App Data Research Articles

What to expect when generating data through the use of a mobile health application

Abstract Background Digitalization has significantly expanded the possibilities for collecting health data. Besides options such as online surveys, data can be collected through the use of digital applications (apps). Once users have given their consent, contact information from the app registration can be accessed to invite the relevant target group for further surveys. To generate a sufficient amount of data, a high level of willingness to participate and long-term involvement are required. This study aims to illustrate the willingness to share data and what can be expected from it by means of the Neo-MILK app, which supports mothers during lactation. Methods The willingness to share data and the data resulting from the use of the app is shown by the frequency of consent to the different data use options and the users’ participation. Results While over 68% of the n = 1079 registered app users consented to the use of their app data, 47% agreed to be contacted again for scientific purposes. Data from n = 421 active users were available for analysis. From n = 220 mothers, milk pumping data was available. In 36% of cases, little (<10) or no information on pumping was provided. Of the users who agreed to be contacted again for scientific purposes, n = 300 were invited to participate in another survey and 23% (n = 69) participated. Conclusions In this example, the general willingness to share data and participate in further studies shows great potential for data collection. However, the data collection methods differ primarily in the motivation to participate. When collecting data through app usage, ensuring that the design of the app meets the needs of the user is essential to optimize the user experience and thereby generating complete data. If the use of contact information has been agreed to, incentives may be considered to increase the willingness to participate. Required ethics approvals and data protection regulations should also be ensured for app-based data collection. Key messages • Generating data via app usage as well as the collection of data through the use of users’ contact information shows great potential for health research. • App-based data collection should be considered when planning future health research projects.

Public Perceptions and Engagement in mHealth Across Eight European Countries

Abstract Background Mobile health technologies have the potential to revolutionize healthcare delivery and patient engagement. This study explores public attitudes toward health app usage and data sharing across eight European countries, addressing a significant gap in understanding the public’s perspective on mHealth adoption. Methods A cross-sectional survey was conducted involving 6,581 participants from Italy, the Netherlands, France, Germany, Spain, Poland, Romania, and Hungary. The survey assessed current health app usage, future interest, willingness to share health data, and concerns about data privacy and security. Results 21.87% of respondents currently use health apps, with 42.71% expressing interest in future use. Regarding data sharing, 52.82% are willing to share health data with healthcare providers, and 25.48% would share data with research institutions. However, 53.18% fear unauthorized use or hacking of their health data. Significant generational and geographical differences in mHealth engagement were observed, with older generations showing higher willingness to adopt health apps OR 1.22 (CI 1.03 - 1.46) for Baby Boomers, OR 1.45 (CI 0.97 - 2.16) for Silent Generation. Education level emerged as a crucial factor, with tertiary-educated individuals more likely to use health apps (OR 1.41 CI 1.24 - 1.58) and demand transparency (OR 1.61 CI 1.45 -1.78). Conclusions The findings underscore the need for targeted strategies to enhance digital literacy, ensure data privacy, and promote equitable access to mHealth technologies across Europe. Policymakers and healthcare stakeholders should prioritize these areas to foster greater public trust and engagement in mHealth, ultimately improving health outcomes and patient care. Key messages • Public attitudes toward mHealth vary significantly across generations, countries, and education levels in Europe. • Addressing data privacy concerns and enhancing digital literacy are crucial for widespread mHealth adoption.

Optimizing the Measurement of Information on the Context of Alcohol Consumption Within the Drink Less App Among People Drinking at Increasing and Higher Risk Levels: Mixed-Methods Usability Study.

There is a growing public health evidence base focused on understanding the links between drinking contexts and alcohol consumption. However, the potential value of developing context-based interventions to help people drinking at increasing and higher risk levels to cut down remains underexplored. Digital interventions, such as apps, offer significant potential for delivering context-based interventions as they can collect contextual information and flexibly deliver personalized interventions while addressing barriers associated with face-to-face interventions, such as time constraints. This early phase study aimed to identify the best method for collecting information on the contexts of alcohol consumption among users of an alcohol reduction app by comparing 2 alternative drinking diaries in terms of user engagement, data quality, usability, and acceptability. Participants were recruited using the online platform Prolific and were randomly assigned to use 1 of the 2 adapted versions of the Drink Less app for 14 days. Tags (n=31) included tags for location, motivation, and company that participants added to drink records. Occasion type (n=31) included a list of occasion types that participants selected from when adding drink records. We assessed engagement and data quality with app data, usability with a validated questionnaire, and acceptability with semistructured interviews. Quantitative findings on engagement, data quality, and app usability were good overall, with participants using the app on most days (tags: mean 12.23, SD 2.46 days; occasion type: mean 12.39, SD 2.12 days). However, around 40% of drinking records in tags did not include company and motivation tags. Mean usability scores were similar across app versions (tags: mean 72.39, SD 8.10; occasion type: mean 74.23, SD 6.76). Qualitative analysis found that both versions were acceptable to users and were relevant to their drinking occasions, and participants reported increased awareness of their drinking contexts. Several participants reported that the diary helped them to reduce alcohol consumption in some contexts (eg, home or lone drinking) more than others (eg, social drinking) and suggested that they felt less negative affect recording social drinking contexts out of their home. Participants also suggested the inclusion of "work drinks" in both versions and "habit" as a motivation in the tags version. There was no clearly better method for collecting data on alcohol consumption as both methods had good user engagement, usability, acceptability, and data quality. Participants recorded sufficient data on their drinking contexts to suggest that an adapted version of Drink Less could be used as the basis for context-specific interventions. The occasion type version may be preferable owing to lower participant burden. A more general consideration is to ensure that context-specific interventions are designed to minimize the risk of unintended positive reinforcement of drinking occasions that are seen as sociable by users.

A digital health-supported and community pharmacy-based lifestyle intervention program for adults with pre-diabetes: a study protocol for a cluster randomised controlled trial

IntroductionPre-diabetes indicates an elevated risk of developing type-2 diabetes and presents a window for preventive actions. The Pre-diabetes Intervention, Management and Evaluation (PRIME) programme is a community pharmacy-based pre-diabetes management...

Remote monitoring of rheumatoid arthritis (REMORA): study protocol for a stepped wedge cluster randomized trial and process evaluation of an integrated symptom tracking intervention

BackgroundManagement of rheumatoid arthritis (RA) relies on symptoms reported by patients during infrequent outpatient clinic visits. These reports are often incomplete and inaccurate due to poor recall, leading to suboptimal treatment decisions and outcomes. Asking people to track symptoms in-between visits and integrating the data into clinical pathways may improve this. However, knowledge on how to implement this into practice and its impact on services and outcomes remains scarce in RA. Therefore, we evaluate the comparative effectiveness and cost-effectiveness of integrated symptom tracking in people with RA over and above usual care, while generating insights on factors for successful implementation.MethodsIn this superiority stepped wedge cluster-randomized controlled trial with continuous recruitment short exposure design, 16 rheumatology outpatient departments (clusters) recruit a total of 732 people with active RA. They initially offer clinic visits according to standard of care before switching in pairs to visits with integrated symptom tracking. Clusters switch in randomized order every 3 weeks. Integrated symptom tracking consists of (1) a mobile app for patients to track their symptoms daily and other RA aspects weekly/monthly, and (2) an interactive dashboard visualizing the app data, which healthcare professionals access from their electronic health record system. Clinic visits happen according to usual practice, with tracked symptom data only reviewed during visits. Our primary outcome is a difference in marginal mean disease activity score at 12 ± 3 months between standard of care and integrated symptom tracking, after accounting for baseline values, cluster, and other covariates. Secondary outcomes include patient-reported disease activity, quality of life and quality-adjusted life-years, medication/resource use, consultation and decision-making experience, self-management, and illness perception. We also conduct interviews and observations as part of a parallel process evaluation to gather information on implementation.DiscussionOur trial will generate high-quality evidence of comparative and cost-effectiveness of integrated symptom tracking compared to standard of care in people with RA, with our process evaluation delivering knowledge on successful implementation. This optimizes the chances of integrated symptom tracking being adopted more widely if we find it is (cost-) effective.Trial registrationRegistered 4-Jun-2024 on https://www.isrctn.com/, ISRCTN51539448.Trial open science framework repositoryhttps://osf.io/sj9ha/.

Mobile Apps for Vaccination Services: Content Analysis and Quality Assessment.

Vaccination services are increasingly in demand by the public, and mobile apps are an effective tool to meet that demand. However, the characteristics and quality of these apps are unknown. Commonly used vaccination service apps on the market were surveyed with regard to quality, service content, and user experience to evaluate and guide users. The Qimai Data mobile app data analytics platform was used to search for common vaccination service apps by keyword, and the WeChat and Alipay platforms were searched for apps. The apps included in the study were independently evaluated by two reviewers using the Mobile Application Rating Scale, and the service content and user experience of the apps were analyzed. The intragroup correlation coefficient between raters was used to measure interrater reliability. In the app stores of the four major Android platforms and the iOS app store, 1092 and 207 apps were found, respectively; 189 WeChat applets and 30 Alipay applets were also found. A total of 29 apps was ultimately included in this study according to the inclusion criteria, including 21 independent apps, 4 WeChat applets, and 4 Alipay applets. Significant differences were found between independent apps and applets in terms of the quality score (t449.57=-5.301; P<.001) and the subjective quality score (z=-4.753; P<.001). No significant differences were found between iOS and Android platforms in terms of the quality score (t1404=-2.55; P=.80) and the subjective quality score (z=-0.137; P=.89). There was good intragroup consistency among the raters. In this study, independent apps and nonindependent apps that rely on social and payment platforms for implementation were included in the vaccination services category. The overall quality of these apps was acceptable. Nonindependent running apps were found to have slightly lower scores and showed room for improvement, and scores for the participatory apps were found to be generally low overall.

Do You “Relay” Want to Give Me Away? – Forensic Cues of Smart Relays and Their IoT Companion Apps

As IoT devices become more prevalent in everyday environments, their relevance to digital investigations increases. The product class of “smart relays”, which are connected to the low-voltage grid and usually installed in sockets behind walls, has not yet received much attention in the context of smart home forensics. To close a category-specific gap in the device forensics literature, we conducted a multi-device analysis of 16 smart relays from 9 manufacturers, which support six different companion apps in total. Our examination shows that forensic artifacts can be found locally on the smart relays and in the companion app data, as well as remotely on cloud servers of the vendors. Based on our findings, we developed a Python framework to extract forensic artifacts automatically from obtained firmware dumps, from companion app data, and from captured network traffic.

Logical methods of forensic expertise of Android devices

Mobile devices are now so ubiquitous that they have revolutionized the way we do most of our activities. As a result, the mobile device is now a huge repository of confidential and personal information about its owner. This has led to the development of mobile forensics, which focuses on the recovery and investigation of mobile data. The purpose of the diagnostic process is to retrieve and recover any information from a mobile device without changing the data on the device. Over the years, digital forensics has expanded with the rapid growth of mobile devices. There are different branches of digital forensics depending on the type of digital device, such as computer forensics, network forensics, mobile forensics, etc. Data obtained from telephones becomes an invaluable source of evidence for investigations in criminal and civil cases. It's rare to conduct digital forensics without using a phone. Third-party applications are an integral part of the investigation mobile device. This requires understanding where app data is stored on the device, what app data is stored for that platform, and which tool best helps uncover the evidence. Although some commercial tools, such as Magnet IEF, are known for supporting application parsing, no tool is perfect, and it is nearly impossible for tools to keep up with the frequent updates that are released for each application. The most commonly available commercial tools analyze the most popular programs on the market. For example, when Facebook acquired WhatsApp, Cellebrite, IEF, and Oxygen Forensics started supporting the app. This is where all programs differ.

Tri-channel visualised malicious code classification based on improved ResNet

As malicious code attacks continue to evolve, attackers leverage techniques like packing and code obfuscation to generate numerous variants, challenging traditional detection methods. Addressing the limitations of current deep learning-based malicious code classification approaches in feature extraction and accuracy, this paper introduces an innovative RGB visualization detection method based on a hybrid multi-head attention mechanism. Initially, a feature representation method utilizing RGB images is introduced. This approach focuses on semantic relationships between a malware’s binary information, assembly details, and API data, generating images with richer textural information. This technique effectively uncovers the deep dependencies between the original and variant versions of malicious code, providing stronger support for subsequent classification tasks. Furthermore, to tackle the issues of malware encryption and obfuscation, a deep neural network framework is adopted, incorporating a modular design philosophy and integrating a multi-head attention mechanism. This design not only enhances the expressiveness of critical features but also helps the model better focus on key aspects of the malicious code, thereby improving classification accuracy. Through comparative experiments and in-depth analysis, the effectiveness and superiority of the proposed RGB visualization method and MSA-ResNet model in the field of malicious code variant classification are validated. The accuracy rates achieved on the Kaggle and DataCon datasets are 99.49% and 97.70%, respectively, representing significant improvements over other methods. This approach demonstrates strong generalization capabilities and resistance to obfuscation, offering a new and effective tool for malicious code detection.Graphical

Examining the Integrity of Apple’s Privacy Labels: GDPR Compliance and Unnecessary Data Collection in iOS Apps

This study investigates the effectiveness of Apple’s privacy labels, introduced in iOS 14, in promoting transparency around app data collection practices with respect to the GDPR. Specifically, we address two key research questions: (1) What special categories of personal data, as regulated by the GDPR, are collected and used by apps, and for which purposes? (2) What disparities exist between app-stated permissions and the apparent unnecessary data gathering across various categories in the iOS App Store? By analyzing a comprehensive dataset of 541,662 iOS apps, we identify common practices related to prevalent use of sensitive and special categories of personal data, revealing widespread instances of unnecessary data collection, misuse, and potential GDPR violations. Furthermore, our analysis uncovers significant inconsistencies between the permissions stated by apps and the actual data they gather, highlighting a critical gap in user privacy protection within the iOS ecosystem. These findings underscore the need for stricter regulatory oversight of app stores and the necessity of effective privacy notices to build accountability and trust and ensure transparency. This study offers actionable insights for regulators, app developers, and users towards creating secure and transparent digital ecosystems.

SP5.13 - Assessing the use of electronic QLIK app in Mortality and Morbidity meetings compared with traditional manual hand collected data in colorectal surgery

Abstract Aims To validate and compare the effectiveness of the QLIK app as an electronic method of data collection compared to traditional manual methods for mortality and morbidity meetings (M&amp;Ms). Methods All data from the theatre operating management system was linked in the Trust Data Warehouse to clinical test and vital statistics data and displayed using a QLIK App. A retrospective study was conducted comparing the manual collection of data of elective colorectal M&amp;M with the QLIK app data for the years 2021 and 2022. Data collected included: total operations, number of deaths, readmissions, reoperations and post-operative complications. The combined data was then analysed in three different rounds of validation. Results There was a high level of concordance between the manual data and the QLIK app, with both correctly identifying the same number of deaths. In 2021 the app recorded 569 total operations compared to 584 in the manual data, and 127 post operative complications to 126 respectively. In 2022, the app recorded 607 operations to 489 in the manual data, and 120 complications compared to 149. Of the complications recorded in the manual data but not the app, 96.8% of these were Clavien-Dindo Grade 1 and 2 complications. Manual data collection of the M&amp;M data was estimated to take approximately 250 hours per year from collection to analysis, whilst the app displayed the data in near real time. Conclusions Use of data visualization tools and coded electronic hospital data for M&amp;Ms can minimise errors and allow for greater efficiency. This approach gives real time surgical performance data.

Privacy in Chinese iOS apps and impact of the personal information protection law

Privacy in apps is a topic of widespread interest because many apps collect and share large amounts of highly sensitive information. In response, the Chinese legislator introduced a range of new data protection laws over recent years, notably the Personal Information Protection Law (PIPL) in 2021. So far, there exists limited research on the impacts of these new laws on apps’ privacy practices. To address this gap, this paper analyses data collection in pairs of 634 Chinese iOS apps, one version from early 2020 and one from late 2021.Our work finds that many more apps now implement consent. Yet, those end-users that decline consent will often be forced to exit the app. Fewer apps now collect data without consent but many still integrate tracking libraries. Market concentration in app data collection has seen limited change. At the same time, there exists a larger number of influential and equal market participants than in the West. Among them, Apple was the only relevant foreign company.We see our findings characteristic of a first iteration at Chinese data regulation with room for improvement. With the help of enhanced technological capabilities, we expect increased enforcement of the new data rules. There is also room to refine the new laws and make them more targeted at mobile apps and the online sphere, particularly through clear and up-to-date technical specifications for software developers. As such, our findings could also be motivation for non-Chinese policy- and lawmakers to enhance their own data protection regimes.

RetrieveDroid: k-NN Performance Evaluation of Distance Measurement Schemes for Android Malware Detection Using Case-Based Retrieval

One of the big challenges in cybersecurity is the detection of Android attacks since Android is the most popular mobile operating system. Within this system, applications require certain permissions to access critical resources. Investigation of the use of permissions is a concern to check whether an application is not mislead to divulge sensitive information. This work aims to determine whose distance schemes offers the best malware detection based on permission similarities. Case based reasoning (CBR) is a concept which aims to find a solution based on historical experiences. CBR performance relies on finding similarities between actual cases and stored cases and then to deduce solutions. This paper proposes to transform app data as vector of appearance of dangerous permissions and to store such vectors based on CBR structure. Then we investigate k-NN classification performance related to five distance-based metrics such as Euclidean, Cosine, Manhattan, Minkowski, and Mahalanobis. Experiments were carried out with a set of 419 applications, including 203 malicious and 216 benign samples. The whole dataset has been split in training set of 291 samples with 162 benign and 129 malicious, and the testing set of 128 samples with 54 benign and 74 malicious samples. k-Nearest Neighbor (k-NN) are used as the similarity algorithm in which the distance model is varied in each of the five distance models. Results reveal that Minkowski and Manhattan models provide the best overall performance to detect Android malware based on permissions, in terms of accuracy (99.21%) and precision (97%). This work is a good start to recommend to researchers distance metrics exploitable when performing permission similarity-based detection.

Quantification of antimicrobial use on Irish dairy farms: A comparison of three recording methods

Antimicrobial use (AMU) data are essential for monitoring usage over time, facilitating reduction strategies to combat the threat of antimicrobial resistance (AMR) to both human and animal health. The objective of this study was to measure and describe AMU over a 12-mo period in Irish dairy herds and compare 3 different recording methods to a reference method. A sample of 33 Irish dairy herds were randomly selected from 6 private veterinary practices across Ireland. The herds were followed for a 12-mo period and their AMU was monitored using 3 recording methods: 1. Veterinary prescription data (VET), 2. The inventory of medicine bins on the farms (BIN), and 3. Farmer treatment records from herd recording software (APP). Each recording method was compared with a previously developed reference method for AMU. The reference method used was based on pre- and poststudy medicine stock on the farms combined with veterinary prescription data. Antimicrobial use was analyzed using both mass- and dosed-based metrics, including mass (mg) of antimicrobial active ingredient per population correction unit (mg/PCU), defined daily doses for animals (DDDVET) and defined course doses for animals (DCDVET). Median AMU was 16.24, 10.47, 8.87 and 15.55 mg/PCU by mass, and 2.43, 1.55, 1.19 and 2.26 DDDVET by dose for VET, BIN, APP, and reference method data, respectively. Reliability of the agreement between each pair of methods was quantified using the concordance correlation coefficient (CCC). When compared with the reference method, VET data had excellent reliability [95% confidence interval (CI) of CCC: 0.992-0.998]. The BIN data had good to excellent reliability [95% CI of CCC: 0.776-0.936]. The APP data had poor reliability when compared with the reference method [95% CI of CCC: -0.167-0.156]. Our results highlight that a small number of herds were contributing most to overall use and farmers showed varying levels of consistency in recording AMU. Veterinary data were the most reliable approach for assessing AMU when compared with a reference method of AMU. This is an important finding for the future monitoring of AMU at a national level.

Measuring Expenditure with a Mobile App: Do Probability-Based and Nonprobability Panels Differ?

Abstract In this case study, we examine a novel aspect of data collected in a typical probability and a typical nonprobability panel: mobile app data. The data were collected in Great Britain in 2018, using the Innovation Panel of the UK Household Longitudinal Study and the Lightspeed online access panel. Respondents in each panel were invited to participate in a month-long study, reporting all their daily expenditures in the app. In line with most of the research on nonprobability and probability-based panel data, our results indicate differences in the data gathered from these data sources. For example, more female, middle-aged, and highly educated people with higher digital skills and a greater interest in their finances participated in the nonprobability app study. Our findings also show that resulting differences in the app spending data are difficult to eliminate by weighting. The only data quality aspect for which we do not find evidence of differences between the nonprobability and probability-based panel is behavior in using the spending app. This finding is contrary to the argument that nonprobability online panel participants try to maximize their monetary incentive at the expense of data quality. However, this finding is in line with some of the scarce existing literature on response behavior in surveys, which is inconclusive regarding the question of whether nonprobability online panel participants answer questions less conscientiously than probability-based panel respondents. Since the two panels in our case study differ in more aspects than the sample selection procedure, more research in different contexts is necessary to establish generalizability and causality.

Graph structure estimation neural network-based service classification

PurposeWith the introduction of graph structure learning into service classification, more accurate graph structures can significantly improve the precision of service classification. However, existing graph structure learning methods tend to rely on a single information source when attempting to eliminate noise in the original graph structure and lack consideration for the graph generation mechanism. To address this problem, this paper aims to propose a graph structure estimation neural network-based service classification (GSESC) model.Design/methodology/approachFirst, this method uses the local smoothing properties of graph convolutional networks (GCN) and combines them with the stochastic block model to serve as the graph generation mechanism. Next, it constructs a series of observation sets reflecting the intrinsic structure of the service from different perspectives to minimize biases introduced by a single information source. Subsequently, it integrates the observation model with the structural model to calculate the posterior distribution of the graph structure. Finally, it jointly optimizes GCN and the graph estimation process to obtain the optimal graph.FindingsThe authors conducted a series of experiments on the API data set and compared it with six baseline methods. The experimental results demonstrate the effectiveness of the GSESC model in service classification.Originality/valueThis paper argues that the data set used for service classification exhibits a strong community structure. In response to this, the paper innovatively applies a graph-based learning model that considers the underlying generation mechanism of the graph to the field of service classification and achieves good results.

Associations of smart device apps with and without a perceived self-efficacy component in a physical activity context with BMI.

Smart device apps can be used to promote physical activity, decrease sedentariness, or increase self-efficacy to get active in college students, possibly reducing their obesity risk. This study assessed associations of college students' smart device use with body mass index (BMI), expecting associations between physical activity parameters and self-efficacy with BMI, moderated by app type. In an online survey, app data of a week from student's devices, weight, height, physical activity, and self-efficacy to get active were collected from 120 students. Apps were categorized "physical activity," "physical activity with a self-efficacy component," or "unrelated." Use time of physical activity apps is not associated with BMI, while self-efficacy app time show negative and unrelated app time positive associations. Self-efficacy may be an important component in app design for obesity risk reduction of college students. Test economy was low, which is why future studies need to concentrate on better data extraction methods.

Unveiling the hidden dangers: Security risks and forensic analysis of smart bulbs

People often dispose of their useless smart digital gadgets without realizing the potential presence of useful information inside these devices. This is also true for faulty smart bulbs, which cybercriminals might exploit to gain unauthorized access to a smart home and manipulate or steal private information. This research delves into the potential security risks associated with smart bulbs and provides recommendations for mitigating such risks. Through a comprehensive analysis of the functionality of smart bulbs, this study introduced the data extraction framework DEF-IoTF for collecting both hardware and application-level digital artifacts from smart bulbs. This paper presents the FIvM-IoT model for collecting and analyzing evidence from companion app data on mobile phones and Wifi modules at the hardware level. We conduct examinations on the smart bulb's Wifi module and extract its firmware using the developed Wifi_Cred tool. These include evidence related to user credentials, log time stamps, Wifi details, potential forensic information, and investigation procedures for IoT devices. Finally, this study provides prominent IoT forensic use cases along with the key requirements for hardware-level forensic investigation of Wifi modules.

Friend or foe? A spatial approach to overlay bicycle and scooter trajectories

Dockless e-scooter schemes have seen increasing popularity in 28 German cities. Increasing use on insufficiently dimensioned bicycle infrastructure can lead to conflicts between e-scooter riders and cyclists. A new approach was developed in order to detect potential zones of conflict by overlaying aggregated bicycle and e-scooter trajectories in the City of Dresden, Germany. Bicycle data is being obtained by the annual STADTRADELN campaign where cyclists record and transmit daily trips via GPS for a period of three weeks. Simultaneously, e-scooter API data has been collected over a course of 8 weeks from June to September 2021. Origin/Destination data has been generated and routed over a OSM network in order to obtain aggregate d e-scooter flows. We extrapolated the aggregated bicycle data to match them with the timeframe of the e-scooter data acquisition. Afterwards we spatially joined both: bicycle and e-scooter flows and calculated the link wise proportion of e-scooter trips in relation to bicycle trip volumes. Two important findings emerged: (1) Residential roads have a higher proportion of e-scooter trips. (2) E-scooters are exposed to high bicycle trip volumes on primary roads with bicycle infrastructure. We conclude that this approach can detect possible links of conflict, where overtaking cyclists or insufficient space can lead to dangerous situations. That approach is biased towards a missing route choice model for e-scooter riders or better route data of e-scooters, which needs further research.

Analysis of Google Play Store Apps Data Using Tableau Data Visualization Application

This research aims to enhance understanding of big data management and processing. One of the challenges faced is the complexity and large volume of data, which requires effective tools and techniques for analysis and visualization. The objective of this study is to analyze Google Play Store app data based on categories and ratings, and to visualize the results using Tableau. The research method employs a quantitative approach with a framework that includes problem formulation, data collection from the Google Play Store Apps database obtained from kaggle.com, data processing, and analysis using Tableau. The results of the study indicate that the use of data visualization in the form of management graphics, such as horizontal bars and treemaps, is highly effective in identifying the comparison of the number of applications based on categories and ratings. These visualizations facilitate understanding the distribution and trends of applications on the Google Play Store. In conclusion, this research demonstrates that data visualization with Tableau can optimize big data processing and provide valuable insights into the distribution of app categories and ratings on the Google Play Store. These findings underscore the importance of using visualization tools in big data analysis to enhance understanding and improve decision-making.