Demographics details for Berlin, NJ vs Mountain pine, AR
Population Overview
Compare main population characteristics in Berlin, NJ vs Mountain pine, AR.
| Data | Berlin | Mountain pine |
|---|---|---|
| Population | 7,506 | 577 |
| Median Age | 40.4 years | 44.2 years |
| Median Income | $98,706 | $28,542 |
| Married Families | 37.0% | 28.0% |
| Poverty Level | 5% | 20% |
| Unemployment Rate | 3.2 | 4.5 |
Population Comparison: Berlin vs Mountain pine
- In Berlin, the population is higher at 7,506, compared to 577 in Mountain pine.
- The median age in Mountain pine is higher at 44.2 years, compared to 40.4 years in Berlin.
- Berlin has a higher median income of $98,706 compared to $28,542 in Mountain pine.
- A higher percentage of married families is found in Berlin at 37.0% compared to 28.0% in Mountain pine.
- The poverty level is higher in Mountain pine at 20%, compared to 5% in Berlin.
- Mountain pine has a higher unemployment rate at 4.5% compared to 3.2% in Berlin.
Demographics
Demographics Berlin vs Mountain pine provide insight into the diversity of the communities to compare.
| Demographic | Berlin | Mountain pine |
|---|---|---|
| Black | 12 | 8 |
| White | 76 | 85 |
| Asian | 1 | Data is updating |
| Hispanic | 5 | 2 |
| Two or More Races | 6 | 5 |
| American Indian | Data is updating | Data is updating |
Demographics Comparison: Berlin vs Mountain pine
- A higher percentage of Black residents are in Berlin at 12% compared to 8% in Mountain pine.
- The percentage of White residents is higher in Mountain pine at 85% compared to 76% in Berlin.
- The Asian population is larger in Berlin at 1% compared to 0% in Mountain pine.
- The Hispanic community is larger in Berlin at 5% compared to 2% in Mountain pine.
- More residents identify as two or more races in Berlin at 6% compared to 5% in Mountain pine.
- The percentage of American Indian residents is the same in both Berlin and Mountain pine at 0%.
Health Statistics
The health statistics provide insights into prevalent health conditions in two communities.
| Health Metric | Berlin | Mountain pine |
|---|---|---|
| Mental Health Not Good | 15.9% | 20.8% |
| Physical Health Not Good | 9.9% | 14.8% |
| Depression | 23.8% | 26.8% |
| Smoking | 14.6% | 24.8% |
| Binge Drinking | 19.5% | 14.8% |
| Obesity | 28.0% | 39.5% |
| Disability Percentage | 13.0% | 23.0% |
Health Statistics Comparison: Berlin vs Mountain pine
- In Mountain pine, a higher percentage report poor mental health at 20.8% compared to 15.9% in Berlin.
- Higher depression rates are seen in Mountain pine at 26.8% versus 23.8% in Berlin.
- Mountain pine has a higher smoking rate at 24.8% compared to 14.6% in Berlin.
- Binge drinking is more common in Berlin at 19.5% compared to 14.8% in Mountain pine.
- Mountain pine has higher obesity rates at 39.5% compared to 28.0% in Berlin.
- There is a higher percentage of disabled individuals in Mountain pine at 23.0% compared to 13.0% in Berlin.
Education Levels
The educational attainment in the area helps gauge the workforce's skill level and economic potential.
| Education Level | Berlin | Mountain pine |
|---|---|---|
| No Schooling | 0.4% (27) | 0.0% (Data is updating) |
| High School Diploma | 17.0% (1,279) | 30.8% (178) |
| Less than High School | 8.2% (612) | 9.4% (54) |
| Bachelor's Degree and Higher | 22.7% (1,706) | 2.9% (17) |
Education Levels Comparison: Berlin vs Mountain pine
- A higher percentage of residents in Berlin have no formal schooling at 0.4% compared to 0.0% in Mountain pine.
- In Mountain pine, the rate of residents with high school diplomas is higher at 30.8% compared to 17.0% in Berlin.
- The percentage of residents with less than a high school education is higher in Mountain pine at 9.4%, compared to 8.2% in Berlin.
- A higher percentage of residents in Berlin hold a bachelor's degree or higher at 22.7% compared to 2.9% in Mountain pine.
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.