57701 Zip Code Historical Type of Heating Fuel in a House Data
ACS 2010-2014 data
| 57701 Zip Code | South Dakota | U.S. |
Total Housing Units | 17,443, 100% | 327,101 | 116,211,092 |
Utility Gas | 11,347, 65.05%, see rank | 48.20% | 48.85% |
Bottled, Tank, or LP Gas | 331, 1.90%, see rank | 16.53% | 4.86% |
Electricity | 5,407, 31.00%, see rank | 28.34% | 36.68% |
Fuel Oil, Kerosene, etc. | 41, 0.24%, see rank | 2.70% | 5.86% |
Coal or Coke | 0, 0.00%, see rank | 0.08% | 0.12% |
Wood | 146, 0.84%, see rank | 1.94% | 2.12% |
Solar Energy | 0, 0.00%, see rank | 0.05% | 0.05% |
Other Fuel | 116, 0.67%, see rank | 1.52% | 0.47% |
No Fuel Used | 55, 0.32%, see rank | 0.63% | 1.00% |
ACS 2008-2012 data
| 57701 Zip Code | South Dakota | U.S. |
Total Housing Units | 17,612, 100% | 320,467 | 115,226,802 |
Utility Gas | 11,804, 67.02%, see rank | 49.23% | 49.42% |
Bottled, Tank, or LP Gas | 300, 1.70%, see rank | 17.01% | 5.03% |
Electricity | 4,975, 28.25%, see rank | 26.60% | 35.51% |
Fuel Oil, Kerosene, etc. | 19, 0.11%, see rank | 3.21% | 6.46% |
Coal or Coke | 0, 0.00%, see rank | 0.08% | 0.12% |
Wood | 185, 1.05%, see rank | 1.94% | 2.08% |
Solar Energy | 0, 0.00%, see rank | 0.04% | 0.04% |
Other Fuel | 213, 1.21%, see rank | 1.34% | 0.43% |
No Fuel Used | 116, 0.66%, see rank | 0.56% | 0.90% |
US Census 2000 data
| 57701 Zip Code | South Dakota | U.S. |
Total Housing Units | 15,638, 100% | 290,245 | 105,480,101 |
Utility Gas | 11,670, 74.63%, see rank | 48.34% | 51.22% |
Bottled, Tank, or LP Gas | 413, 2.64%, see rank | 21.69% | 6.52% |
Electricity | 3,005, 19.22%, see rank | 19.74% | 30.35% |
Fuel Oil, Kerosene, etc. | 55, 0.35%, see rank | 7.04% | 8.97% |
Coal or Coke | 22, 0.14%, see rank | 0.13% | 0.14% |
Wood | 120, 0.77%, see rank | 1.68% | 1.68% |
Solar Energy | 20, 0.13%, see rank | 0.03% | 0.04% |
Other Fuel | 207, 1.32%, see rank | 0.92% | 0.39% |
No Fuel Used | 126, 0.81%, see rank | 0.43% | 0.69% |
* ACS stands for U.S. Census American Community Survey. According to the U.S. Census, if the date is a range, you can interpret the data as an average of the period of time.