Generating Moving Regions from Snapshots of Complex Regions

2020

AGILE GIScience Ser.

Abstract. This work presents a methodology for the comparison and evaluation of region interpolation methods proposed in the spatiotemporal databases literature. An evaluation performed using the methodology proposed is also presented. To the best of our knowledge this is the first time that such a methodology is discussed and presented in the spatiotemporal databases literature, and that region interpolation methods are evaluated and compared using the same dataset and using data from the evolution of real-world phenomena.

Section: Resultsmentioning

confidence: 99%

On the Evaluation and Comparison of Region Interpolation Methods

Duarte

Dias

2020

AGILE GIScience Ser.

“…The spatial transformations in a slice are given by region interpolation methods (e.g. [4,[10][11][12][13]) that provide representations with different characteristics and behaviors, and there is no consensus on a method that provides realistic interpolations for all use cases. In fact, the evaluation the spatio-temporal transformations generated using region interpolation methods is commonly performed visually and case by case.…”

Section: Background and Related Workmentioning

confidence: 99%

“…This section presents how to express user-defined rules using SQL and illustrative examples over real-world data. Three region interpolation methods are used: Secondo [10], PySpatioTemporalGeom [11] and SPTMesh [4]. The source images and WKT representations of geometries are available at http://most.web.ua.pt/datasamples.zip.…”

Section: Applications On Real-world Datamentioning

confidence: 99%

Towards the automatic selection of moving regions representation methods

Costa

Miranda

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation

2020

Moving region is an abstraction used to represent the spatio-temporal behavior of real-world phenomena in database systems. The most common approach to model moving regions uses geometries to represent their position and shape at different times (observations), and interpolation functions to generate the evolution of the geometries between observations. Several region interpolation methods have been proposed in the databases literature, but as there is no suitable method for all use cases, users must select the most adequate algorithm to represent each region by visual inspection. This can be infeasible when dealing with large datasets. This paper presents the first steps towards a system that suggests which methods (and configurations) can generate representations fitting the requirements of a particular application. It includes an abstract specification of user-defined rules on the spatio-temporal evolution of moving regions to assess the suitability of region interpolation functions, a discussion on optimization strategies for efficient implementation of the rules and illustrative examples using real-world data to show how to use this approach to select the best methods to represent a spatio-temporal phenomena. CCS CONCEPTS • Information systems → Temporal data; Spatial-temporal systems.

“… Works on finding : (Heinz & Güting, 2016;Mckenney & Webb, 2010;Mckennney & Frye, 2015;Tøssebro & Güting, 2001)  Morphing techniques using polygons compatible triangulations and rigidity-preserving interpolation methods: (Alexa, Cohen-Or, & Levin, 2000;Baxter et al, 2008;Gong, 2011;Craig Gotsman & Surazhsky, 2001;Haesevoets & Kuijpers, 2004).…”

Section: Representation Of Moving Regionsmentioning

confidence: 99%

“…Our goal is to obtain a realistic representation of the evolution between these geometries. (Amaral, 2015) shows that the use of compatible triangulation and rigid interpolation methods obtains more realistic representations of the evolution of these geometries when compared to other methods proposed in the literature (Tøssebro & Güting, 2001) and (Mckennney & Frye, 2015).…”

Section: A Discrete Data Model For Moving Objectsmentioning

confidence: 99%

A Framework for the Management of Deformable Moving Objects

Duarte

Dias

Lecture Notes in Geoinformation and Cartography

2018

There is an emergence of a growing number of applications and services based on spatiotemporal data in the most diverse areas of knowledge and human activity. The Internet of Things (IoT), the emergence of technologies that make it possible to collect information about the evolution of real world phenomena and the widespread use of devices that can use the Global Positioning System (GPS), such as smartphones and navigation systems, suggest that the volume and value of these data will increase significantly in the future. It is necessary to develop tools capable of extracting knowledge from these data and for this it is necessary to manage them: represent, manipulate, analyze and store, in an efficient way. But this data can be complex, its management is not trivial and there is not yet a complete system capable of performing this task. Works on moving points, that represent the position of objects over time, are frequent in the literature. On the contrary there are much less solutions for the representation of moving regions, that represent the continuous changes in position, shape and extent of objects over time, e.g., storms, fires and icebergs. The representation of the evolution of moving regions is complex and requires the use of more elaborate techniques, e.g., morphing and interpolation techniques, capable of producing realistic and geometrically valid representations. In this dissertation we present and propose a data model for moving objects (moving points and moving regions), in particular for moving regions, based on the concept of mesh and compatible triangulation and rigid interpolation methods. This model was implemented in a framework that is not client or application dependent and we also implemented a spatiotemporal extension for PostgreSQL that uses this framework to manipulate and analyze moving objects, as a proof of concept that our framework works with real applications. The tests' results using real data, obtained from satellite images of the evolution of 2 icebergs over time, show that our data model works. Besides the results obtained one important contribution of this work is the development of a basic framework for moving objects that can be used as a basis for further investigation in this area. A few problems still remain that must be further studied and analyzed, in particular, the ones that were found when using the compatible triangulation and rigid interpolation methods with real data.