Geodata template to KML¶

In this notebook, we will convert the geodata template into a kml file. The kml file can be used to visualize the geodata in Google Earth. We will proceed as follows:

1. Import the necessary libraries.
2. Load the geodata template. We created a test file which contains two test points. It can be further extended in the future to include more information.
3. Visualize the test data.
4. Convert the geodata template into a kml file.

We will work with the following libraries:

• pandas: Python library to work with data.
• geopandas: Python library to work with geodata.
• matplotlib: Python library to work with plots.
• fiona: Python library to import and export to different geodata file formats.
• shapely: Python library to work with spatial objects.

Data import¶

In a first step, we import the necessary libraries.

In [1]:

# for xlsx import
import pandas as pd

# for geocoding
import geopandas as gpd
from shapely.geometry import Point

# for plotting
import matplotlib.pyplot as plt

# for export to KML at the end
import fiona

fiona.supported_drivers["KML"] = "rw"

# for xlsx import import pandas as pd # for geocoding import geopandas as gpd from shapely.geometry import Point # for plotting import matplotlib.pyplot as plt # for export to KML at the end import fiona fiona.supported_drivers["KML"] = "rw"

After we imported the necessary libraries, we load the geodata from the xlsx file.

In [2]:

file_name = "data/test-data.xlsx"
df = pd.read_excel(file_name, header=0, skiprows=1)

# Now we have to clean out all the rows that are empty. Given that 'unique_id' is
# prefilled we have to put it into a temporary variable, drop it and add it at the end
# after to we dropped all the empty rows.
temp = df["unique_id"]
df = df.drop("unique_id", axis=1)

# Remove all rows that are empty
df = df.dropna(how="all")

# Add the unique_id column again
df["unique_id"] = temp

df.head()

file_name = "data/test-data.xlsx" df = pd.read_excel(file_name, header=0, skiprows=1) # Now we have to clean out all the rows that are empty. Given that 'unique_id' is # prefilled we have to put it into a temporary variable, drop it and add it at the end # after to we dropped all the empty rows. temp = df["unique_id"] df = df.drop("unique_id", axis=1) # Remove all rows that are empty df = df.dropna(how="all") # Add the unique_id column again df["unique_id"] = temp df.head()

/Users/fredjendrzejewski/miniconda3/envs/geodata-model/lib/python3.11/site-packages/openpyxl/worksheet/_reader.py:329: UserWarning: Data Validation extension is not supported and will be removed
  warn(msg)

Out[2]:

	KfW Project -No.\n(INPRO)	Project-specific location identifier	Location name	Author of the Data \n(=the legal owner)	Publishing restrictions due to security reasons	Date of data collection or latest update	Location Activity Status	Planned or actual start date of activity at the location	Activity-Description (general)	Additional Activity-Description	...	DAC 5 Purpose Classification	Geographic Exactness	Latitude	Longitude	Related Community / Village / Neighborhood	Additional Geo-data submitted as KML (Lines/Polygons)	GADM GID	GID Level	GADM version	unique_id
0	NaN	NaN	School 1	Freddy Fred	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	50.12057	8.65660	NaN	NaN	NaN	NaN	NaN	1
1	NaN	NaN	School 2	Freddy Fred	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	28.56464	77.15487	NaN	NaN	NaN	NaN	NaN	2

2 rows × 22 columns

As we can see all the required data were imported into the object df.Now it is time to transform this into a geodata object.

In [3]:

df["geometry"] = df.apply(lambda row: Point(row["Longitude"], row["Latitude"]), axis=1)

df["geometry"] = df.apply(lambda row: Point(row["Longitude"], row["Latitude"]), axis=1)

And now create a geopandas dataframe from the geodata object.

In [4]:

gdf = gpd.GeoDataFrame(df, geometry="geometry")

gdf = gpd.GeoDataFrame(df, geometry="geometry")

Visualization¶

To make sure that the import worked out nicely, we can plut the existing points on a map.

In [5]:

# Load the world map
world = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))

# Create a figure with one subplot
fig, ax = plt.subplots()

# Plot the world map
world.plot(ax=ax)

# Draw the points on the map
gdf.plot(ax=ax, color="C0")

# Load the world map world = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres")) # Create a figure with one subplot fig, ax = plt.subplots() # Plot the world map world.plot(ax=ax) # Draw the points on the map gdf.plot(ax=ax, color="C0")

/var/folders/_v/vw5sh5fd4lv4x0cnk8cxz__w0000gn/T/ipykernel_17834/3205200294.py:2: FutureWarning: The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.
  world = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))

Out[5]:

<Axes: >

Data export¶

And now that we have validated the appropiate import, we should also export it into a kml file.

In [6]:

gdf.to_file("data/test-data.kml", driver="KML")

gdf.to_file("data/test-data.kml", driver="KML")

The resulting file can be opened in Google Earth.

Conclusion¶

In this notebook, we have seen how to convert the geodata template into a kml file. The kml file can be used to visualize the geodata in Google Earth.