scholarly journals From Big to Little Data for Natural Disaster Recovery: How Online and On-the-Ground Activities are Connected?

2014 ◽  
Author(s):  
Nader Afzalan ◽  
Jennifer S. Evans-Cowley ◽  
Maziar Barijough
Keyword(s):  
Natural Disaster ◽  
Disaster Recovery

Enhancing the Disaster Recovery Plan through Virtualization

2013 ◽  
pp. 220-243
Author(s):  
Dennis Guster ◽  
Olivia F. Lee
Keyword(s):  
Cloud Computing ◽  
Data Management ◽  
Natural Disasters ◽  
Natural Disaster ◽  
Disaster Recovery ◽  
Fault Tolerant ◽  
Equipment Failure ◽  
Recovery Plan ◽  
Continuity Of Operations ◽  
Security Breaches

Currently, organizations are increasingly aware of the need to protect their computer infrastructure to maintain continuity of operations. This process involves a number of different concerns including: managing natural disasters, equipment failure, and security breaches, poor data management, inadequate design, and complex/impractical design. The purpose of this article is to delineate how virtualization of hosts and cloud computing can be used to address the concerns resulting in improved computer infrastructure that can easily be restored following a natural disaster and which features fault tolerant hosts/components, isolates applications security attacks, is simpler in design, and is easier to manage. Further, because this technology has been out for a number of years and its capabilities have matured an attempt has been made to describe those capabilities as well as document successful applications.

The road to recovery Reimagining Kaikoura after a natural disaster

2019 ◽  
Author(s):  
Joanna Fountain ◽  
Nicholas Cradock-Henry
Keyword(s):  
Natural Disasters ◽  
Natural Disaster ◽  
Disaster Recovery ◽  
Recovery Process ◽  
The Road ◽  
Tourist Destinations ◽  
Stakeholder Groups ◽  
Response And Recovery ◽  
Post Disaster ◽  
New System

It is widely recognized that tourist destinations are vulnerable to disruptions caused by natural disasters, and understanding tourism response and recovery to natural disasters is a critical topic of research internationally (Mair et al., 2016). Post-disaster recovery is defined as: “the development and implementation of strategies and actions to bring the destination back to a normal (pre-event) condition or an improved state” (Mair et al., 2016: 2). Recovery may commence immediately following a crisis or disaster, or can be delayed if a destination has been considerably damaged and residents and businesses profoundly affected. Scott et al. (2008) have suggested that the disaster recovery process contains three phases – recovery of damaged infrastructure, marketing responses (revolving around communication and recovery marketing), and adaptations to the new system. These phases may occur sequentially or simultaneously, with different stakeholder groups managing them (Mair et al., 2016).

scholarly journals Disease and Social Media in Post-Natural Disaster Recovery Philippines

2021 ◽  
Author(s):  
Lauren E. Charles ◽  
Courtney D. Corley
Keyword(s):  
Social Media ◽  
Natural Disasters ◽  
Natural Disaster ◽  
Disease Surveillance ◽  
Disaster Recovery ◽  
Recovery Period ◽  
Disease Outbreaks ◽  
The Philippines ◽  
Surveillance Program ◽  
Multilinear Regression

AbstractIntroductionThe Philippines is plagued with natural disasters and resulting precipitating factors for disease outbreaks. The developing country has a strong disease surveillance program during and post-disaster phases; however, latent disease contracted during these emergency situations emerges once the Filipinos return to their homes. Coined the social media capital of the world, the Philippines provides an opportunity to evaluate the potential of social media use in disease surveillance during the post-recovery period. By developing and defining a non-traditional method for enhancing detection of infectious diseases post-natural disaster recovery in the Philippines, this research aims to increase the resilience of affected developing countries through advanced passive disease surveillance with minimal cost and high impact.MethodsWe collected 50 million geo-tagged tweets, weekly case counts for six diseases, and all natural disasters from the Philippines between 2012 and 2013. We compared the predictive capability of various disease lexicon-based time series models (e.g., Twitter’s BreakoutDetection, Autoregressive Integrated Moving Average with Explanatory Variable [ARIMAX], Multilinear regression, and Logistic regression) and document embeddings (Gensim’s Doc2Vec).ResultsThe analyses show that the use of only tweets to predict disease outbreaks in the Philippines has varying results depending on which technique is applied, the disease type, and location. Overall, the most consistent predictive results were from the ARIMAX model which showed the significance in tweet value for prediction and a role of disaster in specific instances.DiscussionOverall, the use of disease/sick lexicon-filtered tweets as a predictor of disease in the Philippines appears promising. Due to the consistent and large increase use of Twitter within the country, it would be informative to repeat analysis on more recent years to confirm the top method for prediction. In addition, we suggest that a combination disease-specific model would produce the best results. The model would be one where the case counts of a disease are updated periodically along with the continuous monitoring of lexicon-based tweets plus or minus the time from disaster.

scholarly journals CHANGE DETECTION IN MULTI-TEMPORAL IMAGES USING MULTISTAGE CLUSTERING FOR DISASTER RECOVERY PLANNING

2018 ◽  
Vol 11 (2) ◽  
pp. 110
Author(s):  
Muhamad Soleh ◽  
Aniati Murni Arymurthy ◽  
Sesa Wiguna
Keyword(s):  
Image Analysis ◽  
Change Detection ◽  
Natural Disaster ◽  
Disaster Recovery ◽  
Fuzzy C Means ◽  
Recovery Planning ◽  
Change Detection Analysis ◽  
Detection Analysis ◽  
Disaster Recovery Planning ◽  
Multi Temporal

Change detection analysis on multi-temporal images using various methods have been developed by many researchers in the field of spatial data analysis and image processing. Change detection analysis has many benefit for real world applications such as medical image analysis, valuable material detector, satellite image analysis, disaster recovery planning, and many others. Indonesia is one of the most country that encounter natural disaster. The most memorable disaster was happened in December 26, 2004. Change detection is one of the important part management planning for natural disaster recovery. This article present the fast and accurate result of change detection on multi-temporal images using multistage clustering. There are three main step for change detection in this article, the first step is to find the image difference of two multi-temporal images between the time before disaster and after disaster using operation log ratio between those images. The second step is clustering the difference image using Fuzzy C means divided into three classes. Change, unchanged, and intermediate change region. Afterword the last step is cluster the change map from fuzzy C means clustering using k means clustering, divided into two classes. Change and unchanged region. Both clustering’s based on Euclidian distance.

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