Using the internet to meet dating partners is increasingly popular and may have ramifications that are not yet fully realized. Although many dating sites have been operating for years, new online dating platforms continue to draw millions of new users. By using a large sample of people who use online dating platforms (n=1,286) we identified similarities and differences in what motivates people to use geosocial apps and dating sites. Motivations previously considered in the literature were supported and brought together in a single theory driven confirmatory factor analysis for each type of dating platform. A motivation to seek amusement was a latent factor unique to geosocial app use. Implications for researchers, clinicians, and dating platform users and developers may include helping users be better matched to others who have similar motivations to improve the online dating experience.

Geosocial apps on mobile phones use location data to introduce many young adults to other people to initiate various types of relationships. This study examined how established racial preferences affect Straight White Men's (SWM) selection decisions of potential partners in a pseudo-geosocial app when controlling for age, attractiveness, and other profile factors of potential matches. A sample comprising exclusively of SWM was selected because historically, this demographic has benefited most from gender and racial inequalities (Thompson, 2009), and they make up the largest portion of people in interracial relationships in the United States (Livingston & Brown, 2017). We found that SWM were significantly less likely to select profiles of women of color compared to profiles of White women when considering friendship, sexual encounters, dating relationships, or long-term committed relationships such as marriage. Established predictors of negative attitudes toward interracial relationships (e.g., religiosity, political beliefs) had no correlation with SWM's selection behavior in the app, but self-reported openness had a consistent correlation to higher odds of selecting women of all races.

abstract: Social media has become popular in the past decade. Facebook for example has 1.59 billion active users monthly. With such massive social networks generating lot of data, everyone is constantly looking for ways of leveraging the knowledge from social networks to make their systems more personalized to their end users. And with rapid increase in the usage of mobile phones and wearables, social media data is being tied to spatial networks. This research document proposes an efficient technique that answers socially k-Nearest Neighbors with Spatial Range Filter. The proposed approach performs a joint search on both the social and spatial domains which radically improves the performance compared to straight forward solutions. The research document proposes a novel index that combines social and spatial indexes. In other words, graph data is stored in an organized manner to filter it based on spatial (region of interest) and social constraints (top-k closest vertices) at query time. That leads to pruning necessary paths during the social graph traversal procedure, and only returns the top-K social close venues. The research document then experimentally proves how the proposed approach outperforms existing baseline approaches by at least three times and also compare how each of our algorithms perform under various conditions on a real geo-social dataset extracted from Yelp. / Dissertation/Thesis / Masters Thesis Computer Science 2016

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.

This diploma thesis deals with the issue of cultural routes that are a product of cultural tourism. Based on the definition of product of cultural tourism diploma thesis defines the key factors of cultural route proposal. On selected examples of already implemented cultural routes identifies important facts for own proposal of cultural route. The subject of the proposed cultural route is Josipa (Jože) Plečnik's architecture. Plečnik was an architect who was active in the first half of the 20th century. The cultural route connects architectural heritage of three European countries, the Czech Republic, Slovenia and Austria

A transportation system is a critical infrastructure that is key for mobility in any community. Natural hazards can cause failure in transportation infrastructure and impede its routine performance. Ecological systems are resilient systems that are very similar to transportation systems. Diversity is a fundamental factor in ecological resilience, and it is recognized as an important property of transportation resilience. However, quantifying transportation diversity remains challenging, which makes it difficult to understand the influence of diversity on transportation performance and resilience. Consequently, three studies are undertaken to remedy this circumstance. The first study develops a novel approach – inspired by biodiversity in ecological stability theory – to characterize and measure transportation diversity by its richness (availability) and evenness (distribution). This transportation diversity approach is then applied to New York City (NYC) at the zip code level using the GIS data of transportation modes. The results demonstrate the variation of transportation diversity across the city. The characterized inherent and augmented complementarities start to uncover the dynamics of modal compensation and to demonstrate how transportation diversity contributes to this phenomenon. Moreover, the NYC zip codes with low transportation diversity are mainly in hurricane evacuation zones that are more vulnerable. Consequently, low transportation diversity in these areas could affect their post-disaster mobility. In the second study, the influence of transportation diversity on post-disaster mobility is examined by investigating the patterns of mobility in New York City one month before and after Hurricane Sandy using Twitter data. To characterize pre- and post-Sandy mobility patterns, the locations that individuals visited frequently were identified and travel distance, the radius of gyration, and mobility entropy were measured. Individuals were grouped according to the transportation diversity of their frequently visited locations. The findings reveal that individuals that lived in or visited zip codes with higher transportation diversity mostly experienced less disturbance in their mobility patterns after Sandy and the recovery of their mobility patterns was faster. The results confirm that transportation diversity affects the resilience of individual post-disaster mobility. The approach used in this study is one of the first to examine the root causes of changes in mobility patterns after extreme events by linking transportation infrastructure diversity to post-disaster mobility. Finally, the third study employs the transportation diversity approach to investigate modal accessibility and social exclusion. Transportation infrastructure is a sociotechnical system and transport equity is crucial for access to opportunities and services such as jobs and infrastructure. The social exclusion caused by transport inequity could be intensified after natural disasters that can cause failure in a transportation system. One approach to determine transport equity is access to transportation modes. Common catchment area approaches to assess the equity of access to transportation modes cannot differentiate between the equity of access to modes in sub-regions of an area. The transportation diversity approach overcomes this shortcoming, and it is applied to all transportation modes in NYC zip codes to measure the equity of access. Zip codes were grouped in quartiles based on their transportation diversity. Using the American Community Survey data, a set of important socioeconomic and transport usage factors were compared in the quartile groups. The results indicated the relationship between transportation diversity and income, vehicle ownership, commute time, and commute mode. This relationship highlighted that social exclusion is linked with transport inequity. The results also revealed that the inequity of the transport system in zip codes with low transportation diversity affects poor individuals more than non-poor and the zip codes with a majority of black and Hispanic populations are impacted more. Further consideration of the impacts of Hurricanes Irene and Sandy in NYC shows that people in areas with a lower transportation diversity were affected more and the transport inequity in these areas made it difficult to cope with these disasters and caused post-disaster social exclusion. Therefore, enhancing transportation diversity should support transport equity and reduce social exclusion under normal situations and during extreme events. Together, these three studies illustrate the influence of transportation diversity on the resilience of this infrastructure. They highlight the importance of the provision and distribution of all transportation modes, their influence on mobility during normal situations and extreme events and their contribution toward mitigating social exclusion. Finally, these studies suggest that transportation diversity can contribute to more targeted and equitable transportation and community resilience planning, which should help decision-makers allocate scarce resources more effectively. / Doctor of Philosophy / Transportation systems are very important in every city. Natural disasters like hurricanes and floods can destroy roads and inundate metro tunnels that can cause problems for mobility. Ecological systems like forests are very resilient because they have experienced disturbances like natural disasters for millions of years. Ecological systems and transportation systems are very similar; for example, both have different components (different species in an ecological system and different modes in a transportation system). Because of such similarities, we can learn from ecological resilience to improve transportation resilience. Having a variety of species in an ecological system makes it diverse. Diversity is the most important factor in ecological resilience, and it is also recognized as an important factor in transportation resilience. Current methods cannot effectively quantify transportation diversity – the variety of modes in a system – so determining its impact on transportation resilience remains a challenge. In this dissertation, principles of ecological diversity are adapted to characterize transportation infrastructure to develop a new approach to measure transportation diversity; metrics include the availability of transportation modes and their distribution in a community. The developed approach was applied in New York City (NYC) at the zip code level. Locations with low transportation diversity (fewer modes and/or unequal distribution) were identified, and most of these zip codes are located in hurricane evacuation zones. Consequently, these zip codes with the least diverse transportation systems are the most vulnerable, which can cause serious issues during emergency evacuations and the ability of people to access work or essential services. Therefore, in a city hit by a natural disaster, understanding the relationship between people's mobility and a transportation system's diversity is important. Twitter data was used to find the places that people in NYC visited regularly for one month before and one month after Hurricane Sandy. Subsequently, using different methods, the pre- and post-disaster mobility patterns of these individuals were characterized. The results show that after the disaster, individuals had a higher chance of maintaining their pre-disaster mobility patterns if they were living in and/or visiting areas with high transportation diversity. Based on these findings, we confirmed the influence of transportation diversity on post-disaster mobility. In addition, the transportation infrastructure should provide equitable service to all individuals, during normal operations and extreme events. One of the ways to determine this equality is equity of access to transportation modes. Hence, transportation diversity was used as an indicator for equity of access to transportation modes to overcome the limitations of current methods like catchment area approaches. NYC zip codes were grouped based on their transportation diversity and a set of important socioeconomic and transport related factors were compared among these groups. The comparison of socioeconomic and transport related factors in zip codes showed that the zip codes with lower transportation diversity are also more socioeconomically deprived. This highlights the likely influence of transportation diversity on social exclusion. Further consideration of the impacts of Hurricanes Irene and Sandy in NYC shows that people in areas with a lower transportation diversity were affected more and the transport inequity in these areas made it difficult to cope with these disasters and caused post-disaster social exclusion. Therefore, enhancing transportation diversity should support transport equity and reduce social exclusion under normal situations and during extreme events. The investigations conducted highlight the importance of the provision and distribution of all transportation modes, their influence on mobility during normal situations and extreme events and their contribution toward mitigating social exclusion. Finally, the collective results suggest that transportation diversity can contribute to more targeted and equitable transportation and community resilience planning, which should help decision-makers allocate scarce resources more effectively.

Social networks are popular platforms that simplify user interaction and encourage collaboration. They collect large amounts of media from their users, often reported from mobile devices. The value and impact of social media makes it however an attractive attack target. In this thesis, we focus on the following social media vulnerabilities. First, review centered social networks such as Yelp and Google Play have been shown to be the targets of significant search rank and malware proliferation attacks. Detecting fraudulent behaviors is thus paramount to prevent not only public opinion bias, but also to curb the distribution of malware. Second, the increasing use of mobile visual data in news networks, authentication and banking applications, raises questions of its integrity and credibility. Third, through proof-of- concept implementations, we show that data reported from wearable personal trackers is vulnerable to a wide range of security and privacy attacks, while off-the-shelves security solutions do not port gracefully to the constraints introduced by trackers. In this thesis we propose novel solutions to address these problems. First, we introduce Marco, a system that leverages the wealth of spatial, temporal and network information gleaned from Yelp, to detect venues whose ratings are impacted by fraudulent reviews. Second, we propose FairPlay, a system that correlates review activities, linguistic and behavioral signals gleaned from longitudinal app data, to identify not only search rank fraud but also malware in Google Play, the most popular Android app market. Third, we describe Movee, a motion sensor based video liveness verification system, that analyzes the consistency between the motion inferred from the simultaneously and independently captured camera and inertial sensor streams. Finally, we devise SensCrypt, an efficient and secure data storage and communication protocol for affordable and lightweight personal trackers. We provide the correctness and efficacy of our solutions through a detailed theoretic and experimental analysis.