The role of earth observation in an integrated deprived area mapping “system” for low-to-middle income countries

Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups

PICES Press, Vol. 20, No. 2, Summer 2012

•The 2012 Inter-sessional Science Board Meeting: A Note from Science Board Chairman (pp. 1-4) ◾PICES Interns (p. 4) ◾2012 Inter-sessional Workshop on a Roadmap for FUTURE (pp. 5-8) ◾Second Symposium on “Effects of Climate Change on the World’s Oceans” (pp. 9-13) ◾2012 Yeosu Workshop on “Framework for Ocean Observing” (pp. 14-15) ◾2012 Yeosu Workshop on “Climate Change Projections” (pp. 16-17) ◾2012 Yeosu Workshop on “Coastal Blue Carbon” (pp. 18-20) ◾Polar Comparisons: Summary of 2012 Yeosu Workshop (pp. 21-23) ◾2012 Yeosu Workshop on “Climate Change and Range Shifts in the Oceans" (pp. 24-27) ◾2012 Yeosu Workshop on “Beyond Dispersion” (pp. 28-30) ◾2012 Yeosu Workshop on “Public Perception of Climate Change” (pp. 31, 50) ◾PICES Working Group 20: Accomplishments and Legacy (pp. 32-33) ◾The State of the Western North Pacific in the Second Half of 2011 (pp. 34-35) ◾Another Cold Winter in the Gulf of Alaska (pp. 36-37) ◾The Bering Sea: Current Status and Recent Events (pp. 38-40) ◾PICES/ICES 2012 Conference for Early Career Marine Scientists (pp. 41-43) ◾Completion of the PICES Seafood Safety Project – Indonesia (pp. 44-46) ◾Oceanography Improves Salmon Forecasts (p. 47) ◾2012 GEOHAB Open Science Meeting (p. 48-50) ◾Shin-ichi Ito awarded 2011 Uda Prize (p. 50

Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms

Question categorization and expert retrieval methods have been crucial for information organization and accessibility in community question & answering (CQA) platforms. Research in this area, however, has dealt with only the text modality. With the increasing multimodal nature of web content, we focus on extending these methods for CQA questions accompanied by images. Specifically, we leverage the success of representation learning for text and images in the visual question answering (VQA) domain, and adapt the underlying concept and architecture for automated category classification and expert retrieval on image-based questions posted on Yahoo! Chiebukuro, the Japanese counterpart of Yahoo! Answers. To the best of our knowledge, this is the first work to tackle the multimodality challenge in CQA, and to adapt VQA models for tasks on a more ecologically valid source of visual questions. Our analysis of the differences between visual QA and community QA data drives our proposal of novel augmentations of an attention method tailored for CQA, and use of auxiliary tasks for learning better grounding features. Our final model markedly outperforms the text-only and VQA model baselines for both tasks of classification and expert retrieval on real-world multimodal CQA data.Comment: Submitted for review at CIKM 201

What's unusual in online disease outbreak news?

Background: Accurate and timely detection of public health events of international concern is necessary to help support risk assessment and response and save lives. Novel event-based methods that use the World Wide Web as a signal source offer potential to extend health surveillance into areas where traditional indicator networks are lacking. In this paper we address the issue of systematically evaluating online health news to support automatic alerting using daily disease-country counts text mined from real world data using BioCaster. For 18 data sets produced by BioCaster, we compare 5 aberration detection algorithms (EARS C2, C3, W2, F-statistic and EWMA) for performance against expert moderated ProMED-mail postings. Results: We report sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), mean alerts/100 days and F1, at 95% confidence interval (CI) for 287 ProMED-mail postings on 18 outbreaks across 14 countries over a 366 day period. Results indicate that W2 had the best F1 with a slight benefit for day of week effect over C2. In drill down analysis we indicate issues arising from the granular choice of country-level modeling, sudden drops in reporting due to day of week effects and reporting bias. Automatic alerting has been implemented in BioCaster available from http://born.nii.ac.jp. Conclusions: Online health news alerts have the potential to enhance manual analytical methods by increasing throughput, timeliness and detection rates. Systematic evaluation of health news aberrations is necessary to push forward our understanding of the complex relationship between news report volumes and case numbers and to select the best performing features and algorithms