open data

Building Trust in Data and Statistics

Curated on February 14, 2018August 3, 2018 by Stefaan Verhulst

Shaida Badiee at UN World Data Forum: …What do we want for a 2030 data ecosystem?

Hope to achieve: A world where data are part of the DNA and culture of decision-making, used by all and valued as an important public good. A world where citizens trust the systems that produce data and have the skills and means to use and verify their quality and accuracy. A world where there are safeguards in place to protect privacy, while bringing the benefits of open data to all. In this world, countries value their national statistical systems, which are working independently with trusted partners in the public and private sectors and citizens to continuously meet the changing and expanding demands from data users and policy makers. Private sector data generators are generously sharing their data with public sector. And gaps in data are closing, making the dream of “leaving no one behind” come true, with SDG goals on the path to being met by 2030.

Hope to avoid: A world where large corporations control the bulk of national and international data and statistics with only limited sharing with the public sector, academics, and citizens. The culture of every man for himself and who pays, wins, dominates data sharing practices. National statistical systems are under-resourced and under-valued, with low trust from users, further weakening them and undermining their independence from political interference and their ability to control quality. The divide between those who have and those who do not have access, skills, and the ability to use data for decision-making and policy has widened. Data systems and their promise to count the uncounted and “leave no one behind” are falling behind due to low capacity and poor standards and institutions, and the hope of the 2030 agenda is fading.

With this vision in mind, are we on the right path? An optimist would say we are closer to the data ecosystem that we want to achieve. However, there are also some examples of movement in the wrong direction. There is no magic wand to make our wish come true, but a powerful enabler would be building trust in data and statistics. Therefore, this should be included as a goal in all our data strategies and action plans.

Here are some important building blocks underlying trust in data and statistics:

Building strong organizational infrastructure, governance, and partnerships;
Following sound data standards and principles for production, sharing, interoperability, and dissemination; and
Addressing the last mile in the data value chain to meet users’ needs, create value with data, and ensure meaningful impacts…(More)”.

The Entrepreneurial Impact of Open Data

Curated on February 8, 2018October 9, 2018 by Stefaan Verhulst

Sheena Iyengar and Patrick Bergemann at Opening Governance Research Network: “…To understand how open data is being used to spur innovation and create value, the Governance Lab (GovLab) at NYU Tandon School of Engineering conducted the first ever census of companies that use open data. Using outreach campaigns, expert advice and other sources, they created a database of more than 500 companies founded in the United States called the Open Data 500 (OD500). Among the small and medium enterprises identified that use government data, the most common industries they found are data and technology, followed by finance and investment, business and legal services, and healthcare.

In the context of our collaboration with the GovLab-chaired MacArthur Foundation Research Network on Opening Governance, we sought to dig deeper into the broader impact of open data on entrepreneurship. To do so we combined the OD500 with databases on startup activity from Crunchbase and AngelList. This allowed us to look at the trajectories of open data companies from their founding to the present day. In particular, we compared companies that use open data to similar companies with the same founding year, location and industry to see how well open data companies fare at securing funding along with other indicators of success.

We first looked at the extent to which open data companies have access to investor capital, wondering if open data companies have difficulty gaining funding because their use of public data may be perceived as insufficiently innovative or proprietary. If this is the case, the economic impact of open data may be limited. Instead, we found that open data companies obtain more investors than similar companies that do not use open data. Open data companies have, on average, 1.74 more investors than similar companies founded at the same time. Interestingly, investors in open data companies are not a specific group who specialize in open data startups. Instead, a wide variety of investors put money into these companies. Of the investors who funded open data companies, 59 percent had only invested in one open data company, while 81 percent had invested in one or two. Open data companies appear to be appealing to a wide range of investors….(More)”.

Views on Open Data Business from Software Development Companies

Curated on February 6, 2018August 3, 2018 by Stefaan Verhulst

Antti Herala, Jussi Kasurinen, and Erno Vanhala in the Journal of Theoretical and Applied Electronic Commerce Research: “The main concept of open data and its application is simple; access to the publicly-funded data provides greater returns from the public investment and can generate wealth through the downstream use of outputs, such as traffic information or weather forecast services. However, even though open data and data sharing as concepts are forty years old with the open data initiative reaching ten, the practical actions and applications have tended to stay on the superficial level, and significant progress or success stories are hard to find. The current trend is that the governments and municipalities are opening their data, but the impact and usefulness of raw open data repositories to citizens – and even to businesses – can be questioned Besides the governments, a handful of private organizations are opening their data in an attempt to unlock the economic value of open data, but even they have difficulties finding innovative usage, let alone generate additional profit.

In a previous study it was found that companies are interested in open data and that this mindset spans over different industries, from both publicly available data to the private business-to-business data access. Open data is not only a resource for software companies, but also for traditional engineering industries and even for small, nonfranchised local markets and shops. In our previous study, it was established that there is evidence on recognizing the applicability of open data, and opening the data to the clients by private organizations leads to business opportunities, creating new value.

However, while there is interest towards open data in a wide variety of businesses, the question still remains whether or not open data is actually used to generate income or are there some other sharing methods in use that are more efficient and more profitable.

For this study, four research questions were formulated. The first three are concentrating on the usage of open data as well as the interest towards opening or sharing data and the fourth research question revolves around the different types of openness:

How do new clients express interest towards open data?
What kind of open data-based solutions is the existing clientele expecting?
How does the product portfolio of a software company respond to open data?
What are the current trends of open initiatives?…(More)”.

Rights-Based and Tech-Driven: Open Data, Freedom of Information, and the Future of Government Transparency

Curated on January 30, 2018August 3, 2018 by Stefaan Verhulst

Beth Noveck at the Yale Human Rights and Development Journal: “Open data policy mandates that government proactively publish its data online for the public to reuse. It is a radically different approach to transparency than traditional right-to-know strategies as embodied in Freedom of Information Act (FOIA) legislation in that it involves ex ante rather than ex post disclosure of whole datasets. Although both open data and FOIA deal with information sharing, the normative essence of open data is participation rather than litigation. By fostering public engagement, open data shifts the relationship between state and citizen from a monitorial to a collaborative one, centered around using information to solve problems together. This Essay explores the theory and practice of open data in comparison to FOIA and highlights its uses as a tool for advancing human rights, saving lives, and strengthening democracy. Although open data undoubtedly builds upon the fifty-year legal tradition of the right to know about the workings of one’s government, open data does more than advance government accountability. Rather, it is a distinctly twenty-first century governing practice borne out of the potential of big data to help solve society’s biggest problems. Thus, this Essay charts a thoughtful path toward a twenty-first century transparency regime that takes advantage of and blends the strengths of open data’s collaborative and innovation-centric approach and the adversarial and monitorial tactics of freedom of information regimes….(More)”.

Open Data Risk Assessment

Curated on January 25, 2018August 3, 2018 by Stefaan Verhulst

Report by the Future of Privacy Forum: “The transparency goals of the open data movement serve important social, economic, and democratic functions in cities like Seattle. At the same time, some municipal datasets about the city and its citizens’ activities carry inherent risks to individual privacy when shared publicly. In 2016, the City of Seattle declared in its Open Data Policy that the city’s data would be “open by preference,” except when doing so may affect individual privacy. To ensure its Open Data Program effectively protects individuals, Seattle committed to performing an annual risk assessment and tasked the Future of Privacy Forum (FPF) with creating and deploying an initial privacy risk assessment methodology for open data.

This Report provides tools and guidance to the City of Seattle and other municipalities navigating the complex policy, operational, technical, organizational, and ethical standards that support privacyprotective open data programs. Although there is a growing body of research regarding open data privacy, open data managers and departmental data owners need to be able to employ a standardized methodology for assessing the privacy risks and benefits of particular datasets internally, without access to a bevy of expert statisticians, privacy lawyers, or philosophers. By optimizing its internal processes and procedures, developing and investing in advanced statistical disclosure control strategies, and following a flexible, risk-based assessment process, the City of Seattle – and other municipalities – can build mature open data programs that maximize the utility and openness of civic data while minimizing privacy risks to individuals and addressing community concerns about ethical challenges, fairness, and equity.

This Report first describes inherent privacy risks in an open data landscape, with an emphasis on potential harms related to re-identification, data quality, and fairness. To address these risks, the Report includes a Model Open Data Benefit-Risk Analysis (“Model Analysis”). The Model Analysis evaluates the types of data contained in a proposed open dataset, the potential benefits – and concomitant risks – of releasing the dataset publicly, and strategies for effective de-identification and risk mitigation. This holistic assessment guides city officials to determine whether to release the dataset openly, in a limited access environment, or to withhold it from publication (absent countervailing public policy considerations). …(More)”.

Improving journeys by opening data: The case of Transport for London (TfL)

Curated on January 20, 2018July 19, 2019 by Stefaan Verhulst

Merlin Stone and Eleni Aravopoulou in The Bottom Line: “This case study describes how one of the world’s largest public transport operations, Transport for London (TfL), transformed the real-time availability of information for its customers and staff through the open data approach, and what the results of this transformation were. Its purpose is therefore to show what is required for an open data approach to work.

This case study is based mainly on interviews at TfL and data supplied by TfL directly to the researchers. It analyses as far as possible the reported facts of the case, in order to identify the processes required to open data and the benefits thereof.

The main finding is that achieving an open data approach in public transport is helped by having a clear commitment to the idea that the data belongs to the public and that third parties should be allowed to use and repurpose the information, by having a strong digital strategy, and by creating strong partnerships with data management organisations that can support the delivery of high volumes of information.

The case study shows how open data can be used to create commercial and non-commercial customer-facing products and services, which passengers and other road users use to gain a better travel experience, and that this approach can be valued in terms of financial/economic contribution to customers and organisations….(More)”.

Data-Intensive Approaches To Creating Innovation For Sustainable Smart Cities

Curated on January 10, 2018August 3, 2018 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)”.

Government data: How open is too open?

Curated on January 10, 2018August 3, 2018 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)”.

Selected Readings on Data, Gender, and Mobility

Curated on January 9, 2018July 18, 2019 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:

Insights on how data can inform gender empowerment initiatives,
Emergent research into the capacity of new data sources – like call detail records (CDRs) and satellite imagery – to increase our understanding of human mobility patterns, and
Publications exploring data-driven policy for gender equity in mobility.

Readings are listed in alphabetical order.

We selected the readings based upon their focus (gender and/or mobility related); scope and representativeness (going beyond one project or context); type of data used (such as CDRs and satellite imagery); and date of publication.

Annotated Reading List

Data and Gender

Blumenstock, Joshua, and Nathan Eagle. Mobile Divides: Gender, Socioeconomic Status, and Mobile Phone Use in Rwanda. ACM Press, 2010.

Using traditional survey and mobile phone operator data, this study analyzes gender and socioeconomic divides in mobile phone use in Rwanda, where it is found that the use of mobile phones is significantly more prevalent in men and the higher class.

The study also shows the differences in the way men and women use phones, for example: women are more likely to use a shared phone than men.

The authors frame their findings around gender and economic inequality in the country to the end of providing pointers for government action.

Bosco, Claudio, et al. Mapping Indicators of Female Welfare at High Spatial Resolution. WorldPop and Flowminder, 2015.

This report focuses on early adolescence in girls, which often comes with higher risk of violence, fewer economic opportunity, and restrictions on mobility. Significant data gaps, methodological and ethical issues surrounding data collection for girls also create barriers for policymakers to create evidence-based policy to address those issues.

The authors analyze geolocated household survey data, using statistical models and validation techniques, and creates high-resolution maps of various sex-disaggregated indicators, such as nutrition level, access to contraception, and literacy, to better inform local policy making processes.

Further, it identifies the gender data gap and issues surrounding gender data collection, and provides arguments for why having a comprehensive data can help create better policy and contribute to the achievements of the Sustainable Development Goals (SDGs).

Buvinic, Mayra, Rebecca Furst-Nichols, and Gayatri Koolwal. Mapping Gender Data Gaps. Data2X, 2014.

This study identifies gaps in gender data in developing countries on health, education, economic opportunities, political participation, and human security issues.

It recommends ways to close the gender data gap through censuses and micro-level surveys, service and administrative records, and emphasizes how “big data” in particular can fill the missing data that will be able to measure the progress of women and girls well being. The authors argue that dentifying these gaps is key to advancing gender equality and women’s empowerment, one of the SDGs.

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.

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

Curated on December 19, 2017August 3, 2018 by Stefaan Verhulst

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)”.