Spatial Data Mining: Recent Trends in the Era of Big Data

With tremendous development in the field of science and technology, there is vast amount of data which are used in analytics for decision making. Considering its spatial characteristics for mining will enhance the accuracy of decision. So, obtaining knowledge from spatial data becomes very essential and meaningful. The spatial database contains very numerous amounts of spatial and non-spatial data of different forms. Interpretation and analyzing of vast data is far beyond human ability. In order to acquire knowledge on such scenario we need spatial data mining. The challenges involved in spatial mining are to deal with different objects that represent the spatial characteristics. This makes spatial data mining a dominant research field. This chapter briefs about the characteristics of spatial data mining and the methods of spatial data mining in recent years.

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Spatial Data Mining

International Journal of Data Warehousing and Mining ◽

10.4018/ijdwm.2014100103 ◽

2014 ◽

Vol 10 (4) ◽

pp. 50-70 ◽

Cited By ~ 22

Author(s):

Shuliang Wang ◽

Hanning Yuan

Keyword(s):

Data Mining ◽

Big Data ◽

Spatial Data ◽

Environmental Issues ◽

Spatial Data Mining ◽

Academic Community ◽

Primary Role ◽

Big Data Mining ◽

Spatial Big Data ◽

Heavy Pollution

Big data brings the opportunities and challenges into spatial data mining. In this paper, spatial big data mining is presented under the characteristics of geomatics and big data. First, spatial big data attracts much attention from the academic community, business industry, and administrative governments, for it is playing a primary role in addressing social, economic, and environmental issues of pressing importance. Second, humanity is submerged by spatial big data, such as much garbage, heavy pollution and its difficulties in utilization. Third, the value in spatial big data is dissected. As one of the fundamental resources, it may help people to recognize the world with population instead of sample, along with the potential effectiveness. Finally, knowledge discovery from spatial big data refers to the basic technologies to realize the value of big data, and relocate data assets. And the uncovered knowledge may be further transformed into data intelligences.

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Spatial Data Mining Features between General Data Mining

2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing ◽

10.1109/ettandgrs.2008.167 ◽

2008 ◽

Cited By ~ 1

Author(s):

Tie-li Yang ◽

Ping-Bai ◽

Yu-Sheng Gong

Keyword(s):

Data Mining ◽

Spatial Data ◽

Spatial Data Mining ◽

General Data

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Utilizing MapReduce to Improve Probe-Car Track Data Mining

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7070287 ◽

2018 ◽

Vol 7 (7) ◽

pp. 287 ◽

Cited By ~ 2

Author(s):

Li Zheng ◽

Meng Sun ◽

Yuejun Luo ◽

Xiangbo Song ◽

Chaowei Yang ◽

...

Keyword(s):

Data Mining ◽

Traffic Safety ◽

Spatial Data ◽

Road Traffic ◽

Spatial Data Mining ◽

Tracking Data ◽

Mining System ◽

Computational Performance ◽

True Path ◽

Probe Car

With the rapidly increasing popularization of the automobile, challenges and greater demands have come to the fore, including traffic congestion, energy crises, traffic safety, and environmental pollution. To address these challenges and demands, enhanced data support and advanced data collection methods are crucial and highly in need. A probe-car serves as an important and effective way to obtain real-time urban road traffic status in the international Intelligent Transportation System (ITS), and probe-car technology provides the corresponding solution through advanced navigation data, offering more possibilities to address the above problems. In addition, massive spatial data-mining technologies associated with probe-car tracking data have emerged. This paper discusses the major problems of spatial data-mining technologies for probe-car tracking data, such as true path restoration and the close correlation of spatial data. To address the road-matching issue in massive probe-car tracking data caused by the strong correlation combining road topology with map matching, this paper presents a MapReduce-based technology in the second spatial data model. The experimental results demonstrate that by implementing the proposed spatial data-mining system on distributed parallel computing, the computational performance was effectively improved by five times and the hardware requirements were significantly reduced.

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