Photo-sharing websites such as Flickr and Panoramio contain millions of geotagged images contributed by people from all over the world. Characteristics of these data pose new challenges in the domain of spatio-temporal analysis. In this paper, we define several different tasks related to analysis of attractive places, points of interest and comparison of behavioral patterns of different user communities on geotagged photo data. We perform analysis and comparison of temporal events, rankings of sightseeing places in a city, and study mobility of people using geotagged photos. We take a systematic approach to accomplish these tasks by applying scalable computational techniques, using statistical and data mining algorithms, combined with interactive geo-visualization. We provide exploratory visual analysis environment, which allows the analyst to detect spatial and temporal patterns and extract additional knowledge from large geotagged photo collections. We demonstrate our approach by applying the methods to several regions in the world.
"For instance, Li and Goodchild (2012) studied point patterns of georeferenced Flickr imagery in conjunction with toponyms in their metadata to identify places through user references to them. Other efforts attempt to recognize activity and behavioral patterns by analyzing these spatiotemporal points of geotagged entries, such as identifying attractive destinations (Kisilevich et al. 2010) or constructing travel itineraries (De Choudhury et al. 2010). Despite these efforts, the multimedia content of social media remains underexplored. "
[Show abstract][Hide abstract] ABSTRACT: The analysis of social media content for the extraction of geospatial information and event-related knowledge has recently received substantial attention. In this article we present an approach that leverages the complementary nature of social multimedia content by utilizing heterogeneous sources of social media feeds to assess the impact area of a natural disaster. More specifically, we introduce a novel social multimedia triangulation process that uses both Twitter and Flickr content in an integrated two-step process: Twitter content is used to identify toponym references associated with a disaster; this information is then used to provide approximate orientation for the associated Flickr imagery, allowing us to delineate the impact area as the overlap of multiple view footprints. In this approach, we practically crowdsource approximate orientations from Twitter content and use this information to orient Flickr imagery accordingly and identify the impact area through viewshed analysis and viewpoint integration. This approach enables us to avoid computationally intensive image analysis tasks associated with traditional image orientation, while allowing us to triangulate numerous images by having them pointed towards the crowdsourced toponym location. The article presents our approach and demonstrates its performance using a real-world wildfire event as a representative application case study.
Transactions in GIS 12/2014; DOI:10.1111/tgis.12122 · 0.54 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This article provides a methodology of assessing the (Big)/(Open) Data quality in Data Driven Decision Making with the Value Network Analysis approach discovering the value creation failure point(s) in the network and evaluating the impact of loss of vale of data in DDDM process.
Proceedings of the 2013 International Conference on Signal-Image Technology & Internet-Based Systems; 12/2013
[Show abstract][Hide abstract] ABSTRACT: Geographic Information Systems (GIS) handle a wide range of problems that involve spatial distribution data from fields as
diverse as housing, healthcare, transportation, cartography, criminology, and many others. Selecting the right GIS is determined
by the task the user wishes to perform. Some considerations may include the availability of analytical functions, performance,
ease of use, interoperability, extensibility, etc. Nowadays, as positioning technology proliferates and affects almost all
aspects of our lives, GIS tools have become more and more complex as researchers seek to address and implement, respectively,
new spatial tasks and techniques. Due to the rapid growth of GIS in recent years, many provide APIs for extending their software
for specific needs. But learning a complex set of API functions, is not feasible in scenarios that demand rapid prototyping
like in academic research or when the tool is supposed to be used by non-professionals. In this paper, we present three case
studies that are performed on top of a GEO-SPADE, a Google Earth-based .NET-based framework that we developed. Unlike the
typical APIs, that may often contain more than hundred or thousand of functions from different logical layers, the GEO-SPADE
framework provides only the minimal set of functions for plug-in development, networking, and visualization using Google Earth
engine. The extended functionality is provided by the developer and depends on her knowledge of the underlying language. At
the same time, the ease of use is achieved by the functionality of Google Earth. The three presented case studies showcase
the applicability of the Google Earth-based framework in different domains with different analytical tasks (spatial analysis,
spatial exploration, and spatial decision support).
Enterprise Information Systems - 12th International Conference, ICEIS 2010, Funchal, Madeira, Portugal, June 8-12, 2010, Revised Selected Papers; 01/2010
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.