Multiple representations in geospatial databases, the brain’s spatial cells, and deep learning algorithms

ABSTRACT

Buttenfield (1988) pioneered research on multiple representations in the dawn of GIScience. Her efforts evoked inquiries into fundamental issues arising from the selective abstractions of infinite geographic complexity in spatial databases, cartography, and application needs for varied geographic details. These fundamental issues posed ontological challenges (e.g. entity classification) and implementational complications (e.g. duplication and inconsistency) in geographic information systems (GIS). Expanding upon Buttenfield’s line of research over the last three decades, this study reviewed multiple representations in spatial databases, spatial cognition, and deep learning. Initially perceived as a hindrance to GIS, multiple representations were found to offer new perspectives to encode and decipher geographic complexity. This paper commenced by acknowledging Buttenfield’s pivotal contributions to multiple representations in GIScience. Subsequent discussions synthesized the literature to outline cognitive representations of space in the brain’s hippocampal formation and feature representations in deep learning. By cross-referencing related concepts of multiple representations in GIScience, the brain’s spatial cells, and machine learning algorithms, this review concluded that multiple representations facilitate learning geography for both humans and machines.

KEYWORDS:

• Multiple representations
• spatial cognition
• spatial cells
• deep learning
• spatial abstraction

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

This is a review paper and involves no original data.

Additional information

Funding

This research is based upon work supported by (while serving at) the National Science Foundation and the (US) National Institute of Health (NIH) grant R21 AG069267.