Transforming cities into low-carbon, resilient, and sustainable places will require action encompassing most segments of society. However, local governments struggle to overview and assess all ongoing climate activities in a city, constraining well-informed decision-making and transformative capacity. This paper proposes and tests an assessment framework developed to visualize the implementation of urban climate transition (UCT). Integrating key transition activities and process progression, the framework was applied to three Swedish cities. Climate coordinators and municipal councillors evaluated the visual UCT representations. Results indicate that their understanding of UCT actions and implementation bottlenecks became clearer, making transition more governable. To facilitate UCT, involving external actors and shifting priorities between areas were found to be key. The visual UCT representations improved system awareness and memory, building local transformative capacity. The study recommends systematic assessment and visualization of process progression as a promising method to facilitate UCT governance, but potentially also broader sustainability transitions. Electronic supplementary material The online version of this article (10.1007/s13280-018-1109-9) contains supplementary material, which is available to authorized users.

Figure 1: A schematic representation of the work flow. A sketched pattern of interest is matched to the time-series data. Efficient approximation, classification and symbol assignment, based on gradient ratios, enables real-time pattern searching within very large time-series. The steps depicted with green boxes are executed when a new input time series is loaded or when it undergoes hierarchical approximation, yellow boxes only when a new sketch is entered. ABSTRACTLong time-series, involving thousands or even millions of time steps, are common in many application domains but remain very difficult to explore interactively. Often the analytical task in such data is to identify specific patterns, but this is a very complex and computationally difficult problem and so focusing the search in order to only identify interesting patterns is a common solution. We propose an efficient method for exploring user-sketched patterns, incorporating the domain expert's knowledge, in time series data through a shape grammar based approach. The shape grammar is extracted from the time series by considering the data as a combination of basic elementary shapes positioned across different amplitudes. We represent these basic shapes using a ratio value, perform binning on ratio values and apply a symbolic approximation. Our proposed method for pattern matching is amplitude-, scale-and translation-invariant and, since the pattern search and pattern constraint relaxation happen at the symbolic level, is very efficient permitting its use in a real-time/online system. We demonstrate the effectiveness of our method in a case study on stock market data although it is applicable to any numeric time series data.

Eye-tracking has become an invaluable tool for the analysis of working practices in many technological fields of activity. Typically studies focus on short tasks and use static expected areas of interest (AoI) in the display to explore subjects' behaviour, making the analyst's task quite straightforward. In long-duration studies, where the observations may last several hours over a complete work session, the AoIs may change over time in response to altering workload, emergencies or other variables making the analysis more difficult. This work puts forward a novel method to automatically identify spatial AoIs changing over time through a combination of clustering and cluster merging in the temporal domain. A visual analysis system based on the proposed methods is also presented. Finally, we illustrate our approach within the domain of air traffic control, a complex task sensitive to prevailing conditions over long durations, though it is applicable to other domains such as monitoring of complex systems.

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