Astronomy Tourism Project

Loading light pollution data and cloud cover data and making choropleth maps of regions in kenya using shapefiles like this

Objective

Learn how to extract data light pollution and cloud cover data with Data Science Tools to analyze possible suitable AstroTourism locations in Kenya

Initial Setting Up(Ubuntu 20.04 python environment) - Visualizing LP DaTa

• Make or navigate into a directory.

mkdir Astronomy-Tourism-Project

• Create a virtual environment

python3 -m venv venv

• Activate the Virtual Environment

source venv/bin/activate

• Install required Python libraries

pip install geopandas rasterio rasterstats plotly pandas

• Download Kenya Shapefile from GADM

wget https://geodata.ucdavis.edu/gadm/gadm4.1/shp/gadm41_KEN_shp.zip
unzip gadm41_KEN_shp.zip -d kenya_shapefile

• Load Kenya Shapefile, extract Light Pollution Data from the .tif File(assuming it was downloaded earlier from with the light pollution data), convet the Data for Plotly and create a Choropleth Map with Plotly. The script below does that and outputs the map into the output.html file as well.
• Vim lp_analysis.py

import geopandas as gpd
import rasterio
from rasterstats import zonal_stats
import plotly.express as px
import pandas as pd
import json
import webbrowser
import plotly.io as pio


# -------------------------------
# 1. Load Kenya Shapefile
# -------------------------------
kenya_shp_path = "kenya_shapefile/gadm41_KEN_1.shp"
kenya_gdf = gpd.read_file(kenya_shp_path)

# -------------------------------
# 2. Read Light Pollution Data from TIF
# -------------------------------
tif_path = "./World_Atlas_2015-data/World_Atlas_2015.tif"

# Compute mean light pollution per county
stats = zonal_stats(kenya_shp_path, tif_path, stats=["mean"])

# Add extracted LP values to the GeoDataFrame
kenya_gdf["light_pollution"] = [s["mean"] for s in stats]

# -------------------------------
# 3. Convert to GeoJSON for Plotly
# -------------------------------
kenya_gdf = kenya_gdf.to_crs(epsg=4326)  # Convert to Lat/Lon projection
kenya_geojson = json.loads(kenya_gdf.to_json())  # Convert to proper JSON format

# Debugging output
print("GeoJSON keys:", kenya_geojson.keys())  # Should print: dict_keys(['type', 'features'])
print("Sample feature properties:", kenya_geojson["features"][0]["properties"])  # Should show properties

# -------------------------------
# 4. Plot Choropleth Map with Plotly
# -------------------------------
df = pd.DataFrame(kenya_gdf)

fig = px.choropleth(
    df,
    geojson=json.loads(json.dumps(kenya_geojson)),  # Ensures proper JSON
    locations="NAME_1",  # Use county names
    featureidkey="properties.NAME_1",  # match GeoJSON properties
    color="light_pollution",
    # color_continuous_scale="YlOrRd",
    color_continuous_scale="Viridis",
    # range_color=(0, 1),
    title="Light Pollution Levels in Kenya",
)

# Adjust map settings
fig.update_geos(
    fitbounds="locations",
    visible=False,
    projection_type="mercator"  # Proper projection
)

# Show or Save Output
fig.write_html("output_map.html")  # Save as HTML
# webbrowser.open("output_map.html")  # Auto-open in browser

# Set browser-based rendering
pio.renderers.default = "browser"

# Now, show the figure
fig.show()