GIS Portfolio - Michael Malone

  The above layout shows the precision of the data collected by comparing the data to the average point (the star) It was found that the data was precise within 4.5 meters of this location (the yellow area). The data was also found to be accurate within 9.3 meters. Precision was determined by comparing the test waypoints to the average location and determining the distance where 68% of the points fell were within the distance. Accuracy was determined using the same method but rather than using an average location it was determined where 68% of the data was within range of a benchmark location known to be the 'correct' location. With this it was determined that the data is more precise than accurate since the data is clustered more around the average than the reference point.

  The second part of this weeks module focused on creating a corridor modeling the ideal locations for black bear travel between two protected areas in the Coronado National Forest, AZ. The factors considered for suitable travel zones were distance from roads, elevation, and landcover. Elevation and landcover rasters were given and were reclassified to fit the given criteria. The roads shapefile was ran through the Euclidian Distance tool and the resulting raster was reclassified to fit the given criteria. All were combined using the Weighted Overlay tool and the resulting raster was used as the cost raster in the Cost Distance tool with the two protected areas. The two resulting rasters from the Cost Distance tool were processed with Corridor Tool. A threshold was chosen to limit the suitable area to within 10% of the most suitable locations resulting in the corridor seen above. 

  The first part of this module focused on creating a suitability map for a property developer interested in purchasing property. The above map shows the results. For analysis Land cover, soils, slope, distance to streams, and distance to roads were the factors considered for suitability. Suitability was calculated either by reclassifying existing rasters, or creating new rasters and reclassifying to fit the given criteria. Then each reclassified raster was combined using the Weighted Overlay tool. The map on the left shows the results from using equal weights for each raster in the Weighted Overlay. The map on the right shows distributed weights as described in the map above. 

  This weeks module involved completing a damage analysis of the land parcels contained within the study area (blue square). In order to do this a point feature class was created and domains were setup (structure damage, wind damage, inundation, and structure type) to restrict the fields to only allow for the desired entries. Then points were created on each parcel and populated using the built in swipe function to compare the pre and post-storm photos. Once these were all created the estimated coastline was digitized using the pre-storm photo and three buffers were created (100 meter, 200 meter, and 300 meter) on the coastline. I created query fields on the damage structure layer and using select by location and select by attribute queries I was able to determine the quantities of the buildings in each zone which were used for the analysis seen below.