Smoke Detector Risk
After coming across the open source data analysis tool created by Enigma, Bloomington FD (BFD) decided to pursue this data driven prevention project. After uploading 16 years of fire response data, the department was given a spreadsheet with our data analysis. The complexity of the project exceeded internal Fire Department capabilities and quickly moved to the collaborative project list established by our interdisciplinary team. This team based out of Indiana University includes data science researchers from the School of Informatics and Computing, leading technologists from the University’s Information Technology Services (UITS), and BFD members. The mapped data was created by Logan Paul, a graduate researcher in Prof. David Wild’s Integrative Data Science Laboratory and is much easier to use than the raw data. BFD plans to use the data to help focus smoke detector installations to areas that will have the biggest impact. If Bloomington's results are similar to other Cities across the nation, this data driven approach will increase our accuracy of smoke detector installations from 5-8 percent to nearly 65 percent. This represents a more efficient delivery of service that will also save lives.
How it works:
https://www.enigma.com/blog/developing-a-risk-model-for-residences-without-smoke-alarms
Open Source algorithm:
http://labs.enigma.io/smoke-signals/
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| contactPoint |
{ "fn": "Chief Moore", "@type": "vcard:Contact", "hasEmail": "mailto:no-reply@data.bloomington.in.gov" } |
| description | After coming across the open source data analysis tool created by Enigma, Bloomington FD (BFD) decided to pursue this data driven prevention project. After uploading 16 years of fire response data, the department was given a spreadsheet with our data analysis. The complexity of the project exceeded internal Fire Department capabilities and quickly moved to the collaborative project list established by our interdisciplinary team. This team based out of Indiana University includes data science researchers from the School of Informatics and Computing, leading technologists from the University’s Information Technology Services (UITS), and BFD members. The mapped data was created by Logan Paul, a graduate researcher in Prof. David Wild’s Integrative Data Science Laboratory and is much easier to use than the raw data. BFD plans to use the data to help focus smoke detector installations to areas that will have the biggest impact. If Bloomington's results are similar to other Cities across the nation, this data driven approach will increase our accuracy of smoke detector installations from 5-8 percent to nearly 65 percent. This represents a more efficient delivery of service that will also save lives. How it works: https://www.enigma.com/blog/developing-a-risk-model-for-residences-without-smoke-alarms Open Source algorithm: http://labs.enigma.io/smoke-signals/ |
| distribution |
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| identifier | https://data.bloomington.in.gov/api/views/w28u-hpg5 |
| issued | 2023-05-12 |
| keyword |
[ "untagged" ] |
| landingPage | https://data.bloomington.in.gov/d/w28u-hpg5 |
| license | http://opendatacommons.org/licenses/pddl/1.0/ |
| modified | 2023-05-12 |
| publisher |
{ "name": "data.bloomington.in.gov", "@type": "org:Organization" } |
| theme |
[ "Fire" ] |
| title | Smoke Detector Risk |
