GIS5935 M1.2: Data Quality - Standards

    Within this study analysis, I compared different sets of street/road intersection data for Alburquerque, New Mexico that was collected separately by the City of Alburquerque and StreetMaps USA. Using an orthophoto base layer as the reference for this analysis, I then compared the accuracy of both datasets using the procedure set out by the National Standard for Spatial Data Accuracy (NSSDA).

    I started off by sectioning the study area into four quadrants, which allowed for each section to have 20% of the points needed to determine data accuracy and I ensured that points were at least 10% of diameter apart. Per NSSDA (1999), “Twenty or more test points are required to conduct a statistically significant accuracy evaluation regardless of the size of the data set or area of coverage. Twenty points make a computation at the 95 percent confidence level reasonable.”   There were 208 sub-quadrants, and these were evenly divided to create four quadrants. I chose (5) random intersection points in each quadrant (20% per quadrant) that was in each of the three different datasets (City of Albuquerque dataset, StreetMap USA dataset, and within the orthophoto).

 

Map 1: City of Albuquerque selected random points dataset 

Map 2: SteetMap USA selected random points dataset

    After each dataset was given 20 random points and a specific ID was assigned to points of each dataset, coordinates of each point was added via Add XY Coordinates (data management tool). The Attribute Table for each dataset was then exported into .dbf files for use in Microsoft Excel, which allowed us to easily place the x-coordinate and y-coordinate into the horizontal accuracy statistic worksheet provided from the NSSDA. This allowed us to find the National Standard for Spatial Data Accuracy statistic, which provided the value of accuracy at the 95% confidence level.


Accuracy Statements

City of Albuquerque

Using the National Standard for Spatial Data Accuracy, the City of Albuquerque data set tested 16.58 meters horizontal accuracy at 95% confidence level.

 

StreetMap USA

Using the National Standard for Spatial Data Accuracy, the StreetMaps USA data set tested 166.06 meters horizontal accuracy at 95% confidence level.

 

Works Cited

    National Standard for Spatial Data Accuracy. (Oct 1999) Positional Accuracy Handbook.  https://www.mngeo.state.mn.us/committee/standards/positional_accuracy/positional_accuracy_handbook_nssda.pdf 

Comments

Post a Comment

Popular posts from this blog

GIS5007 Orientation: About Me

  Hello! My name is Abby Starling, and I’m currently in the GIS Graduate Certificate program. I started this program to move into a career shift, as I currently work for a county government doing more admin related items full-time. While my job is fine, it isn't what I envisioned - it is time for a change! Through this program, I hope to gain more understanding in GIS and form a solid foundation for a career in GIS. I took several GIS classes in my undergraduate Geology program at Valdosta State University, but am certainly no pro. In my free time, I always end up playing copious amounts of Stardew Valley.    Abby Starling's Story Map Links to an external site.
Read more

GIS 5007 Module 6: Isarithmic Mapping

Image
     Module 6 of Computer Cartography focused on isarithmic mapping and using PRISM (Parameter-elevation Regressions on Independent Slopes Model) derived data to showcase the annual precipitation of Washington state, USA over a ~30 year period (1981-2010). Originally created by the PRISM Group at Oregon State University, the PRISM system has proved to be a leading system in producing quality precipitation maps. A climate-elevation regression function is calculated in PRISM with consideration of climate variables that may influence the data, such as proximity to coast, atmospheric layer, topography of region, and more. As precipitation tends to increase with elevation, elevation is an included variable in PRISM using a digital elevation model (DEM) and each DEM grid cell has its own climate-elevation regression function calculated within. Annual Precipitation of Washington, USA from 1981-2010      The above isarithmic map depicts the annual precipitation of...
Read more

GIS5935 Module 2.1: Surfaces - TINs and DEMs

Image
                In this lab, we explored two types of elevation data models - Digital Elevation Model (DEM) and Triangulated Irregular Network (TIN). The DEM is raster-based and information is stored in grid format, while TIN is vector-based and uses irregularly spaced points that were calculated according to the connections of sets of triangles for contouring. Because of this, the DEM has smoother curves in its contour lines, while TIN has pointed/jagged contour lines. The DEM contour lines in this instance also have a few more lines due to the data parameters.                 The TIN contour lines are likely more accurate, as TIN can differentiate between more complex terrain than DEM contour typically can (depending on resolution). TIN uses irregularly placed elevation points that vary based on data density, but DEM is a grid with elevation values stored within the grid cel...
Read more