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Virginia Tech

Prediction of Building Count and Dimensions from U.S. Census Data Using Multiple Regression

Abstract

dc:description.abstract

Providers of high-speed, wireless data services need to know where in their service area to place transmitters to reach potential customers. Viewshed analysis, a technique found in Geographic Information Systems (GIS) software, can be used to model propagation of the wireless signals from different locations to find the best transmitter site. To carry out viewshed analysis, digital data are required for all obstructions the signal may encounter along its path. One such obstruction, terrain, can be represented in the GIS by easily available Digital Elevation Models (DEMs). Another obstruction is buildings, which are common in populated areas, and therefore of particular concern to wireless providers. Unfortunately, digital data for buildings in U.S. cities and towns are often non-existent, difficult to obtain, or very costly. In light of the difficulties surrounding acquisition of building data for wireless propagation studies, this study used Multiple Regression analysis to construct models to predict building count and dimensions. U.S. Census Housing and Demographic data, aggregated at the Census Block level, served as the predictor variables in the regression equations. The models were built from sample data collected from four U.S. cities. For each variable to be predicted (Y), the top models were compared to find the optimum one. The model chosen for Building Count (per Block) showed quite good results, and future research in the prediction of this variable shows promise. Results for the models of Average Building Height and Average Building Footprint Area (both per Block) were not nearly as encouraging, but additional work modeling these variables may still yield insights.

Degree

thesis:*
Name thesis:degree_name
Master of Science
Level thesis:degree_level
masters
Discipline thesis:degree_discipline
Geography
Department dc:contributor.department
Geography
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2001

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Leach, Jamison Dane
Chair dc:contributor.committeechair
  • Carstensen, Laurence W.
Committee members dc:contributor.committeemember
  • Campbell, James B. Jr.
  • Zahm, Diane L.

Subjects

dc:subject × 3

Rights

dc:rights
Statement dc:rights
  • In Copyright

Identifiers

dc:identifier.*
Dc Identifier Other
etd-10182001-213742
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/35424

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Leach, Jamison Dane. Prediction of Building Count and Dimensions from U.S. Census Data Using Multiple Regression. masters thesis, Virginia Tech, 2001. http://hdl.handle.net/10919/35424