Cartography Portfolio

Map Description

My final layout includes a title, subtitle, neat line, legend, inset map, north arrow, scale bar, spatial reference information, and an explanatory blurb that briefly describes the purpose, relevance, and results of the map. The area of interest is framed by the title and subtitle at the top, legend and blurb to the left, neat line to the right, and the inset map, scale bar, credits, and north arrow to the bottom. This framing also balances the layout, since the bulk of the layout’s visual weight is the area of interest at the middle of the layout. The area of interest also employs a drop shadow to establish a figure ground, pushing it toward the top of the visual hierarchy, with one caveat. The City Hall label is the peak of the hierarchy due to its black lettering and semitransparent white halo that contrasts with the dark gray choropleth coloring. Below the area of interest rests the inset map due to its dark grey color and layout-matching, off-white basemap. This level of the hierarchy also includes the legend, due to the bivariate choropleth patch containing both dark and light colors. I would say that the legend may even be below the inset map in the hierarchy due to the semitransparent white background. Similarly, the title, subtitle, and blurb rest below the legend and inset map in the hierarchy due to their use of 80% gray coloring and semitransparent white halos. I think the halos minimize contrast between the lettering and the basemap, helping to create a seamless flow to the layout. At the bottom of the hierarchy we see the interstate and waterway labels as well as the literature credits in the bottom left, and the north arrow and map credits in the bottom right. Hierarchy is also established among the percent tree cover and percent impervious surfaces through the use of graduated colors to represent different levels of percent cover. I used ColorBrewer to set this color scheme by setting the tool to 7 classes diverging between brown and green, and using the lighter green colors. I juxtaposed the green with shades of gray provided by ESRI to represent impervious surfaces like paved roads, sidewalks, and parking lots. Hierarchy is achieved among the waterway labels through different size labels to represent the size of the respective feature. Contrast is achieved through use of semitransparent halos and backgrounds, as well as the bright green firefly symbology used to represent the area of interest within the inset map.

The data shows a concentration of population density and impervious surfaces, as well as a significant lack of tree cover, in the downtown area surrounding City Hall and the Northwest Harbor. The clustering of dots laid over the dark gray choropleth symbology signifies the population living in downtown Baltimore is vulnerable to heat exposure resulting from an imbalance between impervious surfaces and tree cover, affecting a significant proportion of the city’s population. Consequently, the areas surrounding City Hall, primarily to the north and east of the building, are excellent candidate locations for installing new green spaces. What’s more is that this area also has the highest concentration of transportation infrastructure, noted by the presence of interstates in downtown Baltimore that is disproportionate to other areas of the city. Considering that the city is pretty well-established by now, I would recommend revitalizing existing green spaces by planting new vegetation (which could be a community-building activity) in existing parks, installing green sidewalks, or building rooftop and/or community gardens.

Challenges:

There were a couple of small challenges I faced that I was able to solve with graphic design techniques. In an earlier draft, my legend represented population dot density, urban cover bivariate choropleth, city government points, and interstate shapefiles. The symbol patches for my dot density and City Hall layers were illegible and almost unrecognizable due to their tiny size. This affected the overall formatting of the legend, making the organization of the overall layout a tad more challenging than it needed to be. So, I removed the City Hall and interstate symbologies from the legend to free up more room. I decided that labels would be sufficient to relay the information that each symbology represents, especially if I used clear verbiage like the “I-“ to indicate Interstate-Number. I used this opportunity to make City Hall the peak of my hierarchy and the central focus of the map as well. This left me with only the dot density and bivariate choropleth symbologies in my legend, but the dot density was still small and nearly illegible. My original plan to fix this was to simply zoom in on the symbol and take a screenshot to place over the patch in the legend. This worked fine, but it would have added a couple of steps every time I wanted to resize or move the legend. I ultimately converted the legend to a graphic, which allowed me more freedom to play with the size, shape, and position of the legend and its contents. This allowed me to reformat the dot density patch to a larger size without impacting the rest of the legend contents. It also allowed me to reformat the legend to follow the classic standard of reading left to right and top to bottom. I did this by moving the dot density legend information to the top of the legend. I placed the reformatted dot patch to the left of its descriptions, but between the two pieces of information to create some sense of continuity that tied all three elements together. Converting the legend to a graphic also allowed me to reformat the bivariate choropleth legend patch to make the “High Low” labels legible. Before the conversion they were quite crowded and would only become legible when resizing the entire legend. From there, I was able to reformat each patch for percent tree canopy and percent impervious surfaces (the 3-color graduated colors, not the 9-color matrix). Before the conversion, it was nearly impossible to distinguish the lightest color in the color scheme. I was able to fix this by placing a very thin (0.1 pt) outline around the path that helped differentiate the colors through a very small instant of contrast.

Assignment Objectives

This lab assignment tasked us with developing a map showing European population density and wine consumption for the year 2012, using choropleth and either graduated or proportional symbology.

Process Summary

1: What projection is in use?

The Europe Albers Equal Area Conic is being used.

2: Explain why this projection would be good to use for choropleth mapping, especially on the continental scale?

The relative size of landmasses and area is relevant to the data, so we need to use an equal area projection. The conic shape of this projection minimizes possible distortion near the poles through its use of two standard parallels and the conic axis aligned with the planet’s polar axis. This makes it good for regional, mid-latitude maps and those at a continental scale. The Albers Equal Area Conic is also an equivalent projection, making it good for mapping areas that are to be compared. This ties in nicely with the goal of choropleth mapping, which is to compare different enumeration units like cities, counties, or countries. Also, we want to use an equal area projection to maintain relative size when we compare population density data, which is normalized by area. This is a good projection to use for making a choropleth map of Europe because its characteristics support the map’s purpose.

3: Why would we choose to map population density over raw population counts in the choropleth map?

Density measurements divide the raw population total for each country, which isn’t an area-based measurement, by the area of the country. This gives us a better idea of how much of the population is concentrated in each country. A choropleth map is a good choice to render this information because it can visually compare the difference in population density among countries.

4: What type of color scheme did you choose and why?

I chose a graduated color scheme because it is excellent at representing unipolar data through a series of progressively darker (or lighter) shades of the same color or scheme. This allows us to see the variation among the enumeration units and make comparisons.

5: What classification scheme did you choose and why?

I chose the Natural Breaks method because it adjusts intervals around natural clusters, maximizing homogeneity in each enumeration unit and each class. This will allow for minimal influence from outliers within each class. Also, having a manageable amount of classes, in this case, 5, to represent the data can make processing the map easier for the audience.

6: Why do we not need to normalize this data?

The data is already normalized (consumption per capita), so there is no need to normalize it further.

7: Why would you not employ the Flannery Compensation?

The Flannery Compensation is meant to be used as a scaling factor to correct the size of circle point features so that a graduated point symbol of circular shape could be easily understood by the audience. However, it fails to actually help those who already have poor size judgment by reducing the size variation between classes. It can also lead to the Ebbinghaus Illusion misleading the audience for the same reason. It’s not particularly useful in this case, so I won’t use it.

8: Which method did you choose – Graduated or Proportional? Why?

I chose a graduated symbology because it best reflects the nature of the data with less room for misinterpretation than a proportional symbology would, in this case. Proportional symbology is good to use when you need to know exact values of an enumeration unit and renders point symbols proportionate to the value of the point, whereas a graduated symbology classifies points into bins depending on the classification method. Applying a proportional symbology to this map would leave most of the map obscured by point symbols and negates the purpose of the map. Considering we don’t need to know the exact values of each enumeration unit, a graduated symbology is much more manageable.

9: Why is the projection information relevant to include on this map?

The map projection is important information that tells both the cartographer and the audience how to perceive the image they’re looking at. The purpose is to inform what area is being preserved, and how. They should also be able to recreate the map easily with the given information, thus, giving information about the projection is necessary.

The Module 4 lab tasked us with creating a map of neighborhoods and public schools throughout Washington, D.C.'s 7th Ward. I mapped the neighborhoods with a borderless polygon symbology to avoid too much intersection with the roads layer, and added a couple of extra neighborhood labels to maximize balance in the layout. I used a graduated symbology for the schools to represent the different education levels (Elementary, Middle, High).

This map shows geopolitical and environmental characteristics of the State of Florida, using data from the Florida Geospatial Open Data Portal. Much of the data was already wrangled when I loaded it to ArcGIS Pro. I tried establishing an even balance throughout the layout by including similarly-sized elements that are evenly spaced throughout the page, but it turned out to be too cluttered. The labels, titles, and legend should also be moved to optimize balance and reduce clutter.