Academic study participants willing to donate personal data to research if it could lead to public good (Skatova, Ng, and Goulding, 2014)
A term for data accumulated by an institution for operational purposes and does not fall under any traditional data protection policies.
Organizations across all sectors accumulate a massive amount of data just by virtue of operating alone, and universities are among such organizations. In a paper, Christine L. Borgman categorizes these as grey data and further suggested that universities should take a lead in demonstrating stewardship of these data, which include student applications, faculty dossier, registrar records, ID card data, security cameras, and many others.
“Some of these data are collected for mandatory reporting obligations such as enrollments, diversity, budgets, grants, and library collections. Many types of data about individuals are collected for operational and design purposes, whether for instruction, libraries, travel, health, or student services.” (Borgman, p. 380)
Grey data typically does not fall under traditional data protection policies such as Health Insurance Portability and Accountability Act (HIPAA), Family Educational Rights and Privacy Act (FERPA), or Institutional Review Boards. Consequently, there are a lot of debates about how to use (or misuse) them. Borgman points out that universities have been “exploiting these data for research, learning analytics, faculty evaluation, strategic decisions, and other sensitive matters.” On top of this, for-profit companies “are besieging universities with requests for access to data or for partnerships to mine them.”
Recognizing both the value of data and the risks arising from the accumulation of grey data, Borgman proposes a model of Data Stewardship by drawing on the practices of data protection in the University of California which concern information security, data governance, and cyber risk.
This model is an example of a good Data Stewardship practice that the GovLab is advocating amidst the rise of public-private collaboration in leveraging data for public good.
The GovLab’s Data Stewards website presents the need for such practice as follows:
“With these new practices of data collaborations come the need to reimagine roles and responsibilities to steer the process of using private data, and the insights it can generate, to address some of society’s biggest questions and challenges: Data Stewards.
“Today, establishing and sustaining these new collaborative and accountable approaches requires significant and time-consuming effort and investment of resources for both data holders on the supply side, and institutions that represent the demand. By establishing Data Stewardship as a function, recognized within the private sector as a valued responsibility, the practice of Data Collaboratives can become more predictable, scaleable, sustainable and de-risked.”
Borgman, C. L. (2018). Open Data, Grey Data, and Stewardship: Universities at the Privacy Frontier. ArXiv. https://doi.org/10.15779/Z38B56D489
Young, A. (2018, November 26). About the Data Stewards Network. Retrieved March 6, 2019, from https://medium.com/data-stewards-network/about-the-data-stewards-network-1cb9db0c0792
A process of making datasets raw in three steps: reformatting, cleaning, and ungrounding (Denis and Goeta).
Hundreds of thousands of datasets are now made available via numerous channels from both public and private domains. Based on the stage of processing, these datasets can be categorized as either raw data or processed data. According to an Open Government Data principle, raw data (or primary data) “are published as collected at the source, with the finest possible level of granularity, not in aggregate or modified forms.” While processed data is data that has been through some sort of adulteration, categorization, codification, aggregation, and other similar processes.
A large amount of data that is made publicly available come in processed form. For example, population, trade, and budget data are often presented in aggregated forms, preventing researchers from understanding the underlying stories behind these data, such as the differences in patterns or trends when gender, location, or other variables come into factor. Therefore, a rawification process is oftentimes needed in order for a dataset to be useful for a more detailed, secondary and valuable analysis.
Jérôme Denis and Samuel Goëta define ‘rawification’ as a process of reformatting, cleaning, and ungrounding data in order to obtain a truly ‘raw’ datasets.
According to Denis and Goëta, reformatting data means making sure that data that has been opened can also be easily readable by the users. This is usually achieved by reformatting the data so that it can be read and manipulated by most processing programs. One of the most commonly used formats is CSV (Comma Separated Values).
The next step in a rawification process is cleaning. In this stage, cleaning means correcting mistakes within the datasets, which include but not limited to, redundancies and incoherence. In many cases, datasets can have multiple entries for the same item, for example ‘New York University’ and ‘NYU’ might be interpreted as two different entities or ‘the GovLab’ and ‘the Governance Lab’ might experience a similar issue. Cleaning helps address issues like this.
The final step in a rawification process is ungrounding, which means taking out any ties or links from previous data use. Such ties include color coding, comments, and subcategories. This way the datasets can be purely raw and free of all associations and bias.
Opening up data is a clear step for increasing public access to information held within institutions. However, in order to ensure the utility of that data for those accessing it, a rawification process will likely be necessary.
- Denis, J., & Goëta, S. (2017).
Rawificationand the careful generation of open government data. Social Studies of Science, 47(5), 604–629. https://doi.org/10.1177/0306312717712473
- Denis, J., & Goëta, S. (2014). Exploration, Extraction and ‘Rawification’. The Shaping of Transparency in the Back Rooms of Open Data (SSRN Scholarly Paper No. ID 2403069). Rochester, NY: Social Science Research Network. Retrieved from https://papers.ssrn.com/abstract=2403069
Open data demand in New York City in
A person or a business that manages individual data in a trustworthy manner. Also ‘information fiduciary’, ‘data trust’, or ‘data steward’.
‘Fiduciary’ is an old concept in the legal world. Its latin origin is fidere, which means to trust. In the legal context, a fiduciary is usually a person that is trusted to make a decision on how to manage an asset or information, within constraints given by another person who owns such asset or information. Examples of a fiduciary relationship include homeowner and property manager, patient and doctor, or client and attorney. The latter having the ability to make decisions about the trusted asset that fall within the conditions agreed by the former.
Jack M. Balkin and Jonathan Zittrain wrote a case for “information fiduciary”, in which they pointed out the urgency of adopting the practice of fiduciary in the data space. In the Atlantic, they wrote:
“The information age has created new kinds of entities that have many of the trappings of fiduciaries—huge online businesses, like Facebook, Google, and Uber, that collect, analyze, and use our personal information—sometimes in our interests and sometimes not. Like older fiduciaries, these businesses have become virtually indispensable. Like older fiduciaries, these companies collect a lot of personal information that could be used to our detriment. And like older fiduciaries, these businesses enjoy a much greater ability to monitor our activities than we have to monitor theirs. As a result, many people who need these services often shrug their shoulders and decide to trust them. But the important question is whether these businesses, like older fiduciaries, have legal obligations to be trustworthy. The answer is that they should.”
Recent controversy involving Facebook data and Cambridge Analytica provides another reason for why companies collecting data from users need to act as a fiduciary. Within this framework, individuals would have a say over how and where their data can be used.
Another call for a form of data fiduciary comes from Google’s Sidewalk Labs project in Canada. After collecting data to inform urban planning in Quayside area in Toronto, Sidewalk Labs announced that they won’t be claiming ownership over the data that they collected and that the data should be “under the control of an independent Civic Data Trust.”
In a blog post, Sidewalk Labs wrote that:
“Sidewalk Labs believes an independent Civic Data Trust should become the steward of urban data collected in the physical environment. This Trust would approve and control the collection of, and manage access to, urban data originating in Quayside. The Civic Data Trust would be guided by a charter ensuring that urban data is collected and used in a way that is beneficial to the community, protects privacy, and spurs innovation and investment.”
Realizing the potential of creating new public value through an exchange of data, or data collaboratives, the GovLab “ is advancing the concept and practice of Data Stewardship to promote responsible data leadership that can address the challenges of the 21st century.” A Data Steward mirrors some of the responsibilities of a data fiduciary, in that she/he is “responsible for determining what, when, how and with whom to share private data for public good.”
Balkin and Zittrain suggest that there is an asymmetrical power between companies that collect user generated data and the users themselves, in that these companies are becoming indispensable and having more control over individuals data. However, these companies are currently not legally obligated to be trustworthy, meaning that there is no legal consequence for when they use this data in a way that breach privacy or in the least interest of the customers.
Under a data fiduciary framework, individuals who are trusted with data are attached with legal rights and responsibilities regarding the use of the data. In a case where a breach of trust happens, the trustee will have to face legal consequences.
- Balkin, J. M. (2016). Information Fiduciaries and the First Amendment. UC Davis Law Review, 49(4), 1183–1234.
- Balkin, J. M., & Zittrain, J. (2016, October 3). A Grand Bargain to Make Tech Companies Trustworthy.
- Pearson, J. (2018, October 16). Why Does Google Want to Hand Its Smart City Data to a Third Party “Civic Data Trust?”
- Schwartz, A., & Cohn, C. (2018, October 25). “Information Fiduciaries” Must Protect Your Data Privacy.
Percentage of people around the world who trust their government (Edelman Trust Barometer, 2019).
By Michelle Winowatan, Andrew J. Zahuranec, Andrew Young, Stefaan Verhulst
The Living Library Index – inspired by the Harper’s Index – provides important statistics and highlights global trends in governance innovation. This installment focuses on trust in institutions.
Please share any additional, illustrative statistics on open data, or other issues at the nexus of technology and governance, with us at email@example.com
Global Trust in Public Institutions
- Percentage of citizens globally with trust in institutions: 52% – 2019
- Percentage of people around the world who trust their government: 47% – 2019
- Countries with the lowest trust in their public institutions: Russia (29%), Japan (39%), Spain (40%), Ireland (42%), and United Kingdom: 43% – 2019
- Most Trusted Institution in Latin America: The Church (65%) – 2017
Trust in Government
- Americans who say their democracy is working at least “somewhat well:” 58% – 2018
- Number who believe sweeping changes to their government are needed: 61% – 2018
- Percentage of Americans expressing faith in election system security: 45% – 2018
- Percentage of Americans expressing an overarching trust in government: 40% – 2019
- How Americans would rate the trustworthiness of Congress: 4.1 out of 10 – 2017
- Number who have confidence elected officials act in the best interests of the public: 25% – 2018
- Amount who trust the federal government to do what is right “just about always or most of the time”: 18% – 2017
- Americans with trust and confidence in the federal government to handle domestic problems: 2 in 5 – 2018
- International problems: 1 in 2 – 2018
- US institution with highest amount of confidence to act in the best interests of the public: The Military (80%) – 2018
- Most favorably viewed level of government: Local (67%) – 2018
- Most favorably viewed federal agency: National Park Service (83% favorable) – 2018
- Least favorable federal agency: Immigration and Customs Enforcement (47% unfavorable) – 2018
- Overall trust in government: 42% – 2019
- Trust the national government handling of personal data:
- Public trust in the European Union: 59% – 2018
- Citizens who are optimistic about the future of the European Union: 66% – 2018
- Most trusted institution in the European Union: European Parliament (50%) – 2018
- Highest source of concern about the internet during the pre-election period: Disinformation and Misinformation on the Internet (almost 3 in 4 expressed concerns) – 2018
- Country with the highest concern: Spain and Greece (84%) – 2018
- Country with the lowest concern: Estonia (56%) – 2018
- Overall Concern for elections being manipulated through cyberattacks: 61% – 2018
- Most confident their country was doing what was needed to prevent illegal or fraudulent election activities: Finland (88%) – 2018
- Least confidence: Bulgaria (31%) – 2018
- Number of EU member-states satisfied with free and fair elections in their country: 27 – 2018
- Number of EU member-states dissatisfied with free and fair elections 1 (Bulgaria) – 2018
- Europeans who are convinced that the EU is a secure place to live in: 7 in 10, declining since 2015 – 2018
- EU citizens trust in the European national governments: 49% – 2018
- Most trusted actor to address corruption: Police – 2017
- Those who are confident that justice always prevails over injustice in their country: 39% – 2017
- Support for the independence of European courts and judges: almost 1 in 2 – 2018
- General public who think the independence of European courts and judges is good: 56% – 2018
- Trust in government across Africa: 57% – 2016
- Most trusted institutional actor across Africa: Religious Leaders (72%) – 2016
- Least trusted: Opposition Party (36%) – 2016
- Country with highest level of trust in the state (president or prime minister, army and police): Niger (86%) – 2016
- Lowest: Nigeria (31%) – 2016
- Highest trust in the courts: Niger (82%) – 2016
- Lowest: Madagascar (29%) – 2016
- Highest Trust in Parliament: Namibia (74%) – 2016
- Lowest: Nigeria (25%) – 2016
- Confidence in government across Latin America: Only 1 in 4 trust their leaders – 2018
- Highest confidence in government: Nicaragua (42%) – 2017
- Lowest confidence in government: Brazil (8%) – 2017
- Highest confidence in the legislature: Venezuela (37%) – 2017
- Lowest confidence in the legislature: Paraguay (10%) – 2017
- Highest Confidence in Political Parties: Uruguay (25%) – 2017
- Lowest Confidence in Political Parties: Brazil (7%) – 2017
- Dissatisfaction with democracy: 13 in 20 – 2017
- Confidence in political parties: 3 in 20 – 2017
- Overall Trust in the Judiciary: 34% – 2018
- Most trusted institution in China: Government (86%) – 2019
- What the Chinese public considers to be the largest problem facing the country: Corrupt Officials (49%) – 2016
- Number of people who trust their government in India: 17 in 20 – 2017
Trust in Media
- Percentage of people around the world who trust the media: 47% – 2019
- Rating of news trustworthiness in the United States: 4.5 out of 10 – 2017
- In South Korea: 4.0 out of 10 – 2017
- Number of citizens who trust the press across the European Union: Almost 1 in 2 – 2019
- France: 3.9 out of 10 – 2019
- Germany: 4.8 out of 10 – 2019
- Italy: 3.8 out of 10 – 2019
- Slovenia: 3.9 out of 10 – 2019
- Percentage of European Union citizens who trust the radio: 59% – 2017
- EU citizens who do not actively participate in political discussions on social networks because they don’t trust online social networks: 3 in 10 – 2018
- Those who are confident that the average person in the United Kingdom can tell real news from ‘fake news’: 3 in 10 – 2018
Trust in Business
- Global trust in business: 56% – 2019
- Number of Americans who believe pharmaceutical companies have too much power: More than 4 in 5 – 2018
- The most trusted business sector globally: Technology (78%) – 2019
- Americans who trust technology companies to “do what is right:” More than 17 in 20 – 2018
- British citizens who feel technology companies will seek their consent on data collection: 25% trust; 27% distrust – 2018
- British citizens who feel technology companies will be transparent on how they use data: 23% trust; 33% distrust – 2018
- British citizens who feel technology companies will protect personal data: 26% trust; 29% distrust – 2018
- British citizens who trust technology companies to encrypt their personal data: 31% trust; 25% distrust – 2018
- Least trusted business sector globally: Financial Services (57%) – 2019
- Millennials who trust in companies to keep their personal information private: 44%, the highest compared to other generations – 2016
- Percentage of American citizens confident that business leaders will act in the best interests of the public: 45% – 2018
- Professions least trusted to tell the truth in the United Kingdom: Advertising Executives (16%) – 2018
- 1. Many unhappy with
currentpolitical system. Pew Research Center, 2017.
- Afrobarometer: Round 6. Afrobarometer. 2016.
- Ambitious SDG goal confronts challenging realities: Access to justice is still elusive for many Africans. Afrobarometer, 2017.
- Chinese Public Sees More Powerful Role in World, Names U.S. as Top Threat. Pew Research Center, 2016.
- Confiana en los partidos políticos cae nueve puntos porcentuales desde 2013, se acentúa la crisis de respresentación. Latinobarometro, 2017.
- Confianza en el congreso cae doce puntos porcentuales desde 2010 en América Latina. La crisis de representación se acentúa. Latinobarometro, 2017.
- Corruption. European Commission, 2017.
- Data Security: Not a Big Concern for Millennials. Gallup, 2016.
- Desde 2010 la satisfacción con la democracia en América Latina ha caído 14 puntos porcentuales. Latinobarometro, 2017.
- Edelman Trust Barometer. Edelman, 2019.
- Eurobarometer interactive: And, for each of them, please tell me if you tend to trust it or not to trust it? The European Parliament. European Commission, 2018.
- Eurobarometer Interactive: I would like to ask you a question about how much trust you have in certain media and institutions. For the following media and institutions, please tell me if you tend to trust it or tend not to trust it: Radio. European Commission, 2017.
- Eurobarometer Interactive: I would like to ask you a question about how much trust you have in certain media and institutions. For the following media and institutions, please tell me if you tend to trust it or tend not to trust it: Television. European Commission, 2017.
- Eurobarometer Interactive: I would like to ask you a question about how much trust you have in certain media and institutions. For the following media and institutions, please tell me if you tend to trust it or tend not to trust it: The Internet. European Commission, 2017.
- Eurobarometer Interactive: I would like to ask you a question about how much trust you have in certain media and institutions. For the following media and institutions, please tell me if you tend to trust it or tend not to trust it: Online social networks. European Commission, 2017.
- Eurobarometer: Democracy and elections. European Commission, 2018.
- European Parliament the most trusted EU institution. European Parliament, 2018.
- Fairness, inequality and inter-generational mobility. European Commission, 2018.
- Ipsos MORI Almanac 2018. Ipsos MORI, 2018.
- Job performance of MPs, local
councillors: Are representatives serving voters or themselves? Afrobarometer, 2016.
- La confianza en los gobiernos latinoamericanos alcanza su nivel más bajo desde 2004. Latinobarometro, 2017.
- Latin American Economic Outlook 2018: Rethinking Institutions for Development. OECD Development Centre, 2018.
- Perceived independence of the national justice systems in the EU among companies. European Commission, 2018.
- Perceived independence of the national justice systems in the EU among the general public. European Commission, 2018.
- The State of the Nation. Ipsos MORI, 2018.
- Trust in Government. Gallup Poll, 2018.
- Trust in the Digital Era. Pew Research Center, 2018.
- Trust in the military exceeds trust in other institutions in Western Europe and U.S. Pew Research Center, 2018.
- Trust, Facts, and Democracy: 1. Democracy and government, the U.S. political system, elected officials and governmental institutions. Pew Research Centre, 2018.
- Trustlab. OECD, 2017.
Amount of money allocated to community projects through Participatory Budgeting in the United States as of June 27, 2017.
“a new radical, practice-based ideology […] based on the values of sharing, common (intellectual) ownership and new social co-operations.”
Distinctive, yet with perhaps an interesting hint, from “communism”, the term “Commonism” was first coined by Tom DeWeese, the president of the American Policy Center yet more recently redefined in a new book “Commonism: A New Aesthetics of the Real” edited by Nico Dockx and Pascal Gielen
According to their introduction:
“After half a century of neoliberalism, a new radical, practice-based ideology is making its way from the margins: commonism, with an o in the middle. It is based on the values of sharing, common (intellectual) ownership and new social co-operations. Commoners assert that social relationships can replace money (contract) relationships. They advocate solidarity and they trust in peer-to-peer relationships to develop new ways of production.
“Commonism maps those new ideological thoughts. How do they work and, especially, what is their aesthetics? How do they shape the reality of our living together? Is there another, more just future imaginable through the commons? What strategies and what aesthetics do commoners adopt? This book explores this new political belief system, alternating between theoretical analysis, wild artistic speculation, inspiring art examples, almost empirical observations and critical reflection.”
In an interview excerpted from the book, author Gielen, Vrije Universiteit Brussel professor Sonja Lavaert, and the philosopher Antonio Negri discuss how commonism has the ability to transcend the ideological spectrum. The commons, regardless of political leanings, collaborate to “[re-appropriate] that of which they were robbed by capital.” Examples put forward in the interview include “liberal politicians write books about the importance of the basic income; neonationalism presents itself as a longing for social cohesion; religiously inspired political parties emphasize communion and the community, et cetera.”
In another piece, Louis Volont and Walter van Andel, both of the Culture Commons Quest Office, argue that an application of commonism can be found in blockchain. They argue that Blockchain’s attributes are capable of addressing the three elements of the tragedy of the commons, which are “overuse, (absence of) communication, and scale”. Further, its decentralization feature enables a “common” creation of value.
Although, the authors caution of a potential tragedy of blockchain by asserting that:
“But what would happen when that one thing that makes the world go around – money (be it virtual, be it actual) – enters the picture? One does not need to look far: many cryptocurrencies, Bitcoin among them, are facilitated by blockchain technology. Even though it is ‘horizontally organized’, ‘decentralized’ or ‘functioning beyond the market and the state’, the blockchain-facilitated experiment of virtual money relates to nothing more than exchange value. Indeed, the core question one should ask when speculating on the potentialities of the blockchain experiment, is whether it is put to use for exchange value on the one hand, or for use value on the other. The latter, still, is where the commons begin. The former (that is, the imperatives of capital and its incessant drive for accumulation through trade), is where the blockchain mutates from a solution to a tragedy, to a comedy in itself.”
By Michelle Winowatan, Andrew Young, and Stefaan Verhulst
The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of data, gender, and mobility was originally published in 2017.
This edition of the Selected Readings was developed as part of an ongoing project at the GovLab, supported by Data2X, in collaboration with UNICEF, DigitalGlobe, IDS (UDD/Telefonica R&D), and the ISI Foundation, to establish a data collaborative to analyze unequal access to urban transportation for women and girls in Chile. We thank all our partners for their suggestions to the below curation – in particular Leo Ferres at IDS who got us started with this collection; Ciro Cattuto and Michele Tizzoni from the ISI Foundation; and Bapu Vaitla at Data2X for their pointers to the growing data and mobility literature.
Daily mobility is key for gender equity. Access to transportation contributes to women’s agency and independence. The ability to move from place to place safely and efficiently can allow women to access education, work, and the public domain more generally. Yet, mobility is not just a means to access various opportunities. It is also a means to enter the public domain.
Women’s mobility is a multi-layered challenge
Women’s daily mobility, however, is often hampered by social, cultural, infrastructural, and technical barriers. Cultural bias, for instance, limits women mobility in a way that women are confined to an area with close proximity to their house due to society’s double standard on women to be homemakers. From an infrastructural perspective, public transportation mostly only accommodates home-to-work trips, when in reality women often make more complex trips with stops, for example, at the market, school, healthcare provider – sometimes called “trip chaining.” From a safety perspective, women tend to avoid making trips in certain areas and/or at certain time, due to a constant risk of being sexually harassed on public places. Women are also pushed toward more expensive transportation – such as taking a cab instead of a bus or train – based on safety concerns.
The growing importance of (new sources of) data
Researchers are increasingly experimenting with ways to address these interdependent problems through the analysis of diverse datasets, often collected by private sector businesses and other non-governmental entities. Gender-disaggregated mobile phone records, geospatial data, satellite imagery, and social media data, to name a few, are providing evidence-based insight into gender and mobility concerns. Such data collaboratives – the exchange of data across sectors to create public value – can help governments, international organizations, and other public sector entities in the move toward more inclusive urban and transportation planning, and the promotion of gender equity.
The below curated set of readings seek to focus on the following areas:
- Insights on how data can inform gender empowerment initiatives,
- Emergent research into the capacity of new data sources – like call detail records (CDRs) and satellite imagery – to increase our understanding of human mobility patterns, and
- Publications exploring data-driven policy for gender equity in mobility.
Readings are listed in alphabetical order.
We selected the readings based upon their focus (gender and/or mobility related); scope and representativeness (going beyond one project or context); type of data used (such as CDRs and satellite imagery); and date of publication.
Annotated Reading List
Data and Gender
Blumenstock, Joshua, and Nathan Eagle. Mobile Divides: Gender, Socioeconomic Status, and Mobile Phone Use in Rwanda. ACM Press, 2010.
- Using traditional survey and mobile phone operator data, this study analyzes gender and socioeconomic divides in mobile phone use in Rwanda, where it is found that the use of mobile phones is significantly more prevalent in men and the higher class.
- The study also shows the differences in the way men and women use phones, for example: women are more likely to use a shared phone than men.
- The authors frame their findings around gender and economic inequality in the country to the end of providing pointers for government action.
Bosco, Claudio, et al. Mapping Indicators of Female Welfare at High Spatial Resolution. WorldPop and Flowminder, 2015.
- This report focuses on early adolescence in girls, which often comes with higher risk of violence, fewer economic opportunity, and restrictions on mobility. Significant data gaps, methodological and ethical issues surrounding data collection for girls also create barriers for policymakers to create evidence-based policy to address those issues.
- The authors analyze geolocated household survey data, using statistical models and validation techniques, and creates high-resolution maps of various sex-disaggregated indicators, such as nutrition level, access to contraception, and literacy, to better inform local policy making processes.
- Further, it identifies the gender data gap and issues surrounding gender data collection, and provides arguments for why having a comprehensive data can help create better policy and contribute to the achievements of the Sustainable Development Goals (SDGs).
Buvinic, Mayra, Rebecca Furst-Nichols, and Gayatri Koolwal. Mapping Gender Data Gaps. Data2X, 2014.
- This study identifies gaps in gender data in developing countries on health, education, economic opportunities, political participation, and human security issues.
- It recommends ways to close the gender data gap through censuses and micro-level surveys, service and administrative records, and emphasizes how “big data” in particular can fill the missing data that will be able to measure the progress of women and girls well being. The authors argue that dentifying these gaps is key to advancing gender equality and women’s empowerment, one of the SDGs.
- This article analyzes global and national data in the banking sector to fill the gap of sex-disaggregated data in Chile. The purpose of the study is to describe the difference in spending behavior and priorities between women and men, identify the challenges for women in accessing financial services, and create policies that promote women inclusion in Chile.
Ready to Measure: Twenty Indicators for Monitoring SDG Gender Targets. Open Data Watch and Data2X, 2016.
- Using readily available data this study identifies 20 SDG indicators related to gender issues that can serve as a baseline measurement for advancing gender equality, such as percentage of women aged 20-24 who were married or in a union before age 18 (child marriage), proportion of seats held by women in national parliament, and share of women among mobile telephone owners, among others.
Ready to Measure Phase II: Indicators Available to Monitor SDG Gender Targets. Open Data Watch and Data2X, 2017.
- The Phase II paper is an extension of the Ready to Measure Phase I above. Where Phase I identifies the readily available data to measure women and girls well-being, Phase II provides informations on how to access and summarizes insights from this data.
- Phase II elaborates the insights about data gathered from ready to measure indicators and finds that although underlying data to measure indicators of women and girls’ wellbeing is readily available in most cases, it is typically not sex-disaggregated.
- Over one in five – 53 out of 232 – SDG indicators specifically refer to women and girls. However, further analysis from this study reveals that at least 34 more indicators should be disaggregated by sex. For instance, there should be 15 more sex-disaggregated indicators for SDG number 3: “Ensure healthy lives and promote well-being for all at all ages.”
- The report recommends national statistical agencies to take the lead and assert additional effort to fill the data gap by utilizing tools such as the statistical model to fill the current gender data gap for each of the SDGs.
Reed, Philip J., Muhammad Raza Khan, and Joshua Blumenstock. Observing gender dynamics and disparities with mobile phone metadata. International Conference on Information and Communication Technologies and Development (ICTD), 2016.
- The study analyzes mobile phone logs of millions of Pakistani residents to explore whether there is a difference in mobile phone usage behavior between male and female and determine the extent to which gender inequality is reflected in mobile phone usage.
- It utilizes mobile phone data to analyze the pattern of usage behavior between genders, and socioeconomic and demographic data obtained from census and advocacy groups to assess the state of gender equality in each region in Pakistan.
- One of its findings is a strong positive correlation between proportion of female mobile phone users and education score.
Stehlé, Juliette, et al. Gender homophily from spatial behavior in a primary school: A sociometric study. 2013.
- This paper seeks to understand homophily, a human behavior characterizes by interaction with peers who have similarities in “physical attributes to tastes or political opinions”. Further, it seeks to identify the magnitude of influence, a type of homophily has to social structures.
- Focusing on gender interaction among primary school aged children in France, this paper collects data from wearable devices from 200 children in the period of 2 days and measure the physical proximity and duration of the interaction among those children in the playground.
- It finds that interaction patterns are significantly determined by grade and class structure of the school. Meaning that children belonging to the same class have most interactions, and that lower grades usually do not interact with higher grades.
- From a gender lens, this study finds that mixed-gender interaction lasts shorter relative to same-gender interaction. In addition, interaction among girls is also longer compared to interaction among boys. These indicate that the children in this school tend to have stronger relationships within their own gender, or what the study calls gender homophily. It further finds that gender homophily is apparent in all classes.
Data and Mobility
Bengtsson, Linus, et al. Using Mobile Phone Data to Predict the Spatial Spread of Cholera. Flowminder, 2015.
- This study seeks to predict the 2010 cholera epidemic in Haiti using 2.9 million anonymous mobile phone SIM cards and reported cases of Cholera from the Haitian Directorate of Health, where 78 study areas were analyzed in the period of October 16 – December 16, 2010.
- From this dataset, the study creates a mobility matrix that indicates mobile phone movement from one study area to another and combines that with the number of reported case of cholera in the study areas to calculate the infectious pressure level of those areas.
- The main finding of its analysis shows that the outbreak risk of a study area correlates positively with the infectious pressure level, where an infectious pressure of over 22 results in an outbreak within 7 days. Further, it finds that the infectious pressure level can inform the sensitivity and specificity of the outbreak prediction.
- It hopes to improve infectious disease containment by identifying areas with highest risks of outbreaks.
Calabrese, Francesco, et al. Understanding Individual Mobility Patterns from Urban Sensing Data: A Mobile Phone Trace Example. SENSEable City Lab, MIT, 2012.
- This study compares mobile phone data and odometer readings from annual safety inspections to characterize individual mobility and vehicular mobility in the Boston Metropolitan Area, measured by the average daily total trip length of mobile phone users and average daily Vehicular Kilometers Traveled (VKT).
- The study found that, “accessibility to work and non-work destinations are the two most important factors in explaining the regional variations in individual and vehicular mobility, while the impacts of populations density and land use mix on both mobility measures are insignificant.” Further, “a well-connected street network is negatively associated with daily vehicular total trip length.”
- This study demonstrates the potential for mobile phone data to provide useful and updatable information on individual mobility patterns to inform transportation and mobility research.
Campos-Cordobés, Sergio, et al. “Chapter 5 – Big Data in Road Transport and Mobility Research.” Intelligent Vehicles. Edited by Felipe Jiménez. Butterworth-Heinemann, 2018.
- This study outlines a number of techniques and data sources – such as geolocation information, mobile phone data, and social network observation – that could be leveraged to predict human mobility.
- The authors also provide a number of examples of real-world applications of big data to address transportation and mobility problems, such as transport demand modeling, short-term traffic prediction, and route planning.
Lin, Miao, and Wen-Jing Hsu. Mining GPS Data for Mobility Patterns: A Survey. Pervasive and Mobile Computing vol. 12,, 2014.
- This study surveys the current field of research using high resolution positioning data (GPS) to capture mobility patterns.
- The survey focuses on analyses related to frequently visited locations, modes of transportation, trajectory patterns, and placed-based activities. The authors find “high regularity” in human mobility patterns despite high levels of variation among the mobility areas covered by individuals.
Phithakkitnukoon, Santi, Zbigniew Smoreda, and Patrick Olivier. Socio-Geography of Human Mobility: A Study Using Longitudinal Mobile Phone Data. PLoS ONE, 2012.
- This study used a year’s call logs and location data of approximately one million mobile phone users in Portugal to analyze the association between individuals’ mobility and their social networks.
- It measures and analyze travel scope (locations visited) and geo-social radius (distance from friends, family, and acquaintances) to determine the association.
- It finds that 80% of places visited are within 20 km of an individual’s nearest social ties’ location and it rises to 90% at 45 km radius. Further, as population density increases, distance between individuals and their social networks decreases.
- The findings in this study demonstrates how mobile phone data can provide insights to “the socio-geography of human mobility”.
Semanjski, Ivana, and Sidharta Gautama. Crowdsourcing Mobility Insights – Reflection of Attitude Based Segments on High Resolution Mobility Behaviour Data. vol. 71, Transportation Research, 2016.
- Using cellphone data, this study maps attitudinal segments that explain how age, gender, occupation, household size, income, and car ownership influence an individual’s mobility patterns. This type of segment analysis is seen as particularly useful for targeted messaging.
- The authors argue that these time- and space-specific insights could also provide value for government officials and policymakers, by, for example, allowing for evidence-based transportation pricing options and public sector advertising campaign placement.
Silveira, Lucas M., et al. MobHet: Predicting Human Mobility using Heterogeneous Data Sources. vol. 95, Computer Communications , 2016.
- This study explores the potential of using data from multiple sources (e.g., Twitter and Foursquare), in addition to GPS data, to provide a more accurate prediction of human mobility. This heterogenous data captures popularity of different locations, frequency of visits to those locations, and the relationships among people who are moving around the target area. The authors’ initial experimentation finds that the combination of these sources of data are demonstrated to be more accurate in identifying human mobility patterns.
Wilson, Robin, et al. Rapid and Near Real-Time Assessments of Population Displacement Using Mobile Phone Data Following Disasters: The 2015 Nepal Earthquake. PLOS Current Disasters, 2016.
- Utilizing call detail records of 12 million mobile phone users in Nepal, this study seeks spatio-temporal details of the population after the earthquake on April 25, 2015.
- It seeks to answer the problem of slow and ineffective disaster response, by capturing near real-time displacement pattern provided by mobile phone call detail records, in order to inform humanitarian agencies on where to distribute their assistance. The preliminary results of this study were available nine days after the earthquake.
- This project relies on the foundational cooperation with mobile phone operator, who supplied the de-identified data from 12 million users, before the earthquake.
- The study finds that shortly after the earthquake there was an anomalous population movement out of the Kathmandu Valley, the most impacted area, to surrounding areas. The study estimates 390,000 people above normal had left the valley.
Data, Gender and Mobility
Althoff, Tim, et al. “Large-Scale Physical Activity Data Reveal Worldwide Activity Inequality.” Nature, 2017.
- This study’s analysis of worldwide physical activity is built on a dataset containing 68 million days of physical activity of 717,527 people collected through their smartphone accelerometers.
- The authors find a significant reduction in female activity levels in cities with high active inequality, where high active inequality is associated with low city walkability – walkability indicators include pedestrian facilities (city block length, intersection density, etc.) and amenities (shops, parks, etc.).
- Further, they find that high active inequality is associated with high levels of inactivity-related health problems, like obesity.
Borker, Girija. “Safety First: Street Harassment and Women’s Educational Choices in India.” Stop Street Harassment, 2017.
- Using data collected from SafetiPin, an application that allows user to mark an area on a map as safe or not, and Safecity, another application that lets users share their experience of harassment in public places, the researcher analyzes the safety of travel routes surrounding different colleges in India and their effect on women’s college choices.
- The study finds that women are willing to go to a lower ranked college in order to avoid higher risk of street harassment. Women who choose the best college from their set of options, spend an average of $250 more each year to access safer modes of transportation.
Frias-Martinez, Vanessa, Enrique Frias-Martinez, and Nuria Oliver. A Gender-Centric Analysis of Calling Behavior in a Developing Economy Using Call Detail Records. Association for the Advancement of Articial Intelligence, 2010.
- Using encrypted Call Detail Records (CDRs) of 10,000 participants in a developing economy, this study analyzes the behavioral, social, and mobility variables to determine the gender of a mobile phone user, and finds that there is a difference in behavioral and social variables in mobile phone use between female and male.
- It finds that women have higher usage of phone in terms of number of calls made, call duration, and call expenses compared to men. Women also have bigger social network, meaning that the number of unique phone numbers that contact or get contacted is larger. It finds no statistically significant difference in terms of distance made between calls in men and women.
- Frias-Martinez et al recommends to take these findings into consideration when designing a cellphone based service.
Psylla, Ioanna, Piotr Sapiezynski, Enys Mones, Sune Lehmann. “The role of gender in social network organization.” PLoS ONE 12, December 20, 2017.
- Using a large dataset of high resolution data collected through mobile phones, as well as detailed questionnaires, this report studies gender differences in a large cohort. The researchers consider mobility behavior and individual personality traits among a group of more than 800 university students.
- Analyzing mobility data, they find both that women visit more unique locations over time, and that they have more homogeneous time distribution over their visited locations than men, indicating the time commitment of women is more widely spread across places.
Vaitla, Bapu. Big Data and the Well-Being of Women and Girls: Applications on the Social Scientific Frontier. Data2X, Apr. 2017.
- In this study, the researchers use geospatial data, credit card and cell phone information, and social media posts to identify problems–such as malnutrition, education, access to healthcare, mental health–facing women and girls in developing countries.
- From the credit card and cell phone data in particular, the report finds that analyzing patterns of women’s spending and mobility can provide useful insight into Latin American women’s “economic lifestyles.”
- Based on this analysis, Vaitla recommends that various untraditional big data be used to fill gaps in conventional data sources to address the common issues of invisibility of women and girls’ data in institutional databases.