Demographics details for Big sky, MT vs Pittsburgh, PA
Population Overview
Compare main population characteristics in Big sky, MT vs Pittsburgh, PA.
| Data | Big sky | Pittsburgh |
|---|---|---|
| Population | 2,825 | 302,898 |
| Median Age | 37.2 years | 33.5 years |
| Median Income | $94,176 | $60,187 |
| Married Families | 43.0% | 26.0% |
| Poverty Level | 5% | 15% |
| Unemployment Rate | 3.5 | 3.4 |
Population Comparison: Big sky vs Pittsburgh
- The population in Pittsburgh is higher at 302,898, compared to 2,825 in Big sky.
- Residents in Big sky have a higher median age of 37.2 years compared to 33.5 years in Pittsburgh.
- Big sky has a higher median income of $94,176 compared to $60,187 in Pittsburgh.
- A higher percentage of married families is found in Big sky at 43.0% compared to 26.0% in Pittsburgh.
- The poverty level is higher in Pittsburgh at 15%, compared to 5% in Big sky.
- The unemployment rate in Big sky is higher at 3.5%, compared to 3.4% in Pittsburgh.
Demographics
Demographics Big sky vs Pittsburgh provide insight into the diversity of the communities to compare.
| Demographic | Big sky | Pittsburgh |
|---|---|---|
| Black | Data is updating | 23 |
| White | 85 | 62 |
| Asian | Data is updating | 6 |
| Hispanic | 12 | 4 |
| Two or More Races | 2 | 5 |
| American Indian | 1 | Data is updating |
Demographics Comparison: Big sky vs Pittsburgh
- In Pittsburgh, the percentage of Black residents is higher at 23% compared to 0% in Big sky.
- Big sky has a higher percentage of White residents at 85% compared to 62% in Pittsburgh.
- In Pittsburgh, the Asian population stands at 6%, greater than 0% in Big sky.
- The Hispanic community is larger in Big sky at 12% compared to 4% in Pittsburgh.
- The percentage of residents identifying as two or more races is higher in Pittsburgh at 5%, compared to 2% in Big sky.
- A greater percentage of American Indian residents live in Big sky at 1% compared to 0% in Pittsburgh.
Health Statistics
The health statistics provide insights into prevalent health conditions in two communities.
| Health Metric | Big sky | Pittsburgh |
|---|---|---|
| Mental Health Not Good | 12.8% | 16.3% |
| Physical Health Not Good | 8.0% | 11.7% |
| Depression | 20.6% | 21.1% |
| Smoking | 10.7% | 18.8% |
| Binge Drinking | 24.3% | 19.3% |
| Obesity | 24.1% | 35.0% |
| Disability Percentage | 5.0% | 14.0% |
Health Statistics Comparison: Big sky vs Pittsburgh
- In Pittsburgh, a higher percentage report poor mental health at 16.3% compared to 12.8% in Big sky.
- Higher depression rates are seen in Pittsburgh at 21.1% versus 20.6% in Big sky.
- Pittsburgh has a higher smoking rate at 18.8% compared to 10.7% in Big sky.
- Binge drinking is more common in Big sky at 24.3% compared to 19.3% in Pittsburgh.
- Pittsburgh has higher obesity rates at 35.0% compared to 24.1% in Big sky.
- There is a higher percentage of disabled individuals in Pittsburgh at 14.0% compared to 5.0% in Big sky.
Education Levels
The educational attainment in the area helps gauge the workforce's skill level and economic potential.
| Education Level | Big sky | Pittsburgh |
|---|---|---|
| No Schooling | 0.0% (Data is updating) | 0.6% (1,955) |
| High School Diploma | 10.1% (284) | 13.9% (42,015) |
| Less than High School | 4.2% (118) | 6.6% (20,087) |
| Bachelor's Degree and Higher | 44.6% (1,261) | 32.1% (97,219) |
Education Levels Comparison: Big sky vs Pittsburgh
- In Pittsburgh, a larger percentage of residents lack formal schooling at 0.6% compared to 0.0% in Big sky.
- In Pittsburgh, the rate of residents with high school diplomas is higher at 13.9% compared to 10.1% in Big sky.
- The percentage of residents with less than a high school education is higher in Pittsburgh at 6.6%, compared to 4.2% in Big sky.
- A higher percentage of residents in Big sky hold a bachelor's degree or higher at 44.6% compared to 32.1% in Pittsburgh.
Crime and Safety
Understanding crime rates and safety measures is crucial for assessing the livability of a city or town. Crime levels can vary significantly from one neighborhood to another, influenced by various factors such as population density and local amenities. For instance, areas with high foot traffic, like train stations, might experience different crime dynamics compared to quieter residential neighborhoods. Evaluating these patterns helps in making informed decisions about safety and community well-being.