A New Method to Represent Temporal Data Sets

An increasing number of GIS users need to represent temporal or time dependent data on maps in a meaningful and concise manner. The ability to analyze data for both spatial and temporal patterns in a single graphical presentation provides a powerful incentive to develop visualization tools for communication to map users and decision makers. Although there are many different methods to display scientific data to help discern either spatial or temporal trends, few visualization software packages allow for a single graphical presentation within a geographic context.

The authors’ challenge was to display chemical concentration data for many different groundwater monitoring wells for a single chemical or analyte sample over time. Sample data had been collected for many years; some wells were sampled as early as 1950. The goal was to present this complex temporal data on a map so that one could easily visualize spatially the movement and change in concentration of the analyte, both horizontally and vertically, and to assess the potential for the analyte reaching the water table after many years.

The authors developed the clock diagram code and method for temporal and spatial visualization. The clock diagrams are analogous to “rose diagrams” often used to depict wind direction or strike direction in geologic applications. The clock diagram method consists of three steps: (1) sample location data, including depth, is compiled along with attribute data such as concentration and time; (2) all data is read into a computer code, and clock diagram graphics are created; and (3) clock diagram graphics are placed on the map according to geographic location of the well where samples were collected.

Although this method of representing temporal data through the use of clock diagrams is not entirely new, the use of multiple clock diagrams as GIS symbology to emphasize movement of sampled data over time is a new concept. The clock diagram code and method have been proven to be an effective means in identifying temporal and spatial trends when analyzing groundwater data.