Sector: future of work

Open Banking ‘revolution’ to challenge banks’ dominance

Curated on Posted on by Stefaan Verhulst

Shane Hickey at the Guardian: “This week sees the beginning of a quiet revolution in banking which some have championed as one of the greatest shake-ups in personal finance in years, while others have warned it could have serious implications for people’s private data.

It’s the start of a new series of rules concerning “open banking”, where customers will be able to share their personal financial information with companies other than their bank, opening up opportunities to get better deals on mortgages, overdrafts and comparing insurance and broadband deals.

For eager enthusiasts, it will revolutionise banking, make the system more competitive, and give consumers access to the best products for them.

For the more sceptical, among them consumer groups, it could cause problems with the security of previously private data…..

Financial data about how you spend your money, how often you are overdrawn and other details are currently held by your bank.

Under the new rules, the ownership of this data will essentially be transferred to the consumer, meaning that account holders will be able to give companies, other than their own bank, permission to access their details. Underlying this new regulation, which will spread across Europe this month, are EU rules that mean financial institutions must let customers share their data easily and securely. Extra measures are being taken in the UK to push through the changes, with the setting up of standards so data can be shared securely….

While consumers may feel empowered that their data is now in their own hands to do with as they please, with the potential to save money, it could also lead to unease, particularly when dealing with third parties that have brand names they might not recognise, says Shaw.

“One of the things to be mindful of is that consumers could find themselves in a complicated chain of providers. If you authorise one third party to access your money, and if there are potential losses, where does that fall?

“I think data regulators and financial regulators need to be really clear with consumers about how that is going to work. In order for consumers to really engage with this, they need to be confident that there are safeguards in place to protect them. There has been good progress on that.”…(More)”.

Using new data sources for policymaking

Curated on Posted on by Stefaan Verhulst

Technical report by the Joint Research Centre (JRC) of the European Commission: “… synthesises the results of our work on using new data sources for policy-making. It reflects a recent shift from more general considerations in the area of Big Data to a more dedicated investigation of Citizen Science, and it summarizes the state of play. With this contribution, we start promoting Citizen Science as an integral component of public participation in policy in Europe.

The particular need to focus on the citizen dimension emerged due to (i) the increasing interest in the topic from policy Directorate-Generals (DGs) of the European Commission (EC); (ii) the considerable socio-economic impact policy making has on citizens’ life and society as a whole; and (iii) the clear potentiality of citizens’ contributions to increase the relevance of policy making and the effectiveness of policies when addressing societal challenges.

We explicitly concentrate on Citizen Science (or public participation in scientific research) as a way to engage people in practical work, and to develop a mutual understanding between the participants from civil society, research institutions and the public sector by working together on a topic that is of common interest.

Acknowledging this new priority, this report concentrates on the topic of Citizen Science and presents already ongoing collaborations and recent achievements. The presented work particularly addresses environment-related policies, Open Science and aspects of Better Regulation. We then introduce the six phases of the ‘cyclic value chain of Citizen Science’ as a concept to frame citizen engagement in science for policy. We use this structure in order to detail the benefits and challenges of existing approaches – building on the lessons that we learned so far from our own practical work and thanks to the knowledge exchange from third parties. After outlining additional related policy areas, we sketch the future work that is required in order to overcome the identified challenges, and translate them into actions for ourselves and our partners.

Next steps include the following:

 Develop a robust methodology for data collection, analysis and use of Citizen Science for EU policy;

 Provide a platform as an enabling framework for applying this methodology to different policy areas, including the provision of best practices;

 Offer guidelines for policy DGs in order to promote the use of Citizen Science for policy in Europe;

 Experiment and evaluate possibilities of overarching methodologies for citizen engagement in science and policy, and their case specifics; and

 Continue to advance interoperability and knowledge sharing between currently disconnected communities of practise. …(More)”.

Developing online illustrative and participatory tools for urban planning: towards open innovation and co-production through citizen engagement

Curated on Posted on by Stefaan Verhulst

Virpi Oksman and Minna Kulju in the International Journal of Services Technology and Management: “This article examines the challenge of involving various stakeholders in urban planning through user-driven innovation and collaborative design and leveraging these processes to achieve mutually beneficial outcomes. Consequently, we introduce a novel illustrative and participatory tool combining mixed reality visualisations with user-centred interactions and feedback-tools so as to promote user insights and involve them in design.

This article analyses how these co-design services should be designed and offered to users in order to effectively support public participation and citizen-governance collaboration in future urban planning projects. We conclude that, in order to provide real benefit and value for urban planning and smart city solutions, participatory service should be integrated as part of the decision-making. Adoption of this kind of services system also means reforming of some of work processes in governance and planning how to exploit the results of the participatory processes to make informed decisions….(More)”

Data-Intensive Approaches To Creating Innovation For Sustainable Smart Cities

Curated on Posted on by Stefaan Verhulst

Science Trends: “Located at the complex intersection of economic development and environmental change, cities play a central role in our efforts to move towards sustainability. Reducing air and water pollution, improving energy efficiency while securing energy supply, and minimizing vulnerabilities to disruptions and disturbances are interconnected and pose a formidable challenge, with their dynamic interactions changing in highly complex and unpredictable manners….

The Beijing City Lab demonstrates the usefulness of open urban data in mapping urbanization with a fine spatiotemporal scale and reflecting social and environmental dimensions of urbanization through visualization at multiple scales.

The basic principle of open data will generate significant opportunities for promoting inter-disciplinary and inter-organizational research, producing new data sets through the integration of different sources, avoiding duplication of research, facilitating the verification of previous results, and encouraging citizen scientists and crowdsourcing approaches. Open data also is expected to help governments promote transparency, citizen participation, and access to information in policy-making processes.

Despite a significant potential, however, there still remain numerous challenges in facilitating innovation for urban sustainability through open data. The scope and amount of data collected and shared are still limited, and the quality control, error monitoring, and cleaning of open data is also indispensable in securing the reliability of the analysis. Also, the organizational and legal frameworks of data sharing platforms are often not well-defined or established, and it is critical to address the interoperability between various data standards, balance between open and proprietary data, and normative and legal issues such as the data ownership, personal privacy, confidentiality, law enforcement, and the maintenance of public safety and national security….

These findings are described in the article entitled Facilitating data-intensive approaches to innovation for sustainability: opportunities and challenges in building smart cities, published in the journal Sustainability Science. This work was led by Masaru Yarime from the City University of Hong Kong….(More)”.

From Principle to Practice: Humanitarian Innovation and Experimentation

Curated on Posted on by Stefaan Verhulst

Essay by Sean Martin McDonald, Kristin Bergtora Sandvik, & Katja Lindskov Jacobsen in the Stanford Social Innovation Review: “Humanitarian organizations have an almost impossible task: They must balance the imperative to save lives with the commitment to do no harm. They perform this balancing act amidst chaos, with incredibly high stakes and far fewer resources than they need. It’s no wonder that new technologies that promise to do more with less are so appealing.

By now, we know that technology can introduce bias, insecurity, and failure into systems. We know it is not an unalloyed good. What we often don’t know is how to measure the potential for those harms in the especially fragile contexts where humanitarians work. Without the tools or frameworks to evaluate the credibility of new technologies, it’s hard for humanitarians to know whether they’re having the intended impact and to assess the potential for harm. Introducing untested technologies into unstable environments raises an essential question: When is humanitarian innovation actually human subjects experimentation?

Humanitarians’ use of new technologies (including biometric identification to register refugees for relief, commercial drones to deliver cargo in difficult areas, and big data-fueled algorithms to predict the spread of disease) increasingly looks like the type of experimentation that drove the creation of human subjects research rules in the mid-20th century. In both examples, Western interests used untested approaches on African and Asian populations with limited consent and even less recourse. Today’s digital humanitarians may be innovators, but each new technology raises the specter of new harms, including biasing public resources with predictions over needs assessment, introducing coordination and practical failures through unique indicators and incompatible databases, and significant legal risks to both humanitarians and their growing list of partners….(More)”.

Government data: How open is too open?

Curated on Posted on by Stefaan Verhulst

Sharon Fisher at HPE: “The notion of “open government” appeals to both citizens and IT professionals seeking access to freely available government data. But is there such a thing as data access being too open? Governments may want to be transparent, yet they need to avoid releasing personally identifiable information.

There’s no question that open government data offers many benefits. It gives citizens access to the data their taxes paid for, enables government oversight, and powers the applications developed by government, vendors, and citizens that improve people’s lives.

However, data breaches and concerns about the amount of data that government is collecting makes some people wonder: When is it too much?

“As we think through the big questions about what kind of data a state should collect, how it should use it, and how to disclose it, these nuances become not some theoretical issue but a matter of life and death to some people,” says Alexander Howard, deputy director of the Sunlight Foundation, a Washington nonprofit that advocates for open government. “There are people in government databases where the disclosure of their [physical] location is the difference between a life-changing day and Wednesday.”

Open data supporters point out that much of this data has been considered a public record all along and tout the value of its use in analytics. But having personal data aggregated in a single place that is accessible online—as opposed to, say, having to go to an office and physically look up each record—makes some people uneasy.

Privacy breaches, wholesale

“We’ve seen a real change in how people perceive privacy,” says Michael Morisy, executive director at MuckRock, a Cambridge, Massachusetts, nonprofit that helps media and citizens file public records requests. “It’s been driven by a long-standing concept in transparency: practical obscurity.” Even if something was technically a public record, effort needed to be expended to get one’s hands on it. That amount of work might be worth it about, say, someone running for office, but on the whole, private citizens didn’t have to worry. Things are different now, says Morisy. “With Google, and so much data being available at the click of a mouse or the tap of a phone, what was once practically obscure is now instantly available.”

People are sometimes also surprised to find out that public records can contain their personally identifiable information (PII), such as addresses, phone numbers, and even Social Security numbers. That may be on purpose or because someone failed to redact the data properly.

That’s had consequences. Over the years, there have been a number of incidents in which PII from public records, including addresses, was used to harass and sometimes even kill people. For example, in 1989, Rebecca Schaeffer was murdered by a stalker who learned her address from the Department of Motor Vehicles. Other examples of harassment via driver’s license numbers include thieves who tracked down the address of owners of expensive cars and activists who sent anti-abortion literature to women who had visited health clinics that performed abortions.

In response, in 1994, Congress enacted the Driver’s Privacy Protection Act to restrict the sale of such data. More recently, the state of Idaho passed a law protecting the identity of hunters who shot wolves, because the hunters were being harassed by wolf supporters. Similarly, the state of New York allowed concealed pistol permit holders to make their name and address private after a newspaper published an online interactive map showing the names and addresses of all handgun permit holders in Westchester and Rockland counties….(More)”.

Why the deliberative democracy framework doesn’t quite work for me

Curated on Posted on by Stefaan Verhulst

Essay by Peter Levine: “In some ways, I came of age in the field of deliberative democracy. I had an internship at the Kettering Foundation when I was a college sophomore (when the foundation defined itself more purely in deliberative terms than it does today). By that time, I had already taken a philosophy seminar on the great deliberative theorist Jürgen Habermas. In the three decades since then, I’ve served on the boards of Kettering, Everyday Democracy, and AmericaSPEAKS. I wrote a book with “deliberative democracy” in its subtitle and co-edited The Deliberative Democracy Handbook with John Gastil. I was one of many co-founders of the Deliberative Democracy Consortium and have served on its steering committee since the last century.

None of these groups is committed to deliberation in a narrow sense (although opinions differ within the field). For me, these are the main limitations of focusing on deliberation as the central topic or unit of analysis:

Deliberative values are worthy ones, but they are not the only worthy ones. My own values would also include personal liberties and nonnegotiable rights, concerns for nature, and virtues of the inner life, such as equanimity and personal development. Stating my values doesn’t substitute for an argument, but it may suffice to make the point that deliberation is not the only good thing, and it’s in tension with other goods. A deliberative democrat will reply that I should discuss my values with other people. And so I should–but that doesn’t mean that the norms intrinsic to deliberation trump all other norms. Nor are fellow citizens the only sources of guidance; introspecting, reading ancient texts, consulting legal precedents, and conducting scientific experiments are helpful, too.

By the same token, deliberative virtues are not the only civic virtues. Deliberation is about discourse–talking and listening–so its virtues are discursive ones: humility and openness, empathy, sincerity, and perhaps eloquence. (The list is contested.) But a good citizen may be hard-working, physically courageous, or aesthetically creative instead of especially good at deliberating. The people who physically built the Athenian agora were as important as the people who exchanged ideas in it.

Deliberation depends on social organization. In order for people to have something that’s worth discussing, they must already make, control, or influence things of value together. That requires social organization, whether in the form of a market, a commons, a voluntary association, a functional network, or a political institution. Discussion rarely precedes these forms, because people can’t and won’t come together in completely amorphous groupings. Discussion is more typically a moment in an ongoing process of governance. Often a small group of founders chooses the rules-in-use that create a group in which deliberation can occur.

Thus we should ask about leadership and rules, not just about deliberation….(More)”.

Selected Readings on Data, Gender, and Mobility

Curated on Posted on by Michelle Winowatan

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. 

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 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:

  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 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.

Catalyzing Inclusive FInancial System: 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 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.

Social Theory After the Internet: Media, Technology and Globalization

Curated on Posted on by Stefaan Verhulst

(Open Access) Book by Ralph Schroeder: “The internet has fundamentally transformed society in the past 25 years, yet existing theories of mass or interpersonal communication do not work well in understanding a digital world. Nor has this understanding been helped by disciplinary specialization and a continual focus on the latest innovations. Ralph Schroeder takes a longer-term view, synthesizing perspectives and findings from various social science disciplines in four countries: the United States, Sweden, India and China. His comparison highlights, among other observations, that smartphones are in many respects more important than PC-based internet uses.

Social Theory after the Internet focuses on everyday uses and effects of the internet, including information seeking and big data, and explains how the internet has gone beyond traditional media in, for example, enabling Donald Trump and Narendra Modi to come to power. Schroeder puts forward a sophisticated theory of the role internet plays, and how both technological and social forces shape its significance. He provides a sweeping and penetrating study, theoretically ambitious and at the same time always empirically grounded….(More)”.

A.I. and Big Data Could Power a New War on Poverty

Curated on Posted on by Stefaan Verhulst

Elisabeth A. Mason in The New York Times: “When it comes to artificial intelligence and jobs, the prognostications are grim. The conventional wisdom is that A.I. might soon put millions of people out of work — that it stands poised to do to clerical and white collar workers over the next two decades what mechanization did to factory workers over the past two. And that is to say nothing of the truckers and taxi drivers who will find themselves unemployed or underemployed as self-driving cars take over our roads.

But it’s time we start thinking about A.I.’s potential benefits for society as well as its drawbacks. The big-data and A.I. revolutions could also help fight poverty and promote economic stability.

Poverty, of course, is a multifaceted phenomenon. But the condition of poverty often entails one or more of these realities: a lack of income (joblessness); a lack of preparedness (education); and a dependency on government services (welfare). A.I. can address all three.

First, even as A.I. threatens to put people out of work, it can simultaneously be used to match them to good middle-class jobs that are going unfilled. Today there are millions of such jobs in the United States. This is precisely the kind of matching problem at which A.I. excels. Likewise, A.I. can predict where the job openings of tomorrow will lie, and which skills and training will be needed for them….

Second, we can bring what is known as differentiated education — based on the idea that students master skills in different ways and at different speeds — to every student in the country. A 2013 study by the National Institutes of Health found that nearly 40 percent of medical students held a strong preference for one mode of learning: Some were listeners; others were visual learners; still others learned best by doing….

Third, a concerted effort to drag education and job training and matching into the 21st century ought to remove the reliance of a substantial portion of the population on government programs designed to assist struggling Americans. With 21st-century technology, we could plausibly reduce the use of government assistance services to levels where they serve the function for which they were originally intended…(More)”.

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