Skip to main content

Large Cities and Waterways of Florida

In Module 2 of Cartography, we learned about map typography and different ways to properly use map elements to highlight land and water data. Utilizing point (cities), line (water ways), and polygon (swamps) data in Florida, I created the map below. 

Figure 1: Map of large cities and waterways in Florida.

Utilizing ArcGIS pro, I first deleted any data that I did not need from the attribute tables. I then converted each category's symbology from single to unique depending on their data's correct name. Now I was able to create labels for the data I needed. (I originally used the Labels SQL function to narrow down the labels to only those that I needed, but that resulted in a very cluttered legend at the end.) I played around with colors/fonts for each category and saved all my actions. I also changed the positioning of the labels for the rivers to 'river placement." I then converted the data category to annotation (i.e. Rivers). (I learned the hard way to make sure to change the GroupAnno name into something different, like GroupAnno2, because I left it the same and when you go to create an annotation for a new group, it deletes the previous groups' labels.) Lastly, I changed the labels to locations that were correct and pleasing to the eye.

A couple of customizations I performed are stated below,  to make this a neat and organized map:

1) I removed the excess data not needed for this map to have a simple legend.

2) I removed the basemap as it had labels for cities that caused duplicates and additional information that I did not need. I decided not to add a colored background so the highlighted features stood out more. The Florida keys also faded away and blended into the colored background. 

3) I added a "quote line" to the Okefenokee Swamp label outside the map, so the label would not cover parts of the Suwannee River. 

4) I bolded Tallahassee so the capitol would stand out, as well as placed a more vibrant symbol; a star. 
 

Comments

Popular posts from this blog

Positional Accuracy: NSSDA

 In this analysis, I compared the street and road intersect data collected for Alburquerque, NM by the City of Alburquerque and the application StreetMaps. I used an orthophoto base layer as the reference for this analysis, to compare and determine the accuracy of both the City and Streetmap layers using NSSDA procedures. The most difficult part of this analysis for me was how to determine what 20% per quadrant looks like. Because the reference map was divided into 208 quadrants, I had to determine how to subdivide all the quadrant's equality into 20%. After multiple trials and error, I decided to subdivide the entire area (208 sub-quadrants) into 4 equal-area subsections. In this way, I could do 5 random right intersection points per subsection or 20% per subsection.  Map 1: City of Albuquerque city map data.  Map 2: City of Alburquerque SteetMap data When selecting a random intersection to place the points within each quadrant, I choose a location that had data f...

Isarithmic Mapping

  Map 1: Annual Precipitation, Washington State Map 1 is an Isarithmic map that follows the continuous phenomenon of rainfall in Washington state over a 30 year period. The data was created by the PRISM group at the Oregon State University in 2006, and then downloaded and amended by the U.S. Department of Agriculture, Natural Resources Conservation Service, National Geospatial Management Center in 2012. Eden Santiago Gomez, analyzed the data on 5/2/2021, to create the map above. Santiago Gomez created continuous tones for the data, also adding a hillshade effect. She then converted the floating raster data into Integer data via the geoprocessing tool Int (Spatial Analyst Tool) to bring out hypsometric tinting. Lastly, she added contours of the data via the Contour List tool.   How the precipitation data was derived and interpolated? The PRISM system has been continually developed over the past couple decades, utilizing physiographical maps and climate fingerprints as its ...

Supervised Classification

 This week we learned how to perform supervised and unsupervised classification on ERDAS Imagine. On the figure 1 map below, you can see my attempt at classifying land use/cover of Germantown, Maryland. This week my map falls short of the assignment goals, as I was not able to get ArcGIS pro to replicate the recoded feature classification outputs I performed in ERDAS. I am not sure what I did wrong, but in the future, I will make sure to analyze signature points more carefully via their histograms and mean plots to identify potential errors.  Figure 1: Supervised classification of Germantown, Maryland with a smaller distance map of the main feature map.