Exploring Malaria Burden in Nigeria with Pixel

{adm1_name}In this example, we will demonstrate how to use [Pixel](https://geopixel.xyz) and [Akuko](https://www.akuko.io) to explore malaria burden in Nigeria. For this, we will use the malaria Pf Incidence rate. This represents the proportion of children, 2-10 years of age, that show on a given year, a detectable level of Plasmodium falciparum parasite. This information is available in raster format (see image above) from the [Malaria Atlas Project](https://data.malariaatlas.org/maps?layers=Malaria:202206_Global_Pf_Parasite_Rate&extent=-415565.45612819144,302345.0073265458,2549295.708332153,1951223.935631685). ### Pixel As a reminder, Pixel creates a uniform grid of the world at ~0.5km resolution. Data including population, building counts, drive time, malaria incidence rate, administrative areas, etc. is calculated at the pixel level and assigned a unique geospatial index (quadkey). This conversion of geospatial data to a tabular format greatly simplifies analysis. The first thing we can do is calculate the average the Pf incidence rate by LGA. Which can be visualized in a choropleth map. To do this we can calculate the average Pf Incidence rate grouped by LGA. We can also use a table to show the LGAs with the highest malaria rates along with their populations. While Pixel and Akuko make this analysis very quick and easy to do, one can argue that with a bit of training this is not THAT hard to with your favorite GIS tool. Fair enough. Let's try something a bit more complex. Bauchi state suffers from the highest malaria rate in Nigeria with an average rate of 0.418. Next we would like to calculate the number of people in Bauchi that live in an area with a very high transmission rate (0.5 or higher). This analysis shows that only about 5% of the population of Bauchi live in area with very high malaria rates of 0.5. This would seem to indicate the highest malaria areas are found in rural areas. Let's use the drive time layer in Pixel to verify this.While we can see there high malaria rates in hard to reach places, we don't know the population in these areas. To better understand this, we can create disaggregate the population of people living in high malaria areas by their drive time to the nearest health facility. When we do this, it clearly shows that the majority of people exposed to high rates of malaria also live within close proximity to a health facility. To better understand where people are distributed in these areas, we can use the data from Pixel to create a population layer (blue circles) to the map below to indicate where people are. You could also use the point enrichment feature in Pixel to enrich and add settlements to the map.