M1: Calculating Metrics for Spatial Data Quality

New class, new tasks. This is Module 1 of Special Topics in GIS. This week we have learned about the difference between accuracy and precision. Accuracy is the absence of error and is determined by comparing a coded value in the database of interest to some independent reference value. For numerical values we can use a metric like the Root Mean Square Error to describe accuracy. Precision is, in this context, the variance of measurement. In other words, how close together are multiple observations of the same coded value? This does not use a reference value, but instead uses a metric like the standard deviation of a sample.

Below you will see 2 things: the first is a map layout from Part A of the lab where we were tasked to show accuracy and precision from projected waypoints with circular buffers of precision estimates. The second thing is the numerical results for horizontal accuracy and precision.

Numerical results: Horizontal accuracy of 4.279 and horizontal precision of 4.293

Module 6: Scenario 4 - Suitability and Least-Cost Analysis

Last, but not least, the final post for Applications in GIS. This scenario (2nd deliverable for Mod 6) was a on your own corridor analysis. As a park ranger in the Coronado National Forest we were asked to carry out a corridor analysis using the two National Forest polygons to create a meaningful corridor between them taking into consideration roads, landscape and elevation. Tools to accomplish this were Reclassify, Weighted Overlay, Cost Distance, and Corridor Tool in that order respecfully. Once the suitability output was achieved, and the corridor analysis completed a meaningful symbology was applied based on the minimum values. See layout below.

Module 6: Analysis C

This module is a multi-part analysis through various scenarios. This is blog post #1. For this analysis we were tasked to estimate how much land would be suitable to build on. The analysis itself was based on land cover, soils, slopes, streams and roads. Every item was reclassified into suitability classes using tools: Reclassify, Euclidean Calculator, Polygons to Raster, Slope, and finally Weighted Overlay to get the final result image below. The Weighted Overlay tool was used twice for 2 different scenario with different weights adjusted or being equal.

Module 5: Damage Assessment

This week we explored the damage assessment aspect of Hurricane Sandy's landfall near Atlantic City, New Jersey. We tracked Sandy, created and designed attribute geodatabases for editing purposes. As well as exploring imagery effects tools to visualize pre/post Sandy imagery. We used parcel data, the imagery, and the crewly created damage assessment files to catelog a street on the New Jersey coastline. General steps to accomplish this included, creating line features from points for the track, adjusting the symbology to look like a hurricane graphic, creating a mosaic dataset for pre and post imagery rasters to be. Then created more data using new feature classes and domains and finally filling in those attribute tables with the points/data for our structures in the study area. Below there is a screenshot of the structural damage points created and a table of the distance to coastline to examine patterns.

Module 4: Coastal Flooding

Well we can't win them all! That is the major lesson from this lab. Attempts to follow the directions were fraught with misaligned data and things that just did not make sense. Luckily I was not alone in my efforts coming to bitter ends. The assignment was to conduct a analysis on storm surge in Florida, by comparing a traditional USGS DEM and a DEM derived from LiDAR. We were to compare the differences in results between the two elevation models assuming a storm surge of 1 meter. The layout below is my best attempt to show this.