Gain insights into GIS with GeoPandas, learn to manipulate geospatial data, delve into advanced geoprocessing, and create interactive maps, culminating in a comprehensive project.

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Geospatial analysis plays a crucial role in various applications, from urban planning and environmental monitoring to transportation logistics and emergency response. In this course you will be introduced to GIS applications in Python through the GeoPandas library.
 
You'll begin with the essentials of geospatial data and its formats. You'll then learn to import, manipulate, and export geospatial data using Geopandas. Moving forward, you’ll explore advanced geoprocessing techniques and create captivating visualizations with static and interactive maps. The course concludes with a hands-on project combining various concepts and skills.
 
After completing this course, you can manipulate geospatial data, create insightful visualizations, and undertake complex geoprocessing tasks. These skills can open new doors and opportunities in the dynamic realm of GIS, empowering you in your career or academic pursuits.

Using GeoPandas for Geospatial Analysis in Python

import geopandas as gpd

# open the datasets
brazil = gpd.read_file('brazil')
stations = gpd.read_file('ozone_stations.csv')

# create the geometry for the stations
stations['geometry'] = gpd.points_from_xy(x=stations['X'], y=stations['Y'], crs='epsg:4326')

# filter just the stations in Brazil
br_stations = stations.query("country == 'Brazil'")

# create the coverage area for each station
br_stations.geometry = br_stations.buffer(5)

# project both datasets to equal area CRS
brazil = brazil.to_crs('ESRI:54034')
br_stations = br_stations.to_crs('ESRI:54034')

# perform an overlay difference
uncovered = brazil.overlay(br_stations, how='difference')

# calculate the not covered area
ncarea = format(round(uncovered.area.sum()/1e6), ',')

# plot the results
ax = uncovered.plot(facecolor='green', edgecolor='black')
ax.set_ylabel('Latitude (deg)')
ax.set_xlabel('Longitude (deg)')
ax.set_title(f'Uncovered area is {ncarea} km²')

ax.figure.savefig('output/uncovered.png', dpi=300)


Check your understanding of overlay operations.

Introduction to Geospatial Data and Geopandas

Importing and Exporting Geospatial Data

Geoprocessing with Geopandas

Spatial Data Visualization

Transportation Planning

Conclusion

Appendix

Exploring Geospatial Data Using GeoPandas, Geoplot and Contextily

Solution: Overlays