Outliers detection of cultivated land quality grade results based on spatial autocorrelation

Yan, Mingyang; Tong, Qiuying; Wang, Rumin; Luo, Chao; Land, Wuhan; Gao, Yunbing; Pan, Yuchun

doi:10.1109/agro-geoinformatics.2016.7577629

Cited by 9 publications

(7 citation statements)

References 1 publication

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“…ICs are technical facilities that provide the water required for proper crop growth. To some extent, ICs can determine the quality of arable land and directly affect the abundance and failure of agricultural production [35]. In economically underdeveloped areas, irrigation on abandoned arable land is generally not high, and more than 70% of abandoned arable land is distributed in areas that lack irrigation facilities.…”

Section: Explanation Of the Factors Affecting Arable Land Abandonmentmentioning

confidence: 99%

What Drives Land Abandonment in Core Grain-Producing Areas? Evidence from China

Wang

Kong

2022

IJERPH

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Food security remains a major issue for developing countries. Reducing arable land abandonment (ALA) is crucial to ensuring food security. In China, the ‘decline in both quantity and quality’ of arable land resources, especially in major grain-producing areas, has become increasingly serious. This study uses fuzzy set qualitative comparative analysis (fsQCA) to explore the core conditions and combinations of paths leading to explicit and implicit abandonment using 30 typical cases in the main grain-producing areas of Hubei Province. The results show that (1) three combined pathways lead to explicit ALA (EALA) and that two pathways lead to implicit ALA (IALA); (2) laborer health (LH) is the core condition leading to EALA; and (3) LH, agricultural laborer (AL), per capita income (PCI) and social relationships (SRs) are the core conditions leading to IALA. To effectively alleviate ALA, the government should improve production conditions, pay attention to laborer health issues, improve agricultural returns and strengthen food security publicity and guidance, thereby promoting the rational use of arable land in these areas. The findings in this study link the changes in arable land use and provide a reference for other developing countries in ensuring food security.

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Section: Explanation Of the Factors Affecting Arable Land Abandonmentmentioning

confidence: 99%

What Drives Land Abandonment in Core Grain-Producing Areas? Evidence from China

Wang

Kong

2022

IJERPH

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“…Steps for applying the Delaunay triangulation method for outlier detection. [16] This paper [19] utilizes the Moran's "I index" which can be generally divided into Global Moran's I index and Local Moran's I index for spatial autocorrelation. These methods encompass spatial relationships and characteristics of variables and their adjacency between data points.…”

Section: Literature Surveymentioning

confidence: 99%

Outlier Detection and Spatial Analysis Algorithms

John¹

2021

Preprint

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Outlier detection is a significant area in data mining. It can be either used to pre-process the data prior to an analysis or post the processing phase (before visualization) depending on the effectiveness of the outlier and its importance. [1] Outlier detection extends to several fields such as detection of credit card fraud, network intrusions, machine failure prediction, potential terrorist attacks and so on. Outliers are those data points with characteristics considerably different. They deviate from the data set causing inconsistencies, noise and anomalies during analysis and result in modification of the original points. [2] However, a common misconception is that outliers have to be immediately eliminated or replaced from the data set. Such points could be considered useful if analyzed separately as they could be obtained from a separate mechanism entirely making it important to the research question. This study surveys the different methods of outlier detection for spatial analysis. Spatial data or geospatial data are those that exhibit geographic properties or attributes such as position or areas. An example would be weather data such as precipitation, temperature, wind velocity and so on collected for a defined region.

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“…Sedangkan untuk yang tidak ada, autokorelasi spasial mengindikasikan pola lokasi acak. Indikator autokorelasi spasial dapat dibagi menjadi dua kategori, yaitu indeks global dan indeks lokal [6]. Tahap selanjutnya akan dilakukan analisis peramalan untuk memprediksi pola spasial dengan menggunakan model Autoregresif Integrated Moving Average (ARIMA) berdasarkan data runtun waktu dari tahun 2011-2017.…”

Section: B Dasar Teoriunclassified

Autokorelasi Spasial Untuk Analisis Pola Pengawasan Kawasan Lindung Di Kota Ambon Maluku

Simatauw

Sediyono

Prasetyo

2019

teknika

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Abstrak -Kota Ambon memiliki kawasan lindung yang ditetapkan untuk melindungi kelestarian lingkungan dan sangat sensitif terhadap dampak negatif untuk memenuhi kebutuhan manusia dalam penggunaan lahan. Penelitian ini bertujuan untuk menganalisis pola pengawasan kawasan lindung dalam bentuk data perubahan penutupan lahan pada 50 wilayah di Kota Ambon dengan menggunakan uji autokorelasi indeks Global Moran's I dan Local Indicator of Spatial Association (LISA), selanjutnya melakukan peramalan pola spasial pada data perubahan penutupan lahan di tahun 2020. Penelitian ini menunjukkan terdapat autokorelasi spasial dari perubahan penutupan lahan untuk kawasan lindung tetapi korelasi melemah dengan nilai indeks Moran's I berubah dari 0,283362665 menjadi -0,042523054. Peramalan tahun 2020 menunjukkan terdapat autokorelasi spasial pada tingkat perubahan penutupan lahan yang berkorelasi negatif dengan nilai indeks Moran's I -0,011095491. Kata Kunci: Autokorelasi Spasial, Global Moran's I, Local Indicator of Spatial Association (LISA), Peramalan.

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Outliers detection of cultivated land quality grade results based on spatial autocorrelation

Cited by 9 publications

References 1 publication

What Drives Land Abandonment in Core Grain-Producing Areas? Evidence from China

What Drives Land Abandonment in Core Grain-Producing Areas? Evidence from China

Outlier Detection and Spatial Analysis Algorithms

Autokorelasi Spasial Untuk Analisis Pola Pengawasan Kawasan Lindung Di Kota Ambon Maluku

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