Brooke's GIS Blog

 This week's lab focused around data visualization for maps in the form of scatterplots, bar charts and pie charts. To begin, the first task involved selecting two normalized variables from a spreadsheet provided by the Robert Wood's Foundation Health Rankings & Roadmaps. The normalized variables I selected were teen birth rate and child poverty rate. I created a quick scatterplot to determine if a relationship existed, which proved the relationship to be positively correlated. A summary of the design strategies I employed related to the chart aspect was choosing to learn the method of creating charts on Arc Pro vs. Microsoft Excel, which I was already more or less familiar with. After getting used to Arc Pro's layout for creating graphs, I was impressed with the ability to customize. The charts I chose were a scatterplot detailing the relationship of teen pregnancy to childhood poverty, and two bar graphs showing the top and bottom 3 States for rates of teen pregnancy....

 Looking at my color ramps, you can easily see I struggled pretty significantly with calculating an even color ramp, from linear progression to adjusted progression. Although performing the calculations was basic math (which can still be challenging!) I found myself having to readjust the values manually, regardless of my calculation. Clearly, the results from Colorbrewer are clean and without deviation, demonstrating that once again, computers can do things best! For the choropleth population change map, I calculated North Dakota's percent change from 2014 to 2010. I found this interesting because so much of North Dakota has decreased in population or is barely increasing. My design choices including using Jenks Natural Breaks to classify values in classes of 5. I chose this method because I felt it best represented the severity of many of North Dakota's Counties which have a declining or barely increasing population, in comparison with the Equal Interval Classification and Qu...

 The objectives of this week's lab focused on creating contours from a DEM, applying labeling techniques using a mask, comparing hillshade techniques and using hillshading to create maps. This lab was of particular interest to me, because the other day at work, I was creating a basemap to use in maps of St. Johns County and wanted to add some topographical features to give the basemap some dimension, but struggled with how to do it. Well, now I know! I think another useful technique learned in this lab was using variable depth masking for creating labels. I'm eager to learn other ways I can apply this technique when creating maps.

I chose to use the USA Contiguous Lamber Conformal Conic coordinate system for the State of Ohio. I had chosen this one over coordinate systems that I compared like WGS 1984 UTM Zone 18N and NAD 1983 StatePlane Ohio North and NAD 1983 StatePlane Ohio South. The reason I chose the USA Contiguous Lamber Conformal Conic System was because I felt it best represented Ohio as a whole, rather than Northern or Southern Ohio.

The objectives of this week's lab were focused on cartographic design in ArcGIS Pro, applying map design principles, explaining design choices based on an audience, and applying different typographic styles, including placement, to achieve legibility, visual contrast and hierarchy. Looking the attached map, I believe I achieved the five map design principles represented in my map.  Map one: I addressed the map principles of figure-ground organization and visual contrast by choosing a darker color to represent Travis County, and brighter, lighter colors to represent the individual layers. I achieved legibility by choosing a legible font in black text, and sticking with the same font/color for the entire map. Lastly, I achieved hierarchical organization and balance by placing the features like recreation centers on top of features like the County, water bodies and roads, which centering Travis County with a landscape template in the center of the paper. Map three: I used text (type, ...

 The objectives of the final week's lab were to analyze vector data to determine effects on scale, and analyze raster data to determine the effects on resolution. Additionally, another task was to learn the effects of the Modifiable Area Unit Problem (MAUP) using Ordinary Least Squares (OLS), as well as identifying multipart features and measuring compactness to view Congressional Districts impacted by gerrymandering. Beginning the post with vector data scale and raster resolution, as scale increases, vector data becomes less detailed. As scale decreases, vector data becomes more detailed because the map is more "zoomed" in. Considering raster resolution, as the resolution increases, the raster becomes less detailed and more pixelated, making it difficult to make out independent visual features, like elevational differences on a DEM. Gerrymandering is the process of modifying or redistricting congressional districts for political gain. This can be measured by calculating ...