Granular computing as a tool of human data interaction - a cognitive cities use case

The article proposes granular computing as a theoretical, formal and methodological basis for the newly emerging research field of human–data interaction (HDI). We argue that the ability to represent and reason with information granules is a prerequisite for data legibility. As such, it allows for extending the research agenda of HDI to encompass the topic of collective intelligence amplification, which is seen as an opportunity of today’s increasingly pervasive computing environments. As an example of collective intelligence amplification in HDI, we introduce a collaborative urban planning use case in a cognitive city environment and show how an iterative process of user input and human-oriented automated data processing can support collective decision making. As a basis for automated human-oriented data processing, we use the spatial granular calculus of granular geometry.

Slow Cities: Sustainable Places in a Fast World

This article examines the Slow Food and Slow City movement as an alternative approach to urban development that focuses on local resources, economic and cultural strengths, and the unique historical context of a town. Following recent discussions about the politics of alternative economic development, the study examines the Slow City movement as a strategy to address the interdependencies between goals for economic, environmental, and equitable urban development. In particular, we draw on the examples of two Slow Cities in Germany—Waldkirch and Hersbruck, and show how these towns are retooling their urban policies. The study is placed in the context of alternative urban development agendas as opposed to corporate-centered development. We conclude the article by offering some remarks about the institutional and political attributes of successful Slow Cities and the transferability of the concept.

Psychological predictors of eco-driving: A longitudinal study

Eco-driving has well-known positive effects on fuel economy and greenhouse-gas emissions. Moreover, eco-driving reduces road-traffic noise, which is a serious threat to the health and well-being of many people. We investigated the psychological predictors of the adoption of eco-driving from the perspective of road-traffic noise abatement. The data came from 890 car drivers who participated in a longitudinal survey over four months. Specifically, we tested the effects of the intention to prevent road-traffic noise, variables derived from the theory of planned behavior (social norm, perceived behavioral control, and attitude), and variables derived from the health action process approach (implementation intention, maintenance self-efficacy, and action control) on the intention to practice eco-driving and on eco-driving behavior. The intention to prevent road-traffic noise was not linked to the intention to practice eco-driving. The strongest predictors of the intention to practice eco-driving were attitude and perceived behavioral control. The strongest predictor of eco-driving behavior was action control. The link between behavioral intention and behavior was weak, indicating that drivers have difficulties putting their intention to practice eco-driving into action. Therefore, intervention efforts should directly address and support the transition from intention to behavior. This could be accomplished by providing reminders, which help to maintain behavioral intention, and by providing behavior feedback, which helps car drivers to monitor their behavior.

Promoting a low-noise driving style: An eco-driving intervention study

Road-traffic noise impairs the well-being and health of many people. Motivating car drivers to voluntarily adopt a low-noise driving style (i.e., eco-driving) contributes to the reduction of road-traffic noise, complementary to requirements, bans, and laws. In a field study with employees of a municipality (N = 88), we investigated the effects of an intervention on car drivers’ motivation to prevent road-traffic noise, motivation to practice eco-driving, and driving behavior. The intervention consisted of a leaflet intended to enhance participants’ motivation, a practical eco-driving course, and weekly driving-performance feedbacks. We used a switching-replications design with two intervention groups. In both groups, eco-driving behavior was significantly strengthened by the intervention. The effects on the motivational variables were significant in only one of the groups (however, it should be noted that the average motivation was already relatively high before the intervention). For one of the groups, the study design allowed testing for the effects at an additional follow-up assessment (4 months after the intervention). The results showed that the intervention effect on driving behavior held across this period. The findings of the present research suggest that it is possible to improve car driver’s behavior with regard to a low-noise driving style.

A Dynamic Route Planning Prototype for Cognitive Cities

A software prototype for dynamic route planning in the travel industry for cognitive cities is presented in this paper. In contrast to existing tools, the prototype enhances the travel experience (i.e., sightseeing) by allowing additional flexibility to the user. The theoretical background of the paper strengthens the understanding of the introduced concepts (e.g., cognitive cities, fuzzy logic, graph databases) to comprehend the presented prototype. The prototype applies an instantiation and enhancement of the graph database Neo4j . For didactical reasons and to strengthen the understanding of this prototype a scenario, applied to route planning in the city of Bern (Switzerland) is shown in the paper.

Cognitive Cities: An Application for Nairobi. Text Message Participation of Slum Inhabitants to Improve Sanitary Facilities

Population growth is always increasing, and thus the concept of smart and cognitive cities is becoming more important. Developed countries are aware of and working towards needed changes in city management. However, emerging countries require the optimization of their own city management. This chapter illustrates, based on a use case, how a city in an emerging country can quickly progress using the concept of smart and cognitive cities. Nairobi, the capital of Kenya, is chosen for the test case. More than half of the population of Nairobi lives in slums with poor sanitation, and many slum inhabitants often share a single toilet, so the proper functioning and reliable maintenance of toilets are crucial. For this purpose, an approach for processing text messages based on cognitive computing (using soft computing methods) is introduced. Slum inhabitants can inform the responsible center via text messages in cases when toilets are not functioning properly. Through cognitive computer systems, the responsible center can fix the problem in a quick and efficient way by sending repair workers to the area. Focusing on the slum of Kibera, an easy-to-handle approach for slum inhabitants is presented, which can make the city more efficient, sustainable and resilient (i.e., cognitive).

Synchronizing Mind Maps with Fuzzy Cognitive Maps for Decision-Finding in Cognitive Cities

Synchronizing mind maps with fuzzy cognitive maps can help to handle complex problems with many involved stakeholders by taking advantage of human creativity. The proposed approach has the capacity to instantiate cognitive cities by including cognitive computing. A use case in the context of decision-finding (concerning a transportation system) is presented to illustrate the approach.

Experimental Evidence of an Eco-evolutionary Feedback during Adaptive Divergence

Differences in how organisms modify their environment can evolve rapidly and might influence adaptive population divergence [1, 2]. In a common garden experiment in aquatic mesocosms, we found that adult stickleback from a recently diverged pair of lake and stream populations had contrasting effects on ecosystem metrics. These modifications were caused by both genetic and plastic differences between populations and were sometimes comparable in magnitude to those caused by the presence/ absence of stickleback. Lake and streamfish differentially affected the biomass of zooplankton and phytoplankton, the concentration of phosphorus, and the abundance of several prey (e.g., copepods) and non-prey (e.g., cyanobacteria) species. The adult mediated effects on mesocosm ecosystems influenced the survival and growth of a subsequent generation of juvenile stickleback reared in the same mesocosms. The prior presence of adults decreased the overall growth rate of juveniles, and the prior presence of stream adults lowered overall juvenile survival. Among the survivors, lake juveniles grew faster than co-occurring stream juveniles, except in mesocosm ecosystems previously modified by adult lake fish that were reared on plankton. Overall, our results provide evidence for reciprocal interactions between ecosystem dynamics and evolutionary change (i.e., eco-evolutionary feedbacks) in the early stages of adaptive population divergence.

Personal Digital Assistant 2.0 - A Software Prototype for Cognitive Cities

This paper presents a software prototype of a personal digital assistant 2.0. Based on soft computing methods and cognitive computing this mobile application prototype improves calendar and mobility management in cognitive cities. Applying fuzzy cognitive maps and evolutionary algorithms, the prototype represents a next step towards the realization of cognitive cities (i.e., smart cities enhanced with cognition). A user scenario and a test version of the prototype are included for didactical reasons.

Effects, but no interactions, of ubiquitous pesticide and parasite stressors on honey bee (Apis mellifera) lifespan and behaviour in a colony environment

Interactions between pesticides and parasites are believed to be responsible for increased mortality of honey bee (Apis mellifera) colonies in the northern hemisphere. Previous efforts have employed experimental approaches using small groups under laboratory conditions to investigate influence of these stressors on honey bee physiology and behaviour, although both the colony level and field conditions play a key role for eusocial honey bees. Here, we challenged honey bee workers under in vivo colony conditions with sublethal doses of the neonicotinoid thiacloprid, the miticide tau-fluvalinate and the endoparasite Nosema ceranae, to investigate potential effects on longevity and behaviour using observation hives. In contrast to previous laboratory studies, our results do not suggest interactions among stressors, but rather lone effects of pesticides and the parasite on mortality and behaviour, respectively. These effects appear to be weak due to different outcomes at the two study sites, thereby suggesting that the role of thiacloprid, tau-fluvalinate and N. ceranae and interactions among them may have been overemphasized. In the future, investigations into the effects of honey bee stressors should prioritize the use of colonies maintained under a variety of environmental conditions in order to obtain more biologically relevant data.

Granular computing as a basis of human–data interaction: a cognitive cities use case

The article proposes granular computing as a theoretical, formal and methodological basis for the newly emerging research field of human–data interaction (HDI). We argue that the ability to represent and reason with information granules is a prerequisite for data legibility. As such, it allows for extending the research agenda of HDI to encompass the topic of collective intelligence amplification, which is seen as an opportunity of today’s increasingly pervasive computing environments. As an example of collective intelligence amplification in HDI, we introduce a collaborative urban planning use case in a cognitive city environment and show how an iterative process of user input and human-oriented automated data processing can support collective decision making. As a basis for automated human-oriented data processing, we use the spatial granular calculus of granular geometry.