Spatial data analysis and integration for regional-scale geothermal potential mapping, West Java, Indonesia

Geothermics ◽  
2008 ◽  
Vol 37 (3) ◽  
pp. 267-299 ◽  
Author(s):  
Emmanuel John M. Carranza ◽  
Hendro Wibowo ◽  
Sally D. Barritt ◽  
Prihadi Sumintadireja
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Regional Scale ◽  
Spatial Data Analysis ◽  
West Java ◽  
Geothermal Potential ◽  
Potential Mapping

scholarly journals Measuring Spatial Cluster for Leading Industries in Surakarta with Exploratory Spatial Data Analysis (ESDA)

2017 ◽  
Vol 18 (1) ◽  
pp. 64
Author(s):  
Muhammad Arif ◽  
Didit Purnomo
Keyword(s):  
Data Analysis ◽  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Regional Scale ◽  
Industrial Sector ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Spatial Approach ◽  
Leading Industry ◽  
The City

Economic clusters are significant to support the economic growth, particularly at regional scale. The approach in the analysis has evolved from the emphasis on the comparison between the intra and extra regional into the spatial approach that is capable to detect the prevailing movement and concentration pattern in particular economic activity, hence the generated data is more informative and analyzable. This paper concentrates in identifying the location and assessing the economic clusters of leading industries in Surakarta City, Indonesia based on the number of units and labor absorption by using the Exploratory Spatial Data Analysis (ESDA). In association with the first objective, ArcGis was employed to find out how the concentration of leading industries in Surakarta was formed. The analysis revealed that the industries in Surakarta City have a propensity to be remote from downtown and concentrated in the northern part of the city. The second objective was revealed by performing the Moran’s index on GeoDa software to determine the spatial autocorrelation among the observed areas as the basis in finding the leading industrial cluster. The analysis indicated that all leading industries have relatively low Moran’s index meaning there was no dominant leading industry in Surakarta. These results have been confirmed by the LISA method to reveal the areas having spatial autocorrelation for each industrial sector.

Spatial data analysis for exploration of regional scale geothermal resources

2013 ◽  
Vol 266 ◽  
pp. 69-83 ◽  
Author(s):  
Majid Kiavarz Moghaddam ◽  
Younes Noorollahi ◽  
Farhad Samadzadegan ◽  
Mohammad Ali Sharifi ◽  
Ryuichi Itoi
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Regional Scale ◽  
Spatial Data Analysis ◽  
Geothermal Resources

Analyzing The Violent Crime Patterns In Peninsular Malaysia : Exploratory Spatial Data Analysis (ESDA) Approach

2014 ◽  
Vol 72 (1) ◽  
Author(s):  
Syerrina Zakaria ◽  
Nuzlinda Abd. Rahman
Keyword(s):  
Data Analysis ◽  
Spatial Autocorrelation ◽  
Violent Crime ◽  
Spatial Data ◽  
Hot Spot ◽  
Peninsular Malaysia ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Policy Makers ◽  
Violent Crime Rate

The objective of this study is to analyze the spatial cluster of crime cases in Peninsular Malaysia by using the exploratory spatial data analysis (ESDA). In order to identify and measure the spatial autocorrelation (cluster), Moran’s I index were measured. Based on the cluster analyses, the hot spot of the violent crime occurrence was mapped. Maps were constructed by overlaying hot spot of violent crime rate for the year 2001, 2005 and 2009. As a result, the hypothesis of spatial randomness was rejected indicating cluster effect existed in the study area. The findings reveal that crime was distributed nonrandomly, suggestive of positive spatial autocorrelation. The findings of this study can be used by the goverment, policy makers or responsible agencies to take any related action in term of crime prevention, human resource allocation and law enforcemant in order to overcome this important issue in the future. 

Interactive Spatial Data Analysis.

Ecology ◽  
1996 ◽  
Vol 77 (5) ◽  
pp. 1642
Author(s):  
Michael W. Palmer ◽  
Trevor C. Bailey ◽  
Anthony C. Gatrell
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Spatial Data Analysis
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