Update (25/02/2016): Article now publised at dx.doi.org/10.1080/03098265.2016.1144729

Original (30/11/2015): Just recently I have had an article accepted (but not yet published) that I wrote using Google Docs. It was a collaborative article from a writing retreat with 5 people contributing. We needed some way of all being able to contribute to the article and I had heard of people using Google Docs for this before, so I suggested we give it a go. We actually started using Google Docs to write notes and outlines during the writing retreat and then developed this into the final article.

Using Google Docs has a number of advantages over sending email attachments back and forth and bookmarking the page allowed me to have easy access to the article whenever I wanted it. It didn’t solve all of the problems of writing a joint article by any means, as we still needed a lead author to coordinate people, set deadlines and remind people to contribute by the deadlines!

One thing I observed was that is wasn’t very easy to tell different contributors apart – all of the text by default was the same colour, so we ended up changing the text colour manually for our contributions. Later on I discovered the “suggestions” option which did highlights changes in different colours. I didn’t find a way to put this on by default, but had to ask everyone to make sure they had that set before starting their contributions. Fortunately everyone did remember though! We also used the discussion option quite a bit to talk about specific changes. However you still needed someone to “accept” or “reject” the suggestions, which I took on as lead author.

Automatically, I received a notification every time a change was made, which was useful in some ways so I could see when people had been making changes, but I’m not sure I found it that useful. Being not completely trusting of Google, I did take regular backups (through export as Word doc) in case our text just disappeared on us, but we didn’t suffer any of these issues.

Overall, Google Docs was very useful for collaboration, allowing people to write whenever was convenient for them, without having to worry about different file versions. However we still needed someone to lead the paper (me in this case!) to encourage, remind and cajole co-authors to contribute and meet deadlines, like any other writing collaboration.

Cross-posted from https://www.linkedin.com/grp/post/6509105-6011573350550302720

About 6 months ago Sarah Dyer suggested an e-reading group on a recently published paper – Peters K, Turner J, 2014, “Fixed-term and temporary: teaching fellows, tactics, and the negotiation of contingent labour in the UK higher education system” Environment and Planning A 46(10) 2317 – 2331 (http://www.envplan.com/abstract.cgi?id=a46294). The original post is at https://www.linkedin.com/grp/post/6509105-5943727838417948672. THE GEES is a closed group on LinkedIn, but if you would like to join, please just submit a join request. 

Sarah, Helen Walkington, Stephanie Wyse and I met up on Skype to discuss our thoughts on the paper and wrote our discussion up as a Letter to the Editor for Environment and Planning A, which has now been published (http://www.envplan.com/abstract.cgi?id=a4704l)! (The preprint is available on my publications page if you can’t access EPA). 

I really enjoyed the process and it didn’t take too much of our time. If there’s an article you think it would be interesting to discuss, post it up here and see who else is interested.

Thanks very much to Sarah for starting this off for us, and coordinated THE GEES group!

We have recently published a paper in the Journal of Transport and Health where we modelled the impact on CO₂ emissions of an increased uptake of active travel for the home to school commute. The paper is freely available to anyone under Gold Open Access, with a CC-BY Attribution license.

One of the challenges in this paper, building upon (Singleton, 2014) was being able to model individual routes from home to school for all ~7.5 million school children in England. In addition to origin and destination locations, we also know what modes of travel are typically used to get to school, thanks to the School Census (also known as the National Pupil Database). While modelling a small number of routes is relatively straight forward to perform within a GIS, the challenge was to complete the routing for all 7.5 million records in the data set.

To calculate the route, we used a combination of two different pieces of software – Routino and pgRouting. Routino allows us to use OpenStreetMap data to derive a road-based route from given start and end points, using a number of different profiles for either car, walking, cycling or bus. The profile used is important, as it allows the software to take into account one-way streets (i.e. not applicable to walking, but applicable to driving), footpaths (i.e. applicable to walking only), cycle lanes, bus lanes, etc.. The screenshot below shows an example route, calculated by Routino.

Example of the route calculated using Routino for a car travelling from Rosslyn Street (1) to Granby Street (2). © OpenStreetMap contributors, http://www.openstreetmap.org/copyright.

For railway, tram or tube travel, this was implemented using pgRouting from both Ordnance Survey and edited OSM data. The different networks were read into the PostgreSQL database, and routes calculated using the Shortest Path Dijkstra algorithm. This returned a distance for the route, which was stored alongside the original data.

Routino and pgRouting were called using R, which also managed the large amounts of data, subsequently calculated the CO₂ emissions model, and created graphical outputs (see below).

Map of CO₂ emissions (grouped by residence LSOA) for Norfolk.

To run the routing for each pupil for four years worth of data (we had data from 2007/8-2010/11, although we only used data from academic year 2010-2011 in the paper) took about 14 days on my 27″ iMac. We considered using a cloud solution to shorten the run times, but given we were using sensitive data this was deemed too problematic (see related blog post from Alex on this). This work highlights that it is possible to perform some types of big data analysis using a standard desktop computer, which allows us to perform this type of analysis on sensitive data without needing to make use of cloud or remote processing services, which are often not compatible with restrictions on sensitive data.

*As you would expect, the postcode unit is sensitive data and we had to apply to the Department of Education to use this data. Any postcodes or locations used in this blog post will be examples – e.g. L69 7ZQ is the postcode for my office!

Singleton, A. 2014. “A GIS Approach to Modelling CO₂ Emissions Associated with the Pupil-School Commute.” International Journal of Geographical Information Science 28 (2): 256–73. doi:10.1080/13658816.2013.832765.

Cross-posted from http://geographicdatascience.com/r/2014/11/20/Home-School-Routes/