In today's age of pervasive computing and social media people make extensive use of technology for communicating, sharing media and learning. Yet while in the outdoors, on a hike or a trail we find ourselves inept of information about the natural world surrounding us. In this paper I present in detail the design and technological considerations required to build a location based mobile application for learning about the avian taxonomy present in the user's surroundings. It is designed to be a game for better engagement and learning. The application makes suggestions for birds likely to be sighted in the vicinity of the user and requires the user to spot those birds and upload a photograph to the system. If spotted correctly the user scores points. I also discuss some design methods and evaluation approaches for the application.
@cite_5 use gravity models to explain the spatial structure of location based social networks like Foursquare, BrightKite. @cite_6 investigate research issues in realizing location recommendation services for large-scale location-based social networks, by exploiting the social and geographical characteristics of users and locations places. While the above papers discuss social location based applications, @cite_8 present the new concept of asocial hiking application where the goal is to avoid other hikers while on a hike. The app helps users generate solitary hiking routes based on photographs posted on sites like Flickr. Their mobile application prototype also scans Wi-Fi signals to detect other hikers nearby and warns of their approach. Like above approaches my application uses the location data of the user's device but is not designed to be a social(or asocial) application. Above applications use location information of the device to calculate the proximity of other users whereas my application uses the location based data to compute the proximity of birds on a trail.
@cite_5 @cite_6 @cite_8
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In the age of mobile communications and social media, users are connected to interact with other people, and often obliged to be socially active as technology drives to connect us. In this paper, we harness the technology for the opposite use: helping people to avoid company instead of encouraging interaction. We have developed the concept of an asocial hiking application (app), in which users can generate routes that avoid meeting other people. We developed the concept based on user feedback data derived from an online survey (n=157) and two focus groups, and created a tool that generates solitary hiking routes based on OpenStreetMap data and additional information from the web. In addition, to make the application react to dynamic changes in the environment, we developed a mobile application prototype that scans Wi-Fi signals to detect other hikers nearby and warn of their approach. The prototype was tested and evaluated with 8 hikers in-the-wild. In addition to the concept design and the functional prototype, we present findings on people's, especially hikers, need for solitude, and introduce user feedback from each stage of the prototype design process as well as design recommendations for an asocial navigation application. This paper studies the problem of recommending new venues to users who participate in location-based social networks (LBSNs). As an increasingly larger number of users partake in LBSNs, the recommendation problem in this setting has attracted significant attention in research and in practical applications. The detailed information about past user behavior that is traced by the LBSN differentiates the problem significantly from its traditional settings. The spatial nature in the past user behavior and also the information about the user social interaction with other users, provide a richer background to build a more accurate and expressive recommendation model. Although there have been extensive studies on recommender systems working with user-item ratings, GPS trajectories, and other types of data, there are very few approaches that exploit the unique properties of the LBSN user check-in data. In this paper, we propose algorithms that create recommendations based on four factors: a) past user behavior (visited places), b) the location of each venue, c) the social relationships among the users, and d) the similarity between users. The proposed algorithms outperform traditional recommendation algorithms and other approaches that try to exploit LBSN information. To design our recommendation algorithms we study the properties of two real LBSNs, Brightkite and Gowalla, and analyze the relation between users and visited locations. An experimental evaluation using data from these LBSNs shows that the exploitation of the additional geographical and social information allows our proposed techniques to outperform the current state of the art. The prevalence of smartphones and mobile social networks allow the users to share their location-based life experience much easier. The large amount of data generated in related location-based social networks provides informative cues on user's behaviors and preferences to support personalized location-based services, like point-of-interest (POI) recommendation. Yet achieving accurate personalized POI recommendation is challenging as the data available for each user is highly sparse. In addition, the computational complexity is high due to the large number of users. In this paper, a novel methodology for personalized successive POI recommendation is proposed. First, the preferred successive category of location is predicted using a third-rank tensor computed based on the partially observed transitions between the categories of user's successive locations where the missing transitions are uncovered by inferring the group preference. The group is achieved according to users' demographics and frequently visited locations. Then, a bipartite graph is constructed based on the recommended categories for each user. To obtain the personalized ranking of locations, a distance weighted HITS algorithm is proposed so that the location authority score is updated iteratively according to the visiting frequency of the group and some distance constraints. The proposed two-step approach with the category prediction incorporated aims to boost the location prediction performance via the smoothing and at the same time reduce the complexity. Experimental results obtained based on the real-world location-based social network data show that the proposed approach outperforms the existing state-of-the-art methods by a large margin. Location-based mobile social network services such as foursquare and Gowalla have grown exponentially over the past several years. These location-based services utilize the geographical position to enrich user experiences in a variety of contexts, including location-based searching and location-based mobile advertising. To attract more users, the location-based mobile social network services provide real-world rewards to the user, when a user checks in at a certain venue or location. This gives incentives for users to cheat on their locations. In this paper, we investigate the threat of location cheating attacks, find the root cause of the vulnerability, and outline the possible defending mechanisms. We use foursquare as an example to introduce a novel location cheating attack, which can easily pass the current location verification mechanism (e.g., cheater code of foursquare). We also crawl the foursquare website. By analyzing the crawled data, we show that automated large scale cheating is possible. Through this paper, we aim to call attention to location cheating in mobile social network services and provide insights into the defending mechanisms.