Influence of quadrat characteristics on the evolution of the dispersion effect for fiber–water dispersions

Dispersing fibers in a water dispersion is an important issue for many fiber-based materials that significantly affects the mechanical and many other properties of materials. However, the measurement and assessment of the dispersion effect remains a significant challenge. In this study, we presented an image analysis method based on quadrat analysis from ecology and geography, transforming the issue of the dispersion effect into the statistics of point distribution. Furthermore, we changed the type of sampling and adjusted the shape, size and numbers of each quadrat to investigate its influences on the evaluation results. Our results showed that the area of one quadrat had a more obvious effect on the evaluation results compared to the number of quadrats. In addition, having a quadrat of an optimum shape enlarged the difference in various dispersion effects; the results of a square quadrat exhibited stably in both complete coverage and random sampling. Quadrat analysis realizes good measurement of dispersion states as a result of image processing and offers an assessment of the dispersion effect in a fiber–water dispersion.

Analisis Moran’s I, Geary’s C, dan Getis-Ord G pada Penerapan Jumlah Penderita Kusta di Kabupaten Gowa

Analisis spasial merupakan salah satu metode yang sering digunakan dalam melihat pola penyebaran penyakit menular. Penyakit Kusta atau lepra merupakan penyakit menular kronis yang disebabkan oleh bakteri Mycrobacterium Leprae yang penyebarannya melalui droplet. Penelitian ini bertujuan untuk mengetahui pola spasial pada Kusta dengan menggunakan metode Quadrat Analysis, untuk mengetahui ada atau tidaknya autokorelasi spasial antar daerah dengan menggunakan Moran’s I, Geary’s C, Getis-Ord G, dan pemetaan penyebaran penyakit Kusta di Kabupaten Gowa. Pada penelitian ini diperoleh bahwa pola spasial penyebaran penyakit Kusta pada Tahun 2016 dan 2017 di Kabupaten Gowa bersifat mengelompok (clustered). Pada Tahun 2016 terdapat autokorelasi spasial dengan pengujian Moran’s I dan Geary’s C, sedangkan pengujian Getis-Ord G tidak terdapat autokorelasi spasial antar daerah. Pada Tahun 2017 tidak terdapat autokorelasi spasial antar daerah dengan menggunakan ke tiga pengujian tersebut. Pada Tahun 2016 daerah yang rawan adalah Barombong, daerah yang harus berhati-hati dengan daerah sekitarnya adalah Bontonompo dan daerah yang termasuk kategori aman adalah Tompobulu. Sedangkan pada tahun 2017 daerah yang rawan terhadap penyakit Kusta adalah Bajeng dan Manuju.Kata kunci : Moran’s I, Geary’s C, Getis-Ord G, Moran Scatterplot, Kusta Spatial analysis is one of the methods that is often used to observe spreading pattern of infectious diseases. Leprosy is a chronic infectious disease caused by bacterium Mycrobacterium Leprae which spreads through droplets. This study aims to determine the spatial pattern of leprosy using the Quadrat Analysis method, to determine whether there is spatial autocorrelation between regions using Moran's I, Geary’s C, Getis-Ord G, and mapping the spread of leprosy in Gowa Regency. In this study it was found that the spatial patterns of the spread of leprosy in 2016 and 2017 in Gowa Regency was clustered. In 2016 there were spatial autocorrelations with the tests of Moran's I and Geary's C, while the testing of Getis-Ord G did not have spatial autocorrelation between regions. In 2017 there is no spatial autocorrelation between regions using the three tests. In 2016 the vulnerable areas was Barombong, the area that had to be careful with the surrounding areas was Bontonompo and the area included in the safe category was Tompobulu. Whereas in 2017 areas prone to leprosy were Bajeng and Manuju.Keywords : Moran's I, Geary's C, Getis-Ord G, Moran Scatterplot, Leprosy

POLA SEBARAN ALAMI ANGGREK (Orchidaceae) DI CAGAR ALAM PADANG LUWAY KABUPATEN KUTAI BARAT

Cagar Alam Padang Luway merupakan salah satu habitat anggrek (Orchidaceae) di Kalimantan Timur. Untuk melakukan kegiatan pelestarian diperlukan data dan informasi dasar tentang faktor–faktor ekologi spesies anggrek yang ada serta pola penyebarannya sehingga dapat menjadi dasar pertimbangan dalam pengelolaannya. Penelitian ini dilakukan untuk mengetahui sebaran Anggrek dan komposisi spesies anggrek dengan menggunakan metode analisis vegetasi. Areal penelitian merupakan pulau-pulau anggrek yang terdapat di Cagar Alam Padang Luway. Berdasarkan hasil penelitian berdasarkan Indeks Dispersi Morisita Anggrek rata-rata mengelompok dan Quadrat Analysis sebaran anggrek mengelompok. Sedangkan nilai INP tertinggi adalah Coelogyne pandurata Lind 47,675 yang paling rendah adalah Bulbophylum sp. 1,686 dan Dendrobium sp. 1,686. Sebaran jenis–jenis anggrek di Cagar Alam Padang Luway hasil penelitian ini dapat dijadikan dasar pertimbangan dalam pengelolaan Cagar Alam Padang Luway dimasa yang akan datang

Exploratory Data Analysis

Exploratory data analysis (EDA) tries to summarize datasets main characteristics such as nearest neighborhood indexes, standard deviation, scatterplots or quadrat analysis. This EDA chapter is divided into several sections to cover myGeoffice© options not forgetting the graphical mode when facing outputs: file data input (after all, any analysis demands data); Descriptive study of the variable (mean, kurtosis, distribution plot, etc.); 2D-3D data posting (spatial location of the data samples); Cutoff layout map (a spatial colorful plot according to the data samples values that are higher and lower against any particular threshold); G and Kipley's K Index (to disclose clustered, uniform and random space sampling); Kernel Gaussian density (a non-parametric way to estimate the probability space density function of a variable); T-Student and F-tests (a parametric approach to check statistical differences between two sub-regions), including a brief section regarding the two-way ANOVA technique; Quadrat analysis (comparison of the statistically expected and actual counts of objects within spatial sampling areas to test randomness and clustering); XX profile scatterplot (silhouette view of the data along XX axis); and YY profile scatterplot (silhouette view of the data along YY axis).

Quadrat Analysis of Wildland Fuel Spatial Variability

Quadrat analysis of two fuel properties (loading and depth) was used to assess the relation between variation and sample plot size. By this method, an optimum range of spatial resolutions was established for sampling Artemisia tridentata subsp. wyomingensis (Wyoming big sagebrush) and Pinus contorta (lodgepole pine) fuel types in Colorado. Results of the analysis demonstrated that quadrats provide homogeneous strata and precise measures of central tendency on both fuelbeds studied. Findings indicated that field inventories in which A. tridentata is viewed as a fire fuel could use sample spacings up to 60 m (i.e., reasonably small sample sizes). The optimum range of resolution for the P. contorta fuel type was down to 20-30 m so that larger sample sizes are required. Quadrat analysis shed light on more precise fuel sampling schemes by accounting for the microsite variation of fuel characteristics. Thus, analogous studies can reveal further the semi-stochastic phenomena that govern wildland fire behaviour and effects.