Gender

Exploration

• Women participation ranged from 5-49%
• Top three studies recruited the crowd to gather data while the other retrieved data vice versa

Causation between Gender and Outcome

Zoe et al., 2020

• Gender differences in object types and modes of editing
  • Nodes: Men 5486 vs Women 21832
  • Ways: Men 4696 vs Women 18556
  • Relations: Men 3762 sv Women 21211
• Gender differences in use of tagging keys
  • differences in the proportions of tags made to the ‘buildings’ category between men and women, in that the spread for the interquartile range extends to a higher percentage (60%) than that for men (53%).

Ferster et al., (2017)

• Majority of trips were made by males (68%), and people over 35 years of age (70%).
• Females had higher ridership in younger age classes, while males had higher ridership in the older age classes.
• For incidents reported to BikeMaps.org, the overall median age was lower for males at 34 years of age and higher for females at 37 years of age.

Sun and Li, 2015

• Gender Differences in Activity Patterns at the Aggregate Level
  • Area of the standard deviational ellipse (SDE) (km2): M 135 W 85
  • Distinct location count (DLC): M 4.6, W 5.5
  • Distinct location category count (DLCC): M 4.0, W 4.3
  • Check-ins: M 9016 W 9799
  • Daily trajectories: M 1944 W 1790

Das et al. (2019): OSM Sample from the US

• Who is contributing?

  • Males are less likely to contribute to urban regions vs. rural regions than their female counterparts
  • For %pop-urban, a male editor produces only 0.81 times what a female produces in urban regions, but produces 4.17 times that in rural regions, 95% CI [1.56, 11.14]. (For rural-urban continuum these values are 0.78, 6.01 and 95% CI [2.14, 16.90], respectively).
  • In regards to racial/ethnic diversity, men concentrate a lower proportion of their edits in the most diverse counties vs. the least diverse counties, compared to women.
  • A male produces only 0.51 times what a female produces in the most diverse counties, but produces nearly 3.78 times that in the least diverse counties
  • Women disproportionately contribute to areas with greater racial and ethnic diversity and poorer areas compared to their male counterparts

• What are they contributing?: Male and Female Edits in Gendered Spaces (See Table below)

  • Are there any similarities or differences in what editors are mapping?
  • Women editors were more likely to contribute a higher proportion of information about masculinized spaces relative to men, and men were more likely to contribute a higher proportion of information about feminized spaces relative to women
  • We see that on a proportional basis, the men in our sample produced a higher proportion of their contributions in the feminized spaces compared to women, while the masculinized spaces received a higher proportion of their contributions from women compared to men

Broad Focus Database

Type	Female Edits	Male Edits
Feminicized	60 (68.18%)	5025 (85.90%)
Masculinized	28 (31.82%)	825 (14.10%)

Narrow Focus Database (Survey-based)

Type	Female Edits	Male Edits
Feminicized	30 (53.57%)	2780 (78.07%)
Masculinized	26 (46.43%)	781 (21.93%)

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Age