A generalized approach for producing,quantifying, and validating citizen science data from wildlife images

Citizen science has the potential to expand the scope and scale of research in ecology and conservation, but many professional researchers remain skeptical of data produced by nonexperts. We devised an approach for producing accurate, reliable data from untrained, nonexpert volunteers. On the citizen science website www.snapshotserengeti.org , more than 28,000 volunteers classified 1.51 million images taken in a large‐scale camera‐trap survey in Serengeti National Park, Tanzania. Each image was circulated to, on average, 27 volunteers, and their classifications were aggregated using a simple plurality algorithm. We validated the aggregated answers against a data set of 3829 images verified by experts and calculated 3 certainty metrics—level of agreement among classifications (evenness), fraction of classifications supporting the aggregated answer (fraction support), and fraction of classifiers who reported “nothing here” for an image that was ultimately classified as containing an animal (fraction blank)—to measure confidence that an aggregated answer was correct. Overall, aggregated volunteer answers agreed with the expert‐verified data on 98% of images, but accuracy differed by species commonness such that rare species had higher rates of false positives and false negatives. Easily calculated analysis of variance and post‐hoc Tukey tests indicated that the certainty metrics were significant indicators of whether each image was correctly classified or classifiable. Thus, the certainty metrics can be used to identify images for expert review. Bootstrapping analyses further indicated that 90% of images were correctly classified with just 5 volunteers per image. Species classifications based on the plurality vote of multiple citizen scientists can provide a reliable foundation for large‐scale monitoring of African wildlife.     

The Crisis Map of the Czech Republic: the nationwide deployment of an Ushahidi application for disasters

Crisis mapping is a legitimate component of both crisis informatics and disaster risk management. It has become an effective tool for humanitarian workers, especially after the earthquake in Haiti in 2010. Ushahidi is among the many mapping platforms on offer in the growing field of crisis mapping, and involves the application of crowdsourcing to create online and interactive maps of areas in turmoil. This paper presents the Crisis Map of the Czech Republic, which is the first such instrument to be deployed nationwide in Central Europe. It describes the methodologies used in the preparatory work phase and details some practices identified during the creation and actual employment of the map. In addition, the paper assesses its structure and technological architecture, as well as its potential possible development in the future. Lastly, it evaluates the utilisation of the Crisis Map during the floods in the Czech Republic in 2013.     

Evaluating the aesthetic value of cultural ecosystem services by mapping geo-tagged photographs from social media data on Panoramio and Flickr

Traditional approaches for evaluating aesthetic value rely on surveys or interviews. Social media provides a new type of crowdsourcing data to examine the relationships between aesthetic value and citizens' activities. The main goal of this paper was to evaluate aesthetic value in Nebraska by using citizen-reported geo-tagged photographs posted on two social media sites – Panoramio and Flickr. The study used the application programming interface from Panoramio and Flickr to obtain the latitude and longitude of the geo-tagged photographs, and then used ArcGIS spatial statistical tools to examine spatial patterns with areas of aesthetic value in Nebraska. The outcomes identified the clusters of pictures with the locations of ecosystem service provisioning areas of aesthetic value in Nebraska, identified new areas with aesthetic value, and drew comparisons with local and regional population distribution. This study provided an innovative approach to using crowdsourcing data to verify the value of ecosystem services.     

Untapped potential of collective intelligence in conservation and environmental decision making

Environmental decisions are often deferred to groups of experts, committees, or panels to develop climate policy, plan protected areas, or negotiate trade-offs for biodiversity conservation. There is, however, surprisingly little empirical research on the performance of group decision making related to the environment. We examined examples from a range of different disciplines, demonstrating the emergence of collective intelligence (CI) in the elicitation of quantitative estimates, crowdsourcing applications, and small-group problem solving. We explored the extent to which similar tools are used in environmental decision making. This revealed important gaps (e.g., a lack of integration of fundamental research in decision-making practice, absence of systematic evaluation frameworks) that obstruct mainstreaming of CI. By making judicious use of interdisciplinary learning opportunities, CI can be harnessed effectively to improve decision making in conservation and environmental management. To elicit reliable quantitative estimates an understanding of cognitive psychology and to optimize crowdsourcing artificial intelligence tools may need to be incorporated. The business literature offers insights into the importance of soft skills and diversity in team effectiveness. Environmental problems set a challenging and rich testing ground for collective-intelligence tools and frameworks. We argue this creates an opportunity for significant advancement in decision-making research and practice.     

New tools for emergency managers: an assessment of obstacles to use and implementation

This paper focuses on the role of the formal response community's use of social media and crowdsourcing for emergency managers (EMs) in disaster planning, response and recovery in the United States. In‐depth qualitative interviews with EMs on the Eastern seaboard at the local, state and federal level demonstrate that emergency management tools are in a state of transition—from formal, internally regulated tools for crisis response to an incorporation of new social media and crowdsourcing tools. The first set of findings provides insight into why many EMs are not using social media, and describes their concerns that result in fear, uncertainty and doubt. Second, this research demonstrates how internal functioning and staffing issues within these agencies present challenges. This research seeks to examine the dynamics of this transition and offer lessons for how to improve its outcomes—critical to millions of people across the United States.