Why are events important and how to compute them in geospatial research?

Abstract

Geospatial research has long centered around objects. While attention to events is growing rapidly, events remain objectified in spatial databases. This paper aims to highlight the importance of events in scientific inquiries and overview general event-based approaches to data modeling and computing. As machine learning algorithms and big data become popular in geospatial research, many studies appear to be the products of convenience with readily adaptable data and codes rather than curiosity. By asking why events are important and how to compute events in geospatial research, the author intends to provoke thinking into the rationale and conceptual basis of event-based modeling and to emphasize the epistemological role of events in geospatial information science. Events are essential to understanding the world and communicating the understanding, events provide points of entry for knowledge inquiries and the inquiry processes, and events mediate objects and scaffold causality. We compute events to improve understanding, but event computing and computability depend on event representation. The paper briefly reviews event-based data models in spatial databases and methods to compute events for site understanding and prediction, for spatial impact assessment, and for discovering events' dynamic structures. Concluding remarks summarize key arguments and comment on opportunities to extend event computability.

A Spatial-Temporal Knowledge Management Framework

“…Others like Yuan [6] highlights events and processes as key to the creation of event-driven knowledge within geospatial research. The researcher discusses the importance of events in scientific inquiries and reviews event-based data modeling and computing.…”

“…The researcher discusses the importance of events in scientific inquiries and reviews event-based data modeling and computing. Instead of just applying machine learning (ML) algorithms to readily available data, Yuan highlights the need to add curiosity by asking why events are important and how these can be computed within geospatial research [6]. Events are important to understanding the world and the objects involved in the events.…”

“…Historically knowledge processing has been characterized within a framework comprised of six phases in data mining literature; business understanding, data collection, data processing, modeling, evaluation, and deployment [12]. Several methods have been suggested in the literature that utilizes this framework particularly in processing spatial-temporal data already created in database systems [6]. However, as noted in [4], data within a database only contain a geometric representation of time and space but not the relationships that are derived by knowledge representation.…”

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“…Series of images taken by advanced Earth-observing technologies over long periods of time, combined with historical climate records, constitute the main source of continuous and consistent information about the marine environment [11,12] and offer new opportunities for monitoring oceanic dynamics and understanding their evolutionary patterns [10]. These evolutionary patterns generally have lifespans ranging over generations through development, merging, splitting and dissipation [13,14], playing significant roles in regional and global climate change [15,16].…”

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“…This challenge is accepted in a very different context by Fotheringham [6] who suggests that the challenge in local modeling is recognising that the "unobservable processes producing the observable outcomes we want to change are not the same everywhere and need to be examined locally." Yuan [12] makes a plea for more research on events, which she argues are essential to geospatial understanding, yet constrained by domain-specific attempts at modeling. A more pluralistic understanding of events, and their implications both individually and societally would require us to think much harder about how we model, analyse and communicate with and about spatio-temporal processes and data.…”