The New Tech Tools in Data Sharing


Essay by Massimo Russo and Tian Feng: “…Cloud providers are integrating data-sharing capabilities into their product suites and investing in R&D that addresses new features such as data directories, trusted execution environments, and homomorphic encryption. They are also partnering with industry-specific ecosystem orchestrators to provide joint solutions.

Cloud providers are moving beyond infrastructure to enable broader data sharing. In 2018, for example, Microsoft teamed up with Oracle and SAP to kick off its Open Data Initiative, which focuses on interoperability among the three large platforms. Microsoft has also begun an Open Data Campaign to close the data divide and help smaller organizations get access to data needed for innovation in artificial intelligence (AI). Amazon Web Services (AWS) has begun a number of projects designed to promote open data, including the AWS Data Exchange and the Open Data Sponsorship Program. In addition to these large providers, specialty technology companies and startups are likewise investing in solutions that further data sharing.

Technology solutions today generally fall into three categories: mitigating risks, enhancing value, and reducing friction. The following is a noncomprehensive list of solutions in each category.

1. Mitigating the Risks of Data Sharing

Potential financial, competitive, and brand risks associated with data disclosure inhibit data sharing. To address these risks, data platforms are embedding solutions to control use, limit data access, encrypt data, and create substitute or synthetic data. (See slide 2 in the slideshow.)

Data Breaches. Here are some of the technological solutions designed toprevent data breaches and unauthorized access to sensitive or private data:

  • Data modification techniques alter individual data elements or full data sets while maintaining data integrity. They provide increasing levels of protection but at a cost: loss of granularity of the underlying data. De-identification and masking strip personal identifier information and use encryption, allowing most of the data value to be preserved. More complex encryptions can increase security, but they also remove resolution of information from the data set.
  • Secure data storage and transfer can help ensure that data stays safe both at rest and in transit. Cloud solutions such as Microsoft Azure and AWS have invested in significant platform security and interoperability.
  • Distributed ledger technologies, such as blockchain, permit data to be stored and shared in a decentralized manner that makes it very difficult to tamper with. IOTA, for example, is a distributed ledger platform for IoT applications supported by industy players such as Bosch and Software AG.
  • Secure computation enables analysis without revealing details of the underlying data. This can be done at a software level, with techniques such as secure multiparty computation (MPC) that allow potentially untrusting parties to jointly compute a function without revealing their private inputs. For example, with MPC, two parties can calculate the intersection of their respective encrypted data set while only revealing information about the intersection. Google, for one, is embedding MPC in its open-source Private Join and Compute tools.
  • Trusted execution environments (TEEs) are hardware modules separate from the operating system that allow for secure data processing within an encrypted private area on the chip. Startup Decentriq is partnering with Intel and Microsoft to explore confidential computing by means of TEEs. There is a significant opportunity for IoT equipment providers to integrate TEEs into their products….(More)”

A new approach to problem-solving across the Sustainable Development Goals


Alexandra Bracken, John McArthur, and Jacob Taylor at Brookings: “The economic, social, and environmental challenges embedded throughout the world’s 17 Sustainable Development Goals (SDGs) will require many breakthroughs from business as usual. COVID-19 has only underscored the SDGs’ central message that the underlying problems are both interconnected and urgent, so new mindsets are required to generate faster progress on many fronts at once. Our recent report, 17 Rooms: A new approach to spurring action for the Sustainable Development Goals, describes an effort to innovate around the process of SDG problem-solving itself.

17 Rooms aims to advance problem-solving within and across all the SDGs. As a partnership between Brookings and The Rockefeller Foundation, the first version of the undertaking was convened in September 2018, as a single meeting on the eve of the U.N. General Assembly in New York. The initiative has since evolved into a two-pronged effort: an annual flagship process focused on global-scale policy issues and a community-level process in which local actors are taking 17 Rooms methods into their own hands.

In practical terms, 17 Rooms consists of participants from disparate specialist communities each meeting in their own “Rooms,” or working groups, one for each SDG. Each Room is tasked with a common assignment of identifying cooperative actions they can take over the subsequent 12-18 months. Emerging ideas are then shared across Rooms to spot opportunities for collaboration.

The initiative continues to evolve through ongoing experimentation, so methods are not overly fixed, but three design principles help define key elements of the 17 Rooms mindset:

  1. All SDGs get a seat at the table. Insights, participants, and priorities are valued equally across all the specialist communities focused on individual dimensions of the SDGs
  2. Take a next step, not the perfect step. The process encourages participants to identify—and collaborate on—actions that are “big enough to matter, but small enough to get done”
  3. Conversations, not presentations. Discussions are structured around collaboration and peer-learning, aiming to focus on what’s best for an issue, not any individual organization

These principles appear to contribute to three distinct forms of value: the advancement of action, the generation of insights, and a strengthened sense of community among participants….(More)”.

Legislative Performance Futures


Article by Ben Podgursky on “Incentivize Good Laws by Monetizing the Verdict of History”….There are net-positive legislative policies which legislators won’t enact, because they only help people in the medium to far future.  For example:

  • Climate change policy
  • Infrastructure investments and mass-transit projects
  • Debt control and social security reform
  • Child tax credits

The (infrequent) times reforms on these issues are legislated — which happens rarely compared to their future value — they are passed not because of the value provided to future generations, but because of the immediate benefit to voters today:

  • Infrastructure investment goes to “shovel ready” projects, with an emphasis on short-term job creation, even when the prime benefit is to future GDP.  For example, Dams constructed in the 1930s (the Hoover Dam, the TVA) provide immense value today, but the projects only happened in order to create tens of thousands of jobs.
  • Climate change legislation is usually weakly directed.  Instead of policies which incur significant long-term benefits but short-term costs (ie, carbon taxes), “green legislation” aims to create green jobs and incentivize rooftop solar (reducing power bills today).
  • (small) child tax credits are passed to help parents today, even though the vastly larger benefit is incurred by children who exist because the marginal extra cash helped their parents afford an extra child.

On the other hand, reforms which provide nobenefit to today’s voter do not happen; this is why the upcoming Social Security Trust Fund shortfall will likely not be fixed until benefits are reduced and voters are directly impacted.

The issue is that while the future reaps the benefits or failures of today’s laws, people of the future cannot vote in today’s elections.  In fact, in almost no circumstances does the future have any ability to meaningfully reward or punish past lawmakers; there are debates today about whether to remove statues and rename buildings dedicated to those on the wrong side of history, actions which even proponents acknowledge as entirely symbolic….(More)”.

Policy 2.0 in the Pandemic World: What Worked, What Didn’t, and Why


Blog by David Osimo: “…So how, then, did these new tools perform when confronted with the once-in-a-lifetime crisis of a vast global pandemic?

It turns out, some things worked. Others didn’t. And the question of how these new policymaking tools functioned in the heat of battle is already generating valuable ammunition for future crises.

So what worked?

Policy modelling – an analytical framework designed to anticipate the impact of decisions by simulating the interaction of multiple agents in a system rather than just the independent actions of atomised and rational humans – took centre stage in the pandemic and emerged with reinforced importance in policymaking. Notably, it helped governments predict how and when to introduce lockdowns or open up. But even there uptake was limited. A recent survey showed that of the 28 models used in different countries to fight the pandemic were traditional, and not the modern “agent-based models” or “system dynamics” supposed to deal best with uncertainty. Meanwhile, the concepts of system science was becoming prominent and widely communicated. It became quickly clear in the course of the crisis that social distancing was more a method to reduce the systemic pressure on the health services than a way to avoid individual contagion (the so called “flatten the curve” project).

Open government data has long promised to allow citizens and businesses to build new services at scale and make government accountable. The pandemic largely confirmed how important this data could be to allow citizens to analyse things independently. Hundreds of analysts from all walks of life and disciplines used social media to discuss their analysis and predictions, many becoming household names and go-to people in countries and regions. Yes, this led to noise and a so-called “infodemic,” but overall it served as a fundamental tool to increase confidence and consensus behind the policy measures and to make governments accountable for their actions. For instance, one Catalan analyst demonstrated that vaccines were not provided during weekends and forced the government to change its stance. Yet it is also clear that not all went well, most notably on the supply side. Governments published data of low quality, either in PDF, with delays or with missing data due to spreadsheet abuse.

In most cases, there was little demand for sophisticated data publishing solutions such as “linked” or “FAIR” data, although particularly significant was the uptake of these kinds of solutions when it came time to share crucial research data. Experts argue that the trend towards open science has accelerated dramatically and irreversibly in the last year, as shown by the portal https://www.covid19dataportal.org/ which allowed sharing of high quality data for scientific research….

But other new policy tools proved less easy to use and ultimately ineffective. Collaborative governance, for one, promised to leverage the knowledge of thousands of citizens to improve public policies and services. In practice, methodologies aiming at involving citizens in decision making and service design were of little use. Decisions related to lockdown and opening up were taken in closed committees in top down mode. Individual exceptions certainly exist: Milan, one of the cities worst hit by the pandemic, launched a co-created strategy for opening up after the lockdown, receiving almost 3000 contributions to the consultation. But overall, such initiatives had limited impact and visibility. With regard to co-design of public services, in times of emergency there was no time for prototyping or focus groups. Services such as emergency financial relief had to be launched in a hurry and “just work.”

Citizen science promised to make every citizen a consensual data source for monitoring complex phenomena in real time through apps and Internet-of-Things sensors. In the pandemic, there were initially great expectations on digital contact tracing apps to allow for real time monitoring of contagions, most notably through bluetooth connections in the phone. However, they were mostly a disappointment. Citizens were reluctant to install them. And contact tracing soon appeared to be much more complicated – and human intensive – than originally thought. The huge debate between technology and privacy was followed by very limited impact. Much ado about nothing.

Behavioural economics (commonly known as nudge theory) is probably the most visible failure of the pandemic. It promised to move beyond traditional carrots (public funding) and sticks (regulation) in delivering policy objectives by adopting an experimental method to influence or “nudge” human behaviour towards desired outcomes. The reality is that soft nudges proved an ineffective alternative to hard lockdown choices. What makes it uniquely negative is that such methods took centre stage in the initial phase of the pandemic and particularly informed the United Kingdom’s lax approach in the first months on the basis of a hypothetical and unproven “behavioural fatigue.” This attracted heavy criticism towards the excessive reliance on nudges by the United Kingdom government, a legacy of Prime Minister David Cameron’s administration. The origin of such criticisms seems to lie not in the method shortcomings per se, which enjoyed success previously on more specific cases, but in the backlash from excessive expectations and promises, epitomised in the quote of a prominent behavioural economist: “It’s no longer a matter of supposition as it was in 2010 […] we can now say with a high degree of confidence these models give you best policy.

Three factors emerge as the key determinants behind success and failure: maturity, institutions and leadership….(More)”.

Open Data Day 2021: How to unlock its potential moving forward?


Stefaan Verhulst, Andrew Young, and Andrew Zahuranec at Data and Policy: “For over a decade, data advocates have reserved one day out of the year to celebrate open data. Open Data Day 2021 comes at a time of unprecedented upheaval. As the world remains in the grip of COVID-19, open data researchers and practitioners must confront the challenge of how to use open data to address the types of complex, emergent challenges that are likely to define the rest of this century (and beyond). Amid threats like the ongoing pandemic, climate change, and systemic poverty, there is renewed pressure to find ways that open data can solve complex social, cultural, economic and political problems.

Over the past year, the Open Data Policy Lab, an initiative of The GovLab at NYU’s Tandon School of Engineering, held several sessions with leaders of open data from around the world. Over the course of these sessions, which we called the Summer of Open Data, we studied various strategies and trends, and identified future pathways for open data leaders to pursue. The results of this research suggest an emergent Third Wave of Open Data— one that offers a clear pathway for stakeholders of all types to achieve Open Data Day’s goal of “showing the benefits of open data and encouraging the adoption of open data policies in government, business, and civil society.”

The Third Wave of Open Data is central to how data is being collected, stored, shared, used, and reused around the world. In what follows, we explain this notion further, and argue that it offers a useful rubric through which to take stock of where we are — and to consider future goals — as we mark this latest iteration of Open Data Day.

The Past and Present of Open Data

The history of open data can be divided into several waves, each reflecting the priorities and values of the era in which they emerged….(More)”.

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The Three Waves of Open Data

Selected Readings on Data, Gender, and Mobility


By Michelle Winowatan, Uma Kalkar, 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, and updated in 2021.

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. 

Introduction

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’s 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 multiple 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 times due to a constant risk of being sexually harassed n 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:

  1. Insights on how data can inform gender empowerment initiatives,
  2. 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,
  3. 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  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 identifying these gaps is key to achieving SDG 5: advancing gender equality and women’s empowerment.

Catalyzing Inclusive Financial Systems: Chile’s Commitment to Women’s Data. Data2X, 2014.

  • 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 information on how to access this data and summarizes insights extracted from it.
  • 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 the 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 that characterizes interactions 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 applied 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 measures 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. This means 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.

Strengthening Gender Measures and Data in the COVID-19 Era: An Urgent Need for Change. Paris 21, 2021.

  • COVID-19 has exacerbated gender disparities, especially with regard to women’s livelihoods, unpaid labor, mental health, and risk of gender-based violence. Gaps in gender data impedes robust, data-driven, and effective policies to quantify, analyse, and respond to these issues. 
  • Without this information, the full effects of the COVID-19 pandemic cannot be understood. This report calls on National Statistical Systems, survey managers, funders, multilateral agencies, researchers, and policymakers to collect gender-intentional and disaggregated data that is standardized and comparable to address key areas of concern for women and girls. Additionally, it seeks to link non-traditional data sources, such as social media and news media, with existing frameworks to fill in knowledge gaps. Moreover, this information must be rendered accessible for all stakeholders to maximize the potential of the information. Post-pandemic, conscious collection and collation of gendered data is vital to preempt policy problems.

The Sex, Gender and COVID-19 Project: The COVID-19 Sex-Disaggregated Data Tracker. 2021.

  • This data tracker, produced by Global Health 50/50, the African Population and Health Research Center, and the International Center for Research on Women, tracks which countries and datasets have reported sex-disaggregated data on COVID-19 testing, confirmed cases, hospitalizations, and deaths.

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 cases 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.

Gauvin, Laetitia et al. Gender gaps in urban mobility. Humanities and Information Science. Humanities & Social Sciences Communications vol. 7, issue 11, 2020.

  • This article discusses how urbanization affects mobility of women in realizing their rights. It points out the historic lack of gender disaggregated data for urban planning, leading to transportation designs that do not best accommodate the needs of women.
  • Examining the case study of urban mobility through a gendered lens in the large and growing metropolitan area of Santiago, Chile, the article examines the mobility traces from Call Detail Records (CDRs) of an anonymized cohort of mobile phone users, sorted by gender, over 3 months. It then mapped differences between men and women with regard to socio-demographic indicators and mobility differences across the city and through the Santiago transportation network structure and identified points of interests frequented by either sex to inform gendered mobility needs in urban areas.

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 patterns 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 operators, 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 more people  than 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 users 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, Borker 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 Artificial 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.

The Landscape of Big Data and Gender. Data2X, February, 2021.

  • Under the backdrop of COVID-19, this report reaffirms that big data initiatives to study mobility, health, and social norms through gendered lenses have greatly progressed. More private companies and think tanks have launched data collection and sharing efforts to spur innovative projects to address COVID-19 complications.
  • However, economic opportunity, security, and civic action have been lagging behind. Big data collection among these topics is complicated by the lack of sex-disaggregated datasets, gender disparities in technology access, and the lack of gender-tags among big data.
  • Large technology firms, especially social networks like Facebook, LinkedIn, Uber, and more, create a large amount of gender-organized data. The report found that users and data-holding companies are willing to share this information for public policy reasons so long as it provides value and is protected. To this end, Data2X, alongside its partners, champion the use of data collaboratives to use gender sorted information for social good.

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.

Improving Governance by Asking Questions that Matter


Fiona Cece, Nicola Nixon and Stefaan Verhulst at the Open Government Partnership:

“You can tell whether a man is clever by his answers. You can tell whether a man is wise by his questions” – Naguib Mahfouz

Data is at the heart of every dimension of the COVID-19 challenge. It’s been vital in the monitoring of daily rates, track and trace technologies, doctors appointments, and the vaccine roll-out. Yet our daily diet of brightly-coloured graphed global trends masks the maelstrom of inaccuracies, gaps and guesswork that underlies the ramshackle numbers on which they are so often based. Governments are unable to address their citizens’ needs in an informed way when the data itself is partial, incomplete or simply biased. And citizens’ in turn are unable to contribute to collective decision-making that impacts their lives when the channels for doing so in meaningful ways are largely non-existent. 

There is an irony here. We live in an era in which there are an unprecedented number of methods for collecting data. Even in the poorest countries with weak or largely non-existent government systems, anyone with a mobile phone or who accesses the internet is using and producing data. Yet a chasm exists between the potential of data to contribute to better governance and what it is actually collected and used for.

Even where data accuracy can be relied upon, the practice of effective, efficient and equitable data governance requires much more than its collection and dissemination.

And although governments will play a vital role, combatting the pandemic and its associated socio-economic challenges will require the combined efforts of non-government organizations (NGOs), civil society organizations (CSOs), citizens’ associations, healthcare companies and providers, universities, think tanks and so many others. Collaboration is key.

There is a need to collectively move beyond solution-driven thinking. One initiative working toward this end is The 100 Questions Initiative by The Governance Lab (The GovLab) at the NYU Tandon School of Engineering. In partnership with the The Asia Foundation, the Centre for Strategic and International Studies in Indonesia, and the BRAC Institute of Governance and Development, the Initiative is launching a Governance domain. Collectively we will draw on the expertise of over 100 “bilinguals”– experts in both data science and governance — to identify the 10 most-pressing questions on a variety of issues that can be addressed using data and data science. The cohort for this domain is multi-sectoral and geographically varied, and will provide diverse input on these governance challenges. 

Once the questions have been identified and prioritized, and we have engaged with a broader public through a voting campaign, the ultimate goal is to establish one or more data collaboratives that can generate answers to the questions at hand. Data collaboratives are an emerging structure that allow pooling of data and expertise across sectors, often resulting in new insights and public sector innovations.  Data collaboratives are fundamentally about sharing and cross-sectoral engagement. They have been deployed across countries and sectoral contexts, and their relative success shows that in the twenty-first century no single actor can solve vexing public problems. The route to success lies through broad-based collaboration. 

Multi-sectoral and geographically diverse insight is needed to address the governance challenges we are living through, especially during the time of COVIDd-19. The pandemic has exposed weak governance practices globally, and collectively we need to craft a better response. As an open governance and data-for-development community, we have not yet leveraged the best insight available to inform an effective, evidence-based response to the pandemic. It is time we leverage more data and technology to enable citizen-centrism in our service delivery and decision-making processes, to contribute to overcoming the pandemic and to building our governance systems, institutions and structures back better. Together with over 130 ‘Bilinguals’ – experts in both governance and data – we have set about identifying the priority questions that data can answer to improve governance. Join us on this journey. Stay tuned for our public voting campaign in a couple of months’ time when we will crowdsource your views on which of the questions they pose really matter….(More)”.

Why Transparency Won’t Save Us


Essay by Sun-ha Hong: “In a society beset with black-boxed algorithms and vast surveillance systems, transparency is often hailed as liberal democracy’s superhero. It’s a familiar story: inject the public with information to digest, then await their rational deliberation and improved decision making. Whether in discussions of facial recognition software or platform moderation, we run into the argument that transparency will correct the harmful effects of algorithmic systems. The trouble is that in our movies and comic books, superheroes are themselves deus ex machina: black boxes designed to make complex problems disappear so that the good guys can win. Too often, transparency is asked to save the day on its own, under the assumption that disinformation or abuse of power can be shamed away with information.

Transparency without adequate support, however, can quickly become fuel for speculation and misunderstanding….

All this is part of a broader pattern in which the very groups who should be held accountable by the data tend to be its gatekeepers. Facebook is notorious for transparency-washing strategies, in which it dangles data access like a carrot but rarely follows through in actually delivering it. When researchers worked to create more independent means of holding Facebook accountable — as New York University’s Ad Observatory did last year, using volunteer researchers to build a public database of ads on the platform — Facebook threatened to sue them. Despite the lofty rhetoric around Facebook’s Oversight Board (often described as a “Supreme Court” for the platform), it falls into the same trap of transparency without power: the scope is limited to individual cases of content moderation, with no binding authority over the company’s business strategy, algorithmic design, or even similar moderation cases in the future.

Here, too, the real bottleneck is not information or technology, but power: the legal, political and economic pressure necessary to compel companies like Facebook to produce information and to act on it. We see this all too clearly when ordinary people do take up this labour of transparency, and attempt to hold technological systems accountable. In August 2020, Facebook users reported the Kenosha Guard group more than 400 times for incitement of violence. But Facebook declined to take any action until an armed shooter travelled to Kenosha, Wisconsin, and killed two protesters. When transparency is compromised by the concentration of power, it is often the vulnerable who are asked to make up the difference — and then to pay the price.

Transparency cannot solve our problems on its own. In his book The Rise of the Right to Know, journalism scholar Michael Schudson argues that transparency is better understood as a “secondary or procedural morality”: a tool that only becomes effective by other means. We must move beyond the pernicious myth of transparency as a universal solution, and address the distribution of economic and political power that is the root cause of technologically amplified irrationality and injustice….(More)”.

How can stakeholder engagement and mini-publics better inform the use of data for pandemic response?


Andrew Zahuranec, Andrew Young and Stefaan G. Verhulst at the OECD Participo Blog Series:

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“What does the public expect from data-driven responses to the COVID-19 pandemic? And under what conditions?” These are the motivating questions behind The Data Assembly, a recent initiative by The GovLab at New York University Tandon School of Engineering — an action research center that aims to help institutions work more openly, collaboratively, effectively, and legitimately.

Launched with support from The Henry Luce Foundation, The Data Assembly solicited diverse, actionable public input on data re-use for crisis response in the United States. In particular, we sought to engage the public on how to facilitate, if deemed acceptable, the use of data that was collected for a particular purpose for informing COVID-19. One additional objective was to inform the broader emergence of data collaboration— through formal and ad hoc arrangements between the public sector, civil society, and those in the private sector — by evaluating public expectation and concern with current institutional, contractual, and technical structures and instruments that may underpin these partnerships.

The Data Assembly used a new methodology that re-imagines how organisations can engage with society to better understand local expectations regarding data re-use and related issues. This work goes beyond soliciting input from just the “usual suspects”. Instead, data assemblies provide a forum for a much more diverse set of participants to share their insights and voice their concerns.

This article is informed by our experience piloting The Data Assembly in New York City in summer 2020. It provides an overview of The Data Assembly’s methodology and outcomes and describes major elements of the effort to support organisations working on similar issues in other cities, regions, and countries….(More)”.

As Jakarta floods again, humanitarian chatbots on social media support community-led disaster response


Blog by Petabencana: “On February 20th, #banjir and #JakartaBanjir were the highest trending topics on Twitter Indonesia, as the capital city was inundated for the third major time this year, following particularly heavy rainfall from Friday night (19/2/2021) to Saturday morning (20/02/2021). As Jakarta residents turned to social media to share updates about the flood, they were greeted by “Disaster Bot” – a novel AI-assisted chatbot that monitors social media for posts about disasters and automatically invites users to submit more detailed disaster reports. These crowd-sourced reports are used to map disasters in real-time, on a free and open source website, PetaBencana.id.

As flooding blocked major thoroughfares and toll roads, disrupted commuter lines, and cut off electricity to over 60,000 homes, residents continued to share updates about the flood situation in order to stay alert and make timely decisions about safety and response. Hundreds of residents submitted flood reports to PetaBencana.id, alerting each other about water levels, broken infrastructures and road accessibility. The Jakarta Emergency Management Agency also updated the map with official information about flood affected  areas, and monitored the map to respond to resident needs. PetaBencana.id experienced a 2000% in activity in under 12 hours as residents actively checked the map to understand the flooding situation, avoid flooded areas, and make decisions about safety and response. 

Residents share updates about flood-affected road access through the open source information sharing platform, PetaBencana.id. Thousands of residents used the map to navigate safely as heavy rainfall inundated the city for the third major time this year.

As flooding incidents continue to occur with increasing intensity across the country, community-led information sharing is once again proving its significance in supporting response and planning at multiple scales. …(More)”.