Determining optimal neighborhood size for ecological studies using leave-one-out cross validation

Area of Science:

• Geographic Information Systems (GIS)
• Spatial Statistics
• Geostatistics

Background:

• Determining the appropriate neighborhood size is critical for accurate spatial analysis.
• Existing methods may lack a robust approach for optimizing neighborhood selection.

Purpose of the Study:

• To develop and validate a method for identifying the optimal neighborhood size in spatial data analysis.
• To provide a systematic approach for selecting appropriate filter sizes for geographic and population data.

Main Methods:

• Employed leave-one-out cross-validation to systematically evaluate neighborhood sizes.
• Calculated mean squared error (MSE) for various filter sizes across all data points.
• Identified the optimal neighborhood as the filter size yielding the lowest MSE.

Main Results:

• The leave-one-out cross-validation effectively identified an optimal neighborhood size.
• The method demonstrated a quantifiable approach to minimize spatial analysis errors.
• Mean squared error (MSE) served as a reliable metric for optimal filter selection.

Conclusions:

• The proposed cross-validation technique provides a robust and objective method for determining optimal neighborhood sizes.
• This approach enhances the reliability of spatial analysis for both geographic and population-based studies.
• The method is broadly applicable to various spatial filtering techniques.