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.

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)”.

Democratising the future: How do we build inclusive visions of the future?

Curated on Posted on by Stefaan Verhulst

Chun-Yin San at Nesta: “In 2011, Lord Martin Rees, the British Astronomer-Royal, launched a scathing critique on the UK Government’s long-term thinking capabilities. “It is depressing,” he argued, “that long-term global issues of energy, food, health and climate get trumped on the political agenda by the short term”. We are facing more and more complex, intergenerational issues like climate change, or the impact of AI, which require long-term, joined-up thinking to solve.

But even when governments do invest in foresight and strategic planning, there is a bigger question around whose vision of the future it is. These strategic plans tend to be written in opaque and complex ways by ‘experts’, with little room for scrutiny, let alone input, by members of the public….

There have been some great examples of more democratic futures exercises in the past. Key amongst them was the Hawai’i 2000 project in the 1970s, which bought together Hawaiians from different walks of life to debate the sort of place that Hawai’i should become over the next 30 years. It generated some incredibly inspiring and creative collective visions of the future of the tropical American state, and also helped embed long-term strategic thinking into policy-making instruments – at least for a time.

A more recent example took place over 2008 in the Dutch Caribbean nation of Aruba, which engaged some 50,000 people from all parts of Aruban society. The Nos Aruba 2025 project allowed the island nation to develop a more sustainable national strategic plan than ever before – one based on what Aruba and its people had to offer, responding to the potential and needs of a diverse community. Like Hawai’i 2000, what followed Nos Aruba 2025 was a fundamental change in the nature of participation in the country’s governance, with community engagement becoming a regular feature in the Aruban government’s work….

These examples demonstrate how futures work is at its best when it is participatory. …However, aside from some of the projects above, examples of genuine engagement in futures remain few and far between. Even when activities examining a community’s future take place in the public domain – such as the Museum of London’s ongoing City Now City Future series – the conversation can often seem one-sided. Expert-generated futures are presented to people with little room for them to challenge these ideas or contribute their own visions in a meaningful way. This has led some, like academics Denis Loveridge and Ozcan Saritas, to remark that futures and foresight can suffer from a serious case of ‘democratic deficit‘.

There are three main reasons for this:

  1. Meaningful participation can be difficult to do, as it is expensive and time-consuming, especially when it comes to large-scale exercises meant to facilitate deep and meaningful dialogue about a community’s future.

  2. Participation is not always valued in the way it should be, and can be met with false sincerity from government sponsors. This is despite the wide-reaching social and economic benefits to building collective future visions, which we are currently exploring further in our work.

  3. Practitioners may not necessarily have the know-how or tools to do citizen engagement effectively. While there are plenty of guides to public engagement and a number of different futures toolkits, there are few openly available resources for participatory futures activities….(More)”

Powering Community Participation in Planning for Indianapolis’ Future

Curated on Posted on by Stefaan Verhulst

Thomas Kingsley at the National Neighborhood Indicators Partnership (NNIP): “Thanks to IndyVitals – an award-winning online data tool – residents and organizations can actively contribute to continued planning to achieve Marion County’s vision for 2020. The NNIP Partner, the Polis Center at Indiana University-Purdue University at Indianapolis, leveraged their years of experience in providing actionable data through their Social Assets and Vulnerabilities Indicators (SAVI) to create this new resource for the county.

IndyVitals supports Plan 2020: the initiative of the City of Indianapolis, the Greater Indianapolis Progress Committee and others to revitalize the city’s plans and planning processes in recognition of its 2020 bicentennial. These groups decided to give neighborhood data a considerably more pivotal role in their approach than it has typically played in local planning efforts in the past.

SAVI was one of the first comprehensive online and interactive neighborhood indicators systems ever developed for any city. But IndyVitals incorporates three notable changes to past practice. First is a new configuration of neighborhood geographies for the city. Indianapolis has nearly 500 self-defined neighborhood associations registered with the City, with many overlapping boundaries. Neighborhoods defined at that level would be too small and fragmented to motivate coherent action. Accordingly, the City defined a set of 99 larger “neighborhood areas” that all actors who influence neighborhood change – community groups, public agencies, nonprofit service providers, private businesses – could understand, build their own plans around, and use as a basis for coordinating with each other to achieve progress. The City intends to use the new neighborhood areas as building blocks for revising the boundaries of its police districts, public works areas and other internal administrative units.

The second change pertains to the indicators selected and the tools developed to make use of them. A set of over 50 indicators for IndyVitals was selected to be regularly updated and monitored in the future (drawn from the literally hundreds of possible indicators that could be created with SAVI data). SAVI staff suggested a list of candidates which was then vetted and modified by an advisory committee made up of representatives of community and other stakeholder organizations. The 50 include measures that help explain the forces causing neighborhood change as well as those considered to be markers of goal achievement. They include well known indicators on population characteristics, but also a number of metrics that have powerful implications: for example, percent of families with access to a quality preschool or percent of residents employed in their own neighborhood; percent of students graduating from high school on time, neighborhood “walkability” ratings, crimes committed by minors per 1,000 population, demolitions ordered due to hazardous building conditions….

The third, and probably most important change in practice, is the type of data-informed planning and implementation process envisaged….(More)”.

The 8p banana that showed Bogotá needed more open public spending

Curated on Posted on by Stefaan Verhulst

María Victoria Angulo in The Guardian: “On a typical school day in Bogotá, Colombia’s capital city, about a million pupils, from four to 18 years old, will sit down for a meal at one of our 384 public schools.

Balanced nutrition is crucial for children’s development. The food we provide may well be their main meal for the entire day. So when concerns were raised in 2016 over the quality, delivery, price, and even the origin of our meals, we took them very seriously.

Colombia had recently started publishing detailed public contracting records as open data for the first time. So our first port of call was to work with our national procurement agency, Colombia Compra Eficiente, to analyse the US$136m that we were spending on meals and other services. What we found shocked us: severe inefficiency, or worse.

Mayor Enrique Peñalosa and I set out radical reforms based on an open contracting approach. We established minimum and maximum prices for meals and we made the whole contracting process competitive and fully open. Sourcing, packing and distribution of food would no longer be a single contract, and the lowest bid price would not be the deciding factor when choosing a supplier. Instead, it would be about quality.

We began sharing all the information about how meals were procured, from their planning to their delivery, on a public online platform for anyone to see, in a way that was easy to understand.

We faced resistance from all directions. Some of the existing suppliers threatened to sue, with nine lawsuits attempting to halt the process, and tensions flared in our politically polarised city, with more than 10 debates in the city council over the process. On top of that, a media smear campaign attempted to discredit and sabotage the reforms by spreading misleading information about, for example, food arriving damaged because of the new system.

In December 2016, we opened up for bids to procure 74 products. By March 2017, suppliers had been found for all of them, except one: no company put in a bid to provide fresh fruit at the set cost.

This made us suspicious….(More)”.

The nation state goes virtual

Curated on Posted on by Stefaan Verhulst

Tom Symons at Nesta’s Predictions for 2018: “As the world changes, people expect their governments and public services to do so too. When it’s easy to play computer games with someone on the other side of the world, or set up a company bank account in five minutes, there is an expectation that paying taxes, applying for services or voting should be too…..

To add to this, large political upheavals such as Brexit and the election of Donald Trump have left some people feeling alienated from their national identity. Since the the UK voted to leave the EU, demand for Irish passports has increased by 50 per cent, a sign that people feel dissatisfied by the constraints of geographically determined citizenship when they can no longer relate to their national identity.

In response, some governments see these changes as an opportunity to reconceptualise what we mean by a nation state.

The e-Residency offer

The primary actor in this disruption is Estonia, which leads the world in digital government. In 2015 they introduced an e-Residency, allowing anyone anywhere in the world to receive a government-issued digital identity. The e-Residency gives people access to digital public services and the ability to register and run online businesses from the country, in exactly the same way as someone born in Estonia. As of November 2017, over 27,000 people have applied to be Estonian e-Residents, and they have established over 4,200 companies. Estonia aims to have ten million virtual residents by 2025….

While Estonia is a sovereign nation using technology to redefine itself, there are movements taking advantage of decentralising technologies in a bid to do away with the nation state altogether. Bitnation is a blockchain-based technology which enables people to create and join virtual nations. This allows people to agree their own social contracts between one another, using smart contract technology, removing the need for governments as an administrator or mediator. Since it began in 2014, it has been offering traditional government services, such as notaries, dispute resolution, marriages and voting systems, without the need for a middleman.

As of November 2017, there are over 10,000 Bitnation citizens. …

As citizens, we may be able to educate our children in Finland, access healthcare from South Korea and run our businesses in New Zealand, all without having to leave the comfort of our homes. Governments may see this as means of financial sustainability in the longer term, generating income by selling such services to a global population instead of centralised taxation systems levied on a geographic population.

Such a model has been described as ‘nation-as-a-service’, and could mean countries offering different tiers of citizenship, with taxes based on the number of services used, or tier of citizenship chosen. This could also mean multiple citizenships, including of city-states, as well as nations….

This is the moment for governments to start taking the full implications of the digital age seriously. From electronic IDs and data management through to seamless access to services, citizens will only demand better digital services. Countries such as Azerbaijan, are already developing their own versions of the e-Residency. Large internet platforms such as Amazon are gearing up to replace entire government functions. If governments don’t grasp the nettle, they may find themselves left behind by technology and other countries which won’t wait around for them….(More)”.

Migration Data Portal

Curated on Posted on by Stefaan Verhulst

New portal managed and developed by IOM’s Global Migration Data Analysis Centre (GMDAC)“…aims to serve as a unique access point to timely, comprehensive migration statistics and reliable information about migration data globally. The site is designed to help policy makers, national statistics officers, journalists and the general public interested in the field of migration to navigate the increasingly complex landscape of international migration data, currently scattered across different organisations and agencies.

Especially in critical times, such as those faced today, it is essential to ensure that responses to migration are based on sound facts and accurate analysis. By making the evidence about migration issues accessible and easy to understand, the Portal aims to contribute to a more informed public debate….

The five main sections of the Portal are designed to help you quickly and easily find the data and information you need.

  • DATA – Our interactive world map visualizes international, publicly-available and internationally comparable migration data.
  • THEMES – Thematic overviews explain how various aspects of migration are measured, what are the data sources, their strengths and weaknesses and provide context and analysis of key migration data.
  • TOOLS – Migration data tools are regularly added to help you find the right tools, guidelines and manuals on how to collect, interpret and disseminate migration data.
  • Sustainable Development Goals (SDGs) and the Global Compact on Migration (GCM) – Migration Data, the SDGs and the new Global Compact on Migration (GCM) – Reviews the migration-related targets in the SDGs, how they are defined and measured, and provides information on the new GCM and the migration data needs to support its implementation.
  • BLOG – Our blog and the Talking Migration Data video series provide a place for the migration data community to share their opinion on new developments and policy, new data or methods….(More)”.

Business Models For Sustainable Research Data Repositories

Curated on Posted on by Stefaan Verhulst

OECD Report: “In 2007, the OECD Principles and Guidelines for Access to Research Data from Public Funding were published and in the intervening period there has been an increasing emphasis on open science. At the same time, the quantity and breadth of research data has massively expanded. So called “Big Data” is no longer limited to areas such as particle physics and astronomy, but is ubiquitous across almost all fields of research. This is generating exciting new opportunities, but also challenges.

The promise of open research data is that they will not only accelerate scientific discovery and improve reproducibility, but they will also speed up innovation and improve citizen engagement with research. In short, they will benefit society as a whole. However, for the benefits of open science and open research data to be realised, these data need to be carefully and sustainably managed so that they can be understood and used by both present and future generations of researchers.

Data repositories – based in local and national research institutions and international bodies – are where the long-term stewardship of research data takes place and hence they are the foundation of open science. Yet good data stewardship is costly and research budgets are limited. So, the development of sustainable business models for research data repositories needs to be a high priority in all countries. Surprisingly, perhaps, little systematic analysis has been done on income streams, costs, value propositions, and business models for data repositories, and that is the gap this report attempts to address, from a science policy perspective…..

This project was designed to take up the challenge and to contribute to a better understanding of how research data repositories are funded, and what developments are occurring in their funding. Central questions included:

  • How are data repositories currently funded, and what are the key revenue sources?
  • What innovative revenue sources are available to data repositories?
  • How do revenue sources fit together into sustainable business models?
  • What incentives for, and means of, optimising costs are available?
  • What revenue sources and business models are most acceptable to key stakeholders?…(More)”

A New City O/S: The Power of Open, Collaborative, and Distributed Governance

Curated on Posted on by Stefaan Verhulst

Book by Stephen Goldsmith and Neil Kleiman: “At a time when trust is dropping precipitously and American government at the national level has fallen into a state of long-term, partisan-based gridlock, local government can still be effective—indeed more effective and even more responsive to the needs of its citizens. Based on decades of direct experience and years studying successful models around the world, the authors of this intriguing book propose a new operating system (O/S) for cities. Former mayor and Harvard professor Stephen Goldsmith and New York University professor Neil Kleiman suggest building on the giant leaps that have been made in technology, social engagement, and big data.

Calling their approach “distributed governance,” Goldsmith and Kleiman offer a model that allows public officials to mobilize new resources, surface ideas from unconventional sources, and arm employees with the information they need to become pre-emptive problem solvers. This book highlights lessons from the many innovations taking place in today’s cities to show how a new O/S can create systemic transformation.

For students of government, A New City O/S: The Power of Distributed Governance presents a groundbreaking strategy for rethinking the governance of cities, marking an important evolution of the current bureaucratic authority-based model dating from the 1920s. More important, the book is designed for practitioners, starting with public-sector executives, managers, and frontline workers. By weaving real-life examples into a coherent model, the authors have created a step-by-step guide for all those who would put the needs of citizens front and center. Nothing will do more to restore trust in government than solutions that work. A New City O/S: The Power of Distributed Governanceputs those solutions within reach of those public officials responsible for their delivery….(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)”.