Slava Kisilevich

P-DBSCAN: a density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos

The rapid spread of location-based devices and cheap storage mechanisms, as well as fast development of Internet technology, allowed collection and distribution of huge amounts of user-generated data, such as peoples movement or geo-tagged photos. These types of data produce new challenges for research in different application domains. In many cases, new algorithms should be devised to better portray the phenomena under investigation. In this paper, we present P-DBSCAN, a new density-based clustering algorithm based on DBSCAN for analysis of places and events using a collection of geo-tagged photos. We thereby introduce two new concepts: (1) density threshold, which is defined according to the number of people in the neighborhood, and (2) adaptive density, which is used for fast convergence towards high density regions. Our approach is demonstrated on the area of Washington, D.C.

A conceptual framework and taxonomy of techniques for analyzing movement

Movement data link together space, time, and objects positioned in space and time. They hold valuable and multifaceted information about moving objects, properties of space and time as well as events and processes occurring in space and time. We present a conceptual framework that describes in a systematic and comprehensive way the possible types of information that can be extracted from movement data and on this basis defines the respective types of analytical tasks. Tasks are distinguished according to the type of information they target and according to the level of analysis, which may be elementary (i.e. addressing specific elements of a set) or synoptic (i.e. addressing a set or subsets). We also present a taxonomy of generic analytic techniques, in which the types of tasks are linked to the corresponding classes of techniques that can support fulfilling them. We include techniques from several research fields: visualization and visual analytics, geographic information science, database technology, and data mining. We expect the taxonomy to be valuable for analysts and researchers. Analysts will receive guidance in choosing suitable analytic techniques for their data and tasks. Researchers will learn what approaches exist in different fields and compare or relate them to the approaches they are going to undertake.

Event-Based Analysis of People's Activities and Behavior Using Flickr and Panoramio Geotagged Photo Collections

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.

Discovering Landmark Preferences and Movement Patterns from Photo Postings

This article presents a geovisual analytics approach to discovering people’s preferences for landmarks and movement patterns from photos posted on the Flickr website. The approach combines an exploratory spatio-temporal analysis of geographic coordinates and dates representing locations and time of taking photos with basic thematic information available through the Google Maps Web mapping service, and interpretation of the analyzed area. The article describes data aggregation and filtering techniques to reduce the size of the dataset and focuses on information addressing research questions. The results of analysis for the Seattle metropolitan area help to distinguish between sites that are occasionally popular among the photographers and can be considered as potential attractions from sites that are regularly visited and already known as city landmarks. The analysis of photographers’ movements across the metropolitan area shows that most photographers’ itineraries are short and highly localized.

Spatio-temporal clustering

Summary. Spatio-temporal clustering is a process of grouping objects based on their spatial and temporal similarity. It is relatively new subfield of data mining which gained high popularity especially in geographic information sciences due to the pervasiveness of all kinds of location-based or environmental devices that record position, time or/and environmental properties of an object or set of objects in real-time. As a consequence, different types and large amounts of spatio-temporal data became available that introduce new challenges to data analysis and require novel approaches to knowledge discovery. In this chapter we concentrate on the spatio-temporal clustering in geographic space. First, we provide a classification of different types of spatio-temporal data. Then, we focus on one type of spatio-temporal clustering - trajectory clustering, provide an overview of the state-of-the-art approaches and methods of spatio-temporal clustering and finally present several scenarios in different application domains such as movement, cellular networks and environmental studies.

A Novel Approach to Mining Travel Sequences Using Collections of Geotagged Photos

In this paper we present a novel approach for analyzing the trajectories of moving objects and of people in particular. The mined data from these sequences can provide valuable information for understanding the surrounding locations, discovering attractive place or mining frequent sequences of visited places. Based on geotagged photos, our framework mines semantically annotated sequences. Our framework is capable of mining semantically annotated sequences of any length to discover patterns that are not necessarily immediate antecedents. The approach consists of four main steps. In the first step, every photo location is semantically annotated by assigning it to a known nearby point of interest. In the second step, a density-based clustering algorithm is applied to all unassigned photos, creating regions of unknown points of interest. In the third step, a travel sequence of every individual is built. In the final step, travel sequence patterns are mined using the semantics that were obtained from the first two steps. Case studies of Guimaraes, Portugal (where the conference takes place) and Berlin, Germany demonstrate the capabilities of the proposed framework.

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

Space- and time-referenced data published on the Web by general people can be viewed in a dual way: as independent spatio-temporal events and as trajectories of people in the geographical space. These two views suppose different approaches to the analysis, which can yield different kinds of valuable knowledge about places and about people. We present several analysis methods corresponding to these two views. The methods are suited to the large amounts of the data.

A GIS-based decision support system for hotel room rate estimation and temporal price prediction

The vastly increasing number of online hotel room bookings is not only intensifying the competition in the travel industry as a whole, but also prompts travel intermediates (i.e. e-companies that aggregate information about different travel products from different travel suppliers) into a fierce competition for the best prices of travel products, i.e. hotel rooms. An important factor that affects revenues is the ability to conclude profitable deals with different travel suppliers. However, the profitability of a contract not only depends on the communication skills of a contract manager. It significantly depends on the objective information obtained about a specific travel supplier and his/her products. While the contract manager usually has a broad knowledge of the travel business in general, collecting and processing specific information about travel suppliers is usually a time and cost expensive task. Our goal is to develop a tool that assists the travel intermediate to acquire the missing strategic information about individual hotels in order to leverage profitable deals. We present a GIS-based decision support system that can both, estimate objective hotel room rates using essential hotel and locational characteristics and predict temporal room rate prices. Information about objective hotel room rates allows for an objective comparison and provides the basis for a realistic computation of the contracts profitability. The temporal prediction of room rates can be used for monitoring past hotel room rates and for adjusting the price of the future contract. This paper makes three major contributions. First, we present a GIS-based decision support system, the first of its kind, for hotel brokers. Second, the DSS can be applied to virtually any part of the world, which makes it a very attractive business tool in real-life situations. Third, it integrates a widely used data mining framework that provides access to dozens of ready to run algorithms to be used by a domain expert and it offers the possibility of adding new algorithms once they are developed. The system has been designed and evaluated in close cooperation with a company that develops travel technology solutions, in particular inventory management and pricing solutions for many well-known websites and travel agencies around the world. This company has also provided us with real, large datasets to evaluate the system. We demonstrate the functionality of the DSS using the hotel data in the area of Barcelona, Spain. The results indicate the potential usefulness of the proposed system. Highlights? The DSS meets the real world business requirements. ? The DSS is not constrained to analyzing a specific region. ? The framework can use any available linear and non-linear estimators.

Large-scale analysis of self-disclosure patterns among online social networks users: a Russian context

Online social network services (SNS) provide an unprecedented rich source of information about millions of users worldwide. However, most existing studies of this emerging phenomenon are limited to relatively small data samples, with an emphasis on mostly “western” online communities (such as Facebook and MySpace users in Western countries). To understand the cultural characteristics of users of online social networks, this paper explores the behavioral patterns of more than 16 million users of a popular social network in the Russian segment of the Internet, namely, My.Mail.Ru (also known as “My World” or “Moj Mir” in Russian). Our main goal is to study the self-disclosure patterns of the site users as a function of their age and gender. We compare the findings of our analysis to the previous studies on Western users of SNS and discuss the culturally distinctive aspects. Our study highlights some important cultural differences in usage patterns among Russian users, which call for further studies in SNS in various cultural contexts.

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

Space- and time-referenced data published on the Web by general people can be viewed in a dual way: as independent spatio-temporal events and as trajectories of people in the geographical space. These two views suppose different approaches to the analysis, which can yield different kinds of valuable knowledge about places and about people. We define possible types of analysis tasks related to the two views of the data and present several analysis methods appropriate for these tasks. The methods are suited to large amounts of the data.