**5. Can big data visualization overcome GIS limitations?**

GIS visualization has a limitation since it is basically rooted at the spatial context and geographic maps. GIS visualization's first priority tends more to be geographic

**129**

*GIS and Big Data Visualization*

at mapping and geography.

visualization.

ping process.

technology.

tion performance [14].

**6. Conclusion**

designed, called, instructed, and allocated.

embraces analysis and demonstration aspects.

as well as more persuasive graphic works.

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

than to be informational or graphical. Location matters at GIS visualization as it did

Big data visualization opens a new horizon in GIS visualization because it does not just strengthen the spatial context, but also it gives new meanings and insights to GIS maps and demonstration. As is compared in **Figures 8** and **10**, dots in GIS visualization turn into human faces in big data visualization. **Figure 11** implies that locations can be read without a map. More big data visualization skills and their outcomes will be brought out with more abundant insights and implications to GIS

However, there are some risks of big data visualization in applying to GIS visual-

First, big data's engineering technologies tend to be ignorant to geographic perspectives. Big data engineers and visual technicians are not necessarily geographers, spatial experts, or even urban planners. Big data visualization workers if loaded with GIS related jobs should be aware of basic spatial principles and map-

Second, GIS experts who is creating big data related visualization should be ready to adapt themselves to engineering guidelines that ask them set their spatial norms aside to set up new GIS-based big data visualization works. When GIS professionals get a step back, they will experience a power of big data visualization

Third, GIS and big data visualization works should be multidisciplinary projects or research, in which all possible fields of study are involved in the final production. Social scientists, data engineers, medical & health experts, graphic designers, and other research fields' professionals can join to generate meaningful GIS visualiza-

Big data visualization can be a good measure if people involved are deliberately

Big data is defined as very large-sized, various-formatted datasets and analytic methods based on engineering technology and social network services, including statistical fusion and new visualization. A narrow definition of big data emphasizes data source, collection, storage and other technical issues, but its wider definition

Among big data' data processing, visualization is a process that analyzed datasets are expressed with graph or table format. Big data's advantage in 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 [Miller] more often than the latter because its data source format and their needs. R programming, Tableau software, and Python language are getting a new attention as effective visualization tool for big data demonstration. GIS data visualization displays the spatial patterns or relationship between or among locations. Popular open source software included here are ArcGIS, Tableau, InstantAtlas, QGIS, SAGA GIS, GeoDa, and MapWindow. These tools are actively adapted to big data based software or systems to build up location oriented systems

Big data visualization opens a new horizon in GIS visualization because it does not just strengthen the spatial context, but also it gives new meanings and insights to GIS maps and demonstration. More big data visualization skills and their outcomes will be brought out with more abundant insights and implications to GIS

ization because their fundamental approaches are different in some ways.

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

*Geographic Information Systems and Science*

*US states' death penalty executions since 1976 [13].*

**128**

**Figure 12.**

**Figure 11.**

Does big data visualization overcome GIS and its limitation? About this issue, I

GIS visualization has a limitation since it is basically rooted at the spatial context and geographic maps. GIS visualization's first priority tends more to be geographic

describe some insights in the following section.

*The flow of human migration with online Tableau public [12].*

**5. Can big data visualization overcome GIS limitations?**

than to be informational or graphical. Location matters at GIS visualization as it did at mapping and geography.

Big data visualization opens a new horizon in GIS visualization because it does not just strengthen the spatial context, but also it gives new meanings and insights to GIS maps and demonstration. As is compared in **Figures 8** and **10**, dots in GIS visualization turn into human faces in big data visualization. **Figure 11** implies that locations can be read without a map. More big data visualization skills and their outcomes will be brought out with more abundant insights and implications to GIS visualization.

However, there are some risks of big data visualization in applying to GIS visualization because their fundamental approaches are different in some ways.

First, big data's engineering technologies tend to be ignorant to geographic perspectives. Big data engineers and visual technicians are not necessarily geographers, spatial experts, or even urban planners. Big data visualization workers if loaded with GIS related jobs should be aware of basic spatial principles and mapping process.

Second, GIS experts who is creating big data related visualization should be ready to adapt themselves to engineering guidelines that ask them set their spatial norms aside to set up new GIS-based big data visualization works. When GIS professionals get a step back, they will experience a power of big data visualization technology.

Third, GIS and big data visualization works should be multidisciplinary projects or research, in which all possible fields of study are involved in the final production. Social scientists, data engineers, medical & health experts, graphic designers, and other research fields' professionals can join to generate meaningful GIS visualization performance [14].

Big data visualization can be a good measure if people involved are deliberately designed, called, instructed, and allocated.

### **6. Conclusion**

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

Among big data' data processing, visualization is a process that analyzed datasets are expressed with graph or table format. Big data's advantage in 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 [Miller] more often than the latter because its data source format and their needs. R programming, Tableau software, and Python language are getting a new attention as effective visualization tool for big data demonstration.

GIS data visualization displays the spatial patterns or relationship between or among locations. Popular open source software included here are ArcGIS, Tableau, InstantAtlas, QGIS, SAGA GIS, GeoDa, and MapWindow. These tools are actively adapted to big data based software or systems to build up location oriented systems as well as more persuasive graphic works.

Big data visualization opens a new horizon in GIS visualization because it does not just strengthen the spatial context, but also it gives new meanings and insights to GIS maps and demonstration. More big data visualization skills and their outcomes will be brought out with more abundant insights and implications to GIS

visualization. Especially, big data visualization can be a good measure if people involved are deliberately designed, called, instructed, and allocated.
