scholarly journals Study of Rainfall and Water Discharge Spatial Variability Using Exploratory Spatial Data Analysis Method in Bondowoso Regency

2021 ◽  
Vol 9 (1) ◽  
pp. 26
Author(s):  
Vide Mirza Faillasuf ◽  
Gusfan Halik ◽  
Retno Utami Agung Wiyono
Keyword(s):  
Data Analysis ◽  
Water Resources ◽  
Spatial Data ◽  
Rainfall Intensity ◽  
Water Discharge ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Analysis Method ◽  
Rainfall Distribution ◽  
Monthly Average

The difference in rainfall intensity affects the hydrological cycle as a process that greatly determines the amount of water discharge. Thus, in water resources management, it is important to determine the distribution pattern of rainfall and discharge. By studying the characteristics of rainfall distribution patterns and water discharge, the potential of water resources can be illustrated well. This study uses the Exploratory Spatial Data Analysis method to examine spatial variability of rainfall intensity and water discharge in Bondowoso Regency. Rainfall and discharge data are collected from 35 rain stations and 227 weirs in 2008 until 2018. This study produces monthly average rainfall distribution values between 190 mm / month with monthly average discharge between 7300lt/sec/month. Meanwhile, the obtained average annual rainfall distribution values are between 2300 mm/year with annual average discharge values between 105000 lt/sec/month. The spatial distribution map using IDW method produces information on the potential of water resources as follow: the higher the height of a place, the higher the average monthly rainfall, while the lower the height of a place, the higher the average monthly discharge. As for the obtained correlation value between rainfall and discharge is R² = 0.665.

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.

scholarly journals Approach to Urban Environmental Justice Using Exploratory Spatial Data Analysis. The Case of Valencia’s Monumental Trees

2020 ◽  
Vol 12 (18) ◽  
pp. 7760
Author(s):  
Alfonso Gallego-Valadés ◽  
Francisco Ródenas-Rigla ◽  
Jorge Garcés-Ferrer
Keyword(s):  
Spatial Distribution ◽  
Data Analysis ◽  
Environmental Justice ◽  
Spatial Data ◽  
Urban Trees ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
The Public ◽  
Object Of Study ◽  
The City

Environmental justice has been a relevant object of analysis in recent decades. The generation of patterns in the spatial distribution of urban trees has been a widely addressed issue in the literature. However, the spatial distribution of monumental trees still constitutes an unknown object of study. The aim of this paper was to analyse the spatial distribution of the monumental-tree heritage in the city of Valencia, using Exploratory Spatial Data Analysis (ESDA) methods, in relation to different population groups and to discuss some implications in terms of environmental justice, from the public-policy perspective. The results show that monumental trees are spatially concentrated in high-income neighbourhoods, and this fact represents an indicator of environmental inequality. This diagnosis can provide support for decision-making on this matter.

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