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Downloading and Processing GHSL Data

This example demonstrates how to download and process data from the Global Human Settlement Layer (GHSL) using the GHSLDataHandler class.

Prerequisites

Ensure you have installed the gigaspatial package and set up the necessary configuration. Follow the Installation Guide if you haven't already.

Example Code

from gigaspatial.handlers import GHSLDataHandler

# Initialize the handler with desired product and parameters
ghsl_handler = GHSLDataHandler(
    product="GHS_BUILT_S",  # Built-up surface
    year=2020,
    resolution=100,  # 100m resolution
)

# Download and load data for a specific country
country_code = "TUR"
downloaded_files = ghsl_handler.load_data(country_code, ensure_available=True)

# Load the data into a DataFrame
df = ghsl_handler.load_into_dataframe(country_code, ensure_available=True)
print(df.head())

# You can also load data for specific points
points = [(38.404581, 27.4816677), (39.8915702, 32.7809618)]  # Example coordinates
df_points = ghsl_handler.load_into_dataframe(points, ensure_available=True)

Explanation

  • GHSLDataHandler: This class provides a unified interface for downloading and processing GHSL data.
  • Available Products:
  • GHS_BUILT_S: Built-up surface
  • GHS_BUILT_H_AGBH: Average building height
  • GHS_BUILT_H_ANBH: Average number of building heights
  • GHS_BUILT_V: Building volume
  • GHS_POP: Population
  • GHS_SMOD: Settlement model
  • Parameters:
  • product: The GHSL product to use
  • year: The year of the data (default: 2020)
  • resolution: The resolution in meters (default: 100)
  • Methods:
  • load_data(): Downloads and loads the data
  • load_into_dataframe(): Loads the data into a pandas DataFrame

Next Steps

Once the data is downloaded and processed, you can: 1. Store the data using the DataStore class 2. Visualize the data using geopandas and matplotlib 3. Process the data further using the Processing Examples

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