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Population estimation beyond counts-Inferring demographic characteristics.

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This study predicts resident age and senior proportion using real estate data, advancing population estimation beyond just counts. Machine learning models accurately estimate average age, showing real estate data

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Area of Science:

  • Urban studies and planning
  • Geospatial analysis
  • Demographic modeling

Background:

  • Traditional population estimation focuses on counts, neglecting socio-economic characteristics.
  • Estimating demographics like average age and income remains a challenge in urban planning.
  • Geospatial and statistical methods are commonly used but have limitations.

Purpose of the Study:

  • To enhance population estimation by predicting demographic characteristics beyond counts.
  • To investigate the use of point of interest (POI) and real estate data for demographic prediction.
  • To compare machine learning techniques for predicting average resident age and senior proportion.

Main Methods:

  • Implementation and comparison of Random Forest, Support Vector Machines, and Linear Regression.
  • Utilized novel datasets: property transactions, year of construction, and flat types.
  • Applied methods to administrative areas in Singapore for demographic prediction.

Main Results:

  • Developed a regression model predicting average resident age with a mean error of approximately 1.5 years.
  • Real estate information proved more effective for predicting age patterns than amenities.
  • Demonstrated the potential of specific real estate and POI data for demographic estimation.

Conclusions:

  • Real estate data offers a powerful, previously underexplored resource for demographic estimation.
  • Machine learning models can accurately predict key demographic features like average age.
  • This approach has potential for estimating other characteristics, such as income.