M4- Data Classification

This module was focused on data classification. As mapmakers, the tools we use are embedded with the opportunity for misrepresentation and falsehood. This lesson showed various ways to use this tool and how to account for the issues that come with. The mapping itself was fairly straightforward, nothing we hadn't covered in M3. The real work for this module was understanding the concepts behind the statistic tools we are working with. As you can see below, each map uses a different mode of classifying the data. I tried to balance the maps and maintain continuity in placement of the map elements.

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Vectors, how Vexing!!

Hey everyone, hope your lab went better than mine this week. It was going really well, until I got to the end and didn't have the correct number of features. It had been looking good, I was nearing down on a suitable campsite that didn't fall in the buffer of our water and road features. I had just removed the overlap between the buffer union and the conservation lands, but my end result didn't make sense. See my results below:

M5 - Explore and Manipulate Data

Good evening classmates, This week we tackled dictionaries, cursors, and lists. Oh my! But seriously, these are incredibly powerful tools that you'll implement frequently in your GIS solutions. I've seen them in most of the scripts I've encountered. There was a lot of material this week and I'll admit, I did struggle to keep up with it. The part of the lab that I struggled with most was updating the dictionary with values from the feature class. Beyond the two lines in the lab assignment, I couldn't find any references in the readings or exercise about populating an empty dictionary. I got as far as the below error (cannot concatenate 'str' and 'int'. I tried converting the integer field to string , but that didn't affect the error message. Even put together a try/except statement to see if anything was making it to the dictionary, but nothing was. Unfortunately, I had to stop my efforts on the lab there this week.

Lab 4- Working with Color

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 is the map place

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