Exploring Subjective Survey Classification of a Photographic Archive using Visual Analytics

We use an interactive visual analytics approach to explore the results of a survey and utilise parallel coordinate plots and small multiples as key visualization techniques. The scope of the survey is a set of 900 photographs from 3 origins, which were to be subjectively classified by the participants in a number of ways. In this poster we describe the interface of the designed tool and also highlight the findings it allowed us to make. By visualizing the collected survey data and navigating through it we could estimate the proportions of different types of photographs, identify qualitative differences between their sources and correlate the responses with image metadata. We were also able to support the survey process itself: with various visual representations of the collected results we could detect inappropriate behaviour of a number of participants, handle issues related to unavailability of some photographs and also ensure responses sampled the image database appropriately

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

Summarising the structure of an organisation and reconstructing a chain of events - VAST 2014 Mini-Challenge 1 Submission Honourable Mention for Novelty in Visualization

The 2014 VAST Mini-challenge 1 asked participants to summarise the structure of two organisations with overlapping members, reconstruct the chain of events of a kidnapping and to provide two possible explanations. Our solution to the first part of the challenge - the "chalkboard" - received an Honourable Mention for Novelty in Visualization

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 year’s 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 year’s 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

Design Exposition with Literate Visualization

We propose a new approach to the visualization design and communication process, literate visualization, based upon and extending, Donald Knuth’s idea of literate programming. It integrates the process of writing data visualization code with description of the design choices that led to the implementation (design exposition). We develop a model of design exposition characterised by four visualization designer architypes: the evaluator, the autonomist, the didacticist and the rationalist. The model is used to justify the key characteristics of literate visualization: ‘notebook’ documents that integrate live coding input, rendered output and textual narrative; low cost of authoring textual narrative; guidelines to encourage structured visualization design and its documentation. We propose narrative schemas for structuring and validating a wide range of visualization design approaches and models, and branching narratives for capturing alternative designs and design views. We describe a new open source literate visualization environment, litvis, based on a declarative interface to Vega and Vega-Lite through the functional programming language Elm combined with markdown for formatted narrative. We informally assess the approach, its implementation and potential by considering three examples spanning a range of design abstractions: new visualization idioms; validation though visualization algebra; and feminist data visualization. We argue that the rich documentation of the design process provided by literate visualization offers the potential to improve the validity of visualization design and so benefit both academic visualization and visualization practice

Monitoring the Health of Computer Networks with Visualization - VAST 2012 Mini Challenge 1 Award: "Efficient Use of Visualization"

The complex computer networks of large organisations contain many machines of many types, used in many geographic locations. Although system administrators should monitor the health of each machine, they need to do so within the context of the whole computer network. Our visualization presents the health of a fictitious financial institution's computer network at a snapshot in time and over a time range, and preserves the important aspects of each facility's administrative and geographic context. Using the "Bank of Money" VAST Challenge dataset, our visualization allowed us to correctly identify several areas of concern, as well as hypothesise about their causes

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

Creative User-Centered Visualization Design for Energy Analysts and Modelers

We enhance a user-centered design process with techniques that deliberately promote creativity to identify opportunities for the visualization of data generated by a major energy supplier. Visualization prototypes developed in this way prove effective in a situation whereby data sets are largely unknown and requirements open – enabling successful exploration of possibilities for visualization in Smart Home data analysis. The process gives rise to novel designs and design metaphors including data sculpting. It suggests: that the deliberate use of creativity techniques with data stakeholders is likely to contribute to successful, novel and effective solutions; that being explicit about creativity may contribute to designers developing creative solutions; that using creativity techniques early in the design process may result in a creative approach persisting throughout the process. The work constitutes the first systematic visualization design for a data rich source that will be increasingly important to energy suppliers and consumers as Smart Meter technology is widely deployed. It is novel in explicitly employing creativity techniques at the requirements stage of visualization design and development, paving the way for further use and study of creativity methods in visualization design

Big Chord Data Extraction and Mining

Harmonic progression is one of the cornerstones of tonal music composition and is thereby essential to many musical styles and traditions. Previous studies have shown that musical genres and composers could be discriminated based on chord progressions modeled as chord n-grams. These studies were however conducted on small-scale datasets and using symbolic music transcriptions. In this work, we apply pattern mining techniques to over 200,000 chord progression sequences out of 1,000,000 extracted from the I Like Music (ILM) commercial music audio collection. The ILM collection spans 37 musical genres and includes pieces released between 1907 and 2013. We developed a single program multiple data parallel computing approach whereby audio feature extraction tasks are split up and run simultaneously on multiple cores. An audio-based chord recognition model (Vamp plugin Chordino) was used to extract the chord progressions from the ILM set. To keep low-weight feature sets, the chord data were stored using a compact binary format. We used the CM-SPADE algorithm, which performs a vertical mining of sequential patterns using co-occurence information, and which is fast and efficient enough to be applied to big data collections like the ILM set. In orderto derive key-independent frequent patterns, the transition between chords are modeled by changes of qualities (e.g. major, minor, etc.) and root keys (e.g. fourth, fifth, etc.). The resulting key-independent chord progression patterns vary in length (from 2 to 16) and frequency (from 2 to 19,820) across genres. As illustrated by graphs generated to represent frequent 4-chord progressions, some patterns like circle-of-fifths movements are well represented in most genres but in varying degrees. These large-scale results offer the opportunity to uncover similarities and discrepancies between sets of musical pieces and therefore to build classifiers for search and recommendation. They also support the empirical testing of music theory. It is however more difficult to derive new hypotheses from such dataset due to its size. This can be addressed by using pattern detection algorithms or suitable visualisation which we present in a companion study