ANALISIS POLA SPASIAL DAN TINGKAT KERAWANAN KECELAKAAN LALU LINTAS DI KABUPATEN SLEMAN

2021 ◽  
pp. 213
Author(s):  
Shinta Wahyu Saputri ◽  
Indrianawati Indrianawati
Keyword(s):  
Nearest Neighbor ◽  
Black Spot ◽  
Kernel Density

Jumlah kecelakaan lalu lintas di Indonesia mengalami peningkatan setiap tahunnya. Provinsi Daerah Istimewa Yogyakarta menempati urutan tertinggi ketujuh dari 34 provinsi lainnya. Kabupaten Sleman merupakan salah satu kabupaten di Provinsi Daerah Istimewa Yogyakarta yang memiliki angka kecelakaan lalu lintas cukup tinggi, sehingga diperlukan upaya pencegahan untuk mengurangi angka kecelakaan lalu lintas tersebut. Salah satu upaya yang dapat dilakukan adalah mengidentifikasi lokasi rawan kecelakaan lalu lintas (black spot) dengan memanfaatkan sistem informasi geografis (SIG). Penelitian ini bertujuan untuk menganalisis pola spasial dan tingkat kerawanan kecelakaan lalu lintas di Kabupaten Sleman. Average Nearest Neighbor (ANN) adalah metode yang digunakan untuk menganalisis pola spasial, sedangkan Kernel Density adalah metode yang digunakan untuk menganalisis tingkat kerawanan kecelakaan lalu lintas. Hasil ANN menunjukkan bahwa pola spasial kecelakaan lalu lintas dalam 2 tahun 5 bulan, baik siang maupun malam hari adalah berkelompok. Hasil analisis Kernel Density menunjukkan bahwa tingkat kerawanan kecelakaan lalu lintas di Kabupaten Sleman yang tinggi terletak pada persimpangan ruas jalan arteri dan kolektor .

ANALISIS MEDIA SOSIAL FLICKR DAN POLA RUANG RDTR PANSELA UNTUK PERENCANAAN PENGELOLAAN PARIWISATA PANTAI (Studi Kasus di Desa Parangtritis, Kabupaten Bantul)

2021 ◽  
pp. 55
Author(s):  
Arief Wicaksono ◽  
Nurul Khakhim ◽  
Nur Mohammad Farda ◽  
Dyah Rahmawati Hizbaron ◽  
Djati Mardiatno
Keyword(s):  
Nearest Neighbor ◽  
Hot Spot ◽  
Kernel Density

Beberapa tahun terakhir, media sosial berkontribusi secara signifikan terhadap penyebaran informasi pariwisata mulai dari tahap pra perjalanan, selama perjalanan, hingga setelah perjalanan. Terdapat lima pantai wisata di Desa Parangtritis yaitu Pantai Parangtritis, Parangkusumo, Cemara Sewu, Pelangi, dan Depok. Flickr dapat membagikan foto pengguna media sosial dan dijadikan sebagai pendekatan dalam identifikasi aktivitas wisatawan di lokasi pantai wisata secara spasio-temporal. Penelitian ini bertujuan untuk menganalisis pola sebaran foto Flickr mengenai pantai wisata dan menyusun rekomendasi pengelolaan pantai wisata berdasarkan hasil analisis foto Flickr dan pola ruang pada Rencana Detail Tata Ruang (RDTR) Pansela. Pengelolaan pariwisata pantai dilakukan berdasarkan konsep permintaan dan penawaran dalam industri pariwisata. Permintaan wisata diperoleh dari hasil analisis foto Flickr, sementara penawaran wisata diperoleh dari data pola ruang RDTR Pansela. Metode analisis yang digunakan, antara lain pola sebaran dengan Average Nearest Neighbor, klaster spasial dengan Hot Spot Getis-Ord Gi*, pola spasio-temporal dengan Emerging Hot Spot, dan kepadatan foto dengan Kernel Density. Hasil penelitian ini menunjukkan bahwa pola sebaran foto Flickr dominan terkonsentrasi di Pantai Parangtritis. Berdasarkan pola ruang RDTR, sub zona yang terdapat di pantai wisata diperuntukkan bagi pariwisata, rumah kepadatan rendah, sempadan pantai, suaka alam, dan transportasi. Melalui overlay antara foto Flickr dan pola ruang RDTR maka dapat diketahui sebaran wisatawan dan alokasi pemanfaatan ruang di lokasi pantai wisata. Informasi ini bermanfaat bagi pengelola pantai wisata dan pemerintah daerah demi memenuhi kebutuhan wisatawan dan mendukung pengelolaan pantai wisata.

scholarly journals Applying GIS in Analysing Black Spot Areas in Penang, Malaysia

2018 ◽  
Vol 50 (2) ◽  
pp. 113
Author(s):  
Wan Muhammad Taufiq Wan Hussin ◽  
Tarmiji Masron ◽  
Mohd Norarshad Nordin
Keyword(s):  
Spatial Pattern ◽  
Nearest Neighbor ◽  
Black Spot ◽  
Primary Objective ◽  
Accident Rate ◽  
Fatal Accident ◽  
Gis Application ◽  
North East ◽  
The North ◽  
Accident Data

This study aims to analyze fatal accident rate involving all vehicle types in the North East District of Penang. It covers fatal accident data within the duration of three years from 2011 till 2013. The primary objective is to analyze the spatial pattern and fatal accident black spot areas using Geographic Information System (GIS) application. Average Nearest Neighbor (ANN) tool is used to analyze fatal accident spatial pattern, while Kernel Density Estimation (KDE) method is utilized for fatal accident analysis. The Fatal Accident rates in 2011, 2012 and 2013 were the highest with each accounted up to 90, 88 and 91 cases. The result of ANN shows that the fatal accident pattern for 2011, 2012 and 2013 is clustered with null hypothesis rejected. The KDE analysis result shows that most fatal accident black spot areas happened at main road areas or segments.

scholarly journals Spatial Distribution Patterns Analysis of Hotspot in Central Kalimantan using FIMRS MODIS Data

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Adisty Pratamasari ◽  
Ni Ketut Feny Permatasari ◽  
Tia Pramudiyasari ◽  
Masita Dwi Mandini Manessa ◽  
Supriatna Supriatna
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Correlation Analysis ◽  
Spatial Pattern ◽  
Forest Fires ◽  
Nearest Neighbor ◽  
Kernel Density ◽  
Distribution Patterns ◽  
Central Kalimantan ◽  
Noaa Avhrr

<p><em>One of the ways to observe the </em><em>hotspot created by </em><em>forest fires in Indonesia </em><em>is </em><em>through </em><em>Remote sensing imagery, such as MODIS, NOAA AVHRR, etc</em><em>. </em><em>Central Kalimantan is one of the areas in Indonesia with the highest hotspot data. In this research, MODIS FIRMS hotspot data in Central Kalimantan collected from 2017 – 2019, covering 13 districts: South Barito, East Barito, North Barito, Mount Mas, Kapuas, Katingan, Palangkaraya City, West Kotawaringin, East Kotawaringin, Lamandau, Murung Raya, Pulang Pisau, Seruyan, and Sukamara. That is four aspects that this research evaluated: 1) evaluating the spatial pattern using the Nearest Neighbor Analysis (NNA); 2) evaluate the hotspot density appearance using Kernel Density; and 3) correlation analysis between rainfall data and MODIS FIRMS. As a result, the hotspot in Central Kalimantan shows a clustered pattern. While the natural breaks KDE algorithm shows the most relevant result to represent the hotspot distribution. Finally, the hotspot is low correlated with rainfall; however, is see that most of the hotspot (~90%) appeared in low rainfall month (less than 3000 mm/month).</em></p><p><strong><em>Keywords</em></strong><em>: Forest fire, Hotspot, NNA, Kernel density, Central Kalimantan</em></p>

scholarly journals Spatial Statistic Analysis of Earthquakes in Aceh Province Year 1921-2014: Cluster Seismicity

2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Muzailin Affan ◽  
Muhammad Syukri ◽  
Linda Wahyuna ◽  
Hizir Sofyan
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Spatial Patterns ◽  
Nearest Neighbor ◽  
Kernel Density ◽  
Statistic Analysis ◽  
Global Index ◽  
Spatial Statistic ◽  
Aceh Province ◽  
Local Moran

The purpose of this study is to apply the analysis of spatial patterns of earthquakes in the province of Aceh by detecting clusters and looking for spatial patterns locally and globally during the period 1921-2014 using GIS (Geographic Information System). The selected techniques are Average Nearest Neighbor, Moran Global Index, the Getis-Ord General G, Anselin Local Moran Index, the Getis-Ord Gi*, and Kernel Density Estimation. Each technique is implemented using GIS so that calculations can be done efficiently and quickly. The results of this study indicate that (1) The techniques can detect clusters of dots on the spatial pattern of earthquakes; (2) Both globally and locally, it shows that earthquakes clustered in the southwestern heading to the northern part of the province; (3) An earthquake with a greater magnitude generally concentrated in the district of Simeulue, the western part of Aceh Besar and northwest of Sabang

scholarly journals Prediction of Top Tourist Attraction Spots using Learning Algorithms

2019 ◽  
Vol 8 (3) ◽  
pp. 1063-1067
Keyword(s):  
Social Media ◽  
Random Forest ◽  
Density Estimation ◽  
Nearest Neighbor ◽  
Kernel Density ◽  
Tourist Attraction ◽  
Tourist Industry ◽  
K Nearest Neighbor ◽  
Related Information ◽  
Past Experiences

Dealing with the growing amount of user posted content like preferences, responses, comments, past experiences and beliefs spread through social media is a vital but challenging task. Being applied in several domains, recommender systems are used to find solutions and suggestions based on users interests including tourism-related opinion detection and tourist-attraction spot identification. Tourists can access and analyze this information for making decisions and predicting best tourist places. This study aims to predict tourist attraction spots and their related information by analyzing the data from social media (Facebook, Twitter etc.) which in turns help the tourist industry by deliberating what kind of attractions tourists can have and how to obtain their preferences. For this purpose four algorithms such as Kernel Density Estimation, K- Nearest Neighbor, Random forest and XG Boost have been used. The findings revealed that XG Boost yields better results in terms of accuracy than other three algorithms.

scholarly journals Accident Hotspot Identification on the Midnapore Kharagpur Development Authority Planning Area.

2020 ◽  
Vol 9 (2) ◽  
pp. 169-174 ◽  
Keyword(s):  
Geographical Information Systems ◽  
Black Spot ◽  
Kernel Density ◽  
Road Accident ◽  
Geographical Information ◽  
Road Accidents ◽  
Police Station ◽  
Black Spots ◽  
The World ◽  
Accident Data

Road accidents are a vital problem in our country for various reasons. According to WHO reports, approximately 1.25 million people died each year, and more than 50 million people injured in road accidents all over the world. Road accident is mostly human-made, and it's affecting your life negatively. Regarding, many studies or research has been performed to reduce road accident and identify the accident blackspot. This paper represents a methodology to find out the accident-prone zone, estimation of Kernel Density and black Site & black Spot identification of major roads Medinipur and Kharagpur development Authority (MKDA) planning area using of Geographical Information Systems (GIS). For this study, road accident data collected from Paschim Medinipur Kotwali Police station from 2016 to 2019. A kernel density estimation was created to identify black spots & black sites of the study area. Based on the result, suggestions are provided to improve the situation in the future.

scholarly journals Hotspot Distribution Analysis In East Kalimantan Province 2017-2019 to Support Forest and Land Fires Mitigation

2020 ◽  
Vol 4 (1) ◽  
pp. 28-33
Author(s):  
Nurwita Mustika Sari ◽  
Nurina Rachmita ◽  
Masita Dwi Mandini Manessa
Keyword(s):  
Distribution Pattern ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
High Intensity ◽  
Nearest Neighbor ◽  
Kernel Density ◽  
Distribution Analysis ◽  
Nearest Neighbor Analysis ◽  
East Kalimantan ◽  
Global Concern

Forest and land fires that have occurred in the territory of East Kalimantan Province have caused immediate disaster to the area from year to year and become a global concern in recent years. Hotspots that potentially cause forest and land fires can be detected using satellites such as NOAA-20. The purposes of this study are to analyze the distribution pattern of hotspots in East Kalimantan Province during 2017-2019, identify areas with the highest risk of fires caused by the high intensity of hotspot. The method used in this study is the Nearest Neighbor Analysis and Kernel Density Estimation analysis. The results showed that the distribution pattern of hotspots in East Kalimantan Province during 2017-2019 was clustered with the highest intensity of hotspots were in Berau, East Kutai and Kutai Kartanegara Districts. And from the result of the analysis, the highest number of days has a peak hotspots on September each year. Keywords: forest and land fires, hotspots, Nearest Neighbor, Kernel Density Estimation

Observation of Superlattice Dislocations on Cube Planes in Ni3Al

1973 ◽  
Vol 31 ◽  
pp. 174-175 ◽  
Author(s):  
J. M. Oblak ◽  
W. H. Rand
Keyword(s):  
Nearest Neighbor ◽  
Antiphase Boundary ◽  
Nearest Neighbors ◽  
High Energy ◽  
Cross Slip ◽  
Octahedral Plane ◽  
Trapping Mechanism ◽  
Slip Traces

The energy of an a/2 <110> shear antiphase. boundary in the Ll2 expected to be at a minimum on {100} cube planes because here strue ture is there is no violation of nearest-neighbor order. The latter however does involve the disruption of second nearest neighbors. It has been suggested that cross slip of paired a/2 <110> dislocations from octahedral onto cube planes is an important dislocation trapping mechanism in Ni3Al; furthermore, slip traces consistent with cube slip are observed above 920°K.Due to the high energy of the {111} antiphase boundary (> 200 mJ/m2), paired a/2 <110> dislocations are tightly constricted on the octahedral plane and cannot be individually resolved.

A Method for Setting the Clearance Angle

1972 ◽  
Vol 30 ◽  
pp. 388-389
Author(s):  
Michael T. Bucek ◽  
Howard J. Arnott
Keyword(s):  
Experimental Evidence ◽  
Light Source ◽  
Black Spot ◽  
Critical Factor ◽  
Clearance Angle ◽  
Crude Approximation ◽  
Ultrathin Sections

It is believed by the authors, with supporting experimental evidence, that as little as 0.5°, or less, knife clearance angle may be a critical factor in obtaining optimum quality ultrathin sections. The degree increments located on the knife holder provides the investigator with only a crude approximation of the angle at which the holder is set. With the increments displayed on the holder one cannot set the clearance angle precisely and reproducibly. The ability to routinely set this angle precisely and without difficulty would obviously be of great assistance to the operator. A device has been contrived to aid the investigator in precisely setting the clearance angle. This device is relatively simple and is easily constructed. It consists of a light source and an optically flat, front surfaced mirror with a minute black spot in the center. The mirror is affixed to the knife by placing it permanently on top of the knife holder.

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