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Sampling Plans01:23

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Empiric recommendations for population disaggregation under different data scenarios.

Marta Sapena1, Marlene Kühnl1,2, Michael Wurm1

  • 1German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Weßling, Germany.

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|September 16, 2022
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Summary
This summary is machine-generated.

This study recommends combining statistical and dasymetric methods for high-resolution population mapping with remote sensing data. Simpler methods are better for highly-resolved data, and using multiple accuracy metrics is crucial for validation.

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Area of Science:

  • Geographic Information Science
  • Remote Sensing
  • Urban Planning

Background:

  • High-resolution population maps are crucial for crisis management and urban planning.
  • Earth Observation aids in disaggregating population data into fine-grained maps.
  • Optimal methods, spatial units, and accuracy metrics for population mapping remain unclear.

Purpose of the Study:

  • To provide recommendations for producing high-resolution population maps using remote sensing and geospatial data in urban areas.
  • To evaluate the suitability of different population disaggregation methods based on data availability and resolution.
  • To clarify the impact of spatial units and accuracy metrics on validation processes.

Main Methods:

  • Conducted experimental research on 36 population disaggregation scenarios.
  • Combined five top-down methods (dasymetric, statistical, hybrid) with varying data resolutions and availability (poor, average, rich).
  • Systematically validated resulting maps using a two-fold approach and six accuracy metrics.

Main Results:

  • Combining statistical and dasymetric methods yields better results with only remotely sensed data.
  • Simpler methods are more suitable for highly-resolved input data.
  • Using at least three relative accuracy metrics is highly recommended for robust validation.

Conclusions:

  • The choice of population disaggregation method and validation strategy depends on data characteristics and landscape heterogeneity.
  • Recommendations are provided to optimize efforts and time in future high-resolution population mapping.
  • Understanding the influence of spatial units and accuracy metrics is key for reliable population mapping.