Geospatial Big data analytics: Analysing spatial IT data is a very complex process, which after selecting and analysing data with spatial attributes can help us understand people’s behaviour and/or look for relationships or patterns in any case of emergency.

About the big data

In this context we don’t need to use any algorithm for phishing or any complex data analysis. The data can be restricted spatially (to a country and particular sets of information) so these are the parts of big data that can be linked to derive achievable outcomes. Possible methods:
  • Twitter sentiment analysis
  • Google search analysis

  • Twitter sentiment analysis

    By using social media (Twitter, Facebook, etc.) - we can get a better profile for what has happened in the examined area. By scanning tweets for epidemic terminology, we can follow a contagion’s velocity.

  • Search features: Spatial characteristics - infected area
  • Special Terminology: epidemiological words, words what are indicating the disease
  • Terminology for each language or dialect

  • Disadvantages – Sociodemographic characteristics

  • 44.2 percent of the population have never attended school - Literacy Rates of Population 10 years and above: 48.6% not literate
  • Households by main source of information: 71.1% radio; 0.6% social media
  • 13% have access to internet

  • Used method

    Data is pulled by RapidMiner from the Twitter, "AYLIEN Text Analysis" identify the sentiment, the result is dropped to Tableau.

    Cinque Terre