GeoPandas

Load store locations as points.

Generate 5km buffers around each store point.

Load census tract polygons with demographic data.

Perform a spatial join between buffers and census polygons.

Aggregate demographic stats (income, age) for the joined records.

Import farm boundary GeoJSON.

Convert sensor point data into a GeoDataFrame.

Filter points residing 'within' the boundary geometry.

Calculate spatial averages for sensor values per parcel.

Visualize yield hotspots using a choropleth map.

Load raw GPS coordinate CSV.

Filter out-of-bounds coordinates using spatial predicates.

Snap coordinates to the nearest road network segments (using sjoin_nearest).

Calculate distance-to-node for every telemetry point.

Export to Parquet for training a neural network.

Load property portfolio Shapefile.

Load FEMA flood zone polygon data.

Perform a spatial intersection between properties and flood zones.

Calculate the percentage of property area overlapping the flood zone.

Assign a risk score based on overlap intensity.

Load bus stop locations as a GeoDataFrame.

Load building footprints.

Use 'sjoin_nearest' to find the closest bus stop for every building.

Calculate the geodesic distance between the building and the stop.

Analyze transit 'deserts' where distances exceed 1000 meters.

Import satellite-derived forest cover polygons.

Import proposed road path geometries.

Apply a buffer to the roads to represent the 'impact zone'.

Subtract road buffers from forest polygons using 'overlay(how="difference")'.

Recalculate polygon areas to quantify habitat loss.

Map cell tower coordinates with specific range buffers.

Dissolve overlapping buffers to create a single coverage layer.

Identify 'void' spaces by calculating the difference between city boundaries and coverage.

Join void spaces with population density data.

Prioritize tower placement in high-population void areas.