Accessibility Inequality in Houston

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

5 Scopus Citations
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Author(s)

  • Armin Akhavan
  • Nolan Edward Phillips
  • Jing Du
  • Bita Sadeghinasr
  • Qi Wang

Detail(s)

Original languageEnglish
Article number7102104
Journal / PublicationIEEE Sensors Letters
Volume3
Issue number1
Online published22 Nov 2018
Publication statusPublished - Jan 2019

Abstract

Recent research has reported that residents from different types of neighborhoods exhibit divergent patterns of urban mobility. However, these analyses have not considered the accessibility enabled by transportation networks. Furthermore, the data are typically not collected at a fine-grained enough temporal resolution, which impedes upon the accurate identification of more frequently visited locations. Here, we develop a data fusion framework to integrate individuals’ mobility, transportation routing, and census data to quantify urban dwellers' accessibility to their frequently visited locations. The framework has been applied to a random sample of 1000 Houstonians. The analytical results show that Houston residents visit a median of nine locations five times or more in the period of one week. Residents from primarily poor and black neighborhoods experience longer travel times compared to individuals from nonpoor and white neighborhoods when using public transit, even though their travel durations by driving are similar. The differences are problematic given that residents from poor and black neighborhoods often have lower rates of car ownership. The results indicate that urban dwellers’ accessibility is strongly impacted by their neighborhood characteristics, which has implications for equitable access in urban environments.

Research Area(s)

  • Accessibility, data fusion, neighborhoods, urban mobility

Citation Format(s)

Accessibility Inequality in Houston. / Akhavan, Armin; Phillips, Nolan Edward; Du, Jing; Chen, Jiayu; Sadeghinasr, Bita; Wang, Qi.

In: IEEE Sensors Letters, Vol. 3, No. 1, 7102104, 01.2019.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review