https://github.com/NicolasDumoulin/distancematrix

Provides an environment for computing the distances between municipalities from France
https://github.com/NicolasDumoulin/distancematrix

Last synced: 3 months ago
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Provides an environment for computing the distances between municipalities from France

• Host: GitHub
• URL: https://github.com/NicolasDumoulin/distancematrix
• Owner: NicolasDumoulin
• Created: 2016-01-15T14:21:04.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2016-01-15T14:23:29.000Z (almost 9 years ago)
• Last Synced: 2024-06-12T18:13:29.436Z (5 months ago)
• Language: Python
• Size: 3.91 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: Readme.md

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README

        
# Distances computations between french municipalities

 

The purpose is to provide an environment for computing the distances between municipalities from France.

For that, I propose to you to build a docker image that will install all required software and tools.

 

## Building the docker image

The first step is to built the docker image that will install the [OSRM backend](https://github.com/Project-OSRM/osrm-backend), the [python library osrm-c](https://github.com/tdihp/osrm-c) as frontend and the scripts for the distances matrix computation.

    docker build -t distances .

## Starting the docker image

Now, you can start the docker image with this command:

    docker run --interactive=true --tty=true --ulimit memlock=68719476736 --privileged --volume=$(pwd):/root/distancesmatrix/data distances

We need to enable the shared memory.

According to [this document](https://github.com/Project-OSRM/osrm-backend/wiki/Configuring-and-using-Shared-Memory), we use the option `--ulimit` of docker with attributes `memlock=68719476736`

## Init the OSRM service / Import the data

Then, as you are in the container, you have to run the script `run.sh` with your osm.pbf extract file as input:

    ./data/run.sh data/cantal-rectangle.osm.pbf 

After that, the data have been extracted by OSRM and loaded into shared memory.

## Distances computations

    python data/computeDistances.py

In France (metropolitain), we have 36571 municipalities, giving 668700735 distances to compute for each couple of municipalities for one direction (municipality A to B) and 1337401470 (1.34E+9) if we consider the two directions (distance from A to B and from B to A).

Considering the two directions can be determinant for some municipality, if for example they can enter easily on a motorway in a direction, but should made a detour in the other one.

Considering only couples of municipalities that have an euclidean distance lesser than 100km, we have 37101055 distances to compute (for each direction).