GIS Portfolio - Michael Malone

  Above is a choropleth map of the population change percentage in North Dakota counties between 2010 and 2014. The colors were chosen to intuitively indicate bad (red) for population decline and good (blue) for a population increase. The legend patches were snapped together to give them an appearance of continuous color that mimic the continuous data in the dataset.

  The above map depicts the basic landcover features in Yellowstone National Park. The symbology colors were decided on in an effort to keep the features analogous to their real world counterparts. Topography was established through the use of hillshading. This was placed behind the land cover layer and the land cover lay was made transparent to allow the shadows to show through.

  Nevada is my area of Interest. There a three separate state planes for Nevada; east, west, and central. For this reason, the state plane was not the appropriate choice. I decided to use NAD 1983 UTM Zone 11N because Nevada fits entirely within that UTM zone.

  General: General features were given large black font with a small white halo to increase legibility and visual contrast. The font size is the largest of any font on the relevant feature. The text was placed in a central location without overlapping other labeled features or important data. Water Features: Water features were given dark blue italic serif font. The color chosen was dark enough to allow legibility and contrast with the light blue water background.   For the Golden Gate and San Francisco Bay labels they are placed in a central location within the water feature at a level angle with the page. The Lake Merced label was angled to line up and be contained within the lake. Park Names: Park labels were given a dark green color with a thin white halo to allow visibility and contrast while still keeping the labels intuitive. Font size was chosen to keep the labels within the boundaries of the park. However, due to the shape of Lincoln Park the label overhangs however it’s

  This weeks module focused on classifying images using multispectral signatures. Above you can see the completed classified land cover of Germantown, Maryland. To create this image above signatures were collected that correlated to each required feature. Then bands were chosen (R:4 G:6 B:5) that contained the largest separation amongst features to minimize spectral confusion. In the above image roads and urban areas were often confused leading to a much larger area of roads than actually exist. The inset map contains a classification distance map which displays the distance each cell is (spectrally) from the sample points with brighter pixels being further than darker pixels. This indicates that brighter areas have a higher chance of error.

  This weeks lab focused on manipulating multispectral images by adjusted which layers are displayed in order to reveal information that is otherwise omitted. The above image shows snowcaps that were identified by searching for a spike in pixel brightness in layers 1-5, a RGB combination of 3 5 4 was used to make the snowcaps contrast heavily with the surrounding area.