Jan 28, 2014 | Risk and resilience
Interview with IIASA risk expert Nadejda Komendantova
In a new study, IIASA Risk, Policy, and Vulnerability Program researcher Nadejda Komendantova and colleagues from Germany and Switzerland examined how natural hazards and risks assessments can be incorporated into decision-making processes in Europe on mitigation of multiple risks.
A cyclist rides along the flooded Danube River in Braila, Romania, in 2010. Credit: cod_gabriel on Flickr
Why did you decide to conduct this study?
European decision makers currently have a number of methods that they can use to assess natural hazards and risks and apply to the decision-making process. These methods include risk and hazard assessments, probabilistic scenarios, and socio-economic and engineering models. The variety of tools is enormous and volume of knowledge and data is growing. However, the process of communication between science and practice leaves a lot of open questions for research.
Researchers have developed a few tools to provide multiple risk assessment of a given territory. But even though these models have been tested by operational and practicing stakeholders, there is limited information about how useful the models are for civil protection stakeholders to use in practice. In order to communicate results from science to practice and make it possible for decision-makers to use such tools, it helps to involve decision-makers in the development process. Participatory modeling, which is an important part of risk governance, allows us to not only to take into consideration the facts, but also values and judgments that decision-makers bring to their actions.
What questions did you aim to answer in your study?
The decision-making process becomes even more complex when we talk about situations with multiple risks – multi-risks – which involve interactions between several risks. How will decision-maker will prioritize their actions on risk mitigation or on resources allocation when facing not single but multiple risks? We also wanted to find out if the tools developed by science such as decision support models could be suitable for these tasks. Another question is if there are differences in perceptions of the usability of decision-support tools between different stakeholders, such as academia (based on more theoretical considerations) and civil protection (based on practice).
What are the multiple risks or hazards that face Europe?
Across Europe, people suffer losses not just from single hazards, but also from multiple events in combination. The most important hazards for Europe are earthquakes, landslides, volcanic eruptions, tsunamis, wildfires, winter storms, and floods along both rivers and coastlines.
What methods did you use to conduct your study?
To answer our research questions we collected feedback from civil protection stakeholders on existing risk and hazard assessment tools as well as on the generic multi-risk framework to understand interrelations between different risks, such as conjoint and cascade effects. The new study was based on a method developed by Arnaud Mignan at ETH Zürich, with a decision-support tool developed by Bijan Khazai at the Karlsruhe Institute of Technology. Through a participatory approach, the decision-support tool allowed stakeholders to assign relative importance to the losses for different sectors for each of the scenarios likely to occur in the region.
We collected data through questionnaires on existing risk assessment tools in Europe and their implementation. Then, using the new framework, we conducted focus group discussions in Bonn and Lisbon, and decision-making experiments applying the developed tools. Afterwards we had a chance to collect feedback from stakeholders.
What did you find?
The study showed that general standards for multi-risk assessment are still missing—there are different terminologies and different methodologies related to data collection, monitoring, and output. According to stakeholders from practice, this variety of data, assessment methods, tools and terminology might be a barrier for implementation of the multi-risk approach.
The study also found a sharp divide in understanding of the usability of the tools and areas for their application. Academic stakeholders saw the risk-assessment tools as being useful to understand loss and communication of multi-risk parameters. The stakeholders from practice instead saw the tool as more useful for training and educational purposes as well as to raise awareness about possible multi-risk scenarios.
What should be done to help decision-makers make better decisions?
The study made it clear that we need to work on training and education, both for policymakers and the public. The models we have developed could be useful for educating stakeholders about the usefulness of a multi-risk approach, and to disseminate these results to the general public. It was recommended to use the tools during special training workshops organized for decision-makers on multi-risk mitigation to see possible consequences of a multi-hazard situation for their region. Participatory modeling, involving cooperation between scientists and decision-makers from practice, could not only improve communication processes between science and policy. In addition, decision-support models can become a part of dialogue to help to avoid judgment biases and systematic errors in decision-making and to help in complex decision-making process grounded on human rationality and judgment biases.
Reference:
Nadejda Komendantova, Roger Mrzyglocki, Arnaud Mignan, Bijan Khazai, Friedemann Wenzel, Anthony Patt, Kevin Fleming. 2014. Multi-hazard and multi-risk decision support tools as a part of participatory risk governance: Feedback from civil protection stakeholder. International Journal of Disaster Risk Reduction. http://www.sciencedirect.com/science/article/pii/S221242091300068X
Note: This article gives the views of the interviewee, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.
00
Jan 14, 2014 | Citizen Science, Environment
Steffen Fritz has just been awarded an ERC Consolidator Grant to fund a research project on crowdsourcing and ground data collection on land-use and land cover. In this interview he talks about his plans for the new project, CrowdLand.
Farmers in Kenya are one group which the Crowdland Project aims to involve in their data gathering. Photo credit: Neil Palmer, CIAT
What’s the problem with current land cover data?
There are discrepancies between current land cover products, especially in cropland data. It’s all based on satellite data, and in these data, it is extremely difficult to distinguish between cropland and natural vegetation in certain parts of the world if you do not use so-called very high resolution imagery, similar to a picture you take from space. With this high-resolution data you can see structures like fields and so on, which you can then use to distinguish between natural vegetation and cropland. But this is a task where currently people are still better at than computers–and there is a huge amount of data to look at.
In our Geo-Wiki project and related efforts such as the Cropland Capture game, we have asked volunteers to look at these high-resolution images and classify the ground cover as cropland or not cropland. The efforts have been quite successful, but our new project will take this even further.
How will the new project expand on what you’ve already done in Geo-Wiki?
The big addition is to go on the ground. Most of the exercises we currently do are based on the desktop or the phones, or tablets, asking volunteers to classify imagery that they see on a screen.
What this project aims to do is to improve data you collect on the ground, known as in-situ data. You can use photography, GPS sensors, but also your knowledge you have about what you see. We will use volunteers to collect basic land cover data such as tree cover, cropland, and wetlands, but also much more detailed land-use information. With this type of data we can document what crops are grown where, whether they are irrigated, if the fields are fertilized, what exact type of crops are growing, and other crop management information which you cannot see in satellite imagery. And there are some things you can’t even see when you’re on the ground, thus you need to ask the farmer or recruit the farmer as a data provider. That’s an additional element this project will bring, that we will work closely with farmers and people on the ground.
For the study, you have chosen Austria and Kenya. Why these two countries?
In Austria we have much better in situ data. For example, the Land Use Change Analysis System (LUCAS) in Europe collects in situ data according to a consistent protocol. But this program is very expensive, and the agency that runs it, Eurostat, is discussing how to reduce costs. Additionally the survey is only repeated every three years so fast changes are not immediately recorded. Some countries are not in favor of LUCAS and they prefer to undertake their own surveys. Then however you lose the overall consistency and there is no Europe-wide harmonized database which allows for comparison between countries. Our plan is to use gaming, social incentives, and also small financial incentives to conduct a crowdsourced LUCAS survey. Then we will examine what results you get when you pay volunteers or trained volunteers compared to the data collected by experts.
In Kenya, the idea is similar, but in general in the developing world we have very limited information, and the resources are not there for major surveys like in Europe. In order to remedy that the idea is again to use crowdsourcing and use a “bounded crowd” which means people who have a certain level of expertise, and know about land cover and land use, for example people with a surveyor background, university students, or interested citizens who can be trained. But in developing countries in particular it’s important to use financial incentives. Financial incentives, even small ones, could probably help to collect much larger amounts of data. Kenya is a good choice also because it has quite a good internet connection, a 3G network, and a lot of new technologies evolving around mobile phones and smartphone technology.
What will happen with the data you collect during this project?
First, we will analyze the data in terms of quality. One of our research questions is how good are the data collected by volunteers compared to data collected by experts. Another research question is how can imperfect but large data collected by volunteers be filtered and combined so that it becomes useful and fulfills the scientific accuracy requirements.
Then we will use these data and integrate them into currently existing land use and land cover data, and find ways to make better use of it. For example, in order to make projections about future land-use and to better quantify current yield gaps it is crucial to get accurate current information on land-use, including spatially explicit information on crop types, crop management information and other data.
Once we have done some quality checks we will also make these data available for other researchers or interested groups of people.
Crowdsourcing for land cover is in its infancy. There have been lots of crowdsourcing projects in astronomy, archaeology, and biology, for example, but there hasn’t been much on land use, and there is huge potential there. ”We need to not only better understand the quality of the data we collect, but also expand the network of institutions who are working on this topic.”
Note: This article gives the views of the interviewee, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.
00
Jan 7, 2014 | IIASA Network, Systems Analysis
On October 15, 2012, a young man from Bangladesh named Quazi Mohammad Rezwanul Ahsan Nafis parked next to New York Federal Reserve Bank in a van with what he believed was a 1000-pound bomb, walked a few blocks away, and then attempted to detonate the bomb by mobile phone.
In fact, the bomb was a fake, supplied by undercover agents for the United States FBI. The agents, posing as radical jihadists, had led Nafis along for months, allowing him to believe they were fellow terrorists and gathering information about his plot. The cover was maintained until the moment when his bomb failed to detonate, and Nafis was arrested. Disaster averted.
Researchers at IIASA study many risks to society, from floods, hurricanes, and natural disasters, to the impacts of climate change on future generations. They use models that can help disentangle the costs and benefits of different policies that could help prevent damage or deaths, or mitigate the impacts of global problems like climate change and air pollution. Could the same techniques apply to the dangers of terrorism and jihadists attacks? Could systems analysis help inform intelligence agencies in order to stop more terrorist attacks?
Could systems analysis techniques help guide policies to prevent terrorist attacks? Image Credit: Vjeran Pavic
Yale University Professor Ed Kaplan has done just that in work that he presented at IIASA in late December 2013. His research, which has intersected with IIASA in the past through collaborations with former IIASA Directors Howard Raiffa and Detlof von Winterfeldt, uses operations research to find ways to improve intelligence operations so as to catch more terrorists, before an attack can take place.
Kaplan, an expert on counterterrorism research, refined a simple economic model of customer service, known as a “queuing model” to instead represent the evolution of terror plots by terrorists, and interaction between the terrorists and the undercover agents who are working to uncover those plots.
“The best way to stop an attack is to know it’s about to happen beforehand,” says Kaplan. That means, in large part, having enough agents in the right places to detect attacks. But how many agents is the right number?
At IIASA, Kaplan described his terrorist “queuing model,” which can be applied to show how much a given number of agents would be likely to decrease attacks. Queuing models are an operations research method used to understand waiting times in lines, such as what happens at restaurants, offices, telephone queues or even internet servers.
But in the standard model, customers want to be served, and the servers know who the customers are. In Kaplan’s terrorist model, the terrorists – customers –don’t want to be served, and the servers—the agents—don’t know where their customers are. By modifying the model to account for those differences, Kaplan can answer some tricky questions about the best way for intelligence agencies to fight terrorism.
“Even if you don’t know how many terrorists there are or where they are, you can make it more likely that they will show themselves, you can make it more difficult for them to carry out an attack,” says Kaplan.
Kaplan’s method provides estimates of the numbers of undetected terrorist plots, as well as what it would take to increase detection rates.
Using data from court records of terrorism cases, Kaplan refined his models to include the average time that a terror plot is active – that is, the time from when a terrorist group first starts a plot, to the time that they are either caught, or the attack takes place. Based on the data, he could then calculate how many terror plots were likely to be in progress at any one time. He could also estimate the probability of detecting those plots, and how much that probability could be increased by employing more agents. For example, the model calculates that by increasing FBI agents by a factor of two would increase the detection rate from 80% to 89%.
But the data also point to one disturbing conclusion: A 100% detection rate is impossible. As the number of agents increases, the detection rate increases in ever smaller increments. Kaplan says, “We have to decide how safe is safe enough. When should we stop putting money into Homeland Security, and start putting more back into education and health?”
Download Kaplan’s IIASA presentation (PDF, 2.8 KB)
Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.
00
You must be logged in to post a comment.