Module 10- Dot Mapping

Dot Maps. I have seen plenty of them but I had no idea what went into making one until this week. I used ArcMap 10.2 to create a dot map to display the population density in Southern Florida and in general the project was pretty simple. The symbology display options and statistical tools in ArcMap significantly reduce the amount of work required to count and place dots on a map when the data is already included in a file's attributes. The real work for me was working with cartographic principles, specifically color schemes.

 To create the dot density map I displayed the shapefile I was working with using the Dot Density option hidden in the file's Properties under the Symbology tab. It took a little while to decide which dot size and dot value to use because the clarity of the dots changed as I zoomed in and out of the map and it took me a while to realize this. Eventually I settled with a dot size of 2 and a dot value of 10,000. I was able to adjust the placement of the dots (excluding certain areas like large water features) by using the Properties option within the Dot Density window. As a note to anyone who wants to use this feature, it does not work well if you turn these options on and off several time so save your map often.

Aside from ArcMap being a little finicky the map was not difficult to make. The difficult part was finding a color scheme that worked well with all of the required map information. I had to include multiple water features that needed to be displayed in blues and greens. This major use of blues and greens washed out most of the other color options for the dots and since they are the most important feature, they need to stand out. I tried several bright colors and dark colors but the only dot color that stood out well against all of the water features was a bright pink. I even made most of the other features on the map partially transparent but pink was the only color that seemed to work.

Week 10- Vector Analysis

For the last two weeks in Intro2GIS I worked with ArcMap to conduct vector analysis. I practiced identifying ans selecting features based off of their proximity to other features, working with file geodatabases instead of standard shapefiles, creating buffers around existing features, and using multiple attributes to analyze datasets. The map graphic I created this week shows potential areas for campsites based on their proximity to roads, surface water features, and existing conservation areas.

In order to identify these locations I used the Buffer Tool in ArcToolbox and created buffers around the existing feature classes for roads and surface water features. After creating the buffers I combined the using the Overlay Intersect Tool to find areas that met all of the proximity criteria. Then I used the Overlay Erase Tool to remove any areas that fell within known conservation areas. In addition to using the tools available in ArcToolbox I also dabbled with a little Python Scripting to run multiple buffer processes at the same time. I can see now that Python will be an invaluable time saver once I get a little more comfortable with it. Fortunately it is pretty user friendly inside ArcMap and if you make a mistake in the code, it will point out your error when the program tells you it is unable to run the process.

Module 9- Flow Mapping

This week I created another thematic map and the focus continued to be on design. I created a distributive flow map depicting the worldwide immigration flow into the United States using statistics from 2007.

We were provided with outlines for both the world map and the associated scale bar in a .cdr file to work with in CorelDRAW 7. The initial graphic also included the U.S. Map and scale bar. All of the editing this week was completed in Corel so maintaining map scale while moving elements around was crucial. 

The major topic this week was flow mapping which is why the world map with flow lines is the largest element in my graphic. All of the flow lines are proportional to the amounts of immigrants flowing from each continent. Originally I planned on only using black flow lines but with the overlapping lines I decided that using colors made the map a little easier to follow. I also added a drop shadow to all of the lines to make them stand out against the background without distracting from the map like a bold solid outline would. The background is an elliptical fountain fill instead of a solid fill because it helped balance the different shades of light colors on the map.

The inset map is a choropleth map depicting the percent of immigrants per state. I only applied minimal changes to the choropleth map, primarily adjusting the location of Alaska and Hawaii so everything would fit inside the neatline. I created the legend for the inset map solely using Corel. I thought this would be one of the more difficult aspects of this week's lab but quickly found out how simple it could be. With the help of the color dropper tool to identify fill colors the legend only took about a two minutes to create. I cannot say the same about the drop shadows around the flow lines because they took me at least an hour to adjust. 

Module 8- Isarithmic Maps

This week covered isarithmic maps and I have to say this was my favorite section so far. I'm not sure if it was the weatherman aspect or the fact that this topic was easy to understand but I definitely enjoyed it. For the my lab I used ArcMap to create two maps depicting the average rainfall in Washington using the same precipitation raster dataset. The raster data came from the U.S. Department of Agriculture but was originally produced by the PRISM Group out of the University of Oregon. PRISM is an analytic method used to interpolate point data including the climatological affects of terrain features. This means the precipitation values that were assessed for areas without measuring points were based off of surrounding precipitation measurements as well as the mountains and hills in the region.

The first map displays the data using continuous tone shading. The shading was done using Stretched Symbolgy with the precipitation color ramp in the data's properties. I think this map is better suited for displaying the precipitation values because the changes look natural and smooth. As for the graphic, I used a dark background with light text because it was easier to see the edges of the map against the dark colors; all of the colors besides the map itself are somewhere in the gray scale.

Continuous Tone

The second map displays the precipitation data using hypsometric tinting with contour lines. I created the hypsometric tinting by using a Classified Symbology with the precipitation color ramp in the data's properties. I created ten classes and manually set the class boundaries. This created a map with color blocks of related colors. After classifying the data I also created contour lines using the Contour List Tool in the ArcToolbox. This tool requires the Spatial Analyst Extension to be enabled before you can use it but once it is turned on, it is fairly easy to use. To create the contour lines you simply add the original raster data to the Input section, select the Output location (place to save it), and then add the values you want contour lines drawn at. My contour lines were drawn at the class boundaries for the hypsometric tinting which makes the color changes even more defined.

 

Hypsometric Tinting with Contour Lines

Both maps also use a hillshade to give an idea of what the terrain looks like. I think that is important for these graphics because the dataset was interpolated with respect to these features.

Week 7-8: The Data Hunt

This week was the midterm for Intro2GIS and it was definitely a piece of work. By piece of work I mean it was a two week period of hunting down data, trying it out in ArcMap, and then searching for more data. My task was to find nine different pieces of data for Highlands County, FL and then create a graphic displaying all of it. The data consisted of the county boundary, cities and towns within the county, public lands within the county, all surface water within the county, major roads within the county, wetlands within the county, invasive plants within the county, one DOQQ inside the county, and one DEM file covering the entirety of the county. Fortunately all of the data was available from labins.org or the Florida Geographic Data Library website (http://www.fgdl.org/metadataexplorer/explorer.jsp). It took me a long time to find all of the data but it was all there from reputable publishers complete with metadata and spatial references. I tried a couple of quick Google searches for data and checked the county GIS page but I had more faith in the data I found from FGDL so I dedicated my time to looking at a lot of their files. The final product is pictured below.

There was a lot of information to include and the whole region was covered in water so I created three separate data frames to display it all. Besides finding the data the other two big aspects of this project were ensuring all of the data was displayed in the same projection and and clipping large files to work with a smaller extent. The projections part was pretty simple since we spent the last two weeks practicing with spatial references. I was lucky and most of the data I found was already projected in Albers Conical Equal Area. Clipping data files was also a simple process once I worked through it the first time and found the Clip Tool in ArcToolbox. Almost all of the files I downloaded covered the entire state of Florida so I clipped them all the the county boundary for Highlands. Not only does clipping the data make for a prettier picture, it also makes the map layers load faster in ArcMap. This was not a big issue when we were working with two or three layer but when I had 6-12 layers open in three different data frames it made all the difference in the world.

I have one other thing I'd like to note from this week that cannot be seen in this graphic. Data organization and naming conventions are crazy with GIS data. Every file ii clipped was given a straightforward name and placed in a separate folder from all of my downloaded data. I did not want to have to search through all the funny named files again after I found the one piece I needed.