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.

Universities must prepare for a technology-enabled future

Curated on Posted on by Stefaan Verhulst

 in the Conversation: “Automation and artificial intelligence technologies are transforming manufacturing, corporate work and the retail business, providing new opportunities for companies to explore and posing major threats to those that don’t adapt to the times. Equally daunting challenges confront colleges and universities, but they’ve been slower to acknowledge them.

At present, colleges and universities are most worried about competition from schools or training systems using online learning technology. But that is just one aspect of the technological changes already under way. For example, some companies are moving toward requiring workers have specific skills trainings and certifications – as opposed to college degrees.

As a professor who researches artificial intelligence and offers distance learning courses, I can say that online education is a disruptive challenge for which colleges are ill-prepared. Lack of student demand is already closing 800 out of roughly 10,000 engineering colleges in India. And online learning has put as many as half the colleges and universities in the U.S. at risk of shutting down in the next couple decades as remote students get comparable educations over the internet – without living on campus or taking classes in person. Unless universities move quickly to transform themselves into educational institutions for a technology-assisted future, they risk becoming obsolete….(More)”

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

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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)”.

Humanitarian group uses blockchain tech to give Rohingya digital ID cards

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Techwire Asia: “A Non-Governmental Organization is using blockchain technology to provide stateless Rohingya refugees who fled Burma (Myanmar) with digital identity cards in a pilot project aimed at giving access to services like banking and education.

The first 1,000 people to benefit from the project in 2018 will be members of the diaspora in Malaysia, Bangladesh and Saudi Arabia, decades-old safe havens for the Rohingya, who are the world’s biggest stateless minority.

“They are disenfranchised,” Kyri Andreou, co-founder of The Rohingya Project, which is organising the initiative, said at its launch in Kuala Lumpur on Wednesday.

“They are shut out. One of the key aspects is because of the lack of identification.”

More than 650,000 Rohingya Muslims – who are denied citizenship in Buddhist-majority Burma – have fled to Bangladesh since August after attacks by insurgents triggered a response by Burma’s army and Buddhist vigilantes….

According to The Sun, Muhammad Noor said the project focuses on two aspects – identity and opportunity – in which the system will provide the first verified data on Rohingya census across the world.

Individual Rohingya, he said, shall have their ancestry authentically identified to link them directly to their original land of dispersion…(More)”.

Inside China’s Vast New Experiment in Social Ranking

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Mara Hvistendahl at Wired: “…During the past 30 years, by contrast, China has grown to become the world’s second largest economy without much of a functioning credit system at all. The People’s Bank of China, the country’s central banking regulator, maintains records on millions of consumers, but they often contain little or no information. Until recently, it was difficult to get a credit card with any bank other than your own. Consumers mainly used cash….

In 2013, Ant Financial executives retreated to the mountains outside Hangzhou to discuss creating a slew of new products; one of them was Zhima Credit. The executives realized that they could use the data-collecting powers of Alipay to calculate a credit score based on an individual’s activities. “It was a very natural process,” says You Xi, a Chinese business reporter who detailed this pivotal meeting in a recent book, Ant Financial. “If you have payment data, you can assess the credit of a person.” And so the tech company began the process of creating a score that would be “credit for everything in your life,” as You explains it.

Ant Financial wasn’t the only entity keen on using data to measure people’s worth. Coincidentally or not, in 2014 the Chinese government announced it was developing what it called a system of “social credit.” In 2014, the State Council, China’s governing cabinet, publicly called for the establishment of a nationwide tracking system to rate the reputations of individuals, businesses, and even government officials. The aim is for every Chinese citizen to be trailed by a file compiling data from public and private sources by 2020, and for those files to be searchable by fingerprints and other biometric characteristics. The State Council calls it a “credit system that covers the whole society.”…

In 2015 Ant Financial was one of eight tech companies granted approval from the People’s Bank of China to develop their own private credit scoring platforms. Zhima Credit appeared in the Alipay app shortly after that. The service tracks your behavior on the app to arrive at a score between 350 and 950, and offers perks and rewards to those with good scores. Zhima Credit’s algorithm considers not only whether you repay your bills but also what you buy, what degrees you hold, and the scores of your friends. Like Fair and Isaac decades earlier, Ant Financial executives talked publicly about how a data-driven approach would open up the financial system to people who had been locked out, like students and rural Chinese. For the more than 200 million Alipay users who have opted in to Zhima Credit, the sell is clear: Your data will magically open doors for you….

Often, data brokers are flat-out wrong. The data broker Acxiom, which provides some information about what it collects on a site called AboutTheData.com, has me pegged as a single woman with a high school education and a “likely Las Vegas gambler,” when in fact I’m married, have a master’s degree, and have never even bought a lottery ticket. But it is impossible to challenge these assessments, since we’re never told that they exist. I know more about Zhima Credit’s algorithm than I do about how US data brokers rate me. This is, as Pasquale points out in his book The Black Box Society, essentially a “one-way mirror.”…(More)”.

Civic Technology: Open Data and Citizen Volunteers as a Resource for North Carolina Local Governments

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Report by John B. Stephens: “Civic technology is an emergent area of practice where IT experts and citizens without specialized IT skills volunteer their time using government-provided open data to improve government services or otherwise create public benefit. Civic tech, as it is often referred to, draws on longer-standing practices, particularly e-government and civic engagement. It is also a new form of citizen–government co-production, building on the trend of greater government transparency.

This report is designed to help North Carolina local government leaders:

  • Define civic technology practices and describing North Carolina civic tech resources
  • Highlight accomplishments and ongoing projects in civic tech (in North Carolina and beyond)
  • Identify opportunities and challenges for North Carolina local governments in civic tech
  • Provide a set of resources for education and involvement in civic tech….(More)”.

7 lessons learned from $5 million in open innovation prizes

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Sara Holoubek in the Lab Report: “Prize competitions have long been used to accelerate innovation. In the 18th century, Britain offered a significant prize purse for advancements in seafaring navigation, and Napoleon’s investment in a competition led to innovation in food preservation. More recently, DARPA’s Grand Challenge ignited a decade of progress in autonomous vehicle technology.

Challenges are considered a branch of “open innovation,” an idea that has been around for decades but became more popular after the University of California’s Henry Chesbrough published a book on the topic in 2003. Chesbrough describes open innovation as “a paradigm that assumes that firms can and should use external ideas as well as internal ideas, and internal and external paths to market, as the firms look to advance their technology.”…Here’s what we’ve learned…:

1. It’s a long game.

Clients get more out of open innovation when they reject a “one and done” mentality, opting instead to build an open innovation competency, socialize best practices across the broader organization, and determine the best moments to push the innovation envelope. …

2. Start with problem statement definition.

If a company isn’t in agreement on the problem to be solved, its challenge won’t be successful. …

3. Know what would constitute a “big win.”

Many of our clients are tasked with balancing near-term expectations while navigating what it will take for the organization to thrive in the long term. Rather than meeting in the middle, we ask what would constitute a “big win.” …

4. Invest in challenge design.

The market is flooded with platforms that aim to democratize challenges — and better access to tools is great. But in the absence of challenge design, a competition run on the best platform will fail. ….

5. Understand what it takes to close the gap between concept and viability.

…Solvers often tell us this “virtual accelerator” period — which includes education and exercises in empathy-building, subject matter knowledge, rapid prototyping, and business modeling — is of more value to their teams than prize money.

6. Hug the lawyers — as early as possible.

… Faced with unique constraints, we encourage clients to engage counsel early in the process. …

7. Really, really good marketing is essential.

A key selling point for challenge platforms is the size of their database. Some even monetize “communities.” …(More)”

Transitioning Towards a Knowledge Society: Qatar as a Case Study

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Book by Julia Gremm, Julia Barth, Kaja J. Fietkiewicz and Wolfgang G. Stock: “The book offers a critical evaluation of Qatar’s path from oil- and gas-based industries to a knowledge-based economy. This book gives basic information about the region and the country, including the geographic and demographic data, the culture, the politics and the economy, the health care conditions and the education system. It introduces the concepts of knowledge society and knowledge-based development and adds factual details about Qatar by interpreting indicators of the development status. Subsequently, the research methods that underlie the study are described, which offers information on the eGovernment study analyzing the government-citizen relationship, higher education institutions and systems, its students and the students’ way into the labor market. This book has an audience with economists, sociologists, political scientists, geographers, information scientists and other researchers on the knowledge society, but also all researchers and practitioners interested in the Arab Oil States and their future….(More)”.

How Software is Eating the World and Reprogramming Democracy

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Jaime Gómez Ramírez at Open Mind: “Democracy, the government of the majority typically through elected representatives, is undergoing a major crisis. Human societies have experimented with democracy since at least the fifth century BC in the polis of Athens. Whether democracy is scalable is an open question that could help understand the current mistrust in democratic institutions and the rise of populism. The majority rule is a powerful narrative that is fed every few years with elections. In Against elections, the cultural historian Van Reybrouck claims that elections were never meant to make democracy possible, rather the opposite, it was a tool designed for those in power to prevent “the rule of the mob”. Elections created a new elite and power remained in the hands of a minority, but this time endowed with democratic legitimacy….

The 2008 financial crisis have changed the perception of, the once taken for granted, complementary nature of democracy and capitalism. The belief that capitalism and democracy go hand by hand is not credible anymore. The concept of nation is a fiction in need of a continuous stock of intergenerational believers. The nation state successfully assimilated heterogeneous groups of people under a common language and shared cultural values. But this seems today a rather fragile foundation to resist the centrifugal forces that financial capitalism impinges upon the social fabric.

Nation states will not collapse over night, but they are an industrial era device in a digital world. To do not fall into obsolescence they will need to change their operative system. Since the venture capitalist Marc Andreessen coined the phrase “software is eating the world” the logic of financial capitalism has accelerated this trend. Five software companies: Facebook, Apple, Amazon, Netflix and Google parent Alphabet (FANG) equal more than 10 per cent percent of the S&P 500 cap. Todays dominant industries in entertainment, retail, telecom, marketing companies and others are software companies. Software is also taking a bigger share in industries that traditionally exist in the physical space like automakers and energy. Education and health care have shown more resistance to software-based entrepreneurial change but a very profound transformation is underway. This is already visible with the growing popularity of MOOCs and personalized health monitoring systems.

Software-based business not only have up trending market share but more importantly, software can reprogram the world. The internet of things will allow to have full connectivity of smart devices in an economy with massive deflationary costs in computing. Computing might even become free. This has profound consequences for business, industry and most importantly, for how citizens want to organize society and governance.

The most promising technological innovation in years is the blockchain technology, an encrypted and distributed ledger system. Blockchain is an universal and freely accessible repository of documents including property and insurance contracts, publicly auditable, and resistant to special group interests manipulation and corruption. New kinds of governance models and services could be tested and implemented using the blockchain. The time is ripe for fundamental software-based transformation in governance. Democracy and free society will ignore this at its own peril…(More)”.

Scientists can now figure out detailed, accurate neighborhood demographics using Google Street View photos

Curated on Posted on by Stefaan Verhulst

Christopher Ingraham at the Washington Post: “A team of computer scientists has derived accurate, neighborhood-level estimates of the racial, economic and political characteristics of 200 U.S. cities using an unlikely data source — Google Street View images of people’s cars.

Published this week in the Proceedings of the National Academy of Sciences, the report details how the scientists extracted 50 million photographs of street scenes captured by Google’s Street View cars in 2013 and 2014. They then trained a computer algorithm to identify the make, model and year of 22 million automobiles appearing in neighborhoods in those images, parked outside homes or driving down the street.

The vehicles seen in Street View images are often small or blurry, making precise identification a challenge. So the researchers had human experts identify a small subsample of the vehicles and compare those to the results churned out by their algorithm. They that the algorithm correctly identified whether a vehicle was U.S.- or foreign-made roughly 88 percent of the time, got the manufacturer right 66 percent of the time and nailed the exact model 52 percent of the time.

While far from perfect, the sheer size of the vehicle database means those numbers are still useful for real-world statistical applications, like drawing connections between vehicle preferences and demographic data. The 22 million vehicles in the database comprise roughly 8 percent of all vehicles in the United States. By comparison, the U.S. Census Bureau’s massive American Community Survey reaches only about 1.6 percent of American householdseach year, while the typical 1,000-person opinion poll includes just 0.0004 of American adults.

To test what this data set could be capable of, the researchers first paired the Zip code-level vehicle data with numbers on race, income and education from the American Community Survey. They did this for a random 15 percent of the Zip codes in their data set to create a “training set.” They then created another algorithm to go through the training set to see how vehicle characteristics correlated with neighborhood characteristics: What kinds of vehicles are disproportionately likely to appear in white neighborhoods, or black ones? Low-income vs. high-income? Highly-educated areas vs. less-educated ones?

That yielded a number of reliable correlations….(More)”.