Spatial Analysis and Geospatial Data Science With Python

Learn to read and transform geographic data with Geopandas, read csv files as geospatial data, subset data, compare GeoDataFrame and GeoSeries, and master coordinate reference systems and projections.

Learn to read geospatial data with geopandas by loading formats like shapefiles and GeoJSON using read_file, inspect a data frame with a geometry column, and plot maps with geopandas.

Learn to read a subset of geospatial data with pandas and geopandas by using geometry masks and row limits, filtering conflicts in Africa and inspecting the coordinate reference system.

Differentiate geo data frames and geo series in Geopandas with clear examples, showing how a geometry column defines a geo data frame or geo series, and how to plot.

Practice spatial data analysis by reading shapefiles with geopandas, converting csv to a geo data frame with pandas, plotting with geopandas and matplotlib, and reprojecting to a crs before exporting.

Explore spatial analysis in Python by performing spatial joins between latitude and longitude points and neighborhood boundaries. Apply buffer and overlay analyses, and conduct nearest neighborhood analysis to locate services.

explore table joins and spatial joins using geopandas, merging country shapes and conflicts data, and performing point-in-polygon operations with within, contains, and intersect, plus inner, left, and right joins.

Perform nearest neighbor analysis with geopandas to find the closest bike stations to random points in Chicago, then visualize linestring connections and set geometry for interactive maps.

Practice spatial operations with geopandas: load and plot datasets, index to Jagdalpur, apply buffer, reproject to crs epsg 2272, perform a spatial join, count incidents per neighborhood, export to geojson.

Develop geospatial visuals with geopandas by plotting geographic data, tweaking vector parameters for points, polygons, and lines, and exploring multi-layer maps with context data via contextily, choropleth, and bubble maps.

Create multi-layer maps by combining geospatial datasets with pandas and matplotlib, arranging side-by-side plots or overlaying smaller areas, roads, and preschools on a single axis.

Overlay the neighborhood and swimming pool datasets in Geopandas using a dot plot after reprojecting to a common CRS, then create a choropleth map using counts normalized by area.

Explore interactive geospatial visualization in Python using Volume Library, Folium, and ipyleaflet; visualize conflicts with markers, circles, and heat maps, and preview Plotly Express for easy interactive maps.

Learn to visualize geospatial data with Folium heatmaps and time animations, prepare data with GeoPandas, convert coordinates to NumPy arrays, and apply color gradients.

Explore interactive geospatial visualization in Jupyter using ipyleaflet with geopandas and geojson to create choropleth and cluster maps, add markers, base maps, and search and full-screen controls.

learn to create interactive geospatial visualizations in python by building volume and cluster maps from an incidence dataset in philadelphia, switching to cartodb positron and plotly scatter mapbox.

Geocode address data with open source Python tools and OpenStreetMap data via Osmnx; reverse geocode latitude and longitude to extract street names, zip codes, and points of interest.

Convert coordinates to readable addresses using reverse geocoding with OpenStreetMap. Apply to a pandas DataFrame, plot results, and manage processing time with a progress bar.

Learn how to retrieve OpenStreetMap data in Python using the osmnx package. Download street networks and building footprints for a city like Liverpool, convert to geopandas, and visualize with folium.