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Gender and Individual Access to Urban Opportunities: A Study Using Space–Time Measures

Kwan, Mei-Po·1999·The Professional Geographer

Abstract

Conventional accessibility measures based on the notion of locational proximity ignore the role of complex travel behavior and space–time constraints in determining individual accessibility. As these factors are especially significant in women's everyday lives, all conventional accessibility measures suffer from an inherent “gender bias.” This study conceptualizes individual accessibility as space–time feasibility and provides formulations of accessibility measures based on the space–time prism construct. Using a subsample of European Americans from a travel diary data set collected in Franklin County, Ohio, space–time accessibility measures are implemented with a network-based GIS method. Results of the study indicate that women have lower levels of individual access to urban opportunities when compared to men, although there is no difference in the types of opportunities and areas they can reach given their space–time constraints. Further, individual accessibility has no relationship with the length of the commute trip, suggesting that the journey to work may not be an appropriate measure of job access.

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Metadata

Authors
Kwan, M.-P.
Venue
The Professional Geographer
Published
1999-05-01
Volume
51
Issue
2
Pages
211-227
ISSN
  • 0033-0124 (ISSN-L)
  • 1467-9272
OpenAlex
W1963839094
Cited by
603
References
0
FWCI
95.37
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Gender and Individual Access to Urban Opportunities: A Study Using Space–Time Measures. https://app.cochat.ai/papers/10.1111/0033-0124.00158
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Kwan, M.-P. (1999). Gender and Individual Access to Urban Opportunities: A Study Using Space–Time Measures. The Professional Geographer. https://doi.org/10.1111/0033-0124.00158

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.