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The Maturation of GPT-4 Mapping: Handling Complex GIS Data Queries
Data Visualization   Latest   Machine Learning

The Maturation of GPT-4 Mapping: Handling Complex GIS Data Queries

Last Updated on January 25, 2024 by Editorial Team

Author(s): John Loewen, PhD

Originally published on Towards AI.

A modular approach to creating maps with Python plotly & dash
Dall-E 2 image: AI-rendered (beautiful, but definitely not accurate) map image of Canada Forest Fires.

Data visualization tools in Python are notoriously nit-picky.

With generative AI at our fingertips, we can ask GPT-4 to streamline the process for us.

And recently, it seems to be a lot better at handling large CSV files for data visualizations

To test this theory, today I am revisiting a story that I wrote back in April/2023 — a story that took me a long time (with a LOT of hassle) to write due to the then nit-picky nature of GPT-4.

I based the article on a static map that I viewed on LinkedIn, based on NASA data, showing forest fires for a region for a period of time.

A more useful representation for a static map is to provide the user/viewer with an interactive map — to let them choose the period of time to display.

This would tell a better story.

I‘m on a tight schedule today, so help me out, GPT-4! Can you whip this off?

Let’s give it a go!

The forest fire situation in western Canada over the past 10 years or so has been pretty horrendous. Particularly in British Columbia, where I am from.

Leading to awful pollution days like… Read the full blog for free on Medium.

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