 Hello and welcome to video 4 in the DHIS2 Duos Academy. In this video, we'll be covering how to find out the population living within organization unit boundaries. We'll be calculating the total population count for our downloaded gridded population data. Secondly, we'll be calculating the population counts for our downloaded district or chiefdom vector data using zonal statistics and lastly we'll be visualizing the outputs from the zonal statistics calculations. To start, open QGIS to the last point we reached within the videos. This was the styling of our downloaded data. Firstly, we're going to find the total population of the gridded population raster. In this case, I'm going to find out the total population for Sierra Leone using the 2020 gridded population raster. To do this, I'm going to open the processing toolbox by going to processing and clicking on toolbox. As you will see, the processing toolbox will be loaded on the right hand side of your screen. We need to go to raster analysis and scroll down to raster layer statistics and click on it. This will bring up the raster layer statistics box. We can leave as is and press run. We're interested in the total sum of all of the pixels in the raster which gives us the total estimated population of the country. Here, the estimated total population is just under 8 million at 7.97 million. We can go ahead and close this and move on to the next section of the video which is to provide zonal statistics using the Kinema chiefdoms layer. So, I'm quickly going to right click on my chiefdoms dataset and zoom to layer. To provide zonal statistics, again, go to the processing toolbox, raster analysis and click on zonal statistics this time. This brings up the parameters for the zonal statistics tool. We need to set our downloaded districts as our input layer. This is the zonal area in which the zonal statistics is going to be calculated. As this states, this algorithm calculates statistics of a raster layer for each feature of an overlapping polygon vector layer. For the raster layer, we need to make sure we enter our downloaded gridded population data. Next, we need to change our output column prefix to pop underscore and then we can change our statistics to calculate to simply calculate the count, the sum, and the mean. The count is simply the number of pixels that are located within each area. The sum is the total calculated population count of each area, i.e. the total number of people living in a certain chiefdom in Canema district, and then the mean is the average population value within each chiefdom. Next, we need to set our output layer. This is done by going to the final box and under zonal statistics, clicking the browse button, save to file, and in our DHIS2 underscore training, saving to appropriate dataset output, which could be a geojson, a csv for example, or a shapefile among many other file formats. In this case, I'm going to use a shapefile and I'll call this kinema underscore zonal statistics and press save. I can now run the process. As you can see, the current output of the process is a new vector layer containing the chiefdoms for Canema district. We can have a look at the actual output by going and right clicking the new layer in our layers window, going to open a tribute table, and in our tribute table, scrolling across to see the pop count, pop sum, and pop mean attribute fields. These are calculated for each chiefdom within the district and the tool automatically populates this attribute table with the information from the geojson we downloaded from the DHIS2 platform, and in addition it adds the zonal statistics it's calculated. We're able to symbolize this better by double clicking, going to symbology, and changing it so we use a graduated symbology making use of the population sum and changing our color ramp to an appropriate color ramp. Here I'm just going to change the reds and press classify. As you can see from the values and legends this will split up our districts into the different areas of lower populated chiefdoms and higher populated chiefdoms, and if I press apply we can see that quite quickly and easily we've been able to find out which chiefdoms have greater populations and which chiefdoms have a lower population, and in this output the areas that are darker red are the greater populated areas, and the areas that are lighter red or almost pink and white here have a lower population total. That brings us to the end of this video.