After lots of time spending on documents and search, I found this data set is very rich in information, although the problems and challenges are huge too. I found the NFIRS data promising since it is the most complete public data set of fire incidents on national level. These were the starting point for us to work on. They also got several years National Fire Incident Reporting System (NFIRS) data, listed every fire incident’s time and address. The American Red Cross Diaster response for fire per county Red Cross provided the data of their smoke alarm home visits in 10 months and the data of Red Cross disaster response to us for our analysis. The American Red Cross project is a national level campaign in much larger scale compared to New Orleans project. They don’t have fire risk data in national level, but provided an API to upload local fire risk data into the model. With these two factors combined, they ranked the high priority regions for smoke alarm outreach program.Īfter this New Orleans program, Enigma.io further expanded the acs/ahs method to national level, and produced a visualization website showing risk of lacking smoke alarms on map. Thus they calculated the fire risk per housing unit according to New Orleans historical data, then added age adjustment based on census population data. According to NFPA research, very young and very old are most susceptible to fire fatalities. Then they studied the areas of most likely to suffer structure fire fatalities. Using the shared questions in these two survey, they were able to build a model to predict smoke alarm presence in block group level, thus same number of home visits could cover more homes without smoke alarms. The American Housing Survey(AHS) provided some data on this in county level, and the American Community Survey(ACS) have more detailed results about many other questions in census block group level. They first targeted the areas of least likely to have smoke alarm installed. There was a similar project in New Orleans – Smoke Alarm Outreach Program.
The ranking could came from multiple factors, like the possibility of don’t have smoke alarm installed, the risk of catching fire, the possible casualty and losses, maybe even taking the constraints of each Red Cross chapter into consideration. Given limited resources and the ambitious project goal, the American Red Cross want to have a priority list for regions backed with data and analysis.
The choropleth map of Red Cross Smoke Alarm Home Visits per county in 10 months Project Goal The Red Cross Smoke Alarm Home Visits is already a very successful campaign, but they were looking forward to the power of data and data science to further improve the project. I joined a project The Effectiveness of Smoke Alarm Installs and Home Fire for the American Red Cross. In May, 2015 I went to a MeetUp organized by DataKind, where Volunteers came from all kinds of background joined together to help non-profit organizations with Data Science. Geocoded 18 millions address in NFIRS data with AWS server, Postgresql, PostGIS.Major contribution on model design, implemented NFIRS related predictors, and built interactive visualizations.Discovered the hidden information in NFIRS dataset, obtained and analyzed 10G NFIRS data.The project used public data to help Red Cross directing limited resources to homes that more vulnerable to fire risk and loss.This is a write-up for my volunteer Data Science project for the American Red Cross.
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