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

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 systems that generally dealt with structured datasets.

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

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 traditions.

**Figure 2.** *Data warehousing with Apache Hadoop [5].*

#### **Figure 3.**

*Twitter image that contains some personal information.*

**123**

*GIS and Big Data Visualization*

analysis modules recently.

GIS data.

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

ization tool for big data demonstration.

of these six steps of big data processing.

usually large-sized as is big data.

datasets and their attributes.

mapping coordinates in the database.

other desktop or online GIS systems.

**3. Big data and geographic information system**

engine robot is also useful tool for obtaining data in GIS.

Third, data storage is a step that engineering technologies are concentrated. Big data managers have to control unstructured data with Not Only SQL (NoSQL), extract data with MapReduce, and execute a distributed parallel processing with Hadoop. In big data analysis, researchers use neurolinguistic programming for natural language processing, machine learning for data pattern identification, and serialization for assigning orders among data. Researchers pay attention to R programming to conduct big data analysis because it is an efficient statistical tool compared to other packages. Many statistical packages begin to equip themselves with big data

Big data visualization and demonstration is a process that analyzed datasets are expressed with graph or table format. Merits of big data visualization in comparison with traditional data visualization is that the former uses word/text/tag clouds, network diagrams, parallel coordinates, tree mapping, cone trees, and semantic networks [8] more often than the latter because its data source format and their needs. R, Tableau, Python language are getting a new attention as effective visual-

In the next section, I find out a relationship between big data and GIS in terms

Big data and GIS are able to share several aspects together because they are similar in elements of data processing. In **Figure 5**, I show GIS data processing with its elements. There are popular open source or commercialized software and webbased online GIS systems, which play an important role in processing and analyzing

First, GIS uses data that contains a location or space, therefore it is displayed in a map or picture form. Recently, aerial or satellite data becomes more and more important as new technologies are introduced. As a location based data, GIS data is

Second, GIS collects field data such as street information, Closed Circuit TV (CCTV), or other location-based datasets. If the datasets do not provide location information, GIS technicians should perform a geo-coding process to convert into GIS datasets. People's participation is also an important way to get GIS data; so the participatory GIS system becomes a significant field of GIS. Crawling with search

Third, GIS has web server, geospatial data server, or cloud server for its data storage. These servers can be overlapped one another sometimes, but they have their own territories that cannot be shared. In **Figure 6**, I introduce a basic principle of geo-database for single-user and multi-users with the ESRI's official website information. Geo-database system is crucial to manage complicated structured GIS

Fourth, GIS desktop and online software plays a pivotal role in the rest of process including data processing (building), analysis, and visualization. In the GIS data processing (building), efficient systems included are ArcGIS Online, Google Maps JavaScript API, Here Maps JavaScript API, Microsoft Bing Geocode Dataflow API, and US Census Geocoder. They are helpful for building up geo-coding and

Fifth, GIS data analysis contains several functions as **Table 1** briefly shows with ArcGIS analysis toolbox summary. Similar analyses are conducted with other software such as ArcGIS, QGIS, GRASS GIS, GeoDa, CartoDB, Mapbox, and the

**Figure 4.** *Big data processing and elements [4, 9].*

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

*Geographic Information Systems and Science*

*Twitter image that contains some personal information.*

**122**

**Figure 4.**

**Figure 3.**

*Big data processing and elements [4, 9].*

Third, data storage is a step that engineering technologies are concentrated. Big data managers have to control unstructured data with Not Only SQL (NoSQL), extract data with MapReduce, and execute a distributed parallel processing with Hadoop.

In big data analysis, researchers use neurolinguistic programming for natural language processing, machine learning for data pattern identification, and serialization for assigning orders among data. Researchers pay attention to R programming to conduct big data analysis because it is an efficient statistical tool compared to other packages. Many statistical packages begin to equip themselves with big data analysis modules recently.

Big data visualization and demonstration is a process that analyzed datasets are expressed with graph or table format. Merits of big data visualization in comparison with traditional data visualization is that the former uses word/text/tag clouds, network diagrams, parallel coordinates, tree mapping, cone trees, and semantic networks [8] more often than the latter because its data source format and their needs. R, Tableau, Python language are getting a new attention as effective visualization tool for big data demonstration.

In the next section, I find out a relationship between big data and GIS in terms of these six steps of big data processing.
