Spatial interpolation has been widely and commonly used in many studies to create surface data based on a set of sampled points, such as soil properties, temperature, and precipitation. Currently, there are many commercial Geographic Information System (GIS) or statistics software ofering spatial interpolation functions, such as inverse distance weighted (IDW), kriging, spline, and others. To date, there is no "rule of thumb" on the most appropriate spatial interpolation techniques for certain situations, though general suggestions have been published. Many studies rely on quantitative assessment to determine the performance of spatial interpolation techniques. Most quantitative assessment methods provide a numeric index for the overall performance of an interpolated surface. Although it is objective and convenient, there are many facts or trends not captured by quantitative assessments. This study used 2D visualization and 3D visualization to identify trends not evident in quantitative assessment. This study also presented a special case, a closed system in which all interpolated surfaces should sum up to 100%, to demonstrate the interaction between interpolated surfaces that were created separately and independently.

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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.