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Geospatial Data Science with Python: GeoPandas
Rating: 4.5 out of 5(505 ratings)
3,582 students

Geospatial Data Science with Python: GeoPandas

Vector based geospatial analysis
Created byMichael Miller
Last updated 1/2021
English

What you'll learn

  • How to analyze geospatial data using the python data science ecosystem
  • Using Jupyter notebooks to provide complete documentation of your workflow and interactive code examples
  • The basics of the python data science ecosystem: NumPy, Matplotlib, Pandas, etc.
  • Geospatial extensions to the Python data science ecosystem: Fiona, Shapely, GDAL, and most importantly GeoPandas
  • Perform common vector analysis tasks with GeoPandas

Course content

8 sections • 51 lectures • 9h 54m total length

  • Introduction5:46
  • Differences between data science and GIS19:48
  • Advantages of the data science approach10:52
  • The python data science ecosystem for non-spatial data9:40
  • The python data science ecosystem for spatial data8:29
  • Introduction to Jupyter notebooks8:34

Requirements

  • Basic understanding of GIS operations for data analysis (buffers, intersections, etc)
  • Basic understanding of Python (You can get what you need from my course Survey of Python for GIS analysis)

Description

Learn why the Geospatial Data Science tools are becoming so popular in the Geospatial sector.  The combination of Jupyter Notebooks with Python and GeoPanda's allows you to analyze vector data quickly, repeatably, and with full documentation of every step along the way so your entire analysis can be repeated at the touch of a button in a notebook format that can be shared with colleagues.

If you ever get asked to explain your analysis, either for a scientific paper, to defend your results in a court, or simply to share what you've done with others so they can follow your steps than you will be glad that you conducted your analysis in Jupyter notebooks with GeoPanda's rather than in a traditional desktop GIS system.

If you ever get frustrated with limitations in desktop GIS software, some of which is still 32 bit, single core software that uses decades old technology under the hood then you will appreciate the performance that can be achieved with this approach.

Who this course is for:

  • GIS analysts who want to increase their understanding of data science
  • Data scientists who want to increase their understanding of geospatial analysis

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