The benefits of cycling are well known and many communities are investing in cycling infrastructure in order to encourage and promote ridership. Safety is a primary concern for new cyclists and remains a barrier for increasing ridership. Understanding what influences cyclist safety requires knowing how many cyclists are riding in an area. Lack of ridership data is a common challenge for cycling research and limits our ability to properly assess safety and risk. The goal of our research was to incorporate new data available through crowdsourcing applications to advance cycling research on ridership and safety in the Capital Regional District (CRD), British Columbia (BC), Canada. To meet our goal, our first analysis assessed how crowdsourced fitness app data can be used to map and to quantify the spatial and temporal variation of ridership. Using a dataset from a popular fitness app Strava, we compared how manual cycling counts conducted at intersections during peak commuting hours in Victoria compared to the number of crowdsourced cyclists during these same count periods. In order to estimate ridership at unsampled manual count locations, we used Poisson regression to model the association between manual counts and infrastructure variables found to influence ridership. Our results found that there was a linear association (r2 between 0.4 and 0.58) between crowdsourced cyclists and manual count cyclists, which amounted to one crowdsourced cyclist representing 51 riders. Crowdsourced cyclist volumes, traffic speeds, on street parking, slope, and time of year were found to significantly influence the amount of cyclists in different count locations with a predictive accuracy of 62%. Overall, crowdsourced data from fitness apps are a biased sample of ridership; however, in urban areas in mid-size North American cities, cyclists using fitness apps may choose similar routes as commuter cyclists. Our second analysis used crowdsourced data on cyclist incidents to determine the factors that influence incident reporting at multiuse trail and roadway intersections. Using incident reports from BikeMaps.org, we characterized attributes of reported incidents at intersections between multiuse trails and roads and also examined infrastructure features at these intersections that are predictors of incident frequency. We conducted site observations at 32 multiuse trail-road intersections in the CRD to determine infrastructure characteristics that influence safety. Using Poisson regression we modeled the relationship between the number of incidents (collision and near misses) and the infrastructure characteristics at multiuse trail-road intersections. We found that collisions were more commonly reported (over near misses) at multiuse trail-road intersections than road-road intersections (38% versus 27%), and incidents involving an injury were more common (35% versus 21%). Cycling volumes, vehicle volumes, and lack of vehicle speed reduction factors were associated with incident frequency. Our analysis was able to use crowdsourced cycling incident data to provide valuable evidence on the factors that influence safety at intersections between multiuse trails and roadways where diverse transportation modes converge. Through this thesis we help to overcome limitations for cycling research and planning by demonstrating how crowdsourced ridership and safety data can help fill gaps and supplement available data. Our methodology integrates the high spatial and temporal resolution of crowdsourced cycling data with the detailed attributes provided by traditional ridership counts. We also demonstrate how volunteered safety data can allow new questions on safety to be explored. Improving data available for cycling research allows for a more comprehensive understanding of the factors that influence ridership and safety and, in turn, informs decisions targeted at increasing cycling. / Graduate

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In light of the massive growth of creation and consumption of video, in this thesis I explore the concept of user-enhanced video browsing and evaluate the quantitative and qualitative effects of this approach. For this purpose, I create a unique prototype based on the VLC media player. The player interprets user-generated video tagging and annotations in my designed format, and allows the viewing of multiple event layers to create a personalized video playback. I perform evaluations in two user studies for this work. Among my observations I find benefits in navigation, personalization, consumption, and comprehension. I then look at the way viewers behave when contributing data of annotations and tagging. I find their preferred tasks, their perceived quality of other contributions, as well as their opinions on this system. I conclude with a discussion of the results and list possible use-cases for this concept.

The aim of this thesis is to choose the proper measurement system for evaluating the performance of the LTE mobile networks based on long-term measurements. The thesis describes mobile network LTE, its architecture and introducts the Quality of Service in the LTE. In the next part are studied the known measurement methods and furthermore, there is proposed a solution in the form of long-term measurement. This measurement uses the mobile phone application Open-RMBT, which stores the measurement results into the database on the chosen server. The following part focuses on the issue of choosing a suitable mobile device for the measurement, then it describes a pilot testing and the followed run of the long-term measurements. In the last part is introduced the interractive application to plot graphs. Measurement results are discussed in the conclusion.

The purpose of this thesis is to research how consumer recommendations can be used in terms of marketing and sales purposes in the Fast Moving Consumer Goods sector. Due to technological changes and a shift from traditional to digital marketing, the way of communicating to the audience has changed. Consumer recommendations, micro influencers and social media platforms are a part of digital marketing that is growing progressively. The paradigm of the thesis is interpretivism with following a qualitative research method, inductive reasoning and a case study in focus. The findings show that digital marketing, thanks to aspects as social media and e-commerce, has been growing, however has not completely taken over the marketing field. Influencers are getting a bigger role as marketing tools with the shift of power evolving. Due to these reasons is why and how consumer recommendations have become such a big trend nowadays.

自2008年Youtube熱潮席捲全球之後，以影音分享為主的媒介消費行為逐漸成為常態，在這股集體協作並開放分享的浪潮下，新聞組織亦不免於其外，開始盛行以取材於眾(crowdsourcing)的新聞產製模式增加新聞素材來源的多樣性。電視新聞工作者取用網友拍攝後上傳的第三方影音內容（third party video,TPV）作為新聞素材的一部分，使得眾多使用者生產內容(User-generated content/ produsage)漸成為電視新聞畫面的一大主軸。 本研究欲瞭解電視新聞工作者取用TPV的過程，因此採內容分析法觀察TPV做成的新聞內容，並深度訪談及實際參與觀察以瞭解電視新聞工作者取用TPV的動機、處理素材的方式與新聞查證的過程。研究結果分成兩部分，首先，在取用情況上發現電視台確實大量使用TPV，但超過半數以上的素材難以辨識來源。題材選定偏好具爭議性的內容，而處理素材的方式則以剪接為主。其次，由於TPV的特性使然，電視新聞工作者在查證時容易面臨原始資料不明、消息來源不確定與情境脈絡斷裂等挑戰，而他們通常採取單一查證方式、尋找單一採訪對象，且最常比對網路資料。此外，在需查證的新聞中，僅半數的記者會到現場勘查與採訪。而在取得報導素材後，為規避查證責任，電視新聞工作者經常以白描畫面、翻攝網友留言與重新議題設定等方式作新聞，藉此模糊具爭議性的部份。 本研究因此針對TPV的查證提供若干建議：（1）從第三方的角度客觀觀察，避免淪為有心人士操弄的工具；（2）設法從發文者的字面敘述中或直接從影像中找到可辨識的地點、物件與人物，據此找到當事者；（3）盡力找到消息來源，若無法與消息來源取得聯繫，則應轉而尋找當事者、公正第三方如警察、或是向媒體同業詢問；（4）到事發現場勘查；（5）題材為各種網路傳言時，可請專家以實驗的方式證實。本研究並整理出幾項適用於各種情境的查證原則：質疑優先、詰問必備、資料比對、邏輯至上與經驗輔助。 / Crowdsourcing is an important trend and affects major parts of our lives. More notably, bulk of our news consumption consists of crowdsourced content; these content are what we call third party video or TPV for short. In recent years, we see an increased use of TPV in the Taiwanese media landscape, especially in news reporting and creation. Using crowdsourced content in news presents two major problems: 1. Over half of the TV news today uses crowdsourced TPV. These content often contain controversial materials. These controversial materials are often selected in bid to manipulate its audience. . 2. Due to the nature of crowdsourced TPV, news reporters have difficulties identifying the source of these content. Since more than half of the news reporting today uses TPV, this makes news highly suspicious. Although this research finds that reporters do attempt to verify its sources, these attempts are far from vigorous: new reporters often adopt one of the following ways in order to verify their news sources: cross validating their data using the Internet, remake opinions based on netizens/cyber-citizens or changing the agenda of the news content. In the worst case, news reporters avoid or skip validation of their news sources altogether. This research provides the following suggestions in solving the above mentioned problems: (1) prevention of content manipulation by malicious parties so that consumer of news content are not manipulated or swayed. This ensures that the news presented is unbiased, objective and fair. (2) Verifying TPV can include attempts to find any recognizable location, object, or figure from TPV as tips to trace origin. (3) News reporters can try their best to get in touch with the news sources. If this cannot be done, try to get in touch with unbiased third parties, such as the police force or people same as your profession.(4) News reporters can also attempt to perform on-site investigation may be contributed to find the truth. (5) If the issue is about rumors on the internet, news reporters may consult experts on the subject matter for clarification. Finally, this study summarized essential factors for verification applied in a various situations: Doubting, Cross-examination, Cross validation, logic, and experience.