scholarly journals ANALISIS KESESUAIAN LAHAN BUDIDAYA KEMIRI SUNAN (Aleurites trisperma) DI KABUPATEN LAMPUNG SELATAN PROVINSI LAMPUNG

2018 ◽  
Vol 6 (2) ◽  
pp. 98
Author(s):  
Rangga Agus Fauzi ◽  
A. Bintoro ◽  
A. Darmawan
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Development Planning ◽  
Land Suitability ◽  
Spatial Data Analysis ◽  
Spatial Plan

Sunan Candlenut (Reutealis trisperma) is kind of species that able to produce biodisel, and serve function as conservation plant. This research aimed to analyze the land suitability for the Sunan Candlenut cultivation in South Lampung District, Lampung Province. The suitability classes were categorized as S1 (very appropriate), S2 (quite appropriate), S3 (less appropriate) and N (not applicable).Spatial data analysis used to obtain theSunan Candlenut’s land suitability classes based on the criteria parameters of the Ministry of Agriculture Regulation and another research. Therafter, the result of the criteria based on those parametersisoverlaidto obtainthe land suitability classes of Sunan Candlenut cultivation in South Lampung District.The Results showed that the area of the suistability land for Sunan Candlenut cultivation in South Lampung District is about43.421 hawhich categorized as S2.Reffering to the Spatial Plan of South Lampung district, the distribution of land that could be referredas Sunan Candlenut cultivationis in the Katibung District which about10.520 ha and the Merbau Mataram District about7.237 ha.This reasearch is expected to be subject of consideration for relevant stakeholders regarding development planning of Sunan Candlenut cultivation.Kemiri sunan (Reutealis trisperma) merupakan tanaman yang dapat menghasilkan minyak nabati untuk biodiesel, dan juga dapat berfungsi sebagai tanaman konservasi.Penelitian ini bertujuan untuk menganalisis kelas kesesuaian lahan untuk arahan budidaya kemiri sunan di Kabupaten Lampung Selatan Provinsi Lampung.Kelas kesesuaian yang dimaksud meliputi S1 (sangat sesuai), S2 (cukup sesuai),S3 (kurang sesuai) dan N (tidak sesuai).Analisis data spasial digunakan untuk mendapatkan kelas kesesuaian lahan kemiri sunan berdasarkan parameter kriteria menurut Peraturan Kementerian Pertanian dan penelitian lain. Kemudian hasil dari kedua parameter kriteria kesesuaian lahan tersebut dilakukan overlay, untuk mendapatkan hasil kelas kesesuaian lahan budidaya kemiri sunan di Kabupaten Lampung Selatan. Hasil penelitian menunjukkan luas kesesuaian lahan untuk budidaya kemiri sunan di Kabupaten Lampung Selatan Provinsi Lampung adalah 43.421 ha dengan kelas kesesuaian S2.Mengacu pada Rencana Tata Ruang Wilayah Kabupaten Lampung Selatan, persebaran lahan yang dapat diarahkan untuk budidaya kemiri sunan yaitu wilayah Kecamatan Katibung seluas 10.520 ha dan Kecamatan Merbau Mataram seluas 7.237 ha.Penelitian ini diharapkan menjadi bahan pertimbangan pemerintah terkait dalam perencanaan pengembangan budidaya kemiri sunan.

SARS spatial data analysis and time series modelling

2015 ◽  
Author(s):  
M. Zhang ◽  
J.H. Wen ◽  
Y.L. Fan ◽  
S.J. Jin
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Spatial Data ◽  
Spatial Data Analysis ◽  
Time Series Modelling

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

Spatial Data Analysis by Example—Volume I: Point Pattern and Quantitative Data

Technometrics ◽  
1987 ◽  
Vol 29 (1) ◽  
pp. 114-116
Author(s):  
Noel Cressie
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Quantitative Data ◽  
Spatial Data Analysis ◽  
Point Pattern

Spatial Data Analysis

2009 ◽  
pp. 2691-2695
Author(s):  
Michael F. Goodchild
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Spatial Data Analysis

Predictive spatial data analysis in the geosciences

2019 ◽  
pp. 147-160 ◽  
Author(s):  
Andrea G. Fabbri ◽  
Chang-Jo F. Chung
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Spatial Data Analysis
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