Approaches for Evaluating the Impact of Urban Encroachment on Installation Training/Testing

Abstract

Military installations intended for training and testing purposes have historically been placed in areas remote from human settlements. Over time, land uses and land ownership near installations can change. After such changes, nearby land owners may demand that installations curtail mission-related activities that are incompatible with civilian residential areas. This research was undertaken was to provide a foundation for a research agenda to produce software capabilities that predict: (1) the impact of current/planned military installation training/testing activities on surrounding communities, and (2) the impact of projected urban growth on the opportunities to train/test on military installations and other areas. This work identified and analyzed approaches for predicting: urban land-use change off-installations, land-use change on installations, the impact of installation training and testing on surrounding communities, and the impact of urban growth on the future options to train and test on installations. Research and development recommendations are offered to provide future tools that will help regional planners understand the impacts of proposed investments and policies on an installation's training and testing opportunities.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2004
Accession Number
ADA431772

Entities

People

  • James D. Westervelt

Organizations

  • Engineer Research and Development Center

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • 5G Wireless Networks
  • Bandwidth
  • Commerce
  • Computer Programs
  • Doctrine
  • Environment
  • Environmental Protection
  • Frequency Bands
  • Geographic Information Systems
  • Information Systems
  • Military Training
  • Natural Resources
  • Radio Frequency
  • Radio Waves
  • United States
  • Urban Areas
  • Very High Frequency

Readers

  • Economics
  • Energy Conservation and Renewable Energy Engineering.
  • Neural Network Machine Learning.