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African Armed Conflicts: Visualization & Forecasting

Visualizing Interstate and Internal Conflicts using Small & Medium Arms Imports, Military Expenditures, Fraction of Population in the Army, etc. We use a time-series framework to tackle the problem.

Screenshot

Link to interactive map

Model

Bagged Random Forest (0.9) and Neural Network (0.1)

Overview

Let's first look at all the files in the repo

$ tree --dirsfirst --filelimit 100

.
├── analysis
│   ├── data
│   │   ├── nisat
│   │   │   ├── resources
│   │   │   │   ├── NISAT database public user manual.pdf
│   │   │   │   └── nisat_query.png
│   │   │   ├── arms_trades_exports.csv
│   │   │   ├── arms_trades_imports.csv
│   │   │   ├── arms_trades.zip
│   │   │   ├── preproc_arms_import.csv
│   │   │   └── prio_weapons_code.txt
│   │   ├── output
│   │   │   ├── african_countries.json
│   │   │   ├── arms_imports.json
│   │   │   ├── conflicts.json
│   │   │   ├── mil_exp.json
│   │   │   ├── mil_pers.json
│   │   │   ├── population.json
│   │   │   └── predictions.json
│   │   ├── owid
│   │   │   ├── military-expenditure-as-a-share-of-gdp.csv
│   │   │   └── military-personnel-relative-to-total-population.csv
│   │   ├── population
│   │   │   └── world_population.csv
│   │   ├── ucdp
│   │   │   ├── resources
│   │   │   │   └── ucdp-dyadic-191.pdf
│   │   │   └── ucdp-dyadic-191.csv
│   │   └── world
│   │       ├── african_countries.csv
│   │       ├── country_continent.csv
│   │       └── COW country codes.csv
│   ├── african_countries_dict.ipynb
│   ├── nisat-1-scraping.ipynb
│   ├── owid-1-milexp.ipynb
│   ├── owid-1-milpers.ipynb
│   ├── population.ipynb
│   ├── regression.ipynb
│   └── ucdp-1-preproc.ipynb
├── dataviz
│   ├── assets
│   │   ├── css
│   │   │   ├── leaflet.css
│   │   │   └── style.css
│   │   ├── img
│   │   │   └── search.svg
│   │   └── libs
│   │       ├── d3.js
│   │       ├── d3-tip.js
│   │       ├── leaflet.js
│   │       └── localization-en.js
│   ├── data
│   │   ├── african_countries.json
│   │   ├── arms_imports.json
│   │   ├── conflicts.json
│   │   ├── mil_exp.json
│   │   ├── mil_pers.json
│   │   ├── population.json
│   │   ├── predictions.json
│   │   └── world.json
│   ├── scripts
│   │   ├── 1-preproc.js
│   │   ├── 2-map.js
│   │   └── main.js
│   └── index.html
├── README.md
└── screenshot.png

analysis/ : Contains the notebooks to collect, process, train and predict

dataviz/ : Contains the interactive map, made with the D3.js library

Datasets

Launching jupyter notebook

Make sure to cd in analysis/ before launching jupyter notebook. If not, the paths won't work

Authors

  • Jean-Romain Roy - Co-author: data collection, preprocessing, logistic regression, random forest, svm, interactive map - jeanromainroy