Kelsey Yoder-Ostroski's Portfolio of Geographic Information Science

Testing For Spatial Data Standards This week's lab continued exploring data quality by completing an analysis of horizontal accuracy for two different datasets from 2006. Two different shapefiles were delivered to us: one from the city of Albuquerque itself and the other from TeleAtlas' StreetMap USA. We were also given 6x6 orthophotos of the study area. Initially, twenty reference sites were chosen using ArcGIS's geoprocessing tool Create Random Points with a minimum distance of 5,000 feet between each site. These reference points were then manually adjusted to the nearest "ideal" street intersection as seen on the orthophotos. Ideal locations contained clear, simple intersections with 90-degree intersections. Street intersections provide precise and unambiguous locations that can be easily identified. They are also consistent over time and can be repeatedly located with high precision. Next, test points were created for each dataset along the provided polylines.

  Three statisticians go duck hunting. The first statistician shoots and misses just left of the duck. The second statistician takes aim, shoots, and misses just right of the duck. The third statistician jumps up in joy and shouts, "We got 'em!" Before any spatial project can be underway, a thorough analysis of measurement quality is essential. Accuracy refers to the truthfulness of a measured value. Relatedly, precision refers to the consistency of measurements. Repeated measurements of similar value indicate minimal random error.  A reliable analysis requires both accuracy and precision. It is critical then to check for variability and potential bias. In this scenario, data validity was performed on horizontal accuracy and precision.  Fifty (50) survey points were provided to us. First, we checked for precision by calculating the average of the repeated measurements. A new average point feature class was created by calculating the mean longitude and latitude of all 50 p