User-driven geo-temporal density-based exploration of periodic and not periodic events reported in social networks

International audienceIn this paper we propose a procedure consisting of a first collection phase of social net- work messages, a subsequent user query selection, and finally a clustering phase, de- fined by extending the density-based DBSCAN algorithm, for performing a geographic and temporal exploration of a collection of items, in order to reveal and map their latent spatio-temporal structure. Specifically, both several geo-temporal distance measures and a density-based geo-temporal clustering algorithm are proposed. The approach can be applied to social messages containing an explicit geographic and temporal location. The algorithm usage is exemplified to identify geographic regions where many geotagged Twitter messages about an event of interest have been created, possibly in the same time period in the case of non-periodic events (aperiodic events), or at regular timestamps in the case of periodic events. This allows discovering the spatio-temporal periodic and aperiodic characteristics of events occurring in specific geographic areas, and thus increasing the awareness of decision makers who are in charge of territorial planning. Several case studies are used to illustrate the proposed procedure

SIRENE: A Spatial Data Infrastructure to Enhance Communities' Resilience to Disaster-Related Emergency

Abstract Planning in advance to prepare for and respond to a natural hazard-induced disaster-related emergency is a key action that allows decision makers to mitigate unexpected impacts and potential damage. To further this aim, a collaborative, modular, and information and communications technology-based Spatial Data Infrastructure (SDI) called SIRENE—Sistema Informativo per la Preparazione e la Risposta alle Emergenze (Information System for Emergency Preparedness and Response) is designed and implemented to access and share, over the Internet, relevant multisource and distributed geospatial data to support decision makers in reducing disaster risks. SIRENE flexibly searches and retrieves strategic information from local and/or remote repositories to cope with different emergency phases. The system collects, queries, and analyzes geographic information provided voluntarily by observers directly in the field (volunteered geographic information (VGI) reports) to identify potentially critical environmental conditions. SIRENE can visualize and cross-validate institutional and research-based data against VGI reports, as well as provide disaster managers with a decision support system able to suggest the mode and timing of intervention, before and in the aftermath of different types of emergencies, on the basis of the available information and in agreement with the laws in force at the national and regional levels. Testing installations of SIRENE have been deployed in 18 hilly or mountain municipalities (12 located in the Italian Central Alps of northern Italy, and six in the Umbria region of central Italy), which have been affected by natural hazard-induced disasters over the past years (landslides, debris flows, floods, and wildfire) and experienced significant social and economic losses

Setting Priorities On The Functional Status Of Hydraulic Structures In Mountainous Catchments

The frequent inspection of hydraulic structures in mountain catchments is important due to the changing interaction among water-related processes that take place in the context of hydro-meteorological hazards such as debris flow and flash floods. At first level inspections, reports from survey protocols carried out by trained citizen-volunteers are combined to provide an index that represent the status of the structure. The survey protocols aim at identifying needs for cleaning of obstructions or to pre-screen potential problems for a second level inspection or more technical and detailed inspection. Thus, reports are focused on the functional status for the following parameters: condition of the structure at the opening of the streamflow, level of obstruction at the structure and the level of erosion in the streambank. Each parameter is inspected by means of questions to observe while selecting from the available options in the survey protocol. This research presents a web-based tool to support technicians on the use of these reports for setting priorities for the inspection of structures. The decision support methodology is based on the fuzzy logic theory to handle imprecise judgments coming from the visual inspection, analytical hierarchy process (AHP) to weight the different components of each parameter and multi-criteria TOPSIS method to set priorities for the status of the structures according to each parameter. The experiences of actual technicians in using the tool and the feedback on the methodological approach for decision-making are presented from their implementation in a case study in the Fella Basin, Friuli-Venezia-Giulia region, Italy

Modélisation spatiale de la susceptibilité des versants aux mouvements de terrain

International audienceGIS are more and more used to model slopes landslides susceptibility. Among the different methods generally used, bivariate analysis are considered as the more efficient and robust techniques, particularly the Weight of Evidence technique. However, one major drawback of this technique is the problem of information redundancy between predictive variables. It needs an adapted strategy to reduce this conditional dependence problem. This paper presents a strategy to obtain good landslides susceptibility zonation without statistical problems at the 1/10 000 scale in a complex mountainous environment.Les SIG sont de plus en plus utilisés pour modéliser la susceptibilité des versants aux mouvements de terrain. Parmi les différentes méthodes utilisées, les techniques bivariées sont considérées comme les plus robustes et les plus souples à mettre en place, notamment la technique de la théorie de l'évidence. Cependant, l'inconvénient majeur de ce type de technique reste le problème de redondance d'information entre les variables prédictives, ce qui nécessite une stratégie adaptée pour régler le problème de dépendance conditionnelle. A travers une analyse de sensibilité sur les variables à modéliser et sur les variables prédictives, cet article présente une stratégie pour obtenir des cartes de susceptibilité réalistes et statistiquement acceptables pour une échelle du 1 /10 000éme dans un environnement montagneux complexe

Analyse spatiale de la susceptibilité des versants aux glissements de terrain

International audienceLes techniques d'analyse spatialisée par SIG sont de plus en plus utilisées pour évaluer la susceptibilité des versants aux glissements de terrain. Parmi ces méthodes l'analyse bivariée par théorie de l'évidence est considérée comme la plus robuste. Cependant, l'inconvénient principal de cette technique est le problème de redondance d'information entre variables prédictives. L'analyse bivariée par régression logistique, assez peu utilisée actuellement, donne des résultats prometteurs dans ce domaine sans nécessiter un travail d'évaluation préalable des données. Par une analyse de sensibilité sur les variables prédictives, cet article présente une comparaison de ces deux méthodes pour obtenir des cartes de susceptibilité réalistes et statistiquement acceptables pour une échelle du 1 /10 000e dans un environnement montagneux complexe

Landslide susceptibility assessment by bivariate methods at large scales: Application to a complex mountainous environment

International audienceStatistical assessment of landslide susceptibility has become a major topic of research in the last decade. Most progress has been accomplished on producing susceptibility maps at meso-scales (1:50,000–1:25,000). At 1:10,000 scale, which is the scale of production of most regulatory landslide hazard and risk maps in Europe, few tests on the performance of these methods have been performed. This paper presents a procedure to identify the best variables for landslide susceptibility assessment through a bivariate technique (weights of evidence, WOE) and discusses the best way to minimize conditional independence (CI) between the predictive variables. Indeed, violating CI can severely bias the simulated maps by over- or under-estimating landslide probabilities. The proposed strategy includes four steps: (i) identification of the best response variable (RV) to represent landslide events, (ii) identification of the best combination of predictive variables (PVs) and neo-predictive variables (nPVs) to increase the performance of the statistical model, (iii) evaluation of the performance of the simulations by appropriate tests, and (iv) evaluation of the statistical model by expert judgment. The study site is the north-facing hillslope of the Barcelonnette Basin (France), affected by several types of landslides and characterized by a complex morphology. Results indicate that bivariate methods are powerful to assess landslide susceptibility at 1:10,000 scale. However, the method is limited from a geomorphological viewpoint when RVs and PVs are complex or poorly informative. It is demonstrated that expert knowledge has still to be introduced in statistical models to produce reliable landslide susceptibility maps

La valutazione del rischio di frana

In the Environmental risk evaluation, the most important problem are: to attempt to anticipate the risks, to assess the relationships between causes and effects and to balance the benefits with the costs associated to the control of risks. Risk assessment is mixed with risk management, which is in effect a different area of human behavior. According to mainstream economics refer risk to individual behavior, the Expected utility function (EUF) incorporates risk: risk aversion is strictly individual, it’s necessary a good knowledge of probability occurrence and risk is managed through decision. But, when we use multidimensional data to describe the risk, EUF seems inadequate: environmental risks are complex, and so individual can not manage them. The main consequence is that environmental risk is to be considered exogenous with respect to individual behavior. Environmental economics assesses risks on the basis of the relationship: causes lead to effects. Effects have to be evaluated as physical/technical ones, afterwards it is possible to assess their economic value

GIS and data-driven models for producing vulnerability maps. A case study: nitrate contamination in Milan District groundwater(Italy)

In order to model the distribution of areas where groundwater resources are susceptible to nitrate contamination , the data-driven Weights of Evidence (WofE) and Weighted-Logistic Regression (WLR) methods were used. Using this couple of techniques, different tests were performed considering various combinations of predictor factors, deriving posterior probability maps (probability that a unit area contains a training point). In order to establish the best model, success rate curve and prediction rate curve were calculated. At the end simple statistical techniques were then used to individuate the best model between two tests that showed very similar values of prediction rate. This last test could be useful to determine the distribution of the probabilities to find wells with different values of nitrate concentration. Comparison between simulation shows the best performance of Weights of Evidence

Flexible querying of volunteered geographic information for risk management

The paper presents an approach to manage volunteered geographic information (VGI) to point out anomalous conditions of the environment to help administrators in charge of the governance and maintenance of the territory to plan mitigation and safeguard interventions. To this end they can formulate flexible queries on the VGI reports to analyze their contents. The novelty of the proposal is the search framework of VGI reports designed to support distinct needs, among which the assessment of VGI quality which is an important issue in such applications. Flexible queries are formulated and evaluated within a fuzzy database approach