Non-Connective Linear Cartograms for Mapping Traffic Conditions

Abstract

Cartograms have the advantage of bringing a greater visual impact to map readers. Geographic locations or spatial relationships of objects are intentionally modified to suit the attributes pertaining to objects. In area cartograms, it is the size of the object that is intentionally modified, while in linear cartograms it is the length or direction that is intentionally modified. Traffic conditions in urban transportation networks are very dynamic phenomenon as they change through time. During highly congested hours, travel speeds are low, and travel times are long, and vice versa. In previous studies, traffic conditions were visualized by color and width of road segments. In this paper, non-connective linear cartograms are introduced as a way to represent traffic conditions. Non-connective linear cartograms are linear cartograms that do not show the connectivity between line segments. Lengths of road segments are modified to represent a specific theme in traffic conditions. When the length of road segments represents the congestion level, longer segments indicate higher congestion levels, meaning near road maximum capacity. When the length of the segments represents the travel speed, longer segments indicate higher travel speed and, therefore, shorter travel time. When the length of the segments represents the travel time, longer segments indicate longer travel time, and therefore lower travel speed. In the non-connective linear cartograms, lengths of line segments are not limited to the physical length of represented road segments. The flexibility of adjusting it makes length of line segment a visual variable just like color and width of line segment. All three visual variables work together to create dramatic visual effects and attract greater attention from readers.

Usability evaluation of centered time cartograms

“…Shimizu and Inoue [17] reported that cartograms are a highly effective method for visually representing statistical data. Similarly, Wu and Hung [18] found cartograms very useful for creating strong visual impacts.…”

Recurrent Neural Network for Estimating Speed Using Probe Vehicle Data in an Urban Area

“…Since each link has a different performance, the vehicle group was classified by setting different thresholds for each link. Therefore, the Jenks natural break (JNB) was used as a technique for the clustering of traffic features and for searching for stateidentifying thresholds for a single property of urban traffic flows (37,38). The JNB method searches for thresholds that minimize the variance in the classification group and increase the variance among the classification groups.…”

An alternative method to constructing time cartograms for the visual representation of scheduled movement data

“…Some researchers consider cartograms as a highly effective tool (e.g. Bhatt, 2006;Dent, 1975;Dorling, 1996;Inoue & Shimizu, 2006;Kocmoud & House, 1998;Shimizu & Inoue, 2009;Wu & Hung, 2012), while others have doubts about their effectiveness because of the possible deformation in shape (e.g. Fotheringham, Brunsdon, & Charlton, 2000;Griffin, 1980;Griffin, 1983;Roth, Woodruff, & Johnson, 2010;Tobler, 2004;Yau, 2008).…”

“…In recent years, cartograms have become an increasingly popular tool due to their captivating design (Bhatt, 2006;Kocmoud, 1997;Shimizu & Inoue, 2009), and also due to the availability of tools to create them automatically. A cartogram emphasizes attributes over geographic locations or spatial relationships, thereby attracting greater attention from readers and creating a strong visual impact (Wu & Hung, 2012).…”

“…In the previous studies (e.g. Ahmed & Miller, 2007;Axhausen, Dolci, Fröhlich, Scherer, & Carosio, 2008;Bies & Kreveld, 2013;Campbell, 2001;Campenhout, 2010;Goedvolk, 1988;Spiekermann & Wegener, 1994;Tyner, 1992;Shimizu & Inoue, 2009;Kaiser, Walsh, Farmer, & Pozdnoukhov, 2010;Ramaer, 2011;Wu & Hung, 2012), line and/or point cartograms are often used to visualize travelling-times and are referred to as distance cartograms, distance-by-time cartograms, time-space maps or travel-time maps. However, some of these studies only describe how to deform the network according to travelling-times (e.g.…”