 Okay, welcome to this session on the Maps app. I've called it extending the HS2 maps and it's basically about how you can make the functionality, add to the functionality of the application. So you can, how you can import and do analysis on data, which is not your own, the HS2 data, and also how you can export data from the Maps app and then import it into other application for further analysis. We'll have about an hour. I think we have plenty of time. A little bit about myself. I've not worked on the team here for almost six years this autumn. I'm the core developer of the Maps application. It's basically the only application I work on. I also made Maps my sort of my profession. So I have a master degree in Maps, sorry, in GIS. And I also worked with several other companies to create Maps application. I really enjoyed working on the HS2 and also to see the impact the maps you create can have. So I'm delighted to be here with you. As I said, we have plenty of time. So please use the community of practice. We will share the link. ULS will do it to the page. So I also encourage you to ask questions you might have about the Maps application, even if it's not directly related to the topic today. So please feel free to ask questions and we will take around at the end. So what we will cover today is an exciting new feature in 236, which is that you can combine data from something called Google Earth Engine. It can, for example, be population data. You can combine this data with your own organization units or facilities in your own distance, all from within the Maps application. Then I will show you how easy you can download your data from the Maps application and then import and visualize the same data in a tool called QGIS. And lastly, we will see how you can import external layers into DHS2 Maps. So you can have additional layers in addition to what's already there. And you will even learn how to make your background this painted watercolor look, hopefully not useful. You will use another Maps for the maps you produce with the health data, but it's a good as an example of how you can add your own base map to the tool. So we'll start with the Google Earth Engine layers. In the previous versions, we actually called these Google Earth Engine layers, but it's a quite technical term that end users don't really need to know about. So now we name these layers by their topic. So these are currently the Google Earth Engine layers we have. So we have population, elevation, precipitation, temperature and land cover. And today we have time to go through all of them and show and I can show you how they can be used. So what is Google Earth Engine? It's like come for good project that Google is having. So they are providing their infrastructure in the cloud and not only the capacity to store a lot of data, but also the computing power. So you can run analysis directly on this data and even combine it with your own data. And this is for free. So all the DHS2 users can use it for free. It's for free for non-commercial purposes. The only thing you need to do is to sign up for a Google Earth Engine account. Please note the end user don't need to do it. Not every user of the map application. It only needs to be done one time per DHS2 implementation or DHS2 instance. So it's typically a task for the DHS2 implementer to do this. It might be already done. We have these layers around for some versions. So you could already check today if it's possible to add, for example, the elevation layer. So please check. And if you are troubled with this process, please ask us for assistance. But it should be, and it's only a task you need to do once and then it will work by itself. This is like a high level sketch of how it works. It's a lot of things going on under the hood here. But this is, I think, what you need to know now. So basically we have these organizations which upload their data to the Google Earth Engine. And then we have DHS2. And then in this version, we actually send, we can pass the organization units, these areas here, boundaries to the Google Earth Engine. And then we can combine that with, for example, the data from WorldPop. So in return, we get two things. We get a map that shows the population density. But we also get a table where we have aggregated or counted the population within each organization units you pass in. So I will demo this in a second. I'll start with maybe the easiest layer, which is the elevation layer, which will tell you the height about sea level for all of your area or country. So I will switch to the maps app. So to add this elevation layer, you simply click on Add Layer and then on Elevation. And here you have the different way you could aggregate and the elevation data. So we have tried here to make sensible defaults for each data set. So we think if you pass in some organization units, it could be nice to see the minimum, maximum and the mean or average elevation for that district. So this is by default. But you have, in addition, you can select more ways to aggregate this data. This is a new tab here for this layer. So now you can select the organization units where you want to aggregate the data. So by default, we select the first, the second level below the national level. So we'll just go with the default and see, and to calculate the elevation for all the districts in Sierra Leone. And then style, we will come back to this one. So we skip, go with the default for now. And then we do Add Layer. You will see that it takes a little bit time, more time maybe than before, because we are running these calculations. And this is not done locally. We now send the organization units areas to the Google servers and then it's returned. But it should go fairly quick. And then you can now click on this individual. And this is then calculated when the data was returned. So you can see in this script, you can see just by looking at the map, you will see by the legend that it's a fairly low elevation in this part of the country. And then in the Northeast, it's a higher elevation. We probably also have the highest peak in Sierra Leone. And if you click on it, you will see the values calculated. So within this district, the minimum elevation is 72 meters, and then the highest is 1,933. So all of the country, if I check this one as well, all of the country is below 2,000 meters. You can also right click anywhere on this map to get the value at that location if I zoom in a bit, and then I right click here. And then you will find depending on the layer you have selected, but this is elevation, you can see show elevation. And then you will get the elevation at that exact location. And then you can check over here where it looks like it's higher altitude, show elevation. And there it's 1,600, almost 700 meters. I checked. I found that the highest mountain in Sierra Leone, I didn't know, is the Mount Bintu Mani, and which is 1,945 meters above sea level. The elevation we got, the max elevation was not exactly the same, it was 12 meters below, so 1,933. And you will get these small differences. The main reason for this is that we have elevation data for all over the world by 30 by 30 meters. So for every 30 meter, we have one value. And if the peak, for example, is like a cone like this, that elevation value we have for 30 by 30 meter is a mean value. So there might be small differences. If you look up on Wikipedia, what is the really the highest point? It can be a few meters difference. I'll go back to the app and show one example. So one use of this elevation data could be to detect zones where there is a higher risk for vector-borne diseases or malaria, because maybe the mosquitoes live under a certain altitude. For Sierra Leone, it's not a good example, but it's where I have the data, because the highest point is, or everything is below 2,000 meters. So all of the country is there is a high risk for malaria disease. But I'll still use it as an example, but just so you know that this is only for demoing. So I will create a new, add the layer again, and then I will go to the style tab. So for example, let's say that below 400 meters, there's a high malaria risk. So we add that to the mean value. And then we could say that between 4 and 800 is a medium risk, and then above 800 it will be a low risk. And then we also reduce the number of steps here to make it easier to interpret. So we reduce it all the way to three. So we not only have these three classes before 400, 4 to 800, and above 800. And then also because higher elevation means higher risk, lower elevation means higher risk, we will switch the color legend. So have a dark color. We use red at the bottom and then it gets brighter where it's less risk. And then we add a layer. So this we can do for all the Google Earth engine layers. You can easily change the legend so it sort of fits the country where we are. And again for demo purpose, the high risk areas would then be the one with the dark red, and then it's this zone here, brighter will have medium risk. And then there is only a few areas about 800 meters. So if I go back here, the true maps for Sierra Leone when it comes to malaria risk looks like this already. So this is only for demoing. We made, we made this map we had me and Austin had the GIS Academy one and a half year ago in in Delhi, where we made these more proper malaria map for countries where it actually actually counts. So that in for Bhutan the malaria risk is below high risk is below 1700 meters. So here we have colorized these areas. And so this is not done, you cannot do this in the DHS2 maps but you can use it in QES, which we will look at later. And this is also an example of what you can do also with this elevation data is that you can colorize it after the altitude. So you can have this color scale that sort of mimics the terrain of it. So you have the snow cap mountains and the green arrow valleys. So you can create more like a topography map where that is important. So this is not something we'll cover now but we could cover that in a separate academy. So then I'll move on to the population layers new in this version in 236 is that we have two population layers. So previously we only had the one showing total population. And we now have added one that is divided into age groups. So these are in five years intervals and also cover both gender so you can have divided by sex. And this was especially requested as it could be useful for like a COVID vaccination. So as where you don't have good census data, you could have this as an estimate for where the whole older population or the number of people in the different age groups. So I will demo these layers now. Right on your map. And then add a population layer. Again, we have selected the aggregation methods we think are the most useful. So we have selected some which will calculate a total number of people living, for example, in each of your district or organization units. And then we also have the mean and to understand the meaning of mean or average, you need to look at the unit below. And that is people per hectare. So the resolution of this data set is by 100 by 100 meters. So for every 100 and 100 meter, there is an estimate of the number of people living there. And so the mean will be the mean people per hectare. And that will show you something about the population density in your districts. So basically we have data back to 2000 so a 20 years period, but now I will go for the for the latest. And select for organization units again I will go with the default the district level, and I will still have the default style, and then add this layer. So again, what's happening now is that we are uploading your organization unit boundaries only, not of your not the rest of your data to the Google servers, and then it will return this map. So by looking at the map you can see the darker red, the more people live. And if you click on this, you can see that in this district, there's an estimated population from for 270,000 people living. And the mean number of people, you can see this is the not very densely populated area, it's only 0.33 per hectare. So the data these data's are for well pop. I really recommend you to go on the website and read how they are created and their benefits and shortcomings there is also on our YouTube channel. This is a presentation from the director at well pop from a seminar back in April we had that you where we present the data. So please have a look at that. What I recommend to combine these analysis population data with is to open the data table. So if you click on the more actions button and then select show data table. We have a table view of the same data. And these you can sort so by clicking on the headers here you can sort by the by the population numbers. So now it's sorted in ascending order, clicking once more will sort it so we have the highest population at the top. And here you can also see that you can do the same with the mean population per hectare. So, and this is also a nice little new feature in 236. We added a hover effect so if you're moving the mouse over the table, you will, the area will be highlighted so it's more easy to identify the places on the map. We will see that the western area this district has the highest population and you can also see that from the map, where we have the pre tone of the capital so 1445,000 live there. Another nice feature here is that you can, when you have this view you don't have to go back, if you would like to see within this district. What's the population counts there you can just right click and then select drill down one level this is the same feature we had for for thematic layers already. So that will then load all the levels below all the chiefdoms in this district. And again, this table will automatically updated to find the area with the highest population, you can again, then click on these, or if you want to find the highest population density. We can, we can click on the last column and, and then over here so this district has the highest population density. So further down, we will reach the facility level, and then, as these are not these only points on our maps, these are the location of the health facilities. The way we can sort of aggregate data for each district is that we can draw a buffer around it, and then calculate the population living within this buffer. So this buffer here is for a five kilometers. So within five kilometers if I saw this, this has the highest population of 13,000. And then this one has the second highest. And so this, you can change this value. So if I edit the layer. And then you will see 5000 here, you can we can change this reduce this to 3000. And then click update, and you will have a little bit more space in between them but then at the same time you won't cover all the areas in your district. But you can try different values here and see the results. So this is the total population layer. I will then move on to the population by age and gender. So that is the the second layer here. Let's add it. And here you will find another selection of groups. So let's now see if we are going to check the people we would like to to vaccinate for example and we will start with the elderly population. So let's say we would like to have an estimate of the population above 70 years in our districts. So then we select 70 all these three to have 70 and above. And then now I've selected the men and then we have the women. So let's do the same. So we have these six groups that we have selected together. And then we have the same aggregation method so it's especially we can skip mean for this, we only want to have the totals. So that is layer. You will see that the map gets very bright, because in Sierra Leone, it's a very few people, compared to the total population that is about 70 years. It may be if you switch the base map to dark one. These places where these people live. It's more easy to see what we could also do is to change the legend. So instead of having this from zero to 10, which might work for the whole population, we could see say that this goes only one zero to one. And then the legend will be adjusted for the same number of steps. So we select update, and it will run again. It's a little bit more easy to see where these people are. So now it looks a little bit darker at least. So we have here. So if you click on the district again. We can bind all these groups together. So this shows that in the district of port loop the population about 70 years is estimated to be 10,787 persons. So we can drill down one level. And we know how the different districts, we can open the attribute table. And then sort by the population. And we see that we have most in this district and the least only 358 persons in this district. And again, we can go all the way to facility level and and get the numbers around living around each health facility. So that is the population layer. I think this is probably the one you will use the most and it's the most useful. But I'll also demo the the others that we have added. So it was a quick question. I don't know if you wanted to wait for questions till the end. Now that's fine. Just a quick question about the years that are available for population data in particular. Well that are those fixed with the DHS to version or will they be updated when new new data is available. So, if you have an earlier version of DHS to the we don't have the same population layers that we have in the last version. So there there is limited population data I'm afraid. So, and we don't plan to back forth that. But if you if you are on the latest version or upper to 3236, you will have fairly recent data or from last year for all over the world. Yeah, the question the question was actually about if you're using 236 in 2022 will you see 2021 data. Yeah, that is that yeah that's a good thing with this setup is that well pop will then upload their new data, and it will automatically be available on your instances so yes, yes it's the, as long as well pop is is still alive and updating their data they will also be available on DHS to Sorry for interrupting. No problem. So land cover. So land cover will show you the land use or all the vegetation of your country or units. So we will add it. Here is the land cover. So here you can, you can select how you want the data to be aggregated, either percentage of the total area of of your own units, or you can select in hectares or acres. So, but now I will go with the percentage and then period. This is the latest period available to 2019 organization units still go with the districts. So you can official Land cover classification that is often used. So you will now have the response for Sierra Leone. And then if you click on on these you will actually have all of them listed, which are present in your odd unit, and we have sorted them after the highest value. So you will see in this district, or we can let's select another one, we will go for the western area. You can see that almost 40% of this area is considered to be Savannah, and then also 21% of woody savannas. And then the next category is 30%, which is considered to be urban or build up areas. And that's before for wetlands. So this can be useful. I'm not sure how useful it's regarded related to health issues but can be good to to get some data for for each of your districts. Again, if you are you can see the legend here but they can be a little bit difficult to maybe get the correct color so you can right click and say show land cover. And it will tell you what's at that area where you click. That's the land cover one. Let's move on to temperature and rainfall precipitation. So I'm not sure how useful this is that we might need some extra work on this but I will show you how it works right now. So the aggregation methods we suggest is to have the minimum maximum and mean precipitation for for your audience. And this needs to be selected for a period. And this is collect this data is available in five days periods. And this is an example of how things are automatically updated. We don't have data for it's not like a weather forecast or or it's not totally up to date so this one is updated once a month and then they will add the full month of data. So the largest data available is for the end of May. So we can select this week. Again we go with the default levels. So there are other data sets and some data sets are even like updated on an hourly basis so we might try to have a look and see if we can find more up to date data but we also need to know if I know you can find this data in other other sources so we need a feedback of your requirements as well. So if I open the data table we will see that the go to the mean rainfall. You will see that we have the highest rainfall so this is like the mean rainfall within these five days period and this is the maximum and minimum within the odd unit itself. So it shows that it's more has been more rain in the south than than in the north. The last one I would show is the temperature layer. This has a little bit limitation because this is collected by using satellites and this one is struggling when there is a is a permanent cloud cover of over the area so during the rainy season for example it can be hard to to get the data. So I'll just show an example. This one is newer. This is more frequently updated. So these are in eight days period but you can see it's all the way up to the 18th of June last week. And then if we add a layer you will soon see the challenges with this layer because it's the rainy season now in the African Muslim in Sierra Leone. So it's big areas where we don't have any data. Again we need to look closer to see if we can find some alternatives if these data sets are useful for you to have on the platform. But just to show you an example if you move outside the the rainy season so we go back to January and then update the map you should have more coverage of your country. So we feel right click show temperature you will see that in this week January to January 9 the average temperature with almost 32 degrees during daytime. So that's the Google Earth Engine layers that we currently have just so you know that this is just a tiny tiny bit of what's available on Google Earth Engine. We might also we have some plans that that we would like to make a way that you can configure and add your own layers but that will also be a higher technical barrier for for your users. So if you know of some data set you please have a look on the link here and and if there are some data sets you would like us to add then then please tell us and and we can consider them to add them directly to the platform. Okay, the next topic is how you can download the data. So if if the maps app is not having enough capabilities. We have tried to cover most of your needs but there might be some specific needs that that you have. And then instead of of making the maps application super complex to and also in the matter that we have limited development resources. We have made it easier for you to download the data and then import it into other applications where you can you can do your analysis. So the four layers where we currently support download our thematic events facilities and boundaries. And I'll just show you how easy we have done made this for you. So if I go back to the maps app, I'll create a new and then add a boundary layer. I'll select the district level. Again, add it. So here you have the boundaries and if you want to download these boundaries you click on this button and select download data. So this data will be downloaded in a format called Jason which is probably the most common format for for geographic data to use at least on the web. And it's supported by all all mapping and GIS applications out there, including QGIS and an art GIS if you are using that program. Click download. And then to show you how easy you can import the data is that I'm switching to QGIS and then having my download folder. So I can just drag this over here. And then we have added the layer to our map. You will see it's not looking the same as here because this they do Jason format is only keeping the raw data. It will not keep the style of your map. So you will need to style it again as you like here in this application. And this is not a course on QGIS, but I will show you briefly. I double click on this color symbol. And then I click simple fill and I switch the field style to no brush. And then I click OK. And then it should look like the one we had in the maps application. Next we can add some health facilities. So new layer. Facilities and then. Silty we will style and facility type. And we will select the facility level in organization units. And then add. So these are all the health facilities we have in our demo instance for Sierra Leone. Again, to download very easy, click the more actions and download data. And download. And then switch to QGIS. And open the download folder and just move the file over here. This one by default is just style by adding small dots to the map. If you want to style it differently, double click here. And then. No, I didn't click the correct layer. The facility layer. And then, for example, you can decide to use like a hospital marker. Instead of this, this small dots. So I'll click on this symbol and OK. And you will have these added to the maps. Yeah, I will not show the, the, the thematic layer, but you can also easily add a thematic layer and then upload it to QGIS. And then, and then it will also contain the values for your map. So you can be able to style it for, for the same purpose. I will spend time showing it. Okay, so add a new thematic layer and select indicator. Let's select malaria slept under a bed net last night. Period last 12 months or units. Let's select chief done this time. And then style, we will go with the defaults. So now I have added this layer layer to the map and to download. Same here. Click here. Download data and download. We move it over to QGIS. So here as you see, all the colors from the maps are gone because this format only preserves the raw data. So it preserves the shape of your audience, but the values are here. If you click on this identification tool, you can click on this one and you can see that there is a value and also the color we used in the map application. So again, you can style it here. Double click. I want this is not the course on this. I will do it quickly. But I will select graduated styling and then select the value. And then I will classify the image. Equal intervals. So you see here you have the same possibilities as you have in the maps up and many more. But it also makes it much more complex to use. And then, okay. So you can now see this a different color range, but it's no style according to the slept under the bad net indicator. Okay. This is how it works so you can download Gio Jason and then upload to the application as you like. Also just to mention that now I didn't really show you how you can analyze and combine this data and QGIS. This is actually not possible to do within an hour. But we have a full day course in how to do this. So what we did, especially if you are not on 326, which I expect most of you, you are not yet on this version. And you will still like to use this great world pop data and combine it to your odd units. With there is a course on our YouTube channel, which will go through the steps of how you could do this. And it also adds something that we don't support in the maps up. And that is to see the calculate the number of people living within the walking or driving distance from a health facility and not always inside a buffer. This might be more relevant for the accessibility cashment area for your health facility. So please have a look at these videos if you would like to see how this is done. The last example I will have is to how you can add import external layers, additional data to the maps application. So we will look at two different layers. One is for the for a base map. So this is it's called water color. So it will turn the base map into like a hand painted world map. So not so useful, but but it's a good example of how you can switch the base map. So often maybe you will have a national mapping provider maybe having their own detailed base map or there might also be that you would like a special color for example on your base map. You have this possibility to add your own. And then the other we will add is a layer showing the clearance of forests of the forest loss within the last 20 years. And this is an example not of a base map but we call it an overlay something rely on top of our base maps so I will show you both. And this external layers functionality is in the maintenance app, so I will open that app. So you do this again once for your instance and then it will be these layers will be available for all of your users. So go into the maintenance app. You will find external layers at the bottom. So I will click plus. And then I will take the first there are some URLs that I had so I will add the. Let me see. You can see this URLs here so it's no good rule for how you find these URLs so please ask an expert on this or ask us if you if you have some layers and it would like to add them. I will add the name so this is the water color base map so I call it water color. This is on service is also we support different formats this with any mapping or GS expert will notice this is an XY set format. And the reason is often you see you will see this special XY set here in the URL. And then we will add some attribution so this is from Damon and also based on open street map. And here you can select where you would like this layer to be added either base map or as an overlay. So I will save this. So now we have a new water color layer here. And then we will, while we're here at the other layer. So plus again. This is called forest cover loss. And then I will copy the URL, which I have. This is also a way XY set layer. And this is from an organization called global forest watch. And then this one is not the base map, but it's something you can play plays on top of other layers. What we also added here in 326 is that you can either link this to a predefined legend that you have on your system or link directly to an image, giving an explanation of your layer. But now I just save it. And then if I reload the maps up. These two layers should now be available. So the base map layer is a bit hidden, but the this one is showing the base maps. And if we scroll down, you will now see the new water color base map here. So if I added the map will turn into this beautiful looks like a hand painting. And you will also see as you zoom in. So let's zoom into Oslo. You will see that it has all still all the details. Available, you will have more and more routes and parks. And streets as you zoom in, but still with the same look. So and here could also if I zoom all the way out again, you can see some of the other layers that this is that if you would like a layer that emphasis the terrain. There is a terrain layer as an example. And also the one I showed earlier, adding a dark face map, if you would like that as a as a behind your your health data. But to go back to the water color, we will now look at the other layer and that is an overlay layer and that is added up here at layer. And then you will see the forest cover loss here. So if we add it, you will see the areas where the forest has been cleared in the last 20 years. This is calculated from satellite injury showed in these by these spots here. So we could change to this bright to have it more visible or maybe better to combine it with a satellite injury. So you can also see the intact forest in between. So these are just two few two examples of what you can do with with external layers. I see that I thought we have spent of time but it's actually not so much time left so but we are almost finished this is also an example of external layers. The reason I made this for WHO where because if but the audience needs to be properly defined polygons, but sometimes these boundaries are disputed. So this is a way to sort of add this overlay on top of your thematic maps to show for example disputed areas. In the summary. Three ways you can extend the capability of the maps up one thing is to use the layers from the Google Earth engine and then combine it with your own arguments. You can also export your data easily for further analysis in other programs. And you can add new basemaps and what we call overlays with external layers. Are there any questions. Hey, we are in Scott here. There's actually been a ton of questions very active in the chat. And a few of us are trying to enter this as we go. Thanks. Sure. And Austin and Phil for helping out. There was one question that I thought would be good for you. I know that none of us had entered yet. Lucia Fernandez asks, have you tested the rendering speed and settings with low connection for the maps and any idea how much it takes to load and I think you were presenting at the time, the, the new Google Earth engine layers. So maybe just a word from you on performance and any kind of concerns you might have around performance or ways that people might optimize their performance. So the poem, please test but it even on a low bandwidth connection, the speed should be, of course it will take a little bit more time but the, the most of the time we are waiting is to do these expensive calculations on the Google service. I don't know what the waiting time is there, not actually transferring the data. But it also depends, for example, if you have some very complex boundaries, and especially if you, if you take your full country, I know Scott is sometimes demoing this and taking all the facilities in in Sierra Leone and then I get a little nervous but in most of the time it works it takes just takes a bit of time. But of course when you use these layers just try to do it within one small district or just four or five health facilities in the beginning and then check the speed at the performance before you take your whole country. I get nervous too but it's always worked. So and then also we try so we support something for for yeah now I think that's that should answer the question. Any more questions? Lucina is also asking about getting the data from Google Earth Engine that you that is available in the Maps app and is it possible to get that data into your DHS2 instance so that you can visualize it in other analytics charts pivot tables etc. So we have discussed in supporting this we might in a future version and then it will be part probably be part of the import export app. So the reason we are not yet supporting is is that it's especially if you import the population data and you use that for a denominator or for a lot of calculation for your indicators it can have some big consequences that you might not be aware of by simply importing the data. So right now there is no direct import into the DHS2 but there is a plugin from ICT I think on the app app that actually is doing this. So please check out this plugin. So that will allow you to import the data directly into a data element and then you can use it all over in your instance. Thanks I did post a link to that app the WHO GEE app Google Earth Engine app in the chat there if anyone wants to look at it but just as you pointed out it's not a functionality that we have available now but we are looking into it and we do have it on the Tintive road map for the for the next release to have that as a native functionality in DHS2 to be able to import the data. There's another question from Carla. Legends on the dashboard app I noticed in the previous versions that it's only displays when the mouth is on top of it is it possible to have the legend displayed on the top of the map so just have the legend there all the time I assume it's yeah I think I can switch to the dashboard here so you can see the issue so the question is right now if you have the map you will only see the legend when you hover it so there is a we could maybe make it into a configuration there there is a problem that these late legends for maps often take a lot of space so there is little room left for the maps while often for the for these charts it's it can be much more compact and especially also for maps you can have three or four different layers but yeah we know about the issue and and we could consider and also if you have some good suggestions of how it can be could be achieved then please please tell us and just to point out they were also planning for 237 to add that legend tab or that that legend tile also to the pivot tables as well as the charts because we have legends and pivot tables and charts now also so kind of building from that so another suggestion from Lucia Birn maybe you could start bringing into the analytics with the climate and land cover data since that comes with less risk than the population data true good and also that's also a reason for moving it into the import export app is to sort of hide this feature for the casual user so it's something that you do more with conscious right yeah it's definitely an administrative function it's not like we definitely don't want to allow just any map user to be able to import data so it needs to be something that's tightly controlled within the system which is you know our original plan was to actually have it in the maps app and we had to we had to back away from that because it was a potentially too dangerous to to allow just anyone to be able to do that or afford to be available in the maps up relating to population in particular there is a denominator session I think tomorrow or Thursday I forget which one Thursday Thursday at two o'clock p.m. that might be of interest to anybody relating to population data and in actual calculations on the indicators in DHS too yeah that one's going to be that session is not going to be terribly technical I don't think it's going to be more around like some use cases for from countries on how they've collected some population data like going like doing like household enumerations manually kind of approaches but I think on Friday I will mention a little bit about our continued collaboration with world pop and grid three who are providing these these these population data we're really and I think Bjorn alluded to this we're really right at the tip of the iceberg of what we can actually bring in what you see demonstrated here is we're really just crashing the surface of what's possible and we would love to hear feedback from anyone and everyone who needs population data and how they want to see that data how that data could be useful or any other types of data because we really have the possibility to get like say like individual facility catchment areas kind of data there are folks out there world pop grid three specifically who are doing that kind of work and so if anyone's out there who really needs super high granular data or household enumeration population data or has any kind of request like that very happy to hear those and like I said we we we have space we can we can try to work these kinds of things we just need to use cases in the community to tell us what they need I think we need to end the session it's just a few minutes left please continue to ask questions if you have on this research to community of practice I will keep an eye on that one for the rest of the conference and then thank you for for taking part of this session