scholarly journals Peri-urban Areas and Land Use Structure in Romania at LAU2 Level: An Exploratory Spatial Data Analysis

2016 ◽  
Vol 32 ◽  
pp. 124-137 ◽  
Author(s):  
Cristina Lincaru ◽  
Draga Atanasiu ◽  
Vasilica Ciucă ◽  
Speranţa Pirciog
Keyword(s):  
Land Use ◽  
Data Analysis ◽  
Spatial Data ◽  
Urban Areas ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Land Use Structure

scholarly journals ESDA (Exploratory Spatial Data Analysis) of Vegetation Cover in Urban Areas—Recognition of Vulnerabilities for the Management of Resources in Urban Green Infrastructure

2020 ◽  
Vol 12 (5) ◽  
pp. 1933
Author(s):  
Ana Clara M. Moura ◽  
Bráulio M. Fonseca
Keyword(s):  
Data Analysis ◽  
Population Density ◽  
Spatial Data ◽  
Green Infrastructure ◽  
Vegetation Cover ◽  
Urban Areas ◽  
Spatial Behavior ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Local Index

From the mapping of urban vegetation cover by high-resolution orthoimages, using IR band and NDVI classification (Normalized Difference Vegetation Index), added to three-dimensional representation obtained by LiDAR capture (Light Detection and Ranging), the volumetric values of vegetal cover are obtained as a base to construct spatial analysis in the district of Pampulha, in Belo Horizonte, investigating the role it plays in the neighborhood. The article aims to analyze the relationship between vegetation cover, income distribution and population density, as a support to urban environmental quality management. It applies Exploratory Spatial Data Analysis (ESDA) to identify the presence of clusters and patterns of spatial distribution and to examine spatial autocorrelation. The results confirm the concentration of vegetation cover in areas of high income and lower population density but the main contribution of the study is the use of a method to analyze the spatial behavior of this distribution. Calculating Moran global index and local index (LISA), these spatial combinations are mainly used to identify transformation pressures, which may result in the definition of priorities for public actions and the construction of proposals for parameterization of vegetation cover to support plans related to green infrastructure in urban areas.

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. 

scholarly journals Hotspot analysis: a first prototype Python plugin enabling exploratory spatial data analysis into QGIS

2016 ◽  
Author(s):  
Daniele Oxoli ◽  
Mayra A Zurbarán ◽  
Stanly Shaji ◽  
Arun K Muthusamy
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Point Of View ◽  
Geospatial Data ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Software Environment ◽  
Hotspot Analysis ◽  
Computational Procedures ◽  
Gis Software

The growing popularity of Free and Open Source (FOSS) GIS software is without doubts due to the possibility to build and customize geospatial applications to meet specific requirements for any users. From this point of view, QGIS is one of the most flexible as well as fashionable GIS software environment which enables users to develop powerful geospatial applications using Python. Exploiting this feature, we present here a first prototype plugin for QGIS dedicated to Hotspot analysis, one of the techniques included in the Exploratory Spatial Data Analysis (ESDA). These statistics aim to perform analysis of geospatial data when spatial autocorrelation is not neglectable and they are available inside different Python libraries, but still not integrated within the QGIS core functionalities. The main plugin features, including installation requirements and computational procedures, are described together with an example of the possible applications of the Hotspot analysis.

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