Data to Dashboard: Mapping UN Food Security Stats With GPT-4 Prompting

Author(s): John Loewen, PhD

Originally published on Towards AI.

Interactive food security mapping with GPT-4 and Plotly dash
Dall-E image: impressionist painting of a bubble map on a computer screen with supporting visuals

Recently, GPT-4 prompting for Python code generation has significantly improved.

Particularly with interactive libraries like Plotly dash and Streamlit.

This includes prompting to generate interactive data visualizations, for example, choropleth and scatter geo (bubble) maps.

Choropleth maps are a style of the thematic map where areas are shaded or patterned in proportion to the data point being used (i.e., value to represent relative global safety)

Scatter Geo (Bubble) maps use circles of different size to represent a numeric value on a territory (for example, a bubble per country).

Both of these maps can be created using the Python Plotly library.

Now what if I want to display these maps side-by-side to provide two different perspectives on the same dataset? And can I do this over a period of time, like 20 years? And can I choose the year?

The answer to all these questions is YES! We can even prompt GPT-4 to do it all for us — if we use a modular, careful approach.

Using the UN FAO food security dataset…

Here’s how it works!

My wife works for the UN Food and Agriculture Organization and is always involved with decisions related to global food… Read the full blog for free on Medium.

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Published via Towards AI