Skip to main content

GIS 6005 - Module 1

 


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 positioned in a manner that removes ambiguity.

Landmark: The Golden Gate Bridge label was colored in dark gray to differentiate it from the general labels. It was sized to fit the length of the bridge and angled to run parallel.

Topographic Features: Topographic features were given smaller medium gray labels with no outline. They are legible and available should a viewer search for that information but not so large to take away from the larger more significant map features.

Comments

Popular posts from this blog

Module 5 - Unsupervised and Supervised Image Classification

  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.

Lab 5: M 2.2 Interpolation

  This weeks module focused on identifying the best interpolation method for modeling the air quality over Tampa Bay. Four methods were tested using the same set of sample points Thiessen, Inverse Weighted Distance (IDW), Tensioned Spline (seen above), Regularized Spline. Thiessen Interpolation assigns all cells in the raster with the value of the nearest sample point. IDW calculates the value of all cells by considered multiple sample points nearby and giving closer points a higher weight than further points. Both Spline methods create a smooth surface over the sample points but the regularized version creates a smooth curvature regardless of the range of values in the sample meaning cell values can end up both above and below the minimum and maximum values found in the sample. The tension model attempts to fix this by constricted the curvature of values to the ranges found in the sample points.

GIS 6005 - Module 4

  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.