 In case my face is a little at a faster end, please let me know. I can slow it down so that things are more understandable. In case you think I'm moving fast, please put a message on the zoom chat so that I can slow things down when needed. So in the exercise one and exercise two, we were able to open an existing favorite and make changes to the data selections in the period and the argument and we saw how the system responded to the changes that we made. And then in part two of our exercise, we were able to plot a new map with a different data point and we created a heat map or we can say a Coropletch map which kind of showed us the volume of data by the legend that we had selected. We also discussed the difference between the automatic color legend and the predefined color legend and the concept of low radius, high radius and the classification, the equal intervals and the equal outcomes. Now, moving ahead, what we'll try to do is we will try to see another layer which is the facility layer. So how we can plot the facility layer on the map that we're trying to create. So facility layer is basically your works on the basis of classification of your health facilities. Now the classification could be through different attributes. So I can classify my facilities through their type, whether they are sub centers, whether they are family health centers, whether they are community health centers or district hospitals or medical hospitals or specialty hospitals or I can do it by their ownership, whether they are public hospitals, public health facilities or their private health facilities, whether they are run by NGO or their charitable organization facilities. So these are kind of my attributes which are associated with the facilities when I'm creating. So when I'm creating just a little background on how these specifications can be created and how they are used on the map. So when you're creating your health facilities, your hierarchy in DHRs too, you can group these facilities into different groups and group sets. So when I'm creating my facility master, I am creating a number of health facilities depending upon at the level which they operate. So for example, I create a group for my sub centers. I create a group for my primary health centers. I create a group for my district hospitals. I create a group for my medical colleges. So I've created, say, these four groups which kind of are defining that what type of health facility lie in my hierarchy in DHRs too. Then I can group these organic groups into a group set. So basically a group set is your collection of your organic groups. So these group sets, when you create, they can be used on your facility layers. So you can create these group sets by type, by ownership, by residence. So you can create these respective group or group sets and then you can use these group sets as your dimensions in terms of adding the distribution of the facilities by their type or by their ownership or by their location. So you can do that on the maps. So in order to use a facility layer, you must have group sets defined into the system. So that's a prerequisite. So for adding a particular facility layer in the system, for example, we'll use the same favorite that we created in our last exercise. So we'll, in case you want to load that favorite again, you can go to file, click on open and search for that respective favorite and click on that. So it will again load the favorite for you. Since we already have that favorite there on our map display area, so we can start working on adding the facility layer. So for adding a facility layer, we can click on the add layer option, select facilities. Now you'll see that the first thing that it is asking, it is asking for a group type. Okay. So you need to ensure that your, your DHS2 database has group sets created for organization units so that you could use those group sets here for your data analysis. So we'll select type because you want to see the data or the spread of facilities as per their type. Okay. Next, we need to select the level. Okay. So now we want this facility layer as the name says that it has to be plotted at the facility level. Okay. So within training land, we need to plot all the facilities which are part of the organization group set type. Okay. So we selected the organization group set that we want to plot these facilities by their type and the level should be facility. Okay. Across training land, all the facilities should get plotted by their type. Okay. Now, if you go to the style option, labels is for adding names, but now since we're adding a lot of health facilities, so we should avoid using the labels, what kind of clutter is your whole map. So let's leave that. Buffer is basically you want to show the catchment areas of that respective health facility. So for example, the health facility caters to audiences say five kilometers, the population residing up to five kilometers or up to 10 kilometers, then you can set the radius in meters as that. Okay. So when you're doing catchment specific analysis that this particular facility kind of caters to the population residing five kilometers from it, then you can select those radius in parameters. So it shows the buffer radius as that. Okay. So it's completely optional. If you want to use this, you can use this if not then you can disable it. Okay. But then this option is available for any catchment area related analysis where you want to see that what area this facility covers across the district and to from what respective distances the patients are expected to come to this health facility. Okay. So the basic selections which I need in order to add a facility that I've done, I've selected my group set, I've selected my level and I can click on the ad layer. Okay. So once I click on the ad layer, you'll see that now the facility icons are available on my screen. So I'll just zoom in more and you'll see that this particular Red Deer Health Center has this icon and when you decipher the cycle from your legend, you'll see that this is a type of health center. Okay. Now you see this one, crocodile hospital gate to PFC, then this has an icon says hospital gate to PFC. Okay. So likewise, based on the icons, you can see that what kind of health facility is this. So you kind of now you're overlapping two layers. Okay. One is your thematic layer where you had plotted data by health facility and on top of the thematic layer where you were plotting data by health facility. So you've added the facility layer as well. Okay. So in case you want to overlap these layers, then you can overlap these layers in this manner where you can select a layer of thematic layer with the data. So your thematic layer is always associated with the data and you plot that thematic layer, but you can also overlap that thematic layer through a facility layer as well. Okay. So once you do that, you are able to see the not only the coverage or the volume of testing, which this health facility has done, but also what kind of health facility it is actually. Okay. So this is where you can use your facility layer or even if you don't want to see the data by the based on say thematics, but you just want to see the distribution of the health facilities across the geography. So you want to see in a district how many sub centers are there, how many PFCs are there, how many CSEs are there, how many private partitions are there. In case you have mapped your private clinics as well, then facility layer is of importance. So you can see the spread or the distribution of your facilities through that type across your entire district province or country. Okay. So it has its own independent use, but you can also correlate with the respective thematic layer as well when you're plotting the data. Okay. Now, suppose you don't want to see the facility layer as of now when you're analyzing this data, you can always toggle the visibility of this layer through this eye icon or the visibility icon. If you click on this, then it will hide the facet layer for the moment. It will not remove the layer, but it will hide the layer for that specific moment until you again enable it. Okay. Or if you want to only see the facility distribution right now, you don't want to see the data for HIV test performed, then you can hide the thematic layer. So you'll only see the facility layer plotted. Okay. So this way you can toggle the visibility of the different layers which are plotted on the map which you're going to create. Okay. Now, when you were discussing the low size and the high size options in our thematic layer in the style, so you see there are two options available here, the low radius and the high radius. So the terminology has undergone a slight change. We called it low size high size. Now it's low radius, high radius, but the functionality in itself is the same. So when you're plotting data on at the level of health facility using the point coordinates, the x coordinate, the latitude and the longitude or the x and y, there you could resize these thematic layer or the point layer through the radiuses which you can pass through the system. Okay. So if you want to increase the radius of this respective point layer which you've plotted, you can always change. So you can say that the low radius should show as 10 and high radius should show as 30. So 30 is the last highest range where you can go through. So you can change the radiuses and click on update layer. It will automatically change the radius based on the lowest which I've identified here. So highest becomes a 30. This is slightly less. So the radius is also less and then you have these lowest. So this is your 10, then this is slightly higher because it has more data as compared to this one. So likewise, the radius will correspond to the volume of data available. And of course, the highest value which is set for the high radius. Okay. So this kind of signifies that low radius means low coverage of data, low volume of data. High radius means high volume of data has been reported from this respective health facility. Okay. So this is where you can use the facility layer and you can also overlap facility layer with the thematic layer. And within the thematic layer, you can use the radius options to kind of add more kind of visual effects to the data which kind of gives you more information. So greater the radius, that means greater the volume, lower the radius, that means less is the data plotted or available for that respective health facility. Okay. So now we've seen how we can use the facility layer. So for example, now let's remove the facility layer for now and let's focus on the thematic layer which we have plotted. So if you want to remove the facility layer to go on these more actions and click on remove layers. So this remove layer will get removed automatically from the system. Okay. And let's go and resize the icons that we had. So let's switch it to the default settings. So we can do 5 and let's do 15 and update this. Okay. So now we have, now the radius has changed based on the changes that we have. So now we had left the part where we wanted to see what filter does actually. Okay. So let's edit our thematic layer and look at the filter option. So now when we collect data, we collected two different disaggregations. Okay. So we might use gender disaggregation, we might use age disaggregation. So now those disaggregations could be utilized to filter the data on the maps as well. Okay. So in the visualizer, we saw that there were different disaggregations available. So we could further drill down our data through these disaggregations. Similarly, in the maps app, you can add these filters where you want to drill down to a specific subset of data only. Okay. So now here when you were seeing the HIV test performed in the background when the data is getting collected, we are collecting this data by say male and female disaggregations. So how many males were tested for HIV? How many females were tested for HIV? But right now this map shows us the total volume. This is a sum of male plus female. Okay. But now we want to focus more on the female HIV cases which were tested. So that would warranty that we add a filter here on the data so that we get a filtered subset of data which for which we want to do an analysis for. Okay. So when we click on add filter, you see an option available here. When you click on this arrow icon, you'll see the disaggregations which are available here. Okay. Now all of these disaggregations might not be associated to your data or your data element or indicator which you're trying to plot. So before putting a filter, one should know that in what disaggregations is this data getting collected as of now. So in the visualizer when we saw we were seeing those recommendations, the green dots, but that feature is yet to be implemented in the Maps app. So here in terms of adding these dimensions, you must know that what dimensions are actually applicable to your data element. Okay. So let's see by sex. So if you select the dimension here, you'll see the different disaggregations available. So had I selected an age dimension, then I would have seen the age groups which are available in that dimension, but here I've selected sex. So I see there are two dimensions do disaggregations available, male and female. So I'll select female. Okay. But then you can also create combination of filters. So you want to see female and if you want to do it by age group, then you can add additional filters. Okay. But then you must ensure that the data is available for that combination in the system. So if you're collecting data by only male and female, but here you select sex is equal to female and you just go and add one more for say, for example, urban rural, but then you're not collecting data for urban and rural when you're collecting data for HIV testing, then it won't show any data to you. Okay. It will say that no data found. Okay. So be, I mean, ensure that the selections that you're making in the dimensions are kind of applicable to your data input that you must be collecting data for those disaggregations. So that you can put those respective filters here on the map. Okay. So now I've selected the dimension as sex and I have selected that I want to see the female cases only. And then you can click on update layer. So you'll see that the data will undergo a change and you'll see in the filter, it will show you that right now it is only filtering data where sex is equal to females. Okay. So right now it shows you only the data which has been reported for female cases. Okay. So the filters which you put on the data are shown on the card for the thematic layer and at the bottom you'll see if any filters have been added. Earlier there was no filter and since nothing was shown here, but now since you have a filter, it shows that this data corresponds only to those respective data entries where sex was female. Okay. So likewise you can add either one filter or a combination of filters on the data or the map which you're plotting and based on those filters, the map will get updated. Okay. All right. So now we've seen that how we can add filters to our existing map that we have created. So for now, let's remove this filter for now and proceed on with the next part. So you can click on the remove filter icon to remove filter and click on update layer. This has again been reset to our original map where we see data for all the cases male plus female. Okay. Now let's try and add one more thematic layer on the top of the existing thematic layer which you have been using it in our past example. So let's try to add one more thematic layer. Now we've seen how many tests have been performed. So it's important to correlate this with the number of positives that have come out of this testing exercise. Okay. So in order to add that respective new thematic layer, we again click on add layer. We select thematic and we make our selections. Okay. So we again go to data element, select HIV and we select HIV test positive. So I've made my selections here. I'll go to period. I'll select this as last six months. So it's important when you're overlaying the information, then you also need to take the periods into account. Okay. So if you want to correlate the data between two layers, then the data should belong to the same period or else we might have different interpretations of the data. Okay. So let's select last six months as we've selected for our last thematic layer for the testing. Okay. Orgulate we can select at the district level. So we're seeing testing at the facility level, but then the positives you want to see at the district to understand the overall burden of HIV positive cases at the district level. Okay. So you can select training land and select district. We don't need a filter here and we come to style and we select a different legend here. So let's select this one, for example, and we update the map now. So I'll just quickly review the selection which I made. Okay. So my previous layer is the HIV test performed for all cases male plus female. Okay. I select HIV positive data element to the HIV group. I selected the last six months, same period as I did for my testing. There's a difference here. Now I was looking at testing done at the health facility level, but I want to see the overall burden of positive cases at the district level. Okay. And I did not add any filter in the style. I kept everything same. I just changed the legend, the color palette so that we can differentiate between the the test performed and the test positive. Okay. Let's click on add layer. Now you see the existing layer which we had has now overlaid by this respective new layer which we just added for the total HIV positive cases. Okay. Now we don't see the actual testing done. It is getting overlaid by this HIV positive layer at the district level. So now to make it more clear, you could swap between the layers. Okay. So you see the symbol here for available that could be used for moving layers, one on the top. Okay. So as soon as I move this layer, these cards one over the other, then the display of the map changes. Okay. So I'll again move my test performed on the top because this data is at the facility level and the district data can go below as the second layer but then this needs to come on top as the first layer to make this map more understandable, more visually pleasing. And the first can provide you a better picture for making out any interpretations if you want from this using these two layers together. Okay. So now we see two layers have been superimposed. The first layer shows you the volume of testing done at each health facility and the second layer shows you the overall HIV cases or HIV positive cases in a respective district. Okay. Now we want to save this map again. So we because this created as a as an update on our existing favorites. So we need to create this favorite again. So what we do is we go to file, we click on save as. So if you have made any updates to your existing map, but you want to save that map as a separate object, then you always use save as similar to what we use in our Word and Excel files. If you want to save a different version of our existing documents, then we do save as the same concept applies here. So I click on save as and edit the name of this respective favorite. Okay. So I'll give it a name. So let me just put this so because I'm using multiple layers. So let's put the name which could signify that. So you can say HIV test performed at facility versus HIV positive. And in the bracket, we can put district. Okay. And you can click on save. So now this has been saved as a new favorite. So now you have both the objects available. One object was HIV test performed at the facility in the last six months. Now you have a new object saved, which is comparing your testing data at the facility with the overall HIV positive cases at the district level. Okay. Now let's have a look at this map and try to relate the data closely. So now if you see here in this particular district, so this has the highest number of HIV positive cases and this, these particular health facilities, most of them have done good volume of testing. Okay. So 21,831 cases have been tested here, 22,000 have been tested here, 26,000 have been tested here. So this has this particular district, the health facilities have done and a good volume of testing. Hence you have a higher number of positive cases here. Okay. So if you try to compare it with other districts, you'll see that the districts which have low volume of HIV cases, they have done comparatively less testing. So there's a direct correlation between the quantum of testing which has been carried out and the positive cases which are there as a result. Okay. So we can see for, for example, for the fish district and we can see for the game district and we can see for the dinner district. Okay. So for these three districts, you see that the testing has been at a higher end. Hence the cases are also, the positive cases are also at a higher end. But for the others, maybe they haven't done that aggressive testing, that aggressive testing because they, they kind of show lower numbers of the HIV positive cases. Okay. Had they done the comparable level of testing as related to other districts, then they might have had different results for the HIV positive cases also. Okay. So this is one way to look at the data that whether there are two particular variables available which could be correlated and can an interpretation be taken out of that respective map. Okay. Similarly, we can apply to the recent COVID testing which is done. So if the testing numbers are low, the people are not getting tested. Of course you will get lower case counts every day. But if your testing is, is aggressive, if you're doing a lot of testing, then you might identify a larger people of core or larger positive cases as compared to a district or a state where less testing has been carried out. Okay. So this is how you can kind of use two, two thematic layers, plot different data on those thematic layers and try to create a relationship between those variables. So here we can say, had the testing, there are more chances that you will identify a larger number of positive cases. So there's kind of a correlation between the data which is available. Okay. So then you could again zoom in through each district, identify the facilities which have been a lot of testing and whether that testing has kind of contributed to the overall the disease burden or the case burden for HIV or not. So then you could establish these relationships when you're trying to kind of establish a kind of relationship between the two variables which you are plotting. Okay. So this way the maps kind of give you a good picture of what is happening overall in your districts with respect to the variables which you want to plot into and use the variables for analysis. So let's take some time out for covering the exercises for part 2.12 and 3 and check once what are we covered. Yeah, we covered till part 2.1. So in this session, we've covered part 2.2 and we've covered part 2.3 as well. Yes. So let's take 10 minutes and cover the practice exercises 2.2 and 2.3 and after 10 minutes we will start with 2.4. Yeah. Any questions please do share on the Slack channel and we just try to answer them as quickly as possible. So we resume after 10 minutes and we'll start with the next session.