A software architecture to integrate sensor data and volunteered geographic information for flood risk management

Natural disasters are phenomena that can cause great damage to people in urban and rural areas, and thus require preventive and reactive measures. If they involve multiple sources of information, these measures can be more useful and effective. However, the integration of heterogeneous data still poses challenges due to the differences in their structures and contents. To overcome this difficulty, this paper outlines a service-oriented architecture, as part of the AGORA platform, which aims to support the integration of sensor data and Volunteered Geographic Information (VGI) related to floods. The composition of the architectural components enables sensor data to be integrated with VGI by using several algorithms in a flexible and automated manner. The architecture was implemented by means of a prototype as a proof of concept and the results were used to generate thematic maps. These maps can improve flood risk awareness and support decision-making in flood risk management

Geo-social media as a proxy for hydrometeorological data for streamflow estimation and to improve flood monitoring

Floods are one of the most devastating types of worldwide disasters in terms of human, economic, and social losses. If authoritative data is scarce, or unavailable for some periods, other sources of information are required to improve streamflow estimation and early flood warnings. Georeferenced social media messages are increasingly being regarded as an alternative source of information for coping with flood risks. However, existing studies have mostly concentrated on the links between geo-social media activity and flooded areas. Thus, there is still a gap in research with regard to the use of social media as a proxy for rainfall-runoff estimations and flood forecasting. To address this, we propose using a transformation function that creates a proxy variable for rainfall by analysing geo-social media messages and rainfall measurements from authoritative sources, which are later incorporated within a hydrological model for streamflow estimation. We found that the combined use of official rainfall values with the social media proxy variable as input for the Probability Distributed Model (PDM), improved streamflow simulations for flood monitoring. The combination of authoritative sources and transformed geo-social media data during flood events achieved a 71% degree of accuracy and a 29% underestimation rate in a comparison made with real streamflow measurements. This is a significant improvement on the respective values of 39% and 58%, achieved when only authoritative data were used for the modelling. This result is clear evidence of the potential use of derived geo-social media data as a proxy for environmental variables for improving flood early-warning systems

The effect of intra-urban mobility flows on the spatial heterogeneity of social media activity: investigating the response to rainfall events

Although it is acknowledged that urban inequalities can lead to biases in the production of social media data, there is a lack of studies which make an assessment of the effects of intra-urban movements in real-world urban analytics applications, based on social media. This study investigates the spatial heterogeneity of social media with regard to the regular intra-urban movements of residents by means of a case study of rainfall-related Twitter activity in São Paulo, Brazil. We apply a spatial autoregressive model that uses population and income as covariates and intra-urban mobility flows as spatial weights to explain the spatial distribution of the social response to rainfall events in Twitter vis-à-vis rainfall radar data. Results show high spatial heterogeneity in the response of social media to rainfall events, which is linked to intra-urban inequalities. Our model performance (R2=0.80) provides evidence that urban mobility flows and socio-economic indicators are significant factors to explain the spatial heterogeneity of thematic spatiotemporal patterns extracted from social media. Therefore, urban analytics research and practice should consider not only the influence of socio-economic profile of neighborhoods but also the spatial interaction introduced by intra-urban mobility flows to account for spatial heterogeneity when using social media data

Mineração de padrões de chuvas das redes sociais para apoiar a gestão de risco de inundação

Context. The widespread use of social media platforms and mobile phones in recent years has increased the capability of people to share information anytime, anywhere, and about anything. The past few years have witnessed a growing interest in social media data as a supplementary data source for disaster risk management. Most studies have aimed at extracting spatio-temporal thematic patterns from social media to support the wide range of tasks that comprise disaster risk management. Substantial advances have been made towards the understanding patterns of several natural phenomena, such as floods and earthquakes. Gap. However, scant attention has been given to rain patterns, which are fundamental inputs in many rainfall-runoff models for flood modeling and forecasting, as well as early warning systems of extreme weather. Furthermore, issues such as selection of a representative areal unit of aggregation, temporal validation/calibration with conventional data, and improvement in information retrieval processes have not been thoroughly investigated, and can still be raised as challenges for the establishment of more sophisticated social signals that reflect natural phenomena. Contribution. This doctoral thesis contributes to the extraction of rain patterns from Twitter data for supporting monitoring and forecasting in flood risk management. It advances in establishing (i) a systematic method for the selection of an optimal areal unit, (ii) an approach for the evaluation of the temporal validity of social media activity related to a given phenomenon of interest, (iii) a conceptual specification model for characterization of the spatial units where social signals accurately mirror a given phenomenon of interest, and (iv) a sensitivity analysis of the spatio-temporal patterns of keywords related to a given phenomenon of interest. A series of empirical case studies conducted in Sao Paulo city, Brazil, evaluated such contributions. Results. The results showed the viability of extraction of rain patterns from Twitter data and their potential use to improve the fault tolerance of traditional solutions of flood risk management, especially in areas of lack of conventional data. Conclusions. Social media data can be used as a supplementary data source for rainfall monitoring. Moreover, discussions have provided useful guiding principles to be followed by spatial analysts using social media data as a proxy data source of natural phenomena.Contexto. O uso generalizado de plataformas de rede social e telefones celulares nos últimos anos tem aumentado a capacidade das pessoas de compartilhar informações a qualquer hora, em qualquer lugar, e sobre qualquer tópico. Os últimos anos testemunharam um interesse crescente em dados de rede social como uma fonte suplementar para a gestão de risco de desastres. A maioria dos estudos teve como objetivo extrair padrões temáticos espaço-temporais das redes sociais para apoiar as tarefas de gestão de risco de desastres. Avanços foram feitos no entendimento de padrões temáticos espaço-temporais de fenômenos naturais, tais como padrões de inundações e terremotos. Lacuna. No entanto, pouca atenção foi dada aos padrões de chuva, que são entradas fundamentais em muitos modelos chuva-vazão para a modelagem e previsão de inundação, bem como para sistemas de alerta precoce de condições meteorológicas extremas. Questões como a seleção de uma unidade de agregação de área representativa, validação/calibração temporal com dados convencionais, e melhoria do processo de recuperação de informação não foram investigadas exaustivamente e ainda podem ser levantadas como desafios para o estabelecimento de sinais sociais mais sofisticados que são capazes de refletir fenômenos naturais. Contribuição. Esta tese de doutorado contribui para a extração de padrões de chuva dos dados do Twitter para apoiar o monitoramento e a previsão de riscos de inundação. Também avança no estabelecimento de (i) um método sistemático para a seleção de uma unidade de área ideal, (ii) uma abordagem para a avaliação da validade temporal da atividade de rede social relacionada a um determinado fenômeno de interesse, (iii) um modelo conceitual para caracterizar as unidades espaciais em que o sinal social espelha com precisão um determinado fenômeno de interesse, e (iv) uma análise de sensibilidade dos padrões espaço-temporais de palavras-chaves relacionadas ao fenêmeno de interesse. Uma série de estudos de caso foram conduzidos na cidade de São Paulo, Brasil, a fim de avaliar as contribuições. Resultados. Os resultados mostraram a viabilidade de extrair padrões de chuva dos dados do Twitter e seu uso na tolerância a falhas de soluções tradicionais de gestão de risco de inundação, especialmente em áreas onde há ausência de dados convencionais. Conclusões. Os dados de redes sociais podem ser usados como uma fonte de dados suplementar para monitoramento de chuvas. Além disso, discussões fornecem princípios orientadores úteis a serem seguidos por analistas espaciais ao usar dados de redes sociais como uma fonte de dados proxy de fenômenos naturais

A Software Architecture to Integrate Sensor Data and Volunteered: Geographic Information for Flood Risk Management

Natural disasters are phenomena that can cause great damage to people in urban and rural areas, and thus require preventive and reactive measures. If they involve multiple sources of information, these measures can be more useful and effective. However, the integration of heterogeneous data still poses challenges due to the differences in their structures and contents. To overcome this difficulty, this paper outlines a service-oriented architecture, as part of the AGORA platform, which aims to support the integration of sensor data and Volunteered Geographic Information (VGI) related to floods. The composition of the architectural components enables sensor data to be integrated with VGI by using several algorithms in a flexible and automated manner. The architecture was implemented by means of a prototype as a proof of concept and the results were used to generate thematic maps. These maps can improve flood risk awareness and support decision-making in flood risk management

Mining rainfall spatio-temporal patterns in Twitter: a temporal approach

Social networks are a valuable source of information to support the detection and monitoring of targeted events, such as rainfall episodes. Since the emergence of Web 2.0, several studies have explored the relationship between social network messages and authoritative data in the context of disaster management. However, these studies fail to address the problem of the temporal validity of social network data. This problem is important for establishing the correlation between social network activity and the different phases of rainfall events in real-time, which thus can be useful for detecting and monitoring extreme rainfall events. In light of this, this paper adopts a temporal approach for analyzing the cross-correlation between rainfall gauge data and rainfall-related Twitter messages by means of temporal units and their lag-time. This approach was evaluated by conducting a case study in the city of São Paulo, Brazil, using a dataset of rainfall data provided by the Brazilian National Disaster Monitoring and Early Warning Center. The results provided evidence that the rainfall gauge time-series and the rainfall-related tweets are not synchronized, but they are linked to a lag-time that ranges from −10 to +10 min. Furthermore, our temporal approach is thus able to pave the way for detecting patterns of rainfall in real-time based on social network messages

Analysis of Spatially Distributed Data in Internet of Things in the Environmental Context

The Internet of Things consists of “things” made up of small sensors and actuators capable of interacting with the environment. The combination of devices with sensor networks and Internet access enables the communication between the physical world and cyberspace, enabling the development of solutions to many real-world problems. However, most existing applications are dedicated to solving a specific problem using only private sensor networks, which limits the actual capacity of the Internet of Things. In addition, these applications are concerned with the quality of service offered by the sensor network or the correct analysis method that can lead to inaccurate or irrelevant conclusions, which can cause significant harm for decision makers. In this context, we propose two systematic methods to analyze spatially distributed data Internet of Things. We show with the results that geostatistics and spatial statistics are more appropriate than classical statistics to do this analysis