Swedish school fires and Kaggle open data

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!

BigData.SG and The human face of big data

By an amazing coincidence, I was able to attend a session of the Singapore big data meetup group, BigData.SG, after having attended the NGS Asia 2012 conference here in the Lion City. This group was started earlier this year and tries to meet once a month (a more ambitious schedule than the Stockholm group.) Today, about 40 people were in attendance, and I had a nice time chatting to some of them. The invited speaker was Michael Howard, VP of marketing at Greenplum. He had one nice quip – “big data means so little to so many” and talked a little bit about Chorus, a collaborative data science platform from Greenplum which I hadn’t heard about. He hinted that Chorus and Kaggle have something big going on together – something that will revolutionize the whole crowdsourced prediction “business.” It will be interesting to see what it is.
Earlier today, Howard had announced the Human Face of Big Data project, which has been / will be launched in several cities all over the world today (probably still hasn’t launched in the US).  The project, which “lets people compare themselves to each other”, uses a downloadable app (for Android; the iOS version wasn’t working yet) that you can use to collect data about yourself with. There is “passive data collection”: how far and at what speed you’ve moved, how many Bluetooth hot spots you’ve passed, and so on, and active collection through questions that the app asks you; either “serious” questions such as whether you would modify the genes of your unborn infant if given the opportunity (and if so, what would you improve – immune system, intelligence, …) – apparently men and women answered this very differently – or more open-ended “fantasy” questions.

The app also lets you find your “data doppelganger”, which is of course the user who is most similar to you in terms of the collected data. Howard said that despite the short time since the launch, the app has already yielded interesting information about gender differences and topics of interest.

A good week for (big) data (science)

Perhaps as a subconscious compensation for my failure to attend Strata 2012 last week (I did watch some of the videos and study the downloads from the “Two Most Important Algorithms in Predictive Modeling Today” session), I devoted this week to more big-data/data-science things than usual.

Monday to Wednesday were spent at a Hadoop and NGS (Next Generation [DNA] Sequencing) data processing hackathon hosted by CSC in Espoo, Finland. All of the participants were very nice and accomplished; I’ll just single out two people for having developed high-throughput DNA sequencing related Hadoop software: Matti Niemenmaa, who is the main developer of Hadoop-BAM, a library for manipulating aligned sequence data in the cloud, and Luca Pireddu, who is the main developer of Seal, which is a nice Hadoop toolkit for sequencing data which enables running several different types of tasks in distributed fashion. Other things we looked at was the CloudBioLinux project, map/reduce sequence assembly using Contrail and CSC’s biological high-throughput data analysis platform Chipster.

On Friday, me and blog co-author Joel went to record our first episode of the upcoming Follow the Data podcast series with Fredrik Olsson and Magnus Sahlgren from Gavagai. In the podcast series, we will try to interview mainly Swedish but also other companies that we feel are big data or analytics related in an interesting way. Today I have been listening to the first edit and feel relatively happy with it, even though it is quite rough, owing to our lack of experience. I also hate to hear my own recorded voice, especially in English … I am working on one or two blog posts to summarize the highlights of the podcast (which is in English) and the following discussion in Swedish.

Over the course of the week, I’ve also worked in the evenings and on planes to finish an assignment for an academic R course I am helping out with. I decided to experiment a bit with this assignment and to base it on a Kaggle challenge. The students will download data from Kaggle and get instructions that can be regarded as a sort of “prediction contests 101”, discussing the practical details of getting your data into shape, evaluating your models, figuring out which variables are most important and so on. It’s been fun and can serve as a checklist for my self in the future.

Stay tuned for the first episode of Follow the Data podcast!

New analysis competitions

Some interesting competitions in data analysis / prediction:

Kaggle is managing this year’s KDD Cup, which will be about Weibo, China’s rough equivalent to Twitter (with more support for adding pictures and comments on posts, it’s more like a hybrid between Twitter and Facebook maybe). There will be two tasks, (1) predicting which users a certain user will follow (all data being anonymized, of course), and (2) predicting click-through rate in online computational ad systems. According to Gordon Sun, chief scientist at Tencent (the company behind Weibo), the data set to be used is the largest one ever to have been released for competitive purposes.

CrowdAnalytix, an India-based company with a business idea similar to Kaggle’s, has started a fun quickie competition about sentiment mining. Actually the competition might already be over as it ran for just 9 days starting 16/2. The input consists of comments left by visitors to a major airport in India, and the goal is to identify and compile actionable and/or interesting information, such as what kind of services visitors think are missing.

The Clarity challenge is, for me, easily the most interesting challenge of the three, in that it concerns the use of genomic information in healthcare. This challenge (with a prize sum of $25,000) is, in effect, crowdsourcing genomic/medical research (although only 20 teams will get selected to participate). The goal is to identify and report on potential genetic features underlying medical disorders in three children, given the genome sequences of the children and their parents. These genetics features are presently unknown, which is why this competition really represents something new in medical research. I think this is a very nice initiative, in fact I had thought of initiating something similar at my own institute where I work, but this challenge is much better than what I had in mind. It will be very interesting to see what comes out of it.