Cognitive Aspects on the Representation of Dynamic Environmental Phenomena Using Animations

2001 ◽  
pp. 13-24 ◽  
Author(s):  
Patrick J. Ogao ◽  
Connie A. Blok
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Problem Solving ◽  
Spatial Data ◽  
Scientific Data ◽  
Data Availability ◽  
Data Exploration ◽  
Spatial Data Analysis ◽  
Wide Range ◽  
Interdisciplinary Nature

Measurements from dynamic environmental phenomena have resulted in the acquisition and generation of an enormous amount of data. This upsurge in data availability can be attributed to the interdisciplinary nature of environmental problem solving and the wide range of acquisition technology involved. In essence, users are dealing with data that is complex in nature, multidimensional and probably of a temporal nature. Also, the frequency by which this data is acquired far exceeds the rate at which it is being explored, a factor that has accelerated the search for innovative approaches and tools in spatial data analysis. These attempts have seen both analytical and visual techniques being used as aids in presentation and scientific data exploration. Examples are seen in techniques as in: data mining, data exploration and visualization.

scholarly journals A Statistical Toolbox For Mining And Modeling Spatial Data

2017 ◽  
Vol 19 (5) ◽  
pp. 5-24
Author(s):  
Gérard D’Aubigny
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Point Of View ◽  
Spatial Data Analysis ◽  
Formal Framework ◽  
Mining Projects ◽  
Small Change ◽  
Spatial Entities

Most data mining projects in spatial economics start with an evaluation of a set of attribute variables on a sample of spatial entities, looking for the existence and strength of spatial autocorrelation, based on the Moran’s and the Geary’s coefficients, the adequacy of which is rarely challenged, despite the fact that when reporting on their properties, many users seem likely to make mistakes and to foster confusion. My paper begins by a critical appraisal of the classical definition and rational of these indices. I argue that while intuitively founded, they are plagued by an inconsistency in their conception. Then, I propose a principled small change leading to corrected spatial autocorrelation coefficients, which strongly simplifies their relationship, and opens the way to an augmented toolbox of statistical methods of dimension reduction and data visualization, also useful for modeling purposes. A second section presents a formal framework, adapted from recent work in statistical learning, which gives theoretical support to our definition of corrected spatial autocorrelation coefficients. More specifically, the multivariate data mining methods presented here, are easily implementable on the existing (free) software, yield methods useful to exploit the proposed corrections in spatial data analysis practice, and, from a mathematical point of view, whose asymptotic behavior, already studied in a series of papers by Belkin & Niyogi, suggests that they own qualities of robustness and a limited sensitivity to the Modifiable Areal Unit Problem (MAUP), valuable in exploratory spatial data analysis.

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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