This is the map place

(My Jpeg wasn't uploading so I used a screenshot)   I followed the lab instructions on getting my maps aligned. After that, I organized my two bar charts underneath their respective maps. I also added my scatterplot and used that to get my summary statistic. Then I added a light grey background to make the information stand out more. I felt it over the top to symbolize wealth with greens and death with blacks or red. If I was going for a more radical inflammatory approach, I would've opted for those changes. Instead, I tried to keep my colors as simple as the message "how much you make, increases the likelihood your child survives". I did use italic on my font to make it more eye-catching versus solid blocky text.

 ColorBrewer: Adjusted Progression:  Linear:  The linear and adjusted progression color ramps appear to follow a much more rigid association with the starting and ending colors. The ramp I found from the ColorBrewer website quickly appeared to lose the greenish hue and end on more grey and white notes. While this would appear visually distinct on the map, I find that it loses some of the intrinsic association between features. If I have a dark green and a light green, and I know both come from the same dataset, I can judge that they represent some degree of intensity or increase in amount of data represented. When I compare dark green to white, that doesn’t tell me much about the difference between these two features without referring to the legend. The time saving aspect of ColorBrewer is wonderful in comparison though, and I could have spent more time selecting a color gradient that suited my needs. I used a blue to red color system with percentages shown. Blue shows a...

This week, we worked on terrain visualization. To create the varied products for this lab, we used: hill shading, symbology, and contouring. For my landcover map, I tried to use colors that had a logical consistency with the real world feature they were representing.  

I chose Rhode Island, as I assumed the smallest state would only have one state plane. I was correct in this assumption and used the NAD 1983 (2011) version as it was the most up to date.

  Here is my map. I'm choosing to lose a few points instead of manually identifying and labeling 16 locations just to demonstrate I can use the dropdown options in the element pane. No offense intended, it's just already been a 50+ hour week of GIS for me. I began using the standard blue color for water features, and marked San Francisco in bold lettering as that's the location that contextualizes the map most clearly. If I was going to make this map more effective: I'd use dynamic labeling on the parks to save time and only place one label relevant to the Golden Gate area.

  For this week's lab, we combined a lot of skills into one final map. It almost felt good enough to be a final project in and of itself. Last week we looked at identifying individual features, this week we took those features and reclassified images based on them. This is a handy skill to have as it reduces data load and processing time to view the reclassified data vs. the original raster.