Book by Daniel Aldrich: “Despite the devastation caused by the magnitude 9.0 earthquake and 60-foot tsunami that struck Japan in 2011, some 96% of those living and working in the most disaster-stricken region of Tōhoku made it through. Smaller earthquakes and tsunamis have killed far more people in nearby China and India. What accounts for the exceptionally high survival rate? And why is it that some towns and cities in the Tōhoku region have built back more quickly than others?
Black Wave illuminates two critical factors that had a direct influence on why survival rates varied so much across the Tōhoku region following the 3/11 disasters and why the rebuilding process has also not moved in lockstep across the region. Individuals and communities with stronger networks and better governance, Daniel P. Aldrich shows, had higher survival rates and accelerated recoveries. Less connected communities with fewer such ties faced harder recovery processes and lower survival rates. Beyond the individual and neighborhood levels of survival and recovery, the rebuilding process has varied greatly, as some towns and cities have sought to work independently on rebuilding plans, ignoring recommendations from the national governments and moving quickly to institute their own visions, while others have followed the guidelines offered by Tokyo-based bureaucrats for economic development and rebuilding….(More)”.
This tech tells cities when floods are coming–and what they will destroy
Ben Paynter at FastCompany: “Several years ago, one of the eventual founders of One Concern nearly died in a tragic flood. Today, the company specializes in using artificial intelligence to predict how natural disasters are unfolding in real time on a city-block-level basis, in order to help disaster responders save as many lives as possible….
To fix that, One Concern debuted Flood Concern in late 2018. It creates map-based visualizations of where water surges may hit hardest, up to five days ahead of an impending storm. For cities, that includes not just time-lapse breakdowns of how the water will rise, how fast it could move, and what direction it will be flowing, but also what structures will get swamped or washed away, and how differing mitigation efforts–from levy building to dam releases–will impact each scenario. It’s the winner of Fast Company’s 2019 World Changing Ideas Awards in the AI and Data category.
So far, Flood Concern has been retroactively tested against events like Hurricane Harvey to show that it could have predicted what areas would be most impacted well ahead of the storm. The company, which was founded in Silicon Valley in 2015, started with one of that region’s pressing threats: earthquakes. It’s since earned contracts with cities like San Francisco, Los Angeles, and Cupertino, as well as private insurance companies….
One Concern’s first offering, dubbed Seismic Concern, takes existing information from satellite images and building permits to figure out what kind of ground structures are built on, and what might happen if they started shaking. If a big one hits, the program can extrapolate from the epicenter to suggest the likeliest places for destruction, and then adjust as more data from things like 911 calls and social media gets factored in….(More)”.
Social Entrepreneurship: Concepts, Methodologies, Tools, and Applications
Book edited by the Information Resources Management Association: “Businesses are looking for methods to incorporate social entrepreneurship in order to generate a positive return to society. Social enterprises have the ability to improve societies through altruistic work to create sustainable work environments for future entrepreneurs and their communities.
Social Entrepreneurship: Concepts, Methodologies, Tools, and Applications is a useful scholarly resource that examines the broad topic of social entrepreneurship by looking at relevant theoretical frameworks and fundamental terms. It also addresses the challenges and solutions social entrepreneurs face as they address their corporate social responsibility in an effort to redefine the goals of today’s enterprises and enhance the potential for growth and change in every community. Highlighting a range of topics such as the social economy, corporate social responsibility, and competitive advantage, this multi-volume book is ideally designed for business professionals, entrepreneurs, start-up companies, academics, and graduate-level students in the fields of economics, business administration, sociology, education, politics, and international relations….(More)”.
Negotiating with the future: incorporating imaginary future generations into negotiations
Paper by Yoshio Kamijo et al: “People to be born in the future have no direct
influence on current affairs. Given the disconnect between people who are currently living and those who will inherit the planet left for them, individuals who are currently alive tend to be more oriented toward the present, posing a fundamental problem related to sustainability.
In this study, we propose a new framework for reconciling the disconnect between the present and the future whereby some individuals in the current generation serve as an imaginary
A Review of Citizen Science and Crowdsourcing in Applications of Pluvial Flooding
Jonathan D. Paul in Frontiers in Earth Science: “Pluvial flooding can have devastating effects, both in terms of loss of life and damage. Predicting pluvial floods is difficult and many cities do not have a hydrodynamic model or an early warning system in place. Citizen science and crowdsourcing have the potential for contributing to early warning systems and can also provide data for validating flood forecasting models. Although there are increasing applications of citizen science and crowdsourcing in fluvial hydrology, less is known about activities related to pluvial flooding. Hence the aim of this paper is to review current activities in citizen science and crowdsourcing with respect to applications of pluvial flooding.
Based on a search in Scopus, the papers were first filtered for relevant content and then classified into four main themes. The first two themes were divided into (i) applications relevant during a flood event, which includes automated street flooding detection using crowdsourced photographs and sensors, analysis of social media, and online and mobile applications for flood reporting; and (ii) applications related to post-flood events. The use of citizen science and crowdsourcing for model development and validation is the third theme while the development of integrated systems is theme four. All four main areas of research have the potential to contribute to early warning systems and build community resilience. Moreover, developments in one will benefit others, e.g., further developments in flood reporting applications and automated flood detection systems will yield data useful for model validation….(More)”.
The Lancet Countdown: Tracking progress on health and climate change using data from the International Energy Agency (IEA)
Victoria Moody at the UK Data Service: “The 2015 Lancet Commission on Health and Climate Change—which assessed responses to climate change with a view to ensuring the highest attainable standards of health for populations worldwide—concluded that “tackling climate change could be the greatest global health opportunity of the 21st century”. The Commission recommended that more accurate national quantification of the health co-benefits and economic impacts of mitigation decisions was essential in promoting a low-carbon transition.
Building on these foundations, the Lancet Countdown: tracking progress on health and climate change was formed as an independent research collaboration…
The partnership comprises 24 academic institutions from every continent, bringing together individuals with a broad range of expertise across disciplines (including climate scientists, ecologists, mathematicians, geographers, engineers, energy, food, and transport experts, economists, social and political scientists, public health professionals, and physicians).
Four of the indicators developed for Working Group 3 (Mitigation actions and health co-benefits) uses International Energy Agency (IEA) data made available by the the IEA via the UK Data Service for use by researchers, learners and teaching staff in UK higher and further education. Additionally, two of the indicators developed for Working Group 4 (Finance and economics) also use IEA data.
Read our impact case study to find
Responsible AI for conservation
Oliver Wearn, RobinFreeman and David Jacoby in Nature: “Machine learning (ML) is revolutionizing efforts to conserve nature. ML algorithms are being applied to predict the extinction risk of thousands of species, assess the global footprint of fisheries, and identify animals and humans in wildlife sensor data recorded in the field. These efforts have recently been given a huge boost with support from the commercial sector. New initiatives, such as Microsoft’s AI for Earth and Google’s AI for Social Good, are bringing new resources and new ML tools to bear on some of the biggest challenges in conservation. In parallel to this, the open data revolution means that global-scale, conservation-relevant datasets can be fed directly to ML algorithms from open data repositories, such as Google Earth Engine for satellite data or Movebank for animal tracking data. Added to these will be Wildlife Insights, a Google-supported platform for hosting and
Weather Service prepares to launch prediction model many forecasters don’t trust
Jason Samenow in the Washington Post: “In a month, the National Weather Service plans to launch its “next generation” weather prediction model with the aim of “better, more timely forecasts.” But many meteorologists familiar with the model fear it is unreliable.
The introduction of a model that forecasters lack confidence in matters, considering the enormous impact that weather has on the economy, valued at around $485 billion annually.
The Weather Service announced Wednesday that the model, known as the GFS-FV3 (FV3 stands for Finite Volume Cubed-Sphere dynamical core), is “tentatively” set to become the United States’ primary forecast model on March 20, pending tests. It is an update to the current version of the GFS (Global Forecast System), popularly known as the American model, which has existed in various forms for more than 30 years
A concern is that if forecasters cannot rely on the FV3, they will be left to rely only on the European model for their predictions without a credible alternative for comparisons. And they’ll also have to pay large fees for the European model data. Whereas model data from the Weather Service is free, the European Center for Medium-Range Weather Forecasts, which produces the European model, charges for access.
But there is an alternative perspective, which is that forecasters will just need to adjust to the new model and learn to account for its biases. That is, a little short-term pain is worth the long-term potential benefits as the model improves
The Weather Service’s parent agency, the National Oceanic and Atmospheric Administration, recently entered an agreement with the National Center for Atmospheric Research to increase collaboration between forecasters and researchers in improving forecast modeling.
In addition, President Trump recently signed into law the Weather Research and Forecast Innovation Act Reauthorization, which establishes the NOAA Earth Prediction Innovation Center, aimed at further enhancing prediction capabilities. But even while NOAA develops relationships and infrastructure to improve the Weather Service’s modeling, the question remains whether the FV3 can meet the forecasting needs of the moment. Until the problems identified are addressed, its introduction could represent a step back in U.S. weather prediction despite a well-intended effort to leap forward….(More).
Not so gameful: A critical review of gamification in mobile energy applications
Many of these interventions and products have related apps that use gamification in some capacity in order to improve the user experience, offer motivation, and encourage behavior change. We identified 57 apps from nearly 2400 screened apps that both target direct energy use and employ at least one element of gamification.
We evaluated these apps with
New mathematical model can help save endangered species
The risk of extinction varies from species to species depending on how individuals in its populations reproduce and how long each animal survives. Understanding the dynamics of survival and reproduction can support management actions to improve a specie’s chances of surviving.
Mathematical and statistical models have become powerful tools to help explain these dynamics. However, the quality of the information we use to construct such models is crucial to improve our chances of accurately predicting the fate of populations in nature.
Colchero’s research focuses on mathematically recreating the population dynamics by better understanding the species’s demography. He works on constructing and exploring stochastic population models that predict how a certain population (for example an endangered species) will change over time.
These models include mathematical factors to describe how the species’ environment, survival rates and reproduction determine to the population’s size and growth. For practical reasons some assumptions are necessary.
Two commonly accepted assumptions are that survival and reproduction are constant with age, and that high survival in the species goes hand in hand with reproduction across all age groups within a species. Colchero challenged these assumptions by accounting for age-specific survival and reproduction, and for trade-offs between survival and reproduction. This is, that sometimes conditions that favor survival will be unfavorable for reproduction, and vice versa.
For his work Colchero used statistics, mathematical derivations, and computer simulations with data from wild populations of 24 species of vertebrates. The outcome was a significantly improved model that had more accurate predictions for a species’ population growth.
Despite the technical nature of Fernando’s work, this type of model can have very practical implications as they provide qualified explanations for the underlying reasons for the extinction. This can be used to take management actions and may help prevent