Geospatial Visualization and Big Data

The following are industry-specific examples of the use of geospatial visualization and big data:

Disaster Relief: Maps that capture social media activities are used to assist relief workers responding to disasters worldwide. Mapping the originating location of social media messages, images, and videos allows relief workers to visualize where specific damage has occurred, the severity of the damage, and how to route resources if a response is necessary. This article describes how big data and geospatial analysis was leveraged in responding to Hurricane Irene’s devastation to Florida in 2011.

Financial: Consumer credit card companies are mapping location data from transactional systems (RDBMS), customer information (CRM), and social media streams to build more complete profiles of card users and their behaviors for outbound marketing efforts and fraud detection.

Government: Agencies at the federal, state, and local level use big data for a variety of spatial analyses. Federal government agencies are using geographic information systems (GIS) and big data for many types of activities such as real time location analysis of high volume, high velocity streams of sensor data, fraud detection, and disease surveillance. In local government agencies, big data technology is used to improve the efficiency of call center activities such as city service requests related to asset management and tracking of city service vehicles. This article discusses big data considerations that are relevant for all levels of government.

Insurance: Mapping the originating location of social media streams during natural disasters helps insurers monitor financial impact and deploy claims adjusters. Claims adjusters also use big data analytics to detect fraudulent claims and to improve the accuracy of claims. Read the article.

Natural Resources: The petroleum industry was deeply involved in big data before the term was even invented. Terabytes of location referenced seismic data have been collected from around the world and is available to the petroleum industry for exploration and extraction activities.

Retail: A novel GIS and big data application for one mass retailer monitors and filters social media data based on proximity to a store location. When a message originates within a specified distance from a store, the message is examined to see if a specific follow on activity can be initiated.

Telecommunications: Call centers gather tremendous amounts of data, much of it unstructured or loosely structured. Classifying calls and then mapping them often reveals patterns that indicate localized infrastructure weaknesses.

Utilities: Smart grid applications generate tremendous volumes of utility meter generated data. This near real time data is viewed on maps showing the level of usage on the utility grid and helps managers balance load with supply to avoid outages. Spatial analysis of the grid network can pinpoint assets that could be negatively affected by high usage. Learn how one energy company has overcome its big data challenge. Read a whitepaper about big data in utilities. Watch a video that demonstrates an electric distribution operations dashboard where disparate sources and types of data are brought into one common operating view.

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