# Tag: accuracy

## Geocoding: Delivering High Location Accuracy

Geocoding is a fundamental GIS process for plotting your data on the map. It is often the first step in applying GIS to understand the where. The accuracy of plotted points directly correlates with the success of downstream decisions. Regardless … Continue reading

## Increasing accuracy when using Full Motion Video

Without jumping right into the nitty gritty of how full motion video works—I’ll do that below for the geeks who read this blog—you can use an elevation dataset to increase positional accuracy when working with FMV. To access the Accuracy … Continue reading

## Using Statistical Sampling with Positional Accuracy Assessment Tool

A common question when using Data Reviewer’s Positional Accuracy Assessment Tool (PAAT) is what sample size should be used when evaluating a geospatial data layer. Sometimes the sample size is mandated by a specification; but when it’s not, Data Reviewer’s Sampling check can be used to provide the sample size. In this blog, I’ll discuss how you can use the Sampling check to generate a statistically valid sample size and then explore two options for using it with the PAAT.

The steps include:

1. From the Data Reviewer toolbar, select the Select Data Check dropdown.

2. Expand the Advanced Checks category and select Sampling Check

3. In the Sampling Check Properties dialog, select Auto Calculate.

4. Under Auto Calculate, select your Confidence Level and Margin of Error.

Note: The question you’re looking to answer: given a population size (number of features), what sample size do I need so that I’m “X” percent confident the sample size is statistically significant within a “Y” percent margin of error?

A number of users have commented on occasional differences between computed shadow volumes and observed shadows.

There are a couple of possibilities that could account for the differences observed in the shadow model output and the real world shadow.

• Data discrepancy between your building model and the actual building, or your surface model and the actual surface. Coarse resolution elevation data can introduce a significant variation from the observed proof points.

Make sure your building models represent the actual buildings and increase the resolution of your terrain data when comparing shadows. In ArcScene: Layer properties -> Base Heights -> Raster Surface Resolution.

• Data offset of Sun point feature caused by the transformation of the Sun’s position to the spatial reference of the input feature class. The Sun point is initially calculated in GCS WGS 1984, then projected into the spatial reference of the input feature. If the datum of the input feature is different than WGS 1984, then a default transformation is applied. This would typically be the first available option unless a particular transformation is specified in the environment setting. An improper datum transformation can result in an offset of up to 30 feet, depending on the locale and the method being used.

Please check the spatial reference of your input building feature and see if it necessitates the specification of a datum transformation. This help article offers a guide for determining the appropriate transformation.

Also the impact of such an offset on the final shadow output is reduced if the output Sun distance is increased. For example increase the distance from default 2500 to 10000.

• Our tools use a point source, whereas the shadow measured out in the field is generated by a sun which has a significant angular diameter (0.5 degrees): soft vs hard shadows.
• Another factor might be refraction, which is not considered in the create SunSkyMap script. Refraction causes the sun to appear higher than the theoretical position value, meaning that the shadow will be calculated shorter.

Gert van Maren
3D Product Manager

Posted in 3D GIS | Tagged , , , , | 1 Comment