Saturday, February 16, 2013
Cartographic Skills Lab
For this lab we worked with (or in my case....fought with) Adobe Illustrator again. The main challenge for me was getting all of the layers grouped together correctly so they can be moved and re-sized as one cohesive unit when needed. Our main goal was to create a map that displays appropriate placement of map elements and appropriate map hierarchy.
Projections Lab
In this lab we created a map which
shows 3 different projected coordinate systems. Below you can see
that we used a map of Florida in which 4 counties are highlighted and
their area is shown in each projection's legend. This was an
interesting exercise because you got to see how different projections
distort areas differently.
Monday, February 11, 2013
Grouping with Adobe Illustrator
This week's map shows the location of
Florida cities. It was first created using Arc Maps and was then
exported into Adobe Illustrator as an ai file. Our main objective was
to focus on grouping files properly in order to maintain the correct
scale size. To do this, the scale bar was grouped with the counties
and cities layer.
Thursday, February 7, 2013
ESRI's online global account
This week we worked with ESRI's
online global account. We created our own accounts, took a couple of
the online courses offered, and uploaded two Map Packages to our accounts. The First map we uploaded shows rock
climbing sites in Yosemite National Park. Before uploading this MPK
to our ESRI accounts we made a few small modifications. The second
map we uploaded was a bit more involved. We created a map showing a
study area in the Organ Mountains in N.M. Then, bundled data into two
different groups showing the data at different scales. I was excited to learn the ESRI learning and sharing sites are there to utilize.
Friday, February 1, 2013
GIS3015 Week 3
The maps below were created from data collected during the 2000 census. They show the population of black Escambia County residents by census tract. The first map shows the percentage of residents using 4 different classification methods. The second map is only depicting the Natural Breaks method which I believe to be the most accurate method for this example. Unlike the Equal Interval and Quantile methods,
Natural breaks take into account how that data are distributed along the number
line. Because of that, Natural breaks minimize differences of data values in the same class. This allows each data class to be better represented on the
map.
(I added a border in Arcmaps, I'm not sure why it isn't showing up on the blog)
(I added a border in Arcmaps, I'm not sure why it isn't showing up on the blog)
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