Module 1.2 - Data Quality: Standards

In this lab assignment 1.2 Data Quality – Standards, we measured the quality of two road networks: the streets of Albuquerque, derived from a shapefile provided by the city of Albuquerque, and the Street Map USA data sourced from the TeleAtlas product. The objective was to evaluate the positional accuracy using metrics established by the National Standards for Spatial Data Accuracy (NSSDA). 

The application of the NSSDA standard involves seven distinct steps. 

The first step is to determine whether the assessment apply to horizontal accuracy, vertical accuracy, or both. For this lab, our focus is solely on determining the horizontal accuracy.

The second step requires selecting a study area and identifying a set of test points within that area. These test points correspond to street intersections, ensuring that we select good intersections and include a minimum of 20 street intersection points. The distribution of these points must be such that, when the study area is divided into four quadrants, each quadrant contains 20% of the test points. I selected 24 test points, ensuring that each quadrant included 6 test points. Furthermore, the distance between these points should exceed one-tenth of the length of the study area’s diagonal. Given that the diagonal length of my study area was 6650 feet, I ensured that my test points were spaced at least 665 feet apart. The test points were chosen from both Albuquerque Street data and Street Map USA data correspond to the same street intersection. Both layers of test points are populated with identical point IDs that match to the same intersection. 

Screenshot of study area and sampling locations

The third step involves selecting a higher accuracy dataset for comparison with the data collected from the test area. Initially, we created a new layer designated as Reference, utilizing high-resolution aerial photography to digitize all points that correspond to the true locations of the test points. It was imperative to populate the ID field with the same test point number across all three datasets: Albuquerque Street, Street Map USA, and the Reference true point layer.

To determine the coordinate values for each test point for both the test data sets and the reference data set, I ran Calculate Geometry Attributes tool from Data Management on all three layers to add two new fields for the X and Y coordinates for the test points. At this point, we have three datasets of the same street intersections and have a common field “Point ID”.

Our next step is to assess the accuracy of both the Albuquerque Street layer and the Street Map USA layer. To achieve this, I employed the join tool in ArcGIS Pro to first join the Albuquerque Street layer with the Reference layer based on the common field, Points ID. Subsequently, I applied the join tool to join the Street Map USA test points layer with the Reference layer. Finally, I exported both layers into an Excel spreadsheet.

Using NSSDA accuracy statistic worksheet to calculate the positional accuracy statistic. 

For this step I added five new fields to the excel table they are:

DIFF IN X = the difference between the X coordinate from test point and reference true point

SQUARED DIFF IN X (1) = This is the square root of the x difference 

DIFF IN Y = the difference between the Y coordinate from test point and reference true point

SQUARED DIFF IN Y (2) = This is the square root of the Y difference 

(1) + (2) = SQUARED DIFF IN X + SQUARED DIFF IN Y (2)

From field (1) + (2), three key values can be calculated: The sum, the average and the root mean square error (RMSE).

The sum is the total of the squared differences between the coordinate values of the test data set and those of the Reference data set. The average is obtained by dividing the sum by the number of test points. The root mean square error (RMSE) is the square root of the average. 

The NSSDA statistic is then calculated by multiplying the RMSE by a factor that signifies the standard error of the mean at a 95 percent confidence level which is 1.7308 for the horizontal accuracy. Finally, NSSDA accuracy statement is prepared in a standardized report form.

NSSDA accuracy statement for Albuquerque Street Data:

Horizontal Positional Accuracy: Tested 14.32 feet horizontal accuracy at 95% confidence level

NSSDA accuracy statement for Street Map USA Data:

Horizontal Positional Accuracy: Tested 189.94 feet horizontal accuracy at 95% confidence level