Computational & Data Journalism @ Cardiff

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Chatbots in the Classroom: Education Innovation Research

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The Computational and Data Journalism team has recently been awarded research funding from the University Centre for Education Innovation to investigate the use of chatbots in the classroom.

The project “proposes the development of chat bots as part of the teaching and learning team to support learning and automate everyday issues to alleviate staff workload.

“This would essentially create an on-demand classroom assistant who can provide informational support whatever schedule students choose to keep outside of the classroom environment and increase their overall satisfaction levels as a result.”

We’ve just hired a 3rd year Computer Science student, Stuart Clark to work with us on the project, and he has started swiftly, working to identify sources of data within the university that such a system can plug into, designing system architectures and interfaces, and beginning work on the implementation.

We’ll follow up this development work over the summer with a live trial of the system in Autumn to see how well it works and assess whether this sort of technology can be successfully used by students and lecturers alike to improve information flow and ease administrative pressures.

We’ll continue to blog about the project as it progresses over the next few months.

Empty Properties: simple choropleth maps with leaflet.js

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We’re still working on looking at empty properties around Wales, and so while we wait for the FOI request results to come in, I thought it would be interesting to do a bit of basic mapping. Normally, if I want to create a choropleth I reach straight for d3 and my collection of topojson, but we’re still very early in the course, and we haven’t covered d3 yet (we go into it in some detail in next semester’s visualisation course). As we haven’t covered d3 yet, we need a simple solution, and fortunately the leaflet API makes it very easy to draw polygons on top of a map; all we need to know are the coordinates of the shape that we want to draw.

So, first we need to grab boundary files for the parishes around Wales. A quick hunt through the bafflingly obtuse ONS geoportal brings us to the generalised parish boundaries (E+W). Although it doesn’t seem immediately obvious from that page, there is a download link there that allows us to obtain shapefiles containing the boundary data for every parish in England and Wales. Unfortunately, these files are in a rather complicated shapefile format, when all we really need is a list of coordinates that we can throw into some JavaScript. We could extract and transform this data using command line tools, but as this is an early demo, we’ll use some graphical tools to do the work. So, first of all we open up the shapefile in our favourite GIS software:

England + Wales Parish boundaries in QGIS

England + Wales Parish boundaries in QGIS

This is all the parishes for England and Wales, and we only want the boundaries for Wales, so the next thing we’ll do is extract those. Looking at the attribute table, we see that each parish has a code connecting it to it’s Local Authority District (the LAD14CD). Using a simple filter on the ‘LAD14CD’, we can extract all those parishes that are in a local authority district in Wales, by selecting only those LAD14CDs that begin with a ‘W’:

Filtering based on attributes - substr(LAD14CD, 0, 2) = 'W'

Filtering based on attributes

This gives us our Welsh parishes:

Welsh parishes selected

Welsh parishes selected

Now we can save this selection as geoJSON, which is a nicer format to work with than ESRI shapefiles, and will easily be handled by Leaflet. While we’re at it, we can convert the coordinates of the boundary data to WGS84 (which essentially gives us Lat,Lng coordinates we can use with our map):

Saving the selected parishes

Saving the selected parishes

For this example (because we’ve only had a response from Cardiff Council so far), we only need to deal with the Cardiff parishes, so for simplicity we’ll extract the Cardiff parishes from our large geoJSON file into a smaller Cardiff specific file. A quick bit of Python looking for all the parishes with a LAD14CD of ‘W06000015’ is all that’s needed here:

import json

parishes = json.load(open('Wales_Parish.geojson', 'r'))
cardiff_parishes = {'type': parishes['type'], 'crs': parishes['crs'], 'features': []}

for feature in parishes['features']:
 if feature['properties']['LAD14CD'] == 'W06000015':
 cardiff_parishes['features'].append(feature)

json.dump(cardiff_parishes, open('Cardiff_Parish.geojson', 'w'))

This geojson is all we need to display the parish boundaries on our map. In fact, if we edit the geojson file to include

var parishes = {ALL_OUR_GEOJSON_DATA}

We can import this directly into a webpage and load it into a map with leaflet relatively easily using the geoJson function in leaflet:

<!DOCTYPE html>
<html lang="en">
<head>
 <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
 
 <title>Empty Properties</title>
 <link rel="stylesheet" href="http://cdn.leafletjs.com/leaflet/v0.7.7/leaflet.css" />
 
 <style>
 html, body, #map {
     height: 100%;
     width: 100%;
 }
 </style>
</head>
<body>
 <div id="map"></div>

 <script src="http://cdn.leafletjs.com/leaflet/v0.7.7/leaflet.js"></script>
 <script src="cardiff_parish.js"></script>
 <script>
   // create map and centre on Cardiff
   var map = L.map('map').setView([51.455, -3.19], 12);

   L.geoJson(parishes).addTo(map);

   // add some mapbox tiles
   var tileLayer = L.tileLayer('http://{s}.tiles.mapbox.com/v3/' + 'YOUR_MAPBOX_API_KEY' + '/{z}/{x}/{y}.png', { 
       attribution: 'Map data &copy; <a href="http://openstreetmap.org">OpenStreetMap</a> contributors, <a href="http://creativecommons.org/licenses/by-sa/2.0/">CC-BY-SA</a>, Imagery © <a href="http://mapbox.com">Mapbox</a>',
       maxZoom: 18
   }).addTo(map);
 </script>
</body>
</html>

This gives us a nice map of Cardiff with the parish boundaries:

Cardiff parishes on a map

Cardiff parishes on a map

All we need to do now is alter the colour of our parishes based on the number of empty properties within that parish. So, we go back to the data we extracted preciously, which gave us the total number of empty properties in each parish. We can go back to our code that extracts the Cardiff parishes from the large geojson file, and this time whenever we extract a Cardiff parish, we add a property to the geoJson feature with its value from the empty properties data. We also add min and max values across the whole set of parishes:

import json
import pandas

parishes = json.load(open('Wales_Parish.geojson', 'r'))
cardiff_parishes = {'type': parishes['type'], 'crs': parishes['crs'], 'features': [], 'properties':{}}

parish_totals = pandas.read_csv('parish_totals.csv', index_col=0)

cardiff_parishes['properties']['min'] = parish_totals['value'].min()
cardiff_parishes['properties']['max'] = parish_totals['value'].max()

for feature in parishes['features']:
 if feature['properties']['LAD14CD'] == 'W06000015':
 
 parish_name = feature['properties']['PARNCP14NM'].strip().upper()
 feature['properties']['empty_total'] = parish_totals.loc[parish_name]['value']

 cardiff_parishes['features'].append(feature)
 
json.dump(cardiff_parishes, open('Cardiff_Parish.geojson', 'w'))

Then, we set up a colour scale in our JavaScript code for creating the map (based off a single-hue colorbrewer scale), and style each shape according to its value by adding a style function that gets called by Leaflet when it is drawing each geoJson feature:

<script>
 // create map and centre on Cardiff
 var map = L.map('map').setView([51.455, -3.19], 12);

 var divisor = parishes.properties.max / 9;
 var colour_scale = ["#fff7ec", "#fee8c8", "#fdd49e", "#fdbb84", "#fc8d59", "#ef6548", "#d7301f", "#b30000", "#7f0000"];

 L.geoJson(parishes, {
   style: function(feature){
     var colour = colour_scale[Math.round((feature.properties.empty_total/divisor)-1)];
     return {color: colour, fillOpacity: 0.4, weight: 2}
   }
 }).addTo(map);

 // add some mapbox tiles
 var tileLayer = L.tileLayer('http://{s}.tiles.mapbox.com/v3/' + 'YOUR_MAPBOX_API_KEY' + '/{z}/{x}/{y}.png', { attribution: 'Map data &copy; <a href="http://openstreetmap.org">OpenStreetMap</a> contributors, <a href="http://creativecommons.org/licenses/by-sa/2.0/">CC-BY-SA</a>, Imagery © <a href="http://mapbox.com">Mapbox</a>', maxZoom: 18 }).addTo(map); </script>

And with a refresh of our map page, there we have a choropleth of the parishes in Cardiff, coloured by the number of empty properties:

Choropleth of empty properties in Cardiff

Choropleth of empty properties in Cardiff

This is a nice quick example that has allowed us to begin thinking about mapping data, and some of the issues surrounding such mappings, before we begin to study them it in detail next semester. As more of the data is returned from our FOI requests, we can start expanding this visualisation across Wales.

Visualising data – first try to get the information #foia

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Derelict House © Copyright David Wright and licensed for reuse under this Creative Commons Licence

© Copyright David Wright and licensed for reuse under this Creative Commons Licence

We’re currently looking at how many empty properties there are across Wales.

The students and staff are in the process of using the Freedom of Information act to get data sets as part of our sessions around data journalism and visualising data.

(Martin recently did a post looking at different technologies for wrangling the data from the first request).

We’ve applied to all 22 of the Welsh councils for the same information and are starting to get responses before the 20 day statutory limit.

And it is really interesting to see how that request is being interpreted by different councils.

So, to explain that we’ll go back to our first application – Cardiff

What do they know?

Advanced search on What Do They Now FOI site

Advanced search on What Do They Now FOI site

To keep the application and responses in public, our preferred way of working is to make the  application on What Do They Know.

This is a great resource for anyone intersted in public data, and has an advanced search facility that really helps you find what you are looking for as it uses a syntax familiar to anyone who has used the advanced Google Search techniques.

We asked for:

1 The number of
2 address (including street number and postcode) of homes that:

a) have been empty for over 6 months
b) have been empty for under 6 months
c) your empty homes strategy including what empty homes (if any)
you prioritise.

And we got it, the only issue was we got PDF but asked for Excel. Tabula is a great tool for dealing with information locked in PDF format but we just asked for the new filetype and got them.

The rest of Wales

We then applied to the other 21 authorities in Wales, and have had widely varying results.

We’d already picked up that there might be some issues, given the phrasing coming back from Cardiff, so we made sure that later applications acknowledged (and hopefully dealt with) the anticipated exceptions.

And we hit one in particular so far. Section 31(a) – crime.

Section 31(1)(a) the prevention or detection of crime

  1. Section 31(1)(a) will cover all aspects of the prevention and detection of crime. It could apply to information on general policies and methods adopted by law enforcement agencies. For example, the police’s procedures for collecting forensic evidence, Her Majesty’s Revenue and Customs procedures for investigating tax evasion.
  2. The exemption also covers information held by public authorities without any specific law enforcement responsibilities. It could be used by a public authority to withhold copies of information it had provided to a law enforcement agency as part of an investigation. It could also be used to withhold information that would make anyone, including the public authority itself, more vulnerable to crime for example, by disclosing its own security procedures, such as alarm codes.
  3. Whilst in some instances information held for the purposes of preventing or detecting crime will be exempt, it does not have to be held for such purposes for its disclosure to be prejudicial.

There is a public interest test to this exemption, so we’re off to read up on the Information Commissioner’s rulings on this as we’ve already had a few knock backs.

I will come back to the post, and the applications, when we’ve got all the responses we asked for.

Empty Properties & Postcodes

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As part of the course we hold a weekly session where we try and tie together Journalism and Computer Science: “The Lab”. One of the first sessions we held looked at the results of a Freedom of Information request – tying together a commonly used journalistic tool with some simple coding and data analysis.

Glyn had submitted a Freedom of Information request to Cardiff Council asking for the number of empty properties across the city. This was partially successful, as it turns out the information was already published on the Council website. Unfortunately, as is common with many council documents, the data was made available as a .pdf file. This is a terrible way to have to receive data, as .pdf files are not easily machine readable. So, our first task was to extract the data. (It’s interesting to note that the latest version of this data has been released as an .xls file. It’s still not a fully REST compliant API spitting out lovely well formed JSON, but it’s a step in the right direction at least).

There are many excellent tools for extracting data from .pdf files, such as Tabula for instance. However, often the simplest solutions are the best, and in this case it was completely possible to just copy and paste the contents of the .pdf into a spreadsheet. Once the data was in the spreadsheet we could save it as a Comma Separated Value (.csv) file, which is a fairly simple format to deal with using some python code.

We now have a .csv file listing the postcode and parish of every empty property in Cardiff, along with the date when the property became unoccupied. It is therefore pretty easy to do some simple analysis of the data using Python. For example, we can count the number of occurrences of each parish name, and find the ten areas of Cardiff with the most empty properties:

import csv
from collections import defaultdict

inputfile = open('emptyproperties.csv', 'rU')
csv_reader = csv.DictReader(inputfile)

parish_count = defaultdict(int)

for row in csv_reader:
  parish = row['Parish']
  parish_count[parish] += 1

sorted_parishes = sorted(parish_count.items(), key=operator.itemgetter(1), reverse=True)
print(sorted_parishes[0:10])

Screenshot 2014-12-04 14.40.53

 

Part of creating a story around this result would be to add context to this data. Anyone with local knowledge will recognise that Butetown (including Cardiff Bay) has many blocks of rental flats, which probably explains why there are so many empty properties there. Whitchurch however is a fairly affluent middle class area, so its presence in the top ten is surprising and may require further investigation.

We can also use the dates within the data to find the postcode of the property that has been empty longest:

import csv
import datetime

inputfile = open('emptyproperties_correct.csv', 'rU')
csv_reader = csv.DictReader(inputfile)

earliest_date = datetime.datetime.now()
earliest_postcode = ''

for row in csv_reader:
 date = row['Occupancy Period Start Date ']

 if date is not '':
   py_date = datetime.datetime.strptime(date, "%d-%b-%y")

   if py_date < earliest_date:
     earliest_date = py_date
     earliest_postcode = row['Post Code ']

print earliest_postcode, earliest_date

Screenshot 2014-12-04 14.44.21

 

According to the data, a property in central Cardiff, near to HMP Cardiff, has been empty since 1993. Clearly, further investigation is required to find out whether the data is accurate, and if so, why the property has been empty so long.

These short little examples show how you can start to use simple bits of code to dive into and analyse data quickly, to find the interesting features hidden in the data, that with some investigation may lead on to an interesting story. In future sessions, we can go on to look at interesting ways to visualise this data and examine it further.

Computational Journalism: the manifesto

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While discussing the new MSc course between ourselves and with others, we have repeatedly come up with the same issues and themes, again and again. As a planning exercise earlier in the summer, we gathered some of these together into a ‘manifesto’. Primarily useful to us to ensure we’re thinking consistently, we’re also making it public to show others what we’re talking about when we say ‘Computational Journalism’.

People may agree or disagree with it, and may want to have their own input into it, and that’s part of what it’s there for. The other reason for its existence is to attempt to show how much further this goes than just journalists using computers to analyse data. This not an aggressive ‘this is exactly where the boundaries lie’ sort of exercise, more of a ‘we think there’s something around about these parts’ kind of statement. We expect this to be an organic document that will change as time goes on.

 

 

These are the key themes we’ve used to talk about what’s needed in Computational Journalism:

  • Understand Community

Understand that it’s not an audience that you’re dealing with, but a community of which you are an integral part. For the computational journalist this means understanding not only what content they want to receive and interact with, but how to build it, allow others to build upon it, and help the community as a whole to understand it.

  • Be Creative

This is a key computational thinking principle – the ability to take existing principles and techniques and apply them creatively to solve a new problem. If that’s not possible, then invent new tools, techniques and principles to solve the problem.

  • Be Playful

Another key computational thinking principle that relates closely to the previous theme. What new use can you put that technology to? If you apply this technique in a new way, what happens? It’s important to try and think outside of the usual patterns and workflows to discover new processes and facts.

  • Create your environment

This is about learning to be a developer; creating the workflows and processes that allow you to create efficiently and respond quickly. Know the tools and techniques you use to go from idea to research to analysis to creation to output and dissemination. Improve and refine this environment constantly.

  • Learn by doing

Learning to code is easier when you learn by doing. Dive into projects and use them as the motivation for learning new languages, frameworks and libraries. Examine other projects and see how they’ve solved problems, adapt what you find, and apply it in your code.

  • Be lazy when needed

There’s a reason “Don’t Repeat Yourself” is a popular programming mantra – because it makes sense. Reuse code, reuse tools. Never re-invent the wheel unless it’s absolutely necessary. Make use of others’ libraries if you can.

  • Be understood

When communicating with your community it’s obviously important to make sure you’re being understood. Often you’ve been immersed in a project for a long time, you know the details inside and out. Others don’t, and it’s your job to communicate those details as succinctly and clearly as possible. Don’t overlook what seems ‘simple’ to you and assume everyone else will pick up on it. At the same time, don’t overwhelm others with vast oceans of content that drown your message. There’s a balance – find it.

  • Learn to write clearly

As above, your community needs you to communicate well. But don’t forget that the code you write is also a part of the conversation. It’s not good enough for your output to be clear, your intermediate processes must be understood too. Best practices and coding principles exist for a reason. It’s not just humans that interact with your outputs, machines need to understand your code too. Sloppy coding and design leads to bugs, errors and inaccuracy.

  • See past the numbers

It’s not just about analysing data. Data is important, and without the statistics to prove your statements you have nothing. But never forget the story and the message. Don’t neglect context just because you’ve got a significant p value. Remember that the tools you’re creating have value and are part of the story too.

  • Share everything

Whatever you can share, share. Help others to build upon your work. Licence everything. It’s no good releasing code and data without releasing the terms under which others can use it – a codebase with no licence or terms of reuse is no use to anybody. If you’re building on the work of others, it’s only fair to give back. Communities are stronger when everyone can contribute and receive value.