Dominion Energy’s Proposed Buckingham Compressor & Community Survey Locations overlaid to Virginia Address Points | Mapped by Author.

Dominion Energy & Environmental Racism: a case study in how to lie with maps

Yes, the title is provocative, but its not entirely mine. I simply and liberally borrow from the classic Mark Monmonier primer entitled How to Lie with Maps. But the reality of this ‘case study’ is indeed provocative, and it amounts to nothing less than outright environmental racism under the direction of ‘one of the nation’s largest producers and transporters of energy’, Dominion Energy.

ESRI Demographic and Income Profile Report dated 11/21/2018.
  • Second, census data is always an aggregated product, meaning it is a collection of statistics about a population across geography that typifies that population, not individuals per se. In other words, across distances of census block groups, tracts, counties, states, ect. the census data is organized in such a way as to mask individuals in order to get to a summary of all the individuals in the particular census geography. In the end, census data results in approximations of the population; it doesn’t claim locational precision at the household or individual level. And importantly, in more rural locations such as Buckingham County generally, it spans across relatively large geographies. This can, and does indeed, cause issues when typifying large scale local geographies because the summation at the aggregated census unit stands in for the local dynamics of a large scale local geography.
  • Third, in using a census input for a custom point — in this case the proposed compressor — a unique geographical unit must be created — a .5 mile, 1 mile and 2 mile radius. This unique geography then has to be overlaid to the census geography and the data from the census has to be ‘extracted’ proportionately into the unique geography — .5 mile, 1 mile and 2 miles in this case. Its this ‘extraction’ process — known technically in GIS as apportioning or apportionment that is at play. This is usually acceptable in dense urban areas where the census geographies are ‘tight’ but in more rural locations the aggregation can really produce disastrous results, as has happened here. And this is the BIG lie. That Dominion hasn’t acknowledged this and rather utilizes the implied authority of ESRI and a local university to ‘validate’ inappropriate analysis — that is bad and its literally traumatizing people. This is not an exaggeration; people in Union Hill are very upset and deeply offended by the results of the ESRI profile, in particular.
  • A quick sidenote: the analysis point that was utilized in the Dominion Analysis is incorrect. I’ve utilized the incorrect location (approx. 650 feet southeast in the middle of the road) as the analysis point for consistency.
  • The analysis geometries are recreated at .5, 1 and 2 miles.
  • Geographical apportionment is conducted using the census data as the attribute input data. In the process, the correct proportions of the analysis geometries relative to the whole of the census geometries are determined, and then by that same factor the attribute data is proportioned to the analysis geometries. In effect, the aggregated census data is being ‘weighted’ — just like teachers weight grades — but here the ‘weight’ is the area of the analysis geometry as it overlays to the various census geometries.
  • The attribute data is then collected into a new table and mapped and labeled on top of each analysis geometry.
0.5 Mile Proximity Apportionment with Input Census Tract Boundaries Shown | Mapped by Author.
1 Mile Proximity Apportionment with Input Census Tract Boundaries Shown | Mapped by Author.
2 Mile Proximity Apportionment with Input Census Tract Boundaries Shown | Mapped by Author.

GIS Analyst & Instructor | Shale Gas Impacts, Environmental Justice & Climate Change Issues