Module 8 - Isarithmic Mapping
This week we focused on isarithmic maps which are used to depict smooth, continuous phenomena (such as rainfall, barometric pressure, topography, or elevation). I'm sure you've seen them before on TV for your daily weather news. They are the 2nd most used thematic mapping method after choropleth maps, with its usage dating back to the 18th century.
Since it is nearly impossible to record smooth/continuous phenomena, samples are measured at control points. Data can be collected in 2 different ways at these control points--with true point data or conceptual point data. But how is the "in-between" data determined then? With interpolation. Interpolation is based on the premise that "spatially distributed objects demonstrate spatial autocorrelation". Basically, this means that things that are closer together, tend to share common traits. There are different types of interpolation methods such as Inverse Distance Weighted (IDW), Kriging, Splining, and Triangulation.
So, how do we depict this smooth/continuous phenomena? There are several ways: using contour lines, hypsometric tinting, continuous-tone or fishnet symbolization, and/or a mixture of them.
For our lab assignment, we used ArcMap and the Spatial Analyst Extension Tool to create an isarithmic map depicting precipitation data for the State of Washington. My final product (as shown above), uses hypsometric tinting symbolization, classified manually by 10 categories, a hillshade effect, and a color ramp called 'Precipitation', all of which I configured under Layer Properties. We used the "Int Tool" under Spatial Analyst to convert the raster values in our dataset from floating numbers to integers. Then we used the "Contour List Tool" under Spatial Analyst to create a series of contour lines as depicted in my map. I polished my map by adding the necessary map elements, by utilizing a horizontal legend with contiguous-boxes, and by explaining how the precipitation data was derived and interpolated.
The data was derived using an interpolation method called PRISM (Parameter-elevation Regressions on Independent Slopes Model). The PRISM model used point precipitation data (from a 30-year period from 1981-2010) in the form of digital elevation model (DEM) from the 13,000 weather monitoring stations across the contiguous United States to generate gridded estimates of monthly temperatures and precipitation. The model takes into account physiographic factors influencing climate patterns (such as location, coastal proximity, topographic orientation, and more) when calculating regression for each gridded estimate.
Since it is nearly impossible to record smooth/continuous phenomena, samples are measured at control points. Data can be collected in 2 different ways at these control points--with true point data or conceptual point data. But how is the "in-between" data determined then? With interpolation. Interpolation is based on the premise that "spatially distributed objects demonstrate spatial autocorrelation". Basically, this means that things that are closer together, tend to share common traits. There are different types of interpolation methods such as Inverse Distance Weighted (IDW), Kriging, Splining, and Triangulation.
So, how do we depict this smooth/continuous phenomena? There are several ways: using contour lines, hypsometric tinting, continuous-tone or fishnet symbolization, and/or a mixture of them.
For our lab assignment, we used ArcMap and the Spatial Analyst Extension Tool to create an isarithmic map depicting precipitation data for the State of Washington. My final product (as shown above), uses hypsometric tinting symbolization, classified manually by 10 categories, a hillshade effect, and a color ramp called 'Precipitation', all of which I configured under Layer Properties. We used the "Int Tool" under Spatial Analyst to convert the raster values in our dataset from floating numbers to integers. Then we used the "Contour List Tool" under Spatial Analyst to create a series of contour lines as depicted in my map. I polished my map by adding the necessary map elements, by utilizing a horizontal legend with contiguous-boxes, and by explaining how the precipitation data was derived and interpolated.
The data was derived using an interpolation method called PRISM (Parameter-elevation Regressions on Independent Slopes Model). The PRISM model used point precipitation data (from a 30-year period from 1981-2010) in the form of digital elevation model (DEM) from the 13,000 weather monitoring stations across the contiguous United States to generate gridded estimates of monthly temperatures and precipitation. The model takes into account physiographic factors influencing climate patterns (such as location, coastal proximity, topographic orientation, and more) when calculating regression for each gridded estimate.
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