Data Mining Approach Based on Information-Statistical Analysis: Application to Temporal-Spatial Data

2001 ◽  
pp. 128-140
Author(s):  
Bon K. Sy ◽  
Arjun K. Gupta
Keyword(s):  
Data Mining ◽  
Statistical Analysis ◽  
Spatial Data ◽  
Data Mining Approach ◽  
Analysis Application

Crime hotspot mapping using the crime related factors—a spatial data mining approach

2012 ◽  
Vol 39 (4) ◽  
pp. 772-781 ◽  
Author(s):  
Dawei Wang ◽  
Wei Ding ◽  
Henry Lo ◽  
Tomasz Stepinski ◽  
Josue Salazar ◽  
...  
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
Spatial Data Mining ◽  
Related Factors ◽  
Data Mining Approach

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.

scholarly journals Identify Road Clusters with High-Frequency Crashes Using Spatial Data Mining Approach

2019 ◽  
Vol 9 (24) ◽  
pp. 5282 ◽  
Author(s):  
Zhonggui Zhang ◽  
Yi Ming ◽  
Gangbing Song
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
High Frequency ◽  
Spatial Data Mining ◽  
Spatial Relationships ◽  
Outlier Analysis ◽  
Data Set ◽  
Data Mining Approach ◽  
Spatial Weight ◽  
Research Show

This paper develops a three-step spatial data mining approach to directly identify road clusters with high-frequency crashes (RCHC). The first step, preprocessing, is to store the roads and crashes in a spatial database. The second step is to describe the conceptualization of road–road and crash–road spatial relationships. The spatial weight matrix of roads (SWMR) is constructed to describe the conceptualization of road–road spatial relationships. The conceptualization of crash–road spatial relationships is established using crash spatial aggregation algorithm. The third step, spatial data mining, is to identify RCHC using the cluster and outlier analysis (local Moran’s I index). This approach was validated using spatial data set including roads and road-related crashes (2008–2018) from Polk County, IOWA, U.S.A. The findings of this research show that the proposed approach is successful in identifying RCHC and road outliers.

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