 Today, we will move out of DHS2 and into a program called QGIS, which I hope you all have installed on your computers by now. And we will also be using up-to-date population data from WorldPop and combine that with your data from DHS2. So we have a good crowd of people here today that will help you to move along with the exercises. So in the three persons to the left will be your presenters. So most of the session will be held by Oli Pan from an organization called Grid Tree. He will present himself at Grid Tree later on. And then we are lucky to have Professor Andy Tatum here from who is the director of WorldPop, who will make a short presentation of the WorldPop datasets that you will use. So this is the plan for the day. We will follow the same pattern as yesterday. So we will have four, maybe five exercises if we have time. So there will be a little introduction and then you will have about 15 minutes on each exercise. We know that today might be a little bit harder than yesterday, especially if you have not used QGIS before. And that's why we have prerecorded some videos for you that are quite short, just a few minutes that you can watch along while doing the exercise. So I would just advise you to don't try to do the same as we are having the presentation. Just concentrate and follow along. And then so you know that you will have a recorded version when you start on the exercise. Again, we were very happy with the way you used Slack yesterday. So just please keep up and do the same. So the same as yesterday, we have one Slack channel for each exercise. And this is since we don't have any hand-ins, we would like to show your progress in the Slack channels. And also please use the questions channels if you have any issues or questions. Most of you have also introduced yourself, but if you still have not done that, please do it in the Introduce Yourself channel and follow along in the announcement channel for important information. Also remember that the exercises is not compulsory, but we really advise you to do it to be able to get the certificate for the day. You need to follow all the sessions. And as yesterday, I will tell you a quote that you need to write down in a form. So this quote will be, I will say this later today, but I won't say when. So you need to follow us. We went through this definition yesterday of GIS, so I will not repeat it. But just to say that today we are moving into I would say a proper GIS application that is capable of doing sort of fulfilling this definition. And rather than only just looking at the data, we will analyze the data. And there is also another important distinction here that you can see on the image to the left. And that is the distinction between raster and vector data. And some of this complexity we have sort of decided to hide away from you in the Maps app. But when you're entering QDS, you will see these terms coming up a lot of places. So I will just tell you briefly what to look out for. So the vector and raster data is a common way to sort of classified GIS data. And so far we have worked on vector data in the maps, DHS2 maps. And vector data are often divided into points, lines and polygons. So all your org unit districts will be called a polygon. And then a health facility is just a single location. So that will be represented as a point. We don't have lines. Yeah, we do have some lines actually, some relationships between tracked entities, for example, that can be represented as lines in our system. And then very often these geometry formats, point lines and polygons are linked to the tabular data that can include more information. So in our application, you saw that with the data table, that you could open the data table. And then in addition to have the position on the map, you could get information about that particular point or polygon. And then the other format is a raster data. And the population data we are going to work on today. And also that I demoed at the end of yesterday from WorldPop was a raster data. And that is represented as, we call it a grid of cells or pixels. So you will download this data per country. And then this data set will cover the whole country with cells of equal size. And the data set we will use today, each small cell will be 100 by 100 meters. And within that cell, there can be one or more values. For example, the number of people living within that 100 by 100 meter. We don't have support to store raster data yet in DSS2. It might come later, but we can include it from other providers. So all the data you can include from Google Earth Engine. And also we have support for something we call external layers. They will be raster layers. So you have different options of including them in DSS2. Again, vector data, we divide that into point lines and polygons. And then what we will use today is point data, which is just a single location, and the polygons. And for the polygons, we are going to calculate the population within a polygon or a district. And for points, we are going to make a buffer around them. So like five kilometers from a health facility and then measure the population within. Also in the Maps app, we have been hiding away an important complexity called map projections and coordinate reference systems. So we talked about yesterday that we use only support latitude and longitude. So we have made this decision to reduce the complexity. And we also only support one map projection, which is called the Mercator projection. It's lots of people don't like this projection for good reasons. One reason is that when you look at the map, Greenland has the same size as the whole of Africa. There is a nice website here that I've linked to, the truesize.com, where you can move the countries around on the map. And then you can compare them by placing them next to each other. And this image with Greenland is just to show the true size of Greenland compared to Africa. There are still good reasons. Why would we use a bad projection? And there are still good reasons to use this. One thing is that it preserves shapes. So when you zoom into the map all the way down to the neighborhood or house, the shape will look correct. And that's an important thing for this zoomable map. Another important reason why we use it is that all the big providers like Google and Bing, they use this as the default projection. And as we want you to be able to choose between different base maps, we also need to use this projection where the base maps are provided. We might support other map projections in the future, but this is how it is now. Also a good thing is that the projection is much better around the equator. And many of our DHS2 countries are in this sort of equator belt. And then the projection is not that bad actually. But when we are moving into QAIS and especially when we start to analyze data, you need to know the projection of the region where you operate. And this is a tough matter. There are hundreds of coordinate reference systems or map projections. And often we find them they can be hard to find. So I think that my best advice is to ask a local GIS expert for what is your coordinate reference system or map projection for your area. Big countries will have multiple map projections. Smaller countries will just have one. One universal system that is often used is called UTM. And this divides the globe into different UTM zones. As you can see on this map. And if you're following the Sierra Leone examples here, we will use this special projection code which is listed here. We will mention this later as well. I've also started on a list. I will post it later on. Just for this exercise, it's a map projection you can use. This will be mentioned more later on, but I just want to tell it here as well. So this is a very high level overview of what we are going to do today. We have the QGIS application. And into that we are going to download and import data from WorldPop. And then we are downloading organization units, boundaries, district boundaries, and facility points from DHS too. And then we will run three different analyses on this, which are about the same topic. And the goal is to find the population living in an area. So the first thing we will try is to count the number of people living within your district's polygons, within an organization unit. Then we will draw a buffer around a facility like we did yesterday and then find the population living within that buffer. So it could be 10 kilometers from a health facility. And lastly, we will create something called an isochrome. And that is that we will measure how far can you travel by driving or walking from a health facility. So first we will create a sort of a polygon of that. And then we will use the road network to be able to do that. And then check how many people can actually reach this health facility within 30 minutes driving, for example, one hour of walking. Also as I mentioned, because this is complex, we have prerecorded six videos for you. And these are all already posted for each exercise on Slack. So don't start to watch them now because you need to follow along in this plenary. But while you are working on the exercise, and if you don't remember what should I do here, please just play and try to follow along these YouTube videos. And of course, you can use this later on as well. If you have never used 2GIS before, they might be easier to follow the Sierra Leone example. But if you are a little bit familiar with GIS and you have your own DHS-2 instance or some local data, I think the learning effect will be much better if you use data for your own country. So now I will hand over the word to Andy Tatum from WorldPop. Good morning, yes. Thank you for your time. This is very excited to present to you all and give you an overview of the population data sets that we produce at WorldPop. And yeah, I'm a professor at University of Southampton, and I lead the WorldPop group. And WorldPop is a core member of the Grid3 program. So just to give you an overview here, so oops, sorry. So yeah, we're an applied research and implementation group very much focused on mapping small area demographics in low and middle income countries and making that data open as well as the methods and all the code and details behind those data sets. So today, I'm going to give an overview of those data sets. We're not expecting you to understand all of the methods and equations that go into everything, but hopefully it gives you an overview of some of the data sets, some of the what goes into them and some of the limitations of them. So small area population data, I'm sure all of you know the value of those having accurate data on population numbers at small areas. It underlies so much of government, other decision making, particularly in the health field. And of course, particularly right now with the needs for COVID control and vaccination planning. So it's an important data set. The challenges are often in many settings that the data can be a problematic. So the last population and housing census in a set of countries here, in some cases was many decades ago. There's often lack of registry and administrative data systems to keep those data up to date. And of course, recent disruptions as well to census plan is because of the controls put in place for COVID. So what we often faced with is a set of challenges with population data that sometimes it's outdated, sometimes incomplete, sometimes missing people. And often the resolution sometimes only able at these certificate levels. So this is what we work on within WorldPop to try and produce what beyond this described is raster or gridded population estimates. And this is the kind of recipe of ingredients that go into it on the left. There's some modeling that goes on in the middle and then at the output in these population estimates. So I'll start at the end here of what we're actually trying to produce and the kind of data sets that you'll be working with today. And this goes back to just what Bjorn was presenting. We're trying to divide up the entire world into 100 by 100 meter grid cells. So there's rasters and try and estimate a population number who's living in each of those grid cells. So that gives us this grid that's a consistent comparable format for integrating different data types. It means we can be flexible in the way that we can also summarize those population data. So if we have Nigeria here at the top, we have our 100 by 100 meter estimates. We can summarize by the administrative units to get estimates of those those totals per unit. But we can also be flexible in the way that we analyze those data. And that's what you'll be doing today where you can overlay, for instance, the location of emergency obstetric and neonatal care facilities on these gridded estimates and then summarize them in different ways, whether that's travel times or just the number in this case, the number living within 50 kilometers of one of those facilities and summarize those by the units where decisions are made up to highlight gaps in coverage. So in WorldPop, we're generally producing two broad types of data sets. And I'm going to go through those two today, what we call top down, which we produce data sets for every single country in the world. But also through the the grid three program, we're now producing data sets in collaboration with governments in a situation where census data is outdated or there are gaps in enumeration data. So if I go through each ingredient first that goes into those population data sets, firstly, the population data themselves. And this very much determines how we deal with how we produce a data set. And the two types of situation we're often faced with either that we have a complete enumeration of an area. This square could be an entire country or it could be a province. And maybe we have estimates for enumeration zones and we have full coverage of those that can come from a census or it can come from projections from a from a census. The other situation we often face is that there's a partial census or enumeration surveys. So the data may be very outdated, but we do have some recent data from household surveys or there's a census that's been undertaken, but there are areas of country that cannot be reached or are insecure. So an example of that on the first type on the left is complete enumeration here in an area of Vietnam. And on the right here is an example for Afghanistan where only certain areas have been covered in a provincial census. Or in Nigeria where field teams have been out to collect enumeration data in these areas in the red dots. So that's the kind of population data that goes into producing these gridded estimates. An important ingredient also is mapping of settlements. And this comes from typically satellite imagery. And so there have been lots of efforts to try and map where settlements are, where cities are, where towns are. This is the kind of data set that we've been working with for many years. But recently things have improved substantially and there are many data sets now where every building in a country has been mapped from satellites. And this is really helping to improve the accuracy of population maps. Because if we know where the buildings are, we know likely where the people are. Doesn't mean that it's that we know exactly where everybody is, but it gives us a better estimate. And it also gives us much more detail within urban areas to be able to map those populations correctly. So the final part is what we call geospatial covariates. So it's like this stack of data that beyond showed at the very first slide. So it's a question often of if we have maps of buildings and we have data on people, we really need any of these extra layers. Here's an example of why we do need those extra layers. We may have an example here from Pakistan, where there are from space. There are two settlements here that look identical, really. But when you start to look, you go to the surveys on the ground and you see the mean household size per cluster. You can see some big differences in the northern area there. There's more than 11 people per building per compound. And in the southern area there, it's down to less than five. So there are other factors, meaning that we cannot just put five people into every building and assume that we produced an accurate population map. And this is where GIS comes in a very important way in trying to capture within different data sets some of the variations that determine why we see population densities varying from place to place. And what we try and do is build up a picture of the landscape through different GIS data sets. That can come from things like densities of schools, roads, marketplaces, conflicts. It can also come from mapping of things like household sizes, poverty rates, all of which have some determination of why we see population densities varying in different ways in different parts of countries. An important part of this is also the building footprints, which we can process to get different types of information. So we can map out building densities. Those areas that have very dense numbers of buildings are likely to be the ones that have very high numbers of population. We can map out processes data to identify different types of neighborhoods. Where are their very small buildings all squished together and where are their much bigger, more sparsely distributed buildings? And also try and understand which types of buildings are likely to be residential or non-residential as well. So non-residential buildings typically have certain characteristics of being much bigger, often commercial warehouses. And so we can try and identify those. And some of these data sets are ones that you can go and download from the World Pop website. So this is maps of building patterns throughout every country in sub-Saharan Africa. So those are available to download from this link. And what we do is build up a kind of covariate data sandwich, we call it. So all of these different data sets that describe something about how populations might vary across a landscape, we put into this and that can come from those building footprints. It can be maps of vegetation, satellite images of the Earth at night, where some areas are more brightly lit, which correspond to higher population densities. And then also mapping of health facilities, villages, road networks. So that's the three ingredients that go into them producing population estimates. The middle part here is what brings them all together to try and get to those gridded estimates of population. And there are two types of approach here that we use. You don't need to worry too much about all the details, but one is the situation where we have full census counts matched to boundaries. And the other is where we have those gaps and there's a page on our website. If you're interested in much more detail that takes you to thinking about which types of data sets are useful for your application. But I'll give a short overview here and going back to this example. If we have a full enumeration of an area, that's where we employ these top down methods. And where we have gaps is where we employ these bottom up methods that we're trying to estimate into those areas with question marks. The top down situation is maybe where you have recent reliable census counts matched to boundaries and we want some gridded estimates. Those projections at province level are trusted, but you need some smaller area estimates and maybe need gridded outputs that match my district unit totals. In the other situation, maybe the last census was 1984 and you don't trust that data or any projections from it, so need new estimates. Ultimately, we're aiming for the same outputs, aiming to estimate for each grid square across a country, the number of people in those grid squares. And so we have the same kind of outputs we're aiming for. If I start with the top down, which is the data set that you're going to be using today. And this is essentially making use of the population data at the course level and looking at those relationships with the stack of data in the data sandwich. And then using that to predict at the much smaller level. There's popular entities. So again, don't worry too much about the details here, but to visualize that we have this data here from northern Vietnam. We have each of those units maybe has 50,000 100,000 people in them, but we don't know where we don't know. Don't know how then to turn that into these gridded data sets. But what we do have information that we can use from those more detailed data sets to tell us within this big administrative unit. We know that the buildings are in these locations. We know that the land cover is is like this in certain areas, so certain land covers have higher population densities. We know that some areas are much more brightly lit within those units than others. So we can use all of those relationships to then predict what the population density is likely to be in these grid cells. And this is the kind of data that you will be looking at today where we have an estimate for each one of those grid cells. And this is something that we've done across the entire world. So we gathered together data from subnational estimates from projections, but also the census data themselves match to those boundaries. And you can download and see all the sources that went into this for your country of interest. We've also gathered together as much age and sex structured data as possible as well. And we have a portal here that you can go and explore that data to see what goes in and the kind of populate permits that are produced as part of that. So this is like when it's all put together for the for the whole globe and zoom into an area, you get this kind of information. Estimates for each grid cell of the number of people and patients. We're also producing this for each year from 2000 to 2020. So China in 2000 and here's China in 2020. So go back and forward. You see those big changes that have occurred in China with people moving to urban areas. And we also have the age and sex structures estimated. So this is the population pyramid, the change in this area from the year 2000 up until 2020 as that structure changes over the years. And each data set that you download also comes with a small metadata report. Again, this is something not to worry too much about the details of what goes in. Some of the technical details in there at least shows what are the important variables in predicting those population numbers. For those who are very technical and want to get into the modeling scripts. Those are also available as is a portal to just enable you to zoom in and out and explore that data in a more user friendly way. And today you'll also be there are two options for our data sets. There's a constrained and unconstrained you're here. And this is the simple difference here is that in the unconstrained we're making a prediction of population everywhere. In the constrained the constrained version, we're only predicting into areas where there are building footprints mapped. So then finally, the bottom up approach. This is the situation where we only have a small area data and we have big gaps in between. So an example here is Afghanistan. We have recent data from these areas in pink that we have these big white areas. Where there's no recent data collection at all no data collection since 1979 census. So how can we make use of the data that we have in those those pink areas to predict what the population might be in those white areas. What we do have across the whole country is this data sandwich the stack of data and the mapping of buildings is within there as well. So that we can look at what is the relationship in those areas where we have the data to that stack of variables. So what is the relationship between the population density in those pink areas and maybe the mapping of residential compounds the density of those. What is the relationship between population density where we have it and there's an amount of vegetation. This gives us a set of relationships that we can then use to go from this picture with with some data but gaps and actually build a statistical model to predict into those unsampled areas. And this is a model that works pretty well so that we can complete those estimates. Importantly doing that with with measuring the error that occurs so that we are making predictions into some areas. But those areas of red areas that we were not so certain about our predictions. This is something that we've done within the grid three program to produce new estimates for for many countries. Again, don't need to worry too much about the statistical details here but we're producing estimates in the absence of complete enumeration data. And examples here are Nigeria where field teams went across the country to collect data in these red areas to train all and predict those populations across the country. And these are the data sets that are now used in health intervention micro planning to produce these these types of plans that have been used for polio vaccine distribution and also for COVAX planning at the moment. Similar in Zambia, these estimates have been used in distribution of insecticide treated net planning for malaria. In DL Congo, estimates of population have been produced to provide some of the first data since 1984 in these population estimates. And then with the government of Burkina Faso, census was undertaken recently but the government could not reach those areas in blue. So these approaches enabled the use estimates into those blue areas and these are numbers that went into the recent preliminary results of the census. So all of these bespoke newer forms of estimates without census data are available to download from our open population repository and there are always new countries going in there and updates to those existing countries. And you can explore these newer forms of data sets in this app that we have here, the WAPA vision app, you can zoom in and out, you can calculate different age groups and you can measure the uncertainty in those estimates as well. So some key messages relating to population mapping, of course they form the basis of decision making across multiple fields and the more accurate the data set you have, the more smaller area they are, the more you can ensure that people are not left behind and that you're capturing some of the subnational variation that exists in countries. But that population data can be coarse, outdated, unreliable in some settings. And so there are some methods that exist to try and complement those traditional data sources and add value to them. The models are never perfect, so it's important you have some understanding of the methods that went into producing them and the limitations behind those. And it's also important to engage with whoever you're working with these data on to ensure that there's adoption and sustained use. So just to remind you that there are these two forms of data sets and it's the top down type on the left that you'll be using today where we're taking census data and projections and mapping those down to small area grid cells. So there's a document that we shared before this that gives a recommendation of the types of data set to use. And so for every country in the world, we now have 2020 data sets that are mapped to building footprints in sub-Saharan Africa. And those are adjusted at a national level to match as UN population estimates at a 100 by 100 meter resolution. So these are the kind of data sets that are some of our best data sets. But also through the grid three program for some countries, there are these bespoke bottom up estimates that are likely to be more accurate. So there's those new estimates that I showed for Burkina Faso in Nigeria and also South Sudan and Zambia. So I'd like to thank all of those who did a lot of this work and provided those data sets and of course the world pop group and grid three team. And there's a lot more information on our site and hopefully you have a chance today to experience using some of these data sets and now have a small overview of what went into them and how they're produced. Thank you. So thanks a lot Andy. I think that was a really nice introduction and I would say impressive juice of GIS technology. So it's also a great example of what you're able to achieve with GIS. So thanks. I know you or you will be here or the other from from world pop. So there might be some questions later on. We also have a Q&A session at the end at one o'clock. But feel free to ask questions in the questions channels and we will try to to sum that up at the end. And I'm sure also they can contact world pop or grid three directly the country representatives if they have if they wonder which data set they should use or they need some advice on that. But we will try to help you along today. Yes. Then I will hand over to Oli who will demo how you can download these world pop data sets if you use Yes. Hello everyone. Let me just start my video. Yeah. So I'm going to provide a quick demo of how we can download the world pop data sets. So I'll start sharing my screen. Can we just confirm that everyone can share see my screen? Yes. Yeah. Perfect. So to download the world pop data sets we need to make sure we've gone to world pop.org in our preferred internet browser. And this is the homepage for the world pop research project. And it gives us information on a whole range of demographic and population data relating to research on various sectors that world pop are involved in. But we're interested in downloading population data here. And the population data we'll be using for this instance can be located under data. Population counts. And once we get to the population counts page, we're presented with a paragraph here or two that has information on the different population data sets that world pop creates. As you've just heard from Andy, world pop create a variety of population data sets depending on the methods and inputs used. So we're looking to use today the constrained individual countries 2020 UN adjusted data sets at 100 meter resolution. So to download these, we simply click on the data sets and this brings up the data and resources page where we can look at downloading the data for our respective countries. So here what we want to do is download data for the countries that you're using for your own DHIS to instance, or if you're using the data demo, you can follow along with me. So here we can either scroll and select our countries in the alphabetical list or we can search for our countries in the search bar here. I'm interested in downloading data for Sierra Leone. So if I type Sierra, the box filters the results. And I'm given the population data set for the year 2020 for Sierra Leone. So to download this need to simply click on data and resources and it will take us to the landing page for the Sierra Leone population 2020 data set. And on this page, we simply need to scroll down and click on download entire data set. So if we click this, it begins our data download. Then we can save this in an appropriate place. I've saved mine earlier under a folder called DHIS to underscore training. So I'm now going to pass back to Bjorn who will show us how to download our data from our DHIS to instances. Thank you, Ollie. One second and I'll get my screen up. So let's move back to the maps application. And this is actually the only demo from DHIS to maps today. I will just reload it to make sure that I'm still logged in. So what we are going to do is to download some data that we can use to combine with the population data. And again, please use your own DHIS to instance if you have it available and you have coordinates for your order units on that instance. But for easy way to follow along, you can use the demo workshop instance and then use data for Sierra Leone. So first, we will download some boundary polygons. So I go on add layer and select boundary layer. And then we plan not to do the whole country, but to do the chief dumps in one district. So we will select the Kinema district and then select all chief dumps within that district. And then we add the layer. So you can now see it on the map. And then you can download it by clicking this more actions button and then download data. So this will be in a format called GeoJSON, which is very common supported by all GIS software and also QGIS. As I just mentioned also, we will use QGIS today, but you can do the same thing in ArcGIS for example. But we have chosen QGIS because it's a free and easy available option for you. And then we download. So now we have downloaded the data set. It will be named boundaries GeoJSON by default. So yes, so we know what it is. I would suggest you to rename it Kinema chief dumps. You will see this name in later exercises. So now we have the district polygons. Now we also would like to have some points for health facilities. And I recommend not, because some of these computation to find a population can be a bit intensive for your computer. So I recommend not to download all the facilities in your country, but take a few, maybe around 10 and download that. And then I will add it to the same map. So I click add layer, select facilities. You can select, it doesn't matter what you select as a group set here. So I just select facility type. And then we would like to have facilities, but then only in one chief dump. So we have chosen to go for Dama. So we select this one and add layer. And then this will add six health facilities to your map. Again, we download the data in the same way. Click download. And then download again. And then if I move back to the my download folder, you will see a file called facilities to Jason again renamed the file. So you know what it is about. So we call it Dama facilities. And then we recommend you just to add it to the same folder as you have the other data sets. So you have the population data, and then these two GJs and files within the same folder. And I think can I hand over to you only to present the first exercise? Yes, of course. So the first exercise today is exercise one. And it's going to be on downloading our data. So we've just had a quick overview of how to download our data from both WorldPop and the DHIS2 maps platform. So I'm now going to share my screen. And this is the exercise for downloading the data. So firstly, we want to download our data from WorldPop for today's session. And the links are all in the instructions here. And then secondly, we need to download our data from DHIS2 maps for the session today. If you've got any questions about this exercise, you can use the Slack channel, which is day two, exercise one. So we'll now give you some time to download this data, and then we'll come back and we'll go through exercise two. Thank you very much. Thank you. So you now have 15 minutes for this exercise. And we'll also include five minutes break for you to get some coffee or snacks. So we will be back and start a plenary session again, 10 past 11 Central European time, but basically in 20 minutes. Thank you. Hello, everyone. So we're now ready to reconvene to begin exercise two. So for anyone in exercise two, we'll be importing data to QJS and we'll be setting up our QJS project and importing raster and vector data. We've just downloaded and styling the imported datasets. So I'm just going to quickly open QJS. So anyone who has not opened QJS yet, please make your way to the QJS software and we'll be ready to begin. I'll just give you a few seconds to come back and to start again. And then I'll move forward in exercise two demonstration. Okay, so hopefully everyone was able to complete the exercise for exercise one, where we downloaded our data from the WorldPop website and for our DHIS to facilities and boundaries to JSON files. So the first step in exercise two is to set up our QJS project for use. And the first stage of this is to save our project. And this will allow us to come back to our project to the later date with all of the layers and data saved within the project file. So to do this, we need to go to project in the menu bar. And from project, simply go to save as and navigate to the folder where you've saved your JSON and your population raster file. And within this, you can save your project as anything you like. However, I'm going to save it as DHIS to underscore QJS underscore training. And by saving this, as I say, it allows us to save our progress and keeps all of our data safe in one location that we can access at a later date. It's also a good practice to save your QJS project as you go along. So to do this, you can either go to project and save, or you can press the save button in the toolbars along here and save like that. The next step is to change our default projection coordinate reference systems to an appropriate coordinate reference system based on your location. So for anyone following along with the demo instance, Sierra Leone, you can follow along with me. But for anyone using their own data, you need to find an appropriate coordinate system for your data. And to do that, you can go to this website here, which is EPSG.io. And there's links to this in the slides that will be shared with you at a later date. However, this simply allows you to search for a country. So for example, I could search for France. And it would return appropriate coordinate reference systems for this location. So for Sierra Leone, I'm interested in looking at a projected reference system. So I'd be able to search for it, go to projected. And it would show me the appropriate projected reference systems for use today. So I'm going to use WGS84 UTM Zone 29 North. So I'm now going to return to QJS where I'll show you how you can change the default projection of your QJS project. So to do this, you take your mouse pointer and go to the very right hand side of the screen. Hopefully you're able to see. But in the lower right hand side, there is a box with a globe and a map projection sitting on top looking almost like a hat. And if we click on this, it'll bring up the project properties for our coordinate reference systems. And as you can see here, we can search for our coordinate reference systems, and they can appear in the filter box here. So if you simply click, it'll give you a quick map outline of where your projection is suitable for. And to apply it to your project, simply press apply and OK. As I said before, we should be saving our projects as we go along. So I'm going to press save. The next stage is to import our data into QJS. So to do this, we can go to layer, add layer. And we're going to start off here by adding our raster layer. So under add layer, we'll go to add raster layer. And this will bring up the data source manager QJS. And this is the window that allows you to open up a whole host of different data set formats in QJS. And these are all listed along the left hand side, as you can see. Here we're interested in adding our population layer. So under raster data set, we need to click browse button, which is the small button here with three dots. So we'll click on the button. And this should load a file explorer window. And under your window, you need to search in your local disk for the folder where you downloaded your data. And once you get to this folder, you can simply click on your raster, press open, and simply press add and close. As you can see, this has added the raster to our map canvas. And we can see currently that our lowest population values for Sierra Leone are five. So these are the darker areas in our raster. And currently, our highest values are shown in white, which isn't very convenient for visualizing and understanding the population data. So after we import our vector data, we'll go back and style the data to make it a bit more visually friendly for when using it in QJS. So we're now going to move on to adding our vector data in QJS. So to do this, we need to go to layer, add layer, and add vector layer. Here, it opens up the data source manager, but we're on the vector page. And under vector data sets, we're able to click. And this brings up the file explorer before. So for Windows users here, if you press your control button, we're able to click on both our facilities data and our chiefdoms data at the same time. For anyone using Mac, you're able to do exactly the same using your command button. And we can press open. And as you can see, we have both JSON files in our vector data sets window. If we leave these options to default and simply press add, we're able to see the data has been loaded into the screen. We can reorder our data sets in the layers window by dragging them like so, or by pressing these up and down buttons. So as you can see, we've been able to import our facilities data, our chiefdoms data, and our population raster. So the next stage is to style these. So we're able to use them and visualize them in a better way within QJS. And QJS is really good at this because we can simply double click on the raster, open the layer properties, go to symbology, and then change the band rendering and the symbology of the raster. So it may look like there's quite a lot of settings here. But we're interested in looking at changing the render type to single band pseudo color. And this changes the symbology to set a color ramp where we visualize our different population values for the pixels by the two color extents. Here you can change it by simply clicking on the arrow. And you can set it to any color you want, as long as you think it's visualizing the different distributions of the populations. Okay, so I'm going to keep it the same as Viridis. But under the mode of classification, rather than setting it as continuous, I'm going to set it as equal interval. And again, here, you can change the mode depending on what you want to display the data as. And here I'm just going to increase the classes to six, just for the purposes to show how this changes the visualizations. So here we can see the darker blues are going to be around the smaller values and the greater the population within the cells are going to be shown within yellow and lighter greens. So we now press apply. And if we zoom in quickly, by using the zoom button, we're able to see the difference in population distribution across different areas. And each of these small pixels represents a 100 meter squared area across Sierra Leone. Let's go back to our full map extent by right clicking, going to zoom to layer. And now we'll quickly change the layer symbology for our vector layers. So we're able to present them in a way where we can see all of the data sets at the same time. So again, simply double click on the data, go to the layer properties and symbology. Here we have the symbology for our chiefdoms, which is the district layer I've downloaded. To change how we present this, we'll click on simple fill and under fill style. Instead of having solid, we're going to click no brass. And this will leave us with only the boundary perimeter filled in. And this means that anything outside or inside these parameters will be visible to us when we press apply. So if I press apply and okay, you're able to see that we've changed the symbology to allow us to view the population raster, the boundaries layer and the health facilities we've downloaded at the same time. We can also change the facilities symbology by double clicking and then setting an appropriate symbol in the symbol section. You can scroll and change depending on which symbology you prefer. However, I'm going to change mine to be the top hospitals marker just because this is a universally used marker for hospitals and anyone looking at the map extent will understand that the points are representing health facilities. So if I press apply and okay, this gives me the data and that is the end of the exercise. So I'll now return to our workshop and present the exercise slide. So we're expecting you to post a screenshot of your imported and styled data sets to the Slack channel day two exercise two. So Bjorn, can you just confirm how much time we'll give the participant for this and when we'll reconvene? Yes. So thank you, Ollie. I know it's quite a bit to digest especially if you haven't used QGIS before. I also saw there was some questions about that for them in the chat. So you should not try to do the exercise while we are showing because we need to move in a steady phase to sort of covering all. But this is the reason why we have prerecorded the video so you can watch that in your own space. So you can do now exercise too. I think it's quite a bit to cover. So I would suggest 20 minutes for this one as well. So now the time is so quarter to 12 central European time or yeah. So 20 minutes from now you have on this exercise. And remember if you don't remember the steps watch the video on YouTube and you can just follow along that video and you should be fine. Thank you. Hello everyone. So hopefully everyone's been able to import the data to QGIS and been able to firstly set the coordinate reference system then import the data, install the data. I've seen in the chat in Slack that some people are still facing a few issues when they're looking to project the data. So I'll just go over the kind of steps you need to take. So let's hide the controls. So for anyone looking to use a projected coordinate system from the EPSG.io website, if you search for your country press search, we're looking to use projected data sets. So on the type of results we need to click projected here. And then this is what we're interested in. We're interested in using the UTM zone projection for our data for Sierra Leone. You could use the UTM zone for Sierra Leone 1968. Or if you want to use something slightly more current you could use the UTM zone for the WGS84 data set. And all these are is different datums that being applied to your projection, which is probably too much detail for the session. But just to outline the steps we need to take, if we're interested in using one of these projections we can click on it. And here you either have the choice, you can copy and paste the code here or you can take the code here. So I'm going to copy and paste the numerical code. So what I'll do is right click and copy. And for anyone working inside here we can just go to project properties and within our CRS, our project coordinate reference system, we can just copy this and this brings up the coordinate reference system we found online on the EPSG website. So if we click on this press apply and okay what should happen is your project reference system should change in the lower right hand corner. And you can check it's changed by also going to project properties. And if you go to your project reference system you should see your current project reference system in here. So the CRS you're working in. So that's hopefully going to clear up some of the questions we're getting in the Slack channel. I'm now going to go back to the slides and we'll begin with our next exercise and the demonstration for that. So hopefully everyone was able to import their data, okay. Exercise three will be using the data we've imported. So the population data and both the vector data sets we brought in. So we're going to cover examining the population raster data using the identified tool, calculating the total population of a gridded population data set such as our downloaded raster. And we'll use the raster layer statistics tool to do this. And then we'll look at calculating the population living within our org unit boundaries that we've downloaded using zonal statistics. So if you've got any questions please use the questions channel and Slack and we can post questions and our tasks to day two exercise three. So without further ado I'm going to move back to QJS. So you should have something that looks similar to this in QJS. So to get to the same extent as me you want to right click on your district layer and go to zoom layer and this brings your map canvas to the extent of your district layers. This will just help you focus in on the area we're looking at. So for the session now we're looking at how to find the population living in org units but before that we need to understand a bit more about how these raster cells work and how they're displayed. So if we press our identify button this brings up the identify tool. And using the identify tool we're able to zoom into our raster. Click an individual cell like so and correct me here. So before we use the identify tool we need to make sure we have an active layer and the active layer needs to be set to our population raster. So to do that you simply need to click on your population raster and then return to the identify button. And if we do that as you can see we're now looking at the raster layer as opposed to the previous vector layer. So you can see simply by clicking on each pixel that the value for the raster changes depending on the color and which pixel we click. So that just means the total population for this 100 by 100 meter square is represented by the value stored in the raster. So if we come out of this so that means each of these individual pixels that make up the raster layer are built out of different values and this is really useful for finding out the populations and who lives where but more importantly how many people live in these areas. And this is what today's session is really about is trying to provide us with the different methods and techniques for solving questions that will allow us to understand more about the population distribution in our org units or around facilities. So we're now going to move on to how we can actually work out the total population of our whole raster and to do this we need to open our processing toolbox. So to do this you go to processing and click on toolbox and this should open on the right hand side and within this we can go to the raster analysis section here and we can go down find raster analysis and if you click on the small arrow it brings up all of the individual toolboxes that are used to analyze rasters within QJS. And as you can see there's quite a few different options. We're interested in the raster layer statistics tool. So to use the tool simply double click and it will give you the window for the raster layer statistics tool. So as the tool helps us this algorithm computes the basic statistics from the values given in a band of a raster layer and to do this we simply need to make sure that our raster layer is set as the input the band is correct and we press run. The statistic that we're actually interested in here is the sum value and your results should be displayed in your log. So after you press run you should see something similar to this where you have your max your mean your min etc and if you go to your sum this is the total population which has been calculated from the sum of every single pixel in the raster layer. So if you recall we used our identify button to find out the population within a single layer a single pixel sorry if you imagine all of those pixels have been added up for the total raster. This total of 7,976,985 people is the total of the whole raster. So we can say based on our population raster for 2020 that 7.97 million people are currently living in Sierra Leone. So that gives us a quick overview of how we can do that for our total raster but how we're interested here as we said in solving the problem of how many people live in each chieftain within the Canema district. So to do this we are also going to use a tool within the raster analysis section of QJS but I'm just going to show you how we can quickly search for a tool instead of going through the long route of clicking and finding our tool. So here instead we're going to search for zonal and statistics and this will bring up all of the toolboxes related to zonal statistics that are available in QJS and we're going to use the zonal statistics tool to calculate the total populations living within each org unit in our chieftains. So to do that we'll click on zonal statistics and the zonal statistics window will open and we need to make sure we set our parameters here correctly. So our input layer needs to be our Canema chieftains or if you're using your own country data it needs to be the district level boundaries you downloaded from DHIS2. For the raster layer we need to set it to our downloaded raster and here the output column prefix it's important here to save this as something that makes sense when you look at the attributes. So here I'm going to set it as pop underscore and this means when I come to the attribute table all of the statistics will be called pop underscore and then the statistics type. So if we go to statistics to calculate we're able to see which statistics we're going to use and we're going to use count sum and mean which are the default values but this essentially means that when we calculate this it uses the column prefix and it adds it to the statistic so in a second you'll see the statistics for pop underscore sum pop underscore count etc and for many of you there'll be a zonal statistics output for some it may simply add it onto your district's layer but here we're interested in saving this to file and we can save it in that same area so just to repeat so I'm clear to save it to a file come down to this final box under zonal statistics click the browse button and press save to file and in here we can just click on the kanema chieftains dataset we downloaded and just add to it by going population zonal statistics and this allows us to come back to this dataset at a later date once we've calculated it so press save and we'll press run as you can see we've generated a new dataset within qjs we're now able to use the identified tool to quickly look at the information for each area so as you can see the pop count pop sum and pop mean have been generated for garoma mende and the population sum which is the total population of the chieftain has been calculated as 52,420 so this is a really quick method of finding out the total population within an org unit but we can also have a look at all of these at once and to do that you simply need to right click on your new zonal statistics layer go to open attribute table and within this table you can see that there are quite a few attributes that we're actually probably not interested in and are automatically generated when the layer is downloaded from the dhs to maps up so to organize what's in the layer we need to go to the menu bar on our attribute table and click this button which has the pencil inside the blue table and this is the organized columns button if we click this we're able to change which layers are loaded within the attribute table so I will deselect all and then I'll go back and select name so we know which chieftain we're looking at and we're interested in the population count population sum population mean so I'll press okay and you can see that this has been automatically changed in our attribute table to reflect the columns that are meant to be shown so one of the tasks in this exercise will be to share your output here and you can do that later when I instruct you to do so but also something that's really useful when generating population statistics like this is that we can visualize using our areas so if we double click on our chieftains or your boundary layer we can go to graduated and within graduated we can press classify graduated we can change the value sorry so if we go to graduated change the value to pop sum change the color to whatever color you want I'm going to go for reds and if you press classify and apply we're able to quickly see which are the chieftains in our layer which have the greater population so these are the darker population the darker areas have the greater population in our district and the lower the areas the lower the population the lighter the color so we've just been able to quickly answer that question about who lives in who and how many people live in each unit so I'm going to return to the slides now and give you the opportunity to go through this exercise yourself so if everyone wants to start going through the exercise um firstly you need to examine the population secondly you need to find out the total population for your raster and this needs to be posted in the slack channel then we can use the zonal statistics tool to calculate the population and then you need to open your output and you can post on the slack channel your zonal statistics table so if you've got any questions please use the questions channel or slack or use the day two exercise free to post your answers or any queries so thank you very much I'm now going to give you 15 minutes to do this exercise and we'll reconvene at 11 20 so hopefully you'll be able to post your solutions to the exercise in the slack channel we'll be monitoring it and we'll see you again at 11 20 thank you very much hello everyone so I've seen many of you have been able to post in today's slack channel for exercise free and we've had some wonderful responses in terms of the maps and the the figures you've been able to produce it's been really good to see uh so for those that are still looking at the exercise asthma and and beyond can provide assistance so please make sure you're using the slack channel on the questions channel um the next stage of today's workshop is exercise four and in exercise four we're going to be looking at the population living around health facilities in qjs so we've just seen how useful a qjs has been as a gis tool to find the population living within our district boundaries but what happens when we're faced with a different kind of geospatial question and we're looking to find the catchment areas of specific health facilities or we're looking to plan strategic and informed interventions using as much data as possible so in this exercise we're going to cover how to use qjs to create five kilometer buffers around health facilities and then how to calculate the population living within these five kilometer buffers around the facilities so this is quite a quick but important uh method for calculating population in specific custom areas and remember that you don't have to use these five kilometer buffers but you can use any sort of distance or area that we're looking at so please as always use the slack channel for this exercise which is day two exercise four and if you have any specific questions please make sure you're you're asking these in the questions channel so we're now going to move back to our qjs and for many of you you'll be at the point where we've completed our zonal statistics and what we can do to quickly get rid of the zonal statistics is to simply untick the layer in our layers window so for all of you that have your layers loaded in the layers window or panel on the left hand side we simply need to untick which then turns off the layer and it's no longer visible in our map extent so Bjorn went into some detail about map projections in the start of today's session we're just going to touch on those map projections again now so essentially to be able to produce a buffer measured in meters or kilometers in our case we need to ensure that we're working in a projection where the inherent measurement used is meters the standard projection for the data download is wgs 1984 and the standard measurement of that projection as it is a geographic coordinate system is degrees and this is simply a different measurement but it means we're not able to have the accuracy in our local area as the equivalent measurement in meters for a degree changes depending on your latitude so areas that are closer to the poles will have a different meter measurement for a single degree but we're able to combat that in QJS by using our projected reference systems so at the start of exercise two we covered projecting and setting our standard default map projection in QJS the start of this exercise we're going to cover changing our actual data so to do this we can do it in a few different ways but a very simple and quick way is to simply click on the facilities layer and click on the population layer those are the two layers we'll be projecting quickly now so we want to click on the damer facilities layer or if you have your own facilities click on it need to right click go down to export and press save features as within this window you'll be met with the format so I advise you keep it as a ge adjacent as that's what we've been using previously but under file name click and you'll be hopefully opened within your DHIS to training folder but if not navigate your DHIS to underscore training folder and we can quickly save our facilities as facilities underscore projected or something similar damer facilities underscore UTM 29 north so you have your actual projection and if we click save we're then able to set the CRS which is the coordinate reference system of our data so here you can either click on the select CRS button here or you can go to the drag down which will have your project CRS for the and for those that were able to change your project CRS click this button and it will automatically re-project your data when it is being saved for those that still need to search for your cue for your coordinate reference system press the coordinate reference system button here and this will bring up the system selector in here you can search for your coordinate system in the same way we did in the beginning of exercise two here I'm just going to press okay and as you can see we have a response saying the layer has been exported successfully so if we zoomed to our new layer we can see that we actually have some more points that have been loaded into QJS and we can quickly change the symbology of these by going to our symbology by double clicking going to the symbology tab and changing to top of the hospital but we also need to make sure now we've projected our population data as well because the zonal statistics tool will only work if both data sets are in the same coordinate reference system so we quickly change the coordinate reference system for our population data by going to export save as saving this as the same name so if you click it should populate the file name so same name but we'll add a suffix of utm 29 north and press save for those that are using a different population raster you can save it in the same way and add the information on the CRS at the end just as I have done and under CRS here I'm going to project CRS and that quickly projects our CRS in exactly the same way so as you can see now the layer has been exported but it's now currently in black again so I'm quickly going to change the symbology again to match what we've done previously classify I'm going to press apply and okay so that is the first step in exercise four which looks at creating the buffers so we've projected our data and we're now ready to create our five kilometer buffers around our facilities so to do that we can open our processing toolbox again and I'll just quickly show you how to navigate to that again we go to processing and toolbox and within here we want to delete any previous search that was in there so senile statistics was in there from the last exercise and we can just type buffer and if we click on buffer it brings up the buffer window and I'll just quickly run through the parameters required to create a buffer so under the input layer we need to make sure that our projected facilities layer is in there so for me this is damer underscore facilities underscore utm 29 north for distance as you can see my distance is now set to meters if you have not correctly projected your data this will be shown in degrees and I can quickly show you what that will look like here as you can see we'll have degrees and it will have a an error box so we're looking to make sure our data is in a projected coordinate system and under distance if we select and enter 5000 this is the equivalent of five kilometers of meters we can keep all of these other parameters so segments cap style join style and might eliminate the same and we can press under buffered to save to file and within here if we just click the damer facilities again and simply add buffer five kilometers to the end of the data set and press save this will buffer the data set and if I press run we're able to see the buffers produced in QJS so you've noticed that this produces a new data set within QJS and it's a bit difficult to see our facilities now and the underlying data so I'm going to quickly show you how we can style our facilities data and be able to see both our facilities style our buffer data rather so we can see our facilities and the population data below it so quickly we're going to real reorder our data so to do this take your mouse pointer click on the facilities data and simply drag it beneath your facilities and by doing that that brings the projected facilities to the top of the layer list and you're able to see them the next step is to change the style of the buffers so to do this as we have done with many other data layers today you simply double click go to symbology which opens a symbology settings for our data set and here we're not interested in changing the color we're only interested in changing the opacity and that is how see-through our data is how transparent our data is and to do this we can either drag the setting here or we can use numbers in the setting here so I'm interested in setting my opacity to 30 percent which will allow me when set to look through the data and see both the facilities and the population data as before so we've now set our buffers and this has been quite quick and it shows us in a straight line all of the area that is five kilometers from our facilities data so this is quite useful in understanding like what the exact extent is around our facilities but the actual problem statement here that we're interested in solving is what is the population within this area and for many of you that are now quite used to QJS or can guess what's coming next we're going to use the zonal statistics tool again to calculate the population within these generated custom buffers we've just created so to do this again we're going to go to the processing toolbox so if you press processing and go to toolbox we can then enter zonal for zonal statistics and in here for our input layer please make sure you enter your buffer and under your raster layer you will now see two different raster layer entries we need to make sure we use the correct layer which is projected to the same projection as here so if we look at our input layer we can see the EPSG code is 32629 so we need to make sure our layer here matches the EPSG code for 32629 so we'll use our projected dataset and as we have done previously we're going to change our output column prefix to pop we're going to keep our statistics calculate the same and we're going to save our layer so if we're going to save the file we're able now to set the file name for the zonal statistics i'm just going to type in damer facilities underscore population buffer and press save so i'm now going to press run and as you can see once again this has produced a new output within that area so if you want to start this again i'll just show you quickly how to do it so we'll just double click change our opacity press apply and okay so that's quickly shown us how we can calculate the population and we can now quickly visualize this population by either using the identify tool clicking on the zonal statistics output making sure it's the active layer and clicking on one of the buffers which will produce a red line which means that that buffer has been selected and within the output window you'll see that you've clicked on your population buffer you'll see the name of the facility which is kappan debu and here you can see the population sum for this five kilometer area is 6187 people so this is the total people that are in this five kilometer straight line catchment area and would could be possibly served by this health facility and to just explain what the population count and population mean are the population count is the number of individual population pixels that make up the population sum in this buffer and the mean is the average population value in each of the pixels so each of these one hundred and thirty one pixels so now we can just quickly open the attribute table to see this once again we can organize our table by going to the organized table columns deselecting all pressing name pop count pop sum pop mean pressing okay as you can see these are the population sums within those five kilometer areas so we're now going to return to our presentation and I'll give you an opportunity now to change those coordinate reference system of the two datasets that is required and then you can create a five kilometer buffer and then you can style the buffers and use zonal statistics to calculate the number of people living within the buffers you need to make sure that you're posting your attribute table and a screenshot the buffers to the slack channel as always if you have any questions please make sure you enter them in the slack channel on day two exercise four or you ask them within the questions channel so thank you very much I'm going to give you 20 minutes to do this so we'll come back at 12 and then hopefully we'll be able to go through exercise five which will cover population living within a walking or driving distance around the facilities as well so if you've got any questions again make sure you're asking them in the slack and we look forward to seeing your responses to the exercise thank you very much thanks a lot Ollie I just changed a little bit on the time schedule because I've I've said that only the first three hours are compulsory so that's the thing you need to follow to get the certificate so you will have 15 minutes for the exercise and then we spend the last five minutes before one o'clock our time just to wrap up but then we have a full hour afterwards so those who also want to do the last five exercise with the driving walking distance which is quite fun to do they should just stay on the on the on the video and and we will do that but right now I also share the quote for you to and we will also very soon share the form that where you should put the quote in the announcement channel so Ollie could you just move to the next slide there we are on another slide so then I can share my screen continue share and this top share on that one share screen is you see the quote here or no hi Bill and I can confirm we can see the quote okay good well you thank you yeah there it is add the present here yeah it looks like I was sharing the other screen but here is the quote so we'll just leave it there for a minute because it's a little a little bit long you can't use an old map to explore a new world which was shared by one of you yesterday so and then in the announcements channel I'll check here you should see this attendance form already so please fill it out this out to get the certificates and then we go back to the exercise and we'll meet again here five to two one okay so I'll wrap things up uh for the first part of this webinar just remember we have one more hour and that is optional for you but if you have if you have the time I would really recommend to join the last hour as well we will start with a five minutes break before we do that and in the last hour we will take exercise five with the driving distance and we will also take questions from you so we all hope you will will still be with us but for this part and also are leaving us I just want to say thank you for your great effort I think you have been doing good it's been very nice to follow the slack channels and get some feedback and you have been progressing very nicely and I think you can be really proud of yourself because QEIS as you have seen is a very complex application and we just learned a tiny part of it and but I hope you will see the power in the tool and that you also might use it for for other tasks and we also hope you appreciate that we have tried to make the maps application in DHS too easier to use it comes to a cost because we need to make some decisions for you so for example creating a buffer in in the maps app is very easy just to write the distance and you will get the buffers but then you can't so you don't need to deal with map projections and so on but that also comes with a cost that is some things you can't do in the maps app you need to move to the QEIS or another GIS application we have also learned briefly about the difference between vector and raster data from and we have used raster data from worldpop and big thanks for for Andy for joining us and giving a great presentation I would also really like to thank Oli for having all these sessions today and also for pre-recording the videos I think hope this has been useful and and remember you can always follow these afterwards they will will be available online and we will also keep this slack channel open for a week more at least so you can still log in and check what's there Oli would you like to mention a little bit about further training possibilities from grid 3 yes of course so as you can see on the screen that Bjorns sharing in the slides and hopefully in one of the slack channels there'll be a link to the GIS training sign up for grid 3 so grid 3 I'll just provide a quick overview grid 3 is a sustainable development project run between different organisations so CISIN at Columbia University worldpop at the University of Southampton FlowMinder and UNFBA have all teamed together to produce different geospatial data solutions products and capacity strengthening on GIS so as part of this we are going to be running regular GIS training and if you use the sign-up sheet you'll be able to sign up with your email to find out more information on this the links in the preview of the form will also provide you with more details on grid 3 and the capacity strengthening activities we conduct so if you are willing to find out more and want to increase your skills using GIS for a variety of areas please feel free to fill out the form and we'll be in touch with further details later on after the sessions that you can follow up on thanks beyond back to you thank you and I can also mention that there might be a time after the pandemic when we can actually do training on site again me and Austin did the successful GIS academy then we called it a GIS academy in in Delhi one and a half year ago and with participants from India Nepal and Bhutan and that is also a possibility for later on maybe we will even do it together with the people from from grid 3 so you can contact your local his group and ask for this and and we will see what we can do but yeah it will still be some time from now but please show your your interest so that will end this this session I suggest that we take a break five just a five minute break because we want to finish early before the weekend as well but five minute break and then we'll be back and then we will go to say the exercise five and start with that one so in five minutes we'll meet again thank you hello everyone so just want to say a big thank you to Bjorn for closing the first section of today's training workshop for those still on the call and wanting to cover exercise five with us I'll be providing the demonstration for the exercise and then we'll give you some time to run the exercise yourselves and ask any questions you would like in the slack channel as we've been doing so I'm going to quickly share my screen again so everyone should now be viewing my screen so just to cover where we've got to so far so for everyone on the call we've been able to use QGIS to find the population living within our org unit boundaries and living within five kilometers of our health facilities by using buffers and QGIS so as many of you know as health practitioners and experts in the health sector you'll understand how important it is to know exactly where people are for different planning monitoring and intervention exercises and just having the kind of the correct and up-to-date population data and using it to solve different questions is really useful when it comes to applying this and making informed decisions and that's what we're really trying to promote through the whole workshop so from day one to day two here where we're using the population data and some of the data taken from DHIS2 maps and using it in QGIS and the great thing about GIS is you can solve all sorts of different questions and we're going to be moving on to exercise five here which is looking at solving a question around the population living within driving and walking distance of our facilities so this is quite a complex question that could take quite a while to answer however as I was just saying QGIS is an excellent tool and the way QGIS is developed it's developed by a range of different developers who can all contribute to the development of the software and this means that you'll get new functionalities within QGIS to solve different questions and part of this means that we have tools like the open route service so exercise five will focus at finding the population living within driving and walking distance of facilities using QGIS we'll cover installing and setting up the open route service ORS tools plugin in QGIS and calculating the driving and walking times using ORS tools and then finally calculate the population living within driving and walking distance of each facility so if you have any questions for this exercise please use the questions channel in Slack and use the Slack channel day two exercise five so I'm now going to move back to my QGIS so I'll make it full screen so hopefully many of you have been able to complete exercise four and you've now worked out the population within your buffers we're now looking at trying to find the population living within our driving and walking distances so this is slightly different compared to what we were doing before with our five kilometer buffers these are simply five kilometers in a straight line from our facilities to the perimeter of our buffers we're now going to incorporate more understanding that well actually five kilometers may look like this on the ground but distances and travel distance to facilities may look different depending on your mode of transport i.e. walking or driving so we can actually model that using QGIS and get a greater understanding of well how many people actually live within 30 minutes or 60 minutes of my facility how many people would I be able to serve based on the fact that they drive or they walk to my facility so the first step of this is to install this ORS tools plugin and I'll quickly do that so the first step we need to do is go to plugins manage and install plugins and this will bring up the plugins window and this shows you all of the available plugins that people have developed for QGIS and as you can see there are quite literally hundreds and each of these will have a specific purpose and functionality that allows you to solve a different geospatial problem so we're interested in going to all and in the search bar typing for ORS tools so if you click on ORS tools that will give you some information about the tool and it says here ORS tools provides access to most of the functions of openrouteservice.org and it's based on OpenStreetMap and OpenStreetMap is a volunteer geographic project where the open source community are mapping every feature that is based on the ground so you have a quite up-to-date range of roads, buildings, features such as water and different infrastructure that builds the the map that puts together OpenStreetMap so the tool actually uses different routing and isochrones and matrix calculations to solve different routing problems so to install here I won't have this option but when you click on ORS tools you'll have an install button in your right hand side of the window here and I recommend you press install and you let QJS install it may take a few minutes it'll let you know once it's completed and then you can close the window and that's the tool in installed on your QJS so then we need to make sure firstly that we have it set up correctly to run so to do that you can find the tool in web and then it will be based under ORS tools so you can learn more about the tool by going to about but we're interested in looking at the provider settings and changing the settings for our tool so the most important setting here is our API key and this needs to be entered before we can use the tool and provide outputs so we go to the API key and we enter this long alpha numeric code and this code has been supplied in the Slack channel and it's also supplied on the slides so simply copy and paste the code from the Slack channel into here and we should be able to set up open root service to run correctly so we simply make sure we've entered our API key and our base URL and press OK so we're now ready to use the ORS tool to produce our driving and walking distance isochrones so I'll just untick some of the layers so we're back to our starting point which is our population our facilities and our district boundaries so to use the tool we're going to go to web ORS tools and again follow through to ORS tools and this brings up the window for ORS tools so we're interested here in looking at producing isochrones which are areas of equal travel time or travel distance to a particular facility so to do this you need to go from ORS tools then go to batch jobs which is the second button here on our window which gives all of the different calculations that ORS tools is able to provide so we're interested as I said in isochrones and we're going to produce isochrones from our own dataset so we need to select isochrones from layer so we select this and this brings up the isochrones from layer window so our provider needs to be open root service and we've just set up our API key our input point layer needs to be our facilities and this can be either the facilities layer as long as you then use the same coordinate reference system for the population later on so I'm going to click the damer facilities UTM 29 North and here we need to select an input layer ID field so in the slides this will tell you that we should select name and this will ensure that all of the datasets that are produced by the isochrones from layer tool in ORS tools are based on the name of each health facility that we're looking at driving mode here as you can see or travel mode rather as you can see you can have driving and this can focus on whether you're driving a car or a different vehicle you can have a look at cycling or you can have a look at foot or wheelchair access so we're interested in both footwalking and driving car so quickly I'm going to do this for walking to show you an example so if we click on footwalking dimension can either be the time it takes to get to the facility or the distance to the facility and this is along the road surface of the networks provided by open street map so distance here will not be a simple circular buffer but it will be actually a it will be a polygon based on the distance and you'll see this when we enter our time and the output is generated later on so for comma separated ranges here this is our different distances or all times from our area so as I said before we'll look at 30 minutes so I enter 30 minutes and these need to be comma separated so here I'm going to enter 30 and then separate these with a comma and then finally type 60 and so this covers a polygon generated for areas within 30 minutes of our health facilities and then 60 minutes of our health facilities and we're going to save this by going to our isochrones output area clicking on the browse button and going save to file and I'm going to save this as damer facilities walking time and press save I'm now going to press run and as you can see the tool runs quite quickly and that's because we have relatively few facilities to use for those of you that have downloaded facility layers that have multiple or multiple or lots of facilities in the layer this will take considerable amount of time or you'll get a query limit throwback and my advice would be to return to the isochrones from layer and try and use a smaller data set to run the tool so if we now close our rs tools we can now zoom in to our data sets so quickly we can see the 30 minute the areas that are within 30 minutes of our facility and the areas that are within a 60 minute walking time of our facility and as you can see the areas within 30 minutes are relatively close to our facility and then the areas that extend to 60 minutes extend well beyond the 30 minutes and this will change depending on the underlying features of the open source map below so I'm going to show you what this is quickly built off by going and adding open street map you do not have to do this but this just gives you a quick idea of the layers that will help building this so we have our 30 minutes that extend from the health service here and our 60 minutes extending here so I'll turn off open street map so as we've seen that was very quick and we're able to produce our walking time area but say we're interested in exactly the same time but for driving thing to do that we return to our rs tools go to batch jobs and isochrone from layer enter our point layer enter our field id and I'm going to use name and instead here I'm going to use driving car change the comma comma separated values to 30 and 60 and set our output layer to damer facilities driving time and press okay well save then press run if I now zoom out you can see that there is a considerable increase in the area areas covered by the 30 minutes or 60 minute travel times to the facilities so here we can actually see that 60 minutes of some of these facilities extend all the way out of kanema district and into other areas of Sierra Leone so this just gives you a quick indication of well how far can people actually travel to my facility within 60 minutes or 30 minutes and as you can see if we swap by unticking 30 or 60 we can see that 30 minutes the area is relatively smaller but we can also understand the population therefore that are served by these facilities within a 30 minute radius so that answers the question of well where are these areas of walking and driving times around our facilities but we also have the second component of the question which is well how many people live within these areas and to do this again we're going to use the zonal statistics tool and you're going to be very familiar in the use of the tool by the end of today so to use the tool we're going to go to processing toolbox and here I've previously searched for zonal so I'm going to open zonal statistics I'm going to open my driving time and as you can see I'm using EPS G4326 so my raster layer also needs to match that so I select my 4326 layer here I can set my output here to pop underscore and then I'll create another file called damer facilities driving population and I'll just press save if I run this you can see that the whole areas change color and if we open our tribute table we're able to open and see well which areas within 60 minutes or 30 minutes for each whole facility contain the population so just a word on the actual output of the rs tools it does come with an inbuilt population account this however is not the same data that we're using we're using well pop data whereas this uses a different input so to make accurate comparisons against the data we calculated in exercise three and exercise four we need to repeat the zonal statistic stage to calculate these population sums and just quickly we can see the areas within 30 and 60 minutes for each health facility or health center and we've actually generated the population within each area so therefore we've been able to answer that question of how do we find out how many people live within a driving time of our health facility so if you go back to the zonal statistics you'd be able to run the same for our walking time and I advise that you go back you run the tools in different ways to look at different driving or walking times use different ranges such as 10 minutes 15 minutes and try and work out how best this tool could answer some of the questions that you'd like to to answer so I'm going to return to the slides and return to the exercise page which will give you all of the information needed for answering this exercise so if you have any questions please ask them in the slack uh under day two exercise five or in the question slack and you can post your responses and your answers to the exercise in day two exercise five so thank you very much everyone it's been an absolute pleasure to work with you today and it's been great to see all of your enthusiasm and your your great work in the slack channels and I wish you a great remainder of your day and I hope that you've been able to get as much out of this dh is to GIS workshop as possible so thank you very much and I'm going to return over to Bjorn to finish off the session so thanks a lot Olli you can just keep that slide so I would like to give you 15 minutes on the exercise it might not be enough but then you can continue on your own so just also so we have time for some questions before we end today so you can start on the exercise now but also at the same time if you have any questions related to this exercise or more general questions for us for the everything we have covered then please ask them in the question channels on slack and then we will have a session at the end and go through them together and I'm also very happy to see that most of you are still around and and are following us so so thanks for keeping up okay hello again this time for this is the for the last time today we have a short question section it won't be very long because there are not that many questions but I got one and that was I can share them it's like here as well and that is from Maria M that know that you have learned to use QES and you are able to to create all this new layer the catchment areas the what we call the isochrones which is showing the travel time you might want to import this data back into dhs too and also for example the the population data and there are some ways you can do it we don't have time now to cover it in detail but there are ways of you to import for example the population data back into dhs too it is a little bit risky and you need to know what you are doing especially if this population data is used as like what we call as a denominator for your indicators but there are possibilities and we might cover that in in in a separate workshop also we plan what we plan to do is I showed you yesterday how you can aggregate the population data directly within dhs too and we also plan there is an app called the import export app where we plan to build in support to import this population data directly so that will probably support the population within your org unit boundaries driving distance is quite complex I will probably never supporting that within dhs too and then you need to use QES also I will show you quickly another way to import your data and we have an support for what we call external layers so down here below the default base maps you will also see what we call an external base maps and these are layers that you can configure and add yourself and also here in the overlays we have a something we call the label overlay which is also provided in the same way and this is done in the maintenance app so if we search for maintenance here there is a lot of things you can configure that's the real power with dhs too that everything can be configured and then at the end here we have an external map layer support and if we just list the layers this is just on our demo server this is just an example that we provide a dark base map if you have some really detailed aerial imagery this for example is for Dar es Salaam or maybe you have your own country base map provided you can add that here the only limitation here currently is that this one is only supporting roster layers but there is a way also that you can you can turn vector data into roster data but then you need your own mapping server to be able to serve these layers so I would say as an answer to the question it's a bit complex to get this deep data back into dhs too but if this is something you would like it's nice if you could create like a feature request as you showed you yesterday and then describe your your need and we will see what we can support in the future do you have anything to follow up Oli no nothing on my side I've been responding to a lot of your questions in the slack channel already but yet just extending that if you have any questions beyond today the slack channel will be open and I'll be dipping in and out to provide any questions or assistance with the exercises as required so thank you very much thanks and I'll also do the same we'll keep an eye on the on the slack today and in the beginning of next week so you can continue to post your questions there we've also been thinking about I see there are some of you have not yet been able to import the coordinate data for your org units and this is a really a prerequisite for using the maps up so I will actually share my email so and I think you should just contact me directly if you would like to have training in how to do this because that's a crucial step for you to to move forward so I'll share it in the questions channel my email so it's just Bjorn dsis2.org so please contact me if if you need help on this issue it's really also the the way you import it I have to be sorry it's a bit still a bit complex but since it's something you do only once and then it's there for a long time we have not yet prioritized to to make it easier so there is still this import process but it's still working and and you should be able to to follow it so I think we will end there I'll check in the other channels so there is a question here if it's possible to make a pie I guess the pie is like the donor chart we did in yesterday I think this is possible also in QDS but I've not done it so that's what you need to to check out just to mention QDS there is a lot of material available online so I would just recommend you to google for tutorials there are also some books available I saw some of you mentioned ArcGIS and you should be able to do the same with ArcGIS and I know I guess some of you also have free licenses so one of the reasons we not use it is that it's a very costly software and also we try to stick to open source solutions so that's why we are using QGIS so but you should be able to do the same tutorial and there was also a plugin we found that was calculating driving distance but it seems to cost extra extra money okay see there is one typing but I think I won't spend everyone's time here we will answer your questions directly and come back to you in the Slack channel so thank you for following enjoy your weekend and be proud of yourself for what you have achieved today thank you