Geospatial Data Extra: Exploring the Broad Spectrum of Global Positioning Information
In the realm of data science, geospatial data analysis stands out as a subfield that combines multiple disciplines, including geography, programming, cartography, and geometry. With the increasing demand for understanding our world better, several resources have emerged to help beginners learn geospatial data analysis methods.
Recently, Towards Data Science (TDS) has been publishing a variety of tutorials and guides on this topic. One such publication, "Unveiling Fishing Activity Risk to Marine Megafauna with Geospatial Technologies," written by Bryan R. Vallejo, was unveiled in October 2022. This article discusses a project that leveraged geospatial technology to track the well-being of whale sharks in the Galapagos Islands.
Another noteworthy article, "The Ultimate Beginner's Guide to Geospatial Raster Data," penned by Mattia Gatti, was also published in October 2022. This post provides a thorough, beginner-friendly introduction to raster data, a key building block in many geographic data workflows.
Sutan, a regular contributor to TDS, has covered the most-used programming languages (Python, JavaScript, and HTML) in geospatial analysis in many recent articles. One such article, "Spatial Data Science: Javascript & Python," was published in August 2022.
For those seeking a Python-focused workshop, Adam Symington's workshop, covering essential geospatial data manipulation and visualization, is scheduled for August 2025. This workshop utilizes libraries such as GeoPandas, Shapely, Cartopy, GDAL, Rasterio, and rioxarray for vector and raster data, and includes hands-on exercises on visualizing shapes, managing raster datasets, and combining multiple data types in unified visuals.
GeeksforGeeks has also provided an overview of geospatial analysis workflows, including exploratory analysis techniques, advanced spatial modeling, and visualization methods, with advice on communicating results effectively.
Moreover, a recent beginner-friendly guide on Geo AI explains how artificial intelligence enhances geospatial data interpretation. It breaks down Geo AI workflows into five steps: data preparation, model selection, training, evaluation/refinement, and deployment, highlighting the growing role of AI in automating and accelerating geospatial analysis.
For those interested in R programming, an updated guide introduces geospatial data types, coordinate reference systems, and core R packages such as sf, rgdal, sp, and raster, which provide tools for analysis and visualization of spatial data in R.
A tutorial on the basics of geospatial data science explains foundational geometric concepts, coordinate systems, and using Python packages like GeoPandas, Shapely, and Folium to conduct real-world spatial analysis. It also points toward more advanced topics like spatial joins, network analysis, raster processing, and spatial machine learning, supported with practical examples such as the John Snow Cholera map.
Lan Chu's tutorial, "Simple Yet Stunning and Meaningful Geospatial Visualization Using Happiness and Conflict Data," published in May 2022, demonstrates the power of maps to tell stories about people and communities using public data and open source tools.
Hennie de Harder's tutorial on plotting routes in Python, although no specific date was provided, is a great starting point for newcomers to geospatial data analysis.
TDS has also been sharing a selection of recent posts that cover a wide range of geospatial approaches and methods. The grand finale of the TDS Podcast, hosted by Jeremie Harris, discussed the future of AI and its potential risks in its latest episode.
TDS Editors would like to thank their community members who recently became Medium members for their support. They also encourage readers who are interested in publishing their work to reach out to them.
As we delve deeper into the world of geospatial data analysis, these resources offer comprehensive, practical entry points for beginners to learn geospatial data analysis workflows, tools, and key techniques in Python and R, including recent advances with AI-driven Geo AI methods.
Data-and-cloud-computing technology plays a crucial role in geospatial data analysis, enabling advanced workflows and automation. Many tutorials and guides on this subject are available online, such as Towards Data Science's (TDS) article "Unveiling Fishing Activity Risk to Marine Megafauna with Geospatial Technologies," published in October 2022.
The growing field of Geo AI leverages artificial intelligence to enhance geospatial data interpretation, with tutorials providing step-by-step workflows and introductions to essential tools for Python and R programming.
