Get started with the latest Geospatial Data Science tools and learn what all the hype is about. This approach provides a stark contrast to traditional desktop GIS analysis methods. In this course we use Jupyter Notebooks to provide an interactive python coding environment, and GeoPandas to read, store, analyze, and visualize our data.
Jupyter notebooks allow you to include rich text documentation right along side your code. You can include headings, bold and italic fonts, mathematical equations, images, hyperlinks, etc in your documentation. The python code is interspersed among the documentation in discrete cell blocks that can be executed all at once, or individually. If you want to change a parameter to see the effect on your output its a very simple step. The output can be text, Data Frames, or graphical displays including maps. At the end of your analysis you can share your notebook and provide step-by-step documentation of every step along the way. This is critical in scientific applications, and in cases where your analysis may need to be justified in court. It also has many advantages in the education sector. Even if you don’t work in those areas, the Jupyter notebook approach can save you many hours by reducing the need to re-do your analysis every time the underlying data changes. You can simply re-run the analysis on different data.
GeoPandas provides powerful tools for data management and analysis. It was written as a spatial extension to the extremely popular Pandas library which has long been the essential tool in the toolbox of python based data-scientists. GeoPandas uses the standard set of open source geospatial tools under the hood so you can trust the results of your analysis. GDAL and OGR allow you to read your data in virtually any format, including from PostGIS. GEOS provides low level geometric operations such as spatial predicates for testing relationships (intersects, touches, contains, etc) and spatial operations for creating new geometries (intersection, union, difference, etc). Proj.4 is used to handle coordinate transformations. Everything occurs in memory so it is very fast and does not produce intermediate files, but there is no need to worry about losing information. You can always simply re-run the Jupyter notebook and get back to exactly where you started from the original source data. Of course if you want to save the results of your analysis you can do so in a variety of formats but only when you are ready to create an output file.
Best of all there are no limits when working with GeoPandas. You are not stuck waiting for a new analytical technique you just read about to trickle down into your desktop GIS software. All of the tools available in Python, which is pretty much everything, are available immediately. And if you have massive amounts of data and need more storage or processing capability you can (possibly) parallelize your data with Dask, run the Geospatial data science stack on a cluster of computers, or in the cloud using Google BigQuery or other platform.
GeoPandas is not a replacement for desktop GIS software. It is not a great tool for cartography, or for data management (digitizing, adding new features, etc) or for scrolling through an electronic map. But when it comes time to start performing analysis GeoPandas has become the first tool I reach for. I can’t imagine going back to point and click GUI interfaces ever again.
This course will teach you what you need to know about exactly what GeoPanda’s is, how it works, and how to use it to perform complex geospatial analysis and share it with others.
This course is available NOW through Jan 4. for the introductory price of $9.99 USD (may be less in your country) on Udemy.com. Buy it now, while it is cheap and watch at your leisure. Simply use the coupon code GEOPANDAS at checkout to receive the discount or use the links in this paragraph. The course Survey of Python for GIS applications will also be available at that price with the GEOPANDAS coupon code. This course on GeoPandas will assume that you have the level of expertise in Python programming that is included in the Survey of Python course so if you feel the need to brush up on Python you can do that.
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