Analytics Lab 5

 

For my map I chose to have simple but unique color palettes for each variable so they could be distinguished from each other. Additionally I chose to make the corresponding infographics and charts the same color to avoid confusion and enhance the understanding.  I used simple charts the both shared similar designs so that they were understood they were showing similar data but the data was labeled clearly so that it was clear what the information was. In general I used the five principles of map making such as visual contrast, and balance so that the map is easy to understand. Though in hindsight the scatterplot could have been better in terms of color design and title as it appears to stick out.

Color Choropleth Lab 4

                   

For the first image you will see the color ramps that I created. You can see that the first two ramps are similar in the sense they seem to follow the same color hue but have a different shade. This is base on the RGB values having predictable changes throughout the ramp such as increasing the value by 20 each time. While the color brew is a computer generated color ramp that appears to have two colors of similar hues one for the light side and the other for the dark side. Each have a various amount of work to create with the first two having various calculations with some trial and error while the last have very little.

In the second image you can see the choropleth map I created from population change as well as the legend. I chose to have five classes to make the map simpler to understand with zero being the center of the legend and the two extremes as the ends. I chose colors that were associated with a negative and positive change like green and purple.

 

Terrain Visualization Lab 3

 

In this lab I worked with terrain of maps. Specifically with methods on how to use hillshade to show the topography and terrain features of the natural landscape. For my map that is shown above I chose to use a traditional hillshade features since it worked best with a transparent layer that shows landcover over the topography. Though the multidirectional was viable ai found that with a layer over it it wasn't as visually pleasing. So I managed to create a more visually appealing map with the other hillshade and using the essential map elements to create balance and a good hierarchical structure.   

Lab 2 Coordinate System

 For this lab we worked with Coordinate systems and how to determine which is appropriate for our area of interest. My area of interest is Texas and I determined that a State System would be best. The NAD 1983 Texas Centric Mapping System Albers worked the best since Texas is too large to fit into a UTM and it contains 5 state planes.

Map Design and Typography Lab 1

For the first map I used the 5 design principles to create it. To stat I used different colors to create contract between the area of interest and the background. This contrasting color also helps with the figure ground design principle since the are that is being show is clearly distinguishable. Then I used the balance to space out the required feature like the north arrow, legend etc to fill in the empty space. Then I use font size and weight to create a hierarchy organization and maintain a legible map for the readers. For the second made we explore labels and use individualized text for each of the types of labeling that was to be used.  For example I used font, style, size, and color to organize the categories of the labels into groups like water features with more elegant text that was a dark blue and italicized while natural features were labeled with solid text. I also create general labels to be more bold and large to help with area determination and gave parks more official fonts to increase legibility and help show their importance.
 

Lab 5 Unsupervised and Supervised Image Classification

 

In this lab we learned how to use Unsupervised and Supervised classification techniques in ERDAS Imagine. In this exercise we create signatures using Areas of Interest to show the software what each pixel was and to which class it belonged to so that we could create an image. Once that image was create we recoded the classes in a manageable amount of classes and corrected an spectral confusion using the mean statistical plots, distance maps, and histograms to determine areas that could cause an issue. We corrected this using different combinations of the spectral bands Red, Green, and Blue. And then created the image and maps above. 

Lab 04 Spatial Enhancement, Multispectral Data, and Band Indices

 For this lab we worked with both ERDAS Imagine and ARCGIS pro to gather satellite data. Then use it in identifying features using band indices and histograms to determine the reflection of light shown by the images. This also included using a variety of filters to help to identify features an highlight certain features so that they stand out more and can be accurately identified. Additionally we learned to gather data from online sources and work with bands to display and find certain features like in the images below.