Module 1.2 - Data Quality: Standards
Data accuracy is imperative in conducting assessments and making appropriate conclusions on spatial data. I often find throughout my GIS assignments, data can be skewed and unreliable based on how and when it was acquired. Thus, implementing data quality standards is an excellent way to ensure one's data is accurate before creating projects or making analyses. Making accuracy assessments is a great way to determine if one should use certain data and if it will be reliable for particular assignments or projects. I will certainly be comparing data accuracy in future assignments in an effort to ensure my data is reliable and spatially accurate.
 |
| City of Albuquerque Data |
 |
| StreetMap USA Data |
 |
| Orthophoto Imagery with Digitized Intersections |
2. Select a set of test points from the data set being evaluated.
3. Select an independent data set of higher accuracy that corresponds to the data set being tested.
4. Collect measurements from identical points from each of those two sources.
5. Calculate a positional accuracy statistic using either the horizontal or vertical accuracy statistic worksheet.
6. Prepare an accuracy statement in a standardized report form.
I began the assessment by importing the two datasets and then creating three shape files in the project geodatabase. These shapefiles consisted of points on intersections that would be used to test the reliability of the two datasets. One point on the road file from the Albuquerque City dataset, one from the StreetMap USA dataset, and one from the orthophotos that I digitized and manually placed on where I deemed the intersection to actually be located. Upon creating the 20 points for each class, I manually assigned them identification numbers in their attribute tables so that they could be compared using an excel file. I transferred the attribute table data to excel using the Table to Excel tool in ArcGIS Pro. Once this data was transferred, I used a variety of formulas to calculate my accuracy statistics. These statistics were then used to determine the accuracy of the provided datasets.
After assessing the accuracy statistics, it is clear that the Albuquerque Street dataset was much more accurate than the StreetMap USA dataset. This is because the NSSDA of the Albuquerque Street data was 16.95, while the StreetMap USA data was much higher at 270.88. Also, from observation, I noticed a clear trend in which the StreetMap USA data was very off from the orthophoto intersections, often noticeably far off. This is in contrast to the city data, which was almost always right on the intersection or very close to it. From the calculated statistics, I was able to write the two NSSDA formal statements below.
Using the National Standard for Spatial Data Accuracy, the City of Albuquerque Street dataset tested 16.95 feet horizontal accuracy at 95% confidence level.
StreetMap USA Data Formal Statement
Using the National Standard for Spatial Data Accuracy, the StreetMap USA dataset tested 270.88 feet horizontal accuracy at 95% confidence level.