideas are that we are teaching two different software packages. Then OpenStreetMap data for us are working as a dataset. But those are also a use case of how the things can be done collaboratively. And if you are not part of geofor all and you would like to be, talk to me. I can tell you what that would mean. It’s very simple. There are no requirements. And that’s all. Thank you. Thank you very much. [ Applause ] I’m pretty sure we’ll have time for some questions. But I wanted to check to make sure, is Kazeem in the room at all? Okay. Does anybody have any questions for Vaclav? We have plenty of time before our 6:00 happy hour. AUDIENCE: Thank you. Great presentation and for explaining clearly about your role. Just the courses that you mentioned, all of them are graduate level? Yes. AUDIENCE: And my yes is, what is the experience of in terms of as you mentioned, you ask the students to do the special analysis both in an open source solution and with RTS, special analyst or something else, how many grades is that course? Three, four? How many? AUDIENCE: Credits? I think it’s four. Three. Only three. Okay. AUDIENCE: I’m asking you just in terms of the time commitment. Because the students, it’s very hard to ask them, hey, do this and do it again. Now in the other source. Have you had any experience doing that with undergrads? No. No. That’s our lab is doing the research and the classes which are on the graduate level kind of research. So no. I think on on the undergrad level, maybe the way would be really to have two courses which would really work together in the sense that they would, like, somehow mirror or maybe enhance what was in the other course. I don’t have schema for that. But Actually, a lot of like most of the students Yeah, sure. Most of the students, they already know RGS at this point. So from this point of view it’s easier for them. So they learn mostly just like one new software. And they learn new types of analysis in RGS. But they know RGS already. And we had some undergraduate students there. And so it depends what they are interested in. Like in which program they are. And we sometimes tell them just to do one software if it’s more relevant for their research. So we work usually with students to accommodate their needs. Yeah. Just to extend on that. So with the students, let’s say the important point is that they already know RGS and they usually just have to learn Grass GS there. At the beginning of the course, we ask for feedback. They write a report focused on the theory and explaining the results. And we provide them with like, you do that and you do that. So we don’t want them to actually write the reports. But we want them to write some discussions about what they learned. So they would write at the beginning of the semester, oh, I already knew RGS and I’m struggling with Grass. And the second time it would improve. With some students, it doesn’t improve and they still struggle. But some students are really excited to learn this additional thing. And they don’t have problems with that. With some students, we somehow accommodate very often they would be, like, engineering students. And they would take this course because they want to learn open source GIS. So, then, of course, then it’s a clear choice what they should focus on. Any other questions? I have a question. But I also have kind of a quick comment. When you were talking about the shape file, I just taught an intro GIS workshop the other day. And I was trying to explain the shape file terminology. And one of the students went, oh, it’s like Kleenex. We use that for tissues. And it doesn’t mean everything is anyway. Just that was kind of funny. That’s I think the term for it is “Brand generalization”?” Genericsized brand,” Thank you. But my question is actually, I love this idea of teaching the skills and not software. That’s great. And one of the things that I think about is here in the U.S. for sure there’s so much dominance of Arc in very established places like USGS, things like that. And having trying to talk to students about what they should learn to go and get jobs in these places. I like this idea of the skills part. And I’m just wondering if you think that doing that and then with open source can actually be a good avenue for bringing open source into these institutions? yeah. It’s a circle, right? Why do students why do people in the organizations use RGS? Well, that’s because that’s they learned at university. And that’s the reason to teach them at the university. So it’s a circle. So it’s it would be I’m not sure if it were even possible. I would be all for that, but I don’t think it’s possible right now to teach only open source. But it’s definitely possible to teach both because if they just learn one, then they just learn the clicking, it seems. So then they are not really learning what you actually want them to learn. So teaching both and spending this maybe extra time or sacrificing maybe some part of analysis. Maybe you don’t have to try all these different buffer sizes. So then this will actually give them the understanding of what buffer is. That efficiency of learning that skill, but you have to get past some of that clicking. So I’m tempted to throw two at them at once. But it’s yeah. Not terribly efficient. Yeah. Chop the buffer for analysis. But I would say we have seen examples from at least they also some students, like former students, off the course, they send her an email about what they do and stuff. And I think we’ve seen cases when they actually really like continue using open source tools in their organizations. And you can see, I think, a lot of people now in the City of Raleigh who took the course. And the City of Raleigh has a pretty big open source program. And sometimes I think there are some like rogue employees who just, like, keep using the software they want. So I think I think that’s definitely true that eventual it will kind of propagate through the people. Yeah. Sometimes it’s a really rogue like in the sense that they’ll Grass GS is not approved to be used at this office, no, but QGS is, and you can install. Voila, you can install any software. And you can install virtual machine. I have a collaborator which runs everything in virtual machine. Nice. Nice back door hints. All right. Well, thank you very much. Thank you. I think that’s the end of this session in this room. So feel free to continue having more good conversations. And then the next thing on the schedule is the rooftop terrace reception. So enjoy. It’s a beautiful day. I have stickers for those who like stickers. So come to me. I have selection.