The overall objective of this work is to apply GIS-based cost distance modeling (CDM) to site catchment modeling and analysis of prehistoric (Solutrean) sites in Andalusia. The implementation of a GIS-method for slope-based CDM was explained in detail, so that it can be replicated easily in future studies. Additional cost components, vegetation and stream networks, were included in the method. The presented CDM approach uses slope rasters as input data, which were derived from digital elevation models (DEMs). Various DEMs that differ in cell size, accuracy and other characteristics can be applied to this method. Thus, a major goal of this work is to investigate the influence different publicly available DEMs (SRTM, ASTER GDEM, EU-DEM, official 5-m/10-m cell size DEMs) have on the results of GIS-based CDM. While the investigation was conducted on sites from different chronocultural periods, a case study was performed on Solutrean sites in order to test the CDM approach by producing actual results and then comparing and interpreting them from an archaeological perspective. The results of the DEM evaluation with resampled horizontal resolutions show a clear influence of the DEM cell size on the modeled catchment area sizes. The investigation also indicates that this influence can be superimposed by other factors, such as noise and residuals of filtered anthropogenic features, when using DEMs of different origins.

Paleoenvironmental studies and according information (data) are abundantly published and available in the scientific record. However, GIS-based paleoenvironmental information and datasets are comparably rare. Here, we present an Open Science approach for creating GIS-based data and maps of paleoenvironments, and Open Access publishing them in a web based Spatial Data Infrastructure (SDI), for access by the archaeology and paleoenvironment communities. We introduce an approach to gather and create GIS datasets from published non-GIS based facts and information (data), such as analogous maps, textual information or figures in scientific publications. These collected and created geo-datasets and maps are then published, including a Digital Object Identifier (DOI) to facilitate scholarly reuse and citation of the data, in a web based Open Access Research Data Management Infrastructure. The geo-datasets are additionally published in an Open Geospatial Consortium (OGC) standards compliant SDI, and available for GIS integration via OGC Open Web Services (OWS).