Approach for creating useful, gamified and social map applications utilising privacy-preserving crowdsourcing

The production and use of geographic information have become easier and more social. The interactivity of maps has fundamentally changed, not only because the touch-based interfaces are easier to use, but also because maps offer possibilities to interact with others. Map applications allow citizens to contribute but also share content to others. This contribution and sharing done by regular people is referred to as crowdsourcing. Map applications that utilise crowdsourcing face specific issues regarding the creation process, the usefulness and the crowdsourcing. These issues, however, have not been studied comprehensively and lack real world examples. This dissertation is the initial step to fill this gap by studying map applications that utilise crowdsourcing. These map applications are described using the design science research approach. Three issues relevant for the map application studied are: 1) the creation process, 2) utility requirements and usability heuristics, and 3) crowdsourcing approach. These issues are studied by using the design science research approach to produce theoretical and empirical knowledge of three map applications utilising crowdsourcing. The aim is to use this knowledge to form a design science research based approach suitable for creating map applications utilising crowdsourcing. The results regarding the creation process indicate that following a specific approach will help in creating crowdsourced map applications. This dissertation provides a customised design science research approach for creating crowdsourced map applications. Furthermore, prescriptive knowledge that provides real world examples crowdsourced map applications is provided. The results concerning the usefulness of map applications utilising crowdsourcing indicate that there are specific utility and usability requirements to be accounted for. This dissertation provides key utility requirements and usability heuristics for crowdsourced map applications. In general, a map interface for exploring and sharing content is needed. The map interface should be simple, citizens should be supported and interaction should be intuitive. The results concerning the crowdsourcing approach of map applications indicate that there is a need for specifying how citizens are involved in the process. This dissertation provides key requirements of the crowdsourcing approach of these types of map applications. The community driven crowdsourcing approach should be supported by official content and an engagement approach based on gamified and social elements to motivate content sharing. Privacy of citizens should be preserved by applying the privacy by design approach throughout the creation process. Privacy-preserving map applications utilising community-driven crowdsourcing, in which citizens can be engaged with gamification and social elements to explore and share content can be created by following the designs science research based approach presented in this dissertation.Geospatiaalisen eli paikkaan liittyvän tiedon tuotanto ja käyttö on helpottunut ja muuttunut yhä yhteisöllisemmäksi. Myös karttojen vuorovaikutteisuus on perustavanlaatuisesti muuttunut. Karttapohjaiset käyttöliittymät ovat yhä helppokäyttöisempiä ja niiden avulla kansalaiset voivat tuottaa tietoa, mutta myös jakaa sitä toisilleen. Tätä tavallisten kansalaisten tekemää tiedon tuottamista ja jakamista kutsutaan joukkoistamiseksi. Karttasovelluksiin, jotka hyödyntävät joukkoistettua tiedonkeruuta liittyy kuitenkin erityisiä haasteita niiden luomisen, hyödyllisyyden sekä joukkoistamisen osalta. Näitä haasteita ei ole vielä samanaikaisesti tutkittu kattavasti eikä näistä karttasovelluksista ole tarjolla tarpeeksi käytännön esimerkkejä ja tietoa. Tämä väitöskirja on ensimmäinen askel näiden haasteiden ratkaisemiseen, sillä tässä väitöskirjassa tutkitaan joukkoistamista hyödyntäviä karttasovelluksia. Väitöskirjassa perehdytään kolmeen karttasovelluksiin liittyvään haasteeseen, jotka ovat: 1) luomisprosessin lähestymistapa, 2) toiminnalliset vaatimukset ja käytettävyyden ohjeet ja 3) joukkoistamiseen käytetty lähestymistapa. Näitä haasteita tutkitaan tuottamalla tietoa kolmesta joukkoistamista hyödyntävästä karttasovelluksesta käyttäen kehitystutkimukseen perustuvaa tutkimusmenetelmää. Tätä tietoa käyttäen tavoitteena on muokata kehitystutkimukseen perustuvaa lähestymistapaa, jotta se soveltuisi joukkoistamista hyödyntävien karttasovellusten luomiseen. Luontiprosessin osalta tulokset osoittavat, että tieteellisen lähestymistavan seuraaminen helpottaa joukkoistettujen karttasovelluksien luomisessa. Väitöskirja ehdottaa muokattua kehitystytkimukseen perustuvaa lähestymistapaa joukkoistettujen karttasovellusten luomiseen. Lisäksi väitöskirja tarjoaa kuvailevia sekä ohjailevia tietoja joukkoistetuista karttasovelluksista käytännön esimerkein. Hyödyllisyyden osalta tulokset osoittavat, että joukkoistetuilla karttasovelluksilla on erityisiä toiminnallisia ja käytettävyyden vaatimuksia. Väitöskirja kokoaa keskeisiä toiminnallisia vaatimuksia sekä käytettävyyden ohjeita. Vaatimuksiin kuuluu helppokäyttöinen kansalaista tukeva karttakäyttöliittymä sisältöjen tutkimiseen sekä jakamiseen. Joukkoistamisen osalta tulokset osoittavat, että on tarve määritellä kuinka kansalaisen osallistuvat prosessiin. Tämä väitöskirja ehdottaa keskeisiä vaatimuksia lähestymistavalle joukkoistamiseen. Yhteisölähtöiseen joukkoistamiseen perustuvaa lähestymistapaa tulisi tukea karttasovelluksen sisällöillä, esimerkiksi kiinnostavalla taustakartalla. Lisäksi pelillisyyteen ja yhteisöllisyyteen perustuvalla sitouttamisella kansalaisia voidaan kannustaa sisältöjen jakamiseen. Kansalaisten yksityisyys tulisi turvata seuraamalla sisäänrakennetun tietosuojan lähestymistapaa läpi koko karttasovelluksen luomisprosessin ajan. Tässä väitöskirjassa esitettyä kehitystutkimukseen perustuvaa lähestymistapaa seuraamalla voidaan luoda yksityisyyden suojaavia ja yhteisölähtöistä joukkoistamista hyödyntäviä karttasovelluksia, joissa kansalaiset sitoutetaan pelillisyyden ja yhteisöllisyyden keinoin tutkimaan ja jakamaan sisältöjä

A survey of the use of crowdsourcing in software engineering

The term 'crowdsourcing' was initially introduced in 2006 to describe an emerging distributed problem-solving model by online workers. Since then it has been widely studied and practiced to support software engineering. In this paper we provide a comprehensive survey of the use of crowdsourcing in software engineering, seeking to cover all literature on this topic. We first review the definitions of crowdsourcing and derive our definition of Crowdsourcing Software Engineering together with its taxonomy. Then we summarise industrial crowdsourcing practice in software engineering and corresponding case studies. We further analyse the software engineering domains, tasks and applications for crowdsourcing and the platforms and stakeholders involved in realising Crowdsourced Software Engineering solutions. We conclude by exposing trends, open issues and opportunities for future research on Crowdsourced Software Engineering

行動認識機械学習データセット収集のためのクラウドソーシングの研究

In this thesis, we propose novel methods to explore and improve crowdsourced data labeling for mobile activity recognition. This thesis concerns itself with the quality (i.e., the performance of a classification model), quantity (i.e., the number of data collected), and motivation (i.e., the process that initiates and maintains goal-oriented behaviors) of participant contributions in mobile activity data collection studies. We focus on achieving high-quality and consistent ground-truth labeling and, particularly, on user feedback’s impact under different conditions. Although prior works have used several techniques to improve activity recognition performance, differences to our approach exist in terms of the end goals, proposed method, and implementation. Many researchers commonly investigate post-data collection to increase activity recognition accuracy, such as implementing advanced machine learning algorithms to improve data quality or exploring several preprocessing ways to increase data quantity. However, utilizing post-data collection results is very difficult and time-consuming due to dirty data challenges for most real-world situations. Unlike those commonly used in other literature, in this thesis, we aim to motivate and sustain user engagement during their on-going-self-labeling task to optimize activity recognition accuracy. The outline of the thesis is as follows: In chapter 1 and 2, we briefly introduce the thesis work and literature review. In Chapter 3, we introduce novel gamified active learning and inaccuracy detection for crowdsourced data labeling for an activity recognition system (CrowdAct) using mobile sensing. We exploited active learning to address the lack of accurate information. We presented the integration of gamification into active learning to overcome the lack of motivation and sustained engagement. We introduced an inaccuracy detection algorithm to minimize inaccurate data. In Chapter 4, we introduce a novel method to exploit on-device deep learning inference using a long short-term memory (LSTM)-based approach to alleviate the labeling effort and ground truth data collection in activity recognition systems using smartphone sensors. The novel idea behind this is that estimated activities are used as feedback for motivating users to collect accurate activity labels. In Chapter 5, we introduce a novel on-device personalization for data labeling for an activity recognition system using mobile sensing. The key idea behind this system is that estimated activities personalized for a specific individual user can be used as feedback to motivate user contribution and improve data labeling quality. We exploited finetuning using a Deep Recurrent Neural Network (RNN) to address the lack of sufficient training data and minimize the need for training deep learning on mobile devices from scratch. We utilized a model pruning technique to reduce the computation cost of on-device personalization without affecting the accuracy. Finally, we built a robust activity data labeling system by integrating the two techniques outlined above, allowing the mobile application to create a personalized experience for the user. To demonstrate the proposed methods’ capability and feasibility in realistic settings, we developed and deployed the systems to real-world settings such as crowdsourcing. For the process of data labeling, we challenged online and self-labeling scenarios using inertial smartphone sensors, such as accelerometers. We recruited diverse participants and con- ducted the experiments both in a laboratory setting and in a semi-natural setting. We also applied both manual labeling and the assistance of semi-automated labeling. Addition- ally, we gathered massive labeled training data in activity recognition using smartphone sensors and other information such as user demographics and engagement. Chapter 6 offers a brief discussion of the thesis. In Chapter 7, we conclude the thesis with conclusion and some future work issues. We empirically evaluated these methods across various study goals such as machine learning and descriptive and inferential statistics. Our results indicated that this study enabled us to effectively collect crowdsourced activity data. Our work revealed clear opportunities and challenges in combining human and mobile phone-based sensing techniques for researchers interested in studying human behavior in situ. Researchers and practitioners can apply our findings to improve recognition accuracy and reduce unreliable labels by human users, increase the total number of collected responses, as well as enhance participant motivation for activity data collection.九州工業大学博士学位論文 学位記番号：工博甲第526号 学位授与年月日：令和3年6月28日1 Introduction|2 Related work|3 Achieving High-Quality Crowdsourced Datasets in Mobile Activity Recognition|4 On-Device Deep Learning Inference for Activity Data Collection|5 On-Device Deep Personalization for Activity Data Collection|6 Discussion|7 Conclusion九州工業大学令和3年

Makan@local chatok: mobile eatery recommendation system based on local knowledge

This paper discusses a unique business model of eatery recommended system based on local knowledge using mobile platform. The business model is developed to define the business concept of the innovation which is a rural innovation involving multiple entities (locals, eatery owners and users who are searching for eatery). The innovation highlights on local knowledge crowd sourced from the participation of locals through ramification activities included in the mobile app. In achieving the aim, the design science methodology was adapted in this study which consists of 4 phases: (i) Awareness of Problem, (ii) Suggestion, (iii) Evaluation, and (iv) Conclusion. The proposed business model was developed through a few activities including literature review, comparative study and preliminary study. Then, the study continued with developing a prototype known as Makan@Local Chatok (M@LC) app and evaluated the app in terms of its usability aspects. Results from the usability testing concludes that the app is perceived as easy to use. It was also found that the proposed business model has been well-accepted by users. In conclusion, it is hoped that this study will not only demonstrate the potential and impact of mobile eatery recommendation system using local knowledge, but also provide a capstone on business research in the field of tourism industry

SpaceMaze: Incentivizing Correct Mobile Crowdsourced Sensing Behavior with a Sensified Minigame

Modern mobile phones are equipped with many sensors, which can increasingly be used to sense various environmental phenomena. In particular, mobile sensing has enabled crowdsourced data collection at an unprecedented scale. However, as laypersons are involved in this, concerns regarding the data quality arise. This work explores the gamification of smartphone-based measurement processes in practice by embedding a sensing task into a mobile minigame. The underlying idea is — rather than to educate the user on how to correctly perform a measurement task — to opportunistically execute the measurement in the background once the smartphone is in a suitable context. To this end, this paper presents the design and evaluation of SpaceMaze, a smartphone game with the goal of minimizing user error by introducing appropriate game mechanics to influence the phone context, using the example of mobile noise level monitoring. A large user study that compares SpaceMaze to two non-gamified apps for noise level monitoring (N=360 in total) shows that SpaceMaze can successfully reduce user errors when compared to simple non-gamified ambient noise level monitoring applications and that the minigame is generally perceived as being enjoyable. Solutions for remaining problems, such as noise generated by the players, are discussed

Citizen science and crowdsourcing for Earth observations: An analysis of stakeholder opinions on the present and future

The impact of Crowdsourcing and citizen science activities on academia, businesses, governance and society has been enormous. This is more prevalent today with citizens and communities collaborating with organizations, businesses and authorities to contribute in a variety of manners, starting from mere data providers to being key stakeholders in various decision-making processes. The “Crowdsourcing for observations from Satellites” project is a recently concluded study supported by demonstration projects funded by European Space Agency (ESA). The objective of the project was to investigate the different facets of how crowdsourcing and citizen science impact upon the validation, use and enhancement of Observations from Satellites (OS) products and services. This paper presents our findings in a stakeholder analysis activity involving participants who are experts in crowdsourcing, citizen science for Earth Observations. The activity identified three critical areas that needs attention by the community as well as provides suggestions to potentially help in addressing some of the challenges identified

Identifying success factors in crowdsourced geographic information use in government

Crowdsourcing geographic information in government is focusing on projects that are engaging people who are not government officials and employees in collecting, editing and sharing information with governmental bodies. This type of projects emerged in the past decade, due to technological and societal changes - such as the increased use of smartphones, combined with growing levels of education and technical abilities to use them by citizens. They also flourished due to the need for updated data in relatively quick time when financial resources are low. They range from recording the experience of feeling an earthquake to recording the location of businesses during the summer time. 50 cases of projects in which crowdsourced geographic information was used by governmental bodies across the world are analysed. About 60% of the cases were examined in 2014 and in 2017, to allow for comparison and identification of success and failure. The analysis looked at different aspects and their relationship to success: the drivers to start a project; scope and aims; stakeholders and relationships; inputs into the project; technical and organisational aspect; and problems encountered. The main key factors of the case studies were analysed with the use of Qualitative Comparative Analysis (QCA) which is an analytical method that combines quantitative and qualitative tools in sociological research. From the analysis, we can conclude that there is no “magic bullet” or a perfect methodology for a successful crowdsourcing in government project. Unless the organisation has reached maturity in the area of crowdsourcing, identifying a champion and starting a project that will not address authoritative datasets directly is a good way to ensure early success and start the process of organisational learning on how to run such projects. Governmental support and trust is undisputed. If the choice is to use new technologies, this should be accompanied by an investment of appropriate resources within the organisation to ensure that the investment bear fruits. Alternatively, using an existing technology that was successful elsewhere and investing in training and capacity building is another path for success. We also identified the importance of intermediary Non-Governmental Organizations (NGOs) with the experience and knowledge in working with crowdsourcing within a partnership. These organizations have the knowledge and skills to implement projects at the boundary between government and the crowd, and therefore can offer the experience to ensure better implementation. Changes and improvement of public services, or a focus on environmental monitoring can be a good basis for a project. Capturing base mapping is a good point to start, too. The recommendation of the report address organisational issues, resources, and legal aspects

Build an app and they will come? Lessons learnt from trialling the GetThereBus app in rural communities

Acknowledgements The research described here was supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1.Peer reviewedPostprin

Mobile services for green living

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsUrban cycling is a sustainable transport mode that many cities are promoting. However, few cities are taking advantage of geospatial technologies to represent and analyse behavioural patterns and barriers faced during cycling. This thesis is within the fields of geoinformatics and serious games, and the motivation came from our desire to help both citizens and cities to better understand cyclist behaviour and mobility patterns. We attempted to learn more about the impact of gamified strategies on engagement with cycling, the reasons for choosing between mobile cycling applications and the way such applications would provide commuting information. Furthermore, we explored the potential benefits of offering tools to build decision-making for mobility more transparent, to increase cycling data availability, and to analyse commuting patterns. In general, we found our research useful to enhance green living actions by increasing citizens’ willingness to commute by bicycle or communicating cycling conditions in cities. For urban cycling, data coming from mobile phones can provide a better assessment and enrich the analysis presented in traditional mobility plans. However, the diversity of current mobile applications targeting cyclists does not provide useful data for analysing commuter (inner-city, non-sporting) cycling. Just a few cyclists are adopting these applications as part of their commuting routine, while on the other hand cities are lacking a valuable source of constantly updated cycling information helpful to understand cycling patterns and the role of bicycles in urban transport. This thesis analyses how the incentives of location-based games or geo-games might increase urban cycling engagement and, through this engagement, crowdsource cycling data collection to allow cities to better comprehend cycling patterns. Consequently, the experiment followed a between-groups design to measure the impact of virtual rewards provided by the Cyclist Geo-c application on the levels of intention, satisfaction, and engagement with cycling. Then, to identify the frictions which potentially inhibit bicycle commuting, we analysed the bicycle trips crowdsourced with the geo-game. Our analysis relied on a hexagonal grid of 30-metre cell side to aggregate trip trajectories, calculate the friction intensity and locate the frictions. The thesis reports on the results of an experiment which involved a total of 57 participants in three European cities: M¨unster (Germany), Castell ´o (Spain), and Valletta (Malta). We found participants reported higher satisfaction and engagement with cycling during the experiment in the collaboration condition. However, we did not find a significant impact on the participants’ worldview when it comes to the intentions to start or increase cycling. The results support the use of collaboration-based rewards in the design of game-based applications to promote urban cycling. Furthermore, we validated a procedure to identify not only the cyclists’ preferred streets but also the frictions faced during cycling analysing the crowdsourced trips. We successfully identified 284 places potentially having frictions: 71 in M¨unster, Germany; 70 in Castell ´ o, Spain; and 143 in Valletta, Malta. At such places, participants recorded trip segments at speeds below 5 Km/h indicating a deviation from a hypothetical scenario with a constant cycling speed. This thesis encompasses the cyclist and city perspectives of offering virtual incentives in geo-games and crowdsourcing cycling data collection to better comprehend cycling conditions in cities. We also compiled a set of tools and recommendations for researchers, practitioners, mobile developers, urban planners and cyclist associations interested in fostering sustainable transport and the use of bicycles