scholarly journals CrimeScape: Analysis of socio-spatial associations of urban residential motor vehicle theft

2021 ◽  
pp. 102618
Author(s):  
Thi Hong Diep Dao ◽  
Jean-Claude Thill
Keyword(s):  
Motor Vehicle ◽  
Motor Vehicle Theft ◽  
Vehicle Theft ◽  
Spatial Associations

scholarly journals Algoritma K-Means Clustering dalam Mengklasifikasi Data Daerah Rawan Tindak Kriminalitas (Polres Kepulauan Mentawai)

2021 ◽  
pp. 243-248
Author(s):  
Yoni Aswan ◽  
Sarjon Defit ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Motor Vehicle ◽  
Motor Vehicles ◽  
Hierarchical Data ◽  
Motor Vehicle Theft ◽  
Vehicle Theft ◽  
Mentawai Islands ◽  
Or Groups ◽  
Different Characteristics

Crime is all kinds of actions and actions that are economically and psychologically harmful that violate the laws in force in the State of Indonesia as well as social and religious norms. Ordinary criminal acts affect the security of the community and threaten their inner and outer peace. The research location is the Mentawai Islands Police, which is an agency that can provide security and protection for the community, especially those in the Mentawai Islands Regency. The problem is that it is difficult for the Mentawai Islands Police to classify areas that are prone to crime in the most vulnerable, moderately vulnerable and not vulnerable categories. Especially considering the condition of the Mentawai, there are four large islands consisting of 10 sub-districts, where crime is increasing every year, especially those in the Mentawai Islands Regency area such as motor vehicle theft. Based on the background of the problem above, the researcher is interested in taking research in creating a system to predict the crime rate in the Mentawai Islands Regency in order to anticipate the surge in crime that will come. The method used is the K-Means Clustering Algorithm as a non-hierarchical data clustering method to partition existing data into one or more clusters or groups. This method partitions data into clusters so that data with the same characteristics are grouped into the same cluster and data with different characteristics are grouped into other clusters. Clustering is one of the data mining techniques used to get groups of objects that have common characteristics in large enough data. The data used is data on cases of criminal theft of motor vehicles for the last 5 years from 2016 to 2020. The results of the test show that South Sipora District is an area prone to the crime of motor vehicle theft.

scholarly journals Could Crime Risk Be Propagated across Crime Types?

2019 ◽  
Vol 8 (5) ◽  
pp. 203 ◽  
Author(s):  
Zengli Wang ◽  
Hong Zhang
Keyword(s):  
Motor Vehicle ◽  
Motor Vehicle Theft ◽  
Vehicle Theft ◽  
Crime Type ◽  
Increased Risk ◽  
Short Period ◽  
Public Areas ◽  
Crime Types ◽  
Crime Risk ◽  
Temporal Interaction

It has long been acknowledged that crimes of the same type tend to be committed at the same location or proximity in a short period. However, the investigation of whether this phenomenon exists across crime types remains limited. The spatial-temporal clustered patterns for two types of crimes in public areas (pocket-picking and vehicle/motor vehicle theft) are separately examined. Compared with existing research, this study contributes to current research from three aspects: (1) The repeat and near-repeat phenomenon exists in two types of crimes in a large Chinese city. (2) A significant spatial-temporal interaction between pocket-picking and vehicle/motor vehicle theft exists within a range of 100 m. Some cross-crime type interactions seem to have a stronger ability of prediction than does single-crime type interaction. (3) A risk-avoiding activity is identified after spatial-temporal hotspots of another crime type. The spatial extent with increased risk is limited to a certain distance from the previous hotspots. The experimental results are analyzed and interpreted with current criminology theories.

scholarly journals Did Crime Rates Rise After Colorado Legalized Marijuana?

2016 ◽  
Vol 5 (1) ◽  
pp. 4-7
Author(s):  
Abigail D. Pohl ◽  
Samuel W. Klockenkemper ◽  
Lucas G. Carpinello ◽  
Paul M. Sommers
Keyword(s):  
Motor Vehicle ◽  
Police Department ◽  
Crime Rates ◽  
Motor Vehicle Theft ◽  
Trend Line ◽  
Vehicle Theft ◽  
Aggravated Assault ◽  
Crime Reports

Using monthly crime reports from Denver’s Police Department between January 2010 and December 2014, the authors endeavor to show if there was a break in the trend line of seven different crimes (homicide, rape, aggravated assault, burglary, robbery, larceny, and motor vehicle theft) following Colorado’s legalization of marijuana in late 2012.  After adjusting for seasonal components (some crimes tend to be higher in summer months), the trend lines reveal no break for crimes against persons.  But, three of the four trend lines for crimes against property do reveal a significant decrease after legalization.

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