Special Topics in GIS - M2.1 TIN and DEM

 In exploring and comparing the TIN and DEM data models, I -unsurprisingly perhaps- found DEM more familiar to work with. Based on previous experience in the UWF program and with displaying ecosystem data through my career, I felt like I had a better handle on how the DEM can be manipulated, which tools I had at my disposal, and consistent expectations for what the visual output would be from a given action. Though it is unfortunate that a DEM requires multiple calculations and tool runs to return values for aspect, slope, and elevation, I already had a solid foundation for this process. The neighbor-dependent nature of derived DEM values like this does produce a model that is easy to read, though less accurate than the TIN.

That being said, working with the TIN dataset for this project was extremely smooth, and as a researcher I think I prefer the higher accuracy available in the TIN, even if this somewhat sacrifices visual display for contour lines. The multiple faces of the triangle network in the TIN made it very clear when modeling difference values over a three dimensional structure, and I felt it was much quicker to get the necessary values in the TIN for aspect, slope, and elevation than the successive familiar tool running with the DEM. 

Overall, while I would be more likely to use a DEM for a public-facing map or display, I appreciate the data quality advantages of the TIN model. When working with three dimensional elevation data especially, I think it will be my first choice whenever one is available. 

A screenshot of the DEM model, with layers of calculated DEMs visible in the contents pane. TINs do not require these additional calculations to display the same information. 

Special Topics in GIS - M1.3 Assessment

The goal of an accuracy assessment is to help determine how reliable data is compared to the "actual" - or as close as can be reasonably determined as the actual. With the standardized techniques in the National Standard for Spatial Data Accuracy (NSSDA), we get a determination of positional accuracy that can be used to quantify how "off" a geospatial dataset is from the actual. With standardized protocols like this the accuracy statements become comparable to one another between datasets, allowing a user to select data that meets their needs for this aspect of data quality.

In the NSSDA protocol, the horizontal or vertical distance between a subset (>=20) of dataset features and the "actual" location of the feature is compared, and the difference between the actual and the dataset coordinates are then squared, summed, averaged with mean, and square-rooted to return the Root Mean Square Error. This error is then multiplied by a standard value that represents the average amount of error in the 95th percentile for horizontal or vertical data. The result is the shortest distance in the dataset that can be "trusted", expressed as an accuracy statement to the 95th percentile. 

For this lab a different aspect of data quality was examined; completeness. If accuracy helps determine if a dataset can be used based on how "trustworthy" it is, then completeness determines if a dataset can be used based on the amount of information it contains. In this lab, the completeness of two street datasets was assessed based on amount of data. The street polylines were overlaid and assessed with a grid. This method highlighted areas where the county-provided shapefile contained more lines (reds) and areas where the US Census' Topologically Integrated Geographic Encoding and Referencing (TIGER) dataset contained more lines (blues).

Red areas where the Jackson County file contained more road and blue areas where the TIGER dataset contained more road.

Special Topics in GIS - M1.2 Standards

Reference points representing the "true" location of intersections.

As previously discussed, to determine accuracy a datapoint must be compared to a known reference point for what the "true" value is. In this project the goal was to compare the accuracy of two polyline street datasets; one from the City of Albuquerque, and one from the company StreetMap USA. The reference points were determined by using raster satellite images of the roads and neighborhoods contained in both polyline datasets. I selected 20 reference points at intersections throughout the sample area based on guidelines in the Positional Accuracy Handbook. After that I created point datasets that corresponded with the location of those same intersections in each of the street polyline sets. From here, each of the street datasets could be compared with the "true" location of the intersection. I imported the coordinate values into Excel to run the RMSE, and determine the NSSDA for horizontal accuracy of each street dataset. 

Horizontal positional accuracy: 

Using the National Standard for Spatial Data Accuracy, the City data set tested 24.8 feet horizontal accuracy at 95% confidence level.

Using the same National Standard for Spatial Data Accuracy, the StreetMap USA data set tested 278.1 feet horizontal accuracy at 95% confidence level.