Use of symbolic dynamic time warping in hierarchical clustering of urban fabric evolutions extracted from spatiotemporal topographic databases

Issue title: Environmental Data Mining

Guest editors: Karina Gibert

Article type: Research Article

Authors: Gançarski, Pierre^{a; *} | Puissant, Anne^a | Petitjean, François^b

Affiliations: [a] University of Strasbourg, France. E-mails: [email protected], [email protected] | [b] Monash University, Melbourne, Australia. E-mail: [email protected]

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: This article introduces a new methodology dedicated to classify the evolutions of urban blocks extracted from spatio-temporal topographic databases where an urban block is defined as the smallest area that is surrounded by communication network (roads, railways, …). To achieve that, an ascendant hierarchical clustering is applied to sequences of urban block states (i.e., sequences of class labels to which the block belongs to at each date). The principal originality of this approach is to use a distance measure based on DTW (Dynamic Time Warping) which is able to apprehend temporal behaviors (mainly time lags in dates corresponding to a change of state) and which takes into account the semantic proximity between the different kinds of urban blocks. Several experiments have been carried out on areas in the city of Strasbourg (France). First results are relevant and highlight realistic urban dynamics.

Keywords: Urban dynamics analysis, dynamic time warping, symbolic time series clustering

DOI: 10.3233/AIC-160717

Journal: AI Communications, vol. 29, no. 6, pp. 733-746, 2016