chribsen/inferring-social-ties

This repository contains the data pipeline that was used for inferring social ties from GPS data.

Inferring Social Ties from Human Mobility Patterns

This repository contains code that was used in the thesis "Human mobility Patterns at Large-Scale Events".

The figure above shows how the data pipeline that has been built to infer social ties.

Basic flow of pipeline:

1. PostgreSQL database schema is built using bash scripts (see pipeline/build-schema)
2. Data set is ingested into schema using pg_dump.
3. Database schemas are setup using bash scripts (see pipeline/build-indexes)
4. Features are computed using SQL stored procedures (see pipeline/sql-pipeline)
5. Based on these features supervised learners are trained using Python and sklearn (see pipeline/train-models)
6. Social graph is build using Python and networkx (see pipeline/build-social-graph)
7. Community detection is applied using infomap (see pipeline/graph-statistics)
8. Various graph statistics are applied using networkx (see pipeline/graph-statistics)

Finally, we have throughout the process relied on smaller scripts to plot data and do smaller data tasks.

Folder structure:

.
|-- app
|   |-- static
|   |-- templates
|   |-- app.py
|   `-- config.py
|-- data
|   |-- polygons
|   `-- training-data
|-- figures
|-- misc-scripts
|-- pipeline
|   |-- build-indexes
|   |-- build-schema
|   |-- build-social-graph
|   |-- graph-statistics
|   |-- sql-pipeline
|   `-- train-models
|-- README.md