About our Population Forecast

INTRODUCTION

The Population Dataset is a robust resource offering comprehensive insights into population dynamics. It encompasses data on current population statistics, historical trends, and future projections. This dataset has been carefully assembled by integrating data from the U.S. Census with comprehensive research from Columbia University, merging it with various demographic trends, and developing a feature selection model to assess the importance of a data point in population migration.

Additionally, we employ sophisticated Machine Learning techniques to improve data precision. It serves as a valuable asset for researchers, analysts, and decision-makers seeking to explore and anticipate demographic shifts. With a reputation for reliability and precision, this dataset provides a dependable avenue to gain profound insights into demographic patterns and make informed, data-driven decisions.

DATA AND COVERAGE

Utilizing data from the US Census Bureau, which provides a wide range of demographic data including previous population figures, and innovative population projections from the University of Columbia for the next two decades, Aterio offers comprehensive demographic insights. By integrating alternative sources of demographic data with Machine Learning models, we ensure more frequent updates and refined accuracy.

Our coverage spans 32,000 ZIP codes, encompassing 99% of the nation. We meticulously classify different types of ZIP codes, such as post box categories, to provide detailed and accurate demographic data.

Fast Facts

• Keep informed with monthly updates

• Granularity: ZIP Code

• Coverage: Nationwide

• Forecast values to 2030

VALUE ESTIMATES

Value estimates for Aterio are derived using:

• Advanced time series forecasting models.

• Enhanced from the Component-Coherent method from NASA data for enhanced accuracy.

• Insights from social media users for frequent updates.

For ZIP codes that can be forecasted using more than one method, we employ a cascading model selection algorithm to identify the most accurate modeling approach for the specific geographic area. For instance, if a specific population range in ZIP code 90008 yields the most accurate results with valuation method type A, then all ZIP codes within that specific population range are valued using method A.

ACCURACY

To gauge the accuracy of our time series forecasting models at Aterio, we have used population forecasts for 2022 and compared them with actual census values as backtesting metrics. We calculated and compared our model-predicted values with the actual US census values, and this is our average model accuracy:

• Mean Absolute Error: 23%

• Values within 5% of US Census Values: 95%

• Values within 10% of US Census Values: 96%

We also assessed the model's performance using R2 values, ensuring robust evaluation. By employing stratified sampling to select a representative subset of ZIP codes, we calculated the R2 values and derived 95% confidence intervals to estimate the model's explanatory power across all ZIP codes. Our average R2 value is 0.73 (and a median of 0.84), with a 95% confidence interval of 0.71 and 0.75, indicating that our model reliably explains a significant proportion of the variance in population forecasts.

POPULATION FORECAST EXAMPLE

The population forecast for ZIP Code 74301 in Claremore, Oklahoma, shows an initial rise from 2011 to 2014, peaking at around 12,500, followed by a steady decline. The initial model projections, trained on data up to 2022 and excluding the COVID-19 periods (2020-2021), provided a baseline forecast (orange dashed line).

Starting the population forecast in 2023, adjustments were made using NASA projections to smooth and refine the forecast (green line). These adjustments resulted in a more stable decline, with the population expected to stabilize between 10,500 and 11,000 by 2030. The shaded prediction interval reflects this range, accounting for variability. These refined forecasts are crucial for future planning and resource allocation in Claremore, ensuring infrastructure and services can meet the community's evolving needs.

MODELING PROCESS

Our prediction process at Aterio involves several key steps to ensure accurate and reliable population forecasts. Here’s a concise overview:

Data Collection

• Demographic Features: Factors like age structure, Natural Growth, and migration rates. Understanding these is crucial for grasping population dynamics and how migration rates impact population size and composition.

• Socioeconomic Indicators: Metrics including GDP per capita, education levels, and urbanization rates. These indicators play a significant role in population forecasting and highlight the correlation between economic and social factors and population changes.

• We also incorporate extensive research findings from NASA and Census data, identifying variables correlated with population changes.

Data Handling

• Data Sanity Checks: These involve verifying the accuracy and consistency of data. This can include checking for logical inconsistencies, such as age discrepancies or improbable birth rates.

• Outlier Detection: Identifying and addressing outliers ensures that extreme values do not skew analysis.

• Handling Missing Values: Missing data can significantly impact analysis. Methods such as imputation, where missing values are estimated based on available data.

Geographic Relationships

• Crosswalks of ZIP Codes: Managing the relationships between ZIP codes with cities, counties and MSA’s to ensure accurate geographic representation. This is crucial for demographic studies, where population data needs to be accurately mapped to specific locations.

Forecasting Model Implementation

Employ deep learning time series forecasting models, using historical population data and key demographic indicators to project population trends. Rigorous data quality checks and model adjustments ensure accuracy. Data quality checks are conducted to handle any noise, and models are adjusted accordingly for accuracy.

Additionally, we refine our forecasts using NASA values derived from the Component-Coherent method, enhancing them by adding multiple features and ZIP code granularity, ensuring enhanced precision and reliability. This comprehensive process ensures our population forecasts are accurate, reliable, and grounded in robust data and sophisticated modeling techniques.

Backtesting

To ensure the accuracy of our models, we conduct rigorous backtesting. This helps us understand the weight of different variables in population dynamics and refine our forecasts. We have carefully crafted anomalies and clustering for better analysis and management.

COMMON USE CASES

Market Analysis

Population forecasts enable companies to identify emerging markets by analyzing future demographic trends. By understanding shifts in population size, age distribution, and regional growth, businesses can tailor their marketing strategies and product offerings to meet the anticipated demands of different population segments. For instance, a projected increase in the elderly population might prompt healthcare companies to expand their range of senior care products and services.

Investment Planning

Investors use population and migration trends to make strategic investment decisions. Demographic forecasts provide insights into potential growth areas, helping investors allocate resources effectively. For example, a significant influx of people into urban areas can signal opportunities in the real estate and retail sectors. Understanding these trends allows investors to anticipate market needs and reduce investment risks.

Housing Market Planning

Developers rely on population forecasts to anticipate future housing demand. By analyzing demographic projections, developers can plan residential and commercial real estate projects that align with population growth patterns. This proactive approach helps in preventing housing shortages and ensures that infrastructure developments meet the needs of future residents.

Urban Planning

Local and national governments use population forecasts to plan for infrastructure development, housing, transportation, and other urban amenities. Accurate population projections enable urban planners to design cities that accommodate future growth, ensuring sustainable and well-organized urban environments. This includes planning for schools, hospitals, public transport systems, and recreational facilities.

Resource Allocation

Hospitals and healthcare providers utilize demographic forecasts to plan for future healthcare needs. Understanding population growth and aging trends helps in allocating resources, such as medical staff, facilities, and equipment, to areas where they will be most needed. This foresight ensures that healthcare systems are prepared to meet the demands of a changing population.

Risk Assessment

Population forecasts assist insurance companies in assessing risks related to property, health, and life insurance. By analyzing demographic trends, insurers can predict potential changes in risk profiles and adjust their policies accordingly. For example, an aging population may lead to higher demand for health and life insurance products, while urbanization trends might impact property insurance risks.