Creating KML files¶
Read a shapefile and build a 3D KML visualization.¶
This example shows how to read a shapefile and build a 3D KML visualization from it.
(click on the image to see the KML visualization in Cesium Ion)
You will need to install pyshp (pip install pyshp).
For this example we will use the Data on CO2 and Greenhouse Gas Emissions by Our World in Data, and the Small scale data (1:110m) shapefile from Natural Earth.
First we import the necessary modules:
>>> import csv
>>> import pathlib
>>> import random
>>> import shapefile
>>> from pygeoif.factories import force_3d
>>> from pygeoif.factories import shape
>>> import fastkml
>>> import fastkml.containers
>>> import fastkml.features
>>> import fastkml.styles
>>> from fastkml.enums import AltitudeMode
>>> from fastkml.geometry import create_kml_geometry
Read the shapefile:
>>> shp = shapefile.Reader("examples/ne_110m_admin_0_countries.shp")
Read the CSV file and store the CO2 data for 2020:
>>> shp = shapefile.Reader("examples/ne_110m_admin_0_countries.shp")
>>> co2_csv = pathlib.Path("examples/owid-co2-data.csv")
>>> co2_data = {}
>>> with co2_csv.open() as csvfile:
... reader = csv.DictReader(csvfile)
... for row in reader:
... if row["year"] == "2020":
... co2_data[row["iso_code"]] = (
... float(row["co2_per_capita"]) if row["co2_per_capita"] else 0
... )
...
We prepare the styles and placemarks for the KML file, using random colors for each
country and the CO2 emissions as the height of the geometry. The shapefile offers
a handy __geo_interface__ attribute that we can use to iterate over the features,
just like we would with a GeoJSON object, and extract the necessary information:
>>> placemarks = []
>>> for feature in shp.__geo_interface__["features"]:
... iso3_code = feature["properties"]["ADM0_A3"]
... geometry = shape(feature["geometry"])
... co2_emission = co2_data.get(iso3_code, 0)
... geometry = force_3d(geometry, co2_emission * 100_000)
... kml_geometry = create_kml_geometry(
... geometry,
... extrude=True,
... altitude_mode=AltitudeMode.relative_to_ground,
... )
... color = random.randint(0, 0xFFFFFF)
... style = fastkml.styles.Style(
... id=iso3_code,
... styles=[
... fastkml.styles.LineStyle(color=f"33{color:06X}", width=2),
... fastkml.styles.PolyStyle(
... color=f"88{color:06X}",
... fill=True,
... outline=True,
... ),
... ],
... )
... placemark = fastkml.features.Placemark(
... name=feature["properties"]["NAME"],
... description=feature["properties"]["FORMAL_EN"],
... kml_geometry=kml_geometry,
... styles=[style],
... )
... placemarks.append(placemark)
...
Finally, we create the KML object and write it to a file:
>>> document = fastkml.containers.Document(features=placemarks)
>>> kml = fastkml.KML(features=[document])
>>> outfile = pathlib.Path("co2_per_capita_2020.kml")
>>> kml.write(outfile, prettyprint=True, precision=3)
The resulting KML file can be opened in Google Earth or any other KML viewer.
Build an animated over time KML visualization¶
This example shows how to build an animated KML visualization over time. We will use the same data as in the previous example, but this time we will create a KML file that shows the CO2 emissions accumulating from 1995 to 2022.
(click on the image to see the full visualization in Cesium Ion)
First we import the necessary modules:
>>> import csv
>>> import pathlib
>>> import random
>>> import datetime
>>> import shapefile
>>> from pygeoif.factories import force_3d
>>> from pygeoif.factories import shape
>>> import fastkml
>>> import fastkml.containers
>>> import fastkml.features
>>> import fastkml.styles
>>> import fastkml.times
>>> from fastkml.enums import AltitudeMode, DateTimeResolution
>>> from fastkml.geometry import create_kml_geometry
Read the shapefile, the CSV file and store the CO2 data for each year:
>>> co2_csv = pathlib.Path("examples/owid-co2-data.csv")
>>> co2_pa = {str(i): {} for i in range(1995, 2023)}
>>> with co2_csv.open() as csvfile:
... reader = csv.DictReader(csvfile)
... for row in reader:
... if row["year"] >= "1995":
... co2_pa[row["year"]][row["iso_code"]] = (
... float(row["co2_per_capita"]) if row["co2_per_capita"] else 0
... )
...
This time we will create a folder for each country, and a placemark for each year, with the CO2 emissions per capita as the height of the geometry. We will also create a style for each country, which we store at the document level to prevent creating duplicate styles. Each placemark will have a time-span that covers the whole year:
>>> styles = []
>>> folders = []
>>> for feature in shp.__geo_interface__["features"]:
... iso3_code = feature["properties"]["ADM0_A3"]
... geometry = shape(feature["geometry"])
... color = random.randint(0, 0xFFFFFF)
... styles.append(
... fastkml.styles.Style(
... id=iso3_code,
... styles=[
... fastkml.styles.LineStyle(color=f"33{color:06X}", width=2),
... fastkml.styles.PolyStyle(
... color=f"88{color:06X}",
... fill=True,
... outline=True,
... ),
... ],
... ),
... )
... style_url = fastkml.styles.StyleUrl(url=f"#{iso3_code}")
... folder = fastkml.containers.Folder(name=feature["properties"]["NAME"])
... co2_growth = 0
... for year in range(1995, 2023):
... co2_year = co2_pa[str(year)].get(iso3_code, 0)
... co2_growth += co2_year
... kml_geometry = create_kml_geometry(
... force_3d(geometry, co2_growth * 5_000),
... extrude=True,
... altitude_mode=AltitudeMode.relative_to_ground,
... )
... timespan = fastkml.times.TimeSpan(
... begin=fastkml.times.KmlDateTime(
... datetime.date(year, 1, 1), resolution=DateTimeResolution.year_month
... ),
... end=fastkml.times.KmlDateTime(
... datetime.date(year, 12, 31), resolution=DateTimeResolution.year_month
... ),
... )
... placemark = fastkml.features.Placemark(
... name=f"{feature['properties']['NAME']} - {year}",
... description=feature["properties"]["FORMAL_EN"],
... kml_geometry=kml_geometry,
... style_url=style_url,
... times=timespan,
... )
... folder.features.append(placemark)
... folders.append(folder)
...
Finally, we create the KML object and write it to a file:
>>> document = fastkml.containers.Document(features=folders, styles=styles)
>>> kml = fastkml.KML(features=[document])
>>> outfile = pathlib.Path("co2_growth_1995_2022.kml")
>>> kml.write(outfile, prettyprint=True, precision=3)
You can open the resulting KML file in Google Earth Desktop and use the time slider to see the CO2 emissions per capita grow over time, Google Earth Web does not support time animations, but Cesium Ion can display the time animation.