Data Science for Emergency Services v0.0.1

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Service coverage monitoring (back-end)

Category: back-end application | Status: Working console demo program

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GitHub repo
Project aiming to propose a back-end solution to offer a high resolution of the capacity coverage monitoring for critical dispatch services such as an emergency service.

This project aims to propose a back-end solution to monitor at a high resolution the capacity coverage for a critical dispatch service such as an emergency service.

By capacity coverage, we mean the information relying on: How many teams can reach any point of a jurisdiction within a given timeframe?

Below are examples of how this information could be used by a dispatch application or a monitorig application:

This project will need one or more machine learning models to estimate accurately the routes. For that purpose a data challenge, with the ENS Paris, is under way and a compiled solution will be published under the following repo: https://github.com/ds4es/unit-response-oracle.

See also

Unit response oracle

date_range 19/06/2020

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The response time is one of the most important factors for emergency services because their ability to save lives and rescue people depends on it. A non-optimal choice of an emergency vehicle for a rescue request may lengthen the arrival time of the rescuers and impact the future of the victim. This choice is therefore highly critical for emergency services and directly rely on their ability to predict precisely the arrival time of the different units available.

Real-Time Units Gps Tracking

date_range 16/05/2020

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This project show how to broadcast, consume and plot GPS data in real-time. See the README.md on GitHub for details.

Real-Time Data Replication Between Remote Information Systems

date_range 26/04/2020

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More than ever people are expecting to share, access and get data processed in real-time. Unfortunately on this subject, public services are lagging far behind the private sector.