Data Collection Practices Research Articles

Care, Indifference and Anxiety—Attitudes toward Location Data in Everyday Life

Modern mobile devices are replete with advanced sensors that expand the array of possible methods of locating users. This can be used as a tool to gather and use spatial information, but it also brings with it the specter of “geosurveillance” in which the “location” becomes a product in itself. In the realm of software developers, space/place has been reduced and discretized to a set of coordinates, devoid of human experiences and meanings. To function in such digitally augmented realities, people need to adopt specific attitudes, often marked with anxiety. We explored attitudes toward location data collection practices using qualitative questionnaire surveys (n = 280) from Poznan and Edinburgh. The prevailing attitude that we identified is neutral with a strong undertone of resignation—surrendering personal location is viewed as a form of digital currency. A smaller number of people had stronger, emotional views, either very positive or very negative, based on uncritical technological enthusiasm or fear of privacy violation. Such a wide spectrum of attitudes is not only produced by interaction with technology but can also be a result of different values associated with space and place itself. Those attitudes can bring additional bias into spatial datasets that is not related to demographics.

The Importance of Using Multiple Data Sources in Policy Assessments: Lessons From Two Conditional Cash Transfer Programs in New York City

The high costs of implementing surveys are increasingly leading research teams to either cut back on surveys or to rely on administrative records. Yet no policy should be based on a single set of estimates, and every approach has its weaknesses. A mixture of approaches, each with its own biases, should provide the analyst with a better understanding of the underlying phenomenon. This claim is illustrated with a comparison of employment effect estimates of two conditional cash transfer programs in New York City using survey and administrative unemployment insurance (UI) data. This article explores whether using administrative data and survey data produce different impact estimates and investigates the source of differential effects between data sources. The results of a survey nonresponse bias analysis and an analysis of characteristics of non-UI-covered job characteristics using data collected on 6,000 families who enrolled in either the Family Rewards or Work Rewards evaluation are presented. In both evaluations, survey data showed positive employment effects, while administrative data showed no statistically significant employment effects. Family Rewards increased employment mostly in non-UI-covered jobs, while the positive survey impact estimates in Work Rewards were partially due to survey nonresponse bias. Despite cost pressures leading researchers to collect and analyze only administrative records, the results suggest that survey and administrative records data both suffer from different kinds of sample attrition, and researchers may need to triangulate data sources to draw accurate conclusions about program effects. Developing more economical data collection practices is a major priority.

Internet Users’ Valuation of Enhanced Data Protection on Social Media: Which Aspects of Privacy Are Worth the Most?

As the development of the Internet and social media has led to pervasive data collection and usage practices, consumers’ privacy concerns have increasingly grown stronger. While previous research has investigated consumer valuation of personal data and privacy, only few studies have investigated valuation of different privacy aspects (e.g., third party sharing). Addressing this research gap in the literature, the present study explores Internet users’ valuations of three different privacy aspects on a social networking service (i.e., Facebook), which are commonly captured in privacy policies (i.e., data collection, data control, and third party sharing). A total of 350 participants will be recruited for an experimental online study. The experimental design will consecutively contrast a conventional, free-of-charge version of Facebook with four hypothetical, privacy-enhanced premium versions of the same service. The privacy-enhanced premium versions will offer (1) restricted data collection on side of the company; (2) enhanced data control for users; and (3) no third party sharing, respectively. A fourth premium version offers full protection of all three privacy aspects. Participants’ valuation of the privacy aspects captured in the premium versions will be quantified measuring willingness-to-pay. Additionally, a psychological test battery will be employed to examine the psychological mechanisms (e.g., privacy concerns, trust, and risk perceptions) underlying the valuation of privacy. Overall, this study will offer insights into valuation of different privacy aspects, thus providing valuable suggestions for economically sustainable privacy enhancements and alternative business models that are beneficial to consumers, businesses, practitioners, and policymakers, alike.

Towards Dynamic Transparency: The AppTrans (Transparency for Android Applications) Project

Our digital world relies upon data but its data practices are increasingly opaque -- to users who want to know whether these practices are consistent with their preferences and needs, to those who develop digital technologies and need to understand their workings and effects, and to the regulators who seek to ensure compliance with laws. We need to make data practices more transparent and we need to enlist technology itself in this project through developing stronger transparency-enhancing technologies (TETs). In this project, funded by the Office of the Privacy Commissioner of Canada (OPC), we created a prototype of such a TET, AppTrans (Transparency for Android Applications). It uses digital technologies to compare the declared data practices of mobile apps to their actual (or potential) practices in order to flag potential discrepancies. This involves three steps. The first involves using AI techniques to automate the reading of privacy policies, which are currently the main means through which apps declare their data practices. The second involves the scanning and analysis of an app’s static code in order to determine what the potential permission are in relation to personal data collection. The third involves comparing the first and second to determine potential personal data collection practices that are not declared in the relevant privacy policy. We emphasize that the current tool was developed as a research prototype with a small staff (a couple of graduate students). As such, the goal of the tool was to establish feasibility for tools of this type, and it was not intended in its current form for broad deployment and use. Nonetheless, our results show that this type of tool is feasible and can offer regulators new insights into the activities that they regulate. For example, our empirical results find several interesting phenomena. The first is that we found significant non-compliance between applications and their associated policies (average of 59.5%). We determined that this is often the result of undeclared collection of personally identifiable information by third party code used by the developer, rather than code written by the developer themselves. Apps are often developed through integrating components that have been created by others through the use of third party “libraries”. For example, the easiest way to create ad-supporting apps is through using ad libraries and those libraries can then potentially collect personally identifiable information that is available to the app. Another common type of library used is an analytics library, which measures and documents user engagement. Our results show that such use of personally identifiable information by third party code use is not being properly documented in the app privacy policies. We recommend that the OPC study the issue of third party code use by app developers and issue recommendations. We also found that a large fraction of privacy policies are written at a language level that is above that of many smartphone users. We recommend that the OPC consider using available tools to study the language levels of app privacy policies, perhaps in specific sectors, in order to offer best practice guidelines in relation to the question of language levels.

Principles and practice of acquiring drone-based image data in marine environments

With almost limitless applications across marine and freshwater environments, the number of people using, and wanting to use, remotely piloted aircraft systems (or drones) is increasing exponentially. However, successfully using drones for data collection and mapping is often preceded by hours of researching drone capabilities and functionality followed by numerous limited-success flights as users tailor their approach to data collection through trial and error. Working over water can be particularly complex and the published research using drones rarely documents the methodology and practical information in sufficient detail to allow others, with little remote pilot experience, to replicate them or to learn from their mistakes. This can be frustrating and expensive, particularly when working in remote locations where the window of access is small. The aim of this paper is to provide a practical guide to drone-based data acquisition considerations. We hope to minimise the amount of trial and error required to obtain high-quality, map-ready data by outlining the principles and practice of data collection using drones, particularly in marine and freshwater environments. Importantly, our recommendations are grounded in remote sensing and photogrammetry theory so that the data collected are appropriate for making measurements and conducting quantitative data analysis.

App Users Unwittingly in the Spotlight: A Model of Privacy Protection in Mobile Apps

Mobile apps are increasingly jeopardizing app users' online privacy by collecting, storing, and sharing personal data disclosed via apps. However, little is known about mobile app users' current privacy protection behavior and the factors that motivate it. Drawing on Roger's Protection Motivation Theory (PMT), this study develops and tests the App Privacy Protection Model among 1,593 Western European app users. The results demonstrate that, on the one hand, increased levels of perceived self‐efficacy, vulnerability, and privacy concern enhance mobile app users' motivation to engage in risk‐reducing behavior, while on the other hand, higher levels of knowledge of the data collection practices of mobile apps, app attitude, and perceived response costs diminish it. Being the first study that applies PMT in the mobile app context, this study offers several important implications regarding privacy protection in mobile apps.

Abstract 2276: Efficient curation and ontology mapping of clinical and phenotypic data

Abstract Heterogeneity within and between data sets is one of the primary impediments to sound and efficient data analysis. This heterogeneity can arise from many sources: data collection practices can change over time, even within the same study; rigorous standards for data encoding may be missing, leading to inter-individual heterogeneity in data encoding; or the data may originally have been collected for a different purpose (e.g. EHR data). Additionally, narrative text, such as medical statements, are inherently unstructured and require curation before analysis. No matter which source(s) heterogeneity derives from, curation of the data is necessary for efficient, accurate and reproducible data analysis. This not only includes correcting errors and standardizing encoding, but also extends to enriching the data through mapping it to relevant medical ontologies, such as SNOMED CT, or other standardized terminologies, such as Research Resource Identifiers. To solve the most common issues in clinical and phenotypic data curation, we have developed the AccurateTM data curation and ontology mapping solution. It combines an intuitive web-based user interface for data cleaning with efficient solutions for semi-automated ontology mapping of both structured data and narrative text. For structured data, tokenized and stemmed data items are mapped against ontologies indexed in Elasticsearch. Term names, their synonyms and the local ontology structure are then used to query the target ontology, with a list of best matches returned along with a quality score for the mapping. Ontology tagging of narrative text is based on a sentence-based deep learning approach, analyzing sentences to classify and ontology map identified text units. In practice, combining a bidirectional long short term memory network with a conditional random field model into a named entity recognition system (bio-NER). Preliminary benchmarking of the bio-NER system on the MIMIC III data set suggests good specificity and sensitivity for identification of biomedically relevant concepts. In summary, we here present an intuitive and highly efficient solution for curating clinical and phenotypic data, as well as enriching it using ontology mapping of both structured and narrative data. Citation Format: Henrik Edgren, Beatriz Mano, Maria Laaksonen. Efficient curation and ontology mapping of clinical and phenotypic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2276.

Enhancing national rehabilitation quality development through standardised reporting of functioning in Switzerland

Functioning is the primary outcome of rehabilitation. Currently functioning outcomes are collected with a variety of assessment tools. Unless standards for reporting are in place, data derived from various assessment tools has only applicability within the individual clinical encounter, but cannot be aggregated across and within institutions, thus, lack comparability. Consequently, there is a need for a standardized reporting system to ensure that functioning information can be aggregated, compared, made available for decision-making and continuous improvement at individual, institutional and national levels. The aim is to develop and implement a standardised reporting system for national quality improvement in Swiss rehabilitation, accounting for different assessment tools used for functioning information. We build upon existing data collected for Swiss national quality monitoring to derive a harmonized data set irrespective of the assessment tool used, based on a conceptually and psychometrically-sound system for standardized reporting of functioning outcomes. Change in functioning status within and across health condition groups, and its determinants, throughout the rehabilitation process will be identified. ICF linking rules, Rasch Measurement Theory and regression modelling will be used. The results will be presented to relevant stakeholders conducting Stakeholder Dialogues to develop strategies of how such a system can be implemented for quality improvement. 30 Swiss rehabilitation clinics participate in the project using either the assessment tools FIM ® , Barthel Index, or Health Assessment Questionnaire. First results will be available in April. This poster aims to outline the project–its background, methodology and clinical relevance. First, this project recognizes the important role of functioning as primary health outcome in rehabilitation. Second, the standardized reporting system enables clinicians to maintain their existing data collection practice, while at the same time being able to aggregate and compare information. Third, this project builds upon a learning system, with emphasis on transparency and stakeholder engagement.

Restructuring Data Reported from Jurisdictional Early Hearing Detection and Intervention (EHDI) Programs: A Pilot Study.

To assess the feasibility, benefits, and challenges surrounding individual-level versus aggregate data reporting by jurisdictional EHDI programs to the Centers for Disease Control and Prevention (CDC). Using data reported to CDC by three jurisdictions in 2011, descriptive statistics were used to assess the feasibility of collecting and reporting individual-level data. Comparisons were made on what can be learned from individual-level data as opposed to CDC's aggregate survey data. Individual-level data provided a detailed overview of the population served, services received, and variations across jurisdictions in data collection, reporting, and quality monitoring practices. Several challenges and areas needing improvement were identified: variations in (1) data standardization; (2) data collection and reporting procedures; and (3) protocols for recommended follow-up services. Using individual-level data, CDC was able to perform in-depth statistical analyses and learn more about each jurisdiction's population, their EHDI process, and challenges to data collection, tracking, and surveillance efforts. As a result, CDC was able to provide more targeted technical assistance. All of the above would not be feasible using aggregate survey data. The pilot study demonstrated that individual-level data reporting to CDC is feasible and offers many opportunities for both CDC and jurisdictional EHDI programs.

Geomatics for economic archaeological documentation and management

Restoration of heritage sites from their remains is a challenging task, as the required geospatial and geophysical data are generally incomplete and complex in nature. These data are typically collected using multiple disciplines/techniques such as GIS, remote sensing, photogrammetry and ground penetration radar (GPR), to record and document factual and accurate information. This research proposes an integrated data collection and management practice towards achieving a structured archaeological documentation process with minimal or no damage to the existing structure. The process includes the integration of archaeological information from aerial stereo photographs, satellite images, global positioning systems (GPS) and GPR profiles. This approach is demonstrated through a fourth century ancient Greco-Roman city in Jordan (Umm Qais). Various abstract levels of details have been generated including orthophoto, digital elevation model (DEM) and vector site layout of buildings and infrastructure. Further, the GPR analysis shows the existence of shallow discontinuous and shifted buried walls, which indicates that the site was affected by a possible earthquake during the historical period. The developed 3D geospatial archaeological database for Umm Qais City can be utilised for various purposes such as archaeological documentation, conservation, monitoring, restoration, tourism and urban planning. The study also outlines the schedule of various processes and the cost estimate associated with the development of the integrated 3D geospatial archaeological database.

The Ethical Use of Data

AbstractEvidence‐based decision‐making requires access to good and reliable data. In recent years, there has been a tremendous increase of data available to institutions, from internal as well as external sources. With this increase in data and its analytics, new challenges and questions arise. One of the most pertinent of which is the ethics and best practices of data collection and usage. This chapter explores the role data have in institutional decision‐making, the need for clear data policies and definitions, and concludes with suggestions for elements of a Data Bill of Rights.

Improving Data Quality in eBird- the Expert Reviewer Network

eBird is a global citizen science project that gathers observations of birds. The project has been making a considerable contribution to the collection and sharing of bird observations, even in the data-poorest countries, and is accelerating the accumulation of bird records globally. On 22 March 2018 eBird surpassed ½ billion bird observations. A primary component of ensuring the best quality data is the network of more than 1300 volunteer reviewers who scour incoming data for accuracy. Reviewers provide active feedback to participants on everything from bird identification to best practices for data collection. Since eBird’s inception in 2002, almost 23 million observations have been reviewed, requiring more than 190,000 hours of effort by reviewers. In this presentation we review how eBird recruits expert reviewers, describe their responsibilities, and offer some insight in new developments to improve the reviewing process. How are reviewers recruited. There are three primary methods that used to identify new reviewers. First, if we don’t have any active participants in a region (e.g., Kamchatka Russia) eBird staff search birding listserves to find an individual who is reporting a lot of high-quality observations from the area. We then contact those individuals and offer them the opportunity to review records for the region. This option has the lowest likelihood of success. Second, if an individual is submitting a lot of records to eBird from a region that needs a reviewer we contact them and request their participation. Third, in much of the world eBird has partner groups. These partner organizations (e.g., Taiwan, Spain, India, Portugal, Australia, and all of the Western Hemisphere) recruit their own reviewers. The third method is the most effective way to gain expert participation. What does a reviewer do? eBird reviewers work to improve eBird data in three primary areas. First, they develop and manage the eBird checklist filters for a region. These filters generate a checklist of birds for a particular time and location, and determine what records get flagged for further review. Second, if an eBird participant tries to report a species that is not on the checklist, or if the number of individuals of a species exceeds the filter limit, then these records get flagged for review. Reviewers contact the observer and request further documentation. Currently, 57% of all records that are evaluated by reviewers are validated. Finally, eBird reviewers validate whether the participant is eBirding correctly. That is, are they correctly filling out the information on when, where, and how they went birding. It has been our experience that different types of reviewers are required to effectively review eBird submissions: those who are good at reviewing bird records and those who are good at educating observers on how to participate. What are future plans? eBird will move towards more effective reviewer teams, where the volume of observations can be split amongst a number of individuals with different strengths, allowing identification experts to focus on observation-level ID issues; and strong communicators to focus on working with contributors on checklist-level best practices. Currently, a single eBird review platform handles a broad array of different reviewing functions. It is our intent to split some of these functions into multiple platforms. For example, right now all review happens at the database level of the ‘observation’: a record of a taxon at a date and location. Plans are underway to develop tools that will allow reviewers to work at the entire checklist level (i.e., to more easily review the accuracy of how all the observations during a checklist event were submitted), which will enable much more effective review of checklist-level data quality concerns.

Privacy in Doubt: An Empirical Investigation of Canadians' Knowledge of Corporate Data Collection and Usage Practices

AbstractWe investigate Canadians' level of awareness of the ways in which businesses collect and use their data via existing and emerging technologies, including facial and voice recognition, mobile location tracking and the Internet of Things. In a survey of 1,005 Canadians, we found that many respondents are ill informed about how their data are being collected and used. Such low awareness of data practices is partially explained by the shortcomings of the “privacy notice.” We suggest further research into alternative methods of informing Canadians about corporate data practices and of enhancing individuals' control of private data in today's increasingly connected and mobile world. Copyright © 2018 ASAC. Published by John Wiley & Sons, Ltd.

There and Back Again: A Vindication of the Listener's Interests in Regulating Targeted Advertising in the Internet Information Economy

Targeted advertising - the process by which advertisers direct their messaging at a specific demographic - is neither a recent nor irrational phenomenon. However, the growth of consumer dependence on new and social media platforms has led to consumer concerns regarding data privacy. This Note considers the feasibility of regulating targeted advertising used in data-for-service Internet platforms, such as Facebook, given restraints posed by the Commercial Speech Doctrine of the First Amendment and the Supreme Court’s treatment of data collection and transmission practices, most recently in Sorrell v. IMS Health. This Note argues for a return to first principles of the Commercial Speech Doctrine, which prioritizes the interests of the listener, and proposes several policies on how to best regulate a nascent area of technology.

Special Education Teachers' Perceptions and Intentions Toward Data Collection.

Although data-based decision making is an evidence-based practice, many special educators have difficulty applying the practice within daily routines. We applied the Theory of Planned Behavior (TPB) to understand the influences that promote or hinder early childhood special educators' intentions to collect data. We assessed three influences on behavioral intention to collect data derived from the TPB: (a) attitude toward collecting data, (b) social norms for collecting data, and (c) perceived behavioral control for collecting data. All three influences correlated positively with teachers' reported intention to collect data; however, only perceived behavioral control of barriers correlated positively with actual data collection. Additional measures of teacher self-efficacy and administrative support correlated positively with intention to collect data, but not with actual data collection behaviors. Perceived behavior control accounted for the most variance in actual data collection behavior. Implications of the findings for data collection practices in educational settings are discussed.

Generalized Trust, Need for Cognitive Closure, and the Perceived Acceptability of Personal Data Collection

This vignette-based study examines how generalized trust and the need for cognitive closure relate to the perceived acceptability of contemporary business methods of personal data collection. Subjects are exposed to four scenarios that describe a method of personal data collection, involving either brand-name companies or generic descriptors of companies. After each scenario, subjects rate how acceptable they find the practice of data collection, along with the frequency and quality of experiences that they have had with the company (for brand names) or type of company (for generic descriptors). Judgments of perceived acceptability are analyzed, both across the portfolio of judgments and within each separate scenario. While analyses of each separate scenario point to the context-dependency of the perceived acceptability of data collection, several results stand out when analyzing the subjects’ portfolios of responses in the aggregate. Higher generalized trust is linked to a higher average acceptability rating, and the effect is stronger when companies are described with brand names rather than generic descriptors. Uniformly, however, no relationship is found between need for cognitive closure and perceived acceptability. Additionally, positive experiences are found to be a stronger predictor of perceived acceptability of data collection than frequency of use.

Make Up Your Mind

Today, connected software-intensive products permeate virtually every aspect of our lives and the amount of customer and product data that is collected by companies across domains is exploding. In revealing what products we use, when and how we use them and how the product performs, this data has the potential to help companies optimize existing products, prioritize among features and evaluate new innovations. However, despite advanced data collection and analysis techniques, companies struggle with how to effectively extract value from the data they collect and they experience difficulties in defining what values to optimize for. As a result, the impact of data is low and companies run the risk of sub-optimization due to misalignment of the values they optimize for. In this paper, and based on multi-case study research in embedded systems and online companies, we explore data collection and analysis practices in companies in the embedded systems and in the online domain. In particular, we look into how the value that is delivered to customers can be expressed as a value function that combines different factors that are of importance to customers. By expressing customer value as a value function, companies have the opportunity to increase their awareness of key value factors and they can establish an agreement on what to optimize for. Based on our findings, we see that companies in the embedded systems domain suffer from vague and confusing value functions while companies in the online domain use simple and straightforward value functions to inform development. Ideally, and as proposed in this paper, companies should strive for a comprehensive value function that includes all relevant factors without being vague or too simple as is the case in the companies we studied. To achieve this, and to address the difficulties many companies experience, we present a systematic approach to value modelling in which we provide detailed guidance for how to quantify feature value in such a way that it can be systematically validated over time to help avoid sub-optimization that will harm the company in the long run.

Privacy-enhancing aggregation of Internet of Things data via sensors grouping

Big data collection practices using Internet of Things (IoT) pervasive technologies are often privacy-intrusive and result in surveillance, profiling, and discriminatory actions over citizens that in turn undermine the participation of citizens to the development of sustainable smart cities. Nevertheless, real-time data analytics and aggregate information from IoT devices open up tremendous opportunities for managing and regulating smart city infrastructures in a more efficient and sustainable way. The privacy-enhancing aggregation of distributed sensor data, such as residential energy consumption or traffic information, is the research focus and challenge tackled in this paper. Citizens have the option to choose their privacy level by reducing the quality of the shared data at a cost of a lower accuracy in data analytics services. A baseline scenario is considered in which IoT sensor data are shared directly with an untrustworthy central aggregator. A grouping mechanism is introduced that improves privacy by sharing data aggregated first at a group level compared to a baseline scenario in which each individual shares data directly to the central aggregator. Group-level aggregation obfuscates sensor data of individuals, in a similar fashion as differential privacy and homomorphic encryption schemes, thus inference of privacy-sensitive information from single sensors becomes computationally harder compared to the baseline scenario. The proposed system and its generic applicability are evaluated using real-world data from two smart city pilot projects. Privacy under grouping increases, while preserving the accuracy of the baseline scenario. Intra-group influences of privacy by one group member on the other ones are measured and fairness on privacy is found to be maximized between group members with similar privacy choices. Several grouping strategies are compared. Grouping by proximity of privacy choices provides the highest privacy gains. The implications of the strategy on the design of incentives mechanisms are discussed.

A review and an empirical analysis of privacy policy and notices for consumer Internet of things

The privacy policies and practices of six consumer Internet of things (IoT) devices were reviewed and compared. In addition, an empirical verification of the compliance of privacy policies for data collection practices on two voice‐activated intelligent assistant devices, namely the Amazon Echo Dot and Google Home devices was performed. The review shows that IoT privacy policies may not be usable from the human‐computer interaction perspective because IoT policies are included as part of the manufacturers' general privacy policy (which may include policies unrelated to the device), or the IoT policy requires to read (in addition to the IoT policies) the manufacturers' general privacy policy which increase the cognitive load for the user. It was also found that future policy changes along with the approach to provide user consent to changes may adversely affect the privacy of the consumer because changes to policies may not provide choice to consumers to opt out from data collection practices if consumers are not aware of the changes. Finally, the empirical results for the Amazon Echo Dot and the Google Home devices demonstrate they adhere to their privacy policies when voice is collected through these devices.

Evaluation of a Community Development Program in Nepal

Objective:This study aims to evaluate the impact of a community development program meant to improve living standards of poor rural families through income generating activities (IGAs) based on conditional cash transfers (CCTs) in Doti, Nepal.Method:We use cross-sectional field data from a sample of 392 families representative of the village development committees of Pokhari, Ladagada, and Gajari. After running a propensity score analysis to increase comparability between the treatment and comparison groups, we compare mean scores on a series of chosen outcome variables via t-test analyses.Results:Results suggest that, although improvements in family income and living standards are felt subjectively, crop production might have worsened as a result of IGAs, suggesting the possibility of a trade-off and of long-term effects.Discussion and Implications:This article has implications for research and practice in community development programs and data collection and evaluation of such programs.