We present SeeLevelViz, a free, open-source program written in Python for making interactive visualizations of relative sea-level change in landscapes and shorelines. The accurate reconstruction of shoreline positions is a crucial factor in coastal palaeolandscape studies, particularly in areas where the coast is fronted by islands, since the separation of islands from the mainland drives important ecological and sociocultural outcomes. This pro-gram creates accurate time-slice reconstructions of shoreline positions and palaeolandscapes when the user provides two components: 1) a digital elevation model of the target region (including currently submerged areas), and 2) a simple spreadsheet of relative sea-level elevations at different dates derived either from a glacio isostatic adjustment model of relative sea-level change, or from observed past sea-level data points. The tool is presented using the eastern coast of the Adriatic Sea in the Mediterranean as a test case, since this region has a complex coastline articulation due to combined geological and geomorphological factors. In this area, like in many other Mediterranean coastal areas, the separation of islands from the mainland following the last glacial maximum and throughout the Holocene has occurred in connection with important phases of the development, particularly of Mesolithic and Neolithic cultures, influencing human migrations and the spread of seafaring techniques. Reliable palaeolandscape reconstructions at different time slices are thus crucial for supporting archaeological interpretation. Flexibile and user-friendly, SeeLevelViz can compliment reconstructions of coastal landscape changes either based on glacial isostatic adjustment models or on relative palaeo-sea-level evidence, since simple, interactive visualizations are a powerful technique for understanding spatial time-series data, both for the interpretation phase of research, and for presentation to colleagues and the public. The program can be modified or used freely for papers, presentations, etc. By crediting and citing this article.

SeeLevelViz: A simple data science tool for dynamic visualization of shoreline displacement caused by sea-level change

Silas Dean
Primo
;
Marta Pappalardo
2022-01-01

Abstract

We present SeeLevelViz, a free, open-source program written in Python for making interactive visualizations of relative sea-level change in landscapes and shorelines. The accurate reconstruction of shoreline positions is a crucial factor in coastal palaeolandscape studies, particularly in areas where the coast is fronted by islands, since the separation of islands from the mainland drives important ecological and sociocultural outcomes. This pro-gram creates accurate time-slice reconstructions of shoreline positions and palaeolandscapes when the user provides two components: 1) a digital elevation model of the target region (including currently submerged areas), and 2) a simple spreadsheet of relative sea-level elevations at different dates derived either from a glacio isostatic adjustment model of relative sea-level change, or from observed past sea-level data points. The tool is presented using the eastern coast of the Adriatic Sea in the Mediterranean as a test case, since this region has a complex coastline articulation due to combined geological and geomorphological factors. In this area, like in many other Mediterranean coastal areas, the separation of islands from the mainland following the last glacial maximum and throughout the Holocene has occurred in connection with important phases of the development, particularly of Mesolithic and Neolithic cultures, influencing human migrations and the spread of seafaring techniques. Reliable palaeolandscape reconstructions at different time slices are thus crucial for supporting archaeological interpretation. Flexibile and user-friendly, SeeLevelViz can compliment reconstructions of coastal landscape changes either based on glacial isostatic adjustment models or on relative palaeo-sea-level evidence, since simple, interactive visualizations are a powerful technique for understanding spatial time-series data, both for the interpretation phase of research, and for presentation to colleagues and the public. The program can be modified or used freely for papers, presentations, etc. By crediting and citing this article.
2022
Dean, Silas; Bursten, Simon; Spada, Giorgio; Pappalardo, Marta
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1166128
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