TY - JOUR
T1 - Density equalizing distortion of large geographic point sets
AU - Bak, Peter
AU - Schaefer, Matthias
AU - Stoffel, Andreas
AU - Keim, Daniel A.
AU - Omer, Itzhak
N1 - Funding Information:
this work has been funded by the German Research Society (DFG) under grant GK-1042, “Explorative analysis and visualization of Large information Spaces” and by Priority Programme (SPP) 1335 “visual Spatiotemporal Pattern analysis of Movement and Event Data.” the authors wish to thank Halldór Janetzko for implementing the software framework for the proposed techniques and Miklos Bak for inspiring ideas and discussions.
PY - 2009/7/1
Y1 - 2009/7/1
N2 - Visualizing large geo-demographical datasets using pixel-based techniques involves mapping the geospatial dimensions of a data point to screen coordinates and appropriately encoding its statistical value by color. The analysis of such data presents a great challenge. General tasks involve clustering, categorization, and searching for patterns of interest for sociological or economic research. Available visual encodings and screen space limitations lead to over-plotting and hiding of patterns and clusters in densely populated areas, while sparsely populated areas waste space and draw the attention away from the areas of interest. In this paper, two new approaches (RadialScale and AngularScale) are introduced to create density-equalized maps, while preserving recognizable features and neighborhoods in the visualization. These approaches build the core of a multi-scaling technique based on local features of the data described as local minima and maxima of point density. Scaling is conducted several times around these features, which leads to more homogeneous distortions. Results are illustrated using several real-world datasets. Our evaluation shows that the proposed techniques outperform traditional techniques as regard the homogeneity of the resulting data distributions and therefore build a more appropriate basis for analytic purposes.
AB - Visualizing large geo-demographical datasets using pixel-based techniques involves mapping the geospatial dimensions of a data point to screen coordinates and appropriately encoding its statistical value by color. The analysis of such data presents a great challenge. General tasks involve clustering, categorization, and searching for patterns of interest for sociological or economic research. Available visual encodings and screen space limitations lead to over-plotting and hiding of patterns and clusters in densely populated areas, while sparsely populated areas waste space and draw the attention away from the areas of interest. In this paper, two new approaches (RadialScale and AngularScale) are introduced to create density-equalized maps, while preserving recognizable features and neighborhoods in the visualization. These approaches build the core of a multi-scaling technique based on local features of the data described as local minima and maxima of point density. Scaling is conducted several times around these features, which leads to more homogeneous distortions. Results are illustrated using several real-world datasets. Our evaluation shows that the proposed techniques outperform traditional techniques as regard the homogeneity of the resulting data distributions and therefore build a more appropriate basis for analytic purposes.
KW - Geographic visualization
KW - Geospatial data analysis
KW - Point density distortions and scaling
UR - http://www.scopus.com/inward/record.url?scp=70349134695&partnerID=8YFLogxK
U2 - 10.1559/152304009788988288
DO - 10.1559/152304009788988288
M3 - Article
AN - SCOPUS:70349134695
SN - 1523-0406
VL - 36
SP - 237
EP - 250
JO - Cartography and Geographic Information Science
JF - Cartography and Geographic Information Science
IS - 3
ER -