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Demographic Analysis of Zip Code 51015: A Case Study of Low Diversity and Single-Person Population
CATEGORY
Demographics
DATA
Population
Median Income
Racial Distributions
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Demographics
Population
Median Income
Racial Distributions
Zip code 51015, representing Climbing Hill in Iowa, exhibits a unique demographic profile. As of 2022, this area is classified as "Low Diversity" with a population of one person, consistently reported as white throughout the available data. This extremely small population size limits broad conclusions about community trends, but economic data analysis is still possible.
Throughout the period from 2013 to 2022, the area maintains a "Low Diversity" classification, with 100% of the population consistently reported as white. There are no observable shifts in racial demographics due to the minimal population size.
Despite the constant population of one, notable fluctuations in the reported median income have occurred. The median income increased from $60,957 in 2013 to $82,660 in 2021, an increase of about 35.6% over eight years. A slight decrease to $76,108 was reported in 2022, which is still 24.9% higher than the 2013 figure. The highest median income was reported in 2021 at $82,660, while the lowest (excluding years with no data) was in 2013 at $60,957.
These income fluctuations are notable given the constant population of one. They may reflect changes in the individual's employment situation, local economic conditions, or potentially data reporting anomalies given the unusual circumstances of a one-person CDP.
The median income in this zip code has generally been higher than the national median household income, which was $67,521 in 2020. The growth in median income from 2013 to 2021 (35.6%) outpaced the national inflation rate for the same period (about 16.8%), suggesting real income growth. The area's extremely low population density (one person in 0.17 square miles) is far below the national average and even typical rural densities, making it an outlier in demographic studies.
Climbing Hill presents a case study in minimal population areas, but its unique circumstances limit broader conclusions about demographic and economic trends. The consistent "Low Diversity" classification and the substantial income fluctuations for a single individual highlight the limitations of applying standard demographic analysis tools to extremely small population centers. This case underscores the importance of considering population size when interpreting demographic and economic data, especially in rural or sparsely populated areas.
