scholarly journals Using mixed research approaches to understand rural depopulation

2019 ◽  
Vol 19 (1) ◽  
pp. 99
Author(s):  
Luisa Alamá-Sabater ◽  
Vicente Budí ◽  
Jose Maria García-Álvarez-Coque ◽  
Norat Roig-Tierno

<span lang="EN-US">There is a growing consensus on the need to propose specific policies to face rural depopulation. This article applies fuzzy-set Qualitative Comparative Analysis (QCA) to define the presence or absence in each municipality of the conditions leading to the presence or absence of depopulation. We also perform Exploratory Spatial Data Analysis (ESDA) of population growth to identify hotspots of rural depopulation. The methodologies prove useful to evaluate and guide regional policies that address depopulation processes in the context of a relatively urbanized region.</span>

2019 ◽  
Vol 19 (1) ◽  
pp. 99 ◽  
Author(s):  
Luisa Alamá-Sabater ◽  
Vicente Budí ◽  
José María García-Álvarez-Coque ◽  
Norat Roig-Tierno

<span lang="EN-US">There is a growing consensus on the need to propose specific policies to face rural depopulation. This article applies fuzzy-set Qualitative Comparative Analysis (QCA) to define the presence or absence in each municipality of the conditions leading to the presence or absence of depopulation. We also perform Exploratory Spatial Data Analysis (ESDA) of population growth to identify hotspots of rural depopulation. The methodologies prove useful to evaluate and guide regional policies that address depopulation processes in the context of a relatively urbanized region.</span>


2014 ◽  
Vol 72 (1) ◽  
Author(s):  
Syerrina Zakaria ◽  
Nuzlinda Abd. Rahman

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. 


2016 ◽  
Author(s):  
Daniele Oxoli ◽  
Mayra A Zurbarán ◽  
Stanly Shaji ◽  
Arun K Muthusamy

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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