social inclusion

One of the core innovations around Open Data is always the content of what is being openly published and how it's being made use of. In this session of the ISCN Global Mixer we want to look at how Open data can drive more inclusive policies, as shown by care economy indicator systems in Mexico City and Buenos Aires and reuse cases from the Global South.

Key takeaways

• Open data creates public value: Making information accessible enables evidence-based decisions that improve lives.
• Care economy as a lens: Data about caregiving helps expose invisible work and inequalities, informing fairer policies.
• Local action, global relevance: Examples from Latin America highlight transferable strategies for cities worldwide.
• Reusability matters: When data is open, it can be repurposed by researchers, NGOs, and civic innovators to solve real problems.
• Collaboration drives impact: Partnerships between governments, civil society, and international networks enhance sustainability.
• Trust through transparency: Publishing and sharing data openly fosters accountability and strengthens public trust.

When governments commit to open data, communities benefit from more informed, inclusive, and equitable policies. Across the Global South, cities are demonstrating how transparency and data reuse can empower decision-making and strengthen social care systems. Natalia Carfi from Open Data Charter explored how open data initiatives in Mexico City, Buenos Aires, and other regions are driving new approaches to understanding and addressing care needs in urban environments.

While many governments publish datasets, the content – what data is shared and how it is used – determines its real social value. By focusing on care economy indicators, cities like Mexico City and Buenos Aires have turned abstract principles into actionable insight. These systems track who provides and receives care, where support gaps exist, and how policies can better reflect the realities of women and families balancing unpaid care responsibilities.

Beyond Latin America, reuse cases from across the Global South illustrate how open data can serve as a bridge between communities, policymakers, and service providers. Examples include data-driven resource allocation, mapping of care infrastructure, and social policy design based on lived experiences. In each case, the key lesson is that open data becomes transformative only when it connects directly to human needs.

The conversation also underlined the importance of governance frameworks such as the Open Data Charter, which help ensure consistency, ethics, and interoperability. These frameworks enable cities to adapt and scale successful approaches without reinventing the wheel. By prioritising accessibility, clarity, and participation, open data initiatives can strengthen democratic practices and deliver tangible improvements in people’s everyday lives.

Ultimately, the session demonstrated that open data is not only a technical issue but a social and cultural one. By embracing openness as a tool for inclusion and empowerment, cities can better understand and respond to the realities of care work, inequality, and resilience – paving the way toward more equitable, informed, and human-centred urban development.

This event is part of the ISCN Global Mixer, a series of events organized by the International Smart Cities Network. The presentations cover a wide range of topics related to international smart city approaches and provide exciting insights into urban digitalization worldwide - in just 30 minutes.

In this episode of the ISCN Global Mixer we found out how the Data Powered Positive Deviance Method (DPPD) can be used for positive impact in our cities. Robin Nowok from the GIZ Data Lab presented a case that illustrates how safe public spaces for woman in Mexico City can be identified by combining rigorous statistics and qualitative research.

Key takeaways

• Rigorous statistical analysis: Data-powered positive deviance (DPPD) is a method to help detect and track down hitherto unseen good practices for common-good oriented development - and it’s also very much applicable to smart cities.
• First quant, then qual: DPPD informs statistically and in data-driven manner what aspects to investigate. The quantitative method thus informs where to target the often more resource-intensive qualitative investigations.
• Positively deviant factors for safe public spaces: In the described case from Mexico City, DPPD identified the location of spatial units, population density, no. of bars, no. of financial services and (negatively correlated) the distance to the nearest metro-station as factors relevant for women’s safety and to be further investigated.
• Again, data: The case from Mexico City made use of 60+ (mostly open) data banks, underscoring the importance and opportunities of consolidated provision of data in structured environments.

It’s one of the fundamental promises of smart cities: Leveraging data to detect patterns and translate them into adaptations and improvements for an effective and efficient common good-oriented urban development. 

But how to cut through all the noise that big data inadvertently produces as well, especially when applied to thicker problems of policy and public administration? In this edition of the ISCN Global Mixer we looked at the data-powered positive deviance (DPPD) method as one exciting approach for that.

The core idea is to detect and focalize statistical outliers that defy standard distributions – positive deviants. One of the approach’s classics is a project in Vietnam in 1990 where development economists used it to find individual practices that achieved significantly better results in child nutrition among communities facing similar challenges and restricted resources. The scaling of these successful behaviors achieved improvements in a largely closed system without depending on influx of external resources and technology.

Applied to an urban context, GIZ and its Data Lab cooperated with the United Nations Development Programme (UNDP), the University of Manchester, Codeando México and Cohesión Comunitaria e Innovación Social A.C. to identify safe(r) public spaces for women in Mexico City.

In the research design the number of crimes against women in public spaces was the dependent variable set against 25 independent variables (e.g. geography, infrastructure, demographics). The unit of analysis was AGEB, a geostatistical unit in Mexico. For better comparability among AGEBs, they were grouped homogenously. This returned prediction models for expected crime rates in AGEBs and their public spaces against which positive deviants could be detected. A subsequent round of validation checked qualitatively if results are plausible. For example, in single positive deviants there was a large number of closed housing units or a large footprint of a military college, naturally reducing crime rates in public spaces there. The remaining positively deviant AGEBs were targeted for further qualitative analysis and in-depth field studies. As a result of this DPPD process a recommendation catalogue could be formulated including the proposal of physical interventions in public space, such as removing abandoned vehicles in public area, or promoting diverse foot traffic including children, families, women and elderly.

How could DPPD be applied in your city and community to render your city smarter in a common-good oriented way? Join the discussion and feel free to reach out to us via iscn@giz.de 

For more details on the presented case, you can watch the recording of the keynote above and refer to the links below. 

Moreover, there is a short technical Q&A specifically on the conducted statistical analyses and research designs at the end of this page.
 

Further links:

Paper: Data-powered positive deviance: Combining traditional and non-traditional data to identify and characterize development-related outperformers

GIZ Data Lab Blog: What happened and what’s next for DPPD

GIZ Data Lab Blog: Identifying safe(r) public spaces for women in Mexico City

Podcast-Episode: Data-Powered Positive Deviance

DPPD Handbook

Technical Q&A on research design

1.  How was the homogeneous grouping done? Cluster analysis? ANOVA?

The homogeneous grouping was executed using cluster analysis, which was anchored on three pivotal variables: socio-economic level, daily incoming trips, and population density. This method allowed for the identification of natural groupings within the data, enhancing the interpretability and actionable insights derived from the analysis.

2. Why was homogeneous grouping chosen instead of normalization, e.g., crime against women per area per women population?

Clustering and normalization serve different purposes. Clustering enables the control of multiple factors and grouping similar observations whereas normalization ensures values are on the same scale, enhancing model performance. We normalize our variables before the grouping for numeric stability. In the Mexico use case we wanted to control for three variables: socio-economic level, daily incoming trips and population density. This clustering can reveal the underlying structure and groupings within the data, which can take into account unobserved heterogeneity that we didn't control for in the set of clustering variables. Homogeneous grouping allows us to control for multiple dimensions of similarity across different areas, beyond just a single aspect such as crimes against women. This multi-faceted approach provides a more comprehensive understanding of the data, capturing nuances that single-factor normalization might miss.

3. Which statistical model was used? Was machine learning considered?

For the predictions we used three models: First, we ran a multiple linear regression. Then, we did a LASSO linear regression to perform variable selection to enhance the prediction accuracy and interpretability of the statistical model. Finally, we did a negative binomial regression because this type of regression is designed to fit models in which the performance measure consists of counts with overdispersion. We did the three-regression analysis for each one of the four clusters of the homogenous grouping per category of crime severity and for all crimes, so we have several results depending on the type of regression, cluster, and crime severity. 

Year of publication: 2024

Definition: Data feminism is an approach to understanding data, their analysis and how they are displayed. It is rooted in feminist values and thought, tapping into the learnings that stem from feminist theory and practice (cf. the keynote).

Who is who: To reveal established patterns that are specifically discriminatory against Women or other marginalized groups in the city, use the “detector” of the “who-question”. Who benefits from structures and solutions, who talks through them, who is silenced through them, etc. 

Vectors go both ways: Both under- and overrepresentation in samples or data sets can be the root of bias against vulnerable groups in data-based policy interventions or when using algorithmic systems

Multifaceted: A Data Feminist perspective has many applications in the development of smart cities. It can help build advocacy, improve designs and models or ensure adequate representation and close gender data gaps.

Data feminism addresses gender inequalities and recognizes discriminatory power structures in the field of data science. For a conscious handling of data in smart cities, various principles of data feminism can therefore be helpful in detecting and preventing discriminatory patterns.

In this session of the ISCN Global Mixer, Vanessa Hochwald from GIZ Data Lab spoke about Data Feminism and its relevance to Smart Cities. After giving a short introduction about the work and mandate of her organizational unit, which she dubbed as a think-and-do tank, she established two working hypotheses for the data feminist lens of the GIZ Data Lab. These are, drawing from the book "Data Feminism" by Catherine d'Ignazio and Lauren F. Klein, a) Data is Power, b) Feminism is about Power and challenging it in assumption of structural inequalities.

Conceptually, this translates into several principles and operationalizations for project work. In the context of development cooperation, it warrants for example the introduction of design thinking, the setup of gender budgeting or focussing on marginalized groups to build data literacy.

Vanessa then led through some key interventions and examples as to how the data feminist perspective applies to smart cities. The first by now sadly "classical" and notorious examples revolved around the misuse of CCTV images for racial profiling or biases woven into rigid and unfair criteria for the allocation of welfare benefits. Consequences of that according to data feminism should be the application of data minimization and the right to data refusal on behalf of individuals in order to protect them from negative consequences.

Further, data can be used for better advocacy for marginalized groups. The keynote mentioned here a historical example where communities in Detroit were empowered to track where commuters ran over black children, ushering improvements in road safety, or AI models generate visuals for proposals of more feminist and inclusive urban sites.

Another area of intervention can be in the joint design of questions and interventions with the target group. Vanessa's reference here was an algorithm suggesting where the best locations for childcare facilities in Mexico City would be. This is closely related to projects that aim for closing Gender Data Gaps in order to reduce underrepresentations or negative overrepresentations of marginalized groups in datasets for policy analysis and intervention. The Transformative Urban Mobility Initiative (TUMI) for example - which also presented their Mobility Data Hub in one of our past Global Mixers - has done the first large-scale comparative data collection to understand specifically mobility patterns of women in four African cities. 

For a full dive into the keynote, have a look for yourself in the recording above.

Further links:

• Blog of the GIZ Data Lab
• (German) article by executive board member of GIZ Anna Sohpie Herken on Data diversity and inclusive AI [paywall]