USA.com / Michigan / Alger County / Au Train / 49806 / Census Tract 000200 / Block Group 000200-1 / Census Block 000200-1-101
Census Block 000200-1-101 in Alger County, Michigan
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Census Block 000200-1-101 Map, Border, and Nearby Locations
Population by Races
| White: | 13 (100.00%, see rank) |
| Black: | 0 (0.00%, see rank) |
| Hispanic: | 1 (7.69%, see rank) |
| Asian: | 0 (0.00%, see rank) |
| Native (American Indian, Alaska Native, Hawaiian Native, etc.): | 0 (0.00%, see rank) |
| One Race, Other: | 0 (0.00%, see rank) |
| Two or More Races: | 0 (0.00%, see rank) |
Population by Gender
Male: 7 (53.85%, see rank)
Females: 6 (46.15%, see rank)
Household and Family
| Census Block 000200-1-101 | % | |
| Total Households | 8 | 100% |
| Average Household Size | 1.63, see rank | - |
| 1 Person Households | 3 | 37.50%, see rank |
| 2 or More Person Households | 5 | 62.50%, see rank |
| Family Households (Families) | 5 | 62.50%, see rank |
| Average Family Size | 2.00, see rank | - |
| Married-Couple Family | 5 | 62.50%, see rank |
| Nonfamily Households | 3 | 37.50%, see rank |
Housing Occupancy
| Census Block 000200-1-101 | % | |
| Total Housing Units | 28 | 100% |
| Occupied Housing Units | 8 | 28.57%, see rank |
| Owner Occupied | 7 | 25.00%, see rank |
| Owner Occupied with Mortgage | 3 | 10.71% |
| Owner Occupied without Mortgage | 4 | 14.29% |
| Renter Occupied | 1 | 3.57%, see rank |
| Vacant Housing Units | 20 | 71.43%, see rank |
| For Rent | 1 | 3.57%, see rank |
| For Sale Only | 0 | 0.00%, see rank |
| Rented or Sold, Not Occupied | 0 | 0.00%, see rank |
| For Seasonal, Recreational, or Occasional Use | 19 | 67.86%, see rank |
| For Migrant Workers | 0 | 0.00%, see rank |
| Other Vacant | 0 | 0.00%, see rank |
*Based on 2010 data.
*A census block is the smallest geographic area defined by the United States Census Bureau to take census. In total, there are more than 10 million census blocks in the United Status with an average of around 30 residents in each. A census block normally only covers a street block or a small community. It is ideal for studying data relating to the smallest geographic area.