Background: Advancements in imaging technology, alongside increasing longevity and co-morbidities, have led to heightened demand for diagnostic radiology services. However, there is a shortfall in radiology and radiography staff to acquire, read and report on such imaging examinations. Artificial intelligence (AI) has been identified, notably by AI developers, as a potential solution to impact positively the workload of radiology services for diagnostics to address this staffing shortfall. Methods: A rapid review complemented with data from interviews with UK radiology service stakeholders was undertaken. ArXiv, Cochrane Library, Embase, Medline and Scopus databases were searched for publications in English published between 2007 and 2022. Following screening 110 full texts were included. Interviews with 15 radiology service managers, clinicians and academics were carried out between May and September 2022. Results: Most literature was published in 2021 and 2022 with a distinct focus on AI for diagnostics of lung and chest disease (n = 25) notably COVID-19 and respiratory system cancers, closely followed by AI for breast screening (n = 23). AI contribution to streamline the workload of radiology services was categorised as autonomous, augmentative and assistive contributions. However, percentage estimates, of workload reduction, varied considerably with the most significant reduction identified in national screening programmes. AI was also recognised as aiding radiology services through providing second opinion, assisting in prioritisation of images for reading and improved quantification in diagnostics. Stakeholders saw AI as having the potential to remove some of the laborious work and contribute service resilience. Conclusions: This review has shown there is limited data on real-world experiences from radiology services for the implementation of AI in clinical production. Autonomous, augmentative and assistive AI can, as noted in the article, decrease workload and aid reading and reporting, however the governance surrounding these advancements lags. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This paper is based on commissioned research undertaken by the Workforce Observatory, that was funded by the West Yorkshire Integrated on behalf of the West Yorkshire Health and Care Partnership. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Research Ethics Committee of the University of Bradford (reference: EC27051) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present work are contained in the manuscript.

Visualization artifacts have long served as anchors for collaboration and knowledge transfer in data analysis. While effective for human-human collaboration, little is known about their role in capturing and externalizing knowledge when working with large language models (LLMs). Despite the growing role of LLMs in analytics, their linear text-based workflows limit the ability to structure artifacts into useful and traceable representations of the analytical process. We argue that dynamic visual representations of evolving analysis-organizing artifacts and provenance into semantic structures, such as idea development and shifts in inquiry-are critical for effective human-LLM workflows. We demonstrate the current opportunities and limitations of using LLMs to track, structure, and visualize analytic processes, and propose a research agenda to leverage rapid advances in LLM capabilities. Our goal is to present a compelling argument for maximizing the role of visualization as a catalyst for more structured, transparent, and insightful human-LLM analytical interactions.

Background Healthcare staff deliver patient care in emotionally charged settings and experience a wide range of emotions as part of their work. These emotions and emotional contexts can impact the quality and safety of care. Despite the growing acknowledgement of the important role of emotion, we know very little about what triggers emotion within healthcare environments or the impact this has on patient safety.Objective To systematically review studies to explore the workplace triggers of emotions within the healthcare environment, the emotions experienced in response to these triggers, and the impact of triggers and emotions on patient safety.Methods Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, four electronic databases were searched (MEDLINE, PsychInfo, Scopus, and CINAHL) to identify relevant literature. Studies were then selected and data synthesized in two stages. A quality assessment of the included studies at stage 2 was undertaken.Results In stage 1, 90 studies were included from which seven categories of triggers of emotions in the healthcare work environment were identified, namely: patient and family factors, patient safety events and their repercussions, workplace toxicity, traumatic events, work overload, team working and lack of supervisory support. Specific emotions experienced in response to these triggers (e.g., frustration, guilt, anxiety) were then categorised into four types: immediate, feeling states, reflective, and longer-term emotional sequelae. In stage 2, 13 studies that explored the impact of triggers or emotions on patient safety processes/outcomes were included.Conclusion The various triggers of emotion and the types of emotion experienced that have been identified in this review can be used as a framework for further work examining the role of emotion in patient safety. The findings from this review suggest that certain types of emotions (including fear, anger, and guilt) were more frequently experienced in response to particular categories of triggers and that healthcare staff's experiences of negative emotions can have negative effects on patient care, and ultimately, patient safety. This provides a basis for developing and tailoring strategies, interventions, and support mechanisms for dealing with and regulating emotions in the healthcare work environment.

The disconnect between insights generated from data and real-life practices of decision makers presents a number of open questions for visual analytics (VA). In public service planning, routine data are often perceived as unavailable, biased, incomplete and inconsistent across services. Decision makers often rely on qualitative data - sometimes collected through co-production - to understand the lived experience of communities before formulating a decision. We followed a subjectivist case study approach and immersed ourselves in ongoing co-production activities over the course of one year, to capture how VA can support the dialogue between population health decision-makers and the communities they serve. We present a framework for Connecting Lived Experiences with Visualisation of Electronic Records (CLEVER). The framework regards visualisation as a central component in a complex adaptive decision-making ecosystem and highlights the need to structure domain knowledge across decision contexts in Population Health Management (PHM) at clinical-, service- and district-levels. Our process for developing an initial framework comprised three steps: (i) we elicited decision-making tasks through a series of qualitative data collection activities; (ii) we developed a preliminary domain model to capture data views and a subjective view of the world through human stories; and (iii) we developed a series of visualisation prototypes to instantiate the framework and demonstrated them regularly to stakeholders. In future work, we will conduct 'deep dives' to systematically study the role of VA in individual stages of the framework.

Effective strategic workforce planning for integrated and co-ordinated health and social care is essential if future services are to be resourced such that skill mix, clinical practice and productivity meet population health and social care needs in timely, safe and accessible ways globally.This review presents international literature to illustrate how strategic workforce planning in health and social care has been undertaken around the world with examples of planning frameworks, models and modelling approaches.The databases Business Source Premier, CINAHL, Embase, Health Management Information Consortium, Medline and Scopus were searched for full texts, from 2005 to 2022, detailing empirical research, models or methodologies to explain how strategic workforce planning (with at least a one-year horizon) in health and/or social care has been undertaken, yielding ultimately 101 included references.The supply/demand of a differentiated medical workforce was discussed in 25 references. Nursing and midwifery were characterised as undifferentiated labour, requiring urgent growth to meet demand. Unregistered workers were poorly represented as was the social care workforce. One reference considered planning for health and social care workers. Workforce modelling was illustrated in 66 references with predilection for quantifiable projections. Increasingly needs-based approaches were called for to better consider demography and epidemi-ological impacts.This review's findings advocate for whole-system needs-based approaches that consider the ecology of a co -produced health and social care workforce.

The role of non-formal education in increasing female participation in Computer Science (CS) is a hot topic. Short-term interventions, including programming skill outreach activities, have been reported to increase self-efficacy and willingness to pursue computing careers in young women. We explored the impact of a programming outreach activity on three types of measures for 30 female pupils: computing self-efficacy, social participation, and understanding of basic computing concepts. Preliminary results revealed a significant increase in participants' self-efficacy and sense of belonging in computing after the informal learning activity. Students were more focused on tasks when engaging socially with their peers and teachers. A decrease in misconception was observed in uni-structural knowledge but no significant difference was found in multi-structural computing knowledge acquisition. These data provide a baseline for study of the long term impact of outreach activities.

The richness of linked population data provides exciting opportunities to understand local health needs, identify and predict those in most need of support and evaluate health interventions. There has been extensive investment to unlock the potential of clinical data for health research in the UK. However, most of the determinants of our health are social, economic, education, environmental, housing, food systems and are influenced by local authorities. The Connected Bradford Whole System Data Linkage Accelerator was set up to link health, education, social care, environmental and other local government data to drive learning health systems, prevention and population health management. Data spanning a period of over forty years has been linked for 800,000 individuals using the pseudonymised NHS number and other data variables. This prospective data collection captures near real time activity. This paper describes the dataset and our Connected Bradford Whole System Data Accelerator Framework that covers public engagement; practitioner and policy integration; legal and ethical approvals; information governance; technicalities of data linkage; data curation and guardianship; data validity and visualisation.

The richness of linked population data provides exciting opportunities to understand local health needs, identify and predict those in most need of support and evaluate health interventions. There has been extensive investment to unlock the potential of clinical data for health research in the UK. However, most of the determinants of our health are social, economic, education, environmental, housing, food systems and are influenced by local authorities. The Connected Bradford Whole System Data Linkage Accelerator was set up to link health, education, social care, environmental and other local government data to drive learning health systems, prevention and population health management. Data spanning a period of over forty years has been linked for 800,000 individuals using the pseudonymised NHS number and other data variables. This prospective data collection captures near real time activity. This paper describes the dataset and our Connected Bradford Whole System Data Accelerator Framework that covers public engagement; practitioner and policy integration; legal and ethical approvals; information governance; technicalities of data linkage; data curation and guardianship; data validity and visualisation.

Background: National audits aim to reduce variations in quality by stimulating quality improvement. However, varying provider engagement with audit data means that this is not being realised. Aim: The aim of the study was to develop and evaluate a quality dashboard (i.e. QualDash) to support clinical teams’ and managers’ use of national audit data. Design: The study was a realist evaluation and biography of artefacts study. Setting: The study involved five NHS acute trusts. Methods and results: In phase 1, we developed a theory of national audits through interviews. Data use was supported by data access, audit staff skilled to produce data visualisations, data timeliness and quality, and the importance of perceived metrics. Data were mainly used by clinical teams. Organisational-level staff questioned the legitimacy of national audits. In phase 2, QualDash was co-designed and the QualDash theory was developed. QualDash provides interactive customisable visualisations to enable the exploration of relationships between variables. Locating QualDash on site servers gave users control of data upload frequency. In phase 3, we developed an adoption strategy through focus groups. ‘Champions’, awareness-raising through e-bulletins and demonstrations, and quick reference tools were agreed. In phase 4, we tested the QualDash theory using a mixed-methods evaluation. Constraints on use were metric configurations that did not match users’ expectations, affecting champions’ willingness to promote QualDash, and limited computing resources. Easy customisability supported use. The greatest use was where data use was previously constrained. In these contexts, report preparation time was reduced and efforts to improve data quality were supported, although the interrupted time series analysis did not show improved data quality. Twenty-three questionnaires were returned, revealing positive perceptions of ease of use and usefulness. In phase 5, the feasibility of conducting a cluster randomised controlled trial of QualDash was assessed. Interviews were undertaken to understand how QualDash could be revised to support a region-wide Gold Command. Requirements included multiple real-time data sources and functionality to help to identify priorities. Conclusions: Audits seeking to widen engagement may find the following strategies beneficial: involving a range of professional groups in choosing metrics; real-time reporting; presenting ‘headline’ metrics important to organisational-level staff; using routinely collected clinical data to populate data fields; and dashboards that help staff to explore and report audit data. Those designing dashboards may find it beneficial to include the following: ‘at a glance’ visualisation of key metrics; visualisations configured in line with existing visualisations that teams use, with clear labelling; functionality that supports the creation of reports and presentations; the ability to explore relationships between variables and drill down to look at subgroups; and low requirements for computing resources. Organisations introducing a dashboard may find the following strategies beneficial: clinical champion to promote use; testing with real data by audit staff; establishing routines for integrating use into work practices; involving audit staff in adoption activities; and allowing customisation. Limitations: The COVID-19 pandemic stopped phase 4 data collection, limiting our ability to further test and refine the QualDash theory. Questionnaire results should be treated with caution because of the small, possibly biased, sample. Control sites for the interrupted time series analysis were not possible because of research and development delays. One intervention site did not submit data. Limited uptake meant that assessing the impact on more measures was not appropriate. Future work: The extent to which national audit dashboards are used and the strategies national audits use to encourage uptake, a realist review of the impact of dashboards, and rigorous evaluations of the impact of dashboards and the effectiveness of adoption strategies should be explored. Study registration: This study is registered as ISRCTN18289782. Funding: This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme and will be published in full in Health and Social Care Delivery Research; Vol. 10, No. 12. See the NIHR Journals Library website for further project information.

Population Health Management (PHM) relies on the analysis of data from several sources to account for the complex interaction of factors that contribute to the health and well-being of a population, while considering biases and inequalities across sub-populations. Visualisation is emerging as an essential tool for insight generation from data shared and linked across services including healthcare, education, housing, policing, etc. However, visualisation design is challenged by poor data connectivity and quality, high dimensionality and complexity of real-world routinely collected data, in addition to the heterogeneity of users’ backgrounds and tasks. The Creative Visualisation Opportunities (CVO) framework provides a structured approach for working with diverse communities of visualisation stakeholders and defines a set of participatory activities for the effective elicitation of requirements and visualisation design alternatives. We conducted three workshops, applying variations of the CVO framework, with over one hundred participants from the PHM domain, including clinicians, researchers, government and private sector representatives, and local communities. In this paper, we present the results of preliminary analysis of these activities and report on the perceived impact of visualisation in this domain from a stakeholders’ perspective. We report real-world successes and limitations of applying the framework in different formats (through online and in-person workshops), and reflect on lessons learned for task analysis and visualisation design in the PHM domain.

Adapting dashboard design to different contexts of use is an open question in visualisation research. Dashboard designers often seek to strike a balance between dashboard adaptability and ease-of-use, and in hospitals challenges arise from the vast diversity of key metrics, data models and users involved at different organizational levels. In this design study, we present QualDash, a dashboard generation engine that allows for the dynamic configuration and deployment of visualisation dashboards for healthcare quality improvement (QI). We present a rigorous task analysis based on interviews with healthcare professionals, a co-design workshop and a series of one-on-one meetings with front line analysts. From these activities we define a metric card metaphor as a unit of visual analysis in healthcare QI, using this concept as a building block for generating highly adaptable dashboards, and leading to the design of a Metric Specification Structure (MSS). Each MSS is a JSON structure which enables dashboard authors to concisely configure unit-specific variants of a metric card, while offloading common patterns that are shared across cards to be preset by the engine. We reflect on deploying and iterating the design of OualDash in cardiology wards and pediatric intensive care units of five NHS hospitals. Finally, we report evaluation results that demonstrate the adaptability, ease-of-use and usefulness of QualDash in a real-world scenario.

Background Dashboards can support data-driven quality improvements in health care. They visualize data in ways intended to ease cognitive load and support data comprehension, but how they are best integrated into working practices needs further investigation. Objective This paper reports the findings of a realist evaluation of a web-based quality dashboard (QualDash) developed to support the use of national audit data in quality improvement. Methods QualDash was co-designed with data users and installed in 8 clinical services (3 pediatric intensive care units and 5 cardiology services) across 5 health care organizations (sites A-E) in England between July and December 2019. Champions were identified to support adoption. Data to evaluate QualDash were collected between July 2019 and August 2021 and consisted of 148.5 hours of observations including hospital wards and clinical governance meetings, log files that captured the extent of use of QualDash over 12 months, and a questionnaire designed to assess the dashboard’s perceived usefulness and ease of use. Guided by the principles of realist evaluation, data were analyzed to understand how, why, and in what circumstances QualDash supported the use of national audit data in quality improvement. Results The observations revealed that variation across sites in the amount and type of resources available to support data use, alongside staff interactions with QualDash, shaped its use and impact. Sites resourced with skilled audit support staff and established reporting systems (sites A and C) continued to use existing processes to report data. A number of constraints influenced use of QualDash in these sites including that some dashboard metrics were not configured in line with user expectations and staff were not fully aware how QualDash could be used to facilitate their work. In less well-resourced services, QualDash automated parts of their reporting process, streamlining the work of audit support staff (site B), and, in some cases, highlighted issues with data completeness that the service worked to address (site E). Questionnaire responses received from 23 participants indicated that QualDash was perceived as useful and easy to use despite its variable use in practice. Conclusions Web-based dashboards have the potential to support data-driven improvement, providing access to visualizations that can help users address key questions about care quality. Findings from this study point to ways in which dashboard design might be improved to optimize use and impact in different contexts; this includes using data meaningful to stakeholders in the co-design process and actively engaging staff knowledgeable about current data use and routines in the scrutiny of the dashboard metrics and functions. In addition, consideration should be given to the processes of data collection and upload that underpin the quality of the data visualized and consequently its potential to stimulate quality improvement. International Registered Report Identifier (IRRID) RR2-10.1136/bmjopen-2019-033208

UNSTRUCTURED Objective: Dashboards can support data-driven quality improvement in healthcare. They visualise data in ways intended to ease cognitive load and support data comprehension, but how they are best integrated into working practices to impact patient care needs further investigation. This paper reports the findings of a realist evaluation of a web-based, interactive quality dashboard (QualDash) developed to support use of national audit data in quality improvement. Methods: QualDash was co-designed with data users and installed in eight clinical services across five healthcare organisations in England between July and December 2019. Local ‘champions’ were identified to support uptake and adoption. Data to evaluate QualDash were collected between August 2019 and August 2021 and consisted of (1) 148.5 hours of observations including hospital wards and clinical governance meetings, (2) logfiles that captured the extent of use of QualDash and (3) a questionnaire, based on the Technological Acceptance Model, to assess the dashboard’s perceived usefulness and ease of use. Guided by the principles of realist evaluation, data were analysed to understand how, why and in what circumstances QualDash best supported use of national audit data in quality improvement. Results: The services into which QualDash was introduced varied in the amount and type of resources available to support data use. These variations, alongside early staff interactions with QualDash, shaped its use and impact during the evaluation period. Well-resourced sites with skilled audit support staff and local data management systems, continued to use established processes to access and use data. Factors constraining use of QualDash in these contexts included the use of local systems to report metrics not configured in QualDash; staff not being fully aware how QualDash could facilitate their work; and champions’ initial reluctance to lead use of QualDash until some metrics were reconfigured to reflect user expectations. In services less well-resourced to use data, QualDash automated parts of their routine reporting process, streamlining the work of audit support staff, and, in some cases, it highlighted issues with data completeness that they worked to address. Furthermore, questionnaire responses indicated that QualDash was perceived as useful and easy to use despite its variable use in practice. Conclusions: Interactive, web-based dashboards, such as QualDash, have potential to support use of national audit data in quality improvement by facilitating access to and interactions with data. To optimise use and impact, findings suggest that codesign would benefit from greater scrutiny of dashboard visualisations pre-installation, by site staff knowledgeable about metric configurations. Additionally, further consideration should be given to the processes surrounding dashboard use, including data collection, that underpin user confidence in dashboard functions. INTERNATIONAL REGISTERED REPORT RR2-10.1136/bmjopen-2019-033208

Introduction National audits are used to monitor care quality and safety and are anticipated to reduce unexplained variations in quality by stimulating quality improvement (QI). However, variation within and between providers in the extent of engagement with national audits means that the potential for national audit data to inform QI is not being realised. This study will undertake a feasibility evaluation of QualDash, a quality dashboard designed to support clinical teams and managers to explore data from two national audits, the Myocardial Ischaemia National Audit Project (MINAP) and the Paediatric Intensive Care Audit Network (PICANet). Methods and analysis Realist evaluation, which involves building, testing and refining theories of how an intervention works, provides an overall framework for this feasibility study. Realist hypotheses that describe how, in what contexts, and why QualDash is expected to provide benefit will be tested across five hospitals. A controlled interrupted time series analysis, using key MINAP and PICANet measures, will provide preliminary evidence of the impact of QualDash, while ethnographic observations and interviews over 12 months will provide initial insight into contexts and mechanisms that lead to those impacts. Feasibility outcomes include the extent to which MINAP and PICANet data are used, data completeness in the audits, and the extent to which participants perceive QualDash to be useful and express the intention to continue using it after the study period. Ethics and dissemination The study has been approved by the University of Leeds School of Healthcare Research Ethics Committee. Study results will provide an initial understanding of how, in what contexts, and why quality dashboards lead to improvements in care quality. These will be disseminated to academic audiences, study participants, hospital IT departments and national audits. If the results show a trial is feasible, we will disseminate the QualDash software through a stepped wedge cluster randomised trial.

RATIONALE, AIMS, AND OBJECTIVES Healthcare systems worldwide devote significant resources towards collecting data to support care quality assurance and improvement. In the United Kingdom, National Clinical Audits are intended to contribute to these objectives by providing public reports of data on healthcare treatment and outcomes, but their potential for quality improvement in particular is not realized fully among healthcare providers. Here, we aim to explore this outcome from the perspective of hospital boards and their quality committees: an under-studied area, given the emphasis in previous research on the audits' use by clinical teams. METHODS We carried out semi-structured, qualitative interviews with 54 staff in different clinical and management settings in five English National Health Service hospitals about their use of NCA data, and the circumstances that supported or constrained such use. We used Framework Analysis to identify themes within their responses. RESULTS We found that members and officers of hospitals' governing bodies perceived an imbalance between the benefits to their institutions from National Clinical Audits and the substantial resources consumed by participating in them. This led some to question the audits' legitimacy, which could limit scope for improvements based on audit data, proposed by clinical teams. CONCLUSIONS Measures to enhance the audits' perceived legitimacy could help address these limitations. These include audit suppliers moving from an emphasis on cumulative, retrospective reports to real-time reporting, clearly presenting the "headline" outcomes important to institutional bodies and staff. Measures may also include further negotiation between hospitals, suppliers and their commissioners about the nature and volume of data the latter are expected to collect; wider use by hospitals of routine clinical data to populate audit data fields; and further development of interactive digital technologies to help staff explore and report audit data in meaningful ways.

We present a taxonomy-driven approach to requirements specification in a large-scale project setting, drawing on our work to develop visualization dashboards for improving the quality of healthcare. Our aim is to overcome some of the limitations of the qualitative methods that are typically used for requirements analysis. When applied alone, methods like interviews fall short in identifying the full set of functionalities that a visualization system should support. We present a five-stage pipeline to structure user task elicitation and analysis around well-established taxonomic dimensions, and make the following contributions: (i) criteria for selecting dimensions from the large body of task taxonomies in the literature,, (ii) use of three particular dimensions (granularity, type cardinality and target) to create materials for a requirements analysis workshop with domain experts, (iii) a method for characterizing the task space that was produced by the experts in the workshop, (iv) a decision tree that partitions that space and maps it to visualization design alternatives, and (v) validating our approach by testing the decision tree against new tasks that collected through interviews with further domain experts.

This research presents a novel Trajectory-based Tracking Analyst (TTA) that can track and link spatiotemporally variable data from multiple sources. The proposed technique uses trajectory information to determine the positions of time-enabled and spatially variable scatter data at any given time through a combination of along trajectory adjustment and spatial interpolation. The TTA is applied in this research to track large spatiotemporal data of volcanic eruptions (acquired using multi-sensors) in the unsteady flow field of the atmosphere. The TTA enables tracking injections into the atmospheric flow field, the reconstruction of the spatiotemporally variable data at any desired time, and the spatiotemporal join of attribute data from multiple sources. In addition, we were able to create a smooth animation of the volcanic ash plume at interactive rates. The initial results indicate that the TTA can be applied to a wide range of multiple-source data.

Lipreading is the process of interpreting spoken word by observing lip movement. It plays a vital role in human communication and speech understanding, especially for hearing-impaired individuals. Automated lipreading approaches have recently been used in such applications as biometric identification, silent dictation, forensic analysis of surveillance camera capture, and communication with autonomous vehicles. However, lipreading is a difficult process that poses several challenges to human- and machine-based approaches alike. This is due to the large number of phonemes in human language that are visually represented by a smaller number of lip movements (visemes). Consequently, the same viseme may be used to represent several phonemes, which confuses any lipreader. In this paper, we present a detailed study of the machine learning approach for the real-time visual recognition of spoken words. Our focus on real-time performance is motivated by the recent trend of using lipreading in autonomous vehicles. In this paper, machine learning approaches are applied to recognize lip-reading and nine different classifiers has been implemented and tested, reporting their confusion matrices among different groups of words. The classification process went on more than one classifier but these three classifiers got the best results which are GradientBoosting, Support Vector Machine(SVM) and logistic regression with results 64.7%, 63.5% and 59.4% respectively.

Public transportation movement data provide a wealth of information and insights into many aspects of urban life and human behavior. However, huge amounts of raw data, coupled with incomplete or inconsistent records, may turn into an obstacle for the effective use of the available information. The need for effective movement data analysis has resulted in a large number of visual analytics tools and specialized views. There are still many challenges in public transportation and other kinds of cyclic movement data analysis. In this paper we address some of those challenges by presenting an improvement of the standard map view. This improved view is specifically designed to simplify and make the visual analysis of complex movement data easier to perform, especially when integrated in a coordinated multiple views tool and articulated together with other techniques. We illustrate the effectiveness of the view on public transportation data from Bahía Blanca, Argentina.

Widespread use of GPS and similar technologies makes it possible to collect extensive amounts of trajectory data. These data sets are essential for reasonable decision making in various application domains. Additional information, such as events taking place along a trajectory, makes data analysis challenging, due to data size and complexity. We present an integrated solution for interactive visual analysis and exploration of events along trajectories data. Our approach supports analysis of event sequences at three different levels of abstraction, namely spatial, temporal, and events themselves. Customized views as well as standard views are combined to form a coordinated multiple views system. In addition to trajectories and events, we include on-the-fly derived data in the analysis. We evaluate our integrated solution using the IEEE VAST 2015 Challenge data set. A successful detection and characterization of malicious activity indicate the usefulness and efficiency of the presented approach.