ISPRS-Annals ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Annals ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9050 Copernicus Publications Göttingen, Germany 10.5194/isprs-annals-X-4-W3-2022-81-2022 DISCOVERING CLUSTERING PATTERNS FROM E-COUNTER DATA STREAMS Ivari N. E. 1 Wachowicz M. 2 Williams P. A. H. 3 Agnew T. 4 University of New Brunswick, Department of Geomatics Engineering, Fredericton, Canada RMIT University, Geospatial Science School, Melbourne, Australia Flinders University, College of Medicine and Public Health, Adelaide, Australia Flinders University, College of Nursing and Health Sciences, Adelaide, Australia 14 10 2022 X-4/W3-2022 81 88 Copyright: © 2022 N. E. Ivari et al. 2022 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-annals.copernicus.org/articles/X-4-W3-2022/81/2022/isprs-annals-X-4-W3-2022-81-2022.html The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/X-4-W3-2022/81/2022/isprs-annals-X-4-W3-2022-81-2022.pdf

Clustering data streams has gained popularity in recent years due to their potential of generating relevant information for planning building automation, evaluating energy efficiency scenarios, and simulating emergency protocols in indoor spaces. In this paper, a Data Stream Affinity Propagation (DSAP) clustering algorithm is proposed for analyzing indoor localization data generated from e-counters systems. The data streams are a sequence of potentially infinite and non-stationary data points, arriving continuously where random access to the data is not feasible and storing all the arriving data is impractical. The DSAP model is developed based on a two-phase approach (i.e., online and offline clustering phases) using the landmark time window model. It is non-parametric in the sense of not requiring any prior knowledge about the number of clusters and their respective labels. The validation and performance of the DSAP model is evaluated using real-world data streams from a behavioural experiment aimed at finding new spatio-temporal patterns in stair usage due to an educational intervention campaign.