Data and Code for "Out of Africa: To What Extent Does a Text-to-Image Model Sustain Cartographic Projection Bias in Depictions of the African Continent?"
Abstract
This repository contains the dataset and analysis pipeline for the paper: "Out of Africa: To What Extent Does a Text-to-Image Model Sustain Cartographic Projection Bias in Depictions of the African Continent?". The following resources are provided: Data Files SD-curated.zip: The primary dataset consisting of 1,000 binary silhouettes of the African continent generated via the Stability AI API. SD-faux.zip: A collection of 111 "false" representations generated by the model that were manually deselected because they bore no plausible resemblance to the African continent (the 11% failure rate discussed in the paper). truth32.zip: Eleven reference cartographic projections standardized to a 32x32 binary image format to serve as interpretive anchors. epsg.txt: A mapping file used in the scripts, containing the EPSG/ESRI codes or abbreviations for the eleven reference projections. Code stable-diffusion-generate-africa.py: The batch generation script used to access the SD 3.5 Flash model and process raw outputs into binary format. geometric_features_descriptive.py: Extracts six core geometric features (Size, Width, Height, Aspect Ratio, Eccentricity, and Cardinal Angle) to quantify areal, distance, and angular distortion. tsne_hac.py: The main analysis script that performs t-SNE dimensionality reduction using the Kulczynski distance metric and Hierarchical Agglomerative Clustering to identify morphological regimes. tsne_hyperparam_grid.py: A sensitivity analysis tool that generates a…
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