Cartography Week 4: Data Classification




This week I learned about the different ways data can be classified and how it affects the outcome of you map. These maps depicting the distribution of people aged 65 years or over compare the equal interval, quantile, natural breaks, and standard deviation data classifications. You can see how each map is impacted by how the data is classified, and just how different they can be. This is why it is important to know which data classification is best to use for your purposes and audience.
 

Comments

Post a Comment

Popular posts from this blog

GIS Programming Week 3

Image
       This week I learned the basics of fixing errors within your Python script, a process called debugging. I was provided with a few scripts that contained several errors, and it was up to me to address the issues and make the script functional.  To find the problems in the code to debug them, I first ran the script to see what errors occurred. The error messages in the Python Shell were quite helpful as they told me what type of error it was, as well as the specific line it came from. Most of the troubles were simple spelling or syntax errors, which were easy to fix and help further familiarize me with the proper grammar of Python. I found the entire process quite enjoyable and rewarding. Below is the final output of the first two scripts I fixed, which retrieved shapefile information from a project in ArcGIS pro.      For the final script, I was told to not fix any errors found, but to instead employ a "try/except block", a tool used to work ...
Read more

Cartography Week 5: Choropleth and Proportional Symbol Mapping

Image
  This week I created a choropleth map based on the principles I've learned. A choropleth map is a thematic map that uses color mapping symbology to represent data. You have likely seen one of these in the form of an election map. The choropleth map above uses proportional symbolling, symbols that scale with the data, to depict wine consumption in Europe in 2012 compared to the population density in each country.
Read more

GIS Programming Week 5

Image
This week I learned about manipulating spatial data within Python. I used what I learned to create a script that creates a new geodatabase, copies shapefile data to that new database, and creates a Search Cursor and dictionary to isolate and print County Seat cities in New Mexico and their respective population circa 2000. I found the first few steps of the lab straightforward, but first ran into trouble when creating the Search Cursor. At first, the output printed on the same line and looked very messy. To fix this, I ended each line by inserting an escape character <\n> to the script, which fixed that issue. I had the most trouble populating the empty dictionary. The output initially only gave me the last key in the dictionary, but I realized this was because the line of code creating the empty dictionary was blocking the for loop, so I moved it up a few lines and was able retrieve the whole dictionary successfully. I also ran into trouble while trying to place all of my print ...
Read more