Geodata Classification

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Spatial data classification in GIS involves categorizing geographic information into distinct groups or classes based on shared characteristics. This process enhances the organization and interpretation of spatial data.

The attributes of geospatial data are stored in a specific column within the attribute table. Essentially, we choose a column containing the specific characteristics of interest, allowing QGIS to group the data based on these selected attributes.

Fig. 61 Basic classification. Source:

Single symbol classification

By default, QGIS visualizes all layers in the Single symbol setting. This means all the features of a layer are visualized the same. In this setting, you can change many parameters like colour or opacity but you can not classify any data!

To adjust the style of a layer…

1. Right-click on your layer.

2. Click on Symbology.

3. Confirm that the layer setting is on Single Symbol.

4. Select the colour of your choice in the drop-down menu. For more colour options select in the drop-down menu Choose Color.

5. Optional:. You can adjust the opacity/ transparency of the layer. This can be very useful when you want to show multiple overlapping layers.

6. Optional: Here you can set the unit type. This is useful when you want to visualize points in a certain size, for example.

7. Optional: Here you can find standard and previously used styles quickly.

8. Click Apply to put your adjustment into effect.

9. Click OK to close the window.

Fig. 62 Adjust the style of a layer.

Categorized classification

Categorized classification in QGIS groups spatial data into distinct categories based on specific attributes. This classification enhances the organization and interpretation of geospatial information for clearer insights.

Categorized classification is usually used for nominal and ordinal scaled data.

Data Scale	Definition	Example	Typical Data Format
Nominal Scale	Categories without inherent order or ranking	Land cover types, districts, livelihood zones	Text (“Desert”) or Integer (5)
Ordinal Scale	Categories with a meaningful order or ranking	Ranks (e.g., low, medium)	Text (“high”) or Integer (5)

To classify data in categories…

1. Right-click on your layer.

2. Click on Symbology.

3. Click on Categorized.

4. In the Value dropdown menu, select the column based on which you want to categorize your data.

5. Further down the window click on Classify. Now you should see all unique values or attributes of the selected column in Value. To add or delete single values use the - and + buttons.

6. Optional: In the Symbol dropdown menu, you can select the colours and symbols you want to use

7. Optional: In the Color ramp dropdown menu, you can specify the range of colours you want to use

8. Optional: You can open the panel Layer Rendering on the button of the window. Here you can adjust the opacity/ transparency of the layer.

9. Click Apply to put your adjustment into effect.

10. Click OK to close the window.

Fig. 63 Categorized classification.

Graduated classification

Graduated classification in GIS involves categorizing spatial data into classes or ranges based on a progression of values. This method is particularly useful for visualizing quantitative data, allowing for the differentiation of intensity, density, or magnitude across a spectrum, facilitating a nuanced representation of geographic phenomena.

Graduated classification statistic one-o-one

Graduated classification is used for quantitative data usually interval or ratio scaled.

Data Scale	Definition	Example	Typical Data Format
Interval Scale	Equal intervals between values, no true zero point	Temperature (Celsius)	Float (44.5 Degree)
Ratio Scale	Equal intervals with a true zero point	Population, Length, Number of trees	Integer (5 Trees) or Float (12.5 km of Road)

To classify quantitative data there are many methods how to set up the classes. There is no single best way to select a method or to decide how many classes you like to use. It all comes down to what you want to show.

Tip

A good range for number of classes is 3 to 7. Do not use more than 9 classes.

Take the example below. You see a histogram of the district population. That means we have a dataset with districts and how many people living in each district. Just based on the histogram we can make a few general statements.

1. There are no districts with no or zero population

2. There are just a few districts with very low population

3. It seems that there are three general groups of districts

Fig. 64 Histogramm of population data. Source:

However, if we want to show on a map which districts have a higher population than others, we need to classify the districts.

There are seven ways in QGIS to split quantitative data into classes. The four most important ones are: Equal intervals, Quantile, Natural breaks, Manual. Let’s have a look at how the classes of the district population would look like if we split the data into three classes using these methods.

Fig. 65 Different classifications. Source:

Equal Interval

Equal Interval classification divides data into uniform size classes, such as 0-10, 10-20, 20-30, and so on. It is effective for evenly distributed data across the entire range. However, caution is advised when data is skewed or has significant outliers, as this may result in empty classes. The population data used here, lacking large outliers, is suitable for Equal Interval classification.

Equal Count (Quantile)

Quantiles classification ensures an equal number of observations in each class, creating visually appealing maps. However, it may result in classes with significantly different numerical ranges, and in some cases, similar rates may be separated while different rates are grouped together. It’s advisable to use a histogram to assess potential issues. In the district population example, the quantile classification produced a questionable break, combining a portion of the third cluster with class 2 despite its closer numerical proximity to other observations in class 3.

Natural Breaks

Natural Breaks is an optimal classification method that aims to minimize within-class variance and maximize between-class differences for a given number of classes. However, it produces a unique classification solution for each dataset, which can be a drawback when making comparisons across maps, such as in a series or atlas. In such cases, a consistent classification scheme applied across all maps might be preferable.

Manual

Manual classification allows users to set one or all of the class breaks based on specific needs. This approach is useful when certain break points need to be predetermined, such as aligning with the mean or maintaining consistency across a series of maps. Manual classification is recommended when other methods provide a good solution but may benefit from slight adjustments to better suit specific requirements or visualizations.

Logarithmic scale

Logarithmic scale classification is employed when the data spans multiple orders of magnitude, and a linear scale may not effectively represent the variations. This scale applies logarithmic transformation to the data, compressing larger values while expanding smaller ones. It is useful for visualizing data with exponential growth or decay. However, interpreting values on a logarithmic scale may require a nuanced understanding. Consider using a logarithmic scale when there is a wide range of values, and a linear scale may obscure important patterns or trends.

Pretty Breaks

Pretty Breaks is a classification method designed to create visually appealing and easily interpretable maps. This approach seeks to generate class breaks that align with “round” numbers, making the map more intuitive for viewers. Pretty Breaks is particularly useful when communicating complex spatial data to a broad audience, as it enhances the clarity and understandability of the map. Keep in mind that the choice of ‘pretty’ breaks may depend on the specific context and the preferences of the intended audience.”

Standard Deviation

Standard Deviation classification is a method that determines class breaks based on the standard deviation of the data values. This approach organizes data into classes by considering the distribution of values around the mean. Each class represents a certain number of standard deviations from the mean, providing a statistical basis for categorizing data. Standard Deviation classification is effective when wanting to highlight variability within the dataset. However, it’s important to consider the nature of the data distribution and whether this method aligns with the analytical goals of the map

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How to Graduated classification in QGIS

To perform a graduated classification in QGIS is easy. However, unlike the categorised classification, here you have to decide on how many classes and which method you want to use.

To classify data in classes…

1. Right-click on your layer.

2. Click on Symbology.

3. Click on Graduated.

4. In the Value dropdown menu select the column based on which you want to classify your data.

5. Downright select the number of classes you want to use.

6. Under Mode select the classification method you want to use e.g. Equal count (Quantile).

7. Click on Classify. Now you should see all classes and the distribution of values. To add or delete singlegit s classes use the - and + buttons.

8. Optional: Click on Histogram -> Load Values. Now you can see the exact distribution of values over the classes. This is very practical to decide on a classification method. You can also check the mean value and standard deviation.

Fig. 66 Graduated classification. Source:

9. Optional: In the Symbol dropdown menu you can select the colours and symbols you want to use.

10. Optional: In the Color ramp dropdown menu you can specify the range of colours you want to use. To see all color ramps click on the down arrow of the Color ramp -> All Color Ramps.

11. Optional: Under Legend Format you can adjust how precise the range of the classes will be displayed in the legend. Usually, it is practical to not use too complicated numbers in the legend.

12. Optional: You can open the panel Layer Rendering on the button of the window. Here you can adjust the opacity/ transparency of the layer.

13. Click Apply to put your adjustment into effect.

14. Click OK to close the window.

Fig. 67 Graduated classification in QGIS.