Geospatial Data Exploration in its November Issue
In the ever-evolving realm of data science, a new wave of authors has joined TDS, bringing fresh perspectives and expertise to the table. This month, a diverse group of individuals including Eden Zohar, Karen Asmar, Florent CATTANEO, Michio Suginoo, Elías Snorrason, Justin Mackie, Partha Sarkar, James Isbell, João António Sousa, Omer Ginosar, Ian Henderson, Taylor Jensen, Gordon Davis, Sebastien Peytrignet, Mattia Gatti, Shubhankar Rawat, Ido Greenberg, Erich Henrique, Nico Westerbeck, Max Cembalest, Oliver Kramer, Varatharajah Vaseekaran, Nuri, and Helen McKenzie, have joined the TDS community.
These new authors are publishing on a variety of topics, from choosing large language models and the intersection of data science and archaeology, to geospatial data analysis. One of the new authors, Mattia Gatti, has penned a beginner-friendly introduction to geospatial raster data.
For those new to geospatial data analysis, TDS offers a wealth of resources to help you get started. Hennie de Harder has written an accessible tutorial on plotting routes in Python for beginners in geospatial data. Another valuable resource is a hands-on blog post on basics of geospatial data science, which walks you through foundational geometry concepts and a practical workflow for fetching and extracting shapefiles programmatically using Python, heavily based on GeoPandas usage.
For those who prefer visual learning, there are YouTube tutorials available. "Getting Started with ArcGIS Python API and GeoPandas" covers installation, uploading shapefiles, spatial calculations, and creating choropleth maps with Matplotlib and Folium, aimed at beginners learning spatial data visualization and analysis. "Geospatial Data Visualization in Python" by Adam Symington is a more comprehensive workshop-style tutorial that introduces GeoPandas, Shapely, Rasterio, and multiple visualization libraries.
Key libraries featured across these resources include GeoPandas (vector data handling), Shapely (geometry operations), Rasterio (raster data), Matplotlib and Folium (static and interactive visualization). Together, these resources provide a solid, beginner-friendly foundation in Python geospatial analysis, combining conceptual explanations, sample codes, and visualization techniques. Starting with GeoPandas for vector data and gradually integrating other tools as in these guides is a common learning path.
If you're interested in publishing your own work with TDS, they welcome submissions from new authors. The authors listed have recently published posts on TDS about geospatial data analysis.
In other news, several community members have recently become Medium members. This month, a selection of these posts covering a wide range of geospatial approaches and methods is being shared.
Moreover, Lan Chu has demonstrated the power of maps in telling stories about people and communities using public data and open source tools. Sutan has been covering specific tools and skills for working with geospatial data, including the most-used programming languages (Python, JavaScript, and HTML) in geospatial analysis.
Lastly, Bryan R. Vallejo has walked through a project that leveraged geospatial technology to track the well-being of whale sharks in the Galapagos Islands. The TDS Podcast has a grand finale discussing the future of AI and its associated risks.
Geospatial data analysis is a subfield of data science that combines geography, programming, cartography, geometry, and more. Whether you're a seasoned data scientist or just starting out, TDS offers a welcoming community and a wealth of resources to help you explore this fascinating field.
Data-and-cloud-computing technology plays a significant role in facilitating geospatial data analysis, with authors like Mattia Gatti publishing beginner-friendly introductions to geospatial raster data on TDS.
For those looking to publish their own work in the field, TDS welcomes submissions from new authors, as demonstrated by the diverse group of individuals who have recently joined the TDS community to share their expertise on various topics related to geospatial data analysis.
