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The Fire and Smoke Digital Twin is a cutting-edge solution that predicts smoke paths in 2D and 3D and provides real-time analysis of air quality drops due to fires. The digital twin collects and archives fire incident data from cities all over the US, and uploads nightly to this repository.

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Fire and Smoke Digital Twin: API

See the Frontend or the Backend.

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The Fire and Smoke Digital Twin is a cutting-edge solution that predicts smoke paths in 2D and 3D and provides real-time analysis of air quality drops due to fires. The digital twin collects and archives fire incident data from cities all over the US, and uploads nightly to this repository.

Note: This repository serves as an API endpoint for collected data and provides documentation to access the real-time API. To see the code behind querying each city for fire data and generating predicted impact, see the Backend, and for the code behind displaying this data in real-time, see the Frontend.

Getting Started with Fire & Smoke Digital Twin API

This repository serves fire data in the following format:

/api/v1/ {CITY} / {YEAR} / {MONTH} / {DAY} / FireMap.json

An example to get the data from August 9th, 2022, of Los Angeles fires would be:

/api/v1/LosAngeles/2022/08/09/FireMap.json

Notice that spaces are removed from city names, and that all numbers below 10 are zero padded. To visually see the data structure of our API, just look through the repository files above.

You can query this directly from our live server, at

https://smartcity.tacc.utexas.edu/fire/api/v1/LosAngeles/2022/08/09/FireMap.json

Or, for better uptime and performance, from this repo at

https://raw.githubusercontent.com/urbaninfolab/FireIncidentData/master/api/v1/LosAngeles/2022/08/09/FireMap.json

Recommended API Usage

For historical data, which we consider anything from yesterday to the start of our data collection, please use

https://raw.githubusercontent.com/urbaninfolab/FireIncidentData/master/api/v1/

as an endpoint. This relieves strain from our live server, and has GitHub deliver much faster results.

For real-time data, which is anything from the current day, please use our live server at

https://smartcity.tacc.utexas.edu/fire/api/v1/

to get the latest results.

Data Example

All data is in a JSON dictionary with a RSS-like syntax. To access the fire data, it's under ["rss"]["channel"]["item"]. Watch out for singular fire cases, in which the ["rss"]["channel"]["item"] will return a single dict object {} and not a list of dict [{},{}].

Notice

All data from our API is cloned on a nightly basis to this repository. There are no differences between our API and the files this repository lists.

Coverage

We are currently collecting and publishing active fire data from the following cities:

City Name Collection Start Date
Austin, Texas February 1, 2022
Dallas, Texas July 6, 2022
El Paso, Texas July 17, 2022
Houston, Texas July 6, 2022
Los Angeles, California July 18, 2022
Miami, Florida January 23, 2023
Milwaukee, Wisconsin January 23, 2023
Orlando, Florida January 23, 2023
Portland, Oregon January 23, 2023
Riverside, California July 17, 2022
San Antonio, Texas July 7, 2022
San Antonio, Texas July 7, 2022
San Diego, California September 8, 2022
Seattle, Washington July 19, 2022

This table is sorted alphabetically by city.

Please note that due to some outages, we are missing data for a few days out of the year of 2022. Please keep this in mind when using the data.

Contributing

If you know a city we have not listed here that provides an open real-time fire data page, please open an Issue and let us know! Alternatively, open a pull request to our Backend with the city added.

On the off-case someone has fire data in a different format and would like to convert it to our format, and upload past fire data to our API, please review our conversions in our Backend, or reach out via an Issue.

Contributors

This project is a combined effort of the Urban Information Lab and others. Check out the list of all these amazing people. We thank them for their time, hard work and effort.

About

The Fire and Smoke Digital Twin is a cutting-edge solution that predicts smoke paths in 2D and 3D and provides real-time analysis of air quality drops due to fires. The digital twin collects and archives fire incident data from cities all over the US, and uploads nightly to this repository.

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