TINs and DEMs

 This week, we explored Triangulated Irregular Networks (TINs) and Digital Elevation Models (DEMs).  The TIN is unique in that it allows you to evaluate slope, aspect and elevation in a way that is useful for certain types of landscape analysis and quick retrieval of information regarding these types of information.  An example of this is shown in the image below:

A quick click on any triangle and you can get the slope, aspect, and elevation of that area.  So they can act as a good quick reference for a landscape.  They can also give you a new perspective on landscape and allow you to think about your elevation analysis as it would apply to a DEM as well.

Below, I used a DEM and weighted overlay to give a visual analysis of ski slope suitability.  The DEM makes the appearance of elevation data appear more smooth and it better for general map making as it is pleasing on the eye.

Both elevation models are useful and have their place in the toolbox.  This week was a good exercise in understanding the tools and methods used in both applications.

Road Completeness

 This week, I completed an accuracy assessment of two road networks based on their relative completeness using 1km x1km grid for comparison.  The goal of this assessment was to determine which road network is more complete.  TIGER Roads or Street Centerlines, and to determine which locations are similarly accurate and which locations are more accurate with either of the two networks.  The method ology I used to achieve this is outlined below.

I first used the “Clip” tool to remove portions of each road network that were outside of the supplied grid network.  Once the roads outside of the grid area were clipped, I used the “Summarize Within” tool to calculate the lengths of the road system inside each grid polygon.  I specified the output in kilometers so as to eliminate the need to do a separate calculation to convert the results from feet.

Once I had the total feet for each grid and road dataset, I used the “Table to Excel” tool to export this information to Microsoft Excel and calculate the necessary statistics in order to produce the table and maps below.  Once the statistics were calculated, I used the “Excel to Table” tool to import the final Excel table back into ArcGIS.  Once in ArcGIS, I used the “Join Field” tool join the Percent Difference field I created in Excel to the Grid layer.  I then used this field and the Grid layer to generate the map.

As evidenced by the map, the completeness of the road network is within +/- 5%, but there are areas that diverge a lot based on the accuracy of each network as it relates to specific grids.  

TIGER_Roads total length in kilometers: 11,382.7

Street Centerlines total length in kilometers: 10,873.3

The TIGER Roads network is more complete based on this analysis.

Total Grid Cells	297
Cells Where Street_Centerlines is more complete than TIGER_Roads	134
Cells Where TIGER_Roads is more complete than Street_Centerlines	162

Horizontal Accuracy

This week, I tested the horizontal accuracy of two street datasets in Albuquerque, NM.  I tested the City of Albuquerque dataset and the StreetMap USA dataset. To complete this analysis, I overlaid the two street datasets on orthophotos of Albuquerque. I used the orthophotos to identify the true location of 20 intersections.  After establishing these reference points, I identified the location of those intersections within the Albuquerque streets dataset and the StreetMap dataset, added XY data for all points, and used the NSSDA datasheet to compute the accuracy of each dataset.  Please see the map below for the location of intersections used in the analysis.

The Albuquerque dataset proved to be much more accurate than the StreetMap dataset and the accuracy statements for each are listed below:

ABQ Streets

Positional Accuracy: Tested 13.15 feet horizontal accuracy at 95% confidence interval.

 

StreetMap Streets

Positional Accuracy: Tested 353.50 feet horizontal accuracy at 95% confidence interval.

The relative accuracy of these two datasets is not even close, and it is clear which dataset would best be used for navigating the streets of Albuquerque.