scholarly journals A Novel Visual Data Mining Module for the Geographical Information System gvSIG

2013 ◽  
Vol 36_1 (1) ◽  
pp. 98-111
Author(s):  
R. VÁZQUEZ-RODRÍGUEZ ◽  
C. PÉREZ-RISQUET ◽  
J.C. TORRES-CANTERO
Keyword(s):  
Data Mining ◽  
Information System ◽  
Geographical Information System ◽  
Geographical Information ◽  
Visual Data ◽  
Visual Data Mining

Using Data Mining Techniques for the Management of Seismic Vulnerability

2006 ◽  
Vol 326-328 ◽  
pp. 501-504 ◽  
Author(s):  
Florin Leon ◽  
Gabriela Maria Atanasiu ◽  
Dan Gâlea
Keyword(s):  
Data Mining ◽  
Information System ◽  
Geographical Information System ◽  
Seismic Vulnerability ◽  
Geographical Location ◽  
Geographical Information ◽  
Specific Information ◽  
Data Mining Techniques ◽  
Earthquake Losses ◽  
Effective Decision Making

Natural hazards, and especially earthquakes, are often recurring phenomena. Therefore, there is a permanent need for solutions to reduce earthquake losses by developing technologies, procedures, knowledge, and tools for seismic design and rehabilitation of buildings and infrastructure. A key point to an effective decision making process that aims at mitigating their effects is building a model of the underlying facts. A Geographical Information System (GIS) is a framework able to assemble, keep, process and display specific information, identified by geographical location, which can combine layers of information to give the user a better understanding about that location. By using a Geographical Information System containing geospatial data, one can develop useful scenarios to reduce natural disaster risk and vulnerability of structures. In this paper, we describe a way of applying data mining techniques from the artificial intelligence field to earthquake analysis in order to make a better investigation of the available data. These methods are capable of finding “hidden” correlations among different subsets of data, which cannot be revealed by means of simple statistics.

HIGH-RAISED BUILDINGS IN URBAN AREA CONTRIBUTES TO THE SNATCH THEFT INCIDENTS

2017 ◽  
Vol 3 (2) ◽  
pp. 65
Author(s):  
Anith Nabilah Mustafa ◽  
Siti Rasidah Md Sakip
Keyword(s):  
Information System ◽  
Geographical Information System ◽  
Urban Area ◽  
Statistical Data ◽  
3D Modelling ◽  
Geographical Information ◽  
Residential Areas ◽  
Development Area ◽  
Crime Reports ◽  
Index Crime

Snatch theft incidents are on the rise in Malaysia especially in the cities. It tends to happen in cities because there is a possibility to commit it and both crime and urban are often associated with each other. The aim of this paper is to find out whether the building with two or more levels in urban area contributes to the snatch theft incidents. The present study has been taken up to detect the hotspots of snatch theft in Selangor, Malaysia. The crime data were obtained by requesting and analysing the index crime statistical data from the Royal Malaysian Police (RMP). This study made use of the Geographical Information System (GIS) where its 3D modelling function to construct, assess and analyse the area with high snatch theft cases. The crime reports of 2010 until 2015 were geocoded and the crime maps were prepared in ArcGIS 10.2. It was found that the hotspot area is a mix-used development area which consists mainly of commercial and residential areas of more than two-level buildings. 

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