Interpolation & Density Mapping

Trend analysis; examining change over time

Identify Pattern, Distribution & Effects of Disease

We will use a local method of interpolation known as inverse distance weighting (IDW)

• IDW is a useful method for health-related analyses, as it accounts for distance between data points: the farther away a sampled point from the area of interest, the less weight it holds in the calculation

Launch QGIS and open your project

Ensure you have point data of CASE prevalence from health facilities data

Open the attribute table and look at the data

We will create an interpolation surface to see which areas have high prevalence

Install the interpolation plugin Plugins > Manage and Install Plugins

The interpolated surface has been added to your map

Click on Style on the left panel

In the Band rendering section, change the Render type to Singleband pseudocolor

Click Classify

Density maps can be created using either point or line data

Density maps are used to show the concentration of a variable within a given geographic radius

They are represented using the Raster data model, whereby features are represented on a grid of square cells that are all the same size

We will create a kernel density map (using point data) of areas with a high concentration of a CASE

In QGIS, the heat map tool is used for density mapping of point data - for visualisation

Launch QGIS, and open your project

Add a layer with the total number of reported CASES reached at facility

On the main menu, click on Raster > Heatmap > Heatmap to open the tool

Classify under properties

Use Diagrams to find MAX to plan interventions

Use attribute table calculator to compute change over time e.g. 2022-2021 & create new column

Create a chloropleth map