Analysis of community-contributed space- and time-referenced data (example of flickr and panoramio photos)

Andrienko, Gennady; Andrienko, Natalia; Bak, Peter; Kisilevich, Slava; Keim, Daniel A.

doi:10.1109/vast.2009.5333472

Cited by 20 publications

(24 citation statements)

References 3 publications

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“…These objects/surroundings can be spatially referenced either by giving geographic coordinates and/or user-assigned geospatial descriptions of these photographs in the form of textual labels. These photo sharing websites have several uses such as environmental monitoring (Fuchs et al 2013), pedestrian navigation (Robinson et al 2012), event and human trajectory analysis (Andrienko et al 2009), for creating geographical gazetteers (Popescu et al 2008), or even to complement institutional data sources in your locality (Milholland and Pultar 2013).…”

Section: Image-based Vgimentioning

confidence: 99%

A review of volunteered geographic information quality assessment methods

Senaratne

Mobasheri

Ali

et al. 2016

International Journal of Geographical Information Science

364

315

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With the ubiquity of advanced web technologies and location-sensing hand held devices, citizens regardless of their knowledge or expertise, are able to produce spatial information. The phenomena is known as Volunteered Geographic Information (VGI). During the last decade VGI has been used as a data source supporting a wide range of services such as environmental monitoring, events reporting, human movement analysis, disaster management etc. However, these volunteer contributed data also come with varying quality. Reasons for this are: data is produced by heterogeneous contributors, using various technologies and tools, having different level of details and precision, serving heterogeneous purposes, and a lack of gatekeepers. Crowd-sourcing, social, and geographic approaches have been proposed and later followed to develop appropriate methods to assess the quality measures and indicators of VGI. In this paper, we review various quality measures and indicators for selected types of VGI, and existing quality assessment methods. As an outcome, the paper presents a classification of VGI with current methods utilized to assess the quality of selected types of VGI. Through these findings we introduce data mining as an additional approach for quality handling in VGI.

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Section: Image-based Vgimentioning

confidence: 99%

A review of volunteered geographic information quality assessment methods

Senaratne

Mobasheri

Ali

et al. 2016

International Journal of Geographical Information Science

364

315

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“…Common movement analysis techniques include space-centered tasks to study properties of the space and places (e.g., find places of interest and investigate flows between places), and agent-centered tasks (e.g., discover meetings of people and discover routes frequently taken) [19]. Other previous work distinguishes between three main categories of movement data analysis, which describe: (a) movement characteristics of an individual within a group; (b) the dynamics of a given group (e.g., direction, speed, change in group size and shape); and (c) differences between the behaviors of multiple groups (e.g., relative movements) [21].…”

Section: Related Workmentioning

confidence: 99%

“…In this context, the two above-mentioned objectives follow a framework that involves the view of community-based, space-time referenced data in a dual way, which includes: (1) trajectories of people; and (2) independent spatio-temporal events [19]. Trajectory analysis requires a high quantity of observable routes taken by individuals.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Refugee Migration Patterns Using Geo-tagged Tweets

Hübl

Cvetojevic

Hochmair

et al. 2017

IJGI

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Abstract:Over the past few years, analysts have begun to materialize the "Citizen as Sensors" principle by analyzing human movements, trends and opinions, as well as the occurrence of events from tweets. This study aims to use geo-tagged tweets to identify and visualize refugee migration patterns from the Middle East and Northern Africa to Europe during the initial surge of refugees aiming for Europe in 2015, which was caused by war and political and economic instability in those regions. The focus of this study is on exploratory data analysis, which includes refugee trajectory extraction and aggregation as well as the detection of topical clusters along migration routes using the V-Analytics toolkit. Results suggest that only few refugees use Twitter, limiting the number of extracted travel trajectories to Europe. Iterative exploration of filter parameters, dynamic result mapping, and content analysis were essential for the refinement of trajectory extraction and cluster detection. Whereas trajectory extraction suffers from data scarcity, hashtag-based topical clustering draws a clearer picture about general refugee routes and is able to find geographic areas of high tweet activities on refugee related topics. Identified spatio-temporal clusters can complement migration flow data published by international authorities, which typically come at the aggregated (e.g., national) level. The paper concludes with suggestions to address the scarcity of geo-tagged tweets in order to obtain more detailed results on refugee migration patterns.

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“…Doan et al, 2012) and many more tasks that are geared towards discovering spatio-temporal patterns in this kind of data (e.g. Jaffe et al, 2006;Andrienko et al, 2009;Kisilevich et al, 2010;Ç öltekin et al, 2011;Naaman, 2011).…”

Section: User-generated Contentmentioning

confidence: 99%

“…However, these approaches usually rely on a movingwindow in time to construct trajectories. For example, Andrienko et al (2009) used a window of three days to separate sequences of VGI footprints into individual trajectories. With generous time thresholds (or with no time thresholds at all when the time period is short), we contend that these networks constitute a different kind of network: while there will be substantial overlap with a trajectory network in the way we define them, those networks rather equate to networks of interests, since they reflect which points-of-interest (POIs) have been visited by the same individuals at some point in time (within the generous time threshold if one is applied).…”

Section: Trajectory Networkmentioning

confidence: 99%

Towards (Re)Constructing Narratives from Georeferenced Photographs through Visual Analytics

Straumann

Çöltekin

Andrienko

2014

The Cartographic Journal

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We present a study that explores methodological steps towards (re)constructing collective narratives from the phototaking behaviour of two groups (foreign tourists and inhabitants of Switzerland) by analysing spatial and temporal patterns in user-contributed, georeferenced photographs of Zurich, Switzerland. We reason that the photographers typically capture a scene or a moment because they want to remember or share it, thus these scenes or moments are meaningful to them. Various scholars suggest that the human experience (i.e. this meaningfulness) is what separates a place from the mathematical descriptions of space. While this notion is well known in larger geographic literature, it is under-explored in cartographic research. We respond to this research gap and reconstruct static and dynamic patterns of photo-taking and -sharing behaviour to assist in capturing the implicit meaning in the studied locations. These locations may be meaningful to only a certain group of people in certain moments; therefore, studying group differences in spatial and temporal photo-taking patterns will help building a collective and comparative story about the studied place. In our study, we focus on experiences of foreign versus domestic visitors, and in the process, we examine the potential (and feasibility) of georeferenced photographs for extracting such collective narratives using qualitative and quantitative visual analytical methods.

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Analysis of community-contributed space- and time-referenced data (example of flickr and panoramio photos)

Cited by 20 publications

References 3 publications

A review of volunteered geographic information quality assessment methods

A review of volunteered geographic information quality assessment methods

Analyzing Refugee Migration Patterns Using Geo-tagged Tweets

Towards (Re)Constructing Narratives from Georeferenced Photographs through Visual Analytics

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