 Alright, so start with the session. We'll quickly review the learning objectives for today. So basically we'll have a look at how maps can be used in DHIS too and how we can use the maps to analyze data through different layers. So GIS in terms of its overview is quite broad. It's a vast topic in itself but the focus that we will have today is on the maps app which is kind of a concise GIS built within the DHIS2 system. So we will kind of try to look at the possibilities of data analysis using maps and different layers within DHIS2 which may not fulfill the entire scope of services which a broader geographic information system provides. So our focus would entirely lie on what functionalities and what kind of analysis is possible using the maps app in DHIS2. So we will be demonstrating different functions available in the maps application. So we'll go through the maps app interface in terms of what selections available, what features available and what each function contributes to in the overall analysis function. Then we'll be talking about thematic layers which are basically layering your data or one over the other into different layers using different indicators or different data elements. So it's all about thematic layers when we talk about aggregate data, so we could plot the data into these thematic layers at different levels in the hierarchy. So depending upon how the analysis has been set up, the geographic, the GIS has been set up in the DHIS2. Based on that you are able to perform the levels of analysis using different layers in the application. Then we discussed about legends in both the pivot table and the visualizer session. So legends have a key role to play in the maps app as well. So we'll have a look at the automatic legends which are available, the predefined legends which could be configured. So both we'll discuss during the course of the session. Then we'll have a look at the next two type of layers supported which is the boundary and the facility layers. So boundary layers are basically all the administrative geographies of boundaries for different administrative areas in your country and facility is the individual health center or a hospital. So how we could layer these on the maps app. Then how can we utilize customized legends into our analysis? So we'll have focus on that. Then along with the maps there is also a supportive data table available. So we'll have a look how we can review the data table in congruence with the map that we have developed. And then there are certain views available which kind of add more value to the analytical object or the map which we have created through the user split views, the timeline view and bubble maps have been newly introduced in version 2.35. So we'll have a look at that as well. So in terms of nutshell what the DHIS2 maps allows to do is is to create thematic maps of area and points. So you could create your thematic layers or layering of data one on top of each other to kind of analyze different indicators through either use of the areas which are your polygons which could be your administrative areas, it could be region, it could be districts, it could be blocks and it could be drill down till the facility level also. So your areas are basically your administrative areas like your districts blocks and your points are basically your health facilities with that which you could analyze the data. Then you could view facilities based on their classification. So a facility can be grouped by its type, by its ownership, by the areas it lies into, maybe it's an urban facility or a rural facility. So accordingly you can, sorry, just give you one moment. Yeah. So you can group your health facilities based on different classifications. So you can either classify your health facilities by their type, by the ownership or by the area they lie into. So you could view those facilities based on these classifications. Then with the map you can also have a look at the underlying data which is associated. So it could be the data for facilities infrastructure or data for population or other variables which you want to kind of utilize with the indicators of data elements that you're applying to. Then you could download the map for offline use. So if you want to use the maps which you have created for your presentations or your external documents, then you can download images and use those images in your external documents. Then there's also a feature which allows you to download the map data into a shareable format which could allow you to import the map data into different other external GIS applications such as QGIS. So if you want to do some sort of advanced analysis which is not available in DHIS too and you kind of have expertise on other GIS software, then you put download data from DHIS to use that data in other software such as QGIS or RAP GIS. So these are the key functionalities which are available in the maps application in DHIS too. So basically the maps app allows you to do a special representation of data and kind of they're more tangible to the end users because they're able to clearly highlight or clearly see the areas within the old health system which needs more focus depending upon how you are analyzing the indicators. So if you want to show or highlight the problem areas for example for any program if you want to see the coverage of certain vaccines say the BCG immunization or even for now if the larger focus is on the COVID vaccination system or to see what areas have more vaccination coverage, what areas are less vaccination coverage, a map kind of gives you a very clear picture of what's happening on the field, what are the areas which are producing good results, what are the areas which need more focus. So kind of the outcome of the data on the map is more tangible and it's easy to share the present situation with the stakeholders and decision makers so they're able to quickly see which particular geographic areas need more intervention or we need more support and then how our interventions could be directed to those areas to better improve the performance of those respective facilities or those specific districts. Then it's very easy to overlap information one over the other so if they are contributing factors for an indicator where you want to see if there are less number of COVID cases being reported in a certain geography then we could correlate that whether a good amount of testing is really being done in those areas or not okay because these are proportional. So if you're doing low testing then of course you'll get low positives but then if the testing is done adequately and and there are less results that means the infection is contained to a certain extent but then if you are doing larger testing and there are larger positives being identified then it's kind of runs in proportion that because the testing is an adequately hence we're able to identify a good number of positive cases. So there you are kind of using information by different information in different layers and you're overlapping them to kind of see that what are the areas which need more support or any intervention needs to be carried out for that specific area or not. And then if there are relationships available in terms of especially between the different public health concepts then of course you would utilize those concepts and apply those concepts with the maps and come out with an interpretation that based on the analysis which has been carried out these are the areas which are of concern and these are the geographic areas which need more help in terms of providing more resources or giving them more support so that their performance can improve over time. So the different layerings we see for example in DHIS2 we kind of focus on the boundary layers, the thematic layers and the festivity layers so in order for you to generate a map you need to understand the layers of value in the system and in terms of configuration when we are configuring the map the GIS we kind of use these different layers to kind of import these coordinates against each of the administrative areas. So when we talk about a festivity then that's a point layer so in order to generate data at the festivity level we need to have festivity coordinates available in the system. Similarly for district and provinces those would be the shapefiles which basically have these polygons inbuilt and then these polygons kind of come up and these polygons are shown on the maps app as the district and the province layers so on which the data gets plotted. Now you can overlay the data between these thematic layers so if I want to see the overall testing done for HIV in all the health recipes then I'm using the the facility as one of the layers where I'm plotting my data then I want to see how many positive HIV cases have been identified as an outcome of the testing that I wanted to see overall at the district level then I can use the district level and then I can use these two layers to and overlap them to kind of see the data between overall testing done and overall positive so I can plot the data using these different layers and then use those layers to kind of see data for two different indicators together. So each layer will have the boundaries and the data elements associated so when we go into the demonstration we will see that when we are setting up each layer we are selecting that what kind of at what level we are trying to analyze the data and for what data elements are for indicators for what period we are analyzing the data so we'll see these details as we go ahead. So in terms of the overall look and feel of the Maps app so the Maps app looks like this where you have functionalities to add different layers so you have add layer button on the top which kind of gives you access to the layers which are available in the system and then you have a base layer which is integrated with the Bing Maps and also the OpenStreet Maps where you have these different layers which are kind of from the base of the map so you could select any of these three available base maps but it is also possible to import external base maps so if you have any other satellite imagery or any other external base maps available those also can be imported and can be utilized for plotting your data. This is your map display area where the dimensions or the layers that you select they get populated and this is your selection area where you make all the selections which are required for your spatial data analysis. So base map layers are represented by these layer cards in the layer panel so these are the ones which are available in case you do not want the base map to be shown at all then you can toggle on this i option this v option this will disable the base map layer altogether so you'll not see any base map layer in case you do not want these layers to be shown on your map display area. Then within the map layers which are available in DHIS2 in the maps up you have through this academy we'll focus more on the aggregate data analysis using the maps up so when you're analyzing your aggregate data you use three layers primarily so one is your facility layer one is your thematic layer and one is your boundary layer. We'll see the the function of each of these layers moving forward in the demonstration so based on your requirement you can choose the layer and manage the dimensions which you want to see in in these perspective layers. So for example this is a end output of a thematic map which we can plot in the system where we're analyzing that what was the coverage of the Beasley's Rebellor first doors in the last month and at the district level so you're able to see the ones which are in red these are the low coverage districts there are five districts which are falling under low coverage three districts which are under mid coverage between 70 to 80 percent there are two districts which are on high two on highest and one there is no district which is under invalid or shown data data in 100 percent so like this you can create your thematic layers your thematic maps into the system by utilizing the different selections available in the maps app and this is an example of your end product which is available so this map could be then saved as a favorite can be put on a dashboard can be downloaded as an image and can be utilized for further documentation as well then each of the map which you create is supported by a data table so for the map data table gets generated which has these additional filters on the top so you can add a dynamic filters here and the map and the table both will get updated based on the updates which you do in the table itself so if you want to play around with the map add some layer combinations of data which may or may not be part of the legend then you could also pass on dynamic ranges as well so if you want to see let's say districts falling between say greater than 65 but less than 85 then you can put these these combinations of data into the value field and the map and the table will get automatically updated based on the conditions that you pass into the maps app so there are few considerations which you need to take into account when we are talking about the maps application so of course for any visualization that we do we covered a lot of these concepts during the visualization session where we kind of were choosing what are the best mechanism to show a kind of data to different visualizations available so the same concept supply here as well you need to take into your audience into account when you're creating the maps whether these the maps give adequate information to the audiences for which you're creating these reservations then the type and the purpose of data whether the kind of data that you're plotting is easily representable and understandable through the maps that we're creating so we need to take into account that as well then you have the nature of data relationship being presented for example if you want to see the testing versus positives phenomena so that's that's one thing which you can take into account so whenever you are overlapping your layers you need to ensure that the layers that you're overlapping do hold a valid relationship in between or the data can be related and can be further analyzed upon so that means to be taken into account and the last is the accuracy and the applicability of the map since a lot of this GIS configuration happens through the shapefiles and the coordinates which are already available with many of the organizations so these configurations are done before hand the maps application can be used therefore the accuracy in that process when you're doing the configuration of the maps app holds key importance in the final output so we need to ensure that first of all when we are configuring the maps app the administrative boundaries that the source files which we're using for configuration do have the correct data and they kind of show the exact geographical situation which is there on ground in the system as well so these are some of the considerations which we need to take into account when we are talking about the maps applications so there are some questions that we'll try to answer moving forward that how can maps help you to communicate more effectively for example we saw in one of the examples that we used in in the presentation in the past was for the MR1 coverage so through this map I was very easily able to identify the districts which have low coverage and now I know that these districts have low coverage so I can try to do targeted interventions for these districts specifically to see what are the factors which are contributing to such low coverage as stocks in issue or supply chain management issue or a human resources issue or any other thing which is hampering the districts to perform better in terms of covering the vaccinations so it kind of showed me the problem areas the geographic areas which are issues more prominently so that I could focus more on finding solutions to the problems that I've kind of seen in that those respective districts. Then we need to see if we want to print that specific map in a document so what manipulations are necessary so if I am preparing a map for an external document for sharing with wider audiences what are the changes that I can do in the map to make it more accurate make it more context specific so that in one view I'm able to communicate the information that I want to show or discuss with the respective stakeholders and then you'll also discuss some key design considerations that when you're creating your maps what are the design considerations which you can take into account so that you're able to build context specific and useful maps for your own analysis so today we'll break the demo into different parts in the first part we will do an overview of the the maps interface and then we will review the base map layer how we can add boundaries how we're selecting inputs for a specific map that we're trying to build and how we can interpret a map legend when our map gets created through the maps app. Then we'll also discuss the facility layers how we can add this layer to the map and how we can do filtering on the map that we just created then how we can overlap these thematic layers and try to find a relation between the data that we have plotted on these multiple thematic layers then we'll also have a discussion and demonstration on the legend options which are available in the maps how we can use or change these legends the difference between the predefined and the automatic legends which are available and how data gets distributed across these legends so we'll have a discussion and demonstration then we'll see the drill drilling down and drilling up function on the map and see how we can do that we'll also focus upon the data table and downloading of maps into images and downloading the layer data of the map application then we'll have a look at the split blue and the timeline maps how we can utilize these different view options available for the data that goes on the map and in the last part we'll cover the bubble maps which have been just added as a new feature conversion 2.356 so I'll log in into the RHS to instance now and take you through to the first part of the session where we do an overview of the maps interface and move on with the next set of sessions and the practice exercises so I'll just take it through to my DHIS to screen okay so I hope my screen is visible to all so we have a dedicated maps app as we had for our other visualization applications that we have seen over the course of the last week so we click on the maps application from the applications menu available in the DHIS and we have this interface available on the default maps app so let's try to open existing favorite and then we can review the maps interface so I'll go to the file and look for the favorite which is already available so let's have a look at the BTP notified case new and relapse all forms so this map kind of shows you the spread of tuberculosis cases the notified cases in October to December 2020 by each district okay so if you look at the interface so on the left hand side you'll see the layer which is available so this is the thematic layer which is available and this is the legend which is associated with this respective thematic layer you'll see the map type here on the top and the map display will show the values disaggregated by each district because when I was creating this map I selected that I want to see this data at the district level for my analysis for the last quarter okay so let's edit this layer to review the layer selection options which are available so if you want to edit an existing map which is available in DHIS to you click on this pencil icon it shows you the edit options so once you click on the edit items you'll see these different sections available for making the selections when you're creating any app in in DHIS to using the map application so let's review the data section first there are different items available here so whether you want to plot an indicator or data element or you want to do reporting rates or you want to use program indicators so we'll focus more on the indicator and the data elements selections here okay then you have an option to select the indicator group so it is important that the indicators or data elements which you want to analyze through the maps application they need to be part of a group so if your indicator or your data element which you want to create which you want to analyze on the maps application if they are not a part of the group then you won't be able to make the selections here so ensure that your data elements and indicators which you want to utilize for creation of your maps they are part of a group if they're not part of these groups then they are not available for these selections okay so for example if I select the TV indicator group then I'll have these indicators which are already part of the TV indicator group and then all the indicators which are part of this TV indicator group they could be available for analysis okay now aggregation type is by default it will take up the aggregation type which has been set for the data elements which contribute that indicator so we don't need to change that so we'll leave it as by data element okay period so as we saw for pivot table and visualizer we saw there are both the options available one is to select relative periods but then you also have fixed period available if you click on relative then you'll have the list of all the relative periods by the frequencies available for analysis so started from daily you goes into weeks goes into months it goes into years by months quarter six months year financial year five years so depending upon the periods which are available by default you get all those all that information here if you want to use a specific period then you can select a period type and then you get the list of options to choose from so right now this data is for October to December 2020 we can change that period and use January to March 2021 okay then we switch to organics now there are two ways in which you can select your organics one is through the fixed options which are given here either you can select the levels at which you want to plot the data on the map or you can even select these groups if you want to see data for a specific group of organization units only okay so these are your fixed then the parameters which are passing when you're creating the maps but if you want to create a map which is dynamic and it kinds of is applicable to all the users in the system then you can select these user organization units so this is the same principle that we saw in a visualizer app so when you're creating a dynamic chart which could be accessible by users at different level in the hierarchy then you can select these user organization units if I have access to a particular province then I'll only see the data for the districts which are below the province that I'm responsible for or if I am responsible for a district then I'll only see data for the blocks which are below that district now it also depends upon at what level you have configured your maps application whether you have your polygons imported for block level as well if not then you'll not find any data getting plotted that perspective so depending upon how the GIS has been configured in your DHIS2 system it would take up the plotting of data accordingly okay so depending upon for what audience you're creating this respective map you can choose the organization units here so it could either be a fixed level at which you're plotting your map or it could be based on the user organics you could make these selections here filters we not to cover now we'll see it in the later sessions where we can utilize the filter aspect and style we'll go into detail as we go ahead into different sections and we'll see what each of these sections for each of these options contribute to so let's try to change the organics here and let's see the data for districts in the animal region so you're kind of adding a dual filter here in the organics we are saying that you want only want to see data for animal region and for the districts so you can make a change here so now if I put this chart on the dashboard then this data would only be visible to the users who have access to the animal region itself or the district within the animal regions for others in the food region they won't be able to access this particular map because they don't have access to the animal region and its respective organization units so when you're doing these fixed charts you should understand that since we selected these values here so these are kind of fixed to the users who have access to the animal region and the below districts which are there okay then we have altered some selections here we have changed the period of this respective map so we have taken Jan 2 March 2021 or units we have made a change we have selected animal region and within animal region we selected the district and we have made no change to the filter and style okay so once I have made these changes here I can click on the update layer and you'll see now the map has changed it only shows me the data for the animal region and the districts which are there in that respective region okay so likewise you can edit your existing favorites and make changes to either the data or you can make changes to periods you can make changes to arguments and also to the filter and style aspects the filter and style aspects we'll see later on in different examples how we can utilize that so this is the basic interface which is available and the period selections available in in the system okay so if so the selections which are available are your data your period you augment the filter and the style so we have covered these three right now the data the period and the arguments and as you move forward we'll focus more on the filter and the style aspects as well okay so any questions up till now please feel free to put on zoom chat or the staff channel so that we can answer those questions as we move ahead now for example we'll move on to the next topic where we'll see these different layers which are available so now this was a pre-generated map which was part of our favorites which we opened in case we need to start working on a new application new map then we go to the file option and click on new so this will kind of make your canvas blank and you need to start from scratch to make a new application now a new map okay now we see here that these are base maps which are available so I can switch between these base maps depending upon what particular base number I want to utilize so the ones which are related to the Bing's map they will get loaded automatically if you have external base maps imported into your DHS2 system then you can use those external base maps also okay or if you do not want to use any base map you want a wide background then you can click on this particular eye option so this will disable any base map and you'll have a wide canvas to work with okay now let's look at the add layer button so if you click on the add layer button you'll see the types of layers which are available in the system which we can use for plotting our data now there are thematic layers available event layers available fragmented layer available facilities there available boundary layers available so out of these the ones which can be used in our aggregate data are thematic layers are facility layers and the boundary layers events and frag identities are more focused towards our events or tracker data model so they're not we'll not have examples today for those so these are part of the tracker level one course that we run where we discuss how we can use maps into the events and the frag identities if time allows today then I can quickly cover these if not then we focus more on the the aggregate aspects so if we have a look at the boundary layers so boundaries are basically our geographical demarcations between different areas in your geographical hierarchy so if you click on the boundaries layers you'll see some selections available here so depending upon the levels which you have defined in your hierarchy you could select the levels here and based on the availability of the the shapefiles or the configuration which you have done in advance these boundaries can be plotted accordingly okay so country region district these are basically your polygons your your broad geographic areas for which when you import the shapefiles into the system these polygons also get imported and get associated to these respective provinces districts etc and then these kind of form your broad boundaries facility is your point layer which is basically your exact x y coordinates of that facility or has hospital or a pxc okay so let's select the district from here and let's try to plot the the district boundary so if I select training land I select the district from here and I click on add layer so what I see is that I have my district boundaries plotted here so within our training land instance you have these you have these districts and each of the district has a boundary which kind of helps us to be market the areas on the on the map okay now let's try to plot some data so when you're plotting your data you always use the thematic layer okay so thematic layer you can add multiple thematic layers but one thematic layer can have only one particular data element or indicator which you can plot okay so you can plot more than one indicator on one thematic layer so the ratio is one is to one so each thematic layer will have data for either one data element or for one indicator okay so let's click on thematic layer and we'll see this pop-up there we can make these selections so let's try to plot an indicator now so a data element now so we click on data element and we select the data element group so you see in pivot tables and visualizes there's an option to scroll through all groups so you can see all data elements all indicators which are part of your system but in maps app you do not have the all option available so essentially the data elements and indicators which you want to plot on the map they need to be part of any group mandatory so you make sure that your data elements indicators are grouped correctly so that they can be utilized on the maps application okay so let's select the hiv data element group and let's look for hiv test perform so i made my selections here in the respective data element so i want to see data for hiv test perform okay now let's go to the pivot now here i want to see data relative so i'll remain let it be relative and i'll see data for last six months so i'll go down and select last six months now you see there are three display periods one is single one is timeline one is split map so we'll see timeline and split maps in the respective session when we cover these so right now we'll let it be selected as single aggregate then we'll go to the organets and we will try to plot this data at the facility level okay and we remove region from here okay so i'll select the facility and filter and style we'll see we'll modify existing map with the with the different options available so uh yeah filter we'll cover later let's come to style so you see there are two options available here one is coroplet one is bubble map bubble map we have is a dedicated session so by default your your style would be set to coroplet which are basically your heat maps or you can say they show us the the data into different as a as a layer into different colors based on the legend which are identified okay then you have these two legends available one is automatic color legend one is predefined color legend so automatic color legend is controlled through these color scales which are already available which are predefined into the system so depending upon the indicator which or the data element which you're trying to plot you can use that so if your data element is or your indicator kind of follows that lower the value uh lower the performance or lighter the color lower the performance then you can use something like this or if it's opposite where you want to see higher the value higher the performance then you can change the the switch share so lower the better higher the better depending upon how you want to visualize your data so example mortality rates you kind of want to show that the areas with high mortality rates should show up separately and so depending upon your indicator definition you can use these the color patterns available here okay but if you have a specific color legend defined in the system for a specific data element or indicator then you can use that okay so if your program has defined that these are the these are the legends which you need to follow for a specific indicator where you are defining the ranges on uh based on uh the understanding that we have within the program then you could create these additional legends as well okay so for example for epi coverage if they are ranges defined that 0 to 60 is low 60 to 80 is average 80 to 90 is high 90 to 100 is highest and 100 and above is invalid so these are the ranges which are predefined by the program that this is how we would like to divide our districts based on their coverage performance so then you could redefine these legends and use these legends while you are creating that specific map for your data element or indicator so there are two ways to assign these predefined color legends so when you're creating your indicator or your data element as Pramil showed in his pivot table sessions when we discuss the legend so you can pre assign a legend to an indicator or data element when you're creating that indicator or data element so if a legend is pre defined with the data element and the indicator it gets automatically selected here or if you want to use a predefined color legend then you can select from the list of legends which are already available in the system okay or if you want to use automatic color legends you can then use any of the color palette which are available and are applicable to the indicator which you're trying to plot okay then if you're plotting data at the facility level that means a point layer so you are trying to map the data at the level of facility which has a x y coordinate and you want to show the radius of that respective health facility through the data so if the data is high then we can show bigger circles so then the radius could be set from here so if the data is less then it can show the low radius if the data is more then it could show high radius so that you could differentiate between the health facility based on the data which has been reported or the data volume which is available okay so by default the radius of the point layer which gets created is 5 to 30 but you can change this when you're creating your maps and we'll see some examples where we change the respective radius and you'll see that the radius of the point gets increased depending upon the volume of data available for that respective health facility okay then we have these labels option if you want to show the names of the health facilities then you can select labels by default it is set to no labels but you click on it then you could also modify the size of the labels the color in which the labels need to show and whether you want to italicize or highlight or bold that respective names of labels which is shown okay so we'll see show no data later it has significance with how you analyze the data on the map so we'll cover this information later okay so let's review once quickly what we have selected so we have selected the HIV data element group we have selected the HIV test performed data element we have selected relative period for last six months in the organ it was selected that we want to see data at the facility level in the style we have used the automatic color legend and we have let chloropleth be used in advance and the radius we've just set to default values which are available we're not using labels right now so let's click on add layer to see the outcome of this respective selections that we have made okay so now what you see is you see the data which is plotted for HIV test performed at each health facility and you could zoom in and zoom out so if you hover over this respective health facilities you see there are 19,943 HIV test performed at this respective health facility then there are 32,489 test performed so now through the radius of this particular facility the circle you're able to see which facilities have performed the highest number of tests in this district and which have performed a lower number of tests so based on the radius of the facility layer and the the color gradient you're able to highlight the highest performing and the lowest performing facilities in terms of or if you can't say high or low we can just say by the volume of tests which have been done at this respective health facility in the district you can visualize this respective data okay so this is how you can plot the maps at the facility level by choosing your indicator data element and choosing the facility level in your organization units and then assigning the respective legend which you want to use for this respective map now we'll cover one more concept here for equal counts and equal intervals so if I edit this map again here I'll go to style you see the classification here which has two options one is equal intervals one is equal counts okay so both of these have different significance in terms of how the data gets plotted on your map so how the data gets distributed on the map so when you talk about equal intervals so what it would do is that it will try and distribute the range of values equally amongst your data the total lowest in the highest range which is available and how many classes of data is available in your respective legend okay so now if I do a predefined color legend the classes are already defined by me so I want the classes to be 0 to 60 say 60 to 80 and then I can say 80 to 100 okay so then these classes are being predefined by me when I'm creating this legend but when you're using automatic color legend you also need to define the number of classes so by default the number of classes would be five but if you want more then you can make adjustments here as well but based on the classification and the number of classes available the system will try to distribute data across the data values available and how many classes are set here so if I take an example I have data ranging between 0 to 20,000 then if I select five classes then the data range for each legend would be separated by values of 4000 okay so this is how the equal interval works so based on the classes that I have selected the intervals which I have selected based on that and the number of values available or the number the the data ranges available from starting from the lowest to the highest it will divide across these five classes okay so right now the data which you see here is based on equal intervals so if you see here it will take up the lowest value which is 105 and the highest value which is 35,207 and it will try to divide the data equally between these respective classes and then you'll see the distribution accordingly on the map so 140 facilities have data between 105 to 7125.4 so the the figure you see in the brackets is your number of facilities okay but in case you change it to equal counts so now this is your decision based on the data which you're trying to plot which is the best way to show the data whether you would like to use equal intervals or you like to use equal counts now when I select equal counts the distribution will change here okay when you use equal counts they will try to distribute the data values based on the number of classes and the number of facilities available okay so for example if I have total 100 org units available or 100 health facilities available where I want to plot this data and I have four classes then each class will have around 25 values available okay so if I switch this to equal counts then it will see the number of health facilities which are available and the classes which are available so it will roughly distribute the data between the division which comes out between the count of org units and the number of classes that I selected so if I take it to equal counts and click on update layer you see the way the map the data has been distributed now differs and you see there is almost the same number of org units within each class so 34 facilities lie between this 33 between this range 34 between this range so it kind of has changed the way the data has been distributed across the health facilities either based on the number of health facilities and the number of classes so then it kind of fits to the equal count mode but when you talk about equal intervals then it kind of switches to the data which has been reported by these facilities and how it can be distributed between the class intervals which are available when you select equal intervals okay so depending upon how what's the best mechanism the best way to use and interpret the map application the map that you're trying to create you can switch between the equal intervals and the equal counts okay now in the example that we were trying to analyze was the number of test performs and we're kind of trying to see the raw data values so here we'll see that the equal intervals makes more sense because it kind of helps you to see the scale of difference between the tests which are conducted by each health facility or a group of health facilities okay so equal counts might not really give us the information because we're looking at facilities which are performing in terms of the volume of test whether they're doing good or they're doing bad or they're performing adequate number of testing or not so then equal intervals will make much more sense here so we can switch to equal intervals and we can update the layer then it will give us a more tangible data which we can use to kind of understand the spread of testing across the health facilities in the district okay so depending upon your type of data and how you want to interpret it you can always switch between equal intervals and equal counts okay so now you see here that the data is available through equal intervals so you could see the disparity of data between each health facility okay because now the distribution has been based on the volume of testing which has been carried out not the equal number of organics or facilities being pushed through the distribution so now you're easily able to compare that this particular facility has performed a higher number of HIV test as compared to this facility which has which has done a lower number of HIV tests okay so withdraw data when you want to see the difference between the scales of data volume between two health facilities or two districts it's always preferred to use the equal intervals in such cases okay so now I've created this app map which now I want to save it as a favorite so I can go to my file menu and click on save and I can add in a name I can say HIV HIV tests performed performed by facility and I can say last six months okay and I can click on save so now I have a favorite which has been saved by this name and if in future if I want to refer this favorite I can go on the file menu click on open and look for HIV test cases HIV tests performed so there are a couple of more which are available I think this is the one which I did so we can select this one so you can again search for that respective favorite and refer back to the clip that we created so we've covered part one and part two of the session so now what we'll do is we'll go to the exercise document which is available on edx on the on the lms please download that document and there are exercises available for part one and part two so let's take around 10-15 minutes to do that and in the meanwhile if you need a break then you can also do that so let's take 15 minutes to do part one and part two of the exercise and let's regroup at 14-13 local time so let's come back at 14-30 to proceed with the sessions next and in the meanwhile if there are any questions I'll quickly go through slack and quite once those questions as well so let's do the exercise and regroup at 20-30 local time