03 Process: Accessing APIs, geocoding data, then mapping data in ArcGIS

Accessing APIs

Using EIA’s web data tools, I queried data including average electricity price (cents per kilowatt hour (kWh)) by state from 2020-2025. I downloaded the dataset and performed initial data cleaning (such as cleaning up column names and ensuring state name formatting was consistent) in Google Sheets.

Geocoding data & determining price volatility

Next, I loaded the dataset into R to geocode it using libraries including tidyverse, sf, and tigris. Because the dataset gave me average price by state per year, I created a computed column that calculated price volatility, that is, the standard deviation of the average energy price for each state — the volatility values are in cents per kWh and represent how much a state’s electricity price typically deviated from its own average across 2020-2025. I decided to visualize this to add a layer of depth to simple average price data, offering further context around rising prices.

Mapping in ArcGIS

With my newly geocoded dataset, I created individual maps of average electricity price by state for each year 2020-2025. Then, I created a separate map for electricity price volatility. The final visual consists of price volatility as the main map, and average price maps for each year as an inset.

Color

Color is used to visualize the price variations across the US. 

• Price Volatility: The price volatility map uses a red color scheme – lighter shades of red represent lower volatility, darker shades represent higher volatility.

• Average Price: Average price maps use a yellow to brown color scheme. Lighter shades represent lower prices, darker shades represent higher prices.

• Base Map: The map color schemes are visually contrasted with a dark grey base map.

• Text: All map text is white to stand out from the dark background.