Anchorwhat dataset
Description
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.
Files
01_RAW_DATA.zip
Files
(79.0 MB)
Name | Size | Download all |
---|---|---|
md5:dee49a4450441c31d4261b9e678d0194
|
16.8 MB | Preview Download |
md5:124f1a4761842c38e9ca7b768801c855
|
13.4 MB | Preview Download |
md5:5ff11e37c032aa9a4c9c6e5809ce2e22
|
12.2 MB | Preview Download |
md5:40b0a891999d722d1610b85318310699
|
7.3 MB | Preview Download |
md5:a48ad697e941070306193e0c1718c0c9
|
868.2 kB | Preview Download |
md5:d858fd2d1ba9a9f6d2e0656751760ded
|
3.5 MB | Preview Download |
md5:fca12e37843d0fee7155146cf4e458ac
|
21.5 kB | Preview Download |
md5:8508fc9566ab789fdac216e1eee22e84
|
24.9 MB | Preview Download |