Special Topics in GIS, Module 2.1

In this week's lab assignment, we explored TIN (triangulated irregular network) datasets containing terrain data. Unlike typical raster DEMs, TINs consist of a patchwork of triangles using the input sample points as vertices. Thus, they retain the exact input values at the vertices, which is not always the case with interpolated raster DEMs. That means they can easily accommodate an adaptive sampling strategy in variable terrain. Although TINs are more complex than raster DEMs, they can also be better for some applications because of that added precision. However, their angularity means they do not always capture detailed features accurately, as illustrated by the screenshots below of a TIN from Bear Lake, CA. The outline of the lake was not clear in the original TIN, and had to be incorporated by using a shapefile of the lake to create a hard edge in the TIN surface at the correct elevation. 

Special Topics in GIS, Module 1.3

In this week's lab, we compared to street network datasets to determine which was more complete (based on the total length of road segments) overall and for each grid square within the study area. One of the datasets contained TIGER road data and the other contained street centerlines maintained by the county. On the basis of overall length, the TIGER data was more complete.

To compare completeness by grid square, I first split the street data along the grid and then merged the resulting smaller feature classes back into one large feature set in order to have a single layer containing all the roads segmented by grid square. I then attached the grid information to each of the road datasets using a Spatial Join, which allowed me to calculate the sum of the road segment lengths for each grid square. That data could then be brought into Excel to calculate the difference in length between the two datasets for each grid square.  

Special Topics in GIS, Module 1.2

This week's lab assignment was to assess the accuracy of two different sets of street data against reference points taken from high-resolution orthoimagery. The first step was to establish test points according to the National Standard for Spatial Data Accuracy (NSSDA): at least 20 points at well-defined locations (in this case, street intersections) with at least 20% of the points located in each quadrant of the study area and a distance between points of at least 10% the diagonal length of the study area.

Screenshot of test point distribution, with street data:

XY coordinates were obtained at each test point for each of the datasets to be assessed as well as for the actual location of the intersection based on the orthoimagery. Then the errors statistics for each point and the RMSE and NSSDA accuracy statistic were calculated for each dataset.

Result for the first dataset, from the City of Albuquerque: Tested 23.94516 feet horizontal accuracy at 95% confidence level.

Result for the second dataset, from StreetMap USA: Tested 184.40877 feet horizontal accuracy at 95% confidence level.

In other words, 95% of the data is expected to fall within 23.94516 feet or 184.40877 feet, respectively, of its true location.