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povertymaps

Interpreting wealth distribution via poverty map inference using multimodal data

https://github.com/lisette-espin/povertymaps

Science Score: 49.0%

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Keywords

antennas catboost-model catboostregressor cnn-model crowdsourced-data deep-learning demographics explainable-ai infrastructure interpretable-ai machine-learning mean mobility online-data open-street-map poverty-estimates satellite-imagery stddev
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Interpreting wealth distribution via poverty map inference using multimodal data

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antennas catboost-model catboostregressor cnn-model crowdsourced-data deep-learning demographics explainable-ai infrastructure interpretable-ai machine-learning mean mobility online-data open-street-map poverty-estimates satellite-imagery stddev
Created about 3 years ago · Last pushed about 1 year ago
Metadata Files
Readme License Citation

README.md

PovertyMaps

Interpreting wealth distribution via poverty map inference using multimodal data

Python 3.7

screenshot

Analysis (plots)

  • Open Main results (or download figures here)
  • Open Supplementary material (or download figures here)
    • Normal (Gaussian) Test on ground-truth
    • Descriptive Analysis on ground-truth
    • Pre-processing: Sample weights
    • Pre-processing: Data Recency (RMSE vs NRMSE)
    • Pre-processing: Ablation study using RMSE
    • Model and Feature Performance (R^2)
    • Variability all models
  • Download results

Interactive tool

Try out the interactive tool to see the high-resolution poverty map of Sierra Leone and Uganda.

Check how wealth may look now. screenshot

Check how wealth has changed over the years. screenshot

Scripts

cd scripts

Check Pipeline.md for step-by-step guidelines.

  1. Init: ./batch_init.sh -r ../data/Uganda -c UG -y 2016,2018 -n 10
  2. Features GT: ./batch_features.sh -r ../data/Uganda -c UG -y 2016,2018 -n10
  3. Features PP: ./batch_features.sh -r ../data/Uganda -c UG -n10
  4. Pre-processing: ./batch_preprocessing.sh -r ../data/Uganda -c UG -y 2016,2018 -o none -t all -k 5 -e 3
  5. CatBoost train&test: ./batch_xgb_train.sh -r ../data/Uganda -c UG -y 2016,2018 -l none -t all -a mean_wi,std_wi -f all -k 4 -v 1
  6. Augmentation: python cnn_augmentation.py -r ../data/Uganda -years 2016,2018 -dhsloc none
  7. CNN train&test: python cnn_train.py -r ../data/Uganda/ -years 2016,2018 -model cnn_mp_dp_relu_sigmoid_adam_mean_std_regression -yatt mean_wi,std_wi -dhsloc none -traintype all -kfold 5 -epochs 3 -patience 100 -njobs 1 -retrain 3
  8. CNN+XGB train&test: ./batch_xgb_train.sh -r ../data/Uganda -c UG -y 2016,2018 -l none -t all -a mean_wi,std_wi -f all -k 4 -v 1 -n offaug_cnn_mp_dp_relu_sigmoid_adam_mean_std_regression -e 19 -w 1
  9. Fmaps: python cnn_predict.py -r ../data/Uganda/ -years 2016.,2018 -model cnn_mp_dp_relu_sigmoid_adam_mean_std_regression -yatt mean_wi,std_wi -dhsloc none -traintype all -fmlayer 19 -njobs 1
  10. Poverty maps: python batch_infer_poverty_maps.py -ccode UG -model CB
  11. Cross-country testing: python batch_cross_predictions.py

Citations

Model (WWW'2023)

Lisette Espn-Noboa, Jnos Kertsz, and Mrton Karsai. 2023. Interpreting wealth distribution via poverty map inference using multimodal data. In Proceedings of Association for Computing Machinery (TheWebConf 23). ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3543507.3583862 <!--- Pre-print: https://arxiv.org/abs/2302.10793 --->

A Comparative Analysis of Wealth Index Predictions (SoGood@ECMLPKDD'2024)

Pre-print: https://arxiv.org/abs/2408.01631

Credits and Funding

CEU

CSH

SoBigData++

License

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Owner

  • Name: Lisette Espin
  • Login: lisette-espin
  • Kind: user
  • Location: Austria
  • Company: Complexity Science Hub Vienna, Central European University

Postdoctoral Researcher

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