 We can do the same for the regions. If we select region instead of district and update our table, we can now see that the new values reflect regional totals and percentages rather than district totals and percentages. This is because DHIS2 allows us to take that individual data that we've entered and create aggregated data outputs as required. We can also analyze data from tracker and event programs together with data that is submitted through our routine aggregated processes. We'll use the data visualizer to demonstrate this concept. If we go back to our indicator selection and go back to the CERA indicator group, let us select the facility's offering ANC rate. We can then go back and select the antinatal care indicator group. These indicators are derived from our routine data. If we look at one example, ANC fourth visit coverage, we can now compare data from our survey which allows us to calculate the percentage of facilities that are offering antinatal care services with the ANC fourth visit coverage. This is taken from our routine data where we submit the total number of ANC fourth visit consultations each month. This particular indicator takes that as its numerator and divides it by the estimated number of pregnancies for the year to provide us with this particular indicator value. We can then go through the same selection, selecting our periods and our organization units. We'll modify the layout and click on update. In this example, you can see we are now making a correlation between the survey data that we collected and the routine data that we submit through aggregate data entry. DHIS2 allows us to bring in data from a variety of different sources. It does not matter if it is aggregated data, event data, or tracker data. We can take all of that data and correlate it together within our various analyses tools through the use of both program indicators and combined indicators.