Demographics details for Flower mound, TX vs Pekin, IL
Population Overview
Compare main population characteristics in Flower mound, TX vs Pekin, IL.
| Data | Flower mound | Pekin |
|---|---|---|
| Population | 78,486 | 31,260 |
| Median Age | 42.6 years | 41.2 years |
| Median Income | $154,471 | $52,507 |
| Married Families | 51.0% | 34.0% |
| Poverty Level | 4% | 12% |
| Unemployment Rate | 3.7 | 6.4 |
Population Comparison: Flower mound vs Pekin
- In Flower mound, the population is higher at 78,486, compared to 31,260 in Pekin.
- Residents in Flower mound have a higher median age of 42.6 years compared to 41.2 years in Pekin.
- Flower mound has a higher median income of $154,471 compared to $52,507 in Pekin.
- A higher percentage of married families is found in Flower mound at 51.0% compared to 34.0% in Pekin.
- The poverty level is higher in Pekin at 12%, compared to 4% in Flower mound.
- Pekin has a higher unemployment rate at 6.4% compared to 3.7% in Flower mound.
Demographics
Demographics Flower mound vs Pekin provide insight into the diversity of the communities to compare.
| Demographic | Flower mound | Pekin |
|---|---|---|
| Black | 3 | 3 |
| White | 66 | 89 |
| Asian | 13 | 1 |
| Hispanic | 11 | 3 |
| Two or More Races | 7 | 4 |
| American Indian | Data is updating | Data is updating |
Demographics Comparison: Flower mound vs Pekin
- The percentage of Black residents is the same in both Flower mound and Pekin at 3%.
- The percentage of White residents is higher in Pekin at 89% compared to 66% in Flower mound.
- The Asian population is larger in Flower mound at 13% compared to 1% in Pekin.
- The Hispanic community is larger in Flower mound at 11% compared to 3% in Pekin.
- More residents identify as two or more races in Flower mound at 7% compared to 4% in Pekin.
- The percentage of American Indian residents is the same in both Flower mound and Pekin at 0%.
Health Statistics
The health statistics provide insights into prevalent health conditions in two communities.
| Health Metric | Flower mound | Pekin |
|---|---|---|
| Mental Health Not Good | 13.3% | 17.3% |
| Physical Health Not Good | 7.7% | 12.0% |
| Depression | 21.1% | 23.2% |
| Smoking | 9.5% | 19.8% |
| Binge Drinking | 19.7% | 17.3% |
| Obesity | 28.5% | 38.0% |
| Disability Percentage | 6.0% | 15.0% |
Health Statistics Comparison: Flower mound vs Pekin
- In Pekin, a higher percentage report poor mental health at 17.3% compared to 13.3% in Flower mound.
- Higher depression rates are seen in Pekin at 23.2% versus 21.1% in Flower mound.
- Pekin has a higher smoking rate at 19.8% compared to 9.5% in Flower mound.
- Binge drinking is more common in Flower mound at 19.7% compared to 17.3% in Pekin.
- Pekin has higher obesity rates at 38.0% compared to 28.5% in Flower mound.
- There is a higher percentage of disabled individuals in Pekin at 15.0% compared to 6.0% in Flower mound.
Education Levels
The educational attainment in the area helps gauge the workforce's skill level and economic potential.
| Education Level | Flower mound | Pekin |
|---|---|---|
| No Schooling | 0.7% (519) | 0.7% (204) |
| High School Diploma | 6.2% (4,886) | 21.2% (6,641) |
| Less than High School | 2.7% (2,090) | 13.9% (4,351) |
| Bachelor's Degree and Higher | 42.3% (33,161) | 13.2% (4,126) |
Education Levels Comparison: Flower mound vs Pekin
- The percentage of residents with no formal schooling is the same in both Flower mound and Pekin at 0.7%.
- In Pekin, the rate of residents with high school diplomas is higher at 21.2% compared to 6.2% in Flower mound.
- The percentage of residents with less than a high school education is higher in Pekin at 13.9%, compared to 2.7% in Flower mound.
- A higher percentage of residents in Flower mound hold a bachelor's degree or higher at 42.3% compared to 13.2% in Pekin.
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.