A. Starling's GIS Blog

In this lab, we were tasked to determine the suitability of an area for housing development. For this, we must consider: l and cover, soils, slopes, distance to streams, distance to roads of the area. These five considerations will determine which areas in the location are suitable for development. Analysis First, I use the Reclassify tool to convert the values within the “landcover” raster into values of suitability. With the Polygon to Raster tool, I converted the “soils” polygon feature class into a raster. I then reclassified using the Reclassify tool turning soil class into suitability ratings . I used the Slope tool to create a slope of the “elevation” raster in degrees, and   the Reclassify tool to class slope (in degrees) into suitability rating. I used the Euclidean Distance tool to create a distance to rivers, and the Reclassify tool to class distance to rivers into suitability rating. I repeated the above steps for the “road” line feature class (creating a d...

                Module 4 of Computer Cartography focused on the various ways to classify data in cartography and how classification impacts the outcome of what a map may indicate to it’s intended audience. For this exercise, we created a map of the senior citizen (age 65 and above) of the Miami-Dade County, Florida population using data from the U.S. Census Bureau (2010).               We made maps using Equal Interval , Natural Breaks (Jenks), Quantile, and Standard Deviation classification methods. Efforts were made to ensure our non-normalized and normalized data maps followed a design that was easy to follow and created for intuitive data interpretation with visual balance. The gradient indicates amount of the senior citizen population from lighter to darker – lightest hue meaning less concentration, darker hue indicating greater concentration. ...

     Lab 5 in  Aerial Photo Interpretation and Remote Sensing   focused on unsupervised and supervised classification.       We used a provided a high resolution satellite image to determine unsupervised classification processes by first creating a cluster layer and comparing the cluster layer to the original high resolution image. We then reclassified the cluster layer by recoding within the Recode Table from 50 to 5 classes. Some pixels were shown to be a part of two classes relatively equally when we used the Swipe, Flicker, Blend, and Highlight tools. and we added a fifth class to account for Mixed (M) features that were very split between two classes. Unsupervised Classification      To utilize supervise classification, we used a new provided image to determine a new spectral signature with Signature Editor. Using both the “Drawing Polygon” method and “Creating Signature from Seed” method, we were able to look at the Spect...

  Lab 2 this week in Photo Interpretation and Remote Sensing involved using the provided aerial raster image of Pascagoula, Mississippi area to utilize what we’ve learned so far to determine Land Use and Land Cover (LULC), classifying with the Anderson Classification System (USGS). We started by creating a polygon shapefile (“LULC”), ensuring to add two text fields for the Code and Code Description. Once that was created, we identified areas that would fit into Level I Classification, and then further determined the Level II Classification. A lot of polygons were digitized to indicate the different land use and land cover areas as shown below in my map (Figure 1).   I also ended up making two Feature Class files (“WaterBodies”, “Water2”) to use the Trace tool in the Create Features pane to mark the larger bodies of water that I considered Lakes, as well as to mark the Streams and Canals. Figure 1: Land Use and Land Cover of the Pascagoula, MS area Quick note here – while white...