Module 6: Isarithmic Mapping

 Module 6: Isarithmic Mapping

This weeks module the focus is on isarithmic mapping and how to present the data for the map reader to understand without much explanation. The isarithmic map is probably the most widely used thematic mapping method, and is also one of the oldest techniques, dating back to the 18th century, after the choropleth map. Isometric maps depict smooth, continuous phenomena, such as rainfall, barometric pressure, and topography or elevation. The most common form are contour lines.

The isarithmic map below is showing the annual precipitation over a 30-year period (1981-2010) by different techniques of continuous tone, hypsometric tinting, and contours lines. When making the map the first thing that needed to be done is creating the continuous tone. The continuous tone symbology refers to the representation of data values using a continuous range of colors or shades. The way this is implemented in the map is the symbology is of the average annual precipitation across the state of Washington for the years of 1981-2010. Also, on the continuous tone map we added in the hillshade effect to indicate the steep areas more effectively. This is important because we are able to show a 3-D view of the more mountainous areas compared to the flatter areas. Hypsometric tinting uses colors to represent elevation zones across the map. This was implemented on the map by using the data of the annual precipitation and changing the color ranges with equal to or less than 10 being red (lowest number) to greater than 180 (highest number). Contour lines are also a useful surface representation, because they allow you to simultaneously visualize flat and steep areas by the distance between contours, as well as and ridges and valleys by converging and diverging polylines. When using data it is important to understand where it is coming from and how it is collected. The way the precipitation data was derived and interpolated through PRISM, which is an analytical model that uses point data. Also, has an underlying grid of digital elevation model (DEM). This was used to generate a 30-year climate average through the years of 1981-2010 to get gridded estimates of monthly and yearly precipitation and temperature at 800 meters spacing. Lastly, on this map there is a paragraph to help the map reader understand how the information was collected and interpolated for the isarithmic map.

Module 5: Choropleth and Proportional Symbol Mapping

 Module 5: Choropleth and Proportional Symbol Mapping

In this weeks module we learned about choropleth map, proportional symbols, and graduated symbols. Also, we learned about different projections for the choropleth map and which ones would be the best to not cause any distortions on the map. Lastly, we learned the best way to present the data so that all map readers could understand and see the map.

Choropleth maps are a thematic map in which enumeration units are shaded by intensity proportional to the data values associated with those units. The reason one would use a choropleth map is when data is distributed evenly within a boundary. Also, when doing a choropleth map the best way to use it is with standardized data that can be represented easily. One limitation with a choropleth map is that there is no variation within one enumeration unit. When doing a choropleth map it is important to look at the data that is given to make sure there are no outliers that could skew the data to one way or another. At the same time, the cartographer needs to know if they want to class the data and which classification they would like to use. Another aspect of the choropleth map is the color scheme and which one to use. Cynthia Brewer, the creator of ColorBrewer, wanted to figure out color schemes and how they are interpreted. Also, she talked about how data presented determines color schemes, such as unipolar data, bipolar data, and balanced data. Unipolar data is where data has no natural dividing points and should be represented by sequential steps in lightness or a sequential scheme. Bipolar data is data that does have a natural diving point and would use a diverging color scheme. This is where two sequential themes running end to end with the lightest color in the middle. Balanced data is data that represents two complementary coexisting phenomenon with a bivariate or diverging color scheme should be used. Another important aspect when thinking about what color to use for choropleth maps is about people who are colorblind. Even though they make up 4% of the population it helps the cartographer remember that using colors of red or green could make it hard for the map reader to know what the data is indicating. The best projection to use for choropleth maps is Albers because it makes sure the geographic region is not distorted. Lastly, when making a choropleth map the legend is important to show how the data is being represented. When looking at the map the cartographer needs to figure out if the legend needs to be horizontal or vertical. Also, the legend needs to contiguous as opposed to being separated by spaces. 

The next portion of the module is presenting data either through proportional or graduated symbols. The symbols are a method of mapping that uses visuals of sizes to represent differences in the data. The differences between the two symbols are either class or unclassed data. Graduated symbols is when you class your data. Whereas, proportional symbols is when the data is unclassed and the symbols are proportional to the values of the attribute being mapped. There are different types of symbols that can be used on proportional maps. The first type is geometric symbol (i.e. circle, square, triangle) and is most commonly used. Another type is pictograph symbol where the cartographer uses an actual picture to represent the data but can be hard to interpret on the map by size. The third type is a column map where the height of each column is proportional to the data values, but it can be hard to see the smaller values within the map. A final type is 3D symbol that can be eye-catching but hard for map readers to gauge the symbol size and value. When creating proportional maps the most common issue is symbol overlapping. Making sure there is not too much overlap or too little overlap is important to make sure data values are on the map. One way this can be done is by making the symbols either transparent or opaque. Transparent symbols make it possible to see through overlapping symbols. Whereas, opaque symbols can enhance figure ground contrast and make the symbols appear "above" other map information. Lastly, if an area is overcrowded the cartographer could use an inset map to get closer to the data values and help with overlap.

In this weeks map we used ArcGIS Pro to create a choropleth and proportional map on European population density and wine consumption. In the choropleth portion of the map I took the information that was given above to make the map in a way for the map reader to understand the data. First it was important to look at the map projection which was Albers. As stated earlier Albers is the best projection because it makes sure the geographic region is not distorted. Next I looked at the data to see if there were any outliers that would skew the data to one way or another. When trying to show the population density it is important to make sure the data is not raw but standardized because it takes into account square kilometers to the amount of people who live there. An example would be how Russia is massive but there might not be as many people per square kilometer. Another aspect I looked at was the color scheme to use which was a gradual light to dark blue. The reason I used blue was because it was a color many people liked and people who are colorblind are able to see the changes in color with less issues compared to red or green. Next when looking at the legend it was important to make sure the classes were not separated so the map reader to see the gradual change better. Lastly, I did an inset map because there was a portion that had a lot of circles overlapping which made it hard to see the countries and to know where the circles belonged on the map.

On the other hand, the proportional portion on the map is looking at the wine consumption of liters per capita. The method that I chose was graduated because it seemed to show the differences between the countries and wine consumption better than the proportional. Also, I chose graduated because there were ranges that could be shown in the legend unlike proportional where it is just the highest number of the range. Lastly, I chose graduated because you can do different classification methods to see the changes in wine consumption. I decided to use the green circle because it is easier to see on the blue background then other colors. In this legend I did smaller to bigger vertically because it is similar to the smaller numbers being up top for the population density. At the same time, there is map elements that needed to be put on the map such as the title, scale bar, north arrow, and map information. All of this is necessary because it helps the map reader to know what the map is showing, the size of the countries, which way is north, and who the cartographer is, the date it was created, the source of where the material came from, and the map projection. A new aspect was creating a subtext of the information that is shown on the map. Such as the lowest and highest amount of wine consumption within the countries. A subtext is able to give more information without overpowering the map reader with all the information that is given on the map.

While doing this module it took longer to get all the information on the map and make it look good to the map reader. When trying to get a lot of information across it is a fine line on knowing what needs to be said and what needs to be omitted. Also, when making maps it is important to note that not everyone will not like the map and try to make it to the masses. Lastly, before creating any map it is necessary to look at the data to figure out the best way represent the data to the map reader.

Module 4: Data Classification

Module 4: Data Classification

In this weeks lab the focus was on the different data classification methods that can be used to represent data that are given to cartographers. The four classification methods that are common to use in ArcGIS Pro are Equal Interval, Quantile, Standard Deviation, and Natural Break. The Equal Interval classification method represents an equal amount of data values. The problem with the method is that there could be a class that contains no values and could be left blank. The Quantile classification method will never have empty classes or classes with few values. This method is good only if there are no outliers that can skew the data in one way or another. The Standard Deviation classification method is formed by adding/subtracting the standard deviation from the mean of your dataset. It is important to note that identical values cannot be in two different classes. A major disadvantage is that the data needs to be normally distributed to use the method. If the data is not normally distributed there could be classes that contain no values. The Natural Break classification method considers the natural groups within the dataset. Also, the method minimizes the differences between data values in the same class and maximizes the difference between classes. It is important to note that the method does consider outliers within the data and will place them in their own category. At the same time, it places clusters of data in one or more classes. Lastly, class breaks are up to the mapmaker with can make the map more subjective. Each one the classification methods have their advantages and disadvantages that the cartographer needs to make sure they are aware before representing the data.

There were two types of maps that are being represented in this weeks lab using the different classification methods. The first map is the percentage of people 65 and above in each of the census tracts. The classification method that best displays the data for an audience looking for is equal interval. The reason is because the classes are equal in their representation of the data. Also, it makes sure the outliers are not skewing the data in one way or another. Even though there are classes that do not have any data being represented it is still easy for the map reader to know what the census tracts represent. Lastly, the data is able to target the senior citizen population that has the highest amount of population in the census tracts.

The second map that is being represented is the population count normalized by area. This map is able to represent the actual numbers of the people 65 and above along with the area of the census tracts. When looking at the data being presented the map that accurately depicts the distribution of senior citizens is the Natural Breaks from the population count normalized by area. The reason for this is because it gives actual numbers and not a percentage of the population. Being able to see the actual numbers gives a better representation of the distribution of senior citizens. One potential issue of the other presentation method is that it is a percentage of the senior citizens compared to other age groups and not actual numbers. The numbers of senior citizens helps the map reader know how many actual people are in the census tract. Another potential issue is that the percentages are rounded to the second decimal point whereas the normalized group is either rounded to the first decimal point or a whole number. The last potential issue is that the numbers are based off the square miles of the census tracts and not just the percentage of the age group. Having the number relative to the area of the census tracts gives a better representation.

Data can be represented in many different ways and it is up to the cartographer to make sure it is being represented properly. The data can be represented by different classification methods such as Equal Interval, Natural Breaks, Quantile, or Standard Deviation. The cartographer has to make sure the data is not going to confuse the map reader or give them false interpretation of the data. Lastly, the cartographer needs to look at all the data before it is being represented to make sure they are not interpreting the information correctly.

Module 3: Cartographic Design

 Module 3: Cartographic Design

This weeks module is about doing the cartographic design and using Gesalt's Principles of Visual Hierarchy, Contrast, Figure Ground, and Balance. Visual hierarchy also uses the intellectual hierarchy of thematic maps which is starts with: Thematic symbols and type labels that are directly related to the theme; Title, subtitle, and legend; Base information such as boundaries, roads, place-names and so on; Scale and north arrow; Data source and notes; and Frame and neat lines. The next Gesalt Principle Contrast which is the visual differences between map features that can be distinguished. Another Gesalt Principle Figure Ground is the method of accentuating certain chosen objects over other by making the chosen objects appear closer to the map user. Lastly, the Gesalt Principle Balance is the organization of the map elements of empty space that results in visual harmony and equilibrium. Being able to use these principles helps make your map look the best it can be for the viewer. Also, it makes sure the cartographer is constantly working for the best map. 

This weeks map focuses on Ward 7 public schools in Washington D.C. and using the Gesalt's Principles of Visual Hierarchy, Contrast, Figure Ground, and Balance. The way I implemented visual hierarchy in my map was by making sure the symbols were obvious in the map and that you knew what the map was trying to represent. Having the title be bigger but not the main focus is helpful to give the viewer an idea on what I am representing. Also, when trying to represent the roads I made the everyday roads a subdued color so you can see them but not obvious. Whereas the interstates and the state highway I made sure to add the label so the viewer can see where they are in the world. Lastly, I put the north arrow, scale bar, and data source at the bottom of the map. The reason I did that is so the viewer can see it easily but not be in the way of what the map is showing the viewer. The way I achieved adequate contrast in my map was by making the schools the same color but different sizes. I made the elementary school smaller and got progressively bigger when looking at the middle school and high school features. Also, I made sure the area of Ward 7 was a different color to show the exact location of what is being represented. When doing the neighborhoods I made them 10-point size and the same font as the rest of the map. This helped show neighborhoods but not be in the way of the rest of the map. Lastly, having the roads be a lighter color but easily seen shows the landscape but does not cause too much of a jumble on the map. The way I established figure-ground relationship in my map was by making the area of Ward 7 lighter than the rest of the map to show what I am trying to represent. Also, I made the schools purple to show the exact feature I am trying to represent. I made them the pinpoint because I thought it showed the exact location of the feature better than the schools over an area. Also, I made the Anacostia River a lighter blue with a darker blue outline. This is able to show the distinction between Washington D.C. and Ward 7. The way incorporated balance was by putting the legend in the top right, so it was obvious what is being represented on the map. At first, I had put the inset map on the bottom right, but the background of the inset map and the area were the same, so it was hard to tell what the map was showing. Then I moved the inset map on the top left to show you can see the contrast of the inset map and the map. Lastly, I put the north arrow, scale bar, and source data at the bottom right of the map to cover the area but not crowding the area. There was still some open area, but it is okay since it is not completely blank.