Identifying safe(r) public spaces for women in Mexico City. Part 2: From quantitative to qualitative analysis

Mexico City. GIZ-UNDP.
Recap of the first steps of the pilot, recounted in the first blog post of this series.

Finding positive deviants: Which public spaces are safe(r) for women?

One of the strengths of the DPPD method is that it encourages a focus on outliers — communities, or in our case public spaces, where uncommon grassroots solutions may be found — at scale — by using digital data that cover large geographic areas and time frames. We say “may be found because field research is necessary to confirm if an outlier in the data is indeed a positive deviant.

Validation: How do we know if a positive deviant AGEB is one we can learn from in the field?

The conventional positive deviance approach assumes that in every community there are individuals or groups that develop unusual solutions to cope with the challenges they face, unlike their peers, who live in similar conditions and have access to similar resources. These solutions are the ones we seek in our outlier AGEBs; and finding them qualitatively in the field will ultimately confirm the positive deviant nature of those AGEBs. However, before going to the field, the DPPD method prescribes an initial validation step to help better target investigations. This step is a useful way to assess whether the deviance found in the data is due to random noise or whether it is indeed due to signals of outperformance that can be confirmed through qualitative research.

Data Powered Positive Deviance, quantitative analysis serves to frame the photograph and qualitative analysis to focus it, complementing each other to generate a more detailed image.

Fieldwork design

The qualitative phase of the DPPD method serves to investigate the aspects of outperformance for which digital data are not available. For example, it is essential for understanding how the occupation of public spaces according to the time of day, the interactions between people, or the perception of safety impact women’s actual safety in public spaces.

a. Sample selection

We ended the initial validation step with a list of 22 (potential) positively-deviant AGEBs. We further narrowed these down to 10. We believed this was necessary to increase our focus on the tangible and intangible factors that made public spaces more or less safe for women.

b. Design of qualitative research techniques

For the data collection in the field, we designed a set of six instruments and techniques to identify and analyze the characteristics or conditions, both tangible (urban infrastructure such as lightning, green areas, etc.) and intangible (who occupies the space, at what time, etc.), that made public spaces safer for women who live, work, study, visit, or transit through the 16 AGEBs in our sample.

About the Data Powered Positive Deviance initiative

The Data Powered Positive Deviance initiative was established on the belief that lessons on how to tackle complex sustainable development challenges are best learned from the people who face those challenges every day. It is with this mindset that the GIZ Data Lab, the University of Manchester Centre for Digital Development, and the United Nations Development Programme Accelerator Labs are conducting a series of pilots in different countries and domains to uncover effective, locally developed practices and innovations as a response to development challenges.



[1] Homogeneous group 1: AGEBs that are on average less densely populated than group 2, but much more densely populated than group 3, on average receive more daily trips and have lower marginalization conditions.



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