GIS3015 - Module 8 - Isarithmic Mapping

In this week's lab we were tasked with learning about Isarithmic maps which are used to depict smooth, continuous phenomena across an area using different symbology methods.  We also learned about the two different types of data appropriate for this type of mapping, true point data and conceptual point data.  Next, we covered four different methods of data interpolation that are used to derive intermediate points to create the dataset (Inverse Distance Weight [IDW], Kriging, Splining, and Triangulation).

Our lab used precipitation data for the State of Washington that was derived and interpolated using the PRISM (Parameter-elevation Relationships on Independent Slopes Model) analytical model.  This model uses point data and an underlying grid such as a Digital Elevation Model (DEM) to generate gridded estimates of monthly or annual precipitation.  Our precipitation dataset was created from the application of this method on point data collected from weather monitoring stations and the calculated climate elevation regression for each grid cell within the DEM.  The created dataset accounts for physiographic factors that may influence climate patterns.

 

Continuous Tone Map (Map 1)

Map 1 uses Continuous Tone symbology to represent the data with a stretched precipitation color ramp with the hillshade effect option selected.  This effect creates a "relief" to denote elevation.  During this part of the lab, we also spent some time learning different techniques to create/modify a map legend within ArcMap.  This method can sometimes make it difficult to associate numbers of the legend with locations on the map.

Map 2 uses Hypsometric Tint symbology to represent the data with manual classifications (10 classes).  A "stepped" surface is created to more easily visually interpret the precipitation differences across the State.  Contour lines with defined ranges were also added to provide a three dimensional element to the map.  Map viewers can readily associate the color hues on the map with varying precipitation values.  Prior to changing the symbology we used the "int" spatial analysis tool to convert data in the precipitation raster dataset to integers.  Converting to integers enabled us to create crisp contour ranges represented by whole numbers.  Applying some of the legend techniques from this week's lab as well as the previous week, I created a vertical continuous legend to display the precipitation range values.  

Hypsometric Map with Contour Lines (Map 2)