Wednesday, May 3, 2017

2014 National Student SAT Participation


Every year, many high school seniors prepare for college in a multitude of stressful ways, one of the most notable is by standardized testing.  SAT preparation can be a major focus of at the end of a high school career.  SAT scores are a determining factor in the college acceptance process.  This map depicts two data sets: the mean, combined SAT score for each US state and the participation rate of college-bound seniors per state.  The data for this map comes from the US Census Bureau and is for the year of 2014.  This map aids in determining any correlation between participation and grades and any visible patterns.  
This map is a combination of a choropleth and a proportional symbol map.  The participation rate data is represented by a choropleth map, showing the higher participation in darker colors and lower participation in lighter colors.  This method is used in combination with the proportional symbol for the combined score data, so that both data sets are easily distinguished.  The combined score symbol is a blue dot that is smaller with a lighter color for low scores and larger with a darker color for higher scores.  
The data for the mean combined score are unstandardized, so as to show the real values, however, the participation data is standardized by area so that the population of each state does not skew the data. 
Student participation data is symbolized by quantities using graduated colors using five different classes; it is classified using the Manual method.  The Manual method was chosen over the Equal Interval, Defined Interval, Natural Breaks and Geometrical Interval because they showed very little color differentiation between the states.  The Quantile method showed a little more color diversity, but was still primarily orange.  The Standard Deviation method was not used because the standard deviation of the data was of less concern than the raw data.
Variation in color was added to the graduated symbols for the score values for ease of differentiation.
The map is projected in North America Albers Equal Area Conic as it is the projection that preserves the area of the mapped subject.  The participation data is standardized by area, so it is important to maintain accurate area.
The layout for this map is a simple one with the continental United States in the center and Alaska and Hawaii placed in inset maps, allowing for the entire US to be shown up close.
The resulting map shows lower participation and a higher average score in middle america.  High participation is shown in New England, Georgia, Florida and Idaho with a lower than average score.  Mid-range participation rates are shown in the West Coast and parts of the East with lower average scores.

In conclusion, areas with higher participation rates correlate with lower average scores and lower rates with higher scores. It could be deduced that with lower participation rates, students who took the SATs were more motivated and prepared to take the test, whereas in areas where the majority of college-bound seniors take the test, both prepared and unprepared students cause a lower average score.

Sunday, April 9, 2017

Dot Mapping


This week's assignment was really fun, but became quite challenging after a while.
The project is supposed to portray the population of Southern California, using dots to represent groups of individuals.
I symbolized the background of the map pretty minimally so that my pink dots would stand out (though, my masking tool would not work, so the dots are not present here). I added the dots in symbology, applied the masking tool and isolated the dots to urban areas, so the dots were only distributed where people were actually living, not on waterbodies or other areas.
I added the legend in ArcMap, but exported the dot layer to Adobe Illustrator, drew squares around 5, 10 and 15 dots, saved each square as a layer and moved those layers onto my map, adding them to the legend.
I think this map would have benefitted from an inset map, showing specifically the location of focus.

Sunday, March 26, 2017

Immigration to the United States in 2007

This assignment's goals were to make a flow map based on 2007 immigration to the United States data showing the percentage of people immigrating to each state and the amount of people immigrating from each region into the U.S.. 
I separated the regions that data was collected from and circled them around the choropleth map of the United States, while maintaining their general geographic relation to the U.S., slowing for an ideal display of the States map as well as the flow portion of the map. The widths of the flow lines are in proportion to the amount of people moving from the respective region to the States. The hue of each state represents the percentage of people immigrating into those states in particular. 
I placed drop shadows on the United States, continents, and the legends so that they stand out from the background and text on the finished map. 

Monday, March 13, 2017

Washington Precipitation

Creating an isarithmic map of the annual precipitation in Washington was a project that required several steps. The data for this map was processed by using the PRISM interpolation method by the PRISM Group at Oregon State University. I imported this data and symbolized it with a continuous tone, using cooler colors to symbolize light precipitation and warmer colors to symbolize heavier precipitation and checking the hillside effect relief in the symbology box to show elevation.
I had to do some manual editing of the legend to make the intervals make sense. After formatting the legend and ungrouping/regrouping it to line everything up properly, I'm happy with how it came out. I then used the spatial analyst tool to convert my data and display crisper contours.

Monday, March 6, 2017

Wine Consumption in Europe

The goal of this assignment was to compile a choropleth map that showed to data sets: population density and wine consumption in European countries.
I used a color gradient to show the population density data, using a orange color scheme to make my wine consumption symbols pop more. This data has a Quantities Classification, which showed the more dynamic color range amongst the countries. I used graduated wine glass symbols to show the wine consumption in liters per capita. I annotated the labels and rearranged them a bit and added an inset map of the more crowded areas.



Sunday, February 26, 2017

Senior Population Distribution

The data for this map was provided by the U.S. Census Bureau and was meant, in this project, to be used to determine the distribution of the senior (65 and up) population of Dade County in Miami, Florida. I used several different data classification methods for this map to determine which method best presents the data.
The equal interval method breaks down the data into equal classes with the same number of data sets in each class. This method shows where the higher number of seniors are. This map shows a pretty equal distribution across the map, except for the heavy concentration in the North East portion.
the quantile method breaks down the data into classes with an equal number of units in each category. This method made it look like there were a higher concentration of seniors throughout the county (in the non-normalized map), but, again, there is a higher concentration in the North East.
The standard deviation method breaks the data into categories based on how many standard deviations the data set lies from the mean. It is clear from this map that the North East portion of the map lies the farthest from the mean.
The natural break method groups the data into natural classes based on the data distribution. grouping similar values together. This map looked similar to the quantile method map.
All of the maps that I compiled showed the same data, in one level of clarity or another: the North East tracts have the higher concentration of seniors.

Tuesday, February 21, 2017

Western Europe Weather Monitoring Stations


The purpose of this week's map was to determine which areas in Western Europe should issue a freeze warning. Using ArcMap, I symbolized my data points based on temperature and added a circle of directional distribution.
To do much of the data analysis in this laboratory, we utilized the Geostatistical Analyst toolbar. The toolbar supplies cartographers with several helpful data analysis resources, including a histogram builder and a QQ Plot generator. The two stars represent the mean and median centers of my data.  You can see that the median center is further south than the mean center, because all of the higher temperatures on the western side of the map weigh the mean center to the west.
Pian Rosa was the coldest data point in this data set.