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.

Crime Mapping and Crime Analysis of Property Crimes in Jodhpur

2017 ◽  
Vol 55 (2) ◽  
pp. 205-219 ◽  
Author(s):  
Swikar Lama ◽  
Sikandar Singh Rathore
Keyword(s):  
Environmental Factors ◽  
Crime Prevention ◽  
Police Officers ◽  
Hot Spots ◽  
Hot Spot ◽  
Situational Crime Prevention ◽  
Crime Analysis ◽  
Crime Mapping ◽  
Property Crimes

AbstractThis study is based on crime mapping and crime analysis of property crimes in Jodhpur. The property crimes which were selected were house breaking, auto thefts and chain snatching. Data from police stations were used to generate the maps to locate hot spots of crimes. The profile of these hot spots was analyzed through observations supplemented with interviews of police officers and public 100 cases of house breaking and 100 cases of auto thefts were further analyzed to understand the contexts which lead to these crimes. These contexts are in consonance with situational crime prevention theories. This study may help to understand the environmental factors which may be responsible for certain places becoming hot spot areas of property crimes in Jodhpur.

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.

A Crime Prevention System in Spatiotemporal Principles With Repeat, Near-Repeat Analysis and Crime Density Mapping: Case Study Turkey, Trabzon

2018 ◽  
Vol 64 (14) ◽  
pp. 1820-1835 ◽  
Author(s):  
Gamze Bediroglu ◽  
Sevket Bediroglu ◽  
H. Ebru Colak ◽  
Tahsin Yomralioglu
Keyword(s):  
Kernel Method ◽  
Crime Data ◽  
Density Mapping ◽  
Repeat Analysis ◽  
Crime Type ◽  
Density Maps ◽  
Prevention System ◽  
Standardization Process ◽  
Near Repeat

In this study, we investigated crime events with repeat and near-repeat analysis for Turkey’s Trabzon city’s crime data after standardization process on raw crime data. First, a new crime geodatabase model was created. All types of recorded crime data for events between the years 2010 and 2014 were standardized, generalized, and Geo-referenced. We gave certain locations to crime events with geocoding techniques. Then, we created density maps of crime events with Kernel method in Geographic Information Systems (GIS). Repeat and near-repeat methods were tested on Burglary crime type in this geodatabase. Studies focused to applying prediction analysis besides showing current situation. These predictive analyses may be applied for all the security, intelligence, or defense departments at local, national, or international levels.

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>

Holographic Storage Properties of Photochemical SiO2-Nanoparticles Material for Visualization News Features

2016 ◽  
Vol 867 ◽  
pp. 103-107
Author(s):  
Yan Feng
Keyword(s):  
Refractive Index ◽  
Theoretical Basis ◽  
Hot Spot ◽  
Refractive Index Modulation ◽  
Holographic Storage ◽  
Reading And Writing ◽  
Photochemical Properties ◽  
Storage Properties ◽  
Index Modulation ◽  
Inorganic Composite

The excellent photochemical properties of photopolymer material have made them become the hot spot of current for visualization news holographic storage. However, the shortcomings of common photopolymer materials such as exposure shrinkage and limited refractive index modulation affect the reading and writing properties of their holographic storage. In this thesis, the theoretical basis of the fact that organic/inorganic composite materials possess was built, and the advantages of anti-shrinkage and increasing the refractive index modulation of materials was carried out work on organic/inorganic photopolymer materials blended with SiO2-nanoparticles.

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.

Sign in / Sign up

Export Citation Format

Share Document