GIS5935 Lab1 Calculating Metrics for Spatial Data Quality

 In this weeks lab we examined precision vs accuracy.  We also calculated the (RMSE) root-mean-square error and the (CDF) cumulative distribution function. In part a of our lab we were provided with data and determined the precision and accuracy metrics. The average waypoint was calculated using provided waypoints gathered from a handheld GPS unit in order to find the precision of a specific location. The average location was determined by calculating X and Y averages. 

Horizontal precision is the determination of how close together observations are. The most commonly used measurement for this calculation is the distance that includes 68% of the repeated observations. As shown in the map below the 68% horizontal precision value is 6.5 meters.  Horizontal accuracy is determined by how close the average waypoint is to the true benchmark location. For this lab the horizontal accuracy was determined to be 3.25 meters.    

Below is the map results from part a of our lab.

GIS 5100 Least Cost Path Analysis

This lab was conducted as the follow up to the prior blog on suitability analysis. For the first part of this portion of the lab we were given the task of finding the least cost path for a pipeline and generating a corridor analysis. We had to factor in slope, river crossings and river proximity. The goal is to find the least costly projected path for pipeline construction. The layout below illustrates the least cost path.

For the last portion of the lab we were give the scenario of being a Park Ranger in the Coronado National Forest and it was out job to model the potential movement of black bears between two protected areas. To create our corridor we had to factor in distances to roads focusing on those areas farthest from the roadways. Secondly, we needed to find areas of mid-elevation, and lastly we needed to find land cover that was made up of specific types of forest and vegetation. We began by reclassifying to create suitability rasters, Next, we used the weighted overlay to combine the suitability rasters we created. A new concept for me was introduced at this point. When creating our cost surface we had to invert the suitability model. Once this was accomplished we began working with the corridor analysis tool. 

GIS 5100 Suitability Analysis

 For this lab we were examining and area and performing a suitability analysis. We utilized both vector and raster analysis tools. We began with the Boolean method which break the area into "yes"  and "no" areas for suitability. Next, we used a method of scoring and weighting. With this method you don't have a simple yes and no but rather a range from least to most suitable depending on the criteria met. Weighting also allows rank the criteria in the order of what is most important. For example, near a water source is more important than being near a road and so forth.

The scenario we were given was to conduct an analysis for a property developer for areas best suited for development bases on Land cover, Soils, Slopes, Streams and Roads. 

The side-by-side layout below illustrates how different the results are when you use an alternate weighted scenario versus an equal weighted scenario.

This type of analysis is useful in a wide array of "real world" applications from real estate to environmental science, archaeology and more.

 

GIS 5100 Coastal Flooding

 This was by far our most challenging lab. The first portion of the lab we examined data from New Jersey pre and post hurricane Sandy. We started by creating DEM's, utilizing LAS Dataset to TIN, TIN to Raster and the Raster Calculator. We examined the changes in different areas within the map and cross references with the newer imagery basemap to see if any rebuilding had taken place. Additional calculations were made to determine the percentage of Cape May County that might have been affected by the 2-meter storm surge. Below is my change layer map.

The second portion of the lab was the analysis of storm surge in Collier county Florida. We utilized a variety of tools to tabulate how many buildings of each type were impacted in each DEM (USGS and LiDAR). We were also instructed to calculate the Errors of Omission and Errors of Commission. Our final map was to include a table listing the results of the above calculations as well as a may illustrating flood zones and buildings. The map shows buildings in each DEM, those in both DEMs as well as those not flooded. 

This assignment showed us some of the benefits of storms urge mapping for things like evacuation, search and rescue and even damage assessment for insurance claims. We were also introduced to some of the many factors that need to be considered to create the most realistic and accurate storm surge maps.