Route Knowledge in Complex Environments: An Analysis of Pedestrian Recall Using Stereoscopic Panoramic Interactive Navigation

<p>The dissertation explores the influence of emerging technologies on the dynamics of urban areas from a pedestrian-oriented perspective. To evaluate this influence, novel data sources and modern data-driven approaches replace traditional data collection methods and modelling techniques. Part of the dissertation establishes new tools and techniques to predict and explain pedestrian behaviour that concerns the future dynamics in urban areas. Specifically, this dissertation considers: (i) what are the novel data sources that can capture detailed information on pedestrian behaviour and how can they be used efficiently? (ii) how to extract valuable information from these new high- dimensional data sources? (iii) how to utilize the data and tools to predict pedestrian behaviour in the future urban environment? and (iv) how to interpret and explain pedestrian behaviour in the context of smart cities?</p> <p>This thesis is based on four articles introduced in Chapters 3 to 6. Chapter 3 introduces a semi-supervised residual network for transportation mode detection using passively collected labelled and unlabelled Wi-Fi signal data. Chapter 4 provides a survival analysis to model the wait time behaviour of a crossing pedestrian and analyze the effect of smartphone distraction on pedestrian behaviour. Virtual reality is used in this chapter as a means of data collection in a controlled environment. Chapter 5 highlights pedestrian crossing behaviour in the presence of automated vehicles. A large virtual reality data collection campaign is conducted to understand pedestrian behaviour in futuristic scenarios. Data-driven survival analysis is developed to analyze pedestrian wait time before mid-block unsignalized crossings. By using a post-hoc model interpretation, the contributing factors to pedestrian wait time are assessed. In Chapter 6, a neural network architecture is developed to incorporate sequential time-series data and contextual information to predict pedestrian trajectory. The proposed framework is applied to the virtual reality dataset.</p> <p>Methodological and data collection frameworks explored in this dissertation provide solutions for detecting, modelling and predicting pedestrian behaviour in a futuristic context. This dissertation con- tributes to the field of transportation by proposing alternative data collection methods, developing novel data-driven methodologies and analyzing pedestrian behaviour in the context of automated vehicles.</p>

A potentially powerful method of social-scientific data collection and investigation has been created by an unexpected institution: the law. Article 15 of the EU’s 2018 General Data Protection Regulation (GDPR) mandates that individuals have electronic access to a copy of their personal data, and all major digital platforms now comply with this law by providing users with “data download packages” (DDPs). Through voluntary donation of DDPs, all data collected by public and private entities during the course of citizens’ digital life can be obtained and analyzed to answer social-scientific questions – with consent. Thus, consented DDPs open the way for vast new research opportunities. However, while this entirely new method of data collection will undoubtedly gain popularity in the coming years, it also comes with its own questions of representativeness and measurement quality, which are often evaluated systematically by means of an error framework. Therefore, in this paper we provide a blueprint for digital trace data collection using DDPs, and devise a “total error framework” for such projects. Our error framework for digital trace data collection through data donation is intended to facilitate high quality social-scientific investigations using DDPs while critically reflecting its unique methodological challenges and sources of error. In addition, we provide a quality control checklist to guide researchers in leveraging the vast opportunities afforded by this new mode of investigation.

Background. The article presents the results of a study of methods of positioning, localization and data collection from nodes of a mobile wireless sensor network using intelligent adaptive telecommunication air platforms. To implement the study of this research topic, an analysis of literary sources on this topic was carried out. Based on a fairly rich bibliographic material, this work has the main task of examining, analyzing and systematizing already known approaches to positioning objects in wireless sensor networks using intelligent adaptive telecommunication air platforms and suggesting options for their development. Objective. The aim of the work is to improve the methods of direct data collection of TA from the nodes of BSM, the general directions of synthesis of which are defined in the work. Methods. Methods of cluster analysis (network clustering), graph theory (research of analytical models of BSM with TA functioning, construction of cluster topology), theory of telecommunication networks (when calculating bandwidth in BSM with TA radio channels) and theory were used to solve the formulated problem. (when developing a positioning model for telecommunications air platforms) Results. A technique for evaluating the effectiveness of methods for collecting data from wireless sensor networks using intelligent adaptive telecommunication air platforms is proposed. Conclusions. The method of collecting TA monitoring data from the main nodes of clustered BSM has been improved. The method of estimation of efficiency of methods of data collection with BSM by telecommunication air platforms is offered.

Summary A variety of programs have been written for the computer scoring of multiple choice questions, but the design of the program alone is not a complete solution to the problems of scoring. The program is only a part of a much wider system which must cover methods of accurate data collection, through to suitable dissemination of results. Fortunately, the parts of such systems may be manipulated independently, and we have utilized our own methods of data collection, which have been established for some time, together with our own scoring procedures, to introduce a method of result display which maximizes the return of information to students. Because this is only a special method of displaying the candidates’ results, all other facilities of our major scoring program can be used concurrently. We believe that assessment is central to the learning and teaching processes, and we have therefore formulated a set of procedures which we call SLAM (Self Learning Assessment Module). SLAM is more flexible than a...

Background: Community organizations are essential in addressing health and social needs for populations often underserved by traditional healthcare systems. Recognizing the importance of program evaluation, these organizations increasingly seek to improve their services, demonstrate impact, and secure funding. However, they frequently face barriers such as limited resources, time constraints, and lack of in-house expertise. Evaluation training programs have been developed to bridge this gap to support community organizations in building evaluation capacity. Despite the availability of such programs, few undergo rigorous evaluation themselves, resulting in limited evidence of their effectiveness in fostering sustainable evaluation capacity within community settings. Addressing this need, this study presents key practices and findings from LaboEval, a five-year initiative launched in 2019 to strengthen evaluation capacity in community organizations across Quebec, Canada. Approach: LaboEval was developed through an iterative, participatory curriculum design process led by a steering committee of community representatives, students, and evaluation experts. The program offered seven online modules, each accompanied by follow-up coaching sessions grounded in a practical participatory evaluation framework. Modules covered essential topics such as stakeholder engagement, theory of change, data collection methods, knowledge transfer, and strategies for embedding evaluation within organizational practices. Implemented across 16 diverse organizations within the health and social services sectors, LaboEval emphasized flexibility to accommodate the schedules and needs of participants. Mayne's six-step contribution analysis was employed in the fifth year to assess LaboEval's impact on both individual and organizational evaluation capacities. A mixed-methods approach was applied, combining secondary data analysis, semi-structured interviews, document reviews, surveys, and literature reviews to ensure thorough data collection and analysis. Results: Findings indicate that LaboEval significantly enhanced individual and organizational evaluation skills and practices among participating organizations. Data reported increased confidence in conducting evaluations and improved integration of evaluation into their organizations’ processes. These improvements include more consistent use of logic models, incorporating evaluation activities into routine operations, and adopting appropriate data collection and analysis methods. The program's participatory design, which incorporated direct input from community stakeholders, contributed to its relevance, practicality, and success. Additionally, the modular structure and flexible implementation enhanced participant engagement and retention by addressing organizational constraints. However, challenges remained, particularly regarding the dissemination of evaluation findings by the organizations, which limited the broader impact of their evaluations on policy discussions. Implications: LaboEval offers important insights for both researchers and practitioners engaged in capacity-building initiatives. Its participatory curriculum design highlights the importance of involving organizations in the training program development process, enabling the creation of contextually relevant training that aligns with their specific needs and objectives. Contribution analysis provided a structured evaluation approach, yielding concrete, evidence-based insights into the effectiveness of training strategies and the pathways through which they build evaluation capacity. These insights illuminated specific outcomes, such as increased confidence and skills in evaluation practices, and highlighted how these pathways can refine future capacity-building initiatives and enhance their practical impact. The next phase of LaboEval will focus on expanding the program to additional organizations and enhancing mechanisms to support sustained evaluation practices.

This study is an integrative review of research trends, data collection methods, operational definitions of key terms, and rationales in Korean research on textbook use by mathematics teachers over the past 13 years. Based on the Temporal phases of curriculum use framework (Stein, Remillard, & Smith, 2007, p. 322), we examined the phases of curriculum use in each study and identified trends in the data collection and analysis methods of each paper. We identified the understanding of the terms teacher’s perception and textbook use in the studies and analyzed how the theoretical framework of each study justified the research questions and methods. The results showed: First, a significant number of studies focused on the transformation stage from written to intended, including teachers’ beliefs, orientations, and knowledge about curriculum use. Second, surveys were the most common method of data collection, followed by interviews, classroom practice/observation and text analysis in similar proportions. Surveys and interviews often did not provide a specific analytical framework for analyzing the results but teaching practice/observation and text analysis were mostly based on previous research and derived results through an analytical framework. Third, to examine teacher’s perceptions, researchers mainly examined the appropriateness/effectiveness of mathematics textbooks or specific content areas. And for textbook use, they examined the teaching methods of mathematics textbooks or specific content areas and cases of task modification. Fourth, few studies fully justified their research questions with a theoretical framework, and only about half of the studies justified their research methods with a theoretical framework. Implications for research and practice are discussed.

The relationship between spatial ability and environmental knowledge

The demand for the evaluation of the quality of patient and client care has increased in public health nursing as in all disciplines of nursing. In Hong Kong this demand led to the development of a collaborative study with the Department of Health to evaluate the quality of public health nursing in the maternal and child health centres. A multiple case study design was developed to undertake the research using both qualitative and quantitative methods of data collection. The findings from the study demonstrated not only implications for clinical practice which have been reported elsewhere, but also methodological issues for the evaluation of care. These findings indicate three major issues for consideration. The first issue is that of the cultural context of care which includes perceptions of care such as demands on the service and expectations of care. The second issue is the use of health data, including both the methods of recordings as well as the sources of data. The final issue is that of the method of data collection, in particular the implications of the use of language in data collection tools. The findings suggest that both the use of professional language and the need for translation have implications for data collection methods. Indeed in the author's view the development of methodologies for the evaluation of care must address culturally specific issues, particularly where English is not the first language of subjects in the study. In addition the use the use of language in the method of data collection highlights more general issues raised by the use of translation in the collection and analysis of qualitative data.

A digital diary in the form of a mobile messenger service offers a novel method for data collection in cancer research. Little is known about the things to consider when using this data collection method in clinical research for patients with cancer. In this Viewpoint paper, we discuss the lessons we learned from using a qualitative digital diary method via a mobile messenger service for data collection in oncology care. The lessons learned focus on three main topics: (1) data quality, (2) practical aspects, and (3) data protection. We hope to provide useful information to other researchers who consider this method for their research with patients. First, in this paper, we argue that the interactive nature of a digital diary via a messenger service is very well suited for the phenomenological approach and produces high-quality data. Second, we discuss practical issues of data collection with a mobile messenger service, including participant and researcher interaction. Third, we highlight corresponding aspects around technicalities, particularly those regarding data security. Our views on data privacy and information security are summarized in a comprehensive checklist to inform fellow researchers on the selection of a suitable messenger service for different scenarios. In our opinion, a digital diary via a mobile messenger service can provide high-quality data almost in real time and from participants' daily lives. However, some considerations must be made to ensure that patient data are sufficiently protected. The lessons we learned can guide future qualitative research using this relatively novel method for data collection in cancer research.

Many early researchers and students sometimes face the challenges with the type of data they need and the methods that can help them collect data for their academic research. Some of them often struggle with selecting appropriate data types and methods, leading to delays in research progress. Scientific data collection is one of the most important stages of academic research. Poor data leads to poor and unreliable estimated results that cannot be used for shaping public policy, and helping governments and other stakeholders make informed decisions. Thus, this review aims to equip beginners with tools to design robust studies and complete their research efficiently. This study reviewed the relevant literature on the types of data and the data collection methods in social sciences. The narrative literature review focused on the studies that are relevant to the topic and are published in English. The study used published articles from databases such as ResearchGate, Scopus, Google Scholar, and Web of Science. The findings depicted that data can be categorized based on three main dimensions. These include the Nature of data (Qualitative, quantitative data or Mixed methods) and (Categorical or Continuous), Data source (Primary or Secondary data), Data collection (Cross-sectional, Longitudinal or Time series data). The literature search also showed that the widely used methods of data collection in social sciences include Interviews, Focus Group Discussions, Key Informant Interviews, Observations and Secondary data sources such as Archives, Records and Published documents. In general, researchers should choose the right type of data and data collection method that can answer their research objectives. This will ensure that they collect valid and reliable data that can guide policymakers and the government.

PurposeTo evaluate the appropriateness of potential data sources for the population of performance indicators for primary care (PC) practices.MethodsThis project was a cross sectional study of 7 multidisciplinary primary care teams in Ontario, Canada. Practices were recruited and 5-7 physicians per practice agreed to participate in the study. Patients of participating physicians (20-30) were recruited sequentially as they presented to attend a visit. Data collection included patient, provider and practice surveys, chart abstraction and linkage to administrative data sets. Matched pairs analysis was used to examine the differences in the observed results for each indicator obtained using multiple data sources.ResultsSeven teams, 41 physicians, 94 associated staff and 998 patients were recruited. The survey response rate was 81% for patients, 93% for physicians and 83% for associated staff. Chart audits were successfully completed on all but 1 patient and linkage to administrative data was successful for all subjects. There were significant differences noted between the data collection methods for many measures. No single method of data collection was best for all outcomes. For most measures of technical quality of care chart audit was the most accurate method of data collection. Patient surveys were more accurate for immunizations, chronic disease advice/information dispensed, some general health promotion items and possibly for medication use. Administrative data appears useful for indicators including chronic disease diagnosis and osteoporosis/ breast screening.ConclusionsMultiple data collection methods are required for a comprehensive assessment of performance in primary care practices. The choice of which methods are best for any one particular study or quality improvement initiative requires careful consideration of the biases that each method might introduce into the results. In this study, both patients and providers were willing to participate in and consent to, the collection and linkage of information from multiple sources that would be required for such assessments.

The banking world is a business that greatly influences the economic base, with the condition of increasing internet users from year to year, the business world, including the banking sector, also utilizes internet media in conducting business activities. The research that will be conducted at this time focuses on the use of Internet Banking for BRI Butung Unit customers. The process of distributing questionnaires takes 2 weeks. The method of data collection uses the method of data collection through the distribution of questionnaires to respondents. In this study the independent variables (exogenous) are usability (X1), Ease (X2), trust (X3), and Convenience (X4) while the dependent variable (endogenous) studied is interest in using (Y1) and decided to use (Y2). The results of the study indicate that there is an influence of usability on interest in using internet banking. The higher the perception of the usefulness of customers, the higher the interest in using Internet banking because customers believe that the use of Internet banking can have a positive influence and can improve business transactions. The results of the study indicate that there is no effect of ease on interest in using internet banking. It is expected to always update the internet banking system used, both in terms of the type of transaction and the appearance of the system and innovate new facilities so that it can provide direct benefits, reduce the level of difficulty and complexity of transactions and simplify the operation of the system so that customers can more easily transact using Internet banking.

Data collection with microcrystals at synchrotron radiation facilities is challenging because it is difficult to harvest and locate microcrystals. Moreover, microcrystals are sensitive to radiation damage; thus, typically, a complete data set cannot be obtained with a single microcrystal. Herein, we report a new method for data collection with multiple microcrystals having a crystal size ranging from 1 to 30 µm. This method is suitable for not only low-temperature (100 K) data collection but also room-temperature data collection. Thin Kapton membranes were used to capture multiple crystals simultaneously. The microcrystals were visible under an optical microscope and easily located because the membrane was transparent and sufficiently thin. The film was fixed to a bracket that was prepared using a three-dimensional printer. The bracket was fixed on a magnetic base via screwing and employed by the goniometer system for data collection. Multiple data sets of fatty acid-binding protein 4 (FABP4) and lysozyme microcrystals were collected using this novel designed device. Finally, the structures of protein FABP4 and lysozyme were obtained from these data via the molecule replacement method. The data statistics reveal that this method provides a comparable result to traditional methods such as a nylon loop.

Purpose of the study: The Norwegian Cardiac Arrest Registry (NorCAR) is a mandatory national registry of resuscitation attempts that monitors the population incidence, care and outcome for cardiac arrest. Previously, substantial differences in the number of included patients per 100 000 inhabitants were observed in some “atypical” health trusts (HT). It was not known if this is a problem with data collection or reflected genuine population differences. The purpose of this study was to identify all patients that fill NorCAR’s inclusion criteria among these HTs, by imposing consistent methods for data collection. Materials and methods: The registry implemented consistent, standardised and rigorous data collection methods including targeted audits in four “atypical” health trusts, two with a high and two with a low reported population incidence. Registrations from January to March 2019 was compared with similar results from 2018. Results: In 2018, the observed nationwide annual incidence of out-of-hospital cardiac arrest was 64 per 100 000 inhabitants, varying from 40 to 102. At the four study sites the observed population incidence was stable among HT with high incidence (88 and 110) but apparently increased in the two HTs with previous low incidence after implementation of standardised collection method (from 48 to 56, and 46 to 54). Conclusions: The previously “atypical” low incidence probably reflected a problem with data collection. To achieve full coverage of the population, all data collectors need to collect data using the same method with a concise and thorough investigation of ambulance-, air ambulance-, dispatch-, and hospital records. Data collectors need to have a close relationship with ambulance and dispatch, and constant reminders and feedback on reported results are important. Implementing a new method for data collection takes time, and results need to be evaluated over time.

Measurement is a key task in scientific research. This chapter introduces some basic notions on measurement, and it discusses the two major methods of data collection in the field of psychosocial occupational health, self-reported (subjective) and independently assessed (objective) information. As current mainstream research mainly uses quantitative methods of data collection and analysis, this approach is discussed more extensively than a complementary qualitative approach. Basic features of standardized questionnaires, psychometric scales, and event momentary assessment are discussed, followed by a description of observational methods and job exposure matrices. In view of several limitations of data quality, methodological challenges are emphasized before the second part of the chapter addresses two core topics of data collection, the sampling of study participants, and the application of a study design that allows a robust answer to the research question of interest. Here, we prioritize the prospective observational cohort study with its contributions towards identifying occupational determinants of health. As an important practical message, we maintain that scientific research is a privileged, precious activity that merits the observation of the highest standards of quality. Therefore, the standards of data collection, sample composition, and study design mentioned in this chapter deserve special attention.