How to get people to make things for you

By: Alexandra Deschamps-Sonsino, designswarm

We’re a week away from the deadline for the Smart Oxford Challenge and I was asked if this was a hackathon. That expression and the format it has come to represent gives me the heeby-jeebies so I wanted to share why, and what makes the Challenge different. This is coming from ten years of organising events around the internet of things (up to 60 attendees on average) as well as being a producer for the Mozilla Festival during it’s first 2 years in London (600-800 attendees).

What is a hackathon?

Generally speaking this is the format of a “classic” hackathon (obviously there are variations):

  • A company sets a theme
  • Developers are invited to take a day or two (often on a weekend) of their time to address that theme and build prototypes
  • Pizza is dished out
  • A judging panel arrives at the end of the event and teams pitch
  • There may be a prize at the end of it for the “winner”

What’s good about this?

  • This might give developers some dedicated time to work on something they’ve been interested in and not had the time to work on because of other work commitments.
  • This acts as an easy networking opportunity for developers.
  • On a good day, they may take the work they’ve done during the hackathon and quit their day job and work the idea into a company.

What’s wrong with this?

  • The theme is there because the business has a problem it could probably address by hiring a small number of the right experts.
  • If the problem is artificial, then it’s a marketing exercise. As long as its clear to attendees and the business that’s fine, but it’s rarely clear.
  • Developers are highly paid professionals who care about the work they do. Asking them to spend unpaid time on someone else’s problem is hard especially if the event is on the weekend and they have a family. This may work if they are young and looking for freelance work but the good freelance developers I know would run a mile.
  • There isn’t usually a clear IP situation. Who owns the work done? If this isn’t clear you can be sure that the best idea will not be developed on the day and will be squirrelled away by the developer to work on independently.
  • The food is terrible, and usually doesn’t cater to allergies and intolerances. This puts everyone in a bad mood. Productivity and happiness will always be affected by food, unless you’re on Soylent (barf).
  • Offering a prize is tacky and problematic if it’s a low cash prize or an iPad. It’s a weak symbol of your appreciation. If anything, something more interesting (box tickets for a sports event or a trip somewhere) would go a long way.

So how do you do this differently?

There are in my experience two routes to the development of good ideas by smart people: pay smart people to look at what you’re doing and tell you what they think / build alternatives or support smart people in developing their existing ideas.

1. Pay smart people to look at what you’re doing and tell you what they think

No this isn’t about getting a management consultant in. This is about exposing a small but diverse group of people (less than 15 ideally) to what you’re doing as a business and work in teams (2-3 no more) to work out better ways of doing it. The duration of the exercise depends what kinds of results you want. The higher the resolution of the response (functional prototypes etc), the more time and materials you’ll need. I wouldn’t push beyond a week though. They’ll have other things to do with their time 🙂 But everyone is paid and happy to be there and they meet and work with people they’ve not worked with before. This group of people can come outside the business or all over your business. As long as they are all coming at the problem from different spaces this will work. I’ve worked like this with clients like EDF, American Express and the British Standards Institute.

2. Support smart people in developing their existing ideas

There are so many startups who need help at various levels especially when it comes to #iot. I started thinking about how to really help them as I was encountering all sorts of problems with Good Night Lamp and finding it difficult to get expert advice for little money. I started by organising a showcase for British Gas two years ago where over 60 startups were able to show their products to the British Gas venture team and management. There were some cash prizes for the top 3 but that wasn’t really the point. The startups were able to meet others like them who shared similar challenges in the tricky business of energy-based #iot solutions. Last year, I helped the Digital Catapult scope the best ways to help #iot startups and ended up running a pilot event called Boost. Ten experts were invited to offer 30 minute clinics to startups and discuss their problems. We ended the day with nibbles and a showcase. This was a great event but I thought I’d missed a trick.

The Smart Oxford Challenge borrows heavily from that model and will see selected smart cities teams and individuals come and spend time with experts on topics which they have identified and at the end of the day, they will get to meet city officials and pitch their idea. This is really to not only support and help but help champion and open doors for startups. Building a community around what a business does has to be about opening doors over a short period of time (the event is one day) with no strings attached (the event is free and no equity or IP is at play). Only then can it start to understand what are the challenges of startups and how they can help best. And for clients with R&D departments interested in new areas like the internet of things, this is useful model to gauge where your research should lead you if you try to productise it.


This article was first published in the designswarm blog in July 2015.

How big data is breathing new life into the smart cities concept

By: Jonathan Bright, Oxford Internet Institute

man looking at techno-scuplture of a tree
One day all our trees will be electric. Image from Smart City Exhibition 2014 by Forum PA (Flickr).

“Big data” is a growing area of interest for public policy makers: for example, it was highlighted in UK Chancellor George Osborne’s recent budget speech as a major means of improving efficiency in public service delivery. While big data can apply to government at every level, the majority of innovation is currently being driven by local government, especially cities, who perhaps have greater flexibility and room to experiment and who are constantly on a drive to improve service delivery without increasing budgets.

Work on big data for cities is increasingly incorporated under the rubric of “smart cities”. The smart city is an old(ish) idea: give urban policymakers real time information on a whole variety of indicators about their city (from traffic and pollution to park usage and waste bin collection) and they will be able to improve decision making and optimise service delivery. But the initial vision, which mostly centred around adding sensors and RFID tags to objects around the city so that they would be able to communicate, has thus far remained unrealised (big up front investment needs and the requirements of IPv6 are perhaps the most obvious reasons for this).

The rise of big data – large, heterogeneous datasets generated by the increasing digitisation of social life – has however breathed new life into the smart cities concept. If all the cars have GPS devices, all the people have mobile phones, and all opinions are expressed on social media, then do we really need the city to be smart at all? Instead, policymakers can simply extract what they need from a sea of data which is already around them. And indeed, data from mobile phone operators has already been used for traffic optimisation, Oyster card data has been used to plan London Underground service interruptions, sewage data has been used to estimate population levels … the examples go on.

However, at the moment these examples remain largely anecdotal, driven forward by a few cities rather than adopted worldwide. The big data driven smart city faces considerable challenges if it is to become a default means of policymaking rather than a conversation piece. Getting access to the right data; correcting for biases and inaccuracies (not everyone has a GPS, phone, or expresses themselves on social media); and communicating it all to executives remain key concerns. Furthermore, especially in a context of tight budgets, most local governments cannot afford to experiment with new techniques which may not pay off instantly.

This is the context of two current OII projects in the smart cities field: UrbanData2Decide (2014-2016) and NEXUS (2015-2017). UrbanData2Decide joins together a consortium of European universities, each working with a local city partner, to explore how local government problems can be resolved with urban generated data. In Oxford, we are looking at how open mapping data can be used to estimate alcohol availability; how website analytics can be used to estimate service disruption; and how internal administrative data and social media data can be used to estimate population levels. The best concepts will be built into an application which allows decision makers to access these concepts real time.

NEXUS builds on this work. A collaborative partnership with BT, it will look at how social media data and some internal BT data can be used to estimate people movement and traffic patterns around the city, joining these data into network visualisations which are then displayed to policymakers in a data visualisation application. Both projects fill an important gap by allowing city officials to experiment with data driven solutions, providing proof of concepts and showing what works and what doesn’t. Increasing academic-government partnerships in this way has real potential to drive forward the field and turn the smart city vision into a reality.

OII Research Fellow Jonathan Bright is a political scientist specialising in computational and ‘big data’ approaches to the social sciences. His major interest concerns studying how people get information about the political process, and how this is changing in the internet era.


This posting originally appeared in July 2015 on the OII Policy and Internet website.

How do networks shape the spread of disease and gossip?

map of  london

A new approach to exploring the spread of contagious diseases or the latest celebrity gossip has been tested using London’s street and underground networks. Results from the new approach could help to predict when a contagion will spread through space as a simple wave (as in the Black Death) and when long-range connections, such as air travel, enable it to seemingly jump over long distances and emerge in locations far from an initial outbreak.

A team of Oxford Mathematicians together with colleagues from the University of North Carolina at Chapel Hill and Rutgers University used a set of mathematical rules to encode how a contagion spreads, and they used diverse mathematical and computational tools to study outcomes of these rules.

The researchers explored how disease or gossip might spread through London’s transit network. Specifically, they illustrated how the street network overlaid with the London Underground network enables contagions to hop to a distant location. To analyse the behaviour of a contagion, the researchers drew on ideas from ‘topology’, a branch of mathematics used to characterise the structure of complex shapes. By studying the ‘shape’ of the data that results from a contagion, it is possible to distinguish between contagions that take long-distance hops across a network and those that exhibit a local (and slower) wave-like spreading pattern.

This ‘computational topology’ technique has the potential to overcome many of the barriers to extracting useful information from big, ‘noisy’ data sets, such as those gathered during a disease epidemic or from gossip spreading over social media. Computational topology could, for example, yield insights into how fast a new contagion might spread or where it might emerge next.

A report of the research is published in the journal Nature Communications.

“Underlying spatial networks have a big influence over how diseases or information spread, but in our ever-more-connected world, there are numerous ‘shortcuts’ that these can take that makes their spreading patterns difficult to predict,” said Professor Mason Porter (an author of the report) of the University of Oxford’s Mathematical Institute. “Our work shows a way to reconcile a wave-like model of spreading, which might approximate what happens at a local level, with behaviour that includes shortcuts to distant locations.”

To investigate how networks influence spreading processes, the team ran hundreds of scenarios. They considered various subtly different network structures, which encapsulate which ‘nodes’ (representing, for example, people or locations) are directly reachable from each other through a single short-range or long-range connection.

In some scenarios, nodes can be ‘stubborn’ and resist a new infection or idea; but in others, they are not stubborn at all and quickly succumb to a contagion. The team found that the shape of how a contagion spreads is very sensitive to how inclined nodes are to adopt the contagion. Dr Heather Harrington, another author from the Mathematical Institute in Oxford, said “If nodes are very stubborn, a contagion doesn’t spread much at all; whereas if they are compliant, the contagion quickly crops up all over the network. When the nodes are moderately stubborn—a so called ‘sweet spot’—a contagion tends to spread gradually as a wave.”

Professor Porter said: “In other situations, when different nodes have different levels of stubbornness, and if we otherwise make the model more complicated, we still observe both wave-like and shortcut ‘hopping’ behaviour, although naturally the results are messier.”

By varying the location of the initial outbreak on a given network and tracking exactly who gets infected at what time (and stacking these layers of information on top of one another), the researchers constructed a mathematical object that they call a ‘contagion map’.

Using methods from computational topology to examine the shape of the data encompassed by the contagion map, the researchers looked for ‘holes’ in the data. “You can think of it like looking for the hole in a doughnut shape that enables us to distinguish it from a sphere,” said Professor Porter. In simple scenarios, the approach can distinguish between a ‘real’ hole – which could represent where infections tend not to spread over shortcuts between distant locales – and a ‘false’ hole that arises from noise in the data (such that long-range spread could still be common). As the deluge of data gets ever deeper, developing tools that can distinguish genuine features from noise in large, intricate data sets is becoming increasingly important.

Professor Porter said: “Our work illustrates that these topological methods could be useful in a range of different scenarios. It’s a good example of how pure mathematics and applied mathematics are increasingly working together.”

In addition to Porter and Harrington, the research team included postgraduate student Florian Klimm from Oxford’s Mathematical Institute, Dr Dane Taylor and Prof. Peter Mucha from University of North Carolina at Chapell Hill, and Dr Miroslav Kramár and Prof. Konstantin Mischaikow from Rutgers University. Taylor and Klimm are joint lead authors of the study.


This article was first published on the website of the Mathematical Institute, University of Oxford, on 22 July 2015.

Making Oxford a better place

By: Tony Hart, OxLEP

Oxford skyline

By being smarter we can make Oxford more beautiful, greener, safer, fairer, healthier, more resilient and more prosperous. The traffic and transport systems around the county, most notably on A34, A40 and M40, are struggling to cope, and the volume of car traffic into and within Oxford causes major problems. Let’s use technology, sensors and data analytics, to make cycle lanes safer and make public transport easier to use for commuters, shoppers and visitors.

As our population ages, let’s pioneer new monitoring technologies from the research labs of Oxford University Hospitals NHS Trust to provide preemptive treatment for medical conditions and improve patient care.

25% of Oxford’s schoolchildren go to private schools AND 25% of children in Oxford live in families below the poverty line and the children who go to state schools are on average performing worse than the rest of England. Let’s fix both the social divide and the gap in educational attainment by investigating new ways of engaging with younger students.

Traffic is the primary cause of high pollution levels within the city but we also need to reduce the level of carbon consumption within both public and private buildings and handle environmental challenges such as regular flooding. Let’s use technology to monitor the environmental status of the city and its surroundings, investigate ways of improving it and continue to use systems such as the Flood Warning system to inform our citizens.

The objectives of Smart Oxford are that we use smart technologies and processes to improve the effectiveness and efficiency of services in the city, and build sustainable improvements in the quality of life for Oxford’s people socially, economically and environmentally.

We can create and support innovative ideas from the universities, colleges, businesses (large and small), public sector bodies and active citizens to make Oxford a better place to live, improving the transport infrastructure, recycling, education attainments and people’s health, reducing pollution levels, energy consumption and social divisions, and many more issues.

Let’s use the intellectual and commercial assets of Oxford and Oxfordshire to solve the problems that all cities across the world are currently facing. Let’s engage with all the academic, medical, public and private sector organisations and businesses across the county, and lead the way in showing what a smart city can do to improve the lives of its citizens.

This posting originally appeared in July 2015 on the Smart Oxford Challenge site.

New research: low-energy urban mobility in Oxford

By: agile-ox, University of Oxford


Interested in the transition to low carbon transport in Oxford, and curious to know how local innovations compare to those in Brighton?

Dr Tim Schwanen , the incoming director of the Transport Studies Unit, has conducted a case study examining the nature of low-energy innovation in the everyday mobility of people in two UK cities with favourable conditions for a transition away from fossil fuels (Oxford & Brighton).

The resulting paper, The Bumpy Road toward Low-Energy Urban Mobility: Case Studies from Two UK Cities’, published in Sustainability in June 2015, outlines the innovation and progress in both cities and explores the differences between the two locations. In Brighton, innovations relate more to commercial car sharing; cycling oriented retrofitting of road infrastructure; and travel planning; in Oxford, there has been greater activity around electric mobility, “partly because of greater air quality issues and more concerted action from local government”.

Dr Schwanen concludes that, whilst things are happening, there is “still a long way to go toward urban mobility that is genuinely environmentally sustainable”, with (at least for commuting) private car use (in internal combustion vehicles) remaining “by far the most important form of transport in both cities”.

Get involved: get hacking

So there is work to be here, folks! And here’s an opportunity to get involved, coming up in November: ‘Hackathon: Let’s make cycling in Oxford great!’, November 6-7 2015. Following on from the success of June’s ‘Can you see the future of cycling in Oxford’ event, Oxford’s Environmental Sustainability Team and agile-ox are coming together again to bring you a full cycle hackathon. Find out more about the event.

Read more

You can download the full paper here.

Check out other transport-related research, news and events on the transport hot topic page.

 image: Family Ride, Kamyar Adl, Creative Commons 2.0

This article first appeared on the website of agile-ox in July 2015.