**2. What is big data?**

#### **2.1 Big data's characteristics and components**

Big data can be defined as datasets which have various data styles, fast processing speed, and are hard to be managed and analyzed with existing data systems. These characteristics of big data are summarized with '3V', which denotes volume, variety, and velocity [4].

First, big data deals with large volume datasets, usually more than terabyte size that usually comes from Global Positioning System (GPS), social media, and other sensors. A terabyte is a unit of information equal to one million \* million (1012) bytes, or 1024 gigabyte. The brand 'big data' itself implies a size of datasets is very huge compared to past datasets.

Second, big data deals with a variety of datasets such as sound, picture, video stream, map and even social media text message. Big data targets not only structured datasets but also unstructured ones that were usually out of interest to data workers. Its range is beyond our imagination and different kinds of datasets are integrated to

**121**

**Figure 2.**

*Data warehousing with Apache Hadoop [5].*

*GIS and Big Data Visualization*

contents effectively.

traditions.

**2.2 Big data's data process and analysis techniques**

systems that generally dealt with structured datasets.

spatial database [7], which can be called big data.

*DOI: http://dx.doi.org/10.5772/intechopen.82052*

generate new types of database. Big data systems use a computer clouding and other platform such as Hadoop for data combination and integration (see **Figure 2**). Big data's third characteristic is velocity because it's very fast in generating, spreading, and applying in the real world. Big data's speed in generation, spread, and application can be accelerated with social media or social network services such as Facebook or Twitter [6]. When people post photos in Facebook, those are recorded as datasets, which offer the useful real-time evidence of locations, preference, and other personal information (see **Figure 3**). This information will be used for market-

Although a narrow definition of big data emphasizes data source, collection, storage and other technical issues, its wider definition embraces analysis and demonstration aspects. In summary, big data is defined as very large-sized, variousformatted datasets and analytic methods based on engineering technology and social network services, including statistical fusion and new visualization.

Major components of big data are resource, technology, and human capital [4]. Resource here indicates data acquisition and quality management. Big data technology denotes its platform that refers to data storage, management, processing, analysis, and visualization. Human capital in big data is called data scientists who have an ability of mathematics, engineering, economics, statistics, and psychology. They are also asked to have a capacity of communicating with other people, making a creative storytelling, and visualizing their big data

In **Figure 4**, I briefly show big data process with its elements in which the process has data source, collection, storage, processing, with analysis and visualization. Each step of process has a considerably different elements from the past database

First, big data's data sources come from institutions' or organizations' internal database, or external database such as Twitter or Facebook, or pictures and video streams. Generally, urban and geographic researches and projects use a large scale

Second, in the collection process, big data utilizes a crawling method with search engine to get Internet data. It also uses Internet of Things (IoT) based sensors to collect data. This step makes a huge difference to big data from the past data collection

ing and sales by private business or policy measures by government sector.

#### *GIS and Big Data Visualization DOI: http://dx.doi.org/10.5772/intechopen.82052*

*Geographic Information Systems and Science*

*Google Trends Search for big data since 2004 to present.*

Big data's several or more visualization tools with their software are creating a lot of wonderful GIS masterpieces recently. Thereafter, I examine those tools and find some implications from them. Can big data visualization overcomes the limitations of GIS and opens a new horizon? This chapter would provide answers to this question.

Big data can be defined as datasets which have various data styles, fast processing speed, and are hard to be managed and analyzed with existing data systems. These characteristics of big data are summarized with '3V', which denotes volume,

First, big data deals with large volume datasets, usually more than terabyte size that usually comes from Global Positioning System (GPS), social media, and other sensors. A terabyte is a unit of information equal to one million \* million (1012) bytes, or 1024 gigabyte. The brand 'big data' itself implies a size of datasets is very

Second, big data deals with a variety of datasets such as sound, picture, video stream, map and even social media text message. Big data targets not only structured datasets but also unstructured ones that were usually out of interest to data workers. Its range is beyond our imagination and different kinds of datasets are integrated to

The followings constitute sub-sections of the chapter.

• Big data and geographic information system (GIS)

• Big data as an alternative visualization tool for GIS

**2.1 Big data's characteristics and components**

• Can big data visualization overcome GIS limitations?

• What is big data

**Figure 1.**

• Conclusion

**2. What is big data?**

variety, and velocity [4].

huge compared to past datasets.

**120**

generate new types of database. Big data systems use a computer clouding and other platform such as Hadoop for data combination and integration (see **Figure 2**).

Big data's third characteristic is velocity because it's very fast in generating, spreading, and applying in the real world. Big data's speed in generation, spread, and application can be accelerated with social media or social network services such as Facebook or Twitter [6]. When people post photos in Facebook, those are recorded as datasets, which offer the useful real-time evidence of locations, preference, and other personal information (see **Figure 3**). This information will be used for marketing and sales by private business or policy measures by government sector.

Although a narrow definition of big data emphasizes data source, collection, storage and other technical issues, its wider definition embraces analysis and demonstration aspects. In summary, big data is defined as very large-sized, variousformatted datasets and analytic methods based on engineering technology and social network services, including statistical fusion and new visualization.

Major components of big data are resource, technology, and human capital [4]. Resource here indicates data acquisition and quality management. Big data technology denotes its platform that refers to data storage, management, processing, analysis, and visualization. Human capital in big data is called data scientists who have an ability of mathematics, engineering, economics, statistics, and psychology. They are also asked to have a capacity of communicating with other people, making a creative storytelling, and visualizing their big data contents effectively.
