Scale Effect and Spatial Data Aggregation

In this lab we first looked at how the scale at which we analyze data can affect geometric properties. Looking at data at three different scales (1:1200, 1:24000, 1:100000) I was surprised by my final calculations. I thought that there would be a lineal proportional relationship along the scales, but I did not find this. It makes sense that the greatest resolution [1:1200] would have the most detail of geometric properties, but I was surprised that 1:100000 had greater geometric properties than the medium resolution.

I resampled the data using the Bilinear technique and found the following effect on DEM resolution.

As DEM resolution increases the average slope in degrees decreases. This makes sense because as you get closer, more in detail by getting closer, you are zooming in as actively seeing less features altogether.

In the last part of the lab, we looked into the Gerrymandering of districts in the USA. Gerrymandering essentially breaks up congressional districts in a manner that establishes an unfair advantage to a given political party. One can clearly see by the following example how oftentimes the way districts are broken up makes no sense whatsoever. In the following district in Michigan, this district is divided into 4 different sections that could have easily been absorbed by neighboring land.

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Bivariate Choropleth and Proportional Symbols

In the first part of this lab, we used proportional symbols to represent positive and negative values in job increases/decreases in the USA. Because there were negative values in this data set, I created a new map to "fix" the data. In this new map, I created a new field and copied the negative job loss data. I then used the Calculate field data and multiplied it by one to make it positive. Lastly, I overlaid both maps on the data and was able to accurately represent the increase and decrease of jobs in the USA by state. In the second part of this lab, we delved into how to prepare data for a bivariate choropleth map, choose colors for the legend, and create a good layout. I created three separate fields to analyze the data: Class Obese, Class Inactivity, and Class Final. I used the symbology tool to create 3 Quantile for the Obese and Inactivity classes and used each quantile to set the three classifications in the fields I created using the Select by Attributes tool to

Infographic's

This week was a fun and challenging week as we learned about and created infographics. It was fun to create the infographics themselves, but challenging to figure out the best methods and practices in analyzing raw data. We used 2018 County Health Rankings National Data from countyhealthrankings.org. I chose to move forward with the two values: Unhealthy Mental Days and Premature Dealth. I choose these two variables because those that struggle with mental health die before their time due to depression, anxiety, and/or a combination of similar issues. Both variables are normalized by relating their value to all counties within each state in the USA. For example, the poor mental health days is normalized as the average number of reported mentally unhealthy days per month per ctizen. The normalized premature rate is the “age-adjusted years of potential life lost rate per 100,000.” Below, I created a scatterplot of the normalized data. I choose to keep the scatterplot in a pretty tr

Color and Choropleths

This lab was very interesting as we dived into color theory. In the first part of the lab, we created and compared linear and adjusted progression color ramps to themselves as well as a color ramp from the website colorbrewer.org. I found, the colorbrewer color ramps are not as rhythmic when compared to the other methods, as they don’t step up at set intervals or rates. However, I don’t think that a set rate is needed to go from color to color. I preferred the colorbrewer ramp because each color was distinct from its neighbors. In the linear and adjusted color ramps, the colors looked too similar to each other and were not distinct enough for each step. I think that as long as the color ramp is moving in the opposite direction of the same color hue, the step rate or interval is not as relevant. When I first was completing the linear step I started with the purple hue option but had a difficult time, as each step in the color ramp looked the same. At one point, I created my own color

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