Lab 5- Interpolation- Special Topics

Interpolation is a way of making predictions based on known values. We interpolated the surface water quality of Tampa Bay using the BOD Mg/L variable from multiple sampling locations.

Interpolation methods explored were

        - Spline (regular)

        - Spline (tension

        - IDW

        - Thiessen polygons

IDW uses the distance of each data point to neighboring values to weight its predictions, whereas the Spline interpolation techniques fits through a set of input points to make its estimation. Spline is not constrained by the limits of the provided data and can give values greater or less than the highest and lowest data points, while IDW is constrained to the highest and lowest data point. 

Thiessen interpolation creates a series of polygons with each data point associated with the central location of a polygon. Because of this the size of the polygons is variable and dependent on the distance from other sampling locations. Each polygon is given the value of the central point and there is not estimation or continuity to the data. This method was not ideal for measuring water quality.

In this exercise my regular spline interpolation ended up displaying values that were not mathematically possible, giving me large swaths of negative value areas. It is highly unlikely for water quality to be at 0, and impossible for it to be a negative number. The tension method was a more accurate and aside from a greater range of values, data was closer to that displayed by the IDW. I personally chose to map the data along standard water quality monitoring guidelines using the following table for analysis:

and when using these guidelines the spline (tension) method only slightly differed from the IDW with the majority of the data falling into the "very good" range for both data sets, but a slightly bigger circle area falling under "fair" in the spline tension map.

IDW: "Fair" data is mapped in yellow and "very good" data in green

Spline tension: "Fair" data is mapped in yellow and "very good" data in green

Finally, here is more thoroughly symbolized view of my IDW map with BOD estimates. Although I would not necessarily need to use these number for water quality estimation, they give a good visual of how the IDW interpolation method calculated the water quality with regard to the data points.

 

Special Topics- Lab 4- TINS and DEMS

 This week we explored utilizing ArcGIS Pro to create elevation models. Additionally we converted a DEM to a TIN in order to create a ski run suitability map that weighted aspect, slope and elevation and presented ideal locations for a ski slope.

TINs and DEMs are terrain models used to analyze topographic features. TIN data uses a type of vector data to model a 3D surface by linking a series of triangles of different sizes based on elevation point data. These data points vary in density in proportion to the terrain the terrain. More complex terrain contains more data points, while simpler terrain contains fewer data points. A DEM consists of raster data consisting of a regular grid pattern that models elevation. 

We used our TIN and DEM models to depict the contour lines and slope of our data. Although DEM contour lines are smoother and more reminiscent of a typical map, TIN contour lines are more accurate due to the ability of the data to represent various surface features, notably peaks and ridges.

The first image displays my TIN while the second image contrasts my DEM layer of the same data.

The biggest difference I noticed between the two layers was that the DEM seemed to over estimate the uppermost regions of the data, adding contour lines where they were absent from the TIN data.

Special Topics- Data Quality Assessment- Module 3

 This week we were asked to assess the quality of two road network data sets, Street Centerlines and Tiger, by comparing the completeness of the data on a grid-by-grid basis. We then mapped the resulting data onto a choropleth map.

To run this assessment, I split the data so that each grid could be analyzed separately.  I then used spatial join and summary statistics tools to further process the data. I exported my statistics to Excel to more easily analyze it and found that the Street Centerlines had no data for one of my grids. In total the Street Centerline data was 10,671.1 km in length with 134 of the grids being more complete than the Tiger data. The Tiger data was 11,253.4 km in length with 163 grids being more complete than the Street Centerline data. The Street Centerline data had 5.4% less road coverage than the Tiger data.

After assessing the data in Excel, I returned to ArcGIS and created a field to calculate the percentage difference between the datasets. There was an median of -0.18% difference in coverage between the Street Centerline and Tiger data.

I used this percentage difference field to create my choropleth map: