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.

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 .

scholarly journals Crime mapping features

2020 ◽  
Vol 79 (4) ◽  
pp. 66-72
Author(s):  
О. В. Манжай ◽  
А. О. Потильчак
Keyword(s):  
Theoretical Basis ◽  
Hot Spot ◽  
Kernel Density ◽  
Crime Mapping ◽  
Density Mapping ◽  
Criminal Intelligence ◽  
Symbol Mapping ◽  
Near Repeat ◽  
Artificial Intelligence Systems ◽  
Clinical Strategy

In this paper tools, organization and tactics of crime mapping are analyzed. The directions of application of mapping for maintenance of public safety and order, in criminal intelligence process, etc. are outlined. The domestic experience of mapping is briefly analyzed. The main goals that are achieved with the use of mapping are defined. Features of visualization of criminogenic cells are revealed. Pin mapping features (when points which symbolize a certain event are placed on the map on the corresponding coordinates) are outlined. Kernel density mapping is described, which makes it much easier to detect criminogenic foci, as hot-spot maps clearly reflect the concentration of certain events in the region. A method of mapping using proportional symbol mapping is disclosed when the increase in the size of the symbol denoting a point on the map is proportional to the increase in the number of events or other parameters at these coordinates. The building of geographical profiles of criminals is briefly described. The theoretical basis of mapping for the prediction of crimes is outlined. Prediction strategies based on equations and machine calculations and actuarial strategies based on expertise and clinical strategy are analyzed. Considerations are given to the appropriateness of applying appropriate strategies in different countries. The phenomenon of near repeat patterns is studied. Some software solutions for the implementation of the tasks of mapping criminal manifestations and the use of artificial intelligence systems for this purpose are described. Examples are given. It is noted that the use of cartography to prevent and predict crimes in Ukraine is in its infancy. Some solutions are proposed that could improve the situation in the field of crime mapping in Ukraine.

scholarly journals Use of Spatial Analysis Techniques to Identify Statistically Significant Crash Hot Spots in Metropolitan Melbourne

2020 ◽  
Vol 31 (4) ◽  
pp. 36-58
Author(s):  
Elizabeth Hovenden ◽  
Gang-Jun Liu
Keyword(s):  
Land Use ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Hot Spots ◽  
Hot Spot ◽  
Kernel Density ◽  
Spatial Dependency ◽  
Moran's I ◽  
Analysis Techniques ◽  
Global Moran’S I

Understanding where, when, what type and why crashes are occurring can help determine the most appropriate initiatives to reduce road trauma. Spatial statistical analysis techniques are better suited to analysing crashes than traditional statistical techniques as they allow for spatial dependency and non-stationarity. For example, crashes tend to cluster at specific locations (spatial dependency) and vary from one location to another (non-stationarity). Several spatial statistical methods were used to examine crash clustering in metropolitan Melbourne, including Global Moran’s I statistic, Kernel Density Estimation and Getis-Ord Gi* statistic. The Global Moran’s I statistic identified statistically significant clustering on a global level. The Kernel Density Estimation method showed clustering but could not identify the statistical significance. The Getis-Ord Gi* method identified local crash clustering and found that 15.7 per cent of casualty crash locations in metropolitan Melbourne were statistically significant hot spots at the 95 per cent confidence level. The degree, location and extent of clustering was found to vary for different crash categories, with fatal crashes exhibiting the lowest level of clustering and bicycle crashes exhibiting the highest level of clustering. Temporal variations in clustering were also observed. Overlaying the results with land use and road classification data found that hot spot clusters were in areas with a higher proportion of commercial land use and with a higher proportion of arterial and sub-arterial roads. Further work should investigate network based hot spot analysis and explore the relationship between crash clusters and influencing factors using spatial techniques such as Geographically Weighted Regression.

scholarly journals FAST INSIGHT ABOUT THE SEVERITY OF HURRICANE IMPACT WITH SPATIAL ANALYSIS OF TWITTER POSTS

2019 ◽  
Vol XLII-4/W14 ◽  
pp. 221-225
Author(s):  
K. Spasenovic ◽  
D. Carrion ◽  
F. Migliaccio ◽  
B. Pernici
Keyword(s):  
Spatial Distribution ◽  
Hot Spot ◽  
Kernel Density ◽  
Comprehensive Analysis ◽  
High Impact ◽  
Hot Spot Analysis ◽  
The Usa ◽  
Hurricane Impact ◽  
Density Map ◽  
Spot Analysis

<p><strong>Abstract.</strong> Social media could be very useful source of data for a people interested in disasters, since it can provide them with on-site information. Posted georeferenced messages and images can help to understand the situation of the area affected by the event. Considering this type of resource as a real-time crowdsource of crisis information, the spatial distribution of geolocated posts related to an event can represent an early indicator of the severity of impact. The aim of this paper is to explore the spatial distribution of Twitter posts related to hurricane Michael, occurred in the USA in 2018 and to analyse their potential in providing a fast insight about the event impact. Kernel density estimation has been applied to explore the spatial distribution of Twitter posts, after which Hot Spot analysis has been performed in order to analyse the spatiotemporal distribution of the data. Hot Spot analysis has shown to be the most comprehensive analysis, detecting the area of high impact. The Kernel density map has shown to be useful as well.</p>

scholarly journals HOT SPOT ANALYSIS OF MOTORCYCLES OWNERSHIP IN PADURENAN, RAHTAWU AND WONOSOCO VILLAGE, KUDUS REGENCY

2018 ◽  
Vol 5 (1) ◽  
pp. 147
Author(s):  
Sam'ani Intakoris ◽  
Sugiono Soetomo ◽  
Imam Buchori
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Nearest Neighbor ◽  
Hot Spot ◽  
Hotspot Analysis ◽  
Transportation Mode ◽  
Important Relationship ◽  
Arc Gis ◽  
Mode Of Transportation ◽  
Movement System

Urban and rural interaction is a very important relationship, which rural functions as a supplier of natural and human resources to urban areas, while urban provides income streams, processed goods, and income to rural areas. Transportation became one of the main media as a link. The choice of transportation mode is one of the alternatives in creating an effective and efficient movement system. The motorcycle is the preferred mode of transportation because it is able to change direction very flexible compared to other modes of transportation. There is one unique phenomenon happening in Padurenan, Rahtawu and Wonosoco, Kudus Regency. The low transportation access was inversely proportional to the number of motorcycle ownership in the three villages. Thus, there needs to be a spatial pattern mapping of motorcycles ownership in three villages. This research aims to map spatial ownership of motorcycles in the three villages. It used average nearest neighbor, incremental spatial autocorrelation, hotspot analysis. The analytical tool used Arc GIS 10 as GIS (Geographic Information System) software. The result of this research showed that the distribution of the motorcycles ownership forms a cluster pattern. The highest concentration of motorcycle ownership cluster is seen in Padurenan Village, while the lowest cluster of motorcycle ownership is in Wonosoco Village.

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

APPLICATION OF KERNEL DENSITY ESTIMATION TO IDENTIFY MOTORCYCLE THEFT HOT SPOTS IN KUCHING, SARAWAK

2021 ◽  
Vol 19 ◽  
Author(s):  
Norita Jubit ◽  
Tarmiji Masron ◽  
Azizan Marzuki
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Spatial Data ◽  
Hot Spot ◽  
Kernel Density ◽  
City Centre ◽  
Prevention Measures ◽  
Sector Boundary ◽  
Police Station ◽  
Road Data

Motorcycle theft is the most frequently reported cases worldwide, including in Malaysia. This study aims to identify the hot spot areas for motorcycle theft in Kuching. The spatial data include police station sector boundary, road data and latitud and longitude data while attribute data consists of motorcycle theft by year, address of the incident and time. Kernel Density Estimation (KDE) helps to find the hot spot areas of motorcycle theft. Motorcycle theft in Kuching has been reported as more frequent during the day at 54.8% and at 45% during the night from the year 2015 to 2017. Hot spot locations change by year and time. The study found that most of the hot spot areas of motorcycle theft were detected within the Sentral boundary. This indicates that the city centre is an area with a high density of motorcycle theft. This study can help authorities to improve the prevention measures for motorcycle theft while the findings can help in preventing motorcycle theft by police sector boundary.

Spotting Premium Hot Spots for Urban Tourism Based on Facebook and Foursquare Data Using VGI and GIS

2021 ◽  
pp. 159-186
Author(s):  
José Gomes dos Santos ◽  
Liliana Raquel Simões Azevedo ◽  
Luís Carlos Roseiro Leitão
Keyword(s):  
Social Networks ◽  
Cluster Analysis ◽  
Spatial Analysis ◽  
Density Estimation ◽  
Hot Spot ◽  
Kernel Density ◽  
Urban Tourism ◽  
Distance Weighting ◽  
Inverse Distance ◽  
Spot Analysis

Spatial modeling always involves choices. The existence of constraints, the uncertainty and even the reliability of the data, the purposes and the applications of the studies make these reflections a kind of guiding compass for GIS analysts. Building on a previous exercise of data acquisition (check-ins) based on two digital social networks (DSN – Facebook and Foursquare) and on the awareness of the use of volunteered geographic information (VGI) generated by tourists through DSN, this work aims to evaluate the contribution of spatial analysis applied to urban tourism in the “Alta and University of Coimbra” area. Concepts and procedural tasks related to density determination, cluster analysis, and identification of patterns have thus been implemented with the purpose of evaluating and comparing the results obtained through the application of two techniques of spatial analysis, kernel density estimation (KDE) and optimized hot spot analysis (OHSA) and inverse distance weighting (IDW) interpolation.

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