Enhancing disaster situational awareness through scalable curation of social media

Online social media is today used during humanitarian disasters by victims, responders, journalists and others, to publicly exchange accounts of ongoing events, requests for help, aggregate reports, reflections and commentary. In many cases, incident reports become available on social media before being picked up by traditional information channels, and often include rich evidence such as photos and video recordings. However, individual messages are sparse in content and message inflow rates can reach hundreds of thousands of items per hour during large scale events. Current information management methods struggle to make sense of this vast body of knowledge, due to limitations in terms of accuracy and scalability of processing, summarization capabilities, organizational acceptance and even basic understanding of users’ needs. If solutions to these problems can be found, social media can be mined to offer disaster responders unprecedented levels of situational awareness. This thesis provides a first comprehensive overview of humanitarian disaster stakeholders and their information needs, against which the utility of the proposed and future information management solutions can be assessed. The research then shows how automated online textclustering techniques can provide report de-duplication, timely event detection, ranking and summarization of content in rapid social media streams. To identify and filter out reports that correspond to the information needs of specific stakeholders, crowdsourced information extraction is combined with supervised classification techniques to generalize human annotation behaviour and scale up processing capacity several orders of magnitude. These hybrid processing techniques are implemented in CrisisTracker, a novel software tool, and evaluated through deployment in a large-scale multi-language disaster information management setting. Evaluation shows that the proposed techniques can effectively make social media an accessible complement to currently relied-on information collection methods, which enables disaster analysts to detect and comprehend unfolding events more quickly, deeply and with greater coverage.Actualmente, m´ıdias sociais s˜ao utilizadas em crises humanit´arias por v´ıtimas, apoios de emergˆencia, jornalistas e outros, para partilhar publicamente eventos, pedidos ajuda, relat´orios, reflex˜oes e coment´arios. Frequentemente, relat´orios de incidentes est˜ao dispon´ıveis nestes servic¸o muito antes de estarem dispon´ıveis nos canais de informac¸˜ao comuns e incluem recursos adicionais, tais como fotografia e video. No entanto, mensagens individuais s˜ao escassas em conteu´do e o fluxo destas pode chegar aos milhares de unidades por hora durante grandes eventos. Actualmente, sistemas de gest˜ao de informac¸˜ao s˜ao ineficientes, em grande parte devido a limita¸c˜oes em termos de rigor e escalabilidade de processamento, sintetiza¸c˜ao, aceitac¸˜ao organizacional ou simplesmente falta de compreens˜ao das necessidades dos utilizadores. Se existissem solu¸c˜oes eficientes para extrair informa¸c˜ao de m´ıdias sociais em tempos de crise, apoios de emergˆencia teriam acesso a informac¸˜ao rigorosa, resultando em respostas mais eficientes. Esta tese cont´em a primeira lista exaustiva de parte interessada em ajuda humanit´aria e suas necessidades de informa¸c˜ao, v´alida para a utilizac¸˜ao do sistema proposto e futuras soluc¸˜oes. A investiga¸c˜ao nesta tese demonstra que sistemas de aglomera¸c˜ao de texto autom´atico podem remover redundˆancia de termos; detectar eventos; ordenar por relevˆancia e sintetizar conteu´do dinˆamico de m´ıdias sociais. Para identificar e filtrar relat´orios relevantes para diversos parte interessada, algoritmos de inteligˆencia artificial s˜ao utilizados para generalizar anotac¸˜oes criadas por utilizadores e automatizar consideravelmente o processamento. Esta solu¸c˜ao inovadora, CrisisTracker, foi testada em situa¸c˜oes de grande escala, em diversas l´ınguas, para gest˜ao de informa¸c˜ao em casos de crise humanit´aria. Os resultados demonstram que os m´etodos propostos podem efectivamente tornar a informa¸c˜ao de m´ıdias sociais acess´ıvel e complementam os m´etodos actuais utilizados para gest˜ao de informa¸c˜ao por analistas de crises, para detectar e compreender eventos eficientemente, com maior detalhe e cobertura

Improving Situational Awareness in Emergencies through Crowd Supported Analysis of Social Media

In this ongoing research project, we develop an information system that aims to improve situational awareness and shorten response times in emergency response situations. Through a combination of algorithmic and crowdsourcing techniques, the proposed system gathers, analyzes, organizes and then visualizes social media activity around an event in real-time and turns overwhelming streams of status updates into actionable pieces of information. This document is an extended abstract to the poster with the same name

Intrinsic Elicitation : A Model and Design Approach for Games Collecting Human Subject Data

Applied games are increasingly used to collect human subject data such as people’s performance or attitudes. Games a ord a motive for data provision that poses a validity threat at the same time: as players enjoy winning the game, they are motivated to provide dishonest data if this holds a strategic in-game advantage. Current work on data collection game design doesn’t address this issue. We therefore propose a theoretical model of why people provide certain data in games, the Rational Game User Model. We derive a design approach for human subject data collection games that we call Intrinsic Elicitation: data collection should be integrated into the game’s mechanics such that honest responding is the necessary, strategically optimal, and least e ortful way to pursue the game’s goal. We illustrate the value of our approach with a sample analysis of the data collection game Urbanology

Recommendations for Charging Infrastructure in Stockholm County : Targeting Full Electrification of Passenger Cars by 2030

For the County of Stockholm (1 million cars), we calculate the cumulative socio-economic result of a gradual transition to a battery-electric fleet of passenger cars, over the period 2020-2040. Included in the analysis are the direct and indirect costs to deploy necessary charging infrastructure, and the value generated from lowered emissions and operational costs of vehicles. Necessary charging infrastructure investments are derived from a network model of mobility in the city, that ensures sufficient infrastructure to power all passenger cars. The most important conclusions are that cities should prioritize speed of transition rather than cost minimization to maximize cumulative return on investment, due to substantial cumulative cost savings from electrification, and that current undertaxation of fossil greenhouse gas emissions greatly reduces profit margins for private investments contributing to vehicle electrification. We estimate that a total cumulative infrastructure investment of 13 billion SEK can lead to electrification of 90% of the traffic work by passenger cars in Stockholm county by 2030. This would reduce cumulative system costs by 100 billion SEK from 2020 to 2030, with a return on infrastructure investment of 750%. Approximately 50% of this value is from externalized CO2 emission costs.The report is part of the MEISTER project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under the grant agreement Nº 769052.</p

Reconciliation of Electric Road System (ERS) Utilization Estimations from Two Seemingly Conflicting Reports

Data have been published stating that of all heavy trucks (&gt;16 ton) that drive at least 5 days per year on the main Swedish road network (Malmö-Göteborg-Stockholm), only 15% drive more than 50% of their annual mileage on this road network . Intuitively, this gives the impression that charging infrastructure placed on the main road network cannot contribute greatly to electrification of heavy trucks. Meanwhile, route-based simulation of charging preferences on the Swedish road network has concluded that if Electric Road Systems (ERS) infrastructure is deployed on the main Swedish road network, &gt;95% of heavy traffic on this road network would have sufficient financial incentive to becomes users of the ERS charging infrastructure

Visualizing the Ethiopian Commodity Market

The Ethiopia Commodity Exchange (ECX), like many other data intensive organizations, is having difficulties making full use of the vast amounts of data that it collects. This MSc thesis identifies areas within the organization where concepts from the academic fields of information visualization and visual analytics can be applied to address this issue.Software solutions are designed and implemented in two areas with the purpose of evaluating the approach and to demonstrate to potential users, developers and managers what can be achieved using this method. A number of presentation methods are proposed for the ECX website, which previously contained no graphing functionality for market data, to make it easier for users to find trends, patterns and outliers in prices and trade volumes of commodieties traded at the exchange. A software application is also developed to support the ECX market surveillance team by drastically improving its capabilities of investigating complex trader relationships.Finally, as ECX lacked previous experiences with visualization, one software developer was trained in computer graphics and involved in the work, to enable continued maintenance and future development of new visualization solutions within the organization

Recommendations for Charging Infrastructure in Stockholm County : Targeting Full Electrification of Passenger Cars by 2030

For the County of Stockholm (1 million cars), we calculate the cumulative socio-economic result of a gradual transition to a battery-electric fleet of passenger cars, over the period 2020-2040. Included in the analysis are the direct and indirect costs to deploy necessary charging infrastructure, and the value generated from lowered emissions and operational costs of vehicles. Necessary charging infrastructure investments are derived from a network model of mobility in the city, that ensures sufficient infrastructure to power all passenger cars. The most important conclusions are that cities should prioritize speed of transition rather than cost minimization to maximize cumulative return on investment, due to substantial cumulative cost savings from electrification, and that current undertaxation of fossil greenhouse gas emissions greatly reduces profit margins for private investments contributing to vehicle electrification. We estimate that a total cumulative infrastructure investment of 13 billion SEK can lead to electrification of 90% of the traffic work by passenger cars in Stockholm county by 2030. This would reduce cumulative system costs by 100 billion SEK from 2020 to 2030, with a return on infrastructure investment of 750%. Approximately 50% of this value is from externalized CO2 emission costs.The report is part of the MEISTER project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under the grant agreement Nº 769052.</p

Reconciliation of Electric Road System (ERS) Utilization Estimations from Two Seemingly Conflicting Reports

Data have been published stating that of all heavy trucks (&gt;16 ton) that drive at least 5 days per year on the main Swedish road network (Malmö-Göteborg-Stockholm), only 15% drive more than 50% of their annual mileage on this road network . Intuitively, this gives the impression that charging infrastructure placed on the main road network cannot contribute greatly to electrification of heavy trucks. Meanwhile, route-based simulation of charging preferences on the Swedish road network has concluded that if Electric Road Systems (ERS) infrastructure is deployed on the main Swedish road network, &gt;95% of heavy traffic on this road network would have sufficient financial incentive to becomes users of the ERS charging infrastructure

Ruttbaserade simulerade trafikdata för högupplöst analys av tunga godstransporter på det svenska vägnätet

Route-based simulated traffic data for high-resolution analysis of heavy goods transport on the Swedish road network In this report, a national database has been created regarding freight transport with heavy road vehicles. The primary purpose of the work is to serve as input for further analysis of what appropriate charging infrastructure planning and placement should look like given the knowledge of the transport work. It has thus been no ambition to give any recommendations in this report about, for example, expansion of charging infrastructure, but rather to collect and process information/data as well as develop methods and finally generate a data set that is useful and well representative of the traffic on the national road network. By the time of this publication, a dataset is available based on data from the Swedish Transport Administration’s Samgods-model with its simulations of transport connections based on transport demand between producer and consumer zones. In addition, all transport connections have been translated into routes (how trucks drive from A to B) on the road network, to enable analysis of electrification of/at specific road segments. Finally, the dataset has also been calibrated in various ways to better match statistics and actual measurements, as some major differences/deviations compared to some of them were identified. What the data set now consists of can be summarized as the number of truck movements and tons of goods that annually pass each road segment of the Swedish road network (and on some foreign roads). Furthermore, these totals can be easily divided into subsets and linked to specific routes, types of trucks (weight classes), origin, etcetera. Some shortcomings/limitations have been noticed during the production of this data set, such as the fact that the Samgods-model seems to miss a lot of transport in metropolitan areas, that the routing carried out by all flows is not completely perfect (which has partly to do with requests from OpenStreetMap), that the methods for generating new routes based on population density within municipalities are unlikely to be fully representative of where the transport is going, or that the data itself is based on a simulation model that tries to optimize which type of transport should be used to meet which demand. A couple of additional things may be worth clarifying: (1) The data only tells the number of transports or shipped goods between start and end nodes. Thus, there is no way to determine what the movement pattern of individual vehicle individuals looks like between routes, nor when in time each transport is performed. (2) The data only includes freight transport, and thus "misses" for example all passenger car traffic, which should also be seen as potential users of the charging infrastructure and thus be included in the calculations in the future. It would therefore be interesting to include these in some way in the next step