Home » Archives for November 2017

Propensity to Cycle Tool Hackathon

A hackathon was organised by Robin Lovelace and the rest of the PCT team to allow ‘advanced users’ to play around with the tool and come up with ideas for its future development. This follows the desire expressed by the team that the open source nature of the tool will increase its usefulness (Lovelace et al. 2017):

During the event participants learned about how the tool worked, building on a repository of reproducible code and data at https://github.com/npct/pct-demo

Attendees included representatives from academia, the public sector and transport planning consultancy Systra, who are using the the PCT to plan for a strategic cycling in the Northeast.

There were some excellent ‘hacks’ done on the PCT as illustrated below:

BikeSafety demonstrated how now visualisation methods could enhance the ability of the PCT to be used to road traffic accidents – as shown in the video embedded below:

VID_20171020_161256384.mp4

 

CyIPT (the Cycling Infrastructure Prioritisation Toolkit) was a pre-existing project that has built on the PCT. Malcolm Morgan from the PCT team demonstrated how overlaying infrastructure data on PCT values could help prioritise new infrastructure:

Malcolm Morgan demonstrating CyIPT

 

NewScenarios was a demonstration of how the PCT can (relatively) easily be used to generate new scenarios. PCT member Anna Goodman and ITS staff Ian Philips presented new scenario generation based on data from you:

Ian Philips presenting new scenarios for the PCT

 

ShortCarJourneys is an app to identify where short car journeys are taking in a city, potentially a vital evidence-base for local planners to help tackle car dependence. Robin Lovelace presented to tool and deployed a small publicly-accessible web application for testing the app:

https://bookdown.org/robinlovelace/shortcarjourneys/

The app can also be seen in action in the photo below:

ShortCarJourneys in action

 

Another hack that was not acted on was to make it easier for users to select all fastest routes passing through a specific point, as illustrated below:

An idea: make it easy to select routes passing through specific points

 

As documented in the PCT Manual, this can currently be done using the Freeze Lines check-box, but this is not well-known or intuitive.

Overall it was a great event and shows there is high latent demand for training courses for the PCT, for practitioners and advanced users alike. More fundamentally it shows that if publicly funded transport models are commissioned to be open source, others can build on them, greatly increasing their value and reducing the ‘opportunity costs’ of creating new tech products.

I’d like to thank everyone who attended for a fantastic event which was not only productive but enjoyable, as illustrated in the final photo of this post:

Reference
Lovelace, R., Goodman, A., Aldred, R., Berkoff, N., Abbas, A., & Woodcock, J. (2017). The Propensity to Cycle Tool: An open source online system for sustainable transport planning. Journal Of Transport And Land Use, 10(1). doi:http://dx.doi.org/10.5198/jtlu.2016.862

Can activity self-tracking decrease health and social inequalities?

Dr Chris Till, co-investigator on one of the projects funded by the CDRC Innovation Fund, provides his views on using big commercial datasets critically.

 

This month I started working on a new project with colleagues from Leeds Beckett University and The University of Leeds which will use self-tracked data to assess demographic differences in activity patterns.

This research is funded by the Economic and Social Research Council (ESRC) and the Consumer Research Data Centre (CDRC). We will be using data held by the CDRC which has been gathered by an app called Bounts. This app connects to other fitness tracking tracking apps and rewards activity with points which can be exchanged for prizes and discounts. The data generated by the app (which includes the types of activity engaged in, distance, steps, the source of the data as well as basic demographic data on the users) will be combined with consumer profiling datasets and categories (also made available through the CDRC).

Access to these data should help us to get an understanding of who is using activity tracking apps (in relation to gender, income, location and other proxies for social class), in what ways they are using them and whether they are related to higher levels of activity.

This should also help us to investigate if the economic and social environment in which an individual lives is associated with activity (or inactivity) levels and identify if technology use is socially patterned and if it either reduces or increases existing social inequalities.

A second strand of the project will assess the effectiveness of a community national weight management intervention through using similar data matching with consumer datasets. This should also help to investigate if  engagement with weight management interventions is socially patterned.

I think the efficacy of self-tracking approaches to behaviour change is largely taken for granted with many individuals choosing to use step-tracking apps and fitness bands (such as Fitbit). They are also increasingly being pushed through public health campaigns such as such as the NHS “Couch to 5k” app and Sheffield City Council’s “Move More” (which uses an app to facilitate competitions between workplaces and schools).  Workplaces are also increasingly using self-tracking systems to encourage more activity and better engagement at work through competitions and challenges.

While there is some evidence to suggest that these kinds of apps and devices might be effective at behaviour change it is not clear to what extent their use (and effectiveness) are socially patterned. So, for instance, if these interventions are not effective for those who are most in need of them perhaps they need to be re-designed or other approaches entirely are needed.

I am also excited about this project on a methodological level as we will be using commercial datasets and categorisations of populations. Sociologists (including me) have been critical of the collection of these kinds of data and the categorisations which they have enabled. Consequently they are often quite squeamish about using these kinds of data themselves. This is exacerbated by a broader antipathy towards quantitative methods amongst sociologists (at least in UK) who often see it as incompatible with a “critical” approach to analysis. Mark Carrigan has written very well on this recently.

My thinking around this has been influenced by David Beer’s work on the use of “digital by-product” data and “commercial data” and his position that use of these data can enable critical social science. Perhaps sociologists even have a responsibility not to leave the analysis of these kinds of data to commercial enterprises. Big data can be used for control or to sell products but might also be able to highlight social inequalities and to challenge or improve social policy interventions.

 

Chris is part of the research team working on the Data driven, social, economic and spatial profiles; obesity and physical activity project which is funded by the CDRC Innovation Fund.  The views in the above article, which was originally published on THIS IS NOT A SOCIOLOGY BLOG belong to Chris and do not necessarily represent the views of the project team as a whole.

Public Health England’s new interactive FingerTips website features CDRC data

Following the successful launch of the CDRC indicator ‘Access to Healthy Assets and Hazards’ (AHAH), the overall index of how healthy neighbourhoods are has been incorporated into Public Health England’s ‘FingerTips’ Wider Determinants of Health Data Resource. The data resource compiles various indicators by Public Health England that are supplied to Local Governments to aid with Public Health decision making, and the inclusion of AHAH represents the importance of CDRC project in measuring health-related features of environments. The FingerTips website also includes wider information of health-related features of cities and regions, and health patterns

https://fingertips.phe.org.uk/profile/wider-determinants/data#page/0/gid/1938133043/pat/6/par/E12000002/ati/102/are/

CDRC research presented by Cluttons 

The research analysis from one of this year’s Masters Research Dissertation student was presented to professionals at an event hosted by the leading residential real estate agent Cluttons.

The event, ‘Unlocking London’s Migration Patterns’, brought together professionals from the public and private sector to delve into the factors impacting upon London’s migration patterns.  Research conducted by Neelam Kundi, from University College London (UCL), titled “Exploring submarket trends in London: a constrained spatial interaction modelling analysis” will be used further by Cluttons to explore the extent to which factors such as affordability, accessibility and house price growth affect a household’s relocation decision.

Kundi’s Spatial Interaction Model will form the foundation of the analysis, which makes use of data from the Census Data for London, 2011, and the Office for National Statistics (ONS).

For more on Neelam Kundi’s research click here.

To read more about the study Cluttons are undertaking click here.

Data Analysts User Group conference 2017 – videos now available

On 20th October 2017 the CDRC sponsored and supported the 14th annual Data Analysts User Group Conference (DUG), hosted at the Royal Society.
The conference focussed upon Data Sharing: New Challenges, Opportunities and Examples…

There were some great presentations and stimulating audience discussion over the course of the day around the conference’s key questions:
1. What important questions am I currently unable to answer satisfactorily?
2. Where might data reside, that could help address these challenges and what should I put in place to make progress?

Presentations

The presentations are now available to watch online:

Chair’s Introduction
Tim Drye, Director of Analysts User Group

“The Generation Deficit and other challenges that are revealed by data analysis…”
Lord David Willetts, Resolution Foundation

CDRC Master Dissertation Awards Presentation
Guy Lansley, CDRC, UCL

“Training needs and the Industrial Strategy”
Jonathan Reynolds, CDRC, Oxford

“New Research Indices: The opportunities from extended data integration”
Paul Longley, Professor of Geography, UCL and Alex Singleton, CDRC Liverpool

“Innovation at the ONS: Novel exploration of new challenges”
Tom Smith, Head of Data Science Campus, Office for National Statistics

“Opportunities from the new Digital Economy Act”
Paul Jackson, Administrative Data Research Network and Darren Tucker & Kate Davies Office for National Statistics

“Identifying Problem Families and their interactions”
Paul Holme, Manchester Combined Authority

“Barclays Local Insights: An example of delivering data to share”
Chris Fuggle, Barclays

“From Cosmology to Customers: the improbable applications of astrostatistics”
Dr Roberto Trotta, Dept of Physics, Imperial College London

Chair’s Final Reflections and the DUG Award