Gamification has been successfully applied in many domains, but mostly for simple, isolated and operational tasks. The hope for gamification as a method to radically change and improve behavior, to provide incentives for sustained engagement has proven to be more difficult to get right. Applying gamification in large networked organizations with heterogeneous tasks remains a challenge. Applying gamification in such enterprise environments posits different requirements, and a match between these requirements and the institution needs to be investigated before venturing into the design and implementation of gamification. The current paper contributes a study where the authors investigate the feasibility of implementing gamification in Trafikverket, the Swedish transport administration. Through an investigation of the institutional arrangements around data collection, procurement processes and links to institutional structures, the study finds areas within Trafikverket where gamification could be successfully applied, and suggests gaps and methods to apply gamification in other areas.

Decision making and planning of large scale infrastructures within cities is often a long process encompassing years, between multiple institutions represented by experts that require negotiations and consensus of demands and goals. The role big data plays in such design could be crucial, by providing access to otherwise elusive information on movements of people and goods in a city which can then transparently inform the design process, especially about possible demands and related complexities on the infrastructure being planned. To harness this data, it is necessary to formulate the problem technically such that data can inform experts, by articulating their expertise through the data. In this paper we present an application to analyze millions of instances of spatial data to identify potential locations for electrical road installation(s) in a city, to aid urban planners and other relevant stakeholders in planning and designing an Electrical Road System for a city. The dataset being used is gathered from a major vehicle manufacturer in Sweden, containing millions of instances of GPS data emitted by thousands of vehicles. A plan for electrified transport system is formulated by retrieving locations suitable for both static and dynamic charging installations. We investigate the technical formulation of methods and metrics for such a complex design problem, based on criteria set by experts, thus contributing to the science of big data for design of infrastructure and to methodology of data science in an institutional context.

The increasing pervasiveness of location-aware technologies is leading to the rise of large, spatio-temporal datasets and to the opportunity of discovering usable knowledge about the behaviors of people and objects. Applied extensively in transportation, spatial big data and its analytics can deliver useful insights on a number of different issues such as congestion, delays, public transport reliability and so on. Predominantly studied for its use in operational management, spatial big data can be used to provide insight in strategic applications as well, from planning and design to evaluation and management. Such large scale, streaming spatial big data can be used in the improvement of public transport, for example the design of public transport networks and reliability. In this paper, we analyze GTFS data from the cities of Stockholm and Rome to gain insight on the sources and factors influencing public transport delays in the cities. The analysis is performed on a combination of GTFS data with data from other sources. The paper points to key issues in the analysis of real time data, driven by the contextual setting in the two cities.

The advent of ‘big data’ already enables a wide range of conveniences to citizens. However, the dominant utilization of this data for systematic improvement is geared towards operations such as informing on real-time events in cities. The impact of big data on the long-term planning and design purposes in cities is still unclear. This chapter presents an application of big data where locations, suitable for deploying charging infrastructure for vehicles, are mined. We conducted an experiment to observe the impact of this information on designs of Electrical Road Systems (ERS). Results prove that insights mined from big data outside the design process do influence the designing process and the resulting designs. Therefore it seems promising to further explore this influence on the quality of designing.

Along with the increase of digital healthcare providers, theinterest in diagnostic aids for remote diagnosis has increasedas well. As patients write about their symptoms themselves,we have access to a type of data which previously was rarelyrecorded, and which has not been filtered by a healthcareprofessional. Knowledge of similar patients and similarsymptoms is beneficial for doctors to arrive at a diagnosis.Therefore, the remote diagnostic process could be aided bypresenting patient cases together with information aboutsimilar patients and their self-reported symptom descriptions.Apart from online diagnosis, such an aid could bebeneficial in many healthcare settings, such as long-distancevisits and knowledge gain from patient diaries.In this paper, we present the impact of aiding remote diagnosisby presenting clusters of similar symptoms, usingsymptom descriptions collected from a virtual visit applicationby the Swedish telemedicine provider KRY. Symptomdescriptions were represented using the bag-of-words modeland were then clustered using the k-means algorithm. Anexperiment was then conducted with 13 doctors, where patientcases were presented together with the most representativewords of the associated cluster, to measure howtheir work was impacted. Results indicated that it was usefulin more complicated cases, but also that future experimentswill require further instructions on how the information is tobe interpreted.

Designing future systems such as transport or healthcare in a city takes astute expertise. Design aids in such situations usually offer information in the form of projections or what-if analysis, using which experts make a series of decisions to create bounded designs. We present a case in which Reinforcement Learning (RL) is used to design the future transport system of a city. RL is used to create artifacts that reflect where the transport system can be changed. These agent-produced artifacts are then compared with designs made by human experts. This is achieved by analogizing the city as gridworld and using the same information that the human experts acted on as rewards. The interpretation of agent activity as cognitive artifacts of agents, along with measures of precision and recall to compare real and artificial artifacts form the basis of this work. This paper explores the use of RL in a real world context and the interpretability of results of RL with respect to design problems. The results indicate a robust initial approach to imitating expertise of designers and devising valid creativity in Socio-Technical Systems.

The complexity of (re)design of large-scale systems in a metropolitan context is challenging its designers and their design methods. Data science has the potential to influence the designing process, but the question is to what extent and in what way. In this paper, we present an approach with reinforcement learning to imitate design cognition in a use case on Electric Road Systems, by acting on information produced by data science. This takes place in a loose game that is an abstract representation of the real system, thus artificially producing knowledge. We further discuss the episteme of that knowledge produced by the artificial.