baj23_GIS

 How accurate is data?  Many times, we receive data, and as we make the assumption that the data is accurately applied into ArcPro, it is not always the case. The National Standard for Spatial Data Accuracy (NSSDA) creates a methodology to measure and describe the positional accuracy of data features. For our lab assignment, we were given two data sets for roads from USA street data and ABQ street data of Albuquerque, New Mexico. In order to analyze the road data, we were instructed to create 20 points to serve as reference according to the areial imagery. Then gathering intersection points from the USA street data and the ABQ street data and gathering their XY coordinates to enter into excel to calculate SUM, AVERAGE, Root Mean Square Error (RMSE) values.  SUM - The set of the squared differences between the test and the independent data sets.  AVERAGE - The average of the sum.  ROOT MEAN  SQUARE ERROR - The square root of the average. To find the RMSE value at a 95% confidence level

 How important is it to monitor spatial data quality?  Spatial data quality is very important for GIS to accurately present data in a project and consider adjustments to properly format the data. Additionally, it is important to identify the potential errors data can have and summarize our findings. The accuracy and precision of data are two factors to identify errors.  In the lab, we used the Root Mean Square Error (RMSE) calculations to show errors in accuracy and measured the data points to identify variance in find errors of precision in the data.  In our lab, we were asked to summarize 50 GPS points taken on the reference point and create buffers to determine the range of precision at the 50th, 68th, and 95th percentile values.  Our horizontal accuracy was 0.26 m. We find the horizontal accuracy we took the precision value subtracted the averages of the data points to get 0.26m. Since it was still in the range of the horizontal precision value of 2.44m at the 50 percentile; it was