Creating sustainability through Smart City Projects

Smart Cities are a key mechanism for facilitating sustainability – be that in the use of resources (e.g. energy, water), the running of city infrastructure (e.g. transport) or in terms of social policy (e.g. politics). Using our experience of a Smart City project, MK:Smart, we describe what role citizen-led innovation could have in promoting long-term sustainable change. Beyond this we detail some of the barriers to success we have identified in the hope that design patterns might help us address these challenges

Visualising energy: teaching data literacy in schools

As data sets become increasingly complex and pervasive, the importance of citizens to achieve a certain level of data literacy is more important. Citizens need to understand not only how they are contributing data but how this is being used. Data literate citizens have more opportunities for understanding cities through data and informing data driven urban innovations. Current practices around teaching data in schools still focus on using small, personally collected datasets and in teaching graph or chart based visualization. This is a long way away from the types of data and visualisations that are increasingly encountered in daily life. This paper proposes to teach data literacy in schools. Of particular interest in this paper is the idea to engage students with complex data sets to get them thinking about how to produce novel visualisations of this data. Examples are given in which a class of Year 7 and Year 9 students in the U.K. are tasked with creating visualisations of data related to their home energy consumption

Participatory Data Analysis: A New Method for Investigating Human Energy Practices

This paper presents a novel data-driven method to investigate the interdependence between technology design and human energy practices. The method – called Participatory Data – makes use of fine-grained energy data collected via smart meters and smart plugs, and behaviour visualisation during home visits to spark self-reflection among householders

Collective intelligence for community energy initiatives.

In this paper we present an approach aimed at overcoming barriers to the success of community energy initiatives in urban areas. The proposed methods will support communities in identifying and adopting community energy solutions by connecting citizens with a collection of relevant datasets including satellite, energy and socio-economic data. Citizens will be provided with the ability to explore the data and with advanced urban data analytics methods to identify key aspects and potential areas where initiatives could be successful. Communities will be supported with advice and expertise and in identifying dedicated and enthusiastic participants. To meet all these requirements is a difficult task, but the benefits include: reduction of carbon emissions, generation of savings for communities, reduction of fuel poverty and creation of local jobs. The concept of urban area is a key one in this approach, because energy efficient cities will only be possible through a broad-based consensus between science, politics, industry and citizens. Traditionally citizens have played a passive role in the energy market, but now they have the opportunity of being involved in generation, reduction of consumption and management and purchasing of energy, becoming new actors in the market and using collective intelligence for the common good

The Role of Data Literacy within a MOOC Analysis

This paper discusses the role of data literacy in the planning of analysis of data from a six week Smart Cities MOOC delivered on the FutureLearn platform. The aim of the analysis was to discover whether the MOOC had met the aims of engaging participants with topics related to smart cities and to evaluate social interactions and understanding of the key concepts through analysis of MOOC comments. The paper identifies where data literacy impacts on decisions made, such as the need to include both domain and data expertise in the analysis, whether this is provided by a single person or by a team. It also identifies a need for better tools for rapid protoyping of methods for analysing large data sets particularly of non-standard data, such as natural language data. This would be of benefit in cases where the analysis will be used just a few times for a specific purpose, such as analysing the MOOC data across several presentations

Urban Data in the primary classroom: bringing data literacy to the UK curriculum

As data becomes established as part of everyday life, the ability for the average citizen to have some level of data literacy is increasingly important. This paper describes an approach to teaching data skills in schools using real life, complex, urban data sets collected as part of a smart city project. The approach is founded on the premise that young learners have the ability to work with complex data sets if they are supported in the right way and if the tasks are grounded in a real life context. Narrative principles are used to frame the task, to assist interpretation and tell stories from data and to structure queries of datasets. An inquiry-based methodology organises the activities. This paper describes the initial trial in a UK primary school in which twelve students aged 9-10 years learnt about home energy consumption and the generation of solar energy from home solar PV, by interpreting existing visualisations of smart meter data and data obtained from aerial survey. Additional trials are scheduled with older learners which will evaluate learners on more challenging data handling tasks. The trials are informing the development of the Urban Data School, a web-based platform designed to support teaching data skills in schools in order to improve data literacy among school leavers

Towards smart city education

Sustainability has been an important topic in UK schools for some time, most notably since the Sustainable School Strategy was proposed by the UK Department for Education (DFES) in 2006. However, as smart city technologies emerge and show real promise in contributing to a more sustainable future, it is becoming apparent that new skills for working with the big urban data sets that drive these innovations must be taught to upcoming generations to ensure that they can be active smart city citizens. Current practice within schools is to distribute teaching of different aspects of data skills across the curriculum. We ask the question how can data skills be taught using a more unified and practical approach, which facilitates application of skills in genuine, smart city contexts. We propose to use Urban Data Games to set a context for learning, and demonstrating, practical application of skills for handling large complex data sets. This paper focuses on an Appathon challenge, which will shortly be trialled in a Milton Keynes school, in which participants are tasked to design a novel App from real satellite data after first learning and applying data skills to data about home energy consumption

Data Literacy to Support Human-centred Machine Learning

In the past, machine learning applications were mostly developed and deployed in specialist situations where the outputs would be either read by experts, or else interpreted for the public, with the methods hidden from view. In the current data driven society, the general public are increasingly interacting with complex data sets and the outputs of machine learning technologies. Within the domain of the smart city, non-experts are also being brought closer to the design process itself. This paper explores whether improving the overall data literacy of a society can instill within that society a set of core competences that improve the capacity of non-experts in machine learning to engage with machine learning outputs in a more knowledgeable way, or to provide insight and differing perspectives into the design of machine learning applications