Smoothing Spatial Data by Estimating Mean Local Variance.

Abstract

A nearest neighbor nonparametric regression method is used to estimate air pollution levels at other than measured points. The method requires an appropriate smoother. Cross-validation is used to determine the appropriate smoother. An alternative method is introduced to determine an appropriate level of smoothing which involves minimizing mean local variance. Mean local variance is a function of the size of a circular window. It is minimized for two pollutants in Ohio, New York and Florida. The smoother obtained by cross-validation using Ohio's data is compared to that obtained by minimizing mean local variance. Keywords: Air quality; Sulfur dioxide; Suspended particulates; Statistical data; Pollution concentrations.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1988
Accession Number
ADA195302

Entities

People

  • Laura D. Johnson

Organizations

  • Naval Postgraduate School

Tags

DTIC Thesaurus Topics

  • Air Pollution
  • Analysis Of Variance
  • Availability
  • Classification
  • Continents
  • Data Sets
  • Dielectric Gases
  • Geographic Regions
  • Monitoring
  • New York
  • North America
  • Security
  • Shape
  • Spatial Distribution
  • Two Dimensional
  • United States
  • Validation

Fields of Study

  • Mathematics

Readers

  • Environmental Engineering
  • Statistical inference.