Big Data Analytics and Artificial intelligence combine to form Social Intelligence, the outcome of absorbing massive amounts of information that are hard to obtain via standard means as they require detailed contextual information describing individual interests, preferences, and activities.
The emergence of these massive amounts of data (big data) brings new opportunities for us to understand our socioeconomic environments. We use the term social intelligence for such individual-level big geospatial data and the associated analysis methods. First, in social intelligence data, each individual plays the role of a sensor providing rich information about spatial interactions and place semantics, which go beyond the scope of traditional remote sensing data. Second, there is the complement of remote sensing, as big data can effectively capture socioeconomic features while conventional remote sensing data do not have such privileges. In SIMon, multiple social and online shopping interactions provide the rich data source to categorize, identify and analyze behavioral data that uncovers hidden patterns, unknown correlations, consumer trends, customer preferences and other useful information that can help organizations make more-informed business decisions.
Applications of social intelligence can include such diverse and precise services as tracking flu symptoms and occurrences during the winter months in randomly populated areas, or more broadly, haze effect on a population. In Singapore and surrounding countries, the annual haze originating from Indonesia poses a severe current and future health threat to millions of inhabitants. From the social networking and purchase behaviors of eSIMon, interactions can provide rich data on emotions, frequencies, purchasing behaviors along with comments, recommendations and treatments. This data is quantified to provide insight into treatments and their effectiveness.
As technologies develop the service grows to include external date sourced from, for instance, epidermal sensing of a ring, which provide alternative or a correlative dimension of geospatial data.
In social intelligence, we contribute to data patterns and behaviors as a group without divulging personal information, with users remaining anonymous.