 So in this session, we will talk about tracker data analysis will continue from yesterday. So yesterday we saw event reports where Pamod mentioned that how you can create different types of reports in form of aggregation using the data which are capturing for various events and enrollments. Today we'll see, today we'll see another app which is called event visualizer where you can also use your tracker event data to create different visualization items. So we'll see a small presentation with the details of what event visualizer does. And then we'll follow it up with the demo. So the learning objectives of this session now that I'll describe the event visualizer app, the interface and the location of various items which are there. Then we'll have a look at the event visualizer interface, how similar or how different it is with respect to the event reports app. And then we'll see how to create visualizations using tracker data. And there are some limitations of the event visualizer when you work when you work with tracker data so we'll discuss these limitations as we go ahead in the session. So basically event visualizer enables you to analyze the data which you capture for your tracker and events program in charts format. So event reports was more on line listing and creating aggregate tables event visualizer is more on creating charts from the event data. So it's kind of similar to data visualizer app, but this is specifically for tracker and event data, particularly useful when you want to analyze data for these data elements which have option sets associated or which have dropped on menus associated with it. So there it makes more sense to use the event visualizer app. As we saw in the event reports app that you could now use the enrollment type output and select data from different stages that feature is yet to be implemented in the event visualizer. The limitation that we just spoke about is that with event visualizer you can only work with data from a single program stage. So at a time you can either see data from, for example, the clinical and diagnosis program stage in the COVID surveillance program, or you can see data from the lab request or the lab result program stage. You can't select data elements from multiple program stages in one go. Always you'll have one program and one program stage to work with. So this is the difference between the event reports and the event visualizer app at present. There are revamping efforts going on as part of the core features. The app will get replaced in the future, but the timeline is yet not clear. So as Pomod mentioned yesterday, the event reports app is completely being overhauled. So you will have a tracker reports app which should have both charts and tables functions integrated. So eventually this app will get deprecated and it will get replaced by the new event reports app which will have combined functionalities for charts and tables for tracker event data together. So similar to the event reports, you can save your visualizations, the charts that you create, you can download and you can add them to the dashboard. Similar to what we saw in events reports and in general with any DHS to analytics app that you use, you need to understand and identify what program you want to choose to aggregate and visualize the data, what program stage you want to choose for aggregating and visualizing the data. The data elements or attributes you want to choose the periods and the organization units and chart type. So what chart type you want to use for your data presentation. So these are the key parameters you need to know before you start creating your visualization item using event visualizer. So you need to know your program program stage. Within that program stage, which is selected, you should be aware of your data elements, which you need to select in order to analyze them. You need to know of your periods and the organization units or reporting areas, geography for which you want to plot the data and then what chart type you want to use for data presentation. So these are the key parameters you need to keep into account when you're using your event visualizer interface. So we need to recall the tracker model in terms of how the data was structured into the tracker programs with the profile or the attributes of the person that you were collecting and the program stages that were involved. Okay, so when you are using inter event visualizer, you need to understand the intended information which you want to visualize. So as we saw in the previous slide, we need to know in advance what are the data sources that we need to use in order to generate a chart from the data that is already collected. The chart type will determine the analytical report, which is to be presented. There are different chart types available. Most commonly you have columns, you have lines, your bars, but you can also create by charts, etc. When you're using event visualizer, we'll see all the chart types which are possible when we review the interface of the event visualizer app. So once you selected your chart type, you selected your program, program stage, you can select your data elements. If they have options associated to them, then you can select what options you want to plot and you can then select a period and your organization unit. Then you have your layouts and table options. The layout would allow you to change your accesses, what data you want to plot on the x-axis, what data you want to plot on the y-axis, what data you want to keep in filter. So the layout will allow you to do so. So we'll have a look at this when we see the event visualizer interface. So now I'll proceed to the demo and we'll review the app first and then we'll go into the further details. So I've logged in into the instance that we have been using for the academy. From the apps menu, you click on event visualizer and it opens up the app for you. The interface would be more or less similar to what we saw in the event reports. However, there might be slight differences that we'll discuss as we move ahead. So this is my interface for the event visualizer app. On the top, you see different chart tags which are available. So you have your basic bar or column chart, then you have your stack charts, then you have your line charts, your data charts, your pie charts. So the most basic ones which are used are either your line charts, your column chart bar charts and your pie charts. So you can select the chart type from the top in terms of the analytical item which you want to create. Second is your data menu. So you have your list of programs available here. So you'll have both event programs and tracker programs available here. In case you have an event program, then by default, the one and only stage which an event program can have that we get preloaded. In case you are doing the analysis for a tracker program and if it has multiple stages, then the stages will get listed down here. So the COVID vaccination registry does only have the vaccination program stage. Hence you see only one option in the dropdown. In case you are doing analysis for COVID-19 case based surveillance, then you'll see this list of all the stages which are there in the program. Okay. Now, as I mentioned that unlike event reports where you could select data elements from different program stages. Here you can only select data elements from one specific program stage at a time. So in case I change my stage one to stage two, my selection of data element would be reset and I need to check the data elements again that I want to use. So at once I can only use one one program stage and the data elements which are associated to it. So this is the difference as of now between the event reports and the event visualizer that. Okay. Then we go to the periods. So this grid is similar to what you have already seen in event reports. In addition, there are also start and end dates. So in case you don't want to use a fixed period, you want to use custom start date and end date, then you can change the toggle from here. So either you can use relative periods, or you can use fixed periods, or you could use start and end dates. So three kinds of input selection from periods are supported and whatever period you choose on based on which the data will be shown. Okay. Then you have the organization with hierarchy. So you select your organization units from here. Okay. So these are the three main selection criterias that you need to put into place your chart type your data your periods and your organization units. Okay. Then you have your layout menu here so you can change the layout between series and category and your filter. So basically these are your x axis and y axis and the filter you want to keep. So in terms of aggregation, we'll see how we can change the aggregation of the data elements that we choose in terms of what data do we want to see. So when we take some examples, we'll see how we can use this particular option. So this again would allow you to do some further enhancements on the charts that you create, which may be related to sorting which may be related to what kind of output you're looking for, which may be related to giving names to your x axis and y axis, adding chart titles. So, so on and so forth. So the different type of additions you can do one of the additions you can do from the options menu. Okay. So the download again would allow you to download the item that you create. And favorites will have your list of existing chart items that you have created already. Okay. Now, let's try to create a chart using event visualizers, and we take it up from there. So I want to create a column chart so I click on column which is the by default selected chart type. And load the app for the first time. So I'll select my COVID-19 vaccination registry program. And though since it is one, this is track program but has one program stage which is repeatable in nature. So I have just one program stage loaded here. So I'll go ahead and select my data elements for which I want to create a chart for. Okay. So for example, I want to plot the number of doses for AstraZeneca, both first dose and second dose that has been given this year in at the province level in Laos. So what I'll do is I'll select my data element called those number. You see here that it has a search box against it and it has these options available. That means this particular data element has option set associated with hence the app will aggregate the data based on the options options that we choose when you're creating your chart. Okay, so from this I'll select first dose and second dose. Okay, you can also select third dose and booster dose depending upon your requirements. In case you want to remove the options which have already selected you can click on this option this next cell and you can click on the items to deselect. Okay. So you can select from the left inside search box and you can deselect from the right inside search box. Okay. Next, we also want to filter this information by vaccine name. Okay, so we only want to see the doses given from AstraZeneca. Okay. So again, this has an option set associated to it. So you see the selection menus available here. If you see you have vaccines, five vaccine candidates, we're only interested to see the information for AstraZeneca. So we'll select this one from here. Okay. So I made my selections of the data, which I wanted to which I wanted to analyze in my chart. Okay. Next, I go to periods and I select a fixed and relative periods and I want to see for this year. So I select this year. So I'm done with my period selections. And I want to see this data for all the provinces under law pdrs. So we have these menus above user argument user subunits user subjects to units. Basically, when you're creating your charts, where you want the argument selections to remain dynamic based on the users access, you can select your, your disaggregation type from the top options which are given here. So you have a user organ it if you select this, then whatever organ it is assigned to the user, he or she will be able to access the data in this favorite for that perspective organization unit. So if I have law pdr assigned then I'll see data for total data for law pdr. But in case as my username as my users access to a particular province I'll see data for me that perspective problems. Okay. If you select subunits, then you'll see the data for the subunits which are below my respective parent organization which is assigned to me. Okay. So if my chart has been created to show data by subunits. So my reporting centers are on the x axis and my data is on the y axis. Then if I have venting capital assigned to me as my user access, I'll see the data in x axis for all these CHW all these facilities which are below venting capital. Okay. So if I have law pdr assigned to me then I'll see data for all the provinces under law pdr as part of my x axis. Okay. If I do subjects to then I'll see data by the last level but then we need to take into account the user should have that kind of organ it available for him to access. Or else the chart will not load for that specific user, because if I give venting capital and I do subjects to subjects to then. CH mouse is one level and x2 is a level below CH CH mouse which doesn't exist in my system right now so that chart will not load for you. So when you're creating your dynamic visualizations with respect to your organization units, you need to understand what kind of levels exist in your hierarchy and you need to take care of the users assignment also. So whether at such and such level where you're defining your charts whether the user will still have access to that level of audience you need to also take into that account. So basically if you see here you just have a four level hierarchy here. Okay, so you can use in order to make this chart more meaningful and more accessible for all the users, users subunits is the safest level which you go because no matter MSI and LOPDR or venting capital or this particular level, I'll still see data for the last level, which is available for me to analyze. Okay, so based on your levels in the hierarchy and the disaggregations you want to show for your organization units, you need to select what kind of disaggregation you like to have with respect to the organization units. Okay, and it also depends on the layout when you're doing subunits then how you want to show those subunits. Okay, so it makes more sense to do a comparison between the reporting centers or the districts. Then you need to keep them on the x axis in a bar chart so that you can compare the number of doses given from the from one district to another. Okay. So what I'll do is I'll let LOPDR be the basic selection, but I'll do a selection of user subunits from here so that I can see the data by different provinces. Okay. Now, we have done a selections for a chart type, our data and the parameters that we want to see on the chart, the periods that I want to use and the organization units which I want to use on my chart. Next is I want to place my data elements in a way that they make a meaningful visualization. So I go to the chart layouts and I start adjusting my layout so that I can see the information which I want to see. So the objective of my creating this chart is that I want to see the number of doses given by districts this year. So my category dimension is the x axis. So I want to see the comparison between different provinces in terms of the number of first dose and the number of second dose is given. So I'll put the organization units in my category so that I can see the provinces or districts as my x axis. Then I want to see the number of doses given at my y axis. So I switch back the number of doses to the series dimension which is my y axis. So I do this adjustments in order to see the data which I want to see which makes more sense for me to analyze. So I've done these adjustments on my layout. So I click on update. So now you see that you have the information which you wanted to visualize. You have AstraZeneca vaccine which is a part of your filter because you've already selected AstraZeneca from this dropdown. Your period was 2021 because you wanted to see data for this year. The data which you see is the number of AstraZeneca vaccines given as first dose and a second dose because it's selected first and second dose in your dose number. And in the x axis you see it was sub x2. That means it will show the children below Laopedia because I have Laopedia assigned to me as part of my user assignment. Now you see you can't figure out which of these provinces have performed better and which are still performing relatively low number of doses have been given. So you can do some improvements on the chart earlier. So you can go to the options panel and you can use the sort order to see either high to low or low to high. So let's go with low to high. And then you want to also show the end user or whosoever is your audience that what data is plotted and what access so you can also give the names of these respective accesses. So the range access is basically your y axis. So I'll put my label here. So I'll put those number and my domain access is my x axis. So I'll put organization units slash I can also say location. So and my chart does not have a very intuitive title. So I can add a chart title Astra Zeneca first second dose by location. An athlete one update. Okay, so now all the effects have taken place. I have a title on the top. I have my accesses labeled and I see my data sorted from the lowest number of doses which have been given to the highest number of doses. Okay, so you see here the sorting is done now and you could visualize your data in this manner using data visualizer. Okay, then if you want to save this chart as a favorite and used on a dashboard, we can click on favorites and can click on save and we can add a name to it. Okay, so I can just say event visualizer Astra Zeneca. Okay, so you can give a name that you wish to give here. Okay, and you can click on save. Okay, you can also then download. If you want to download this particular chart for using it in your presentations or reports, then you can download and it will open up the chart as an image or a PDF, depending upon depending upon the option that you choose. So you'll see the downloaded report there. Okay, so I'll stop here and please go through your learner guide and do the exercise one. Let's take around 10 minutes to do that and then we resume with the next demonstration. Once you finish that specific exercise. In the meanwhile, any questions please be free to put them on the chat and we resume with the next demo in 10 minutes time. So let's move ahead with the next demo. So in this one we'll see how we can change the kind of what data we want to aggregate or what kind of aggregation we want to see, whether you want to see the aggregation by events or whether you want to see the unique count of patients. So we can do both when we are creating our event visualizer items so just share my screen and then we can see the concept how to use it. Okay, so now what I'll do is I'll open an existing event visualizer charts that we've created. So we'll go to favorites and click on open and I look for CPS test results. Okay, and I'll open this one. So if you see here there is not much difference, but you can see your selections that on the data front you're using your COVID-19 case based surveillance program and you're using stage three lab results. Now the lab result stage is a repeatable stage. So when you're using this analysis by default, it kind of gives you the number of inconclusive negative and positive results for all the events. So if a person has had three lab requests raised, then you'll have three results available so you'll have three events for one person. So data for all three will come here and get aggregated. Okay, so this kind of shows you the total number of tests which have positive negative inconclusive test result. But we would also like to see the number of unique people with these test results. Okay. So when we were looking at event reports yesterday, we were able to use enrollment type output to see the count of enrollments with the latest test result. And we were also able to combine data from multiple stages. But in the event visualizer we do not have that option right now. So what we can do is we can count the number of unique tagged entities, tagged entity instances which have these values in their records. Okay. So then it will show you the unique number of test results available in the system. So how do we do that in order to switch the aggregation mechanism for this chart where currently it is showing you the total number of test results available but not the number of unique test results a unique identity instances which have this result. Then what we do is we go to options. In options we have a parameter called output type. Okay. So here you see event enrollment and frag identity instance. Okay. So if you want to see the total number, the data as per the sum of events or the total number of events we select events. If you want to see data by tagged entity instances, we select frag identity instance. Okay. Enrollment doesn't really make sense here because we're using data elements for a single program stage. So using enrollment is not applicable here. So we can kind of ignore this particular options and we can switch this to frag identity instance to see the unique counts for these test results. Okay. So I'll do this frag identity instance. I'll click on update. You'll see the values have changed now. Here the positives were 11 but now the positives are nine. So that means that it is counting the number of frag identity instances uniquely rather than counting the number of events which were matching the criteria for your filters. Okay. So like this way you can use the output type parameter here in the options to differentiate between the total. Events or the unique frag identity instances. Okay. So this is as I discussed there are certain limitations with the visualizer app and it does not has the enrollment concepts as of now. Hence we are not using the enrollment function here but moving forward as I mentioned before the event reports app is being revamped and all the features of event visualizer and event reports will get merged into the new event reports app and it will give you a much better interface with much better functionalities to work with. Okay. So I'll again stop here. There is another exercise in the learners guided exercise to you can please perform that for say five to seven minutes. Then we will switch over to the map session and continue the academy from there. So let's take five seven minutes to the exercise to and then we move on to the maps part. Okay. All right. So we'll have a look at the event layer for track data analysis using the maps. So the learning objectives quickly will describe the maps app as it relates to the tracker data. We'll have a look at some of the limitations of the maps when working with tracker data. And then we'll create maps using tracker data using the event layers and the track identity instance layer. Okay. So, there are three ways in which you can use the maps app for analyzing our track data. One is through the thematic layer one is through the event layer and one is through the track identity layer. And like the event reports and visualizer app where you could only use the tracker data in maps, you can use both the aggregate and tracker data. So if you want to superimpose layers of your aggregate information along with the data which are capturing through tracker event, you can superimpose both or use both layers for your analysis. Okay. So in the event layer, it will allow you to map the location of the actual event as long as the coordinates are collected during data entry. So this is the prerequisite that you must ensure that if you want to use the maps and the event layers, your events should have coordinates associated. If the events do not have coordinates associated during the data entry part, then these event layers could not be used in maps because they're all coordinate driven or location driven. So when we work with the event data, it could either belong to the event program or belong to a tracker program. In both the scenarios we select the program stages in which we select our data elements which have coordinates associated and based on that, you can visualize your individual events or you can the data for the data elements that you've selected, you can use them on your map on the basis of the selections that you make. So that was about the event layer. So in a second layer which you have is your track identity layer which allows you to map the location of your track identity. As long as you're collecting the coordinates of that specific person during the registration process. This layer is also coordinate dependent unless you select coordinates while registering an individual, you can't use the track identity layer as well. In this particular layer you can also use relationships where there are limitations associated to this layer because this has been newly introduced and enhancements are being made but at present will show what are the functionalities which are available. And of course there are additional features in the pipeline which are under development. You can have the thematic layers. So basically on the thematic layers you can map your aggregated data in two forms. One is a program indicators. So program indicators are basically your aggregation of case based data. Okay, so when you have when you're creating your program indicators, you kind of pass information to the system to aggregate your tracker data on the basis of the conditions that you define while creating a program indicator. So your aggregate data can be plotted using program indicators and then you can aggregate data elements within the tracker or event programs. Okay, so for both these forms you can use the thematic layer with the program indicators aggregate your data elements as part of your programs and you can also bring in data from your indicators that you've created. So for example, you want to show the number of deaths and the overall fatality rate for COVID-19 in one chart. So then your number of deaths come from your events while the fatality rate is an indicator which you've created that you can add as a thematic layer. So you can overlap your numbers of deaths and your overall fatality rate in one map using different data sources. Okay, or you want to see the number of COVID-19 cases in a particular area based on the coordinates which have been added in the system in the event while registering a specific person, then you can use the population data to create a layer and see that out of the total population which is there in that respective district in what pockets do the highest number of cases live for COVID-19. So in the Maps app, you have the advantage of using the data which is getting aggregated from tracker or from event program and you can also use the data which is getting stored in your aggregate part of the system as well, especially your population or your denominators data. And you can also use program indicators and indicators together so you can see the potential clusters of COVID cases and you can also see the total number of COVID cases or population or anything or any positivity rate or whatever indicator you want to see in relation with your total number of cases. Okay, so mixing and triangulating data sources is much easier in Maps app along with the tracker data as compared to event reports and event visualizer. Okay, so then what we'll do now is we move to this demonstration session and I'll take you through to the live demo for the Maps application. So during our previous sessions, we've all seen how we can take up the coordinates for our fragmented instances and our events. So if I can quickly go and maybe review that for you so it would be easier to relate from the previous sessions, how were these coordinates were being captured. Okay. Okay. So I hope you can see my screen. Yes. So when we talk about the tracked tracker coordinates, then you are collecting your point on Maps. So this is from where you kind of will take the coordinates when you're using the track identity instance layer or the tag entity layer so it will take up data from the respective enrollment coordinates. When we talk about event coordinates in your event program, if you have set in the configuration to collect the coordinates which doesn't seem to be done for this respective program, let's look for some other one can look for case based surveillance. Yeah, so for this particular program and program stage we have configured the selection or collection of the coordinates for each event. So this, these set of coordinates which you selected, they will be used for plotting your data on the event layer. Okay, so just to recap that these are the coordinates which are used in the Maps app for doing further analysis of data. Okay. So let's go to the Maps app now and look at the interface. So you have the Maps app as part of your app role. Once you click on it, it will load an interface which allow you to select different objects. The concept still remains the same in the event visualizer event charts in event visualizer for example you were using different chart types here you need to select different layers. Okay, so there are different layers available for you to analyze data. When it comes to the tracker data we either use the events layer, the tracked entities layers or the thematic layer. Okay, so these three layers could be used with your tracker or event data. And you can also plug in your aggregate set of information using the thematic layer along with the tracker data. Okay. So this is the menu where you select your the layers which you want to plot. Okay, and for the events model tracker model we have these three layers the first three which are of relevance. Okay. Then within the map when you're creating your map you're defining your data, period and organization units also. Okay. Let me open a map for you and then we can review the maps and see the layout which has been selected while creating this respective set of information. Okay. So we'll review a map first and then we can see how this was created. So I'll select the COVID CBS lab confirmed cases this year by home location. So you see here that you have a map available here which has clusters of COVID lab confirmed cases by their respective home location. Okay. So we defined that the location that we're choosing to plot these COVID clusters on the basis of the residential location which was collected when we were adding the data for lab results for these patients. So we saw that with each program stage, there are coordinate fields associated. So you can search for a patient's location a locality a city or a specific village, and the instance it is linked to the open street map and its it pulls the matching locations and gives you an option to select and the coordinates are then assigned to that respective event. Okay. So then these clusters are created by the locations or the coordinates which you added with each events and these are all the cases which are lab confirmed this year and the plotting is done by the home location. On the left hand side we see that it has a certain disaggregation also so we are seeing cases by their respective genders, the male and the female and not set. So this is an attribute which has this option set associated. So what we did was we aggregated events as lab confirmed positive. But we wanted to bifurcate that data by using the gender attributes so that they we can see the division or the person the approximate proportion of male confirmed and female confirmed cases in these respective locations. Okay, so these clusters are deliverable clusters. A single dot which you see here is one male PCR positive case, which was tested at this respective health facility. While the cluster is a group of these cases which kind of share the same coordinates of the same location or same residential areas. So the basis of that these clusters are created. Now what we can do is we can review the selections that have been made for creating this respective chart item, map item and then we can see how we can create such charge. So we'll click on the edit option here and we'll review quickly. So when you click on the event layer, edit event layer button here, you'll see the selections for data period organics are inbuilt into this one pop up. So the concept remains the same just the interface is different where you were collecting the data period organic information from a left hand side panel in event reports and visualizer. Whereas in the maps app you have one pop up which has all these tabs which allow you to select data for analysis and what you want to plot here. So we selected a program case with surveillance and it would load the stages which are there in this respective program. This is already a created favorite. Hence you see that this only has lab results selected because it was using lab results program stage for showing the data. But when you create a new event where you see the list of stages which are associated to this program. We'll see that when we create this chart from scratch. Then you have the coordinate field as event location. So as we had previously discussed, one is your default enrollment coordinates or event coordinates which are there as system properties, but you can also define your data elements or attributes which are of coordinate type. You can collect multiple coordinate information in the system. And if you're collecting, say the coordinates of the health facility also and coordinates of the home location also, then you can choose from here that which coordinate field you want to use for creating these clusters. So if we were using the facility location as they went location, but in a separate data element, we were also capturing the home coordinates. You'll see a dropdown and you'll see one more option called home coordinates. Okay, so depending upon how your program is configured and what kind of coordinate metadata objects are available. You can see the list for you so you can see the same data but in different geographies, different locations depending upon what coordinates you select from this respective list. Okay. Event status, you want to see data for a specific event now completed is the most common that we use but in case the end users forget to complete their event and you'd want to miss out events which were complete in terms of data but the end user forgot to complete the event while doing data entry, you can just select all so that it will cover data for all the events which are complete or incomplete irrespective of their status. Okay. So I selected the data which I want to plot the periods again are relative periods, or you also have start and dates, or you also have your fixed periods, which you can select from the dropdown. Okay, so most commonly we use start and end dates because they kind of are more flexible, and you can set a future date as well so that this chart or map regularly gets updated as new data starts coming into the system. Okay. Again you have the same concept main below two weeks below as we saw in the event visualizer. So depending upon the access that I have respect to the organization at hierarchy, and the configuration of the shape files which are there in the system, I can assign a particular map so that the map stays dynamic for the users. If you put this map on the dashboard, then if we load specifically for specific users depending upon their org unit assignment. Okay. So the concepts remain same to do main below two weeks below. So this is your user org unit. This is sub unit. This is sub extra unit. Okay. So you can select the levels from here. If you select a specific organization unit, then it kind of remains a fixed chart. So only those who have access to say allow PDR will have access to it, the venting capital won't be able to see the data because they don't have the data assigned to them. Okay. So when you're creating charts for your dashboard or dynamic sharing with all levels of users ensure that you select the user organization units from this panel so that it remains dynamic for all kinds of users. And on a condition that that GIS is configured for that specific level also because when you're working with countries and working with organizations, you often find that GIS files may not be available that easily till the lowest level. So they're either available till the province or maximum to the district or you can even go level below. But in some implementation use cases we even have facility coordinates available so you can even go down to the lowest facility level and plot data. Okay. So you can see as per implementation how you can set this up so that till whatever level the shapefiles or the GIS configuration is done at least you can see the data disaggregated to that level. Okay. So filter is basically you're choosing your data element, which have already chosen here from the stage three lab result. You put a filter that you want to see the lab test as a data item, but you only want to see the positive cases. Now you can add multiple filters also. So if your analysis requires you to add more than one filter, then you can select one or more data element and then you can also select the location. For example, you want to see that you only want to see the positive PCR tests. Okay. So you added two filters now. So earlier we were seeing lab test results, which was positive. Now we've added one more filter that I only want to see the positive test results which were conducted by PCRs. So I've added one more filter here. So like that you can add multiple filters to kind of improve or further refine your clusters that you're trying to create. And then you have the styling option where you can either group events or you can view all events. If you click group events, then create these clusters for you as you see on the background. If you do view all events, then it will show you individual events. It would no longer cluster those events based on proximity, but will show dot for each individual event. You can also do styling by data element. So as we saw that we wanted to further disaggregate our clusters into male cases and female cases. So I selected sex as my styling data element. And as soon as I select sex, it gives me the options which are associated with that data element. So this is more relevant when you want to do analysis for your data elements which have option sets associated. So for example, if you wanted to see these clusters disaggregated by age, then you see automatically you have the legend set shown here that you have a legend set for age COVID-19. So in case of seeing these clusters by sex, we're now seeing these clusters by age group. So this may not be applicable to all the fields which you see here, but if you need to make a conscious decision on what is the best disaggregation which is more suitable to your analysis. So I changed it to age and for age, I don't have an option set assigned, but I do have a legend set associated. So my age groups are already defined in a legend set for which these clusters will reorganize themselves into age group specific disaggregations for each cluster. So let's try and update this and hopefully let's do this this year. So let's see. Yeah, so now I see the output where I see the data for different lab confirmed cases by the residential locations. Only those who were identified PC positive by PCR and this is the age band that I've set up for my COVID-19 cases. So the colors might not be that bright, but then when you're creating your legends, you can use your own color sets and you can create these legends and you can modify these clusters accordingly based on the data that was reported. So this was in review of what functionalities are available when you're using your event layer. So let's try to refresh this one. Let's go to a blank slate and let's see what steps do we follow to create such charts. So first of all, we add a boundary. So why boundaries are important. You can always create your chart without boundaries. There's no compulsion for you to add boundaries, but kind of these boundary demarcations kind of distinguish one geographic unit from other. So it's important that you do it so that your chart looks cleaner and able to segregate the data by their respective district or all province boundaries or for whatever level the boundaries are available. So let's select boundary and we can select two weeks below and click on add layer to see if we have the boundaries available. Yes, we do have. Then we click on style and add labels. Now the labels, if you're looking at at a very lower levels, then it might make your map clumsy because the labels can tend to get overlapped on each other. So ensure that you use your labels wisely if it kind of gives you a more visually appealing chart or which is easier to read than use them. Or else, if you're doing data plotting for a larger number of organization units, then you should avoid doing that so that your charts don't look overlapped and you can always hover over and see for what specific region you are looking for. Okay. So this is how you can see the boundaries, which you've added now. Then we go to add there again and select events as a layer. This opens a new pop up. You click on the program, select the program. And now you see all four stages here. But let's try to see the lab result example again so we select lab results. We have a single location coordinate field defined here that is event location. That means by default, as the the practice implemented by the ministry, in each event location, you are recording the location of the person's home. Okay, so you have one specific event location which you can select event status we let all so that all incomplete and complete events can become part of a chart, the map. And then you go to periods and you select from save this year, you can also put start in and end it. You switch to organization units, and we do two X below, because we have the boundaries available for that level. So the data is also available because it's getting collected at the individual health facility that we saw. So, but you need to also keep into account that whether a user with just has access to the health facility, would he be able to access this particular map or not so you need to be a little wise and take all considerations into account when you're creating these items so that the users at each level are able to see the visualizations that we're trying to create. Okay, we go to filter, and we add a filter here that we want the lab test result to be positive, and you can add multiple filters if you want to as I showed you in my past overview. Then we go to style, and we want to see individual, let's start with your events, and let's select from here sex, and then I already have these options loaded, I can change the color if I want to say for female I want to switch to another color, then I can do that for male I can do blue, so I can change the colors as well. And let's try to add this layer. Okay, so now it shows me individual events for each male and each female case which had a lab test result positive, kind of shows the data, but I really can't figure out the quantum of the test results positive in my designated area. So I can go back to the my edit event layer option depends like I go and I group events, and I gain update the layer. Okay, so by the proximity of these individual events, it kind of summarizes that data into different clusters. It becomes visually easier for me to see that which particular geographical unit is contributing what number of COVID-19 cases, and visually I can see the proportionate things also that what number of cases are female cases what are male cases. Okay. Now, if you do not want to see the, the base map, then you have this visibility option you can switch it off. So this will become a map without any base map layer. But if you want to change the list then you have some open street map players available where you can use a light layer or the detailed layer or the big roads. So you can use any layer which you want to. You can go use a dark layer if that makes more sense or adds more visual appeal to the specific map that you're trying to create that depends on what you want to keep it clean. You just want to show the boundaries and the distribution you can always switch it off. Okay. So you've seen this, then you can also see the selected data for each of the case on the pop up. So if you click on an individual event, it will show you the data for that respective event. We see here that no person identified information is shown here so the attributes which are collecting for the patient, they cannot be displayed here because that sensitive information. But you can, while configuring your program you can define that what data elements you want to show in reports. So when you do display in report, then those elements are shown here. So this configuration is all covered under the Tracker Configuration Academy. So if you want to learn that then please enroll into that course and see how these finer settings are made to have your data model in congruence to what you want to see on the outputs. Okay. Next. So on this map, we just want to save it so we go back to the file menu, we click on save, and we give this a name, covid cases, lab positive by gender, home location, and we click on save. Okay, so now we have a map saved. Then we can download the map. You have some options to choose whether you want to show the name you want to show the legend and where you want to place the legend. So you can change that and you can click on download. So it will download this particular map as an image, which you can use in your presentations. So I'll stop here. Let's try to do the exercise one in your learner's guide. We take, let's take five to seven minutes for that, and then we can cover the next layer that we have. And the mean of the questions, please feel free to add in the zoom chat on the Slack channel will answer that. Okay, let us continue to the next part of the maps presentation. So we'll have a look at the track entity layer now, and what is feasible to do at present because this is still a feature under development. So, let's try to create a map using the tag entity layers with relationships. So, let me open pre configured map for you and then we can review it and then see how we ended up with that. So, I'll open this COVID-19 CBS cases and contacts. You'll see here is that it is showing a person along with the relationship. In context of the COVID-19 program, it means we're displaying the index cases with their potential contacts based on their locations which are entered. Okay, so the red circle which you see here is the index case or where the relationship was initiated from and the black circles which you see are there contacts for each of the index cases. Okay, so we'll see how we can create this map and we'll also see the limitations that we have right now with this respective feature. So we go to the file menu, let's refresh this one. And let's start adding a layer. We start with boundary layer and just select the ending capital for now. And we click on add layer so we only have a layer added for one of the provinces in law. Then we again click on add layer and select our track entities there and you see there are some selections you need to make. You need to select the fragd entity type here. So when we discuss the tracker data model we spoke of fragd entity type. So it could be person, could be lab sample, could be household, could be village, could be any commodity or any which you want to track. So in this case we have one fragd entity type defined in the system which is person. We select a program so we'll select COVID-19 case this awareness program and program status related as all relationship. You see it already shows a morning that it is still an experimental feature and we are doing more developing around it to make it more, more robust and more usable feature for the tracker data model. But to begin with you need to map the relationship which you are using to create the fragd entity layer on the maps. So you create, you click on display tag and relationship and you select the relationship. Now in our case based surveillance program we had a relationship has been in contact with in the contact tracing program. Okay, so we'll see how is that getting used here. Okay, so selected my relationship type here. I go to period and I select the enrollment date when the date where the tag entity was just enrolled into the program. Okay, or else you can also do select period when fragd entity is the last updated this is still an experimental feature so we'll stick with what works right now that you select enrollment dates. So we'll let it let's go a little back and select 16th of October so we'll select all the enrollments which have been made in the past one year. Now, we select the organization unit, you can either select the whole country or a province but then you'll see a lot of arrows going back and forth, and it will become a complex diagram. So the organization will just stick to one of the health facilities so we'll just select chw metaphas. Okay. Style will leave it as it is for now, I'll go back when moving forward I'll I'll explain how you can change these and how these impact the map that you try to create. If you click on add layer. So this is how the map looks like where it has these index cases and the potential contacts which are added for each index case. Okay. Now, you'll see that that it has a relationship between the index and the contacts case. But the drawback that we have here is that right now the relationship layer works within a single program only or relationships within the same program. For example, the workflow that we had was that I registered a particular index case case in the covert case surveillance program. And then slowly the contact tracing activity happened and I registered the contacts in the contact tracing programs. So we had two programs here. This is the relationship between the case and the contact through my relationship which was connecting my case based surveillance program and my contact tracing and follow up program. Okay. But since the relationship layer right now does not work across programs. Hence, you only see a lesser number of relationships here. Because these are those contacts which were found to cover positive moving forward and they were again enrolled into the covert case surveillance program. And they were then treated as separate new cases of COVID-19 and a separate contact tracing exercise were done for them. Okay. So right now we're only displaying relationships within the surveillance program, that is, both the cases and the contacts are now part of the same program. So if person a had COVID and then person BC and D were their contacts on further moving ahead on further. So we got to know that the contact to be which was added or person be which is added as a contact of person a has now COVID-19. So you enroll that person back into the case surveillance program because you need to track his treatment and testing details also and outcome as well. So you have both the index case through which the contact got COVID and that contact is also part of your case based surveillance program. So based on that relationship. We were able to create a map where we could show the index cases and the contacts, given that the contact is also part of the case based surveillance program so that's the condition here. We are only able to display relationships within the same program on the map, currently using the flag identity layer, so we cannot do cross program analytics. Ideally, we should have been able to use the index case and the contacts between the two programs, which is not happening right now. So this is the limitation that we have so since this is a feature in development soon we'll have updates in the forthcoming releases, which will allow you to create a relationship layer across programs so you can see that more widely across programs linking the index case with all the contacts which are related in the contact tracing program. So I'll come back to the style app so everything style menu everything remains same. What you can change here is that you can change your color by how you are denoting your index case so you can change it by making some selections here. Then, to the related entity or the relationship which you want to show you can change the colors of the dots, and you can also change the color of the lines which are there, which are created between the index and the contact. So you can change the line color, you can change the color of the particular and then we can just update this layer so the colors would change here so this is how you can use the styling tab to denote the index person, the line shows the contact tracing and the dot shows the person who is related. But all this is still working within the ambit of the same program and not across programs so this will further get enhanced where you could also see relationship across two programs plotted on a single map. Now there's one more limitation which we see here that unlike the events layer we're not able to add any further disaggregation so we cannot see male contact separately a female contact separately, because we are not linking the coordinates data with the tracked entity, it's mostly related to the events across so we can't use additional filters here. So that's why we get to implement this functionality in moving forward this should also get implemented and it's expected to come out in future releases. So these were the two things that we wanted to show on the map specifically one was to use our events layer. Again, the most important factor is that you should have your coordinates added with the events and the tag entity instances then only you can use these functionalities in the system. The GIS should be configured with boundaries etc so that you can segregate and choose and select what areas to plot the data in. And you should know that the data sources, as you are aware with the event visualizing event reports, you should know what data element to select what what period to select and what places to select which should have appropriate data which you want to plot on your maps. So I'll stop here and you can do the exercise to in the learner's guide, and we'll also take a break. So we'll start the session next session at 205 that's 15 minutes from now. So you can take a break and do the second exercise as well. The next session is on program indicators, we will discuss different concepts of how you can aggregate your tracker data through program indicators, both for events and enrollment type so that you can count the number of events and you can have unique tag entities or unique enrollments also. So if there are any questions for the maps section please feel free to put on the chat I'll keep on answering them during the break and even during the next session you can refer to them later. So thank you for your patient listening and we'll see you in next 15 minutes for the next session.