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Writing about Arts Funding

If you’ve followed my little blog over the past year or two you’ll have noticed a few things:

  1. Despite blogging about blogging, I don’t do that much blogging.
  2. I occasionally post content partly intended for my MA in Online Journalism.
  3. I like lists.

So, as you may have guessed from this rather tortured introduction, this is one of those blog posts that goes over what I’ve been working on for my current module’s assignment – ‘MA Online Journalism: MED7017 – Multimedia Journalism Assignment 2: Specialist Portfolio’ to be precise.

So, if that’s not for you, go away. I will not take it personally. Honest.

The Plan

According to the closing notes of my last assignment, my plan for Assignment 2 ‘Specialist Portfolio’ would be along the following lines:

“For my next assignment in this module, I wish to build on the skills, research and experience I’ve built up during the course so far, by delving deeper into the technical side of online journalism.

Principally, this means exploring the use of APIs, data visualisations, maps and JavaScript to tell stories that people want to share.

I’m especially interested in the UK government cuts at present, and feel there is a great potential for data visualisations, maps and web tools to tell stories around this topic in a far more digestible, engaging and sharable way.”

Data journalism, snow fall and BuzzFeed, David Allen

My initial exploration during the project did indeed veer down this route. In particular, I wanted to create to aggregate multiple sources of data via API’s and JavaScript to create a web tool that would map the distribution of UK Government cuts along local authority and political boundaries.

The goal of this data-focused project would be to analyse and provide a simple visual tool to assess whether or not specific cuts in funding were politically motivated (Newcastle City Council Cuts, Who’s to Blame?). I.e. Had central government passed the worst of the cuts onto non-Conservative Party voting local authorities or not?

However, despite a substantial amount of research, which included aborted attempts to learn how to build a web app using an API, and learn server-side languages such as .php, unfortunately the scale and technical boundaries of this project were too great given the timeframes of the assignment.

Despite this set-back this albeit frustrating process provided me with a great insight into the server-side programming, API’s and how far my own technical knowledge reach without more in-depth training using sites like CodeAcademy.

Datasets and visualisation

Throughout this process however, I continued to request, collect and research datasets relating to government reductions and distribution of funding.

I made contact with Three Sixty Degree Giving, who aim to make grant-making  more transparent and downloaded and analysed the funding datasets they had already collected and standardised:

  • BIG Lottery
  • Nesta
  • NOMINET Trust
  • Wellcome Trust
  • Arts Council England
  • Sport England
  • Arts Council Wales
  • Sport Northern Ireland
  • DSDNI
  • Sports Wales
  • Creative Scotland
  • Technology Strategy Board

Having worked extensively with Creative England in the past, and not being able to find any of their grant data listed on their site, I contacted/pestered them via Twitter for the data.

Twitter---CE-conversation

This approach meant I was able to source their funding data without the need for a formal and potential costly (for Creative England) Freedom of Information request. Eventually, the  dataset was published in a slightly ‘cut ‘n’ paste’ format on their website, which I scraped and imported into a spreadsheet format.

Unfortunately, as Creative England is a relatively small, and new organisation, the dataset only contained information for grants in 2012. As such, the dataset was too small to really discover trends or significance in the distribution of the data. However, I put them in touch with 360DegreeGiving to standardise their data, and tablized the top 10 funded projects of 2012 below, along with publishing the dataset in full via Google Sheets here.

Creative England’s Top 10 Funded Projects of 2012

# Project Title Organisation Award Award Date Fund Program Area
1 iFeatures 2 iFeatures Ltd £ 260,000.00 7/30/2012 BFI RIFE Lottery iFeatures
2 Building the SiN Partnership Media Archive for Central England £ 61,000.00 1/21/2013 BFI RIFE Lottery CE Film Culture\Organisational Development
3 Learning Networks Xtend (UK) Ltd £ 40,000.00 3/19/2013 BFI RIFE Lottery CE Film Culture\Organisational Development
4 Cornerhouse OD Programme Cornerhouse £ 38,500.00 1/21/2013 BFI RIFE Lottery CE Film Culture\Organisational Development
5 Sustaining SWFTA through Volunteers South West Film and Television Archive £ 37,000.00 1/21/2013 BFI RIFE Lottery CE Film Culture\Organisational Development
6 Staff and Board Development Programme Sheffield Media & Exhibition Centre Ltd £ 35,000.00 1/21/2013 BFI RIFE Lottery CE Film Culture\Organisational Development
7 Cambridge Film Festival Cambridge Film Trust £ 35,000.00 7/17/2012 BFI RIFE Lottery CE Film Culture
8 Doc/20 Sheffield International Documentary Festival (Sheffield Doc/Fest) £ 30,000.00 1/23/2013 BFI RIFE Lottery CE Film Culture\Festivals
9 Organisational Strategic Review Encounters Festival Limited £ 30,000.00 1/21/2013 BFI RIFE Lottery CE Film Culture\Organisational Development
10 26th Leeds International Film Festival Leeds Film £ 30,000.00 7/17/2012 BFI RIFE Lottery CE Film Culture\Festivals

Arts Funding Project

Eventually, after hitting numerous dead ends, I was forwarded an article on ArtsProfessional.co.uk that critiqued the misrepresentation of data in Arts Council England’s report ‘This England: how Arts Council England uses its investment to shape a national cultural ecology.’

As well as paying a homage to Ben Goldacre’s book ‘Bad Science’, which I was reading at the time, Liz Hill’s piece lead me towards the vast and often confusing amounts of data presented in the discussions surrounding arts funding – normally buried within PDF reports or as raw numbers within web copy. In particular, using a variety of Google searches, I found that a group of academics and consultants had recently published the second in three reports researching the perceived imbalances and lack of fairness in Arts Council England’s distribution of National Lottery funding for the arts.

This presented me with a perfect opportunity to fill a gap in the current reporting of arts funding distribution. Using the POST process, I planned the following:

People

Having previously worked in the sector myself (I  briefly ran a web project for New Art Gallery Walsall), and alongside artists in a previous agency role, I had a basic understanding of the issues and tone of conversation within the contemporary art world.

As such, the project would be primarily aimed at those within the sector, alongside Birmingham Eastside’s core audience, people working or living near the emerging ‘Eastside’ area of Birmingham.

Objective

To visually present the complex data published in the PLACE and Rebalancing our Cultural Capital reports, alongside granular datasets I had collected myself, in a far more interactive and shareable way.

This would have the ultimate aim of fostering a more open and public discussion of the issues at a more grass-roots level (rather than in senior funding circles).

Strategy

Given the objectives, I would naturally need to research, experiment with and utilise a variety of data visualisation tools.

However, to provide greater depth and discussion around the issues – both for myself and reader, I wanted to intergrate the data visualisation aspects of my project into a long form piece featuring interviews with the key figures involved.

This approach would then allow me an appropriate model to explore the complex and often jargon-filled issue of public arts funding in the depth it deserved.

Tools

On my last assignment, I experimented with a number of tools designed to present longform pieces in a immersive, interactive way in the manner of ‘snowfall’. For this longform interactive article, I would attempt to build on this research – and use some of the tools I’d blogged about.

In particular, I wanted to try using Aesop Story Engine’s WordPress plugin, as it could potentially allow a ‘Snowfall’ style approach without users having to leave the Birmingham Eastside website to access an off-site, 3rd party tool, such as Creatavist.

With regards to data, I still hadn’t found used data visualisation tools – other than Google Fusion, that I was fully happy with. As a Mac user, the industry standard software, Tableu, was  still not available for my operating system.

As such, one of my personal goals for the project would be to find and familiarise myself with a number of new data visualisation tools, such as Google Charts, Info.gram, Visual.ly and RAW.

Production

As my project had gravitated away from the initial idea of data-driven web tool, and towards an interactive, longform piece, my first task was to find greater context to the Arts Council England public expenditure data I had sourced, and begun to map and produce visualisations for.

To start with, I mapped Arts Council England’s public expenditure between 2003 and 2013 to the English regions:

As you might notice from this attempt – which didn’t make it into the final piece, I spent some time attempting to learn how to integrate the Google Charts API into the ‘info bubble’ windows for each region. I did this by following a PDF tutorial, written and forwarded by the module tutor, although unfortunately – perhaps due to Google officially depreciating the Google Charts API in favour of JavaScript based charts, I could not get the charts to display my data or the tutorial’s test data correctly.

I could adjust the size and layout of the chart, but the chart would not display the data correctly. For the final piece, I reverted to using raw HTML and Fusion strings for the final chart.

This was just one of many frustrations with data visualisation tools on this project. For instance, loading too data into Many Eyes crashed my computer, while the unusual selection of charts amidst the promising, sleek design of RAW was unable to produce any visuals that would actually be useful.

Datasets

The initial dataset I worked with, documented Arts Council England’s distribution of ‘Grants for the Arts Awards’ between 2003 and 2013 (source: Data.gov). To prepare this dataset, I downloaded all the yearly reports, then used Open Refine to merge the ten years worth of datasets into a single workable document.

This was then exported to an Excel document, where pivot tables and sorting functions were used to extrapolate potential data stories for an infographic, which I  created with Infogr.am:

Again, this particular infographic didn’t make it into the final longform piece, due to potential confusion for the reader; as the ‘grants for the arts’ funding programme was effectively a sub-set of the National Lottery arts funding, which became the article’s primary focus. In addition, despite a deliberately local focus, none of the visualisations were shocking or surprising enough to warrant additon to the final piece.

Despite this, my experiments and initial attempts with Infogram, did provide the basis for learning how to best utilise the somewhat limited web application.

Interviews

As part of my research, I read the PLACE and RoCC reports, and their subsequent Arts Council England responses, and attempted to contact  key figures from both sides for interview.

Making contact with one of the report authors, Peter Stark, I arranged a phone interview that covered and challenged the key areas of the reports. As part of this, he also directed me to his co-author, Steve Trow, who had collated the datasets upon which the reports are based.

Due to Stark’s strident criticism of the Arts Council in the interview, it was then important to allow the organisation a right of reply (BBC editorial guidelines point 6.4.25, consulted as guidance) to ensure an objective, editorial balance to the final story. In this instance, I contacted the organisation’s national media relations officer who provided a comprehensive set of answers to my questions, which was edited into the final story.

Putting it all togetherIMAG1176

Upon putting the interview and assets together however, I realised several things:

  • Aesop story engine, which I’d planned to use for the story, and is still in beta, did not work well with the Birmingham Eastside theme. None of the parallax effects seemed to work, whilst character and text effects intended for full-width page design looked cramped and amateurish on my Eastside test post. After experimenting with several free themes, it seems the plugin is only truly compatible to the themes for sale on their website. Eventually I decided to scrap the plugin altogether, in favour of a standard post format.
  • Much of the data I’d already collected concerned only a portion of the arts lottery spend (the Grants for the Arts). As such, I found the source of the PLACE report data, a slightly dated web-app, which searched the grants database. However, search results could be exported as text files, which I converted to CSV and merged together in Excel. This then gave me the basis for the data visualisations that appear in the report.

KML files were sourced and merged with the DCMS and report data to produce a series of Fusion Maps to illustrate different points in the story (rather than a single ‘map to rule them all’).

Law and ethics

Rather than halting the story midway through the article by embedding one big tall, single infographic with all the data included. I utilised Infogram instead to create single, interactive charts and images that would visibly provide BuzzFeed-style breaks in the copy, whilst allowing users to interact and explore the greater context of the story.

Images were all sourced using a Creative Commons search and credited to their respective authors in the captioning. Likewise, source of data was credited appropriately, and linked back to source.

Publication

The article, ‘Midlands Lottery Players Subsiding Cultural Activities for London’s Elite’ was scheduled to publish at 8am, on Friday 6 June 2014. This was timed to coincide with the morning commute to work for the article’s target audience (outlined above in ‘People’), along with scheduled tweets via @BhamEastside and my personal @BrummieDave Twitter account and Facebook profile, throughout the day.

Social engagement with the posts was low, although the article was shared by several animation contacts on Facebook. The webpage itself had a social reach of 1,237 accounts, and has had 80 page visits, of of 14 hours after its publication.

Tweet Reach Report

TweetReach_http_birminghameastside_com_2014_06_06_midlands-lottery-players-subsidising-cultural-activities-for-londons-elite-1WordPress Page Statssite-stats

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Dave Allen

Digital Marketing Manager at Birmingham City University
I manage the social media and online presence for Birmingham City University's Faculty of Performance, Media and English, and provide one-to-one social media consultancy for the Faculty's staff.

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