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crowdsourcing | nexus

Crowdsourcing for food security

By Myroslava Lesiv, IIASA Ecosystems Services and Management Program.

The public can contribute considerably to science by filling the gaps of missing information in many research areas, for example, monitoring land use, biodiversity, or forest degradation. Crowdsourcing campaigns organized by research institutions bring together citizens interested in science and help solving research questions to the benefit of the whole world.

This June, the IIASA Geo-Wiki team ran the Global Field Size campaign, encouraging citizen scientists to classify field sizes on satellite images. Its aim was to develop a global field sizes dataset, which will be used as input to create an improved global cropland field size map for agricultural monitoring and food security assessments. The field sizes dataset can also help us determine what types of satellite data are needed for agricultural monitoring in different parts of the world.

Geo-Wiki interface for collecting field size data. Background layer: Google Maps.

Why are field sizes so important? They provide us with valuable information to tackle challenges of food security. A recent study showed that more than a half the food calories produced globally comes from smallholder farmers, who often make up the most vulnerable parts of population, living in poverty. Within this scope, the field size dataset fills the gaps of missing information, especially for countries that have a limited food supply and lack a well-developed agricultural monitoring system.

The Global Field Size campaign has been one of the most successful crowdsourcing campaigns run through the Geo-Wiki engagement platform. Within one month, 130 participants completed 390,000 tasks – that is, they classified the field sizes in 130,000 locations around the globe!

So we can see that crowdsourcing is powerful, but can we trust the data? Is it accurate enough to be used in different applications? I think it is! The Geo-Wiki team has significant experience in running crowdsourcing campaigns; one of the key lessons we have learned from previous Geo-Wiki campaigns is the importance of training the public to increase the quality of the crowdsourced data.

This campaign was designed so that the participants learned over time how to delineate fields in different regions of the world, and, at the same time, pay special attention to the quality of their submissions. At the end of the campaign, the majority of participants gave us a feedback that, to them, this campaign was indeed a learning exercise. From our end, I have to add, this was also a challenging campaign, as fields are so diverse in shape, continuity of coverage, crop type, irrigation, etc.

Global distribution of dominant field sizes. Cartography by Myroslava Lesiv. Country boundaries: GAUL. Software: ArcMap 10.1.

During the campaign, the crowd was asked to identify whether there were fields in a certain location, and determine the relevant field sizes by the visual interpretation of very high-resolution Google and Bing imagery. A “field” was defined as an agricultural area that included annual or perennial croplands, fallow, shifting cultivation, pastures or hayfields. The collected data can also be used to identify areas falsely mapped as cropland.

Now the team is focused on summarizing the results of the campaign, processing the collected field size data, and preparing them for scientific publication. We will ensure that the published dataset is of high quality and can be used by others with confidence!

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.

From the Himalayas to the Andes: Crowdsourced disaster risk mapping

By Wei Liu, IIASA Risk and Resilience Program

What do Rajapur, Nepal; Chosica, Peru; and Tabasco, Mexico all have in common? Flooding:  these areas are all threatened by floods, and they also face similar knowledge gaps, especially in terms of local level spatial information on risk, and the resources and the capacities of communities to manage risk.

To address these gaps, I and my colleagues at IIASA, in collaboration with Kathmandu Living Labs (KLL) and Practical Action (PA) Nepal are building on our experiences in Nepal’s Lower Karnali River basin to support flood risk mapping in flood-prone areas in Peru and Mexico.

Recent developments in data collection and communication via personal devices and social media have greatly enhanced citizens’ abilities to contribute spatial data, called Crowdsourced Geographic Information (CGI) in the mapping community. OpenStreetMap is the most widely used platform for sharing this free geographic data globally, and the fast growing Humanitarian OpenStreetMap Team has developed CGI in some of the world’s most disaster-prone and data-scarce regions. For example, after the 2015 Nepal Earthquake, thousands of global volunteers mapped infrastructure across Nepal, greatly supporting earthquake rescue, recovery, and reconstruction efforts.

Today there is excellent potential to engage citizen mappers in all stages of the disaster risk management cycle, including risk prevention and reduction, preparedness and reconstruction. In this project, we have successfully launched a series of such mapping activities for the Lower Karnali River basin in Nepal starting in early 2016. In an effort to share the experience and lessons of this work with other Zurich Global Flood Resilience Alliance field sites, in March 2017 we initiated two new mapathons  in Kathmandu, with support from Soluciones Prácticas (PA Peru) and the Mexican Red Cross, to remotely map basic infrastructure such as buildings and roads, as well as visible water surface, around flood-prone communities in Chosica, Peru and Tobasco, Mexico.

@ Wei Liu | IIASA

March 17th, 2017, staff and volunteers conducting remote mapping at Kathmandu Living Labs @ Wei Liu | IIASA

Prior to our efforts very few buildings in these areas were identified on online map portals, including Google Maps, Bing Maps, and OSM. Through our mapathons, dozens of Nepalese volunteers mapped over 15,000 buildings and 100 km of roads. The top scorer, Bishal Bhandari, mapped over 1,700 buildings and 6 km of roads for Chosica alone.

Having the basic infrastructure mapped before a flood event can be extremely valuable for increasing flood preparedness of communities and for local authorities and NGOs.  During the period of the mapathons, the Lima region in Peru, including Chosica, was hit by a severe flood induced by coastal El Niño conditions. Having almost all buildings in Chosica mapped on the OSM platform now makes visible the high flood risk faced by people living in this densely populated area with both formal and informal settlements. These data may support conducting a quick damage assessment, as suggested by Miguel Arestegui, a collaborator from PA Peru during his visit to IIASA in April, 2017.

Recognizing the value of crowdsourced spatial risk information, we are working closely with partners, including OpenStreetMap Peru, to mobilize the creativity, technical know-how, and practical experience from the Nepal study to Latin America countries. Collecting such information using CGI comes with low cost but high potential for modeling and estimating the amount of people and economic assets potentially being affected under different future flood situations, for improving development and land-use plans to support disaster risk reduction, and for increasing preparedness and helping with allocating humanitarian support in a timely manner after disaster events.

Having the basic infrastructure mapped before a flood event can be extremely valuable for increasing flood preparedness of communities and for local authorities and NGOs.  During the period of the mapathons, the Lima region in Peru, including Chosica, was hit by a severe flood induced by coastal El Niño conditions. Having almost all buildings in Chosica mapped on the OSM platform now makes visible the high flood risk faced by people living in this densely populated area with both formal and informal settlements. These data may support conducting a quick damage assessment, as suggested by Miguel Arestegui, a collaborator from PA Peru during his visit to IIASA in April, 2017.

Recognizing the value of crowdsourced spatial risk information, we are working closely with partners, including OpenStreetMap Peru, to mobilize the creativity, technical know-how, and practical experience from the Nepal study to Latin America countries. Collecting such information using CGI comes with low cost but high potential for modeling and estimating the amount of people and economic assets potentially being affected under different future flood situations, for improving development and land-use plans to support disaster risk reduction, and for increasing preparedness and helping with allocating humanitarian support in a timely manner after disaster events.

Flood-inundated houses and local railway in Chosica, Peru, 18/03/2017 @ Miluska Ordoñez | Soluciones Prácticas

The United Nation’s Sendai Framework for Disaster Risk Reduction states that knowledge in “all dimensions of vulnerability, capacity, exposure of persons and assets, hazard characteristics and the environment” needs to be leveraged to inform policies and practices across all stages of the disaster risk management cycle. CGI has a great potential to involve citizens from around the world to help fill this critical knowledge gap. These pilot mapathons conducted between Nepal and Latin America are promising examples of supporting community flood resilience through the mobilization of CGI via international partnerships within the Global South.

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.

What will it take to trust scientific data from citizens?

By Linda See, IIASA Ecosystems Services and Management Program

One of the biggest questions when it comes to citizen science is the quality of the data. Scientists worry that citizens are not as rigorous in their data collection as professionals might be, which calls into question the reliability of the data.  At a meeting this month in Brussels on using citizen science to track invasive species, we grappled with the question: what it will take to trust this data source, particularly if it’s going to be used to alert authorities regarding the presence of an invasive species in a timely manner.

This discussion got me thinking about what other types of data are supplied by citizens that authorities simply trust, for example, when a citizen calls the emergency services to report an incident, such as a fire. Such reports are investigated by the authorities and the veracity of the alert is not questioned. Instead authorities are obliged to investigate such reports.

Yet the statistics show that false alarms do occur. For example, in 2015, there were more than 2.5 million false fire alarms in the United States, of which just under a third were due to system malfunctions. The remaining calls were unintentional, malicious, or other types of false alarms, such as a bomb scare. Statistics for calls to the emergency services more generally show similar trends in different European countries, where the percentage of false reports range from 40% in Latvia up to 75% in Lithuania and Norway. So why is it that we inherently trust this data source, despite the false alarm rate, and not data from citizen scientists? Is it because life is threatened or because fires are easier to spot than invasive species, or simply because emergency services are mandated with the requirement to investigate?

Volunteers monitor butterflies in Mount Rainier National Park, as part of the Cascade Butterfly Project, a citizen science effort organized by the US National Park Service © Kevin Bacher | US National Park Service

A recent encouraging development for citizen science was the signing of an executive order by President Obama on 6 January 2017, which gave federal agencies the jurisdiction to use citizen science and crowdsourced data in their operations. Do we need something similar in the EU or at the level of member states? And what will it really take for authorities to trust scientific data from citizens?

To move from the current situation of general distrust in citizen science data to one in which the data are viewed as a potentially useful source of information, we need further action. First we need to showcase examples of where data collected by citizens are already being used for monitoring. At the meeting in Brussels, Kyle Copas of the Global Biodiversity Information Facility (GBIF) noted that up to 40% of the data records in GBIF are supplied by citizens, which surprised many of the meeting participants. Data from GBIF are used for national and international monitoring of biodiversity. Secondly, we need to quantify the value of information coming from citizen scientists. For example, how much money could have been saved if reports on invasive species from citizens were acted upon? Third, we need to forge partnerships with government agencies to institutionally embed citizen science data streams into everyday operations. For example, the LandSense citizen observatory, a new project, aims to do exactly this. We are working with the National Mapping Agency in France to use citizen science data to update their maps but there are many other similar examples with other local and national agencies that will be tested over the next 3.5 years.

Finally, we need to develop quality assurance systems that can be easily plugged into the infrastructure of existing organizations. The EU-funded COBWEB project began building such a citizen science-based quality assurance system, which we are continuing to develop in LandSense as a service. Providing out-of-the-box tools may be one solution to help organizations to begin working with citizen science data more seriously at an institutional level.

IIASA researchers test the Fotoquest app, a citizen science game developed at IIASA. ©Katherine Leitzell | IIASA

These measures will clearly take time to implement so I don’t expect that the discussion on the quality of the data will be removed from any agenda for some time to come. However, I look forward to the day when the main issue revolves around how we can possibly handle the masses of big data coming from citizens, a situation that many of us would like to be in.

More Information about the meeting: https://ec.europa.eu/jrc/en/event/workshop/citizen-science-open-data-model-invasive-alien-species-europe

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.

 

Picture Pile: Gaming for 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!

PicturePileScreen

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 and Tanzania - two regions where there are problems with existing maps of deforestation. Photo (cc) Aulia Erlangga for Center for International Forestry Research (CIFOR).

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.

Network science and marketing: A virus’ tale

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

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

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.