Precision and Accuracy

 This week we explored the concepts of precision and accuracy and how to analyze the effectiveness of data collected in the real world.  Below is a map of an average waypoint generated from a collection of 50 data points collected in the field.  From the average, we can generate error buffers and determine if this is statistically significant in relation to the actual location of our waypoint (not represented on this map layout).  We can also use this data to determine outliers and to see which data points are actually useful in our location assessment.

For the discussion below, horizontal accuracy is measured as the difference between the true location of our waypoint and the average waypoint as calculated from our GPS collected data points.  Horizontal precision is a measure of how closely grouped together our field collected data points are and is a measure of the location consistency of the data collected.

Our average waypoint was 3.29 Meters from being truly accurate. Our acceptable precision is represented by a 4.55 meter (68%) buffer. The true waypoint falls within the range of the 68% buffer of our average waypoint and therefore falls within our general rule of acceptable accuracy estimation and is not significantly different when going by the established criteria. Even though this does fall within our 68% buffer, I would argue that the purpose of this waypoint would have a big impact on whether or not this level of precision is truly enough to be of concern. For some applications, I believe it would be.

The difference between the average elevation and the true elevation is 5.962 meters. This falls outside of our 68% vertical precision buffer of 5.888 meters. This difference is significant and could be very confusing if you were to try and determine a location based on average elevation from our data.

There is bias towards a higher elevation and a south and east location when using our average location in comparison to the true location.  

Hurricane Sandy Damage Analysis

This week, we assessed property damage along the New Jersey coastline due to hurricane Sandy.  I have included an image of damage assessment points that I created and a summary table of the final results.  In order to create a point layer with the ability to add information based on visual damage analysis, I first created mosaic images of pre and post Sandy imagery and added those images to a damage assessment geodatabase.  I then created attribute domains in the geodatabase and input codes and descriptions for each domain in order to create a drop down list that made creating new points with proper information very easy.  I then created a new feature class and added fields that I could then relate to the domains I previously created.  Once I had attached the proper domains to assess the damage, I created a data point for each parcel in order to assess property damage.  The end result is shown in a screenshot below.

I attempted to identify familiar properties first, particularly residential areas. If I saw something that looked like a house, I assumed it was a residential property. I found all properties that appeared to be standing and in their original location, and judging from the evidence of flooding throughout the entire study area, was able to set Inundation to yes for every property.

I then found properties that were partially destroyed or completely destroyed and used the swipe tool to see just how much damage was done. Parking lots that were now covered with sediment I labeled as “destroyed” even though it could be possible that the sand could simply be removed and the asphalt still intact.

I used a 1:1,500 scale to get a decent view of each property. The most difficult thing to determine was the extent of flood damage to each property. It was obvious that flooding occurred throughout the study area, but impossible to tell the extent of property damage from an aerial photo.

Zoning information would have been useful because I could not tell the difference between government, industrial or other with any reliability. Having a familiarity of the area would also be greatly beneficial. 

 

Structure Damage Category	Count of Structures 0-100 m from coastline	Count of Structures 100-200 m from coastline	Count of Structures 200-300 m from coastline
No Damage	0	0	0
Affected	0	21	29
Minor Damage	0	3	12
Major Damage	1	13	0
Destroyed	12	2	5
Totals	13	39	46

To get the information for the above table, I drew a line on the coast as suggested in the lab instructions. I then used the distance tool to see where the 100m, 200m and 300m lines were and selected those buildings with the polygon selector tool to get the information for the table.

Obvious damage as perceived from viewing aerial photos is much harder to detect as you move farther from the coast. Most buildings were destroyed within 100m and although evidence of flooding was obvious everywhere, damage to buildings is less apparent as you move further from the coast.

I do not believe you can reliably extrapolate this information to other areas of the coast. The damage to the coast was highly variable and more in-depth analysis would be needed to get an accurate picture of the entire coast.