TINs & DEMs

 TINs use vector (point) data while DEMs use raster (grid) data. The most noticeable differences between the Tin and DEM is some of the contour lines on the TIN produce sharp edges that do not close while the contour lines on the DEM are smooth and continuous. The areas where the contour lines are closest together (steepest) show the smallest amount of differences between the TIN and DEM. I infer that the DEM contour lines are more accurate because they have the advantage of containing more reference data. 

Assessment: Road Networks

For this week's module, we learned how to determine the quality of road networks by employing methodology similar to a study conducted by Haklay (2010). The completeness of two road networks, Street Centerlines and TIGER, were determined by measuring the total lengths of road in Jackson County, Oregon. We were provided a polygon grid of the county. By using the summarize within tool, I was able to calculate the total kilometers of road for each road network shapefile in each grid.The Tiger shapefile contains 11382.7 Km of road segments and the Street Centerlines shapefile contains 10873.3 Km of road segments. The Tiger road network is the most complete.
  

I also created a join for the two road shapefiles by joining the Gridcode fields. I did a quality check and selected by attribute for each length field to see if any Grids had a value of zero and discovered two. I changed the field value to Null. I added a field and calculated the field with the statement:

!Grid_SummarizeWithin_Street.SUM_Length_KILOMETERS!>!Grid_SummarizeWithin_Tiger.SUM_Length_KILOMETERS! 

This returned a value of 1 if it was true and a value of 0 if it was false. The value of 1 indicated the Street Centerline was more complete and the value of 0 indicated the Tiger network was more complete. The Street Centerline network was more complete than the Tiger network in 134 out of 297 grids. The Tiger network was more complete than the Street Centerline in 162 out of 297 grids. . One of the grids did not contain any road segments and one only contained 5 km of TIGER road so both were excluded from the map. 

 

Standards

 This week we learned how to determine the quality of road network data. This was accomplished by determining the horizontal accuracy of the ABQ Streets (shapefile of road centerlines from Albuquerque, NM and StreetMap USA (shapefile of street centerlines from StreetMap USA) compared to reference points of true intersection locations. Inside the study area, I created two new feature classes of 20 test points for the ABQ Street shapefile and StreetMap USA shapefile. The criteria followed was using a good intersection, meeting sampling rules (minimum of 20% in each quadrant and >10% diameter apart), and matching locations in the two datasets.  After I completed the test points, I created a new feature class named reference points and created 20 points depicting the true intersection of my test points. To determine the accuracy statistics, I added XY coordinates to the feature classes to later add to the NSSDA Horizontal Accuracy worksheet.

City (ABQ) Accuracy Statement:  Using the National Standard for Spatial Data Accuracy, the data tested 14.47 feet horizontal accuracy at 95% confidence level.

StreetMap USA Accuracy Statement:  Using the National Standard for Spatial Data Accuracy, the data tested 160.00 feet horizontal accuracy at 95% confidence level.

Fundamentals

Many GIS organizations define precision and accuracy limits for their geospatial data and perform reviews of this data to ensure that standards are upheld. Accuracy is defined as "the closeness of agreement between a test result and the accepted reference value". Precision is defined as "the closeness of agreement between independent test results obtained under stipulated conditions". For this module, we determined precision and accuracy metrics based off provided data. The data provided was GPS waypoints mapped using a hand-held GPS device, a Garmn GPSMAP 76 unit.

 The horizontal accuracy of 3.24 meters was determined by measuring the distance between the reference point and the average waypoint location. The horizontal precision is 4.5 meters. There is a significant difference of 1.26 meters. The vertical accuracy (average location elevation-reference location elevation) is 5.92 meters. The vertical precision is 5.9 meters. The difference between the vertical accuracy and vertical precision is 0.02 meters. There is not a significant difference between vertical accuracy and vertical precision.