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Spatio-temporal Clustering of Polygon Objects and per Object Interventions

Authors:
Floris Abrams
Lieve Sweeck
Johan Camps
Dirk Catrysse
Jos Van Orshoven

Keywords: Spatio-temporal clustering; Budget constraint; Disaster management; Multi-Attribute Decision Making; MADM.

Abstract:
Polygons provide a natural representation for many types of geospatial objects such as agricultural parcels, buildings, and polluted sites. These polygon-based entities form the smallest units used in decision making of real-world problems. Acting on these dispersed entities could result in a heterogeneous and difficult to perform an action plan. Clustering of parcels in larger homogeneous actionable units can improve feasibility and reduce cost. Therefore, a polygon-based clustering can be beneficial for environmental disaster management, where due to the large impacted area or limited availability of labor and financial resources, setting priorities of where, how and when to act are indispensable. This paper presents a spatio-temporal clustering algorithm under a budget constraint to prioritize clusters of parcels for intervention in space and time. The proposed algorithm returns homogeneous actionable clusters in space and time, trading off between effectiveness and feasibility and cost of intervention.

Pages: 1 to 6

Copyright: Copyright (c) IARIA, 2022

Publication date: June 26, 2022

Published in: conference

ISSN: 2308-393X

ISBN: 978-1-61208-983-6

Location: Porto, Portugal

Dates: from June 26, 2022 to June 30, 2022