Python Geospatial Analysis Essentials -

Geospatial data is everywhere. From tracking delivery trucks to analyzing climate change, location is the secret ingredient that makes data science actionable.

print(result['name']) # Should output "Brazil" Python GeoSpatial Analysis Essentials

A GeoDataFrame is just a Pandas DataFrame with a special column (usually geometry ) that stores shapely objects. You rarely create geometries by hand, but you must understand them. Geospatial data is everywhere

from shapely.geometry import Point, LineString, Polygon nyc = Point(-74.006, 40.7128) Create a line route = LineString([(-74.006, 40.7128), (-73.935, 40.7306)]) Create a polygon (bounding box around NYC) bbox = Polygon([(-74.05, 40.68), (-73.95, 40.68), (-73.95, 40.75), (-74.05, 40.75)]) Check if point is inside polygon print(bbox.contains(nyc)) # True Step 4: The Magic of Spatial Joins This is where Geopandas shines. Let's find all countries that contain a specific point. You rarely create geometries by hand, but you

Next week, I'll cover spatial autocorrelation (aka: "Is that cluster real or random?"). Until then, map something interesting. What geospatial project are you working on? Let me know in the comments below.

import geopandas as gpd world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')) What is this? print(type(world)) # <class 'geopandas.geodataframe.GeoDataFrame'> print(world.head()) print(world.geometry.name) # 'geometry'