Visualizing Ship Movements with AIS Data


Article by Jon Keegan: “As we run, drive, bike, and fly, humans leave behind telltale tracks of movement on Earth—if you know where to look. Physical tracks, thermal signatures, and chemical traces can reveal where we’ve been. But another type of breadcrumb trail comes from the radio signals emitted by the cars, planes, trains, and boats we use.

Just like ADS-B transmitters on airplanes, which provide real-time location, identification, speed, and orientation data, the AIS (Automatic Identification System) performs the same function for ships at sea.

Operating at 161.975 and 162.025 MHz, AIS transmitters broadcast a ship’s identification number, name, call sign, length, beam, type, and antenna location every six minutes. Ship location, position timestamp, and direction are transmitted more frequently. The primary purpose of AIS is maritime safety—it helps prevent collisions, assists in rescues, and provides insight into the impact of ship traffic on marine life.

Unlike ADS-B in a plane, AIS can only be turned off in rare circumstances. The result of this is a treasure trove of fascinating ship movement data. You can even watch live ship data on sites like Vessel Finder.

Using NOAA’s “Marine Cadastre” tool, you can download 16 years’ worth of detailed daily ship movements (filtered to the minute), in addition to “transit count” maps generated from a year’s worth of data to show each ship’s accumulated paths…(More)”.

Mapping AI Narratives at the Local Level


Article for Urban AI: “In May 2024, Nantes Métropole (France) launched a pioneering initiative titled “Nantes Débat de l’IA” (meaning “Nantes is Debating AI”). This year-long project is designed to curate the organization of events dedicated to artificial intelligence (AI) across the territory. The primary aim of this initiative is to foster dialogue among local stakeholders, enabling them to engage in meaningful discussions, exchange ideas, and develop a shared understanding of AI’s impact on the region.

Over the course of one year, the Nantes metropolitan area will host around sixty events focused on AI, bringing together a wide range of participants, including policymakers, businesses, researchers, and civil society. These events provide a platform for these diverse actors to share their perspectives, debate critical issues, and explore the potential opportunities and challenges AI presents. Through this collaborative process, the goal is to cultivate a common culture around AI, ensuring that all relevant voices are heard as the city navigates to integrate this transformative technology…(More)”.

AI Localism Repository: A Tool for Local AI Governance


About: “In a world where AI continues to be ever more entangled with our communities, cities, and decision-making processes, local governments are stepping up to address the challenges of AI governance. Today, we’re excited to announce the launch of the newly updated AI Localism Repository—a curated resource designed to help local governments, researchers, and citizens understand how AI is being governed at the state, city, or community level.

What is AI Localism?

AI Localism refers to the actions taken by local decision-makers to address AI governance in their communities. Unlike national or global policies, AI Localism offers immediate solutions tailored to specific local conditions, creating opportunities for greater effectiveness and accountability in the governance of AI.

What’s the AI Localism Repository?

The AI Localism Repository is a collection of examples of AI governance measures from around the world, focusing on how local governments are navigating the evolving landscape of AI. This resource is more than just a list of laws—it highlights innovative methods of AI governance, from the creation of expert advisory groups to the implementation of AI pilot programs.

Why AI Localism Matters

Local governments often face unique challenges in regulating AI, from ethical considerations to the social impact of AI in areas like law enforcement, housing, and employment. Yet, local initiatives are frequently overlooked by national and global AI policy observatories. The AI Localism Repository fills this gap, offering a platform for local policymakers to share their experiences and learn from one another…(More)”

Making the Global Digital Compact a reality: Four steps to establish a responsible, inclusive and equitable data future.


Article by Stefaan Verhulst: “In September of this year, as world leaders assemble in New York for the 78th annual meeting of the United Nations (UN) General Assembly, they will confront a weighty agenda. War and peace will be at the forefront of conversations, along with efforts to tackle climate change and the ongoing migration crisis. Alongside these usual topics, however, the gathered dignitaries will also turn their attention to digital governance.

In 2021, the UN Secretary General proposed that a Global Digital Compact (GDC) be agreed upon that would “outline shared principles for an open, free and secure digital future for all”. The development of this Compact, which builds on a range of adjacent work streams at the UN, including activities related to the Sustainable Development Goals (SDGs), has now reached a vital inflection point. After a wide-ranging process of consultation, the General Assembly is expected to ratify the latest draft of the Digital Compact, which contains five key objectives and a commitment to thirteen cross-cutting principles. We have reached a rare moment of near-consensus in the global digital ecosystem, one that offers undeniable potential for revamping (and improving) our frameworks for global governance.

The Global Digital Compact will be agreed upon by UN Member States at the Summit of the Future at the United Nations Headquarters in New York, establishing guidelines for the responsible use and governance of digital technologies. 

The growing prominence of these objectives and principles at the seat of global governance is a welcome development. Each is essential to developing a healthy, safe and responsible digital ecosystem. In particular, the emphasis on better data governance is a step forward, as is the related call for an enhanced approach for international AI governance. Both cannot be separated: data governance is the bedrock of AI governance.

Yet now that we are moving toward ratification of the Compact, we must focus on the next crucial—and in some ways most difficult – step: implementation. This is particularly important given that the digital realm faces in many ways a growing crisis of credibility, marked by growing concerns over exclusion, extraction, concentrations of power, mis- and disinformation, and what we have elsewhere referred to as an impending “data winter”.

Manifesting the goals of the Compact to create genuine and lasting impact is thus critical. In what follows, we explore four key ways in which the Compact’s key objectives can be operationalized to create a more vibrant, responsive and free global digital commons…(More)”.

Align or fail: How economics shape successful data sharing


Blog by Federico Bartolomucci: “…The conceptual distinctions between different data sharing models are mostly based on one fundamental element: the economic nature of data and its value. 

Open data projects operate under the assumption that data is a non-rival (i.e. can be used by multiple people at the same time) and a non-excludable asset (i.e. anyone can use it, similar to a public good like roads or the air we breathe). This means that data can be shared with everyone, for any use, without losing its market and competitive value. The Humanitarian Data Exchange platform is a great example that allows organizations to share over 19,000 open data sets on all aspects of humanitarian response with others.

Data collaboratives treat data as an excludable asset that some people may be excluded from accessing (i.e. a ‘club good’, like a movie theater) and therefore share it only among a restricted pool of actors. At the same time, they overcome the rival nature of this data set up by linking its use to a specific purpose. These work best by giving the actors a voice in choosing the purpose for which the data will be used, and through specific agreements and governance bodies that ensure that those contributing data will not have their competitive position harmed, therefore incentivizing them to engage. A good example of this is the California Data Collaborative, which uses data from different actors in the water sector to develop high-level analysis on water distribution to guide policy, planning, and operations for water districts in the state of California. 

Data ecosystems work by activating market mechanisms around data exchange to overcome reluctance to share data, rather than relying solely on its purpose of use. This means that actors can choose to share their data in exchange for compensation, be it monetary or in alternate forms such as other data. In this way, the compensation balances the potential loss of competitive advantage created by the sharing of a rival asset, as well as the costs and risks of sharing. The Enershare initiative aims to establish a marketplace utilizing blockchain and smart contracts to facilitate data exchange in the energy sector. The platform is based on a compensation system, which can be non-monetary, for exchanging assets and resources related to data (such as datasets, algorithms, and models) with energy assets and services (like heating system maintenance or the transfer of surplus locally self-produced energy).

These different models of data sharing have different operational implications…(More)”.

On Fables and Nuanced Charts


Column by Spencer Greenberg and Amber Dawn Ace: “In 1994, the U.S. Congress passed the largest crime bill in U.S. history, called the Violent Crime Control and Law Enforcement Act. The bill allocated billions of dollars to build more prisons and hire 100,000 new police officers, among other things. In the years following the bill’s passage, violent crime rates in the U.S. dropped drastically, from around 750 offenses per 100,000 people in 1990 to under 400 in 2018.

A chart showing U.S. crime rates over time. The data and annotation are real, but the implied story is not. Credit: Authors.

But can we infer, as this chart seems to ask us to, that the bill caused the drop in crime?

As it turns out, this chart wasn’t put together by sociologists or political scientists who’ve studied violent crime. Rather, we—a mathematician and a writer—devised it to make a point: Although charts seem to reflect reality, they often convey narratives that are misleading or entirely false.

Upon seeing that violent crime dipped after 1990, we looked up major events that happened right around that time—selecting one, the 1994 Crime Bill, and slapping it on the graph. There are other events we could have stuck on the graph just as easily that would likely have invited you to construct a completely different causal story. In other words, the bill and the data in the graph are real, but the story is manufactured.

Perhaps the 1994 Crime Bill really did cause the drop in violent crime, or perhaps the causality goes the other way: the spike in violent crime motivated politicians to pass the act in the first place. (Note that the act was passed slightly after the violent crime rate peaked!) 

Charts are a concise way not only to show data but also to tell a story. Such stories, however, reflect the interpretations of a chart’s creators and are often accepted by the viewer without skepticism. As Noah Smith and many others have argued, charts contain hidden assumptions that can drastically change the story they tell…(More)”.

On Slicks and Satellites: An Open Source Guide to Marine Oil Spill Detection


Article by Wim Zwijnenburg: “The sheer scale of ocean oil pollution is staggering. In Europe, a suspected 3,000 major illegal oil dumps take place annually, with an estimated release of between 15,000 and 60,000 tonnes of oil ending up in the North Sea. In the Mediterranean, figures provided by the Regional Marine Pollution Emergency Response Centre estimate there are 1,500 to 2,000 oil spills every year.

The impact of any single oil spill on a marine or coastal ecosystem can be devastating and long-lasting. Animals such as birds, turtles, dolphins and otters can suffer from ingesting or inhaling oil, as well as getting stuck in the slick. The loss of water and soil quality can be toxic to both flora and fauna. Heavy metals enter the food chain, poisoning everything from plankton to shellfish, which in turn affects the livelihoods of coastal communities dependent on fishing and tourism.

However, with a wealth of open source earth observation tools at our fingertips, during such environmental disasters it’s possible for us to identify and monitor these spills, highlight at-risk areas, and even hold perpetrators accountable. …

There are several different types of remote sensing sensors we can use for collecting data about the Earth’s surface. In this article we’ll focus on two: optical and radar sensors. 

Optical imagery captures the broad light spectrum reflected from the Earth, also known as passive remote sensing. In contrast, Synthetic Aperture Radar (SAR) uses active remote sensing, sending radio waves down to the Earth’s surface and capturing them as they are reflected back. Any change in the reflection can indicate a change on ground, which can then be investigated. For more background, see Bellingcat contributor Ollie Ballinger’s Remote Sensing for OSINT Guide…(More)”.

Building LLMs for the social sector: Emerging pain points


Blog by Edmund Korley: “…One of the sprint’s main tracks focused on using LLMs to enhance the impact and scale of chat services in the social sector.

Six organizations participated, with operations spanning Africa and India. Bandhu empowers India’s blue-collar workers and migrants by connecting them to jobs and affordable housing, helping them take control of their livelihoods and future stability. Digital Green enhances rural farmers’ agency with AI-driven insights to improve agricultural productivity and livelihoods. Jacaranda Health provides mothers in sub-Saharan Africa with essential information and support to improve maternal and newborn health outcomes. Kabakoo equips youth in Francophone Africa with digital skills, fostering self-reliance and economic independence. Noora Health teaches Indian patients and caregivers critical health skills, enhancing their ability to manage care. Udhyam provides micro-entrepreneurs’ with education, mentorship, and financial support to build sustainable businesses.

These organizations demonstrate diverse ways one can boost human agency: they help people in underserved communities take control of their lives, make more informed choices, and build better futures – and they are piloting AI interventions to scale these efforts…(More)”.

Using internet search data as part of medical research


Blog by Susan Thomas and Matthew Thompson: “…In the UK, almost 50 million health-related searches are made using Google per year. Globally there are 100s of millions of health-related searches every day. And, of course, people are doing these searches in real-time, looking for answers to their concerns in the moment. It’s also possible that, even if people aren’t noticing and searching about changes to their health, their behaviour is changing. Maybe they are searching more at night because they are having difficulty sleeping or maybe they are spending more (or less) time online. Maybe an individual’s search history could actually be really useful for researchers. This realisation has led medical researchers to start to explore whether individuals’ online search activity could help provide those subtle, almost unnoticeable signals that point to the beginning of a serious illness.

Our recent review found 23 studies have been published so far that have done exactly this. These studies suggest that online search activity among people later diagnosed with a variety of conditions ranging from pancreatic cancer and stroke to mood disorders, was different to people who did not have one of these conditions.

One of these studies was published by researchers at Imperial College London, who used online search activity to identify signals of women with gynaecological malignancies. They found that women with malignant (e.g. ovarian cancer) and benign conditions had different search patterns, up to two months prior to a GP referral. 

Pause for a moment, and think about what this could mean. Ovarian cancer is one of the most devastating cancers women get. It’s desperately hard to detect early – and yet there are signals of this cancer visible in women’s internet searches months before diagnosis?…(More)”.

The Imperial Origins of Big Data


Blog and book by Asheesh Kapur Siddique: “We live in a moment of massive transformation in the nature of information. In 2020, according to one report, users of the Internet created 64.2 zetabytes of data, a quantity greater than the “number of detectable stars in the cosmos,” a colossal increase whose origins can be traced to the emergence of the World Wide Web in 1993.1 Facilitated by technologies like satellites, smartphones, and artificial intelligence, the scale and speed of data creation seems like it may only balloon over the rest of our lifetimes—and with it, the problem of how to govern ourselves in relation to the inequalities and opportunities that the explosion of data creates.

But while much about our era of big data is indeed revolutionary, the political questions that it raises—How should information be used? Who should control it? And how should it be preserved?—are ones with which societies have long grappled. These questions attained a particular importance in Europe from the eleventh century due to a technological change no less significant than the ones we are witnessing today: the introduction of paper into Europe. Initially invented in China, paper travelled to Europe via the conduit of Islam around the eleventh century after the Moors conquered Spain. Over the twelfth, thirteenth, and fourteenth centuries, paper emerged as the fundamental substrate which politicians, merchants, and scholars relied on to record and circulate information in governance, commerce, and learning. At the same time, governing institutions sought to preserve and control the spread of written information through the creation of archives: repositories where they collected, organized, and stored documents.

The expansion of European polities overseas from the late fifteenth century onward saw governments massively scale up their use of paper—and confront the challenge of controlling its dissemination across thousands of miles of ocean and land. These pressures were felt particularly acutely in what eventually became the largest empire in world history, the British empire. As people from the British isles from the early seventeenth century fought, traded, and settled their way to power in the Atlantic world and South Asia, administrators faced the problem of how to govern both their emigrating subjects and the non-British peoples with whom they interacted. This meant collecting information about their behavior through the technology of paper. Just as we struggle to organize, search, and control our email boxes, text messages, and app notifications, so too did these early moderns confront the attendant challenges of developing practices of collection and storage to manage the resulting information overload. And despite the best efforts of states and companies to control information, it constantly escaped their grasp, falling into the hands of their opponents and rivals who deployed it to challenge and contest ruling powers.

The history of the early modern information state offers no simple or straightforward answers to the questions that data raises for us today. But it does remind us of a crucial truth, all too readily obscured by the deluge of popular narratives glorifying technological innovation: that questions of data are inherently questions about politics—about who gets to collect, control, and use information, and the ends to which information should be put. We should resist any effort to insulate data governance from democratic processes—and having an informed perspective on the politics of data requires that we attend not just to its present, but also to its past…(More)”.