The Integral of Spatial Data Mining in the Era of Big Data

2017 ◽  
pp. 90-126
Author(s):  
Gebeyehu Belay Gebremeskel ◽  
Chai Yi ◽  
Zhongshi He
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Spatial Data ◽  
High Volume ◽  
Spatial Data Mining ◽  
Research Field ◽  
Data Sets ◽  
Data Types ◽  
Basic Principles ◽  
Gis Data

Data Mining (DM) is a rapidly expanding field in many disciplines, and it is greatly inspiring to analyze massive data types, which includes geospatial, image and other forms of data sets. Such the fast growths of data characterized as high volume, velocity, variety, variability, value and others that collected and generated from various sources that are too complex and big to capturing, storing, and analyzing and challenging to traditional tools. The SDM is, therefore, the process of searching and discovering valuable information and knowledge in large volumes of spatial data, which draws basic principles from concepts in databases, machine learning, statistics, pattern recognition and 'soft' computing. Using DM techniques enables a more efficient use of the data warehouse. It is thus becoming an emerging research field in Geosciences because of the increasing amount of data, which lead to new promising applications. The integral SDM in which we focused in this chapter is the inference to geospatial and GIS data.

The Integral of Spatial Data Mining in the Era of Big Data

2019 ◽  
pp. 863-899
Author(s):  
Gebeyehu Belay Gebremeskel ◽  
Chai Yi ◽  
Zhongshi He
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Spatial Data ◽  
High Volume ◽  
Spatial Data Mining ◽  
Research Field ◽  
Data Sets ◽  
Data Types ◽  
Basic Principles ◽  
Gis Data

Data Mining (DM) is a rapidly expanding field in many disciplines, and it is greatly inspiring to analyze massive data types, which includes geospatial, image and other forms of data sets. Such the fast growths of data characterized as high volume, velocity, variety, variability, value and others that collected and generated from various sources that are too complex and big to capturing, storing, and analyzing and challenging to traditional tools. The SDM is, therefore, the process of searching and discovering valuable information and knowledge in large volumes of spatial data, which draws basic principles from concepts in databases, machine learning, statistics, pattern recognition and 'soft' computing. Using DM techniques enables a more efficient use of the data warehouse. It is thus becoming an emerging research field in Geosciences because of the increasing amount of data, which lead to new promising applications. The integral SDM in which we focused in this chapter is the inference to geospatial and GIS data.

Geographic Knowledge Discovery in Multiple Spatial Databases

2017 ◽  
pp. 344-366 ◽  
Author(s):  
Tahar Mehenni
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Association ◽  
Spatial Data Mining ◽  
Research Field ◽  
Geographic Knowledge ◽  
Geographic Knowledge Discovery ◽  
Active Research

Voluminous geographic data have been, and continue to be, collected from various Geographic Information Systems (GIS) applications such as Global Positioning Systems (GPS) and high-resolution remote sensing. For these applications, huge amount of data is maintained in multiple disparate databases and different in spatial data type, file formats, data schema, access mechanism, etc. Spatial data mining and knowledge discovery has emerged as an active research field that focuses on the development of theory, methodology, and practice for the extraction of useful information and knowledge from massive and complex spatial databases. This chapter highlights recent theoretical and applied research in geographic knowledge discovery and spatial data mining in a distributed environment where spatial data are dispersed in multiple sites. The author will present in this chapter, an overall picture of how spatial multi-database mining is achieved through several common spatial data-mining tasks, including spatial cluster analysis, spatial association rule and spatial classification.

scholarly journals MINING CO-LOCATION PATTERNS WITH CLUSTERING ITEMS FROM SPATIAL DATA SETS

2018 ◽  
Vol XLII-3 ◽  
pp. 2505-2509
Author(s):  
G. Zhou ◽  
Q. Li ◽  
G. Deng ◽  
T. Yue ◽  
X. Zhou
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Data Mining ◽  
Data Sets ◽  
Location Pattern ◽  
Spatial Feature ◽  
Geographic Space ◽  
Location Patterns ◽  
Spatial Data Sets

The explosive growth of spatial data and widespread use of spatial databases emphasize the need for the spatial data mining. Co-location patterns discovery is an important branch in spatial data mining. Spatial co-locations represent the subsets of features which are frequently located together in geographic space. However, the appearance of a spatial feature C is often not determined by a single spatial feature A or B but by the two spatial features A and B, that is to say where A and B appear together, C often appears. We note that this co-location pattern is different from the traditional co-location pattern. Thus, this paper presents a new concept called clustering terms, and this co-location pattern is called co-location patterns with clustering items. And the traditional algorithm cannot mine this co-location pattern, so we introduce the related concept in detail and propose a novel algorithm. This algorithm is extended by join-based approach proposed by Huang. Finally, we evaluate the performance of this algorithm.

Geographic Knowledge Discovery in Multiple Spatial Databases

2018 ◽  
pp. 121-143
Author(s):  
Tahar Mehenni
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Association ◽  
Spatial Data Mining ◽  
Research Field ◽  
Geographic Knowledge ◽  
Geographic Knowledge Discovery ◽  
Active Research

Voluminous geographic data have been, and continue to be, collected from various Geographic Information Systems (GIS) applications such as Global Positioning Systems (GPS) and high-resolution remote sensing. For these applications, huge amount of data is maintained in multiple disparate databases and different in spatial data type, file formats, data schema, access mechanism, etc. Spatial data mining and knowledge discovery has emerged as an active research field that focuses on the development of theory, methodology, and practice for the extraction of useful information and knowledge from massive and complex spatial databases. This chapter highlights recent theoretical and applied research in geographic knowledge discovery and spatial data mining in a distributed environment where spatial data are dispersed in multiple sites. The author will present in this chapter, an overall picture of how spatial multi-database mining is achieved through several common spatial data-mining tasks, including spatial cluster analysis, spatial association rule and spatial classification.

Mining Complex Spatial Patterns: Issues and Techniques

2014 ◽  
Vol 13 (02) ◽  
pp. 1450019 ◽  
Author(s):  
Grace L. Samson ◽  
Joan Lu ◽  
Aminat A. Showole
Keyword(s):  
Data Mining ◽  
Spatial Patterns ◽  
Spatial Data ◽  
Predictive Modelling ◽  
Spatial Data Mining ◽  
Data Types ◽  
Data Set ◽  
Spatial Systems ◽  
Spatial Attributes ◽  
Using Data

Spatial data mining is the quantitative study of phenomena that are located in space. This paper investigates methods of mining patterns of a complex spatial data set (which generally describes any kind of data where the location in space of object holds importance). We based this research on the analysis of some spatial characteristics of certain objects. We began with describing the spatial pattern of events or objects with respect to their attributes; we looked at how to describe the spatial nature/characteristics of entities in an environment with respect to their spatial and non-spatial attributes. We also looked at modelling (predictive modelling/knowledge management of complex spatial systems), querying and implementing a complex spatial database (using data structure and algorithms). Critically speaking, the presence of spatial auto-correlation and the fact that continuous data types are always present in spatial data makes it important to create methods, tools and algorithms to mine spatial patterns in a complex spatial data set. This work is particularly useful to researchers in the field of data mining as it contributes a whole lot of knowledge to different application areas of data mining especially spatial data mining. It can also be useful in teaching and likewise for other study purposes.

Recent Trends in Spatial Data Mining and Its Challenges

2016 ◽  
pp. 37-54
Author(s):  
Anuradha Jagadeesan ◽  
Prathik A ◽  
Tripathy B K
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Spatial Data ◽  
Science And Technology ◽  
Spatial Database ◽  
Spatial Data Mining ◽  
Research Field ◽  
Spatial Characteristics ◽  
Human Ability ◽  
Recent Trends

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.

scholarly journals Prediction of Suitability of Soil for Different Crops using Spatial Data Mining

2019 ◽  
Vol 9 (1) ◽  
pp. 2330-2337 ◽  
Keyword(s):  
Data Mining ◽  
Statistical Analysis ◽  
Spatial Data ◽  
Clustering Algorithm ◽  
Geographical Location ◽  
Spatial Data Mining ◽  
Soil Conditions ◽  
Data Sets ◽  
Grid Pattern ◽  
Multiple Data Sets

The main Objective of Data mining in agriculture is to improvise the productivity based on the data observed and timelines of cultivation. Spatial Data mining, a key to capture the data by proposing sensors on a particular geographical location and observe various parameters to enhance the productivity based on the statistical analysis of data collected. In general, Data mining is an anticipating measurement and prognosticates the various data sets and mutate into useful data sets which can be applied on various applications. In this paper, data mining is applied in bridging the soil conditions to the applicable crop for cultivation in enhancing the productivity and multiple crops cultivation for enriched productivity based on the data sets acquired. A Statistical analysis resulted from a backend algorithm with the data sets and displayed as dashboard with the forecasted productivity. A Grid based clustering algorithm is adhered at the backend for performing analysis on the collected data sets results crop selectivity & productivity timelines. Geographical analysis forms a grid pattern with multiple data sets as matrix results in multiple crop selectivity based on the soil conditions and analyzed data sets obtained from various sensor parameters on a particular location. Data visualization is performed after the algorithmic process at the backend and data stored in the cloud server. Spatial Survey & Collective data Sets analyzed with the algorithm are used to elevate the Crop Selectivity and productivity on a soil based on the Biological Predicts, defoliant and manure usage timelines yields Improved Monetary generation.

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