Tracking the Flow of Refugees Using Data Science
Towards the end of last week, the United Nations announced that the Data For Democracy team had won the the Unite Ideas Internal Displacement Event Tagging and Extraction Clustering Tool (#IDETECT) challenge, by building a tool capable of tracking and analyzing refugees and other people forced to flee from or evacuate their homes.
I was a member of this team, along with Aneel Nazareth, George Richardson, Wendy Mak, James Allen, Yane Frenski, Domingo Hui, Charles Neiswender, Daniel Forsyth, Joshua Arnold, Alex Rich, and others from D4D.
This is a project that has been underway since roughly February of this year, and I think is a great example of what can be achieved through communities like D4D.
The idea of this post is to talk a little about the solution we submitted, as well as share some of my own reflections from working on my first ever collaborative data science project.
The competition itself was run by Unite Ideas, on behalf of the Internal Displacement Monitoring Center, which is an independent, non-governmental organization dedicated to collecting and analyzing data about what are known as Internally Displaced Persons (IDPs).
My understanding of the challenge is that, with hundreds — if not thousands — of news articles and other reports being generated every day, it would require a significant amount of manpower to be able to read each one of these, in order to populate the database of internal displacement events on an ongoing basis.
This is (hopefully) where data science and machine learning can come to the rescue. The basic requirement of the challenge was to create a tool capable of automatically “processing” news articles and other websites, and:
- Identifying whether or not they are about IDPs
- Classifying them into events, e.g. whether they were due to Natural Disasters or Conflict/Violence
- Extracting key information, such as the number of people affected, location, date, etc.
- Providing tools for analysis and visualization of the data
Brief Overview of the Solution
For those readers interested in the technical side of things, the solution we created consists of a Python back-end and NodeJS front end, interacting through a PostgreSQL database, all nicely wrapped up in a Docker container.
The back-end is basically a collection of modules that facilitate and support article processing, classification and information extraction. This all comes together in a ‘pipeline’ function that can be fed a specific URL, and then performs the following steps:
- Figure out if the URL links to a PDF file or regular web page
- Access the URL and extract the content along with relevant metadata (e.g. publication date)
- Assess the article relevance (i.e. decide if the subject matter relates to IDPs)
- Further classify relevant articles into three types: Natural Disaster, Violence, or Other
- Extract key facts about the event (more on this in a later section)
- Identify locations mentioned in the text, and further obtain the specific country name as well as latitude/longitude coordinates
- Save everything into a database for later analysis and visualization
Some of the principles that we ended up following during this project, for better or worse, were to base ourselves around Python3, and to use third-party libraries wherever possible, so as to speed things up and make our lives easier. This did mean that we ended up with a pretty extensive ‘requirements.txt’ file. Some of the more notable libraries we used are:
- Newspaper for parsing and extracting text and metadata from webpages
- Spacy, Textacy, and Gensim for Natural Language Processing work such as tagging, tokenizing, Part-Of-Speech extraction etc.
- Good old scikit-learn for our (pretty basic) machine learning models
We also took advantage of mapzen.com’s Geocoding API, which provides extensive and well-structured information for place names, including the underlying Country ISO code, which was a key piece of data we needed to obtain.
The front-end was started much later in the project, and so does not (yet) include all of the visualizations and other functionality we want to provide. However, the idea is that this will serve as an access point for analysts and other users, where they should be able to:
- Filter events based on dates, locations and event type
- Create different types of visualizations of the number of displaced people, destroyed houses, etc.
- Submit new URLs to the database for later processing
Before getting into the nuts and bolts of the technical approach, it is worth explaining in a little more detail what is meant by a report.
For those articles that are identified as being about IDPs, the tool needs to be able to generate a summary of the type of event, where it happened, the number of people or houses impacted, etc.
For the purposes of this competition, a report is required to contain a set of facts including:
- The date of publication of the underlying article
- The location where the displacement event occurred
- The reporting term used in the article (i.e. Displaced, Evacuated, Destroyed Housing, etc.)
- The reporting unit (i.e. People, Residents, Families, Households, etc.)
- The displacement figure, i.e. the number of people or households affected
When we started, there were no training data or other examples available. As such, we began with a rules-based approach to fact-extraction, where the rules were slowly built up by manually reviewing performance on a small selection of articles and examining the fail cases in detail.
A very simple and naïve rule could be to look for the occurrence of certain words within a sentence or paragraph. For example, you could look for co-occurrences of “People” and “Displaced” in order to identify reports.
If you are able to come up with an exhaustive list of keywords, then this sort of approach should be able to correctly identify relevant articles (i.e. it would display high recall). However, it would also be likely to find a lot of irrelevant articles that just happen to contain some of the keywords (i.e. it would display low precision).
Instead, we tried to fashion a series of rules that did not just look for keywords, but also attempted to ensure that the words had the right grammatical or contextual relationships between them.
Here, our first step was to use Spacy to process articles and look at how the different terms were related to each other within sentences. A very useful tool is displacy, which provides a nice online visualization of the syntactic structure of a text fragment:
Initial inspection seemed to indicate that Reporting Terms (i.e. Displaced, Evacuated) typically occur as verbs in a sentence, and Reporting Units (People, Houses etc.) can either be their subject or object.
Thus, a very simple rule could be to find those sentences that have a word like People or Persons as the subject, and a word like Displaced or Flee as the verb.
If, based on these rules, a sentence seems to contain a report, then we can start looking for other pieces of information — such as numbers, dates and locations — by using Named Entity Recognition.
Spacy has pretty good Named Entity Recognition functionality, and also provides another useful online tool for visualizing the different types of entities found within a sentence.
For example, in the sentence below, the tool highlights relevant parts of text that are:
- Numeric (for identifying the number of people affected)
- Place names (for identifying the location of the event)
- Dates (for identifying when the event occurred)
It is worth noting that at this point we were approaching the problem in as general a way as possible; we were aiming to identify all possible references to displaced people in each sentence, paragraph and article, while also trying to build up a highly comprehensive list of relevant vocabulary, rules and keywords.
Machine Learning + Rules
Once we got close to the competition submission deadline, we discovered that the tool would be evaluated in a much more constrained fashion than we had previously envisioned.
In particular, in order to test the tool’s fact extraction capabilities, we would be provided with a selection of sentences or sentence fragments, and have to return the model output along certain dimensions.
In some cases, this amounted to basically being a classification exercise, in which we would need to slot the reporting unit and reporting term into certain categories from a list of pre-defined options.
Meanwhile, finding the quantity of people affected as well as the impacted location continued to present a challenge, as the model would need to extract these directly from the text.
We had a small set of training data to work with, consisting of about 130 tagged text excerpts. However, this was pretty noisy; for example:
- Many sentences were ambiguous, with both people and households being mentioned
- Multiple locations were often mentioned in the text
- Some excerpts were tagged with a location, despite no location being mentioned in the text
However, we decided to go ahead and try building a small machine learning-based model to help complement our initial rules-based approach.
Final Submitted Model
The final model included in the submission was based on a mixed approach, combining the output of the handcrafted rules and machine learning models.
For both the reporting unit and term, we used a simple workflow to combine the two approaches:
- If the classifier and rules output match => Done
- If the handcrafted rules do not find anything => Use the classifier output
- Otherwise => Use the rules output
The Reporting Unit classifier was a Multinomial Naive Bayes model, trained on features extracted using scikit-learn’s Word Vectorizer (single words).
The Reporting Term classifier, meanwhile, combined two separate models:
- A Multinomial Naïve Bayes model, trained using Word Vectorizer bigrams
- A Linear Support Vector Classification, trained on a feature array extracted using a Word2Vec model (from Google)
By combining the rules + classifiers, we saw an F1-score improvement:
- Reporting Unit: Improvement from 0.73 to 0.93
- Reporting Term: Improvement from 0.68 to 0.71
Note: Report Extraction was not the only area where we ended up using Machine Learning. In fact, our team leader, George Richardson, also created several classifiers for other parts of the process, specifically identifying relevant articles and classifying them into Natural Disaster vs. Conflict & Violence. However, I was not particularly involved there, and so have instead focused more on the models that I worked on.
When we submitted our solution, we certainly didn’t feel it was complete, and had already identified a number of improvements to work on across the board. These included:
- Making improvements to both URL and PDF parsing, especially dealing with URL retrieval errors and timeouts.
- Creating and implementing new visualizations and ways of aggregating and presenting the underlying data.
- Making improvements to our Machine Learning models, in particular by obtaining more training data (using some credit very kindly donated by the folks at Crowdflower).
- Starting to work on new metadata fields for articles and reports, with the aim of being able to score the extracted content in terms of reliability and report accuracy.
Around the time I started writing this post, I coincidentally also listened to a Partially Derivative podcast where Chris Albon talks about “Learning Everything Else”, i.e. the non-data science parts of being a data scientist.
One of my motivations for getting involved in this particular project was the opportunity to learn about and work on Natural Language Processing techniques, and while I certainly got to do that, I also feel that I got as much (if not more) value from the “everything else”.
For example, a significant proportion of what we had to do was more software engineering rather than pure data science. Ultimately, we were trying to deliver a tool that could be usable in the real world, and hence we spent a lot of time thinking about modules, pipelines, and data models. This is not to say that I am now an expert software engineer, but I certainly feel the experience I gained in that regard was as useful as what I learned about Natural Language Processing.
It also turned out that our team was spread across the world, with people in Seattle, Florida, England, Australia and Mexico, to name just a few locations. I therefore had to get very comfortable with Slack as our only channel for communication, as well as learning how to use Git and GitHub for collaboration.
Finally, this was my first experience of doing data science as part of a team, and it was a fantastic demonstration of how much more you can achieve by collaborating with other people. I learned a huge amount through those interactions, and I would never have been able to achieve so much if I’d had to do everything myself.