We propose a visual analytics procedure for analyzing movement data, i.e., recorded tracks of moving objects. It is oriented to a class of problems where it is required to determine significant places on the basis of certain types of events occurring repeatedly in movement data. The procedure consists of four major steps: (1) event extraction from trajectories; (2) event clustering and extraction of relevant places; (3) spatio-temporal aggregation of events or trajectories; (4) analysis of the aggregated data. All steps are scalable with respect to the amount of the data under analysis. We demonstrate the use of the procedure by example of two realworld problems requiring analysis at different spatial scales. KEYWORDS: movement, trajectories, spatio-temporal data, spatial events, spatial clustering, spatio-temporal clustering Movement data (also called mobility data) describing changes of spatial positions of discrete mobile objects are nowadays collected in growing amounts by means of current tracking technologies, such as GPS, RFID, radars, and others. Automatically collected movement data are semantically poor as they basically consist of object identifiers, coordinates in space, and time stamps. Despite that, valuable information about the objects and their movement behavior as well as about the space and time in which they move can be gained from movement data by means of analysis [2] . Movement can be viewed as consisting of continuous paths in space and time [18] , also called trajectories, or as a composition of various spatial events [3] . As noted in [6] and [30], there are many definitions of the term event. We adhere to Kim's definition of events as exemplifications of properties or relationships at some times [22] . Spatial events are events localized in space [6] . The event-based view of movement is particularly suitable for applications and tasks where analysts are interested in occurrences of certain movement characteristics such as very high or very low speeds. Each occurrence is a spatial event. Events that are relevant to the goals of analysis need to be extracted from movement data. Such events will be further called movement events, or m-events. There is a class of problems where analysts need to determine places in which m-events of a certain type occur repeatedly and then use these places in the further analysis. For example, having tracks of multiple cars in a city, a traffic analyst may first need to find places where traffic jams occur and then investigate in which times of the day they happen and how long they last. From trails of migratory birds, an ornithologist may wish to extract places where the birds stop for resting and feeding and then analyze the temporal patterns of visiting these places and travelling between the places. We point out that relevant places can only be delineated by processing movement data, that is, there is no predefined set of places (e.g., compartments of a territory division) from which the analyst can select places of interest. The relevant places may have arbitrary shapes and sizes and irregular spatial distribution. They may even overlap in space; hence, approaches based on dividing the territory into non-overlapping areas (as in [4]) are not appropriate. We propose a visual analytics procedure for place-centered analysis of mobility data that includes (1) visually-supported extraction of relevant m-events, (2) finding and delineating significant places on the basis of interactive clustering of the mevents according to different attributes, (3) spatio-temporal aggregation of the m-events and movement data by the defined places or pairs of places and time intervals; (4) analysis of the aggregated data for studying the spatio-temporal patterns of event occurrences and/or connections between the places. We demonstrate the use of the procedure by applying it to two realworld problems differing in the spatial scale and character of the movement that is analyzed. The main contributions of this paper are following: • We suggest a generic visual analytics procedure for a certain class of problems, which is explicitly specified. The existing literature describes particular techniques that can be used in different steps of the procedure. We put the techniques in a workflow, in which outputs of one technique are used as an input for another technique. • The procedure can be scaled to large amounts of data. We explicitly describe the use of the procedure in a case when the data do not fit in the RAM (section 4.5). • We suggest an original approach to clustering of events, which uses a special distance function capable to account for distances between events in geographic space, in time, and with regard to values of thematic attributes. The function properly deals with cyclic attributes such as movement direction or time of a day. The remainder of the paper is structured as follows. After introducing the main concepts in section 2, we discuss the related research (section 3) and describe the suggested procedure (section 4). Sections 5 and 6 present two examples of the use of the procedure; a supplement with larger figures and more details is given at http://geoanalytics.net/and/papers/vast11att.pdf. Section 7 concludes the paper. Movement data consist of position records <object identifier, time, spatial coordinates>. Temporally ordered position records of one object represent the trajectory of this object. The real trajectory is continuous (i.e., the object has some position at any time moment) while the data representing it are discrete (i.e., positions are specified for a sample of time moments). When the time intervals between the known positions are sufficiently short, the
The authors in REF looked at the characteristics of the movement such as instant speed, direction, travelled distance, displacement and the temporal distances to the starts and ends of the trajectories.
From movement tracks through events to places: Extracting and characterizing significant places from mobility data
2011 IEEE Conference on Visual Analytics Science and Technology (VAST)
2011 IEEE Conference on Visual Analytics Science and Technology (VAST)
Computer Science