Communicating GIS - Module 6 Lab - Proportional Symbol and Bi-variate Mapping

Hello Everyone!

This week's module was all about proportional and bi-variate symbol mapping. These two mapping styles presented some unique challenges and really opened my eyes to the possibilities of mapping in ArcGIS Pro. The most interesting map that I made was the Bi-variate map. This map type is unique because it allows you to map two variables in one map using a unique 3x3 color scheme. For my map, I chose to map the correlation between obesity and physical inactivity. My map can be seen below:

As you can see the color ramp legend allows you to show the two variables and their low medium high combinations. The color at the top right illustrates the highest presence of obesity and inactivity percentages while the lowest left shows the lowest of both. Unlike my previous posting for the info-graphic, this map allows you to better show the correlation between the two variables.

~Map On!

Communicating GIS - Module 5 Lab - Analytics and Info-graphics

 Hello Everyone!

This week's lab focused all on infographics and data analytics. These mediums have such a powerful potential to illustrate data in a meaningful way. For my infographic, I attempted to analyze and illustrate the correlation between obesity in America and excessive drinking. To my surprise, there is actually a negative correlation between these two variables even though alcoholic beverages are so high in carbs and calories. Below my analytic page layout can be found.

For this layout, I used a few interesting infographic modules. I first used a chart showing the increasing trend in obesity in the United States. I used a scatterplot illustrating the negative correlation between the two variables I was showing. Additionally, I used a chart showing the top three counties in the US with the highest percentage of obesity and excessive drinking and a bar chart showing the average nationwide percentages for my variables. Finally, I used two choropleth maps illustrating the distribution of my two variables. 

This infographic really was very eye-opening, I thought for sure that these to variables were correlated. As can be seen on the map, the highest percentage of the obese population tend to be in the south, and the highest percentage of drinking populations can be found in the north. But why is this? I believe there are a few factors as to why there is this negative correlation. The first is that the diet of the south is much different than that of the north where the south has a lot of 'comfort foods'. Additionally, the south is the home of the bible belt where alcohol consumption may be less due to religious belief/preference. Finally, the north consists of large beer powerhouses where high alcohol consumption is present.

~ Map On!

Communicating GIS - Module 4 Lab - Choropleth Mappin

Hello Everyone! 

This week's lab we covered choropleth mapping. Before I get into that part, I'd first like to share a bit about color ramps and their creation. Color ramps are comprised of colors which are composed of RGB (Red, Green, and Blue) these color values can be changed to create lighter and darker versions of a color. For example, when you increase the values together you get a lighter variation of that color and vice versa. Below you can see 3 variations of this: 2 I did on my own, and 1 generated by a software called Color Brewer.

As you can see, each progression has varying step values depending on what time of color ramp you were creating. Now that I've explained color ramps in some brief detail, let's see what they can be used for in GIS. Below is a Choropleth map of the population change in counties of Colorado from 2010 to 2014. 

Within choropleth mapping, there are a variety of classification methods. For this map, I used the Equal Interval classification which creates equal class intervals. Additionally, I used a Blue to Red diverging color ramp, counties with the largest population decrease are darker blue while counties with the largest population increase are in a brighter red. Equal interval classification for this data be presented the total range and gave the 0% class a nice middle ground among the data without having to get too complicated in the manual classification.

~Map On!

Communicating GIS - Module 3 Lab - Terrain Visualization

Hello Everyone!

For this week's lab, we covered terrain visualization! There are many methods to visualize the terrain of the earth available in GIS. For this specific post, I want to delve in a little bit to a map I created using land cover and a hill shade map. The map I created can be seen below:

For this map, I was given a digital elevation model (DEM) and a landcover classification raster. For the land cover, I created a custom symbology that helps show the different land covers with distinct color choices. Where I had varieties of the same land cover, such as Fir tree land cover, I combined them together in one land cover group. I then created a hill shade, overlayed the land cover layer on it with increased transparency so the user can see the elevation features of the area and then created my map. All in all this map came out very well, although I would change Nonforested areas to black or a different neutral color for better area boundary distinction.

Map On!

Communicating GIS - Module 2 Lab - Coordinate Projections

Hello Everyone!

This week in Communicating GIS, I learned all about different coordinate projection systems. Prior to these modules exercises, I knew some things about projections but not the level of detail that I learned about this week! Part of this week's lab that I would like to share with you is choosing a state in the United States and mapping that state based on an appropriate projection. For my area of interest, I chose the state of South Carolina. South Carolina is a good choice for this exercise because of two main reasons. The first being that South Carolina has only one State Plane region unlike most states (for example Florida has three state plane regions North, East, and West). Additionally, South Carolina falls within one UTM Zone, that zone being UTM Zone 17. Because these two potential projections fit so well, I chose NAD 1983 StatePlane South Carolina as my projection because state plane projections tend to be the most accurate. The map layout I created can be seen below.

By choosing this projection, the map is catered to South Carolina. In addition to this map, I added both a reference grid and graticule for map use.

~Map On!

Communicating GIS - Module 1 Lab - Map Design & Typography

Hello Everyone!

This week in communicating GIS, I created a variety of maps incorporating map design and typography. These maps essentially help train cartographers in how maps are created using map principals for organization and balance. One of these maps was a typography map. Typography essentially describes the labeling properties to describe specific features. These properties can range from label size, font, style, and placement. The typography label map I created was for the San Francisco area and can be seen below.

For this map, I attempted to best label the features with some kind of typographic structure. Examples of this include label formatting such as water body and park labels, and labels for areas in order of magnitude. One challenge with this map is that ArcGIS Pro is terrible for the creation of feature labels and fails drastically compared to Adobe Illustrator. Overall I tried the best I could with the software to make an effective map that shows the power of typography labels. This map could also be approved if quad boundaries were provided for some of the block level features.

~Map On

Aerial Photography and Remote Sensing - Module Lab 5 - Unsupervised & Supervised Classification

Hello Everyone!

It's hard to believe, but this week's lab is the final lab of this awesome remote sensing class before I start my final project! With that being said, this week's lab was a great way to end the labs for the semester! This week we covered supervised and unsupervised classification. Unsupervised classification essentially gives the user the power to select how the image is classified using pixel values that match across spectral classes. These classes can then be changed individually so when you change the class, all pixel values in that class change. Supervised classification essentially uses the image analyst to supervise the selection and creation of spectral classes within the program. By defining the areas of classification, it then can classify the image based on a handful of 'seeded' areas.

For this week's deliverable, I was tasked with using supervised classification to classify the landcover/use of Germantown, Maryland. For this exercise, I created 8 seeded areas of interest classification signatures that would serve as the classification seeds for my image. I then chose a band combination (within the map title) that caused the least amount of spectral confusion and then recoded my values. Once my image was complete, I added the new class names and area of each class in acres. Additionally, I added and created a Distance map which shows where areas of classification are likely incorrect. The brighter the area, the higher the chance that the classification of that feature was wrong.

I am really glad that everyone has been following me through this incredible journey in the world of remote sensing and aerial photography, it is truly one of the coolest fields of GIS and I would love to work on this for my capstone research!

~Map On!