Lab 1 - Data Quality: Fundamentals

We launched GIS-5935 Special Topics in Geographic Science class by covering important fundamentals regarding data quality. For our lab assignment we practiced calculating metrics to measure accuracy and precision to be able to recognize the differences and their importance when working with spatial data and their inevitable errors. There were multiple things to take away from this first lesson. The first and foremost: spatial data quality matters! Also, an error is defined as "difference between 'true' value and predicted (or observed) value" (Zandbergen). Dimensions of data quality include: spatial, temporal, thematic; and their components include: accuracy, resolution, consistency, and completeness.

For the first part of our lab, we determined both the precision and accuracy of single point locations that were mapped 50 times using a Garmin GPS76MAP unit. The precision of the GPS unit was determined by observing how many points fell within a buffered distance using commonly used percentages of 50, 68, and 95, from an average location. The accuracy was calculated similarly, except that we used an actual reference point or true value, instead of an average location. My final map product is shown below.

My numerical results at a 68% observation are as follows:

- Horizontal precision: 4.3m
- Horizontal accuracy: 6.2m
- Vertical precision: 5.7m
- Vertical accuracy: 6.8m

Lastly, for our second part of the lab, we determined the RSME (Root Mean Square Error) of a dataset and created a Cumulative Frequency Distribution (CDF) scatterplot, using 200 autonomous GPS position fixes, and a survey benchmark point location. My chart is shown below.