Human leptospirosis distribution pattern analysis in Hulu Langat, Selangor

Tujuan penelitian ini adalah untuk menganalisis pola distribusi capung (Odonata) di Kawasan Taman Wisata Alam Suranadi. Jenis penelitian ini deskriptif eksploratif yang dilakukan pada bulan Mei 2017. Metode yang digunakan dalam pengambilan data ialah metode penangkapan capung dengan jaring serangga mengikuti empat jalur transek yaitu jalur pinggir kiri hutan, jalur pinggir kanan hutan, jalur tengah dan jalur air. Pengambilan data dilakukan sebanyak 2 kali pengulangan dalam waktu 1 bulan pada pagi hari pukul 08.00 – 11. 00 wita dan sore hari pukul 15.00–17.00 wita Perhitungan indeks pola penyebaran capung menggunakan rumus dari Southwood. Hasil penelitian menunjukkan bahwa (1) Jumlah spesies capung yang ditemukan di Kawasan Taman Wisata Alam Suranadi terdiri dari 19 spesies yang termasuk kedalam 5 famili (2) Pola penyebaran capung berkategori mengelompok (15 spesies) dan pola penyebaran merata (4 spesies). Kata kunci: Capung, Pola Penyebaran, Suranadi. Abstract The aim of this research is to analyze the distribution pattern of the dragonflies (Odonata) in the Area of Nature Park Suranadi. This is an explorative descriptive study conducted in May 2017. The method used in data collection is survey method with sweeping net technique following observation path (Left Edge, Right Edge, Central Line and Water Line). Data retrieval is done 2 times repetition within 1 month in the morning at 08.00 - 11.00 pm and afternoon at 15.00-17.00 pm. The pattern analysis of the distribution of dragonflies using the formula variance value of Southwood . The results showed that (1) the number of species of dragonflies found in Suranadi Nature Park Area consists of 19 species belonging to 5 families (2) distribution pattern of dragonflies are distributed in groups (15 species) and uniform distribution pattern (4 species). Key words: Dragonflies, distribution Patterns, Suranadi.

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Spatial Distribution Pattern Analysis of Unsafe Setting of Electric Wires in Urban Area Using K-function Methods: A Case Study of Guangming District

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/555/1/012099 ◽

2020 ◽

Vol 555 ◽

pp. 012099

Author(s):

Cong Liao ◽

Xiao Pang ◽

Heng Cai ◽

Yuan Tian

Keyword(s):

Spatial Distribution ◽

Distribution Pattern ◽

Urban Area ◽

Pattern Analysis ◽

Spatial Distribution Pattern ◽

K Function

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Target group distribution pattern analysis with bagged convolutional neural networks for UAV distribution pattern identification

Aircraft Engineering and Aerospace Technology ◽

10.1108/aeat-05-2021-0142 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Xin Xu

Keyword(s):

Distribution Pattern ◽

Pattern Analysis ◽

Distribution Patterns ◽

Pattern Identification ◽

Target Group ◽

Identification Accuracy ◽

Distribution Data ◽

Content Type ◽

Distribution Features ◽

Group Distribution

Purpose The purpose of this study is to address the limitations of existing target group distribution pattern analysis methods and identify subtle distribution differences within and between the groups with no pre-specified distribution features. Classical work generally concentrates on either the group distribution tendency or shape as a whole and simply ignores the subtle distribution differences within the group. Other work is constrained to pre-defined spatial distribution features. Design/methodology/approach This study proposes a novel algorithm for target group distribution pattern analysis. This study first transforms the group distribution data with uncertain measurements into a distributional image. Upon that, a bagged convolutional neural network model is constructed to discriminate the delicate group distribution patterns. Findings Experimental results indicate that our method is robust to target missing and location variance and scalable with dataset size. Our method has outperformed the benchmark machine learning methods significantly in pattern identification accuracy. Originality/value Our method is applicable for complex unmanned aerial vehicle distribution pattern identification.

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Spatial Pattern Analysis on Dengue Cases in Bangi district, Selangor, Malaysia

10.21203/rs.3.rs-32929/v1 ◽

2020 ◽

Author(s):

Nuriah Abd Majid ◽

Ruslan Rainis ◽

Mazrura Sahani ◽

Ahmad Fariz Mohamed ◽

Sarah Aziz Abdul Ghani Aziz ◽

...

Keyword(s):

Spatial Pattern ◽

Distribution Pattern ◽

Pattern Analysis ◽

Nearest Neighbor ◽

Statistical Analyses ◽

Spatial Pattern Analysis ◽

Study Region ◽

Directional Distribution ◽

Standard Distance ◽

Positive Factor

Abstract Background: Dengue outbreak has proliferated around the developing countries, including Malaysia, in recent decades. Thus, understanding the distribution pattern is essential for urbanization livelihood. Method: The objective of this study is to determine the trend of dengue cases reported from year 2014 to 2018 and the spatial pattern for dengue spread with reference to weather elements in Bangi town. Results: Spatial statistical analyses conducted found that the distribution pattern and spatial mean center for dengue cases was clustered at the east of Bangi region. Directional distribution observed that the elongated polygon of dengue cluster stretched from the northeast to the southwest of Bangi district. Standard distance for dengue cases was the smallest for the year 2014 (0.017 m), and the largest was in the year 2016 (0.019 m), whereas dengue cases in year 2015, 2017, and 2018 were measured at 0.018 m. The average nearest neighbor analysis also observed clustered patterns for dengue cases in Bangi district. Pearson’s correlation analysis found that temperature (r = -0.269) was negatively correlated with dengue cases for year 2014 and 2018; however, rainfall amount (r = 0.286) and rain days (r = 0.250) were positively correlated with dengue cases in year 2018.Conclusions: The three spatial statistical analyses (spatial mean center, standard distance, and directional distribution) findings illustrated that the dengue cases from the year 2014 to 2018 are clustered on the northeast to the southwest of the study region. The rainfall element is found to be a significant positive factor correlated for most study years compared to temperature element.

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