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Time‐geographic Derivation of Feasible C o‐presence Opportunities from Network‐constrained Episodic Movement Data
Author(s) -
Versichele Mathias,
Neutens Tijs,
Claeys Bouuaert Manuel,
Van de Weghe Nico
Publication year - 2014
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/tgis.12050
Subject(s) - context (archaeology) , computer science , object (grammar) , event (particle physics) , path (computing) , position (finance) , tracking (education) , calibration , data mining , real time computing , sampling (signal processing) , geography , artificial intelligence , computer vision , mathematics , computer network , psychology , pedagogy , physics , archaeology , finance , filter (signal processing) , quantum mechanics , economics , statistics
Certain datasets on moving objects are episodic in nature – that is, the data is characterized by time gaps during which the position of the object is unknown. In this article, a model is developed to study the sparsely sampled network‐constrained movement of several objects by calculating both potential and feasible (i.e. more likely) co‐presence opportunities over time. The approach is applied to the context of a static sensor network, where the location of an object is only registered when passing a sensor location along a road network. Feasibility is incorporated based on the deviation from the shortest path. As an illustration, the model is applied to a large Bluetooth tracking dataset gathered at a mass event. The model output consists of maps showing the temporal evolution of the distribution of feasible co‐presence opportunities of tracked visitors over the network (i.e. the number of visitors that could have been present together). We demonstrate the model's usefulness in studying the movement and distribution of a crowd over a study area with relatively few sampling locations. Finally, we discuss the results with a special emphasis on the distinction between feasible and actual presence, the need for further validation and calibration, and the performance of the implementation.