It is an exciting time to be part of a city administration team. The proliferation of technologies across the social, mobile, analytics and cloud(SMAC) spectrum means more services can be delivered in exciting new ways. However, the pace of change means evaluating appropriate technology needs to be also done in different ways. Relying on months long procurement processes can lead to loss opportunities and risk citizen dissatisfaction.  We, as consumers, are all more accustomed to new services coming online in a matter of days, and expect the same from our local public services.

Over the past few months we have had numerous enquiries from administrators on how best to keep up-to-date with capabilities available in the market. Individual vendors are doing an amazing job publishing their view of what’s possible and sharing case studies. However, for city technology buyers, a fuller scan looking across the marketplace is as important as understanding the capabilities of individual vendors.

At PublicTechViews, our primary research goal has been to look at how cities are adopting technology. So far we have focused entirely on writing about cities themselves. However, in response to the strength of demand,  we are now embarking on a technology vendor survey. Our interest is only to understand how offerings have evolved to meet the demands of city administration. If you are a Technology vendor with a compelling offering for city administrators we encourage you to contact as and be part of this study.

In order to include as many of the leading benders as possible we are keeping our research focused on five key factors that are of interest to the buyer community:

1. Changes to the technology portfolio – across the technology spectrum, vendor’s have been updating their offerings packages and pricing quite aggressively. Instead of capturing the entire portfolio, we are primarily interested in what has changed since the end of 2012
2. Capability investments – New offerings are only real if capabilities have scaled to deliver. Here we would like to understand investments that have been made in R&D, manufacturing, software or consulting capability
3. Reference case studies – like most buyers city administrators take comfort from successful implementation of the offerings they are reviewing. We are keen to build a library of referenceable case studies as easy source for our readers
4. Partnering – The increasing complexity of jobs we are expecting technology to perform means very often a number of vendors work together on projects or programmes. Understanding strategic alliances and frequent partnerships is one way buyers can gain confidence in the delivery capability
5. Business growth – The long-term sustainability of a vendor will be predicated on the business growth ambitions they invest for. Understanding this will help buyers profile their risk more effectively

Palava, near Mumbai in India, is unlike most other cities. For a start, it’s a new city, and it’s been carefully designed. Most cities across the globe have grown up over time, and large chunks of them have happened more or less by chance. There are a few exceptions – Paris springs to mind – but as a rule, cities, especially old ones, were not planned, and this shows.

A city designed for living

What does this mean in practice? It means that Palava has been designed to make it pleasant to live there. There’s plenty of open space, and it’s possible to walk to work in the city. There will be high quality education, sporting and cultural facilities available, and it’s conveniently located within reach of major transport hubs such as the port at Mumbai, and the planned new airport at Navi Mumbai. Palava is ambitious in its scope, and by the time it is complete will be India’s largest private completely-planned urban development. It hopes to be among the top 50 places to live in the world by 2020.

But there’s more to it than just a pleasant place to live. Palava’s developer, the Lodha Group, has used the opportunity to build in smart city technology, and employed IBM to create the smart city architecture to make this happen. This collaboration, which will bring together data from all the city operations into one place, to not only increase efficiency, but also improve the quality of life for residents.

IBM will  integrate information from multiple city agencies, helping them to collaborate better.  There are several aspects to this:

• Palava’s website describes smart traffic management, designed to make traffic jams a thing of the past, by providing real-time information about congestion. The city’s fleets of buses, rubbish trucks and emergency vehicles will also be managed in a coordinated way, as will parking.
• Integration of energy, water, public safety and smart cards. This will allow a more coordinated response to any problems, and particularly to any emergencies. In future, Big Data analytics will support management of issues that could cause problems or affect quality of life.
• The system will enable better user involvement in government. Palava’s City Management Association already consists of administrators, city planners and citizens, giving end-users a key voice on the central committee running the city. Analytics and an overview of the city’s functions will enable better allocation of resources and preventative maintenance. But more than that, there will be a social media platform that enables service users to interact with the city administration, and manage their own services. This will include the facility to report issues and receive feedback, enabling conversations between officials and service users.
• There’s also a big focus on public safety with centralised city operations for better and more coordinated responses to any incidents with public safety implications.

Delivering an ambitious vision

Lodha’s vision for Palava is certainly ambitious. But it’s also realistic, not least because of the steps that they have already taken to make it happen.  Better information about the infrastructure, used alongside user-provided information, will mean that services can genuinely be user- and citizen-centric. It should also make for a more efficient system, with better allocation of resources to meet the city’s priorities.

Recent projections suggest that India’s urban population will grow from 340 million in 2008 to 590 million by 2030. And there are plenty of other countries, especially in the developing world, that are looking at similar urban growth. Smart cities are likely to be key to managing this growth in an efficient and cost-effective way. Many will argue that it is easier to implement smart solutions into a new city, and certainly having no legacy systems or older infrastructure does make life simpler. Palava’s success might set the trend for the next 20 to 50 years of urban planning.

Where Barcelona leads, London is keen to follow.

It’s not often that you’ll read those words, but in smart city terms, it’s true. Barcelona is widely recognised as a pioneer of the Smart Cities movement. And now the Canary Wharf Group has set up a challenge called the Cognicity Challenge, which is designed to redress the balance in London’s favour. Or if perhaps not exactly London, at least that area of London’s Docklands known as Canary Wharf.

The Cognicity Challenge

Cognicity is a pioneering project set up by the Canary Wharf Group, which is designed to identify and accelerate the development of smart city technology products and services. The Group wants to create a leading smart city area around Canary Wharf. It is, apparently, all about the creation of ‘the integrated city of the future’.

The Cognicity Challenge is the starting point of the development. Participants, who are expected to be technology start-ups, will create and pilot the technologies that will enable the city to integrate. The six winning start-ups will each win a prize of £50,000 and the opportunity to pilot their product or service in Cognicity. It is, in fact, a start-up accelerator programme, a bit like Cisco’s Entrepreneurs in Residence, which we wrote about earlier this year. Participants will be provided with free accelerator space for the duration of the 12-week programme, and will also get advice and mentoring from Canary Wharf Group executives and other leading technology experts.

Challenge streams

There are currently four open challenge streams, and more on the way. All the start-ups applying should be using open or interoperable technology, for obvious reasons. The first two, for which applications close at the end of November, are Sustainable Buildings and Integrated Transportation. The 12-week accelerator will run from the end of January until mid-April 2015. Piloting will start from the end of April.

• Sustainable Buildings is looking for applications from start-ups working on smart and green building technologies that improve efficiency and reduce operating costs. They might, for example, optimise resource use, connect smart devices, or develop new building materials.
• Integrated Transportation is open to start-ups who are working on intelligent mobility projects. The idea is to enable seamless integration of public and individual transport, improving user experience and reducing disruption. They might also increase accessibility and usage, or use data to improve efficiency.

The next two, Integrated Resource Management and Automated Building Management, run slightly later. Applications close in mid-December for a 12-week accelerator programme from mid-February to mid-May, and piloting from the end of May.

• Integrated Resource Management is about improving the integration of infrastructure. This might include load-balancing, demand-side response or CO₂ reduction, but should optimise energy, waste and water use.

• Automated Building Management is for those start-ups who have developed digital platforms that will integrate solutions and interpret the resulting data to inform decision-making. It is envisaged that products will use high quality analytics to detect issues before they become problems. This will allow prioritising of maintenance and make sure that the building runs well.

There are also two further streams forthcoming, Connected Home, for devices that will improve life for residents and building managers, and Virtual Design and Construction, around building information modelling. Applications for those will open in early December, with the accelerator running from mid-April to early July, and piloting from mid-July.

A start-up accelerator with a difference

The Cognicity Challenge is different from other start-up accelerators. Others are usually designed to introduce a more entrepreneurial element to an established company, or even to give something back to the entrepreneurial community. The Cognicity Challenge is fiercely focused on its purpose, to develop a smart city area for the future to rival any other around the world. This has driven its streams of development, and also its urge to learn more and share information about innovation in smart city technology.

However, other smart cities are also building  on and with what is already available. Yes, innovation is a great thing, and of course it is vital to the future of smart cities, but perhaps what has most powered Barcelona’s progress as world-leading smart city is installing a network of fast fibre-optic broadband that enables free WiFi around the city. It’s fairly basic infrastructure, but it’s fundamental. Let’s hope Cognicity is not so focused on the joys of innovation that it forgets the importance of the underpinning basic infrastructure to ensure connectivity.

The Global Open Data Initiative (GODI) is a coalition of civil society organisations sharing principles and resources to get the best value out of open data. Open data, at its simplest, is freely available government data that anyone can access, analyse and use. It has potential to be widely used by citizens and organisations to explore how to get the best out of cities.

The key principle behind the establishment of GODI is that government exists by and for the people. This means that any data that it collects should be freely available for anyone to use. Some people will, inevitably and rightly, use this data to hold governments to account, which goes some way to explaining why it is so hard to persuade governments of the merits of open data.

An organisation for open data

GODI’s work is across a number of areas, including:

• Acting as a global guiding voice on open data

This will ensure that organisations espousing open data do not feel isolated, but instead can discuss and share issues with others facing similar problems around the world.

• Developing and providing a vision for how governments should approach open data

GODI recommendations and resources will help local and national governments to develop pledges on open data, and then to put them into action. They will also help not-for-profit organisations seeking to evaluate government initiatives.

• Increasing awareness of open data and related issues

There are many issues and challenges to sharing data, and GODI will help raise awareness of them, and also how to overcome them. It will support networking and benchmarking and promote best practice in information sharing and open data provision.

• Supporting the development of the open data community

At the moment, not many organisations are engaging with open data, or grasping at the opportunities that it offers. GODI aims to build the global community working with open data by showing organisations how they can use it to develop strategic plans and improve operationally.

• Gathering, expanding and disseminating the evidence base on open data.

The academic community has started to define and deliver a research agenda, but there is still a shortage of information and resources about what works in open data. GODI aims to identify areas for further work, and build evidence-based policies and guidance. It will also draw together resources on open data, and make them widely available.

The current state of play

This all sounds very laudable, but is it working? A recent survey by GODI suggests that there is a long way to go yet. The survey had around 80 respondents in more than 30 countries, so should give a reasonable picture of the global open data situation.

Respondents generally agreed on the definition of open data as data released by governments, but there were question marks around the edges. Many saw governments release information only reluctantly, following freedom of information requests, and therefore suggested that open data was more a response to civil society organisations than a government initiative. Some respondents also suggested that data needed to be usable to count as ‘open’.

Many people appeared unaware that there were standards about open data. This may be because of language and translation issues, since this seemed to be a particular problem in Latin America. Several respondents also felt that the term ‘open data’ was a bit dry and boring.

The challenges were, unsurprisingly, politics, access to data, data quality and engagement. Government resistance to data release was much greater with politically sensitive information. Respondents noted that data were often isolated within government departments. There seem to be issues, too, about how data are presented to make them usable and helpful, with particular problems in Africa, where infrastructure and connectivity are lower.

Good news about community-building

On the plus side, most respondents supported the need for global networking, and felt that GODI had a role in building a community around open data, to help organisations to make best use of it. They would also like a place to connect with potential partners. They felt optimistic that an alliance like GODI could influence national and international views and practice on data sharing.

In general, respondents valued GODI’s work in all areas, and felt that it had potential. It does, however, look like there is still a lot of work to do before open data is more than just an ambition. Interested city administrators could well find potential partners through GODI, and also quickly find themselves a leading light on open data.

Image credit: Put a cork in it by Betty

There has been a lot written about how AI will change the world of work. Fewer commentators, however, have set out how it might help governments around the world to achieve more for less. A report by Deloitte on that issue makes some very good points.

The potential savings from even simple AI applications could be huge – Deloitte’s analysis suggests that millions of working hours could be freed up by use of AI technology. The range of possible savings is from 96.7 million to 1.2 billion working hours per year, with financial savings of $3.3 billion up to $41.4 billion. That is a lot of additional services that could be provided or government debt that could be paid off.

Cognitive automation can help to overcome resource constraints – Cognitive automation can perform routine tasks at unprecedented speeds, freeing up workers’ time to concentrate on the more difficult aspects. For example, electronic document discovery systems can track down 95% of documentation for court cases, compared with the 50% that people can manage, and in considerably less time.

AI can also be used to reduce backlogs – Part of the problem with government systems is the sheer number of applications and cases that are received. Many are straightforward, however, and could be processed relatively quickly and easily by AI systems, which could flag the more difficult cases for human caseworkers to examine and process.

Cognitive insights via real-time tracking offer security and public health benefits – AI systems using real-time tracking are being used by the US Centers for Disease Control and Prevention to track polio virus. The AI tool can classify virus type, and separate disease reports into relevant clusters, supporting ongoing disease surveillance, and helping to manage outbreaks. This type of pattern recognition is widely applicable for complex problems.

AI is also being used to improve resource usage in food safety and hygiene inspections – The Southern Nevada Health District has started to use an AI application to analyse Tweets reporting food poisoning. The app uses geotagging and natural language processing to identify reports of food poisoning and any associated restaurants. An experiment in Las Vegas found that food safety inspections targeted using the app were significantly more likely to find problems than randomly targeted inspections.

AI chatbots have been used to improve customer responses via cognitive engagement – The US Army uses an AI chatbot called SGT STAR to interact with visitors to its recruitment website. SGT STAR can answer questions from potential recruits about likely specialisms, benefits and commitment, and direct them to a human recruiter if necessary. This is not a matter of replacing a human with a chatbot, but supplementing the human provision, and creating a partnership.

The biggest improvements come from putting multiple capabilities together – There are individual ‘wins’ by applying AI in several possible areas and ways, but the biggest potential lies in combining capabilities. For example, automation can be used to free up time, and then cognitive insights can be used to identify where best to use that additional resource. The focus needs to be on maximising the flow of tasks.

There are four ways to use AI: to relieve, split up, replace and augment capacity – Relieving uses AI to automate routine work, freeing up worker time to do higher value work. Splitting up involves separating jobs into steps or tasks, and automating as many as possible. Replacing takes whole tasks or jobs and replaces them with a machine. All these are effectively ways to increase efficiency by automation, and most aim to reduce costs.

Augmentation creates genuine human–machine partnerships – Augmentation makes workers more effective, by giving them additional support to complement their skills. This is very much a partnership, which is better than either machine or human alone. It is designed to help to address problems that could not be managed before, such as detecting insider trading on stock exchanges, or improving treatment choices in healthcare.

Potential uses of AI should be carefully assessed before adoption – There are no obvious answers to where and how to adopt AI technology in government. Deloitte’s report suggests that agencies and governments should consider whether each option is viable, valuable and/or vital, and make a decision on that basis. Things can, for example, be viable without being valuable, and those where AI is vital offer the biggest potential benefit. Policy-makers need to use this type of decision-making framework to maximise public value for money.

Cities can use artificial intelligence and analysis of data in a number of ways to help them become smarter, and provide better services for their citizens.

Controlling parking by variable pricing on parking meters. Boston is planning to raise the prices of its parking meters for the first time in more than five years. But it is not just planning a blanket hike. Instead, it will use the changes to test how parking prices affect occupancy of parking bays, and the way that people use cars. San Francisco also plans to implement demand-driven pricing as a way to control congestion.

Creating smart parking systems to direct drivers to spaces, and reduce congestion. One problem in cities is congestion created by drivers driving round and round a district searching for a parking space. Deutsche Telekom is trying to address this via smart parking systems which will alert drivers to empty spaces, and direct them straight there. Systems are being tested in Dubrovnik in Croatia, Bucharest in Romania, and Pisa in Italy.

Intelligent traffic management helps to reduce congestion and air pollution. Sensors are not just useful for parking meters. Pollution sensors on traffic lights and at bus stops can also help identify areas of congestion, and direct traffic away from those. Providing real-time information about hold-ups and traffic jams to drivers, together with suggestions for alternative routes, helps traffic to avoid those areas. This, in turn, reduces congestion, emissions, and air pollution in busy areas.

Vehicle-to-vehicle communications offer increased potential to avoid congestion. Smart traffic management can also involve direct vehicle-to-vehicle communications. Vehicles including cars and buses can be fitted with RFID tags that can communicate with each other. Information about traffic jams can therefore be passed on ‘from the horse’s mouth’, almost literally, helping drivers to avoid problem areas.

Improving the use of energy by predicting spikes in demand. Artificial intelligence systems can learn from experience, which means that they can analyse past patterns in data, and use them to predict the future, and therefore manage better. This is particularly useful in managing power grids, because the system can learn when spikes in demand are more likely to occur, and enable better use of the power at times of lower demand.

Variable energy pricing could encourage better use of energy by individuals. Managing energy by predicting spikes in demand is one thing. Encouraging people to use energy at particular times is quite another. Artificial intelligence systems can be used to propose variable pricing models to encourage use at low-demand times. These build on, for example, the UK’s Economy 7 tariff, a good example of a tariff that encouraged night-time use of energy for heating.

Improving awareness of patterns in data, to create new theses and models. Bringing together data from different areas and about different issues can create new insights: it is one of the most important benefits of big data and analytics. These insights, in turn, can help smart cities to create new models about how their citizens behave, and therefore improve the way in which they provide services. Insights might be about ‘known’ and ‘unknown’ issues, leading to very different ways of working.

Helping individuals to make better choices. Cities are all about their populations, and populations are made up of individuals. Helping individuals to make better choices—that is, both better for them, and for the community or population—can therefore change the environment, one person at a time. Big data, coupled with good data visualisation technology, can be used to show citizens the consequences of their decisions quickly and simply, and help them to improve their decision-making for everyone’s good.

Encouraging healthy eating among children. Healthy eating may not sound like a smart city initiative. But a healthy population has fewer health problems, resulting in less demand for healthcare and infrastructure. And with the demands on services from aging populations, this is no light matter. The city of Amsterdam, for example, has used data from grocery stores about sales of vegetables to evaluate a city campaign to encourage healthy eating.

Supporting better land use planning around urban areas. Demand for housing is growing exponentially, according to many government models. Data about demographics, income levels, businesses and general land use within an area can help planning officers to ensure that urban brownfield land is used effectively to meet the needs of the population in the area, rather than just to suit developers.

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