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Applied Data Science group project. Visualising data from OSINT practitioner SIPRI to model the act of international arms transfers, and their possible influences on ongoing conflicts across the globe.

HTML 0.01% JavaScript 0.28% CSS 0.01% Jupyter Notebook 54.87% Python 0.34% Rich Text Format 44.50%

osint_visualisation's Introduction

Open Source Intelligence (OS-INT) is defined as the act of extracting and analysing information from publicly accessible sources. Its purposes include, but are not limited to national security, military intrigue, business intelligence and academic research. With the rapid growth of the reach of the internet, OS-INT has reached new highs in levels of practitioners, as well as the breadth of the content illustrated by the data.

For this project, we are working with several different sources of data, and OS-INT practitioners, such as the Stockholm International Peace Research Institute (SIPRI), who provide data which encapsulates military expenditure, armed conflict and the arms trade.

It is without doubt that the collective reliance and funding of the military industrial complex has shown massive benefits and disadvantages for individuals across the world, with corporations raking in giant profits, and conflict decimating communities worldwide. With this project, we hope to clearly visualise the direct correlation between the transfer of arms and equipment to the levels of conflict across the globe.

Getting our project running:

To be able to run our project, first clone it.
Run npm install in the root directory
Replace the webpack.config.js in node_modules\react-scripts\config with the one in the project root
Extract the .tiff file in \public\NE1_LR_LC.zip to \public

Once installed

In the project directory, you can run:

`npm start`

Runs the app in the development mode.
Open http://localhost:3000 to view it in your browser.

The page will reload when you make changes.
You may also see any lint errors in the console.

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radshiadeh

osint_visualisation's Issues

Predict UK's expenditure forecast for 20's

Use statistical methods to forecast spending by the UK.
Predicted 2020 and 2021 to a good level of accuracy, so use this to predict for the rest of 20's

Make basic graphs on the data extracted (Exploration)

Make some graphs using a library such as matplotlib in order to attain some visualisation of the data that has been extracted and cleaned up
Potential Graphs

Countries with the most sales
Countries that have purchased the most ($Mil/Bil)
Countries with the most conflict
Top suppliers for each major war in the past (half) century

Write intro (Report)

Write the intro to the report:

info on the problem at hand and the context (want to visualise and illustrate the correlation between the sales of armaments and the prevelance of conflict across the world)

Data Cleaning and formatting of conflicts datasets

Combination of E.Germany and Germany, Soviet union and russia

Currently I have made some graphs showing top buyers and top sellers since 1950, however in both they have some countries and also their old form. Would be good to have graphs of this also.

Make the page screen-size adjustable

Apply ML techniques to the key features (Data Modelling)

Final Application (Modelling)

The main form of the visualisation of the data
A tiff file of a projection of a map with a slider showing the transfer of arms and a separate indicator showing levels of conflict across the world.