Agnostic Extraction af Quake's Active Area

Context

This project is part of a course issued at the ENSG-Géomatique school, it was proposed to us by BRGM in order to identify a quake's active area once we retrieved tweets related to a seismic activity.

As a case study, we worked on the 2019 Teil Quake by testing different clustering algorithms to find the most active areas. Since tweets have a temporal dimension, we decided to use a spatial temporal clustering algorithm. In our case we used ST-DBSCAN.

You can read the report here.

Setup

To run the ST-DBSCAN Jupyter Notebook, you'll need to setup a conda environment. To do so, run the following command in your terminal:

conda env create -f environment.yml

You'll find the ST-DBSCAN Jupyter Notebook in the notebooks folder. We recommend you to use Visual Studio Code to run the notebook.

Analysis

In order to prepare the tweets for the clustering, we followed these steps:

• We used .hdf format to store the tweets.
• We removed tweets that were not in the area of interest (in this case France).
• We removed tweets with no coordinates.
• We ordered tweets by datetime and calculated a cumulative timestamp.

For us to choose the optimal parameters for ST-DBSCAN, we iterated over the minimum number of tweets and the maximum distance for an interval of time of 10 minutes. The results were as follows:

Results

We adopted the following parameters:

eps1 (distance)	eps2 (time)	MinPts (tweets)
90 km	10 min	22 tweets

The clustering took 1.62 s for a total of 5 clusters.

Time (s)	# of clusters
1.62	5

We use ThreeJS to visualize the results. Visit this link.

Made with ♥ by Léa, Romain and Salaheddine.