 Hi, I'm Matt Beatty, the instructor for Geography 858, Spatial Data Science for Emergency Management. Thanks for taking the time to have a look at this course, this syllabus, and I wanted to just tell you a little bit about the course, and hopefully address any questions that you may have in looking over the material below. The earlier version of this course focused primarily on geographic information systems and the ways that GIS can be implemented in an emergency management context. So the focus is really more on design and particular systems for GIS in emergency management. The course has been redeveloped now, so it's quite different in its focus. Now the emphasis is on spatial data science and the technologies that are emerging to change practice in emergency management to incorporate a whole range of sort of new techniques and data sets. So it's really focusing on how the area is being transformed. So if you have an interest in emergency management, I think this will be of interest. But if you just have a more general interest in emerging geospatial technologies and data and analytical approaches, you'll probably find this an interesting course and application as well. So some of the emerging techniques that we'll encounter are very familiar. So things like unmanned aero vehicles, volunteer geographic information, crowd sourcing, those sorts of things. Others will be a little less familiar, including topics like geospatial artificial intelligence near real time or real time spatial data analysis. As well as just some other novel approaches for dealing with issues around logistics, humanitarian logistics and the roles that agencies play with communities and those sorts of things. So there's a hard sort of technical technological advances, but then there's also different perspectives about how we need to approach emergency management situations. So what will we do in this class? Well the activities are quite varied. So there'll be quite a bit of discussion amongst ourselves, which I'll talk about in a minute. Hands-on exercises, group collaboration work and the development of a term project. So let me just start by talking a little bit about the collaboration component of this assignment. Now sometimes people hear that collaboration or group work kind of roll their eyes, but this set of topics that we'll be considering here really require people to get together and talk through complex problems and bring their understanding of the world together. So we'll accomplish this in a couple ways. So each week there'll be content, so a reading, watching a video, looking at various example websites that will present a topic. So it might be something like geographic artificial intelligence. Then you'll take what you've learned from those readings and you'll bring them into the discussion forum where there's a set of sort of standard deadlines you'll need to meet in terms of what you cover with your classmates in that forum. So the idea isn't that it's like super heavy or anything, but the idea is that you bring insights to it from your understanding of the reading and then it should grow sort of organically. That might not sound very exciting to begin with, but I think if you've found in the past with these courses that students quite like that a lot because it let them sort of talk about their understanding of the material but then also to provide their insights and sort of be creative as well in terms of how they interact and the points they bring. So the interaction is very important in this class. It also comes in in a couple of your hands-on assignments where you'll work in groups, to I think in one instance you are developing scenarios looking at a particular disaster and at how different phases of that disaster were sort of addressed or could have been addressed better with spatial data science. Another example is you'll develop sort of a more critical view of sort of what happened in a particular disaster and do a bit of a critique of that. So you'll be doing a bit of analysis and writing and then the idea is that you'll summarize that at the end of it using an Esri story map. So you'll be using a very sort of media rich environment to tell the story that you have to tell. And this is increasingly a skill that's highly sought I think in terms of being able to communicate the data in these sort of non-traditional ways to different stakeholders. Anyway, so it should be kind of fun. The next bit to mention I guess is around the hands-on assignments. So you'll work with a particular data set. So for example in the second lesson there's a lab on using unmanned aerial vehicle data to look at a few different scenes that might represent a landscape that first responders or others would be going into to try to address a problem. And you'd be the analyst developing the spatial products to provide advice about where to go, where not to go, those sorts of things. So in that you'll consider both 2D and 3D drone imagery and the idea is to really give you a flavor for like what's sort of happening in this space and how we can really narrow that critical time period between the disaster and the production of spatial products. Reduce that as much as possible so that this response can be more effective. The final assessment I guess I'd like to just mention is the term project. This one's really key and quite important because it's your opportunity to really tailor the class for you. You can pick a wide range of topics. I'm pretty easy. Early on in the piece you'll be basically doing a pitch of your idea and what you would like to do and I'll provide you feedback on that. The vehicle for that is an abstract followed soon by an outline where you sort of flesh out the details. So you'll get lots of feedback early on to make sure you're on track and that you're doing something that's possible and the time you have available. Then you develop the first draft and then a final draft. And then through that process I'm with you and I'm providing feedback along the way. You could do topics that relate more to more conceptual and sort of design issues or you could do something that's pretty hardcore data driven. So it really just kind of depends on your interests and the like. At the end of this process we do you'll create a five to seven minute short video that explains what your topic was and what your findings are. And that will be available for the rest of the students to look at. Also it's something that you could sort of take with you. So I should have mentioned that with the term projects in the past a lot of the students have been able to take their work and actually show it to their employer in some cases as a proposal for what they think their organization should be doing. In other cases it's formed a very solid draft of what would be a journal paper. So I think at the very least you can come out of this with a bit of a portfolio. So from your esri story mats to your term paper with a very strong and very strong emphasis on technology and data and that coupled with your communication products like the video that you'll do. So anyway that's really I guess what I wanted to cover. Probably covered more than I meant to say but if you have any questions about this course I would like to know more or I'd like to understand if it fits with what your goals are which you went out of more detailed understanding of spatial data science as applied to emergency management. Please don't hesitate to get in touch with me or with one of the people in the program office that can help hook us up as well. The only hitch on my end is that I'm based in Australia so I live in Melbourne Australia so there can be a bit of a delay in communication but in the past that's been something we've all had to deal with. We've had in a given class people from all over the world but anyway so we can definitely sort of work around any of those issues. Anyway thanks again for listening and please have a look at the rest of the page and the syllabus if you want to kind of know what we're doing. Right now we only have lessons lesson one open and the reason we do that is that this is a paced course because of all the interactions and the requirements of the data assignments we're really trying to limit you to like not working more than a couple weeks ahead. And I know that doesn't always fit well with people's schedules and the like but in a sense that's how this course sort of has to work so if that's an issue we can talk about it because I mean we have examples where students have been able to sort of manage this as well. Great well thanks again and I'll talk to you soon hopefully.