Open-Sourcing Google Earth Enterprise

Curated on Posted on by Stefaan Verhulst

Geo Developers Blog: “We are excited to announce that we are open-sourcing Google Earth Enterprise (GEE), the enterprise product that allows developers to build and host their own private maps and 3D globes. With this release, GEE Fusion, GEE Server, and GEE Portable Server source code (all 470,000+ lines!) will be published on GitHub under the Apache2 license in March.

Originally launched in 2006, Google Earth Enterprise provides customers the ability to build and host private, on-premise versions of Google Earth and Google Maps. In March 2015, we announced the deprecation of the product and the end of all sales. To provide ample time for customers to transition, we have provided a two year maintenance period ending on March 22, 2017. During this maintenance period, product updates have been regularly shipped and technical support has been available to licensed customers….

GCP is increasingly used as a source for geospatial data. Google’s Earth Engine has made available over a petabyte of raster datasets which are readily accessible and available to the public on Google Cloud Storage. Additionally, Google uses Cloud Storage to provide data to customers who purchase Google Imagerytoday. Having access to massive amounts of geospatial data, on the same platform as your flexible compute and storage, makes generating high quality Google Earth Enterprise Databases and Portables easier and faster than ever.

We will be sharing a series of white papers and other technical resources to make it as frictionless as possible to get open source GEE up and running on Google Cloud Platform. We are excited about the possibilities that open-sourcing enables, and we trust this is good news for our community. We will be sharing more information when we launch the code in March on GitHub. For general product information, visit the Google Earth Enterprise Help Center. Review the essential and advanced training for how to use Google Earth Enterprise, or learn more about the benefits of Google Cloud Platform….(More)”

The science of society: From credible social science to better social policies

Curated on Posted on by Stefaan Verhulst

Nancy Cartwright and Julian Reiss at LSE Blog: “Society invests a great deal of money in social science research. Surely the expectation is that some of it will be useful not only for understanding ourselves and the societies we live in but also for changing them? This is certainly the hope of the very active evidence-based policy and practice movement, which is heavily endorsed in the UK both by the last Labour Government and by the current Coalition Government. But we still do not know how to use the results of social science in order to improve society. This has to change, and soon.

Last year the UK launched an extensive – and expensive – new What Works Network that, as the Government press release describes, consists of “two existing centres of excellence – the National Institute for Health and Clinical Excellence (NICE) and the Educational Endowment Foundation – plus four new independent institutions responsible for gathering, assessing and sharing the most robust evidence to inform policy and service delivery in tackling crime, promoting active and independent ageing, effective early intervention, and fostering local economic growth”.

This is an exciting and promising initiative. But it faces a series challenge: we remain unable to build real social policies based on the results of social science or to predict reliably what the outcomes of these policies will actually be. This contrasts with our understanding of how to establish the results in the first place.There we have a handle on the problem. We have a reasonable understanding of what kinds of methods are good for establishing what kinds of results and with what (at least rough) degrees of certainty.

There are methods – well thought through – that social scientists learn in the course of their training for constructing a questionnaire, running a randomised controlled trial, conducting an ethnographic study, looking for patterns in large data sets. There is nothing comparably explicit and well thought through about how to use social science knowledge to help predict what will happen when we implement a proposed policy in real, complex situations. Nor is there anything to help us estimate and balance the effectiveness, the evidence, the chances of success, the costs, the benefits, the winners and losers, and the social, moral, political and cultural acceptability of the policy.

To see why this is so difficult think of an analogy: not building social policies but building material technologies. We do not just read off instructions for building a laser – which may ultimately be used to operate on your eyes – from knowledge of basic science. Rather, we piece together a detailed model using heterogeneous knowledge from a mix of physics theories, from various branches of engineering, from experience of how specific materials behave, from the results of trial-and-error, etc. By analogy, building a successful social policy equally requires a mix of heterogeneous kinds of knowledge from radically different sources. Sometimes we are successful at doing this and some experts are very good at it in their own specific areas of expertise. But in both cases – both for material technology and for social technology – there is no well thought through, defensible guidance on how to do it: what are better and worse ways to proceed, what tools and information might be needed, and how to go about getting these. This is true whether we look for general advice that might be helpful across subject areas or advice geared to specific areas or specific kinds of problems. Though we indulge in social technology – indeed we can hardly avoid it – and are convinced that better social science will make for better policies, we do not know how to turn that conviction into a reality.

This presents a real challenge to the hopes for evidence-based policy….(More)”

Can you crowdsource water quality data?

Curated on Posted on by Stefaan Verhulst

Pratibha Mistry at The Water Blog (Worldbank): “The recently released Contextual Framework for Crowdsourcing Water Quality Data lays out a strategy for citizen engagement in decentralized water quality monitoring, enabled by the “mobile revolution.”

According to the WHO, 1.8 billion people lack access to safe drinking water worldwide. Poor source water quality, non-existent or insufficient treatment, and defects in water distribution systems and storage mean these consumers use water that often doesn’t meet the WHO’s Guidelines for Drinking Water Quality.

The crowdsourcing framework develops a strategy to engage citizens in measuring and learning about the quality of their own drinking water. Through their participation, citizens provide utilities and water supply agencies with cost-effective water quality data in near-real time. Following a typical crowdsourcing model: consumers use their mobile phones to report water quality information to a central service. That service receives the information, then repackages and shares it via mobile phone messages, websites, dashboards, and social media. Individual citizens can thus be educated about their water quality, and water management agencies and other stakeholders can use the data to improve water management; it’s a win-win.

A well-implemented crowdsourcing project both depends on and benefits end users.Source: Figure modified from Hutchings, M., Dev, A., Palaniappan, M., Srinivasan, V., Ramanathan, N., Taylor, J.  2012. “mWASH: Mobile Phone Applications for the Water, Sanitation, and Hygiene Sector.” Pacific Institute, Oakland, California.  114 p.  (Link to full text)

Several groups, from the private sector to academia to non-profits, have taken a recent interest in developing a variety of so-called mWASH apps (mobile phone applications for the water, sanitation, and hygiene WASH sector).  A recent academic study analyzed how mobile phones might facilitate the flow of water quality data between water suppliers and public health agencies in Africa. USAID has invested in piloting a mobile application in Tanzania to help consumers test their water for E. coli….(More)”

What does Big Data mean to public affairs research?

Curated on Posted on by Stefaan Verhulst

Ines Mergel, R. Karl Rethemeyer, and Kimberley R. Isett at LSE’s The Impact Blog: “…Big Data promises access to vast amounts of real-time information from public and private sources that should allow insights into behavioral preferences, policy options, and methods for public service improvement. In the private sector, marketing preferences can be aligned with customer insights gleaned from Big Data. In the public sector however, government agencies are less responsive and agile in their real-time interactions by design – instead using time for deliberation to respond to broader public goods. The responsiveness Big Data promises is a virtue in the private sector but could be a vice in the public.

Moreover, we raise several important concerns with respect to relying on Big Data as a decision and policymaking tool. While in the abstract Big Data is comprehensive and complete, in practice today’sversion of Big Data has several features that should give public sector practitioners and scholars pause. First, most of what we think of as Big Data is really ‘digital exhaust’ – that is, data collected for purposes other than public sector operations or research. Data sets that might be publicly available from social networking sites such as Facebook or Twitter were designed for purely technical reasons. The degree to which this data lines up conceptually and operationally with public sector questions is purely coincidental. Use of digital exhaust for purposes not previously envisioned can go awry. A good example is Google’s attempt to predict the flu based on search terms.

Second, we believe there are ethical issues that may arise when researchers use data that was created as a byproduct of citizens’ interactions with each other or with a government social media account. Citizens are not able to understand or control how their data is used and have not given consent for storage and re-use of their data. We believe that research institutions need to examine their institutional review board processes to help researchers and their subjects understand important privacy issues that may arise. Too often it is possible to infer individual-level insights about private citizens from a combination of data points and thus predict their behaviors or choices.

Lastly, Big Data can only represent those that spend some part of their life online. Yet we know that certain segments of society opt in to life online (by using social media or network-connected devices), opt out (either knowingly or passively), or lack the resources to participate at all. The demography of the internet matters. For instance, researchers tend to use Twitter data because its API allows data collection for research purposes, but many forget that Twitter users are not representative of the overall population. Instead, as a recent Pew Social Media 2016 update shows, only 24% of all online adults use Twitter. Internet participation generally is biased in terms of age, educational attainment, and income – all of which correlate with gender, race, and ethnicity. We believe therefore that predictive insights are potentially biased toward certain parts of the population, making generalisations highly problematic at this time….(More)”

Toward Evidence-Based Open Governance by Curating and Exchanging Research: OGRX 2.0

Curated on Posted on by Stefaan Verhulst

Andrew Young and Stefaan Verhulst at OGRX : “The Open Governance Research Exchange (OGRX) is a platform that seeks to identify, collect and share curated insights on new ways of solving public problems. It was created last year by the GovLab, World Bank Digital Engagement Evaluation Team and mySociety. Today, while more than 3000 representatives from more than 70 countries are gathering in Paris for the Open Government Partnership Summit, we are launching OGRX 2.0 with new features and functionalities to further help users identify the signal in the noise of research and evidence on more innovative means of governing….

What is new?

First, the new OGRX Blog provides an outlet for more easily digestible and shareable insights from the open governance research community. OGRX currently features over 600 publications on governance innovation – but how to digest and identify insights? This space will provide summaries of important works, analyses of key trends in the field of research, and guest posts from researchers working at the leading edge of governance innovation across regions and domains. Check back often to stay on top of what’s new in open governance research.

Second, the new OGRX Selected Readings series offers curated reading lists from well-known experts in open governance. These Selected Readings will give readers a sense of how to jumpstart their knowledge by focusing on those publications that have been curated by those in the known about the topics at hand. Today we are launching this new series with the Selected Readings on Civic Technology, curated by mySociety’s head of research Rebecca Rumbul; and the Selected Readings on Policy Informatics, curated by Erik Johnston of the MacArthur Foundation Research Network on Opening Governance and director of the Arizona State University Center for Policy Informatics. New Selected Readings will be posted each month, so check back often!…Watch this space and #OGRX to stay abreast of new developments….”

How the Circle Line rogue train was caught with data

Curated on Posted on by Stefaan Verhulst

Daniel Sim at the Data.gov.sg Blog: “Singapore’s MRT Circle Line was hit by a spate of mysterious disruptions in recent months, causing much confusion and distress to thousands of commuters.

Like most of my colleagues, I take a train on the Circle Line to my office at one-north every morning. So on November 5, when my team was given the chance to investigate the cause, I volunteered without hesitation.

 From prior investigations by train operator SMRT and the Land Transport Authority (LTA), we already knew that the incidents were caused by some form of signal interference, which led to loss of signals in some trains. The signal loss would trigger the emergency brake safety feature in those trains and cause them to stop randomly along the tracks.

But the incidents — which first happened in August — seemed to occur at random, making it difficult for the investigation team to pinpoint the exact cause.

We were given a dataset compiled by SMRT that contained the following information:

  • Date and time of each incident
  • Location of incident
  • ID of train involved
  • Direction of train…

LTA and SMRT eventually published a joint press release on November 11 to share the findings with the public….

When we first started, my colleagues and I were hoping to find patterns that may be of interest to the cross-agency investigation team, which included many officers at LTA, SMRT and DSTA. The tidy incident logs provided by SMRT and LTA were instrumental in getting us off to a good start, as minimal cleaning up was required before we could import and analyse the data. We were also gratified by the effective follow-up investigations by LTA and DSTA that confirmed the hardware problems on PV46.

From the data science perspective, we were lucky that incidents happened so close to one another. That allowed us to identify both the problem and the culprit in such a short time. If the incidents were more isolated, the zigzag pattern would have been less apparent, and it would have taken us more time — and data — to solve the mystery….(More).”

Open Data Collection (PLOS)

Curated on Posted on by Stefaan Verhulst
Daniella Lowenberg, Amy Ross, Emma Ganley at PLOS: “In the spirit of Open Con and highlighting the state of Open Data, PLOS is proud to release our Open Data Collection. The many values of Open Data are becoming increasingly apparent, and as a result, we are seeing an adoption of Open Data policies across publishers, funders and organizations. Open Data has proven a fantastic tool to help evaluate the replicability of published research, and even politicians are taking a stand in favor of Open Data as a mechanism to advance science rapidly. In March of 2014, PLOS updated our Data Policy to reflect the need for the underlying data to be as open as the paper itself resulting in complete transparency of the research. Two and-a-half years later, we have seen over 60,000 published papers with open data sets and an increase in submissions reflecting open data practices and policies….

To create this Open Data Collection, we exhaustively searched for relevant articles published across PLOS that discuss open data in some way. Then, in collaboration with our external advisor, Melissa Haendel, we have selected 26 of those articles with the aim to highlight a broad scope of research articles, guidelines, and commentaries about data sharing, data practices, and data policies from different research fields. Melissa has written an engaging blog post detailing the rubric and reasons behind her selections….(More)”

Transforming government through digitization

Curated on Posted on by Stefaan Verhulst

Bjarne Corydon, Vidhya Ganesan, and Martin Lundqvist at McKinsey: “By digitizing processes and making organizational changes, governments can enhance services, save money, and improve citizens’ quality of life.

As companies have transformed themselves with digital technologies, people are calling on governments to follow suit. By digitizing, governments can provide services that meet the evolving expectations of citizens and businesses, even in a period of tight budgets and increasingly complex challenges. Our estimates suggest that government digitization, using current technology, could generate over $1 trillion annually worldwide.

Digitizing a government requires attention to two major considerations: the core capabilities for engaging citizens and businesses, and the organizational enablers that support those capabilities (exhibit). These make up a framework for setting digital priorities. In this article, we look at the capabilities and enablers in this framework, along with guidelines and real-world examples to help governments seize the opportunities that digitization offers.

A digital government has core capabilities supported by organizational enablers.

Governments typically center their digitization efforts on four capabilities: services, processes, decisions, and data sharing. For each, we believe there is a natural progression from quick wins to transformative efforts….(More)”

See also: Digital by default: A guide to transforming government (PDF–474KB) and  “Never underestimate the importance of good government,”  a New at McKinsey blog post with coauthor Bjarne Corydon, director of the McKinsey Center for Government.

Beyond nudging: it’s time for a second generation of behaviourally-informed social policy

Curated on Posted on by Stefaan Verhulst

Katherine Curchin at LSE Blog: “…behavioural scientists are calling for a second generation of behaviourally-informed policy. In some policy areas, nudges simply aren’t enough. Behavioural research shows stronger action is required to attack the underlying cause of problems. For example, many scholars have argued that behavioural insights provide a rationale for regulation to protect consumers from manipulation by private sector companies. But what might a second generation of behaviourally-informed social policy look like?

Behavioural insights could provide a justification to change the trajectory of income support policy. Since the 1990s policy attention has focused on the moral character of benefits recipients. Inspired by Lawrence Mead’s paternalist philosophy, governments have tried to increase the resolve of the unemployed to work their way out of poverty. More and more behavioural requirements have been attached to benefits to motivate people to fulfil their obligations to society.

But behavioural research now suggests that these harsh policies are misguided. Behavioural science supports the idea that people often make poor decisions and do things which are not in their long term interests.  But the weakness of individuals’ moral constitution isn’t so much the problem as the unequal distribution of opportunities in society. There are circumstances in which humans are unlikely to flourish no matter how motivated they are.

Normal human psychological limitations – our limited cognitive capacity, limited attention and limited self-control – interact with environment to produce the behaviour that advocates of harsh welfare regimes attribute to permissive welfare. In their book Scarcity, Sendhil Mullainathan and Eldar Shafir argue that the experience of deprivation creates a mindset that makes it harder to process information, pay attention, make good decisions, plan for the future, and resist temptations.

Importantly, behavioural scientists have demonstrated that this mindset can be temporarily created in the laboratory by placing subjects in artificial situations which induce the feeling of not having enough. As a consequence, experimental subjects from middle-class backgrounds suddenly display the short-term thinking and irrational decision making often attributed to a culture of poverty.

Tying inadequate income support to a list of behavioural conditions will most punish those who are suffering most. Empirical studies of welfare conditionality have found that benefit claimants often do not comprehend the complicated rules that apply to them. Some are being punished for lack of understanding rather than deliberate non-compliance.

Behavioural insights can be used to mount a case for a more generous, less punitive approach to income support. The starting point is to acknowledge that some of Mead’s psychological assumptions have turned out to be wrong. The nature of the cognitive machinery humans share imposes limits on how self-disciplined and conscientious we can reasonably expect people living in adverse circumstances to be. We have placed too much emphasis on personal responsibility in recent decades. Why should people internalize the consequences of their behaviour when this behaviour is to a large extent the product of their environment?…(More)”

Crowdsourcing Gun Violence Research

Curated on Posted on by Stefaan Verhulst

Penn Engineering: “Gun violence is often described as an epidemic, but as visible and shocking as shooting incidents are, epidemiologists who study that particular source of mortality have a hard time tracking them. The Centers for Disease Control is prohibited by federal law from conducting gun violence research, so there is little in the way of centralized infrastructure to monitor where, how,when, why and to whom shootings occur.

Chris Callison-Burch, Aravind K.Joshi Term Assistant Professor in Computer and InformationScience, and graduate studentEllie Pavlick are working to solve this problem.

They have developed the GunViolence Database, which combines machine learning and crowdsourcing techniques to produce a national registry of shooting incidents. Callison-Burch and Pavlick’s algorithm scans thousands of articles from local newspaper and television stations,determines which are about gun violence, then asks everyday people to pullout vital statistics from those articles, compiling that information into a unified, open database.

For natural language processing experts like Callison-Burch and Pavlick, the most exciting prospect of this effort is that it is training computer systems to do this kind of analysis automatically. They recently presented their work on that front at Bloomberg’s Data for Good Exchange conference.

The Gun Violence Database project started in 2014, when it became the centerpiece of Callison-Burch’s “Crowdsourcing and Human Computation”class. There, Pavlick developed a series of homework assignments that challenged undergraduates to develop a classifier that could tell whether a given news article was about a shooting incident.

“It allowed us to teach the things we want students to learn about datascience and natural language processing, while giving them the motivation to do a project that could contribute to the greater good,” says Callison-Burch.

The articles students used to train their classifiers were sourced from “TheGun Report,” a daily blog from New York Times reporters that attempted to catalog shootings from around the country in the wake of the Sandy Hook massacre. Realizing that their algorithmic approach could be scaled up to automate what the Times’ reporters were attempting, the researchers began exploring how such a database could work. They consulted with DouglasWiebe, a Associate Professor of Epidemiology in Biostatistics andEpidemiology in the Perelman School of Medicine, to learn more about what kind of information public health researchers needed to better study gun violence on a societal scale.

From there, the researchers enlisted people to annotate the articles their classifier found, connecting with them through Mechanical Turk, Amazon’scrowdsourcing platform, and their own website, http://gun-violence.org/…(More)”