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In this research, we are interested in the use of what we call pan-scalar maps, i.e. interactive, zoomable, multi-scale maps such as Google Maps. It is frequent to feel disorientation when we use these pan-scalar maps, and the absence of consistent landmarks or anchors across scales can be one of the causes of this disorientation \citep{touya_where_2023}. As a consequence, within the virtual environment of a pan-scalar map, we make the hypothesis that map objects, parts of objects, or groups of objects can comport comparably to the qualities of anchors or landmarks in a real space for spatialization purposes.For that we designed a user study where participants were asked to draw on top of the memorable, salient landmarks they saw on the map. - RAW_DATA contains 2 CSV files: the first contains all drawings, the second all participations. - MAP_DRAWING contains all drawings split by view (location, style, zoom). - DRAWING_ANCHORS split drawings by view into pan-scalar anchors (Location, style, zoom, drawings_anchor). - ANCHORS contains the vector delineation of pan-scalar anchors (Location, style, zoom,anchor). -STATISTIC_DRAWING contains anchoress, presence... attribute information in xls of drawings (Location, style, zoom,drawings_statistics) -BOUNDED_ANCHOR contains vector data for anchor lines that have been drawn in the same hue (Location, style, zoom,bounded_anchor) -WORFLOW_ANCHOR : Contains all QGIS workflows used for AnchorWhat analysis. - ILLUSTATIONS contains some illustrations from the AnchorWhat analysis.
cognitive cartography, anchor, landmarks, map design, pan-scalar map, drawing
cognitive cartography, anchor, landmarks, map design, pan-scalar map, drawing
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