Multi-Dimensional and Multiple-Perspective Storytelling

In Reversible, Reverse History and Side-by-Side Storytelling I linked to an example of side-by-side video storie where two videos are played next to each other. @cogdog then shared a link in the comments to the HBO video cube which adds a whole other dimension!

(I seem to remember the BBC experimenting with similar forms in the past – e.g. co-broadcasting different perspectives of the same story on different channels, or even different media (such as radio and television [anyone got an links to write-ups of those?).

Anyway, today, via GigaOm, I came across something related – a virtual choir:

For some reason, that video also reminded me of this?!

Anyway, it struck me that backchannel commentary is another approach that demonstrates this mutliple perspective idea. Which is why I think that the Twitter video captions idea has a long way to run yet. (By the by, Martin Hawksey has a done a great job posting a version of Gordon Brown’s Building Britain’s Digital Future announcement with twitter subtitles. If anyone wants to volunteer or help fund us to develop this app a little further, we have the ideas but just need to secure the developer time…:-)

Should Academic Journal Papers Have Video Trailers?

I don’t read academic journal papers very much any more, partly because folk rarely link to them, but today I read a paper (“Narrative Visualization: Telling Stories with Data”, Edward Segel, Jeffrey Heer, IEEE Trans. Visualization & Comp. Graphics (Proc. InfoVis), 2010) in response to this video trail that brought it to my attention (Journalism in the Age of Data, Ch. 3: Telling “Data Stories”):

I encourage you to watch the video – not necessarily for what it’s about, but for the way that a journal article is used to hold bits of the video together. Note that the video is not just about the paper, but it’s not hard to see how a video could be made that was just about the paper…

new way of discovering journals?

So I wonder: should we be making voiced over “papercasts” of academic papers to provide a quick summary of what they contain, and maybe also enriching them with photos and footage relating to what the content of the paper is about? (I know this might not make sense for the subject matter of every paper, but if a journal paper is about a particular online tool, for example, here would be an opportunity to show a few seconds of the tool in use, and contextualise it/demonstrate it a little more interestingly than a single, simple screenshot can convey?)

UPDATE: @der_no tweets: “Always enjoyed technical papers preview @ #SIGGRAPH (esp considering many of actual papers are beyond me)” See an example conference papers trailer here – SIGGRAPH 2010 : Technical Papers Trailer:

If the conference matter is appropriate (robotics related conferences come to my mind, for example), couldn’t this sort of approach provide an additional legacy resource that can continue to give an event life after the fact?

PS I believe that several of the OpenLearn folk are also looking at ways of pulling together video and audio in the way they package their material, for example looking at the use of Xtranormal videos, or Slideshare slidecasts. (Note that it’s easy (or used to be!) to publish Xtranormal clips into Youtube, and Youtube clips can also be embedded in Slideshare presentations, so all manner of fusions of content become possible!)

PPS Very, very loosely related to the above is another thread I want to link in to, here. That is, the extent to which academics might take up various sorts of (“new”) media training to explore different ways of engaging with (and maybe helping reinvent?) scientific communication. For example, a recent initiative in the OU has seen more than a few brave academic volunteers engaging in podcast training as part of Martin’s Podstars project (I couldn’t find a better link?!).

Running parallel to this, the OBU’s media training team have been helping other academics put together short showreels that have since been published on the OU podcast site – OU Experts:

OU academic experts

In terms of finding training materials that are already out there, it struck me that the BBC College of Journalism might be a good start, particularly in the skills area?

My Presentation at OU Statistics Conference – Visualisation Tools for the Rest of Us

Slides from my presentation to the OU Visualisation and Presentation in Statistics earlier today… will update this post with notes and links as an when I get round to it! In the meantime, you’ll have to use Google…(though other search engines are available). (Slodes via Slideshare)

Playing With R/ggplot2 Online (err, I think..?!)

Trying to get my head round what to talk about in another couple of presentations – an online viz tools presentation for the JISC activity data synthesis project tomorrow, and an OU workshop around the iChart eSTeEM project – I rediscovered an app that I’d completely forgotten about: an online R server that supports the plotting of charts using the ggplot library (err, I think?!): http://www.yeroon.net/ggplot2/


Example of how to use http://www.yeroon.net/ggplot2/

By the by, I have started trying to get my head round R using RStudio, but the online ggplot2 environment masks the stats commands and just focusses on helping you create quick charts. I randomly uploaded one of my F1 timing data files from the British Grand Prix, had a random click around, and in 8(?) clicks – from uploading the file, to rendering the chart – I’d managed to create this:

ggplot - British Grand Prix

What it shows is a scatterplot for each car showing the time on the current leader lap that the leader is ahead. When the plotted points drop from 100 or so seconds behind to just a few seconds behind, that car has been lapped.

What this chart shows (which I stumbled across just by playing with the environment) is a birds-eye view over the whole of the race, from each driver’s point of view. One thing I don’t make much use of is the colour dimension – or the size of each plotted point – but if tweak the input file to include the number of laps a car is behind the leader, their race position, the number of pitstops they’ve had, or their current tyre selection, I could easily view a couple more of these dimensions.

Where there’s a jump in the plotted points for a lap or two, if the step/break goes above the trend line (the gap to leader increases by 20s or so), the leader has lapped before the car. If the jump goes below the trend line (the gap to the leader has decreased), the leader has pitted before the car in question.

But that’s not really the point; what is the point is that here is a solution (and I think mirroring options are a possibility) for hosting within an institution an interactive chart generator. I also wonder to what extent it would be possible to extend the environment to detect single sign on credentials and allow a student to access a set of files related to a particular course, for example? Alternatively, it looks as if there is support for loading files in from Google Docs, so would it be possible to use this environment as a way of providing a graphing environment for data files stored (and maybe shared via a course) within a student’s Google Apps account?

Getting My Eye In Around F1 Quali Data – Parallel Coordinate Plots, Sort of…

Looking over the sector times from the qualifying session for tomorrow’s Hungarian Grand Prix, I noticed that Vettel was only fastest in one of the sectors.

Whilst looking for an easy way of shaping an R data frame so that I could plot categorical values sector1, sector2, sector3 on the x-axis, and then a line for each driver showing their time in the sector on the y-axis (I still haven’t worked out how to do that? Any hints? Add them to the comments please…;-), I came across a variant of the parallel coordinate plot hidden away in the lattice package:

f1 2011 hun quali sector times parralel coordinate plot

What this plot does is for each row (i.e. each driver) take values from separate columns (i.e. times from each sector), normalise them, and then plot lines between the normalised value, one “axis” per column; each row defines a separate category.

The normalisation obviously hides the magnitude of the differences between the time deltas in each sector (the min-max range might be hundredths in one sector, tenths in another), but this plot does show us that there are different groupings of cars – there is clear(?!) white space in the diagram:

Clusters in sectors times

Whilst the parallel co-ordinate plot helps identify groupings of cars, and shows where they may have similar performance, it isn’t so good at helping us get an idea of which sector had most impact on the final lap time. For this, I think we need to have a single axis in seconds showing the delta from the fastest time in the sector. That is, we should have a parallel plot where the parallel axes have the same scale, but in terms of sector time, a floating origin (so e.g. the origin for one sector might be 28.6s and for another, 22.4s). For convenience, I’d also like to see the deltas shown on the y-axis, and the categorical ranges arranged on the x-axis (in contrast to the diagrams above, where the different ranges appear along the y-axis).

PS I also wonder to what extent we can identify signatures for the different teams? Eg the fifth and sixth slowest cars in sector 1 have the same signature across all three sectors and come from the same team; and the third and fourth slowest cars in sector 2 have a very similar signature (and again, represent the same team).

Where else might we look for signatures? In the speed traps maybe? Here’s what the parallel plot for the speed traps looks like:

SPeed trap parallel plot

(In this case, max is better = faster speed.)

To combine the views (timings and speed), we might use a formulation of the flavour:

parallel(~data.frame(a$sector1,a$sector2,a$sector3, -a$inter1,-a$inter2,-a$finish,-a$trap))

Combined parallel plot - times and speeds

This is a bit too cluttered to pull much out of though? I wonder if changing the order of parallel axes might help, e.g. by trying to come up with an order than minimises the number of crossed lines?

And if we colour lines by team, can we see any characteristics?

Looking for patterns across teams

Using a dashed, rather than solid, line makes the chart a little easier to read (more white space). Using a thinking line also helps bring out the colours.

parallel(~data.frame(a$sector1,-a$inter1,-a$inter2,a$sector2,a$sector3, -a$finish,-a$trap),col=a$team,lty=2,lwd=2)

Here’s another ordering of the axes:

ANother attempt at ordering axes

Here are the sector times ordered by team (min is better):

Sector times coloured by team

Here are the speeds by team (max is better):

Speeds by team

Again, we can reorder this to try to make it easier(?!) to pull out team signatures:

Reordering speed traps

(I wonder – would it make sense to try to order these based on similarity eg derived from a circuit guide?)

Hmmm… I need to ponder this…

Over on F1DataJunkie, 2011 Season Review Doodles…

Things have been a little quiet, post wise here, of late, in part because of the holiday season… but I have been posting notes on a couple of charts in progress over on the F1DataJunkie blog. Here are links to the posts in chronological order – they capture the evolution of the chart design(s) to date:

You can find a copy of the data I used to create the charts here: F1 2011 Year in Review spreadsheet.

I used R to generate the charts (scripts are provided and/or linked to from the posts, or included in the comments – I’ll tidy them and pop them into a proper Github repository if/when I get a chance), loading the data in to RStudio using this sort of call:

require(RCurl)

gsqAPI = function(key,query,gid=0){ return( read.csv( paste( sep="",'http://spreadsheets.google.com/tq?', 'tqx=out:csv','&tq=', curlEscape(query), '&key=', key, '&gid=', curlEscape(gid) ), na.strings = "null" ) ) }

key='0AmbQbL4Lrd61dEd0S1FqN2tDbTlnX0o4STFkNkc0NGc'
sheet=4

qualiResults2011=gsqAPI(key,'select *',sheet)

If any other folk out there are interested in using R to wrangle with F1 data, either from 2011 or looking forward to 2012, let me know and maybe we could get a script collection going on Github:-)

Questioning Election Data to See if It Has a Story to Tell

I know, I know, the local elections are old news now, but elections come round again and again, which means building up a set of case examples of what we might be able to do – data wise – around elections in the future could be handy…

So here’s one example of a data-related question we might ask (where in this case by data I mean “information available in: a) electronic form, that b) can be represented in a structured way): are the candidates standing in different seats local to that ward/electoral division?. By “local”, I mean – can they vote in that ward by virtue of having a home address that lays within that ward?

Here’s what the original data for my own local council (the Isle of Wight council, a unitary authority) looked like – a multi-page PDF document collating the Notice of polls for each electoral division (archive copy):

IW council - notice of poll

Although it’s a PDF, the document is reasonably nicely structured for scraping (I’ll do a post on this over the next week or two) – you can find a Scraperwiki scraper here. I pull out three sorts of data – information about the polling stations (the table at the bottom of the page), information about the signatories (of which, more in a later post…;-), and information about the candidates, including the electoral division in which they were standing (the “ward” column) and a home address for them, as shown here:

scraperwiki candidates

So what might we be able to do with this information? Does the home address take us anywhere interesting? Maybe. If we can easily look up the electoral division the home addresses fall in, we have a handful of news story search opportunities: 1) to what extent are candidates – and election winners – “local”? 2) do any of the parties appear to favour standing in/out of ward candidates? 3) if candidates are standing out of their home ward, why? If we complement the data with information about the number of votes cast for each candidate, might we be able to find any patterns suggestive of a beneficial or detrimental effect living within, or outside of, the electoral division a candidate is standing in, and so on.

In this post, I’ll describe a way of having a conversation with the data using OpenRefine and Google Fusion Tables as a way of starting to explore some the stories we may be able to tell with, and around, the data. (Bruce Mcphereson/Excel Liberation blog has also posted an Excel version of the methods described in the post: Mashing up electoral data. Thanks, Bruce:-)

Let’s get the data into OpenRefine so we can start to work it. Scraperwiki provides a CSV output format for each scraper table, so we can get a URL for it that we can then use to pull the data into OpenRefine:

scraperwiki CSV export

In OpenRefine, we can Create a New Project and then import the data directly:

openrefine import from URL

The data is in comma separated CSV format, so let’s specify that:

import as csv comma separated

We can then name and create the project and we’re ready to start…

…but start what? If we want to find out if a candidate lives in ward or out of ward, we either need to know whether their address is in ward or out of ward, or we need to find out which ward their address is in and then see if it is the same as the one they are standing in.

Now it just so happens (:-) that MySociety run a service called MapIt that lets you submit a postcode and it tells you a whole host of things about what administrative areas that postcode is in, including (in this case) the unitary authority electoral division.

mapit postcode lookup

And what’s more, MapIt also makes the data available in a format that’s data ready for OpenRefine to be able to read at a web address (aka a URL) that we can construct from a postcode:

mapit json

Here’s an example of just such a web address: http://mapit.mysociety.org/postcode/PO36%200JT

Can you see the postcode in there? http://mapit.mysociety.org/postcode/PO36%200JT

The %20 is a character encoding for a space. In this case, we can also use a +.

So – to get information about the electoral division an address lays in, we need to get the postcode, construct a URL to pull down corresponding data from MapIt, and then figure out some way to get the electoral division name out of the data. But one step at a time, eh?!;-)

Hmmm…I wonder if postcode areas necessarily fall within electoral divisions? I can imagine (though it may be incorrect to do so!) a situation where a division boundary falls within a postcode area, so we need to be suspicious about the result, or at least bear in mind that an address falling near a division boundary may be wrongly classified. (I guess if we plot postcodes on a map, we could look to see how close to the boundary line they are, because we already know how to plot boundary lines.

To grab the postcode, a quick skim of the addresses suggests that they are written in a standard way – the postcode always seems to appear at the end of the string preceded by a comma. We can use this information to extract the postcode, by splitting the address at each comma into an ordered list of chunks, then picking the last item in the list. Because the postcode might be preceded by a space character, it’s often convenient for us to strip() any white space surrounding it.

What we want to do then is to create a new, derived column based on the address:

Add derived column

And we do this by creating a list of comma separated chunks from the address, picking the last one (by counting backwards from the end of the list), and then stripping off any whitespace/space characters that surround it:

grab a postcode

Here’s the result…

postcodes...

Having got the postcode, we can now generate a URL from it and then pull down the data from each URL:

col from URL

When constructing the web address, we need to remember to encode the postcode by escaping it so as not to break the URL:

get data from URL

The throttle value slows down the rate at which OpenRefine loads in data from the URLs. If we set it to 500 milliseconds, it will load one page every half a second.

When it’s loaded in all the data, we get a new column, filled with data from the MapIt service…

lots of data

We now need to parse this data (which is in a JSON format) to pull out the electoral division. There’s a bit of jiggery pokery required to do this, and I couldn’t work it out myself at first, but Stack Overflow came to the rescue:

that's handy...

We need to tweak that expression slightly by first grabbing the areas data from the full set of MapIt data. Here’s the expression I used:

filter(('[' + (value.parseJson()['areas'].replace( /"[0-9]+":/,""))[1,-1] + ']' ).parseJson(), v, v['type']=='UTE' )[0]['name']

to create a new column containing the electoral division:

parse out the electroal division

Now we can create another column, this time based on the new Electoral Division column, that compares the value against the corresponding original “ward” column value (i.e. the electoral division the candidate was standing in) and prints a message saying whether they were standing in ward or out:

inward or out

If we collapse down the spare columns, we get a clearer picture:

collapse...

Like this:

summary data

If we generate a text facet on the In/Out column, and increase the number of rows displayed, we can filter the results to show just the candidates who stood in their local electoral division (or conversely, those who stood outside it):

facet on inout

We can also start to get investigative, and ask some more questions of the data. For example, we could apply a text facet on the party/desc column to let us filter the results even more…

inout facet filter

Hmmm… were most of the Labour Party candidates standing outside their home division (and hence unable to vote for themselves?!)

Hmm.. labour out

There aren’t too many parties represented across the Island elections (a text facet on the desc/party description column should reveal them all), so it wouldn’t be too hard to treat the data as a source, get paper and pen in hand, and write down the in/out counts for each party describing the extent to which they fielded candidates who lived in the electoral divisions they were standing in (and as such, could vote for themselves!) versus those who lived “outside”. This data could reasonably be displayed using a staggered bar chart (the data collection and plotting are left as an exercise for the reader [See Bruce Mcphereson’s Mashing up electoral data post for a stacked bar chart view.];-) Another possible questioning line is how do the different electoral divisions fare in terms of in-vs-out resident candidates. If we pull in affluence/poverty data, might it tell us anything about the likelihood of candidates living in area, or even tell us something about the likely socio-economic standing of the candidates?

One more thing we could try to do is to geocode the postcode of the address of the each candidate rather more exactly. A blog post by Ordnance Survey blogger John Goodwin (@gothwin) shows how we might do this (note: copying the code from John’s post won’t necessarily work; WordPress has a tendency to replace single quotes with all manner of exotic punctuation marks that f**k things up when you copy and paste them into froms for use in other contexts). When we “Add column by fetching URLs”, we should use something along the lines of the following:

'http://beta.data.ordnancesurvey.co.uk/datasets/code-point-open/apis/search?output=json&query=' + escape(value,'url')

os postcode lookup

The data, as imported from the Ordnance Survey, looks something like this:

o:sdata

As is the way of national services, the Ordnance Survey returns a data format that is all well and good but isn’t the one that mortals use. Many of my geo-recipes rely on latitude and longitude co-ordinates, but the call to the Ordnance Survey API returns Eastings and Northings.

Fortunately, Paul Bradshaw had come across this problem before (How to: Convert Easting/Northing into Lat/Long for an Interactive Map) and bludgeoned(?!;-) Stuart harrison/@pezholio, ex- of Lichfield Council, now of the Open Data Institute, to produce a pop-up service that returns lat/long co-ordinates in exchange for a Northing/Easting pair.

The service relies on URLs of the form http://www.uk-postcodes.com/eastingnorthing.php?easting=EASTING&northing=NORTHING, which we can construct from data returned from the Ordnance Survey API:

easting northing lat -long

Here’s what the returned lat/long data looks like:

lat-long json

We can then create a new column derived from this JSON data by parsing it as follows
parse latlong to lat

A similar trick can be used to generate a column containing just the longitude data.

We can then export a view over the data to a CSV file, or direct to Google Fusion tables.

postcode lat long export

With the data in Google Fusion Tables, we can let Fusion Tables know that the Postcode lat and Postcode long columns define a location:2222

Fusion table edit column

Specifically, we pick either the lat or the long column and use it to cast a two column latitude and longitude location type:

fusion table config cols to location type

We can inspect the location data using a more convenient “natural” view over it…

fusion table add map

By applying a filter, we can look to see where the candidates for a particular ward have declared their home address to be:

havenstreet candidates

(Note – it would be more useful to plot these markers over a boundary line defined region corresponding to the area covered by the corresponding electoral ward. I don’t think Fusion Table lets you do this directly (or if it does, I don’t know how to do it..!). This workaround – FusionTablesLayer Wizard – on merging outputs from Fusion Tables as separate layers on a Google Map is the closest I’ve found following a not very thorough search;-)

We can go back to the tabular view in Fusion Tables to run a filter to see who the candidates were in a particular electoral division, or we can go back to OpenRefine and run a filter (or a facet) on the ward column to see who the candidates were:

refine filter by division

Filtering on some of the other wards using local knowledge (i.e. using the filter to check/corroborate things I knew), I spotted a couple of missing markers. Going back to the OpenRefine view of the data, I ran a facetted view on the postcode to see if there were any “none-postcodes” there that would in turn break the Ordnance Survey postcode geocoding/lookup:

postcode missing...

Ah – oops… It seems we have a “data quality” issue, although albeit a minor one…

So, what do we learn from all this? One take away for me is that data is a source we can ask questions of. If we have a story or angle in mind, we can tune our questions to tease out corroborating facts (possbily! caveat emptor applies!) that might confirm, helpdevelop, or even cause us to rethink, the story we are working towards telling based on the support the data gives us.

Idle Reflections on Sensemaking Around Sporting Events, Part 1: Three Phases of Sports Event Journalism

Tinkering with motorsport data again has, as is the way of these things, also got me thinking about (sports) journalism again. In particular, a portion of what I’m tinkering with relates to ideas associated with "automated journalism" (aka "robot journalism"), a topic that I haven’t been tracking so much over the last couple of years and I should probably revisit (for an previous consideration, see Notes on Robot Churnalism, Part I – Robot Writers).

But as well as that, it’s also got me thinking more widely about what sort of a thing sports journalism is, the sensemaking that goes on around it, and how automation might be used to support that sensemaking.

My current topic of interest is rallying, most notably the FIA World Rally Championship (WRC), but also rallying in more general terms, including, but not limited to, the Dakar Rally, the FIA European Rally Championship (ERC), and various British rallies that I follow, whether as a fan, spectator or marshal.

This post is the first in what I suspect will be an ad hoc series of posts following a riff on the idea of a sporting event as a crisis situation in which fans want to make sense of the event and journalists mediate, concentrate and curate information release and help to interpret the event. In an actual crisis, the public might want to make sense of an event in order to moderate their own behaviour or inform actions they should take, or they may purely be watching events unfold without any requirement to modify their behaviour.

So how does the reporting and sensemaking unfold?

Three Phases of Sports Event Journalism

I imagine that "event" journalism is a well categorised thing amongst communications and journalism researchers, and I should probably look up some scholarly references around it, but it seems to me that there are several different ways in which a sports journalist could cover a rally event and the sporting context it is situated in, such as a championship, or even a wider historical context ("best rallies ever", "career history" and so on).

In Seven characteristics defining online news formats: Towards a typology of online news and live blogs, Digital Journalism, 6(7), pp.847-868, 2018, Thorsen, E. & Jackson, D. characterise live event coverage in terms of "the vernacular interaction audiences would experience when attending a sporting event (including build-up banter, anticipation, commentary of the event, and emotive post-event analysis)".

More generally, it seems to me that there are three phases of reporting: pre-event, on-event, and post-event. And it also seems to me that each one of them has access to, and calls on, different sorts of dataset.

In the run up to an event, a journalist may want to set the championship and historical context, reviewing what has happened in the season to date, what changes might result to the championship standings, and how a manufacturer or crew have performed on the same rally in previous years; they may want to provide a technical context, in terms of recent updates to a car, or a review of how the environment may affect performance (for example, How very low ambient temperatures impact on the aero of WRC cars); or they may want to set the scene for the sporting challenge likely to be provided by the upcoming event — in the case of rallying, this is likely to include a preview of each of the stages (for example, Route preview: WRC Arctic Rally, 2021), as well as the anticipated weather! (A journalist covering an international event may also consider a wider social or political view around, or potential economic impact on, the event location or host country, but that is out-of-scope for my current consideration.)

Once the event starts, the sports journalist may move into live coverage as well as rapid analysis, and, for multi-day events, backward looking session, daily or previous day reviews and forward looking next day / later today upcoming previews. For WRC rallies, live timing gives updates to timing and results data as stages run, with split times appearing on a particular stage as they are recorded, along with current stage rankings and time gaps. Stage level timing and results data from large range of international and national rallies is more generally available, in near real-time, from the ewrc-results.com rally results database. For large international rallies, live GPS traces with update refreshes of ervy few seconds for the WRC+ live tracker map, also provide a source of near real time location data. In some cases, "champaionship predictions" will be available shwoing what the championship status would be if the event were to finish with the competitors in the current positions. One other feature of WRC and ERC events is that drivers often give a short, to-camera interviews at the end of each stage, as well as more formal "media zone" interviews after each loop. Often, the drivers or co-drivers themseleves, or their social media teams, will post social media updates, as will the official teams. Fans on-stage may also post social media footage and commentary in near real-time. The event structure also allows for review and preview opportunities througout the event. Each day of a stage rally tends to be segmented into loops, each typically of three or four stages. Loops are often repeated, typically with a service or other form of regroup, (including tyre and light fitting regroups), in-between. This means that the same stages are often run twice, although in many cases the state of the surface may have changed significantly between loops. (Gravel roads start off looking like tarmac; they end up being completely shredded, with twelve inch deep and twelve inch wide ruts carved into what looks like a black pebble beach…)

In the immediate aftermath of the event, a complete set of timing and results data will be available, along with crew and team boss interviews and updated championship standings. At this point, there is an opportunity for a quick to press event review (in Formula One, the Grand Prix + magazine is published within a few short hours of the end of the race), followed by more leisurely analysis of what happened during the event, along with counterfactual speculation about what could have happened if things had gone differently or different choices had been made, in the days following the event.

Throughout each phase, explainer articles may also be used as fillers to raise general background understanding of the sport, as well as specific understanding of the generics of the sport that may be relevant to an actual event (for example, for a winter rally, an explainer article on studded snow tyres).

Fractal Reporting and the Macroscopic View

One thing that is worth noting is that the same reporting structures may appear at different scales in a multi-day event. The review-preview-live-review model works at the overall event level, (previous event, upcoming event, on-event, review event), day level (previous event, upcoming day, on-day, review day), intra-day level (previous loop, upcoming loop, on-loop, review loop), intra-session level (previous stage, upcoming stage, on-stage, review stage) and intra-stage level (previous driver, upcoming driver, on-driver, review driver).

One of the graphical approaches I value for exploring datasets is the ability to take a macroscopic view, where you can zoom out to get an overall view of an event as well as being bale to zoom in to a particular part of the event.

My own tinkerings will rally timing and results information has the intention not only of presenting the information in a summary form as a glanceable summary, but also presenting the material in a way that supports story discovery using macroscope style tools that work at different levels.

By making certain things pictorial, a sports journalist may scan the results table for potential story points, or even story lines: what happened to driver X in stage Y? See how driver Z made steady progress from a miserable start to end up finishing well? And so on.

Rally timing and stage results review chartable.

The above chart summarises timing data at an event level, with the evolution of the rally positions tracked at the stage level. Where split times exist within a stage, a similar sort of chartable can be used to summarise evolution within a stage by tracking times at the splits level.

These "fractal" views thus provide the same sort of view over an event but at different levels of scale.

What Next?

Such are the reporting phases available to the sports journalist; but as I hope to explore in future posts, I believe there is also a potential for crossover in the research or preparation that journalists, event organisers, competitors and fans alike might indulge in, or benefit from when trying to make sense of an event.

In the next post in this series, I’ll explore in more detail some of the practices involved in each phase, and start to consider how techniques used for collaborative sensemaking and developing situational awareness in a crisis might relate to making sense of a sporting event.