Mapping Environmental Justice

Environmental justice is a relatively new concept that was coined in the 1980s. It combines demographics with environmental factors. GIS can better illuminate the environmental justices or injustices in areas through the comparison and illustration of data.


Above is what could be considered a “story map” of environmental justice in three cities: Atlanta, Charlotte, and Richmond. Story maps are an interesting facet of geography in the digital world. Story maps tell a story through media, in this case video, which helps relate maps with other geographic elements. other and an overarching narrative. Story maps can make maps and geographic concepts accessible which may not be apparent outside a guided narrative.

The video above focuses on the environmental comparison of three cities. It’s interesting to see the concept of environmental justice present in all three cities. In each city there seems to be three types of areas. There is a collection of low-income population areas, which may or may not be contiguous, where environmental factors are the worse. There seems to be areas that doesn’t classify as “low-income” but serve as spillover areas for undesirable environmental factors. For example, the southeast part of Atlanta isn’t classified as “low-income” but it suffers almost as much from the detrimental environmental factors like the amount of diesel particulate matter in the air. These areas could be classified as not low-income but not high-income enough to be exempt from detrimental environmental factors.

And, of course, there are the high-income areas. These areas are spared from all the damaging aspects of environmental pollution. The core of the environmental justice problem arises from this perceived injustice. The argument is that even though the high-income areas might mitigate negative environmental factors through paying higher prices for property, both through taxes and the bottom line of a sale, it is unjust to deny the same environmental consciousness to the lower and mid-income areas. This is operating under the assumption that a quality environment, especially where negative effects are mitigable, is a human right and, when denied, is injust on a humanitarian level.

Traffic is an interesting factor to consider. At first thought, one might not think this is a factor that could be manipulated to fall within this income/environmental schema. Imagine an airport, it is constructed and the value of the residential properties within earshot plummet because of the noise pollution. Considering this evolution of value, we could assume roads follow a similar pattern. However, noise pollution from traffic has several elements that can be employed to either reduce the associate noise pollution or reduce the volume of traffic. Sound barriers can be used to limit the amount of noise that reaches residential properties. These also serve an aesthetic purpose and, while not being a designers first choice, can help dress up a property, thus further increasing its value, creating a positive feedback loop that is not present in less fortunate properties. You’ll likely find these sound barriers in high-income areas. This is not because the desire isn’t there for the low-income residential areas. It’s because the financial incentive isn’t there.

The use of alternative transportation methods alleviates these traffic problems for a price. Implementation of bike-friendly infrastructure reduce the volume of traffic but may require expanding and repaving roads, reducing speed limits, adding sidewalks, and developing areas along roads to accommodate bike paths and bike lanes. This brings us back to our original investment problem. What is the worth of implementing the projects if there is no return? Should bike-friendly infrastructure be legislated as a human right? Pedestrian traffic elements like footpaths, elevated footpaths, trails, and greenways follow the same logic and would provide similar results. If a neighborhood’s local amenities are easily accessible by foot it reduces the amount of local traffic. HOV lanes and carpooling elements follow the same logic as well.

Lead paint is another element that contains some interesting implications. Two important legislative measures have reduced the amount of lead in paint used in commercial and residential applications in the 1970s and 80s. A younger city like Charlotte shows a lower concentration of lead paint compared to a city like Atlanta. Charlotte’s construction boom was in the late 80s and 90s, a time where the widespread use of lead paint was discouraged by legislation. Richmond and Atlanta, however, developed during a time before this preventative legislation and the higher amounts of lead paint cooberate this.

Should these environmental factors be legislated? Should they be retroactive? Should they apply for new developments only? Should the free market continue to dictate the environmental quality of neighborhoods? These are all questions that environmental justice initiatives seek to address. Again, data is the light in the darkness to illuminate these issues and cartography is a medium that can present it to the minds that may one day solve it.

Working with bathymetry, rasters, and environmental models

Working with Arcmap is proving to be astutely rewarding. The more capabilities of the program I become familiar the more the scale of the possible projects becomes apparent. The critical cartographic element is also becoming more obvious. Data on a map is only useful if it’s presented in a legible and easily interpretable manner. Avoiding convolution is something I wanted to include in all my maps.


This map shows elevation both above and below sea level. The sound is a deep blue to represent its depth under sea level. The lighter blues and teals represent waterways that are more shallow compared to the sound. If I could redo the color scheme on the map I would make sea level a more neutral color and exaggerate the blues to make identifying values below sea level easier.  An interesting design choice was the use of the map inset which magnifies the city of Seattle. All of this was done within the Arcmap software using the draw tool. Although it’s not as functional as something like Photoshop, it still allows provides some editing and design functionality. If I were to redesign I would enlarge the insert so the canopy of the city would be better visible, possibly to the point where the individual buildings could be picked out.

The use of color really makes this map stand out. Normally muted color would be my go to schema but this seems to work fine against the light grey background. I’m starting to enjoying the design element of putting maps together. The inset was a unique way to incorporate the second map of seattle  I’d like to use Photoshop for post production to really create some design elements that map be limited by Arcmap.


This project concentrated on identifying habitable areas for the wood turtle in Keene, New Hampshire. Working with this data was interesting because it was a land use raster of the entire state of New Hampshire format of. Arcmap’s robust toolkit gives you many options to achieve a certain result.

To begin I clipped the raster data to the Keene city limits using a provided shapefile. I then used the “build raster attribute table” tool to make the raster’s attribute table editable. After starting the editor, a field was added in the attribute table to indicate suitability of each different category of land use. A “0” indicated uninhabitable land and a “1” indicated inhabitable land. The raster was then exported to a shapefile, symbology adjust, and geometry calculated. The final product is the map above.

The next part of the assignment was to find the amount of land in square feet and square miles that was both inhabitable and uninhabitable. Since the information in the attribute table, labeled “count”, did not have a unit of measurement assigned to it, I felt like getting creative. Wikipedia states Keene, NH is 37.5 square miles. In total, the count for both habitable and uninhabitable land was 118,922 unknown units. Using the statistics option in the attribute table, the count units can be separated by the uninhabitable (38244) and habitable (80678). Using some simple math we can calculate the percentage of the count that is uninhabitable:

38244 / 118922 = 0.32158…

We can then turn around and multiple our earlier measurement of 37.5 square miles for Keene:

0.32158… * 37.5 = 12.05…

This gives us our 12.05 square miles uninhabitable. Doing the same calculation with the 80678 count will give us the habitable square mileage. Finding the square footage is just a simple conversion from there.

While this might be the “long route” to achieving these measurements I believe it is important to do this kind of exploration to really get an understanding of these concepts and the toolkits that are associated with them. It definitely makes the user appreciate the shortcuts more when the longer route is typically taken.