Multivariate and Geospatial Analysis of Technology Utilization in US Counties

Avijit Sarkar, University of Redlands
James Pick, University of Redlands
Jessica Rosales, University of Redlands School of Business

Description

We examine geographic patterns and socio-economic and social capital correlates of information and communication technologies (ICTs) in 3,109 counties of the United States. Access and use of ICTs is found to vary significantly among metropolitan, micropolitan, and rural parts of the country. Clusters of high, moderate, and low ICT utilization counties are characterized by geodemographic and socio-economic attributes. Regression findings indicate that young dependency ratio, college education, working age population, and race and ethnic variables are major correlates of ICT use. We analyze and explain some novel findings on associations of income, ethnicity, and social capital variables with ICT usage in light of advancements in the technology use landscape made in the US. Spatial bias and large sample size fallacy issues are addressed and policy recommendations to remediate the digital divide in US counties are suggested.

 
Aug 11th, 12:00 AM

Multivariate and Geospatial Analysis of Technology Utilization in US Counties

We examine geographic patterns and socio-economic and social capital correlates of information and communication technologies (ICTs) in 3,109 counties of the United States. Access and use of ICTs is found to vary significantly among metropolitan, micropolitan, and rural parts of the country. Clusters of high, moderate, and low ICT utilization counties are characterized by geodemographic and socio-economic attributes. Regression findings indicate that young dependency ratio, college education, working age population, and race and ethnic variables are major correlates of ICT use. We analyze and explain some novel findings on associations of income, ethnicity, and social capital variables with ICT usage in light of advancements in the technology use landscape made in the US. Spatial bias and large sample size fallacy issues are addressed and policy recommendations to remediate the digital divide in US counties are suggested.