An offline–online Web-GIS Android application for fast data acquisition of landslide hazard and risk

Regional landslide assessments and mapping have been effectively pursued by research institutions, national and local governments, non-governmental organizations (NGOs), and different stakeholders for some time, and a wide range of methodologies and technologies have consequently been proposed. Land-use mapping and hazard event inventories are mostly created by remote-sensing data, subject to difficulties, such as accessibility and terrain, which need to be overcome. Likewise, landslide data acquisition for the field navigation can magnify the accuracy of databases and analysis. Open-source Web and mobile GIS tools can be used for improved ground-truthing of critical areas to improve the analysis of hazard patterns and triggering factors. This paper reviews the implementation and selected results of a secure mobile-map application called ROOMA (Rapid Offline–Online Mapping Application) for the rapid data collection of landslide hazard and risk. This prototype assists the quick creation of landslide inventory maps (LIMs) by collecting information on the type, feature, volume, date, and patterns of landslides using open-source Web-GIS technologies such as Leaflet maps, Cordova, GeoServer, PostgreSQL as the real DBMS (database management system), and PostGIS as its plug-in for spatial database management. This application comprises Leaflet maps coupled with satellite images as a base layer, drawing tools, geolocation (using GPS and the Internet), photo mapping, and event clustering. All the features and information are recorded into a GeoJSON text file in an offline version (Android) and subsequently uploaded to the online mode (using all browsers) with the availability of Internet. Finally, the events can be accessed and edited after approval by an administrator and then be visualized by the general public

Exploring on the Role of Open Government Data in Emergency Management

Part 6: Open GovernmentInternational audienceAnalysis of the U.S. government response to Hurricane Katrina in 2005 and Hurricane Sandy in 2012 remind us that inter-governmental and intra-governmental communication plays an important role in effective response to disaster. Hurricane Katrina highlighted the lack of information sharing across levels of government and sectors and showed that such gaps in sharing contribute to slower and uncoordinated response and insufficient deployment of resources. The response to Hurricane Sandy was much more effective because of the lessons learned from Katrina about cross-boundary information sharing but problems still existed. The conclusion that more complex and severe incidents require more coordination and information sharing across levels of government and functional agencies makes it increasingly important to increase information sharing capability as part of EM. This paper presents the argument that the unique and important opportunity of leveraging OGD in this regard requires continued attention and investment in ways that maximize value in the form of more effective and efficient emergency response efforts

A spatio-temporal predictive model inferring the year-to-year probability of occurrence of TBE human cases in Europe

BACKGROUND The number of tick-borne encephalitis (TBE) human cases reported in Europe has increased in recent years, peaking during the Covid-19 pandemic phase. To improve the capability to identify high-risk areas, we developed a spatio-temporal predictive model inferring the year-to-year probability of occurrence of TBE human cases in Europe. METHODS We used data provided by the European Surveillance System (TESSy, ECDC) to infer the distribution of TBE human cases at the regional (NUTS3) level during the period 2017-2021. We included variables related to temperature, precipitation, land cover and ticks’ hosts presence to account for the natural hazard of viral circulation. We also used indexes based on recorded intensities of human outdoor activity in forests as proxies of human exposure to tick bites. We identified the yearly probability of TBE occurrence using a boosted regression tree modeling framework. RESULTS Areas with higher probability for transmission were identified in Central-Eastern Europe and along the coastline of Nordic countries up to the Bothnian Bay. Our results highlighted a westbound and northbound spread of TBEpositive regions throughout the years. Areas at higher risks are characterized by the occurrence of key rodent reservoir and cervid species, intense human recreational activities in forests, steep drops in late summer temperatures and high annual precipitation amounts. The predictive accuracy of the model was assessed through internal and external validation (AUC = 0.81; CBI =0.98). CONCLUSIONS Our study provides an assessment of the European regions at risk of TBE human infections on a yearly basis. Our results can therefore be used for year-to-year disease risk mapping in support of surveillance and prevention campaigns within endemic and potential new risk areas