159 research outputs found
Crowd-sourced Photographic Content for Urban Recreational Route Planning
Routing services are able to provide travel directions for users of all modes of transport. Most of them are focusing on functional journeys (i.e. journeys linking given origin and destination with minimum cost) while paying less attention to recreational trips, in particular leisure walks in an urban context. These walks are additionally predefined by time or distance and as their purpose is the process of walking itself, the attractiveness of areas that are passed by can be an important factor in route selection. This factor is hard to be formalised and requires a reliable source of information, covering the entire street network. Previous research shows that crowd-sourced data available from photo-sharing services has a potential for being a measure of space attractiveness, thus becoming a base for a routing system that suggests leisure walks, and ongoing PhD research aims to build such system. This paper demonstrates findings on four investigated data sources (Flickr, Panoramio, Picasa and Geograph) in Central London and discusses the requirements to the algorithm that is going to be implemented in the second half of this PhD research. Visual analytics was chosen as a method for understanding and comparing obtained datasets that contain hundreds of thousands records. Interactive software was developed to find a number of problems, as well as to estimate the suitability of the sources in general. It was concluded that Picasa and Geograph have problems making them less suitable for further research while Panoramio and Flickr require filtering to remove photographs that do not contribute to understanding of local attractiveness. Based on this analysis a number of filtering methods were proposed in order to improve the quality of datasets and thus provide a more reliable measure to support urban recreational routing
A Big Data Analytics Method for Tourist Behaviour Analysis
Ā© 2016 Elsevier B.V. Big data generated across social media sites have created numerous opportunities for bringing more insights to decision-makers. Few studies on big data analytics, however, have demonstrated the support for strategic decision-making. Moreover, a formal method for analysing social media-generated big data for decision support is yet to be developed, particularly in the tourism sector. Using a design science research approach, this study aims to design and evaluate a ābig data analyticsā method to support strategic decision-making in tourism destination management. Using geotagged photos uploaded by tourists to the photo-sharing social media site, Flickr, the applicability of the method in assisting destination management organisations to analyse and predict tourist behavioural patterns at specific destinations is shown, using Melbourne, Australia, as a representative case. Utility was confirmed using both another destination and directly with stakeholder audiences. The developed artefact demonstrates a method for analysing unstructured big data to enhance strategic decision making within a real problem domain. The proposed method is generic, and its applicability to other big data streams is discussed
A Big Data Analytics Method for Tourist Behaviour Analysis
Ā© 2016 Elsevier B.V. Big data generated across social media sites have created numerous opportunities for bringing more insights to decision-makers. Few studies on big data analytics, however, have demonstrated the support for strategic decision-making. Moreover, a formal method for analysing social media-generated big data for decision support is yet to be developed, particularly in the tourism sector. Using a design science research approach, this study aims to design and evaluate a ābig data analyticsā method to support strategic decision-making in tourism destination management. Using geotagged photos uploaded by tourists to the photo-sharing social media site, Flickr, the applicability of the method in assisting destination management organisations to analyse and predict tourist behavioural patterns at specific destinations is shown, using Melbourne, Australia, as a representative case. Utility was confirmed using both another destination and directly with stakeholder audiences. The developed artefact demonstrates a method for analysing unstructured big data to enhance strategic decision making within a real problem domain. The proposed method is generic, and its applicability to other big data streams is discussed
Recommended from our members
Automated planning of leisure walks based on crowd-sourced photographic content
All walking trips can be classified into two main groups: functional walks and leisure (or recreational) walks. While the goal of functional walks is moving from one point in space to another, the purpose of leisure walks is the process of walking itself. Unlike functional walking, recreational walking implies a more complex combination of factors that form the selection of a particular route in the mind of a pedestrian, and many of these factors are having a psychological nature being related to human perception of space. One of the most hard-to-formalize factors that a person can be considering when planning a leisure walk is the attractiveness of areas that appear on the way. Conventional map data that are informing existing routing algorithms cannot be used for extracting such measure as attractiveness of streets. Indeed, even a very rich description of all road segments including their type, surface, slope, accessibility, etc. does not contain a subjective component, or in other words, does not tell whether or not the pedestrians enjoy their presence at a particular place. In order to resolve this issue external information sources should be used. This project is focusing on data from 4 photo-sharing services (Flickr, Panoramio, Picasa and Gerograph) and is examining how they can be used for road segments weighting in Central London area.
The idea of using the density of geotagged photographs as a measure of attractiveness of urban streets is based on the peculiarity of the process of photography sharing. In order for an image to appear on a photo-sharing website it must be taken and then uploaded by a user. Both of these actions are voluntary and due to the human psychology often happen when a person finds something interesting that is worth showing to others. When such behaviour is repeated among hundreds of people, this results patterns in distributions of photographs that can be potentially turned into a measure of attractiveness of different places and streets in cities.
Following the discussion of the idea at the last yearās UTSG conference, this paper presents the results of the PhD research and covers a number of findings and conclusions.
The first part of the paper is devoted to data analysis and filtering. Because the photographic datasets are not originally collected for the purpose of measuring street attractiveness and thus contain bias, they need to be studied and cleaned in order to increase their reliability and suitability for the chosen purpose. The photographs with different content do not contribute to the measure of street attractiveness equally and the challenge is to classify them into ones that should inform the routing algorithm and those that must be excluded. Because the datasets that this project is working with contain hundreds of thousands of entries and the automated image content classification is unfeasible, the classification is done with the help of an online survey. 900 randomly picked images were shown to a group volunteered participants, who were asked to classify each photograph by a set of criteria: whether an image is a real photograph, is taken outdoors, is taken during daytime, is containing human faces, is featuring something permanent, is made by a pedestrian and is suggesting a nice place for a walk. With 8,434 subjective responses from 359 users (at least 8 subjective responses per photograph), it was possible to suggest filtering methods based on metadata of the photographs as well as their content. The following approaches are discussed in the paper: filtering based on EXIF data, presence of faces in the photographs (involving automated face detection), photo timestamp, tags, title and description, amount of green in the photographs. A combination of successful filtering techniques together with spatiotemporal filtering discussed in the last yearās UTSG paper allows reducing bias in the photographic datasets and makes them more suitable for estimating street attractiveness.
The second part of the paper describes the routing algorithm itself. Based on the filtered versions of the photographic datasets and road network data from OpenStreetMap, a methodology for weighting road segments has been proposed. We discuss the work of the algorithm and possible ways if its improvement
Exploring human mobility patterns based on geotagged Flickr photos
Predicting human mobility behaviour has long been a topic of scientiļ¬c interest. Such studies generally rely on tracking human movements through a range of data collection methodologies such as using GPS trackers, cellular network data etc. Some of this data may be conļ¬dential or hard to acquire. This thesis explores if existing publicly available data on online photo sharing platforms can be used to determine human mobility patterns with reasonable accuracy. We choose the Flickr website as the data collection medium as it has an extensive user base actively sharing photos many of which, have geo tags embedded in them which are preserved by Flickr. Our analysis reveals that while the data from Flickr is sparse and discontinuous making it unsuitable for reliable mobility prediction, typical human mobility trends based on time of day, day of week and month of the year can still be extracted. Such interesting patterns could be potentially used in traļ¬c engineering domains or for user proļ¬ling purposes.
More speciļ¬cally, we describe how to obtain a subset of frequent active users and their information from Flickr, and the sliding window mechanism to ļ¬lter the active periods of the users. Later we explain the various statistical methods applied on the ļ¬ltered subset of data to identify the categories in which users could be classiļ¬ed, mainly short distance travellers and long distance travellers. The short distance travellers are considered for mobility trends prediction
A big-data analytics method for capturing visitor activities and flows: the case of an island country
Ā© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Understanding how people move from one location to another is important both for smart city planners and destination managers. Big-data generated on social media sites have created opportunities for developing evidence-based insights that can be useful for decision-makers. While previous studies have introduced observational data analysis methods for social media data, there remains a need for method developmentāspecifically for capturing peopleās movement flows and behavioural details. This paper reports a study outlining a new analytical method, to explore peopleās activities, behavioural, and movement details for people monitoring and planning purposes. Our method utilises online geotagged content uploaded by users from various locations. The effectiveness of the proposed method, which combines content capturing, processing and predicting algorithms, is demonstrated through a case study of the Fiji Islands. The results show good performance compared to other relevant methods and show applicability to national decisions and policies
The Shortest Path to Happiness: Recommending Beautiful, Quiet, and Happy Routes in the City
When providing directions to a place, web and mobile mapping services are all
able to suggest the shortest route. The goal of this work is to automatically
suggest routes that are not only short but also emotionally pleasant. To
quantify the extent to which urban locations are pleasant, we use data from a
crowd-sourcing platform that shows two street scenes in London (out of
hundreds), and a user votes on which one looks more beautiful, quiet, and
happy. We consider votes from more than 3.3K individuals and translate them
into quantitative measures of location perceptions. We arrange those locations
into a graph upon which we learn pleasant routes. Based on a quantitative
validation, we find that, compared to the shortest routes, the recommended ones
add just a few extra walking minutes and are indeed perceived to be more
beautiful, quiet, and happy. To test the generality of our approach, we
consider Flickr metadata of more than 3.7M pictures in London and 1.3M in
Boston, compute proxies for the crowdsourced beauty dimension (the one for
which we have collected the most votes), and evaluate those proxies with 30
participants in London and 54 in Boston. These participants have not only rated
our recommendations but have also carefully motivated their choices, providing
insights for future work.Comment: 11 pages, 7 figures, Proceedings of ACM Hypertext 201
Recommended from our members
Visual Analytic Extraction of Meaning from Photo-Sharing Services for Leisure Pedestrian Routing
Present-day routing services are able to provide travel directions for users of all modes of transport. Most of them are focusing on functional journeys (i.e. journeys linking given origin and destination with minimum cost) and pay less attention to recreational trips, in particular leisure walks in an urban context. These walks have predefined time or distance and as their purpose is the process of walking itself, the attractiveness of chosen paths starts playing an important role in route selection. Conventional map data that are informing routing algorithms cannot be used for extracting street attractiveness as they do not contain a subjective component, or in other words, do not tell whether or not people enjoy their presence at a particular place. Recent research demonstrates that the crowd-sourced data available from the photo- sharing websites have a potential for being a good source of this measure, thus becoming a base for a routing system that suggests attractive leisure walks.
This PhD research looks at existing projects, which aim to utilize user-generated photographic data for journey planning, and suggests new techniques that make the estimation of street attractiveness based on this source more reliable. First, we determine the artifacts in photo- graphic datasets that may negatively impact the resulting attractiveness scores. Based on the findings, we suggest filtering methods that improve the compliance of the spatial distributions of photographs with the chosen purpose. Second, we discuss several approaches of assigning attractiveness scores to street segments and make conclusions about their differences. Finally, we experiment with the routing itself and develop a prototype system that suggests leisure walks through attractive streets in an urban area. The experiments we perform cover Central London and involve four photographic sources: Flickr, Geograph, Panoramio and Picasa.
A Visual analytic (VA) approach is used throughout the work to glean new insights. Being able to combine computation and the analytical capabilities of the human brain, this research method has proven to work well with complex data structures in a variety of tasks. The thesis contributes to VA as an example of what can be achieved by means of the visual exploration of data
- ā¦