Research Article Open Access

Investigating Commuting Time in a Metropolitan Statistical Area Using Spatial Autocorrelation Analysis

S. Hessam Miri1 and S. Behnam Miri1
  • 1 University of Guilan, Iran
American Journal of Engineering and Applied Sciences
Volume 13 No. 1, 2020, 27-36

DOI: https://doi.org/10.3844/ajeassp.2020.27.36

Submitted On: 10 November 2019 Published On: 11 January 2020

How to Cite: Miri, S. H. & Miri, S. B. (2020). Investigating Commuting Time in a Metropolitan Statistical Area Using Spatial Autocorrelation Analysis. American Journal of Engineering and Applied Sciences, 13(1), 27-36. https://doi.org/10.3844/ajeassp.2020.27.36

Abstract

Commuting is an unavoidable issue as living and working are two spatially separated activities for most people. The most influence of commuting is on land uses and transportation systems and ultimately it poses its consequences to the society. Research on urban commuting is one of the most favorable approaches to lessening the impact and intensity of land use and transportation problems. As urban spatial structure affects commuting patterns, this study aims to understand the spatial distribution of mean commuting time at the block group level in Charlotte-Concord-Gastonia Metropolitan Statistical Area (MSA) using spatial autocorrelation analysis method. The results show that the areas of recent housing boom have longer commuting time and the commuting time decreases as the areas’ age increase. Also, there is no significant difference in Moran’s I values for Rook and Queen methods as they are 0.45939 and 0.45265, respectively. The positive value of Moran’s I (p-values <0.05) shows that block groups with longer average commutes are adjacent to block groups with longer average commutes and shorter commutes next to shorter commutes. Furthermore, it is identified that clustering of low commuting time is in the central part of the cities with old houses and clustering of high commuting time is in suburbs with newer houses.

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Keywords

  • Commuting Time
  • Transportation
  • Land use
  • Spatial Autocorrelation
  • Moran’s I