Nearest neighbor analysis in QGIS

GIS is very useful in analyzing the spatial relationships between features. It is important at times to find the features nearest to a feature in GIS(Nearest neighbor analysis).To achieve this, we can use the Distance Matrix that helps in calculating the distances between each point in the dataset.

Steps
You can use Vector > Analysis Tools > Distance Matrix, and a join to have an output layer that has all the required attributes.
I will use the Towns layer to exemplify. This will help in showing the distances and is quite small for faster processing.
1. Create a distance matrix using your layer as both destination and target.
Don’t forget to tick “Use only the nearest (k) target points” and set it to 1.

Distance Matrix Window
2. Open the CSV with add delimited text layer

Choose “Comma” as a delimiter, and set the geometry definition as no Geometry

Loading CSV Data
3. Create a join in the original layer with the newly created table
 

 

Creating Joins

4. Use Field calculator to populate a field with the desired values
Because of the join, we now have access to the distance table values from within the towns table of attributes, therefore it quite fairly easy to create a new field and populate it with our desired values.

Using Field Calculator
5. Grouping the generated town data.

 We can now group the towns depending on the distances between them. To do that, we write a function to classify the data.

That was a brief task that is very useful when conducting GIS analysis and nearest neighbor analysis to be specific.
Happy hacking….

Leave a Reply

Your email address will not be published. Required fields are marked *

Wanjohi Kibui

Passionate about harnessing the power of geospatial technology to create innovative solutions, I'm a GIS Consultant and Developer dedicated to building cutting-edge geospatial applications. With a keen eye for spatial analysis and a knack for problem-solving, I specialize in crafting solutions that seamlessly integrate technology and geography.

More Reading

Post navigation