Search

Due to increasing urbanization, nowadays, cities are facing challenges spanning multiple domains such as mobility, energy, environment, etc. For example, to reduce traffic congestion, energy consumption, and excessive pollution, big data gathered from legacy systems (e.g., sensors not conformant with modern standards), geographic information systems, gateways of public administrations, and Internet of Things technologies can be exploited to provide insights to assess the current status of a city. Moreover, the possibility to perform what-if analyses is fundamental to analyzing the impact of possible changes in the urban environment. The few available solutions for scenario definitions and analyses are limited to addressing a single domain and providing proprietary formats and tools, with scarce flexibility. Therefore, in this paper, we present a novel scenario model and editor integrated into the open-source Snap4City.org platform to enable several processing and what-if analyses in multiple domains. Different from state-of-the-art software, the proposed solution responds to a series of identified requirements, implements NGSIv2-compliant data models with formal descriptions of the urban context, and a scenario versioning method. Moreover, it allows us to carry out analyses on different domains, as shown with some examples. As a case study, a traffic congestion analysis is provided, confirming the validity and usefulness of the proposed solution. This work was developed in the context of CN MOST, the National Center on Sustainable Mobility in Italy, and for the Tourismo EC project.

The Internet of things has produced several heterogeneous devices and data models for sensors/actuators, physical and virtual. Corresponding data must be aggregated and their models have to be put in relationships with the general knowledge to make them immediately usable by visual analytics tools, APIs, and other devices. In this paper, models and tools for data ingestion and regularization are presented to simplify and enable the automated visual representation of corresponding data. The addressed problems are related to the (i) regularization of the high heterogeneity of data that are available in the IoT devices (physical or virtual) and KPIs (key performance indicators), thus allowing such data in elements of hypercubes to be reported, and (ii) the possibility of providing final users with an index on views and data structures that can be directly exploited by graphical widgets of visual analytics tools, according to different operators. The solution analyzes the loaded data to extract and generate the IoT device model, as well as to create the instances of the device and generate eventual time series. The whole process allows data for visual analytics and dashboarding to be prepared in a few clicks. The proposed IoT device model is compliant with FIWARE NGSI and is supported by a formal definition of data characterization in terms of value type, value unit, and data type. The resulting data model has been enforced into the Snap4City dashboard wizard and tool, which is a GDPR-compliant multitenant architecture. The solution has been developed and validated by considering six different pilots in Europe for collecting big data to monitor and reason people flows and tourism with the aim of improving quality of service; it has been developed in the context of the HERIT-DATA Interreg project and on top of Snap4City infrastructure and tools. The model turned out to be capable of meeting all the requirements of HERIT-DATA, while some of the visual representation tools still need to be updated and furtherly developed to add a few features.

Purpose: The purpose of this paper is to explore employee perceptions of communication in psychologically safe and unsafe clinical care environments., Design/methodology/approach: Clinical providers at the USA Veterans Health Administration were interviewed as part of planning organizational interventions. They discussed strengths, weaknesses, and desired changes in their workplaces. A subset of respondents also discussed workplace psychological safety (i.e. employee perceptions of being able to speak up or report errors without retaliation or ostracism--Edmondson, 1999). Two trained coders analysed the interview data using a grounded theory-based method. They excerpted passages that discussed job-related communication and summarized specific themes. Subsequent analyses compared frequencies of themes across workgroups defined as having psychologically safe vs unsafe climate based upon an independently administered employee survey., Findings: Perceptions of work-related communication differed across clinical provider groups with high vs low psychological safety. The differences in frequencies of communication-related themes across the compared groups matched the expected pattern of problem-laden communication characterizing psychologically unsafe workplaces., Originality/value: Previous research implied the existence of a connection between communication and psychological safety whereas this study offers substantive evidence of it. The paper summarized the differences in perceptions of communication in high vs low psychological safety environments drawing from qualitative data that reflected clinical providers' direct experience on the job. The paper also illustrated the conclusions with multiple specific examples. The findings are informative to health care providers seeking to improve communication within care delivery teams.