02909 Zip Code Historical Type of Heating Fuel in a House Data
ACS 2010-2014 data
| 02909 Zip Code | Rhode Island | U.S. |
Total Housing Units | 14,523, 100% | 409,569 | 116,211,092 |
Utility Gas | 11,004, 75.77%, see rank | 51.13% | 48.85% |
Bottled, Tank, or LP Gas | 253, 1.74%, see rank | 2.34% | 4.86% |
Electricity | 1,335, 9.19%, see rank | 9.15% | 36.68% |
Fuel Oil, Kerosene, etc. | 1,782, 12.27%, see rank | 34.82% | 5.86% |
Coal or Coke | 0, 0.00%, see rank | 0.11% | 0.12% |
Wood | 12, 0.08%, see rank | 1.75% | 2.12% |
Solar Energy | 0, 0.00%, see rank | 0.02% | 0.05% |
Other Fuel | 20, 0.14%, see rank | 0.42% | 0.47% |
No Fuel Used | 117, 0.81%, see rank | 0.26% | 1.00% |
ACS 2008-2012 data
| 02909 Zip Code | Rhode Island | U.S. |
Total Housing Units | 14,305, 100% | 410,639 | 115,226,802 |
Utility Gas | 10,575, 73.93%, see rank | 49.86% | 49.42% |
Bottled, Tank, or LP Gas | 191, 1.34%, see rank | 2.00% | 5.03% |
Electricity | 1,367, 9.56%, see rank | 8.60% | 35.51% |
Fuel Oil, Kerosene, etc. | 1,997, 13.96%, see rank | 37.38% | 6.46% |
Coal or Coke | 28, 0.20%, see rank | 0.13% | 0.12% |
Wood | 0, 0.00%, see rank | 1.49% | 2.08% |
Solar Energy | 0, 0.00%, see rank | 0.02% | 0.04% |
Other Fuel | 17, 0.12%, see rank | 0.30% | 0.43% |
No Fuel Used | 130, 0.91%, see rank | 0.23% | 0.90% |
US Census 2000 data
| 02909 Zip Code | Rhode Island | U.S. |
Total Housing Units | 14,575, 100% | 408,424 | 105,480,101 |
Utility Gas | 9,911, 68.00%, see rank | 46.31% | 51.22% |
Bottled, Tank, or LP Gas | 348, 2.39%, see rank | 2.51% | 6.52% |
Electricity | 1,229, 8.43%, see rank | 7.64% | 30.35% |
Fuel Oil, Kerosene, etc. | 2,990, 20.51%, see rank | 42.08% | 8.97% |
Coal or Coke | 7, 0.05%, see rank | 0.08% | 0.14% |
Wood | 0, 0.00%, see rank | 0.88% | 1.68% |
Solar Energy | 0, 0.00%, see rank | 0.03% | 0.04% |
Other Fuel | 72, 0.49%, see rank | 0.29% | 0.39% |
No Fuel Used | 18, 0.12%, see rank | 0.18% | 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.