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Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data.

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This study introduces a novel semi-automated dasymetric modeling approach using Random Forest to create high-resolution gridded population data. The method enhances accuracy and flexibility for human population distribution mapping.

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Area of Science:

  • Geographic Information Science
  • Demography
  • Environmental Science

Background:

  • Accurate human population distribution data is crucial for environmental monitoring and policy development.
  • Existing methods for creating fine-scale gridded population data have limitations in accuracy and flexibility.
  • Disaggregating census data to finer scales requires sophisticated modeling techniques.

Purpose of the Study:

  • To present a new semi-automated dasymetric modeling approach for generating high-resolution gridded population density data.
  • To improve the accuracy and flexibility of population data disaggregation compared to existing methods.
  • To demonstrate the application of the new algorithm using case studies in Vietnam, Cambodia, and Kenya.

Main Methods:

  • Developed a semi-automated dasymetric modeling approach integrating census and ancillary geospatial data.
  • Employed a Random Forest estimation technique to model dasymetric weights based on remote sensing and geospatial data.
  • Generated gridded population density predictions at approximately 100 m spatial resolution.
  • Utilized the prediction layer to redistribute census counts at a country level.

Main Results:

  • The new Random Forest-based dasymetric modeling approach demonstrated increased accuracy and flexibility.
  • Case studies in Vietnam, Cambodia, and Kenya showed advantages over common gridded population data production methodologies.
  • The method successfully generated high-resolution population density maps (~100 m).

Conclusions:

  • The presented semi-automated dasymetric modeling approach offers a significant advancement in creating accurate and flexible gridded population data.
  • This methodology provides a robust framework for understanding human population distributions and their environmental interactions.
  • Future work includes extending the algorithm to provide freely available gridded population datasets for Africa, Asia, and Latin America.