Surfaces - TINs and DEMs

 

This week the focus was on using and understanding DEMs and TINs.  DEMs are digital elevation models, and TINs are triangular irregular networks.  Both are remotely sensed imagery that are used to convey the three-dimensional surface of the Earth.  This can be used for a wide variety of applications that require knowledge of what the terrain looks like.

DEMs and TINs are similar in many ways but there are differences between the two (Bolstad 2016, 68).  The most obvious difference is that is a DEM is a raster and a TIN is a vector.  The other major difference is that a TIN is able to convey elevation, slope, and aspect simultaneously while a DEM must be geoprocessed into three different layers to show each one of those elements.

TINs can be symbolized to emphasized different characteristics such as slope.  It is also possible to apply an outline to the edge of each triangle, so that when you are looking to click on a particular triangle to find its information it is easier to identify a single one.  Contour lines may also be applied so that differences in elevation are easier to discern.

Bolstad, P. (2016). GIS Fundamentals: A First Text on Geographic Information Systems (5th ed). Eider Press.

Data Quality Assessment

 

The goal of this week's lab was to determine the completeness of two separate road networks for Jackson County, OR.  One of the networks was the TIGER file which is created by the US Census Bureau, and the other was created by the county's GIS team.  The methodology for determining completeness was based off of the method used by Haklay (2010) where the study area was broken into grid squares of equal area and the total length in difference between the two networks was compared.  The network with longer road distance in each particular grid square was considered to be more complete.  This was mapped by using the percent difference which was calculated by (total length of Jackson County-created network - total length of TIGER network)/(total length of Jackson County-created network) * 100.

When looking at the county as a whole, the TIGER network is more complete as it has a little more than 500 km of road than the network made by the Jackson county team.  However, as shown in the map below, this does not mean that the TIGER network is more complete in all areas.  The Jackson County team network was more complete for 45.27% of the grid squares, and the TIGER network more complete for 54.73% of the grid squares.

A comparison of completeness between the two road networks.  The pink areas are where the TIGER network is more complete, and the green squares are where the Jackson County-created network is more complete.

Haklay, M. (2010). How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets. Environment and Planning B: Planning and Design, 37, 682-703. doi:10.1068/b35097

Data Quality

Continuing with the same focus of last week on positional accuracy, we compared two different road networks of the city of Albuquerque for positional accuracy.  The first network tested was made by the city of Albuquerque itself and the second was made by StreetMapUSA.  As to be expected, the network produced by the city of Albuquerque was considerably more accurate of the two since they have a much greater vested interest in having an accurate map of their own city (e.g. for properly dispatching ambulances for 911 calls).

To compare the accuracy of a network map you need to have a reference map to work from.  For the independent reference points we used the orthographic satellite photos to find intersections that exist on both map networks.

The next step of the positional accuracy process is to set the points.  Each point for both networks and the ortho photos has to be for the same intersection.  It is also important to get an even spread of points across the study area.  For best accuracy, greater than 20% of the points should be present in each quadrant and the points should be spaced out from one another.  At least 20 test points are required for reliable results.

After picking the points and then exporting them to an Excel spreadsheet, I took that raw data to process the accuracy assessment.  For each point on the network, the difference in the latitude and longitude is found.  Then, that difference is squared.  And the squared difference of both latitude and longitude are added together.  The sum of the squared differences is calculated, and then the average of that sum is also calculated.  Finding the square root of the average of the sum of the difference in latitude and longitude squared gives the RSME.  Multiplying the RSME by a provided value (in this case, the National Standard for Spatial Data Accuracy statistic determines that to be 1.7308 for horizontal accuracy) gives the final NSSDA value.  The lower the value the better the positional accuracy.

A road network map with the associated points for the intersections.  Both of the network layers and the reference points (from the ortho imagery) each had their own set of 20 points that all corresponded to like intersections.

The final results on accuracy are as follows:

Streetmap:
Tested 478.683 feet horizontal accuracy at 95% confidence level

Vertical positional accuracy: not applicable 

ABQ:

Tested 21.669 feet horizontal accuracy at 95% confidence level Vertical positional accuracy: not applicable