 Thanks, Annela. So just a quick introduction of myself. My name is Mike Andrews. I'm a GIS research assistant with SDGs today. Part of my work is supporting the data development on Annela's and programmatic work on bearings and, but I also assist in the development of new methods and research opportunities at SDGs today. So I'm very excited today to be sharing our first in-house data set looking at school locations. So in July of this year, SDGs today launched the My School Today call to action. This call to action in support of SDG 4 utilizes a bottom up crowd sourcing approach to geo-referencing schools across Africa. And in addition to the call to action itself, which aims to improve the data, we've also launched the first in-house data set mapping school locations which utilizes this data to explore population demographics as it relates to travel time to various school locations. So we have several goals with the My School Today call to action. Primarily, we want to support policymakers and other SDG stakeholders with timely data on school locations and timely is really the key there. And we want that data to be locally informed. So that's where we decided to take a bottom up approach and utilize crowd sourcing in order to gather and solidify the data. So that's where our goal is to engage local communities and organizations in efforts to geo-reference their schools. So the idea is this data is coming from the people who know it best. And we also want to provide students with educational resources and GIS training lessons. So the majority of the call to action is actually targeted at student mappers so that they can gain experience in geo-referencing and a core skill to GIS. As well as being educational resources and how to utilize the data once it's available. So we also develop shared learn lessons, step-by-step mapping guides and several other products to help support students in learning of GIS skills to this call to action. And so in collaboration in concert with the My School Today call to action is also mapping school locations. And so as a data set, mapping school locations aims to utilize the My School Today location data in order to estimate walking only travel time for students to currently OSM recorded schools. And so this is a data set that is constantly improving as my school today, school location data improves. So it's one there to exist as a use case of the data itself, but it's also there to highlight a really important aspect of achieving SDG4, which is school access. And in this case we wanted to use the spatial lens in order to get a better understanding of physical, physical access to schools. And I'll go ahead and actually run through how we move from school location data provided by My School Today to the mapping school locations data set. So we begin with the school locations and within for those of you who are familiar with OpenStreetMap, OpenStreetMap can record schools in two different ways. One is as amenities, which typically outline the school grounds or is just a point feature to show the school itself and then there's also school buildings. And they can work in concert with each other. In our case, we're only utilizing school buildings because we thought that that was a better approach to providing the geo-referencing skills. And it also can work in concert with future potential collaboration with machine learning method methodologies. But we first extract school locations using those school building features convert them to points. And then those are used as source locations when we calculate our travel time isochrones, which I'll explain here in a second. So travel time isochrone is essentially a single isochrone, a polygon that represents a certain amount of time. It's a measure of time within a spatial context. And so constructing an isochrone data set allows us to create a depiction of travel time from any given source point to any given other point. On a map. So in the case on this map to the right, the red dots themselves are the actual school locations and then each of those different colors are different travel times. In this case, that would be 30 minutes, 30 to 60 minutes and 60 plus where 30 minutes is yellow and 60 plus is the red. And so isochrone construction also requires a cost raster. And a cost raster basically provides the information of how difficult it is to move across any given space on a map. And so we utilize a walking only surface friction raster that was developed by West of all in 2018. And this friction raster is fantastic because in addition to accounting to physical features, such as the difficulty of the terrain physical barrier such as as waterways it also accounts for geopolitical elements as well including including political boundaries. So that cost raster is inutilized to provide the idea of how long does each, in this case, sell of movement cost for a student to walk across from to a given school location. So we constructed our isochrones, we then have to construct an idea of the school age population. And so we extracted individual age sex bin images from world pops demographic population count images. We use their constrained demographic population images for for 2020 in this case. And what this does is that this provides us different bands of images of raster images that each provide different population counts for people for males and females aged one to five, five to 10, 10 to 15 so on and so forth. And so we extract each of those for male and female between the ages of five and 20, and then aggregate those into age bin groups, so that we end up with two, two images. One of females aged five to 20 and one of males aged five to 20. And this is on the right here is a depiction of what that population data actually ends up looking like. It's one of my favorite data sets to work with this. It looks so fantastic. But once we have the school age population raster images, we can then overlay that with our isochrones travel time, travel time layers in order to get isochronal population counts. So the population raster images are masked by the travel time isochrome to create six gender specific image outputs. So within a 30 minute walk time between 3060 minutes over 60 minutes in the same group emails as well. And so that masking is essentially, we are removing all for any given image, any given population image we're removing all data that does not exist within a specific isochrone time time grouping so within 30 minute walk time. And so that the population greater than 30 minutes away is essentially removed from that first image and then we utilize zonal statistics in order to count all of the cells that each provide population counts for the specific demographics that we're looking at. So that we end up with counts by travel time for males and females within first level country administration. And this ultimately ends up resulting in our final data set. I will go ahead and give you an example here. So like in Ella highlighted before, once we have our in house data set assembled, we can go about sharing sharing relevant information and statistics using dashboards are very powerful visualization tools and Ella demonstrated earlier. So here we have our isochrone population counts that are counted by first, first level administration within all the countries in Africa. And here you can get an idea of counts within each of those first level administration so the female population within 30 minutes of recording school we have 8504, and so on and so forth. There are certain limitations to the data as it exists now due to the underlying data still being in development. So typically we're assuming that most of these numbers that are greater than 30 minutes away are going to be higher than they are in reality but this is to demonstrate that use case and this will improve over time. And then, you know, with the goal of my school today, this should reach a point where we can get very accurate population counts for each of these, each of these areas. And then we can also aggregate those into statistics and focus down into specific countries. So basically we wanted to look at the name. We can see that, based off of our data, 59% of students live within over 60 minutes from recorded school, and we can get time specific and demographic specific population breakdowns and counts for each of those. And we can also share kind of like the ongoing work at my school today and explore how many schools are getting mapped and where there's where they're getting mapped and you can focus down in specific countries. And we can get very fine geographic resolutions in the school location data. And we can even track the ongoing development of the data set, both for Africa as a whole and for specific countries. So for example, down here on the bottom we have a graph that shows essentially the number of schools Africa wide that have been added since the launch of my school today. And so, there have been a good number of schools added. The good news is that this is really showing that this is like a constantly improving data set, because there's a very active open street map community and online mapping community. And very, very strong local, very amazing and strong local OSM communities and organizations and colleges and universities as well as just volunteer mappers that continually improve this data set so we can see that the data is improving. And we hope to continue to contribute that with my school today. And so that's that's mapping school locations data set. And then just some tools that we have at our disposal for improving school location both that we've already implemented that we will be implementing soon and that we have plans for in the longer one open street map on street map forms the basis of the my school today. Call the action as well as the mapping so locations data set. And it's a very powerful open source open source and crowdsource data set that allows anyone to geo reference schools. Anyone, students, teachers, academics, policymakers, any SDG stakeholder can go in and they can map schools in their area as well as any other public facilities that they they wish to include. And they can add additional details as they fit or they can have it as simple as just starting down a point. And in addition to it being openly available for anyone to contribute there's also an extremely large and extremely active mapping community within open street map that continue continually runs through all of the data and improves it runs various challenges to work for errors can support the efforts with machine learning. So it's a really amazing crowdsourcing tool that we're that can be used to continue, continue to improve education data for us to do for. And so we have developed our own step by step mapping guides that are available on our website that are multilingual currently available in English French Portuguese and Arabic. In order to provide a step by step run through of how a student could hop in and add their school within within the data set. And it's realistically something that shouldn't take much more than 10 minutes but there's also some great GIS lessons that are just a part of that. And then for groups and organizations that want to go much more in depth and to improving your location data than just adding one or two schools. The humanitarian open street map team that we've been working with also has a fantastic resource in the hot toolbox online that provides skills for skills and guidelines for doing really deep dive mapping challenges and map and engaging local communities and local members in order to improve the data set, wherever, wherever the user is. And then outside of open street map. There's also as raise RGS survey 123 and for individuals that may not have access to open street map or desktop open street map. We developed a survey within RGS survey 123 that any individual can do literally just on their phone. And we basically run them through a guided data submission process in which they can geo references and satellite imagery available and as we space maps to draw a border around their school geo references school and then submit it to us at which point we can then upload that data into open street map. As well as gaining potentially supplementary data such as average student class size number of females, female teachers, etc. So we'll be continuing to develop that survey it is currently live, we will be adding more questions and creating more comprehensive survey out of there. And then finally there are mapping challenges which is a very exciting component of being a part of the online mapping community. And so this, this is focused on the online community engagement and the real advantage of mapping challenges data quality improvements. So websites like map roulette can be used to feed users a series of tasks one task at a time so we can complete as many as they'd like they can hop on complete one hop off and essentially feed them. This is a school location. When you know that it's it is because it's labeled as an amenity but we want to get an idea of the school buildings. If you see if you don't see a school building here and go ahead and draw a boundary around it. Under the within the amenity. And so we can use this for data quality improvement and to kind of like bring together the fact that the school location data can exist in like multiple formats. And in the future, we can use it to one actually add new schools that are not mapped as amenities or school buildings. And there's also some some great potential there to be utilized with a machine learning in order to take schools that are that are mapped based off of machine learning criteria, and then bring in these mapping challenges to providing human element to check each those those outputs and ensure. Yes, that does appear to be a school or to fix like any any boundaries or all corners that might not be lined up correctly. So these are just some some opportunities that come with our bottom up crowdsourcing approach. And really exciting opportunities to improve education data. So I believe that's it for me and I'll be passing it back to Mary and so she can discuss storytelling tools and resources.