Milan Janosov, "Geospatial Data Science Essentials: 101 Practical Python Tips and Tricks" (2024)

·36 min·3 clips

Milan Janosov explains how his geospatial data science book started as an accidental cheat sheet.

This episode features host Devika Jain interviewing Dr. Milan Janosov, a geospatial data scientist and Forbes 30 Under 30 honoree, about his 2024 book "Geospatial Data Science Essentials: 101 Practical Python Tips and Tricks." Janosov explains the book originated from his personal collection of Python code snippets and sticky notes, which he compiled into a cheat sheet that organically grew into a full publication. He describes using a Python script to analyze his own notebooks, creating a frequency distribution of packages and commands to determine the book's core content. The book systematically covers vector data, raster data, fundamental geometries, coordinate reference systems (CRS), and map projections. It provides practical guidance on essential Python libraries like GeoPandas, which Janosov calls the "Swiss knife" of spatial data science, alongside Shapely, Rasterio, and visualization tools like Folium. A dedicated chapter explores OpenStreetMap, which Janosov considers a favorite and increasingly vital open-source data source. The later sections touch on advanced applications, including spatial networks for analyzing urban accessibility, such as computing travel times to services in the context of "15-minute cities." A surprising insight is Janosov's observation that visually appealing maps garner significantly more engagement on social media, even for academic work, highlighting the practical importance of visualization. He emphasizes that many professionals work with spatial data without initially recognizing it, often starting with a simple spreadsheet containing coordinates. The book is designed as a "grab-and-go" resource for immediate problem-solving, prioritizing practical code over deep theoretical diving. Janosov reveals he had no formal plan to write a book; it was an "accident" stemming from his personal need to organize knowledge. The conversation is educational and conversational, focused on practical advice and the author's personal journey. This episode is ideal for data scientists, GIS professionals, or Python users who encounter spatial data and seek a concise, code-first reference. Listeners looking for deep theoretical exploration of geographic concepts or narrative storytelling might find it too technically focused.

As heard by us

A steady guide to the hidden mechanics that keep spatial data honest.

Milan Janusov's discussion starts from the basics of geospatial data science and keeps returning to them: geometries, coordinate systems, map projections, and the ways small slips can distort a map.

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Need a careful intro to Python geospatial tooling?

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