Vector Calculator

Vector Calculator#

The Vector Calculator feature in Kalpa allows you to perform advanced calculations on the columns of a vector dataset (GeoDataFrame) and save the results as a new column. This is particularly useful for geospatial data engineering, statistical analysis, and feature generation for machine learning.

Using the VectorLayerCalculator operation, you can define custom operations to compute new columns based on existing data in a vector layer. This chapter guides you through the process of using the Vector Calculator, including practical examples.

How to Use the Vector Calculator#

To compute new columns in your vector data, follow these steps:

  1. Load the Dataset Ensure that the vector dataset is loaded into Kalpa.

  2. Define the New Column Name Choose a descriptive name for the column that will store the calculated values.

  3. Write the Operation Code Use Python expressions to define the calculation. Examples:

    • row['column1'] + row['column2']

    • row['column1'] * 1.5

    • len(row['city_name'])

  4. Save the Result After defining the operation, execute and save the results directly into the dataset.

Using the GUI#

When using Kalpa’s Graphical User Interface (GUI), you will be prompted to provide the following:

  • Select Layer: Choose the vector layer to apply the calculation.

  • New Column Name: Specify the name of the column to store the result.

  • Condition: Write the operation code (Python expression) that defines how to compute the new column.

Examples#

1. Basic Arithmetic Operation Add two columns col1 and col2 to create a new column sum_col:

sum_col
row['col1'] + row['col2']

2. Scaling a Column Multiply a column value by a constant factor:

scaled_value
row['value'] * 1.5

3. String Length Calculation Create a column representing the length of strings in city_name:

city_name_length
len(row['city_name'])

4. Conditional Calculation Create a binary column high_population based on a threshold:

high_population
1 if row['population'] > 1000 else 0

5. Combining String Values Merge city and state into a single Location column:

Location
f"{row['city']}, {row['state']}"

6. Calculating Distance to a Reference Point Calculate the distance of each geometry to a reference point:

distance_to_point
row['geometry'].distance(reference_point)

7. Geometric Area Calculation Compute the area of each geometry (for polygons):

area
row['geometry'].area

8. Custom Transformations Apply a logarithmic transformation to a numeric column:

log_value
math.log(row['value'])

Using the Results#

Once calculations are completed, you can:

  • Visualize the new column directly in Kalpa’s interface.

  • Use the updated vector layer for further geospatial or statistical analysis.

  • Export the enriched dataset as a GeoPackage (.gpkg) file or other supported formats using Kalpa’s export options.

Best Practices#

  1. Column Names Use clear and descriptive names for new columns to keep the dataset understandable.

  2. Error Handling Check your Python expressions for syntax errors or invalid column references before applying them.

  3. Performance Avoid overly complex computations on large datasets to maintain good performance.

  4. Documentation Keep a record of all applied transformations for reproducibility and future reference.