Module 6B--Flow Line Mapping

This was an optional exercise that utilized Adobe Illustrator in total to produce a map that shows immigration by continent and by percentage to U.S. states.

The assignment was great practice using AI and developing flow lines. Once all the objects were placed on the map, I chose to separate my continents and place them in an arc around the inset map of the United States (which already displayed the states as a choropleth map using five manually derived classes.

The biggest portion of the exercise was actually drawing the flow maps and modifying them to an appropriate shape. This was easy using the line tool in AI and then manipulating the anchor points. The challenge was determining the stroke of the line, which was proportional to the amount of immigrants to the United States. In order to determine the stroke width, I used an Excel spreadsheet that provided the immigration numbers for each region. I then determined the proportional weight by first determining the square root of each region's immigration numbers. From there, I used the following formula:

Width of Line Symbol = (Size of Largest Line I Want) * (SQRT of Region Immig/SQRT Max Value)

Though it seems difficult, it was very easy using excel. The only challenge to this was for converging lines, where you split the difference of the lines so that they converge to the proper width. The only reason this was difficult for me was because I tried to use lines that had transparency adjusted. This caused an overlap which I could not remove. If the lines are solid, this is not an issue. Another consideration is to make sure the lines do not overlap other lines or terrain and are presented appropriately. I chose to build my flow lines in a new layer and then placed this layer below the continents layer so that the flow lines originated from behind the continent and stopped short of the inset map. I then changed the colors of the lines to match the color of the continents. 

Once the essential map elements were built, I then modified the flow lines to show an inner glow, a drop shadow, and a bevel. The lesson here was that in the appearance tab, the bevel and extrude effect had to be above the inner glow and drop shadow so that the bevel was on the line and not the other effects. I then adjusted the bevel to make them look three dimensional and in perspective.

A word of caution, many of these effects (especially 3-D) use a lot of memory, which can slow processes or reduce the ability to add new effects. To finish the map, I used file-export to export the map to a .png file.

Flow Map depicting 2008 Immigration Numbers by Region and Percentage to each State.
Projection: Winkel Tripel

Module 5b--Dot Density Mapping

This lab was very straightforward and just showed how to produce a dot density map. Though this map was optional due to the coronavirus, the professor highly suggested that we complete the project because it builds on future maps.

The map again utilized the Albers Conic Equal Area projection for the same reasons as the choropleth map--to maintain equal areas so that data is not skewed or misinterpreted.

Once all my layers were added, I joined the Excel spreadsheet that included population numbers with a feature class that showed the South Florida counties and boundaries. I then populated the symbology using the dot density feature based on the population density included in the feature class/attribute table.

With the dot density symbology, it was trial and error to determine which looked the best and had the correct size and dot value for the extent of the map. Though I could have used a nomograph, I chose to do this using my own intuition. Because the dots are placed randomly throughout the boundaries, I masked those dots that were inside surface water. This way, people would not be shown as living on the water. In order to show the dots only in urban areas, I clipped the South Florida feature class to the Urban Land feature class in place dots only in the urban areas.

I then finalized the map by placing the essential map elements. To provide a visual anchor for population density, I created three equally sized boxes with a specific number of dots to show an example population density. I did this by created one box with dots, grouping the elements, and then copying until I had the appropriate number of dots. I then ensured the dots were placed randomly in each box.

Again, the map was very straightforward and only took a few hours. I believe it is important to get all the practice one can get, especially when this lab builds upon the next.

Dot Density Map of Southern Florida. One dot equals 5,000 People

Criteria 3--Schools and Registered Daycare Centers

For Criteria 3, I had to define and quantify schools and registered daycare centers in proximity to the preferred corridor. I downloaded the U.S. Census Bureau's TIGER edge files for both counties and used imagery for my basemap. As with every map, I ensured that the GCS and projection were consistent.

I got a little over zealous on determining the schools and daycare centers. I prepared two separate Excel spreadsheets to input my data. Unfortunately, I input ALL schools and daycare centers in both counties from information on the school districts' webpages and a childcare center locator.

After I cleaned the spreadsheets, I save them in .csv format and then created my address locator. When I geocoded the schools, more than half were unmatched and would take an inordinate time to match them using Google Earth and school webpages. As a result, I located the zip codes that were impacted by the corridor and removed all schools (and later daycare centers) that were not in these zip codes. I then geocoded the result and matched any schools that were not matched. I did the same with the registered daycare centers.

Just as in the homes and parcels analysis, there were many schools and daycare centers that were new, had moved, or shut down. I had to use my best judgment when determining their status. In the end, only two private schools were within one mile of the corridor midline and no daycare centers were close. Therefore, no schools or daycare centers met the criteria to be impacted.

Schools and Registered Daycare Centers in Proximity of the Corridor.
The colors are consistent with previous maps. No schools or daycare centers
are impacted by the corridor.

Geocoding

This week's lab taught me quite a bit. Though I am still weening myself from Power Point habits, I was excited to learn some Excel techniques. I wish I had known about these time-saving tools several years ago.

The lab started by converting latitude/longitude (DMS format) into decimal format via Excel. Once I set up my headings in the spreadsheet, I used the extract function to remove specific characters (by Left or Mid, place number, and number of characters) from one cell to another. As with all Excel functions, I was then able to drag the formula down the column, saving time. Once the D-M-S (use a comma for degrees) were extracted, I then converted the numbers into X and Y coordinates (decimal form). Once I added the data to a new project, the eagle nest locations were displayed.

Eagle Nests. Hybrid imagery of Santa Clara County, FL. Green point features are eagle's nests obtained by converting lat/long to X/Y decimal coordinates. The XY Point Data was then added to the layer group to display the locations.

The second part of the lab taught geocoding. To accomplish this, I downloaded census data (TIGER) and addresses from Brevard County Schools. Once the data was placed into Excel, I converted the addresses for input into the address locator function. Using Excel extraction functions and offset functions, I stripped specific data from a cell and moved it to another. Following each operation, I copy/pasted the values only into another spreadsheet (tab at bottom). To remove excess cells (many of the input data took up multiple cells), I used the filter function, which allowed me to select/de-select data within columns and then perform a function (such as delete).

Brevard County Schools. This is a hybrid imagery map of Brevard County, Florida. In this map, yellow outlines are roads and boundaries. The purple school point symbols are Brevard County schools. The data was obtained by downloading standard mailing addresses and transforming the data into a usable format to be geocoded in the address locator. After matching and re-matching the output, the schools were displayed with the school house symbol.

When the addresses were formatted properly (saved in an .xls and .csv format), shapefiles were added (roads and county boundaries) and the projection was changed. I imported the .csv table and created an address locator, which allowed me to geocode. By geocoding, each address was matched to a specific map location. Those unmatched schools were rematched using Google Earth and sometimes visiting the school website. From this analysis, I located the school location, I chose its site on my map. For ease of viewing, I changed my school icons and saved my data.

The link to the above web map is http://arcg.is/ai1vP

All in all, it was a fun lab. I always enjoy learning, especially when I can leverage the new knowledge into future projects (such as the new Excel tools I learned).