“We do not feel safe”: A Kabul-based crisis alert app struggles to protect its own employees

Curated on Posted on by Stefaan Verhulst

Q and A with Sara Wahedi by Hajira Maryam: “Ehtesab, a Kabul-based startup, emerged out of a personal security-related incident that Sara Wahedi, a former Afghan government employee, experienced in May 2018. After witnessing a suicide bomb attack firsthand, Wahedi rushed home, where she could see militants roaming the streets from her balcony. The city was put on lockdown for 12 hours and left without electricity. No one, Wahedi said, knew when the electricity would be restored or when roads would be cleared. The authorities were of little help. 

“Since that moment, I kept pondering about the idea of accountability and information provision. I jotted down a few words in different languages for accountability, namely Dari and Pashto. That was the moment the term Ehtesab came to my mind.” 

Ehtesab means “accountability” in Dari and Pashto, and the app, formally launched in March 2020, offers streamlined security-related information, including general security updates in Kabul to its users. With real-time, crowdsourced alerts, users across the city can track bomb blasts, roadblocks, electricity outages, or other problems in locations close to them. The app, which generates push notifications about nearby security risks, is supported by 20 employees working out of the company’s Kabul office, according to Wahedi. 

Despite the company’s single-minded focus on security, the Ehtesab team was caught off-guard by the sudden collapse of the Afghan government over the weekend. “It was inevitable that there would be a significant shift in governance … but we weren’t expecting the Taliban to come in within the first eight hours of the day,” Wahedi said….(More)”.

Satellite Earth observation for sustainable rural development

Curated on Posted on by Stefaan Verhulst

A blog post by Peter Hargreaves: “…We find ourselves in a “golden age for satellite exploration”. ‘Big Data’ from satellite Earth observation – hereafter denoted ‘EO’ – could be an important part of the solution to the shortage of socioeconomic data required to inform several of the goals and targets that compose the United Nations (UN) Sustainable Development Goals (SDGs) [hyperlink]. In particular, the goals that pertain to socioeconomic and human wellbeing dimensions of development. EO data could play a significant role in producing the transparent data system necessary to achieve sustainable development….

Census and nationally representative household surveys are the medium through which most socioeconomic data are collected. It is impossible to understand socioeconomic conditions without them – I cannot stress this enough. But they have limitations, particularly in terms of cost and spatio-temporal coverage. In an ideal world, we would vastly upscale the spatial and temporal reporting of these surveys to cover more places and points in time. But this mass enumeration would be prohibitively expensive and *logistically impossible*. Imagine the quantity of data produced and the burden placed upon National Statistics Offices (NSOs) and governmental institutions? The 2030 end point for the SDGs would be upon us before much of the data was processed leaving very little time to use the outputs for policy.

This is where unconventional data enters the debate, and in this sphere – that of measuring socioeconomic conditions for development – EO data is unconventional. EO data has considerable potential to augment survey and census data for measuring rural poverty development in rural spaces, especially during intercensal periods, and where ground data are patchy, or non-existent. While on the subject, there is an important point to make: you can’t use EO to understand everything about a particular context. It does not matter how elaborate the model or the effort put in. Quite simply, EO cannot give you the full picture.

What EO *does* have is a five-decade temporal legacy (most platforms and data products are near continuous), and its broadly open access with low to negligible acquisition costs. EO data is also availabile across multiple spatial resolutions and is often easily comparable and complementary. When we say, ‘five-decade temporal legacy’, this means that there are roughly 50 years of EO data (if we use the Landsat program as an anchor). Not all EO platforms have operated across the whole timeline – Figure 1 below offers an idea of when different platforms were launched and for how long they were, or have been, operational. What’s more, data will be increasingly available and accessible, catalysed by technological innovation and investment in public and private ventures. A lot of this data is open access e.g. EO platforms operated by NASA or the ESA Copernicus programme, which include Landsat, MODIS, AVHRR, VIIRs, and the Sentinels amongst others. Meanwhile, the availability of EO data across multiple spatial resolutions enables disaggregation of data alongside survey and census data for subnational monitoring of socioeconomic conditions….(More)”.

The controversy over the term ‘citizen science’

Curated on Posted on by Stefaan Verhulst

CBC News: “The term citizen science has been around for decades. Its original definition, coined in the 1990s, refers to institution-guided projects that invite the public to contribute to scientific knowledge in all kinds of ways, from the cataloguing of plants, animals and insects in people’s backyards to watching space.

Anyone is invited to participate in citizen science, regardless of whether they have an academic background in the sciences, and every year these projects number in the thousands. 

Recently, however, some large institutions, scientists and community members have proposed replacing the term citizen science with “community science.” 

Those in favour of the terminology change — such as eBird, one of the world’s largest biodiversity databases — say they want to avoid using the word citizen. They do so because they want to be “welcoming to any birder or person who wants to learn more about bird watching, regardless of their citizen status,” said Lynn Fuller, an eBird spokesperson, in a news release earlier this year. 

Some argue that while the intention is valid, the term community science already holds another definition — namely projects that gather different groups of people around environmental justice focused on social action. 

To add to the confusion, renaming citizen science could impact policies and legislation that have been established in countries such as the U.S. and Canada to support projects and efforts in favour of citizen science. 

For example, if we suddenly decided to call all species of birds “waterbirds,” then the specific meaning of this category of bird species that lives on or around water would eventually be lost. This would, in turn, make communication between people and the various fields of science incredibly difficult. 

A paper published in Science magazine last month pointed out some of the reasons why rebranding citizen science in the name of inclusion could backfire. 

Caren Cooper, a professor of forestry and environmental resources at North Carolina State University and one of the authors of the paper, said that the term citizen science didn’t originally mean to imply that people should have a certain citizenship status to participate in such projects. 

Rather, citizen science is meant to convey the idea of responsibilities and rights to access science. 

She said there are other terms being used to describe this meaning, including “public science, participatory science [and] civic science.”

Chris Hawn, a professor of geography and environmental systems at the University of Maryland Baltimore County and one of Cooper’s co-authors, said that being aware of the need for change is a good first step, but any decision to rename should be made carefully….(More)”.

What is the difference between current awareness and horizon scanning?

Curated on Posted on by Stefaan Verhulst
identifying the trends

An informed perspective is more important than ever in order to anticipate what comes next and succeed in emerging futures”. HBR, October 16, 2015

Article by Clare Brown: “Legal professionals are busy people. They are concerned with doing the best they can for their clients and making sure that their business runs smoothly. Trend spotting or horizon scanning isn’t necessarily at the top of their daily “to do” lists but if they want to grow the firm effectively, everyone – from trainee to managing partner – needs to anticipate future events. 

The best way information people can help to do this is to first understand how everything fits together. We need to look at the difference between current awareness and horizon scanning – and put them both into a wider strategic context. When we present our management teams with evidence that they need automated current awareness, we should also be dazzling them with future information possibilities. 

…The answer might lie in a strategic and collaborative form of foresight, or as Kerstin E. Cuhls defines it, “a systematic debate of complex futures”. Large corporations, governments and intergovernmental organisations have used various methods to use information in their efforts to predict all possible outcomes. For instance, 

Georghiou (2007) reported that foresight activities have been conducted in conjunction with NIS in the USA, Canada, UK, Germany, The Netherlands, Austria, Russia, Australia, New Zealand, Columbia, India, South Korea, Kazakhstan, Taiwan, Malaysia, Egypt, Morocco, South Africa and other countries. In Germany, the Fraunhofer Society has taken the lead in progressively applying foresight not only in NIS but also in the preparation of strategic scenarios at the corporate level (Cuhls, 2015). (Yuichi Washida and Akihisa Yahata “Predictive value of horizon scanning for future scenarios in Foresight, 3 February 2021)

The excellent article on horizon scanning I mentioned above explains how they attempt it. In essence, it involves literature searches, conversations, taking a broad view, and being open to any and all possibilities:

  • Structured: it is a systematic approach by applying methods of futures research, science-based, and based on new theories of futures research
  • Debate: it includes interaction of relevant actors, active preparation for the future or different futures, and orientation towards shaping the future
  • Complex: it includes the consideration of systemic interdependencies, takes a holistic view
  • Futures is plural: it is an open view on different paths into the future with thinking in alternatives. We also envisage different types of futures, in futures research we differentiate between possible, probable and preferable futures…(More)”.

Proposal for a European Interoperability Framework for Smart Cities and Communities (EIF4SCC) published

Curated on Posted on by Stefaan Verhulst

Article by Nóirín Ní Earcáin: “In recognition of the importance of interoperability and the specific challenges it presents in a city context, The Commission (DG DIGIT and DG CONNECT) appointed Deloitte and KU Leven to prepare a “Proposal for a European Interoperability Framework for Smart Cities and Communities”. While an EIF for eGovernment has been in place since 2010, this is the first time the concepts and ideas developed there have been adapted to the local context.

The aim of the EIF4SCC is to provide EU local administration leaders with definitions, principles, recommendations, practical use cases drawn from cities and communities from around Europe and beyond, and a common model to facilitate delivery of services to the public across domains, cities, regions and borders.

The framework was developed by building on and finding complementarities with previous and ongoing initiatives, such as the Living-in.EU movement, the 2017 European Interoperability Framework (EIF), the Minimal Interoperability Mechanisms (MIMs Plus) and the outcomes of EU funded initiatives (e.g.Connecting Europe Facility (CEF) Digital Building Blocks, Smart Cities Marketplace, Intelligent Cities Challenge, Digital Transition Partnership under the Urban Agenda) and EU funded projects (Synchronicity, Triangulum, etc.).

Why do cities and communities need interoperability?

The EIF4SCC is targeted at EU local administration leaders and aims to provide a generic framework of interoperability of all types, and how it can contribute to the development of a Smart(er) City/Community. This will pave the way for services for citizens and business to be offered not only in a single city, but also across cities, regions and across borders.

European Interoperability Framework for Smart Cities and Communities

The EIF4SCC includes three concepts (interoperability, smart city or community, EIF4SCC), five principles (drawing on the Living-in.EU declaration), and seven elements (consisting of the five components of interoperability, one cross-cutting layer – Integrated Service Governance, and a foundational layer of Interoperability Governance)….The European Commission encourages local administrations at regional, city and community level to review the Proposed EIF4SCC, and the accompanying Final Study Report which details the methodology, literature review, and stakeholder engagement process undertaken. It will be discussed through the Living-in.EU community and other fora, with a view to its adoption as an official Commission document, based on users’ and stakeholders’ feedback…(More)”.

An Obsolete Paradigm

Curated on Posted on by Stefaan Verhulst

Blogpost by Paul Wormelli: “…Our national system of describing the extent of crime in the U.S. is broken beyond repair and deserves to be replaced by a totally new paradigm (system). 

Since 1930, we have relied on the metrics generated by the Uniform Crime Reporting (UCR) Program to describe crime in the U.S., but it simply does not do so, even with its evolution into the National Incident-Based Reporting System (NIBRS). Criminologists have long recognized the limited scope of the UCR summary crime data, leading to the creation of the National Crime Victimization Survey (NCVS) and other supplementary crime data measurement vehicles. However, despite these measures, the United States still has no comprehensive national data on the amount of crime that has occurred. Even after decades of collecting data, the 1968 Presidential Crime Commission report on the Challenge of Crime in a Free Society lamented the absence of sound and complete data on crime in the U.S., and called for the creation of a National Crime Survey (NCS) that eventually led to the creation of the NCVS. Since then, we have slowly attempted to make improvements that will lead to more robust data. Only in 2021 did the FBI end UCR summary-based crime data collection and move to NIBRS crime data collection on a national scale.

Admittedly, the shift to NIBRS will unleash a sea change in how we analyze crime data and use it for decision making. However, it still lacks the completeness of national crime reporting. In the landmark study of the National Academy of Sciences Committee on Statistics (funded by the FBI and the Bureau of Justice Statistics to make recommendations on modernizing crime statistics), the panel members grappled with this reality and called out the absence of national statistics on crime that would fully inform policymaking on this critical subject….(More)”

How to predict citizen engagement in urban innovation projects?

Curated on Posted on by Stefaan Verhulst

Blogpost by Julien Carbonnell: “Citizen engagement in decision-making has proven to be a key factor for success in a smart city project and a must-have of contemporary democratic regimes. While inhabitants are all daily internet users, they widely inform themselves about their political electives’ achievements during the mandate, interact with each other on social networks, and by word-of-mouth on messaging apps or phone calls to form an opinion.

Unfortunately, most of the smart cities’ rankings lack resources to evaluate the citizen engagement dynamic around the urban innovations deployed. Indeed this data can’t be found on official open data portals, focused instead on cities’ infrastructure and quality of life. These include the number of metro stations, the length of bike lanes, air pollution, and tap water quality. Some of them also include field investigation such as the amount of investment in this or that urban area and communication dynamics about a new smart city project.

If this kind of formal information provides a good overview of the official state of development of a city, it does not give any insight from the inhabitants themselves and sounds out the street vibes of a city.

So, I’ve been working on filling this gap for the last 3 years and share in Democracy Studio all the elements of my method and tools built for conducting such analysis. To do so, I have notably been collecting inhabitants’ participation in a survey study in three case study cities: Taipei (Taiwan), Tel Aviv (Israel), and Tallinn (Estonia). I collected 366 answers by contacting inhabitants randomly online (Facebook groups, direct messages on LinkedIn, and through messaging apps) and in-person, in events related to my field of interest (Smart-City and Urban Innovation Startups). The resulting variables have been integrated into machine learning models, which finally performed a very satisfying prediction of the citizen engagement in my case studies….(More)”.

The One-Earth Balance Sheet

Curated on Posted on by Stefaan Verhulst

Essay by Andrew Sheng: “Modern science arose by breaking down complex problems into their parts. As Alvin Toffler, an American writer and futurist, wrote in his 1984 foreword to the chemist Ilya Prigogine’s classic book “Order out of Chaos”: “One of the most highly developed skills in contemporary Western civilization is dissection: the split-up of problems into their smallest possible components. We are good at it. So good, we often forget to put the pieces back together again.”

Specialization produces efficiency in production and output. But one unfortunate result is that silos produce a partial perspective from specialist knowledge; very few take a system-wide view on how the parts are related to the whole. When the parts do not fit or work together, the system may break down. As behavioral economist Daniel Kahnemann put it: “We can be blind to the obvious, and we are also blind to our blindness.”

Silos make group collective action more difficult; nation-states, tribes, communities and groups have different ways of knowing and different repositories of knowledge. A new collective mental map is needed, one that moves away from classical Newtonian science, with its linear and mechanical worldview, toward a systems-view of life. The ecologists Fritjof Capra and Pier Luigi Luisi argue that “the major problems of our time — energy, the environment, climate change, food security, financial security — cannot be understood in isolation. They are systemic problems, which means that they are all interconnected and interdependent.”

“Siloed thinking created many of our problems with inequality, injustice and planetary damage.”

A complex, non-linear, systemic view of life sees the whole as a constant interaction between the small and the large: diverse parts that are cooperating and competing at the same time. This organic view of life coincides with the ancient perspective found in numerous cultures — including Chinese, Indian, native Australian and Amerindian — that man and nature are one.

In short, modern Western science has begun to return to the pre-Enlightenment worldview that saw man, God and Earth in somewhat mystic entanglement. The late Chinese scientist Qian Xuesen argued the world was made up of “open giant complex systems” operating within larger open giant complex systems. Human beings themselves are open giant complex systems — every brain has billions of neurons connected to each other through trillions of pathways — continually exchanging and processing information with other humans and the environment. Life is much more complex, dynamic and uncertain than we once assumed.

To describe this dynamic, complex and uncertain systemic whole, we need to evolve trans-disciplinary thinking that integrates the natural, social, biological sciences and arts by transcending disciplinary boundaries. Qian concluded that the only way to describe such systemic complexity and uncertainty is to integrate quantitative with qualitative narratives, exactly what the Nobel Laureate Robert Shiller advocates for in “Narrative Economics.”…(More)”.

The Inevitable Weaponization of App Data Is Here

Curated on Posted on by Stefaan Verhulst

Joseph Cox at VICE: “…After years of warning from researchers, journalists, and even governments, someone used highly sensitive location data from a smartphone app to track and publicly harass a specific person. In this case, Catholic Substack publication The Pillar said it used location data ultimately tied to Grindr to trace the movements of a priest, and then outed him publicly as potentially gay without his consent. The Washington Post reported on Tuesday that the outing led to his resignation….

The data itself didn’t contain each mobile phone user’s real name, but The Pillar and its partner were able to pinpoint which device belonged to Burill by observing one that appeared at the USCCB staff residence and headquarters, locations of meetings that he was in, as well as his family lake house and an apartment that has him listed as a resident. In other words, they managed to, as experts have long said is easy to do, unmask this specific person and their movements across time from an supposedly anonymous dataset.

A Grindr spokesperson told Motherboard in an emailed statement that “Grindr’s response is aligned with the editorial story published by the Washington Post which describes the original blog post from The Pillar as homophobic and full of unsubstantiated inuendo. The alleged activities listed in that unattributed blog post are infeasible from a technical standpoint and incredibly unlikely to occur. There is absolutely no evidence supporting the allegations of improper data collection or usage related to the Grindr app as purported.”…

“The research from The Pillar aligns to the reality that Grindr has historically treated user data with almost no care or concern, and dozens of potential ad tech vendors could have ingested the data that led to the doxxing,” Zach Edwards, a researcher who has closely followed the supply chain of various sources of data, told Motherboard in an online chat. “No one should be doxxed and outed for adult consenting relationships, but Grindr never treated their own users with the respect they deserve, and the Grindr app has shared user data to dozens of ad tech and analytics vendors for years.”…(More)”.

What Is Behavioral Data Science and How to Get into It?

Curated on Posted on by Stefaan Verhulst

Blogpost by Ganna Pogrebna: “Behavioral Data Science is a new, emerging, interdisciplinary field, which combines techniques from the behavioral sciences, such as psychology, economics, sociology, and business, with computational approaches from computer science, statistics, data-centric engineering, information systems research and mathematics, all in order to better model, understand and predict behavior.

Behavioral Data Science lies at the interface of all these disciplines (and a growing list of others) — all interested in combining deep knowledge about the questions underlying human, algorithmic, and systems behavior with increasing quantities of data. The kinds of questions this field engages are not only exciting and challenging, but also timely, such as:

Behavioral Data Science is capable of addressing all these issues (and many more) partly because of the availability of new data sources and partly due to the emergence of new (hybrid) models, which merge behavioral science and data science models. The main advantage of these models is that they expand machine learning techniques, operating, essentially, as black boxes, to fully tractable, and explainable upgrades. Specifically, while a deep learning model can generate accurate prediction of why people select one product or brand over the other, it will not tell you what exactly drives people’s preferences; whereas hybrid models, such as anthropomorphic learning, will be able to provide this insight….(More)”