57702 Zip Code Historical Type of Heating Fuel in a House Data
ACS 2010-2014 data
| 57702 Zip Code | South Dakota | U.S. |
Total Housing Units | 14,034, 100% | 327,101 | 116,211,092 |
Utility Gas | 8,739, 62.27%, see rank | 48.20% | 48.85% |
Bottled, Tank, or LP Gas | 992, 7.07%, see rank | 16.53% | 4.86% |
Electricity | 3,738, 26.64%, see rank | 28.34% | 36.68% |
Fuel Oil, Kerosene, etc. | 49, 0.35%, see rank | 2.70% | 5.86% |
Coal or Coke | 0, 0.00%, see rank | 0.08% | 0.12% |
Wood | 269, 1.92%, see rank | 1.94% | 2.12% |
Solar Energy | 0, 0.00%, see rank | 0.05% | 0.05% |
Other Fuel | 160, 1.14%, see rank | 1.52% | 0.47% |
No Fuel Used | 87, 0.62%, see rank | 0.63% | 1.00% |
ACS 2008-2012 data
| 57702 Zip Code | South Dakota | U.S. |
Total Housing Units | 13,546, 100% | 320,467 | 115,226,802 |
Utility Gas | 8,810, 65.04%, see rank | 49.23% | 49.42% |
Bottled, Tank, or LP Gas | 948, 7.00%, see rank | 17.01% | 5.03% |
Electricity | 3,308, 24.42%, see rank | 26.60% | 35.51% |
Fuel Oil, Kerosene, etc. | 46, 0.34%, see rank | 3.21% | 6.46% |
Coal or Coke | 0, 0.00%, see rank | 0.08% | 0.12% |
Wood | 244, 1.80%, see rank | 1.94% | 2.08% |
Solar Energy | 0, 0.00%, see rank | 0.04% | 0.04% |
Other Fuel | 66, 0.49%, see rank | 1.34% | 0.43% |
No Fuel Used | 124, 0.92%, see rank | 0.56% | 0.90% |
US Census 2000 data
| 57702 Zip Code | South Dakota | U.S. |
Total Housing Units | 11,804, 100% | 290,245 | 105,480,101 |
Utility Gas | 7,319, 62.00%, see rank | 48.34% | 51.22% |
Bottled, Tank, or LP Gas | 1,069, 9.06%, see rank | 21.69% | 6.52% |
Electricity | 2,872, 24.33%, see rank | 19.74% | 30.35% |
Fuel Oil, Kerosene, etc. | 118, 1.00%, see rank | 7.04% | 8.97% |
Coal or Coke | 16, 0.14%, see rank | 0.13% | 0.14% |
Wood | 222, 1.88%, see rank | 1.68% | 1.68% |
Solar Energy | 6, 0.05%, see rank | 0.03% | 0.04% |
Other Fuel | 144, 1.22%, see rank | 0.92% | 0.39% |
No Fuel Used | 38, 0.32%, 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.