An event-based spatiotemporal data model (ESTDM) for temporal analysis of geographical data

Abstract

Representations historically used within GIS assume a world that exists only in the present. Information contained within a spatial database may be added-to or modified over time, but a sense of change or dynamics through time is not maintained. This limitation of current GIS capabilities has recently received substantial attention, given the increasingly urgent need to better understand geographical processes and the cause-and-effect interrelationships between human activities and the environment. Models proposed so-far for the representation of spatiotemporal data are extensions of traditional raster and vector representations that can be seen as location-or feature-based, respectively, and are therefore best organized for performing either location-based or feature-based queries. Neither form is as well-suited for analysing overall temporal relationships of events and patterns of events throughout a geographical area as a temporally-based representation.In the current paper, a new spatio-temporal data model is proposed that is based on rime as its organizational basis. and is thereby intended to facilitate analysis of temporal relationships and patterns of change through time. This model is named the Event-based Spatio Temporal Data Model (ESTDM). It is shown that temporally-based queries relating to locations can be implemented in an efficient and conceptually straightforward manner using ESTDM by describing algorithms for three fundamental temporally-based retrieval tasks based on this model: (I) retrieving location(s) that changed to a given value at a given time, (2) retrieving location(s) that changed to a given value over a given temporal interval, and (3) calculation of the total area that has changed to a given value over a given temporal interval. An empirical comparison of the space efficiency of ESTDM and compressed and uncompressed forms of the 'snapshot' model is also given, showing that ESTDM is also a compact representation of spatio-temporal information.

Spatial process and data models: Toward integration of agent-based models and GIS

“…The focus of data models in GIS has been on spatial, at the expense of temporal, dimensions (Peuquet 2002). Space and time can be defined in absolute or relative terms (Blaut 1961;Couclelis 1997;Wachowicz 1999;Goodchild 2002).…”

“…Space and time can be defined in absolute or relative terms (Blaut 1961;Couclelis 1997;Wachowicz 1999;Goodchild 2002). Whereas absolute space and time offer a rigid geometric structure within which phenomena are referenced, the relative view references location largely in terms of relationships, topological and temporal, between features (Peuquet 2002). Object-oriented data models, which have grown increasingly sophisticated in GIS (Leung et al 1999), assign location as an attribute of features rather than of the space itself, and allow representation of relative space.…”

A multidimensional representation model of geographic features

“…Each of these approaches handles one aspect, such as event-based temporal modeling (Peuquet and Duan, 1995). The representation developed in this research includes the temporality of geographical entities.…”

“…The theory is grounded firmly in geographic and cartographic abstraction and modeling principles (Peuquet, 1984;Peuquet, 1988;Guptill and others, 1990) and in cognitive category theory (Mark, 1993;Usery, 1993Usery, , 1996Frank, 1998). The theory builds from basic research into the three aspects of geographical entities defined by Berry (1964) and used by many researchers since that time (Sinton, 1978;Usery, 1993Usery, ,1996Peuquet, 1994;Peuquet and Duan, 1995;Yuan, 1996Yuan, , 1999. This research is a unique contribution because the developed theory supports a variety of data types including objects, fields, and objects with field-like properties and multiple applications from a single theoretical structure.…”

Modelling Higher Dimensional Data for GIS Using Generalised Maps

“…For instance, event-based models [16] connect successive moments in time with the changes that occurred in them, object-relationship models [17] add information to model the changes themselves, and the original tGAP structure [18] links appropriate 2D objects at different levels of detail. These representations are lightweight and sufficient for many applications, but they do not solve any of the above mentioned problems in their entirety: there is still a fixed number of points along a dimension (e.g.…”