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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The spatial structure of large-scale online social networks has been largely unaccessible due to the lack of available and accurate data about people’s location. However, with the recent surging popularity of location-based social services, data about the geographic position of users have been available for the first time, together with their online social connections. In this work we present a comprehensive study of the spatial properties of the social networks arising among users of three main popular online location-based services. We observe robust universal features across them: while all networks exhibit about 40 of links below 100 km, we further discover strong heterogeneity across users, with different characteristic spatial lengths of interaction across both their social ties and social triads. We provide evidence that mechanisms akin to gravity models may influence how these social connections are created over space. Our results constitute the first large-scale study to unravel the socio-spatial properties of online location-based social networks. In this paper, we study the 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. Through our analysis on a dataset collected from Foursquare, a popular location-based social networking system, we observe that there exists strong social and geospatial ties among users and their favorite locations places in the system. Accordingly, we develop a friend-based collaborative filtering (FCF) approach for location recommendation based on collaborative ratings of places made by social friends. Moreover, we propose a variant of FCF technique, namely Geo-Measured FCF (GM-FCF), based on heuristics derived from observed geospatial characteristics in the Foursquare dataset. Finally, the evaluation results show that the proposed family of FCF techniques holds comparable recommendation effectiveness against the state-of-the-art recommendation algorithms, while incurring significantly lower computational overhead. Meanwhile, the GM-FCF provides additional flexibility in tradeoff between recommendation effectiveness and computational overhead. 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.