Nov 10, 2016 | Risk & Vulnerability
By Wei Liu, IIASA Risk and Resilience and Ecosystems Services and Management programs
Disasters caused by extreme weather events are on the rise. Floods in particular are increasing in frequency and severity, with reoccurring events trapping people in a vicious cycle of poverty. Information is key for communities to prepare for and respond to floods – to inform risk reduction strategies, improve land use planning, and prepare for when disaster strikes.
But, across much of the developing world, data is sparse at best for understanding the dynamics of flood risk. When and if disaster strikes, massive efforts are required in the response phase to develop or update information. After that, communities have an even greater need for data to help with recovery and reconstruction and further enhance communities’ resilience to future floods. This is particularly important for the Global South, such as the Karnali Basin in Nepal, where little information is available regarding community’s exposure and vulnerability to floods.
Karnali Basin in Nepal © Wei Liu | IIASA
That’s why we are working with Practical Action in the Zurich Flood Resilience Alliance to try to remedy this situation. Participatory Vulnerability and Capacity Assessment is a widely used tool to collect community level disaster risk and resilience information and to inform disaster risk reduction strategies. One of our first projects was to digitize a set of existing maps on disaster risk and community resources where the locations of, for example, rivers, houses, infrastructure and emergency shelters are usually hand-drawn by selected community members. Such maps provide critical information used by local stakeholders in designing and prioritizing among possible flood risk management options.
From hand-drawn to internet mapping
While hand-drawn maps are ideal for working in remote rural communities, they risk being damaged, lost, or simply unused. They are also more difficult to share with other stakeholders such as emergency services or merge with additional mapped information such as flood hazard. With the recent increase in internet mapping, platforms such as OpenStreetMap have made it possible for us to transfer existing maps or capture new information on a common platform in such a way that anyone with an internet connection can add, edit, and share maps. As this information is digital, it makes it easier to perform additional tasks, such as identifying households in areas of high risk or measuring the distance to the nearest emergency shelter, to support effective risk-reduction and resilience-building.
Practical Action Nepal, the Center for Social Development and Research, and community members discuss the transfer of community maps to online maps © Wei Liu | IIASA
From theory to practice
In March 2016, the Project team travelled to two Nepal communities in the Rajapur and Tikapur districts, to pilot the idea of working with a local NGO (the Center for Social Development and Research) and community members, to transfer their maps into a digital environment. The latter can easily be further edited, improved and shared within a broad range of stakeholders and potential users. Local residents in both communities were excited seeing their households and other features for the first time overlaid on a map with satellite imagery. The Center for Social Development and Research was also very enthusiastic about integrating their future community mapping activities with digital mapping, without losing the spirit of participation.
Hand drawn maps produced from community mapping exercises in Chakkhapur, Nepal © Practical Action
The resulting online maps in OpenStreetMap of Chakkhapur, Nepal, showing the location of drinking water, an emergency shelter and medical clinic. ©OpenStreetMap
Increasing resilience through improved information management
The first stage pilot study in the Karnali river basin confirmed the great potential of new digital technologies in providing accurate and locally relevant maps to improve flood risk assessment to support resilience building at the community level. The next step is to further engage local stakeholders. A wider partnership has been established between Practical Action, the Center for Social Development and Research, the International Institute for Applied Systems Analysis and Kathmandu Living Labs to further build local stakeholders’ capacity in mapping with digital technologies, including a training workshop for NGO staff members in September, 2016. The plan is to have more communities’ flood risk information mapped for designing more effective action plans and strategies for coping with future flood events across the Karnali river basin. A greater potential can be realized when this effort is further scaled up across the region and the results are placed into shared open online databases such as OpenStreetMap.
Further information
- Flood Resilience Portal
- Geo-Wiki Risk
- McCallum, I., Liu, W., See, L., Mechler, R., Keating, A., Hochrainer-Stigler, S., Mochizuki, J., Fritz, S., Dugar, S., Arestegui, M., Szoenyi, M., Laso Bayas, J.C., Burek, P., French, A. and Moorthy, I. (2016) Technologies to Support Community Flood Disaster Risk Reduction. International Journal of Disaster Risk Science, 7 (2). pp. 198-204. http://pure.iiasa.ac.at/13299/
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.
May 17, 2016 | Citizen Science
By Dilek Fraisl, IIASA Ecosystems Services and Management Program
In October 2015, we launched our latest game, Picture Pile. The idea is simple: look at a pair of satellite images from different years and tell us if you can see any evidence of deforestation. Thanks to the participation of many volunteers, 2.69 million pictures have already been sorted in our pile of 5 million pairs. But we still have a long way to go, and we need your help to get us there!
Screenshot from the game: click for more information (Image credit Tobias Sturn)
Deforestation is one of the most serious environmental problems in the world today. Forests cover a third of the land area on Earth, producing vital oxygen, habitats for a diversity of wildlife, and important ecosystem services. According to the World Wildlife Fund (WWF), some 46,000 to 58,000 square miles of forest are lost each year, which is equivalent to 48 football fields every minute. But this is a rough estimate since deforestation is very difficult to track. Reasons why are that satellite imagery can be of insufficient spatial resolution to map deforestation accurately, deforestation mostly occurs in small chunks that may not be visible from medium-resolution imagery, and very high-resolution data sets are expensive and can require big data processing capabilities, so can only be used for limited areas.
To help contribute to better mapping of deforestation, researchers in IIASA’s Earth Observation Systems (EOS) group, led by Steffen Fritz, have been working on novel projects to engage citizens in scientific data collection that can complement satellite-based traditional deforestation monitoring. One of the latest applications is Picture Pile, a game that makes use of very high-resolution satellite images spanning the last decade. Designed by Tobias Sturn, the aim is to provide data that can help researchers build a better map of deforestation. Players are provided with a pair of images that span two time periods and are then asked to answer a simple question: “Do you see tree loss over time?” After examining the image, the player drags the images to the right for “yes,” left for “no,” or down to indicate “maybe” when the deforestation is not clearly visible.
Every image is sorted multiple times by numerous independent players, in order to build confidence in the results, and also to gain an understanding of how good the players are at recognizing visible patterns of deforestation. Once enough data are collected at a single location, the images are taken out of the game and new ones are added, thereby increasing the spatial coverage of our mapped area over time. Right now we are focusing on Tanzania and Indonesia, two regions where we know there are problems with existing maps of deforestation.
Picture Pile is focusing first on Indonesia (pictured) and Tanzania – two regions where there are problems with existing maps of deforestation. Photo (cc) Aulia Erlangga for Center for International Forestry Research (CIFOR).
Once the pile is fully sorted, the 5 million photos in the data set will be used to develop better maps of forest cover and forest loss using hybrid techniques developed by the group as well as inputs to classification algorithms. We will also use the data to validate the accuracy of existing global land cover maps. Finally, we will mine the data set to look for patterns regarding quality (for example, how many samples do we need to provide to the “crowd” before we can be confident enough to use their data in further research). In short, by integrating citizens in scientific research, Picture Pile will also help us improve the science of land cover monitoring through crowdsourcing mechanisms.
So please join in and help us get to the finish line. You can play Picture Pile in your browser or you can download the free iOS/Android app from the Apple and Google Play stores and play on your smartphone or tablet. Your contributions will help scientists like those at IIASA to tackle global problems such as deforestation and environmental degradation. At the same time you may win some great prizes: a brand new smartphone, a tablet, or a mini tablet.
More information:
Reference
Schepaschenko D, See L, Lesiv M, McCallum I, Fritz S, Salk C, Perger C, Schepaschenko M, Shvidenko A, Kovalevskyi S, Albrecht F, Kraxner F, Bun A, Maksyutov S, Sokolov A, Dürauer M, Obersteiner M. (2015) Global hybrid forest mask: synergy of remote sensing, crowd sourcing and statistics. Remote Sensing of the Environment, 162, 208-220. doi:10.1016/j.rse.2015.02.011
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.
Oct 1, 2015 | Citizen Science
By Linda See, IIASA Ecosystems Services and Management Program
We had another very hot summer this year in Europe and many other parts of the world. Many European cities, including London, Madrid, Frankfurt, Paris and Geneva, broke new temperature records.
Cities are particularly vulnerable to increasing temperatures because of a phenomenon known as the urban heat island effect. First measured more than a half a century ago by Tim Oke, the increased temperatures measured in urban areas are a result of urban land use, or higher amounts of impervious surfaces such as concrete and concentrated urban structures. The urban heat island effect impacts human health and well-being. It’s not just a matter of comfort: during the heat wave in 2003, more than 70,000 people in Europe are estimated to have perished, mostly urban dwellers.
Summer 2015 in Ljubljana, Slovenia. ©K. Leitzell | IIASA
While climate models have many uncertainties, they do all agree that the urban heat island effect will increase in frequency and duration in the future. A recent article by Hannah Hoag in Nature paints a bleak picture of just how unprepared cities are for dealing with increasing temperatures. The paper cites positive and negative examples of mitigation from various cities but it falls short of suggesting a more widely applicable solution.
What we need is a standardized way of approaching the problem. Underlying this lack of standards is the paucity of data on the form and function of cities. By form I mean the geometry of the city–a 3D model of the buildings and road network, and information on the building materials—as well as a map of the basic land cover including impervious surfaces like roads and sidewalks, and areas of vegetation such as gardens, parks, and fields. Function refers to the building use, road types, use of irrigation and air conditioning and other factors that affect local atmospheric conditions. As climate models become more highly resolved, they will need vast amounts of such information to feed into them.
These issues are what led me and my colleagues (Prof Gerald Mills of UCD, Dr Jason Ching of UNC and many others) to conceive the World Urban Database and Access Portal Tools (WUDAPT) initiative (www.wudapt.org). WUDAPT is a community-driven data collection effort that draws upon the considerable network of urban climate modelers around the world. We start by dividing a city into atmospherically distinct areas, or Local Climate Zones (LCZs) developed by Stewart and Oke, which provides a standard methodology for characterizing cities that can improve the parameters needed for data-hungry urban climate models.
Using freely available satellite imagery of the Earth’s surface, the success of the approach relies on local urban experts to provide representative examples of different LCZs across their city. We are currently working towards creating an LCZ classification for all C40 cities (a network of cities committed to addressing climate change) but are encouraging volunteers to work on any cities that are of interest to them. We refer to this as Level 0 data collection because it provides a basic classification for each city. Further detailed data collection efforts (referred to as Levels 1 and 2) will use a citizen science approach to gather information on building materials and function, landscape morphology and vegetation types.
The Local Climate Zone (LCZ) map for Kiev.
WUDAPT will equip climate modelers and urban planners with the data needed to examine a range of mitigation and adaptation scenarios: For example what effect will green roofs, changes in land use or changes in the urban energy infrastructure have on the urban heat island and future climate?
The ultimate goal of WUDAPT is to develop a very detailed open access urban database for all major cities in the world, which will be valuable for many other applications from energy modelling to greenhouse gas assessment. If we want to improve the science of urban climatology and help cities develop their own urban heat adaptation plans, then WUDAPT represents one concrete step towards reaching this goal. Contact us if you want to get involved.
About the WUDAPT Project
The WUDAPT concept has been developed during two workshops, one held in Dublin Ireland in July 2014 and the second in conjunction with the International Conference on Urban Climate in Toulouse; a third workshop is set to take place in Hong Kong in December 2015. More information can be found on the WUDAPT website at: http://www.wudapt.org.
References
Bechtel, B., Alexander, P., Böhner, J., Ching, J., Conrad, O., Feddema, J., Mills, G., See, L. and Stewart, I. 2015. Mapping local climate zones for a worldwide database of form and function of cities. International Journal of Geographic Information, 4(1), 199-219.
Hoag, H. 2015. How cities can beat the heat. Nature, 524, 402-404.
See, L., Mills, G. and Ching. J. 2015. Community initiative counters urban heat. Nature, 526,43 (01 October 2015) doi:10.1038/526043b
Stewart, I.D. and Oke, T.R. 2012. Local Climate Zones for urban temperature studies. Bulletin of the American Meteorological Society, 93(12), 1879-1900.
Wake, B. 2012. Defining local zones. Nature Climate Change, 2, 487.
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.
Sep 2, 2015 | Systems Analysis
By Matthias Wildemeersch, IIASA Advanced Systems Analysis and Ecosystems Services and Management Programs
FotoQuest Austria is a citizen science campaign initiated by the IIASA Ecosystems Services & Management Program that aims to involve the general public in mapping land use in Austria. Understanding the evolution of urban sprawl is important to estimate the risk of flooding, while the preservation of wetlands has important implications for climate change.
But how can we engage people in environmental monitoring, in particular when they are growing increasingly resistant to traditional forms of advertising? Viral marketing makes use of social networks to spread messages, and takes advantage of the trust that we have in the recommendation coming from a friend rather than from a stranger or a company.
Network science and the formal description of spreading phenomena can shed light on the propagation of messages through communities and can be applied to inform and design viral marketing campaigns.
Viral spreading © kittitee550 | Dollar Photo Club
Network science is a multi-disciplinary field of research that draws on graph theory, statistical mechanics, inference, and other theories to study the behavior of agents in various networks. The spreading phenomena in viral marketing show similarities with well-studied spreading processes over biological, social, physical, and financial networks. For instance, we can think about epidemics,which are well understood and allow for the design of optimal strategies to contain viruses. Another example is opinion dynamics, which received renewed research attention over the last years in the context of social media. In contrast to diseases or computer viruses, which we aim to contain and stop, the goal of viral marketing is to spread widely, reaching the largest possible fraction of a community.
What makes viral marketing unique?
But some aspects of viral marketing are very different from what we see in other spreading phenomena. First of all, there are many platforms that can be used to spread information at the same time, and the interaction between these platforms is not always transparent. Human psychology is a crucial factor in social networks, as repeated interaction and saturation can decrease the willingness to further spread viral content. Marketing campaigns have a limited budget, and therefore it is meaningful to understand how we can use incentives and how efficient they are. This also means that it is essential to find the group of most influential people that can be used as seeds for the viral campaign.
Network science has addressed to a great extent all these individual questions, mostly under the assumption of full knowledge of the connections between the agents and their influence. Currently, so-called multiplexes are an active research field that studies the behavior of multi-layer networks. This research unveils the relationships between the dynamics of viral marketing, the connection pattern, and strength between the network layers. Although viral spreading may be unachievable in a single layer, for example a social network like Facebook, the critical threshold may be exceeded by joining different platforms. Within a given platform, people alike can be clustered using community detection algorithms. Once the communities are identified, influence maximization algorithms have been established to select these persons that maximize the spread of viral content. Although this discrete optimization problem is computationally difficult—or NP-hard—mathematicians have proposed algorithms that can efficiently predict who to target to give a campaign the best chance of going viral. On top of that, optimal pricing strategies have been developed to reward recommenders.
The FotoQuest Austria app aims to engage citizen scientists in their campaign – network theory may help them go “viral.” © IIASA
Although the literature is extensive, the nature of the results is often theoretical and involves mathematically complex models and algorithms. Considering that only partial information on the network is usually available, it is not straightforward to bring this knowledge back to a practical marketing campaign. So researchers in this field are trying to bridge the gap between theoretical results and practical problems. The generic, powerful methods of network science are sufficiently versatile to capture the specifics of real-world applications. As such, network science can provide guidelines that can bring great value for the design of heuristic methods in marketing strategies.
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.
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.
