For quite a while now, I have been rather mystified and intrigued by the fact that Sweden has one of the highest rates of school fires due to arson. According to the Division of Fire Safety Engineering at Lund University, “Almost every day between one and two school fires occur in Sweden. In most cases arson is the cause of the fire.” This is a lot for a small country with less than 10 million inhabitants, and the associated costs can be up to a billion SEK (around 120 million USD) per year.
It would be hard to find a suitable dataset to address the question why arson school fires are so frequent in Sweden compared to other countries in a data-driven way – but perhaps it would be possible to stay within a Swedish context and find out which properties and indicators of Swedish towns (municipalities, to be exact) might be related to a high frequency of school fires?
To answer this question, I collected data on school fire cases in Sweden between 1998 and 2014 through a web site with official statistics from the Swedish Civil Contingencies Agency. As there was no API to allow easy programmatic access to schools fire data, I collected them by a quasi-manual process, downloading XLSX report generated from the database year by year, after which I joined these with an R script into a single table of school fire cases where the suspected reason was arson. (see Github link below for full details!)
To complement these data, I used a list of municipal KPI:s (key performance indicators) from 2014, that Johan Dahlberg put together for our contribution in Hack for Sweden earlier this year. These KPIs were extracted from Kolada (a database of Swedish municipality and county council statistics) by repeatedly querying its API.
There is a Github repo containing all the data and detailed information on how it was extracted.
The open Kaggle dataset lives at https://www.kaggle.com/mikaelhuss/swedish-school-fires. So far, the process of uploading and describing the data has been smooth. I’ve learned that each Kaggle dataset has an associated discussion forum, and (potentially) a bunch of “kernels”, which are analysis scripts or notebooks in Python, R or Julia. I hope that other people will contribute script and analyses based on these data. Please do if you find this dataset intriguing!