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Our Alumni: Nikita Vashisth – cutting edge in India

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In our new series of posts we’re taking a look at some of our past students, where they’ve gone, and what they’re up to. First up is Nikita Vashisth, one of the graduates from the first year of our course in 2014/2015.

Two years after leaving the course, Nikita is working with a cutting-edge Indian data journalism team. One of the projects she’s  involved in is to measure air particles to help save lives in cities affected by pollution – something she initially proposed in her major coursework. Nikita said:

“I’m working as a data journalist at IndiaSpend, India’s first data journalism initiative. One of the projects I am currently working on is #Breathe, an air quality monitoring network. We’re analyzing pm2.5 and pm10 levels across Indian cities to understand city-wide high and lows. The vision of the project is to democratize data critical to saving thousands of lives and engage citizens and other stakeholders in a conversation towards solving the life-threatening issue of air pollution.”

And her view of her studies? Nikita added:

 “Being a part of the first COMPJ batch in 2014 was a whirlwind! I was introduced to COMSCI and it opened a whole new world of opportunities in journalism for me. The course takes a practical learning approach in digital journalism, data analysis and coding—which made it all the more fun. The Visual Communication & Information Design and Digital Investigation modules were especially engaging and lead me to understanding the power of data and design in effective storytelling. A big shout out to Glyn and Martin, my course directors/lecturers/mentors whose immense support and knowledge helped me get past the nerve-wracking learning curve by the end of the year.”

We can’t wait to see what Nikita gets up to in the future, and look forward to seeing the projects she comes up with.

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.

Scraping the Assembly

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Glyn is currently teaching the first-semester module on Data Journalism. As part of this, students need to complete a data investigation project. One of the students is looking at the expenses of Welsh Assembly Members. These are all freely available online, but not in an easy to manipulate form. According to the Assembly they’d be happy to give the data out as a spreadsheet, if we submitted an FOI.

To me, this seems quite stupid. The information is all online and freely accessible. You’ve admitted you’re willing to give it out to anyone who submits an FOI. So why not just make the raw data available to download? This does not sound like a helpful Open Government to me. Anyway, for whatever reason, they’ve chosen not to, and we can’t be bothered to wait around for an FOI to come back. It’s much quicker and easier to build a scraper! We’ll just use selenium to drive a web browser, submit a search, page through all the results collecting the details, then dump it all out to csv. Simple.

Scraping the Assembly

I built this as a quick hack this morning. It took about an hour or so, and it shows. The code is not robust in any way, but it works. You can ask it for data from any year (or a number of years) and it’ll happily sit there churning its way through the results and spitting them out as both .csv and .json.

All the code is available on Github and it’s under an MIT Licence. Have fun 😉

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.