Big Data in GIS Environment
GIS or Geographical Information Systems which began in the 1980’s plays a very important role in our understanding of our planet and make it easier for us to visualise and analyse information. Big Data has been gaining popularity since its invention and has been spreading across fields, and it has also affected the GIS environment.
How GIS works
Geographical Information Systems contain software that obtain the geographical information captured from various sources, This Information can include details of the terrains, vegetation, kinds of soil, the positions of water bodies such as streams and can also include information about people’s population, education levels or levels of income. This information is then used by the GIS software to produce different maps and visual entities that make it very much easy for the user to understand the data and also get all the relevant knowledge that is required by Him/Her.
Big Data has been defined by the 3 v’s which are Volume, velocity and variety. Big Data refers to the data sets that are so large that they cannot be analysed computationally by the traditional computer systems and require dedicated systems that analyse and categorize the data and aid in the prediction of patterns, trends and relationships among the data.
Big Data in GIS environment
Big data can have a very significant effect in a GIS system. Since GIS system is mainly focused on the analysis of data and big data deals with a large amount of data, these systems can be combined to provide higher efficiency to the GIS systems and improve the users experience with the GIS. With notable improvements in the technology used for the collection of data, the data available for the GIS is going to be significantly increased, this paves the way for the improvements in the use of big data technologies. The availability of data has essentially exploded in the 21st century and the trends show that this increase is going to be exponential in the future too. This acts as a boon for the GIS industry as the data quality gets considerably improved.
The traditional data categories in GIS include raster, vector and graph, big data is becoming increasingly widespread in all the categories. The Raster type include the elevation, satellite imagery. The vector data include geo location data, data of schools and land boundaries. Graph data include the roads, electricity networks.
Once the data has been made available to the GIS system, the data is combined and analysed so as to produce different kinds of maps. Big data comes in here and helps in aggregating and analysing the large quantities if data in real time and providing visualizations and also for predictions.
How Big data Improves the GIS functionality:
Create Predictive Models: The presence of data and algorithms allow for prediction. This can help in health care, crime management and disaster response.
Real time analysis: Real time unstructured data can be aggregates and analysed. This is helpful for the retail and the finance industries.
Multiple streams and layers: Data can be accessed form multiple sources and can produce multiple layers in the output. This provides a better picture for analysis. The data sources available also increase since the average person can produce very large quantities of data by using the devices available with them such as smartphones.
The use of Big Data along with GIS was done during the Ebola Virus breakout. Several companies including IBM decided to use GPS data to create maps to locate and prevent the spreading of Ebola.
Since data is going to increase day by day and the processing capabilities are also increasing exponentially, Big data combined along with GIS can be a very useful factor for several industries in the future.
About the Author
DataFactZ is a professional services company that provides consulting and implementation expertise to solve the complex data issues facing many organizations in the modern business environment. As a highly specialized system and data integration company, we are uniquely focused on solving complex data issues in the data warehousing and business intelligence markets.