GIS 4043 Lab 9: Vector Analysis 2

This week's lab continues the previous module's exploration of vector analysis.  This module introduced three tools that will undoubtedly be essential for future GIS work:  buffer, overlay and Python.  Buffer is a tool used to create zones of a defined distance around a feature.  Buffers become new layers which can be worked, combined and analyzed further.  In the above map I created for this lab, potential camping sites must be within 300 meters of a road as well as within 500 meters of a river or 150 meters of a lake.  Buffers for each of these can be created and added to the map.  To easily find areas that meet all the above criteria and create a new layer for them, an overlay tool can be used.  Using the overlay intersect tool, for example, would create a layer based upon the areas in which the input layers overlap (think of a Venn diagram).  There are six different overlay tools for different functions.  For example, another requirement for the camping area in the above map were that they not fall within conservation areas.  Using the erase overlay tool with the output of the intersection of the road and water buffers, we can create yet another layer that subtracts locations within conservation areas.

The third tool introduced was ArcPy and the use of Python in ArcMap.  This module included a fairly gentle introduction to using the program language, but it made clear how bulk processing is made easier with Python compared with going through the more user-friendly but more time-consuming ArcMap user interface.  I look forward to future courses diving further into using the Python programming language.

GIS 3015 Lab 10: Flowlines

This week's Cartographic Skills lab introduced us to the somewhat specialized flowline thematic map.  As described in our text, this style of thematic map is used less often than the others we've worked with, yet it may most effectively and efficiently express data showing movement.  This movement may involve people, trade items, ideas, animals, or virtually anything that may be mobile.  While good at communicating such data, the design of flowline maps can be a problem; creating easily interpretable flowlines without cluttering the map and obscuring features can be a challenge.

To create our flowline map, we were given immigration data and two basemap options to work with in Adobe Illustrator.  We were to decide on the size of the largest flowline and then calculate the sizes of the smaller flowlines based upon the regional proportion of immigrants.  Beyond simply creating the flowlines, however, we were to experiment with different effects in Illustrator to give the map a more professional and unique polish.  For my map above, I applied drop shadows to the flowlines and legend, inner glow based upon continent color to the flowlines, rounded corners to the legend and neatline, and (based upon a suggestions in the lab instructions) placed the origins of each flowline within their respective continents.

The main challenge of this lab was getting the appearance of the flowlines just right.  It took several attempts to get each one to "flow" from origin to destination without sharp edges and without obscuring features.  Additionally, I am always tempted to spend too much time in Illustrator experimenting with different effects.  I am never completely satisfied with my end result; I always believe I could spend more time working on a particular aspect of the map.  However, I must cut myself off at some point or else I would never move on.

GIS 3015 Lab 9: Isarithmic Mapping

The ninth lab of our Cartographic Skills course introduced us to another variety of thematic mapping.  Isarithmic maps may be most familiar for depicting elevation and weather-related phenomena (rainfall, pressure, etc.).  However, isarithmic maps can be used to represent any data that are continuous.  As seen in the above two maps, data represented in isarithmic maps can be symbolized in different ways.  The top map depicts the precipitation data of Washington State in a continuous tone while the lower shows a hypsometric tint symbology.  It is simplest to think of the data in the latter category of map as being classed while the former is unclassed.  Continuous tone maps do a good job of accurately representing the data (as interpolated from control points), but it can be difficult to interpret the data value for specific locations.  The hypsometric tint map may be easier to interpret, but the classed data, as in other types of classed data maps, may skew one's interpretation of the data.

My primary creative contributions to these maps involved experimenting with different gradient patterns for the background.  I also increased the width of the legend for the continuous tone map to make the color ramp easier to interpret.  Although the high-to-low value orientation of this legend was accidental at first, I decided to leave it this way since the majority of the low values are in the eastern portion of the state and the high values are in the west.  This decision may cost me a point or two, but it made sense at the time.  I also experimented with different symbologies and labeling styles for the elevation contours on the hypsometric tint map.  However, I could never settle on a symbology that was not cluttered and difficult to read, thus I decided to keep the contours simple and unlabeled.

GIS 4043 Lab 7: Data Search

The seventh lab of Introduction to Geographic Information Systems forced us to find and organize our data layers with little supervision.  Rather than being handed data, we were required to locate, download and organize data representing county boundaries, major roads, hydrography, cities, elevation, and public lands.  We were also to choose one digital orthophoto quarter quad to incorporate into our layouts.  Finally, we were to choose two of four environmental data layers to add into the mix.  Since each of us were given a single county of Florida to focus on, most of the layers needed to be clipped to the extent of our respective counties.  With the amount of data represented, we were forced to carefully organize our layouts so as not to overload the maps with too much information.

I did not run into any overly troublesome difficulties in finding and downloading the required data; I downloaded many more layers than I ended up using as I experimented with different data sources.  Initially I planned on using a land cover layer as one of my optional environmental components.  Unfortunately, the layer I found was so complex and information-rich that I did not want to tackle the task of wrangling it into a presentable form for this lab.  Instead, I went with a shapefile of invasive plant species available from the Florida Natural Areas Inventory website.

After all my layers were downloaded, I tossed several crude maps together with different combinations of layers to see what would work best.  The division that seemed most natural grouped the "human" data (cities, roads, public lands) in one map and the environmental data (invasive species and wetlands as well as elevation) into another.  I could have plausibly included surface water in both maps, but I thought including it in the environmental map would have been unnecessary given the presence of the wetlands data.  I was worried about shoehorning the DOQQ into one of the maps, but using it to expand an especially concentrated section of invasive plants seemed to work well.  The only problem was that, when using the 'clip to shape' option of the data frame properties to clip my DEM raster to the Gadsden County outline, the extent indicators for my orthophoto were also clipped.  After some searching amongst ArcMap's help files and more experimentation, I found the 'Mask (Environment setting)' tool functioned just as well for my purposes without clipping the extent indicators.

GIS 3015 Lab 8: Proportional Symbol Mapping

This week's Cartography lab introduced us to the use of proportional symbols to communicate map data.  As seen in the above two maps, the size of the symbol represents the value of interest.  This method is an intuitive way to communicate information; very little interpretation needs to be done by the viewer to see France's wine consumption is higher than Finland's.  A potential downside, however, is the difficulty of discerning the relative sizes of similar values.  France, Italy and Germany feature similarly sized symbols, for example.  If these three symbols were further apart, discerning their relation would be even more difficult.  We may also run into the difficulty of an overcrowding of symbols; in the second map I was forced to move most of the symbols away from their country's center to allow for legibility.  

The first map features a legend that is fairly worthless.  I would have spent some extra time working with it, but the point of the initial lab was more experimental in nature.  Leaving the symbology unclassed allows a direct representation of wine consumption to symbol size.  The second map was completed in Adobe Illustrator and proved to be more bothersome.  Although I am sure it is operator error, at times Illustrator seems to have a mind of its own.  On more than one occasion I had to deal with disappearing or spontaneously moved symbols.  On the other hand, we did learn how to create the circular text seen above.