Emily's GIS Blog

I began by uploading the tif file for this map and removed the base map. I created a feature class with the create feature class tool. The first feature class was titled “Tone,” which identified the various tones (very light, light, medium, dark, very dark) of the tif file. This feature class has a polygon geometry, which I used to outline the various toned areas. I created a new field in the attribute table titled “name” in which I labeled the tones. I then labeled each polygon and adjusted the font, size, and color. I also adjusted the symbology of the polygons. I then repeated this process with a feature class for “texture,” which included very fine, fine, mottled, coarse, and very coarse. I outlined the texture areas, added the attribute tables data, labeling, and symbology. I used this map to create a layout with a title, a legend, and credits.     For this map, I began by opening a new map and added the Tif file. I also removed the basemap from my map. Next, I used the c...

This week I was tasked with conducting a damage assessment to an area in New Jersey after Hurricane Sandy hit. Below is the result of my analysis. The points are symbolized with a color ramp, with Green representing No Damage and red representing Destroyed.       The assignment called for an analysis of the number of points within 100, 200, and 300 meters of the coastline. In order to conduct this analysis, I first created a coastline feature. After setting up the coastline feature, I then created three buffers for 100, 200, and 300 meters, all on the right side of the line. From there I simply manually counted how many of each category fell into the buffer layers. I did use the “Select layer by attribute” tool to help highlight the different categories.      I found that every building within 100 meters of the coastline was destroyed or received major damage. The parcels that were unaffected were parking lots and did not have structures. This was the ...