• ETRI Journal
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• v.43 no.2
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• pp.272-287
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• 2021

This article presents a multi-dimensional spatial pattern analysis of crime events in San Francisco. Our analysis includes the impact of spatial resolution on hotspot identification, temporal effects in crime spatial patterns, and relationships between various crime categories. In this work, crime prediction is viewed as a classification problem. When predictions for a particular category are made, a binary classification-based model is framed, and when all categories are considered for analysis, a multiclass model is formulated. The proposed crime-prediction model (HotBlock) utilizes spatiotemporal analysis for predicting crime in a fixed spatial region over a period of time. It is robust under variation of model parameters. HotBlock's results are compared with baseline real-world crime datasets. It is found that the proposed model outperforms the standard DeepCrime model in most cases.

• Proceedings of the Korea Contents Association Conference
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• 2015.05a
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• pp.17-18
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• 2015

To improve urban safety there is an increasing social need for environmental design against crime, which is defined as the creation of inconvenient environments or situations for criminal offenders. By using a cluster analysis, we aimed to clarify crime occurrence patterns from the perspective of land-use. Osaka Prefecture was chosen as the study area because it has the highest crime rate in Japan. The results revealed that there are six patterns of crime occurrence, and that cities of medium-level of mixed land-use have the lowest crime rates.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.5
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• pp.23-32
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• 2020

Recently, women's fear of crime continues to increase in space of everyday. By the way, the fear of crime has the spatial properties as crime. Therefore, The purpose of this study is to evaluate the spatial dependence of fear of crime and to suggest the physical environmental factors influencing fear of crime. For this, a spatial regression analysis using spatial weights was conducted based on the location data of the fear of crime measured through a survey. The results of this study are as follows; First, the fear of crime felt by women in their twenties who are vulnerable to crime has spatial dependence. Therefore, it is necessary to consider the spatial characteristics in analyzing the environmental factors affecting this. Second, in order to reduce the fear of crime, it is necessary to improve the environments of old housing and entertainment facilities. There is also a need for ongoing management. Third, careful consideration is needed in the installation of CCTV and street lights, which are factors influencing the fear of crime. It is necessary to establish a reasonable arrangement standard for CCTV and to analyze the street lighting in detail.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.11
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• pp.53-60
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• 2019

Multi-unit house, multi-household house, row house and apartments with less than 500 households were included in the list of anti-crime for buildings following the revision of the "notice of crime prevention building standards" on July 31, 2019. Strengthening the performance of crime prevention buildings is inevitable to increase the cost of building construction, including installation of preventive facilities and use of facilities that have secured performance. Thus an economic analysis on the costs and expected benefits of implementing the standards is required for social consensus. Economic analysis is divided into cost analysis and benefit analysis. This study aims to perform an economic analysis on the installation of crime prevention facilities in the buildings subject to expanded crime prevention obligations. Cost analysis is calculated as the sum of the cost of installation and the price of the crime prevention facilities installed for each target residential building. Benefit analysis is calculated as the social cost of targeted crimes that are expected to decrease due to the installation of crime prevention facilities. Economic analysis shows that the total cost of installing crime prevention facilities in residential buildings is estimated at 107.31 billion won per year, while the total benefit from enhanced crime prevention performance is estimated at 9.38 billion won per year. Considering inflation, benefits are expected to outpace costs in the 28th year since the system was implemented.

• Architectural research
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• v.14 no.2
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• pp.45-56
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• 2012

The high rate of urban crime is a main issue that needs to be dealt with in this high-tech society. With the rapid increase of urban crime, research has mainly focused on topics either on a global or a local scale, such as cities or communities and houses or buildings, without reliable observational data. This study makes the best use of the nationwide surveys carried out by Korean government agencies for the analysis of urban crime patterns and factors in major Korean cities. The aims of this research are threefold: understanding the relationship between urban crime patterns and socio-economic differences in cities, determining the effect of residence types on the urban crime patterns; and uncovering potential influential factors of a crime victim's individual characteristics. The statistical methods used for the analysis of social statistical data are as follows: simple regression, logistic regression, one-way ANOVA and post-hoc test. This research found that the patterns of urban crime rate in cities have a certain tendency toward the cities' socio-economic and geographical differences. The residence type is an influential factor showing a close relation to the crime rate. Personal issues, such as the types of occupation, education, marriage, etc., are directly relevant to victims of crime.

• ETRI Journal
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• v.43 no.6
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• pp.1058-1080
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• 2021

Crime is not a completely random event but rather shows a pattern in space and time. Capturing the dynamic nature of crime patterns is a challenging task. Crime prediction models that rely only on neighborhood influence and demographic features might not be able to capture the dynamics of crime patterns, as demographic data collection does not occur frequently and is static. This work proposes a novel approach for crime count and hotspot prediction to capture the dynamic nature of crime patterns using taxi data along with historical crime and demographic data. The proposed approach predicts crime events in spatial units and classifies each of them into a hotspot category based on the number of crime events. Four models are proposed, which consider different covariates to select a set of independent variables. The experimental results show that the proposed combined subset model (CSM), in which static and dynamic aspects of crime are combined by employing the taxi dataset, is more accurate than the other models presented in this study.

• Korean Security Journal
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• no.14
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• pp.261-283
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• 2007

In recent studies for explaining the causation of crime fear shows interest and effort in studies attempting microscopical individual level and macroscopical local level of sex, age, economic level, crime damage level and etc. However, in this study, it is considered that interest and analysis of individual on characteristics of these local level may has its difference depends on crime damage experience in the past, fragility precision of crime damage and interest on crime relating information and processed positive analysis on characteristics of individual and relation of crime fear on individual level before making an attempt of connecting microscopical level and macroscopical level. Therefore, the purpose of this study is on positive verification of how people feel about crime fear depends on individual's characteristic and also how much effect would they receive. As the result of this study, it is shown that first, population statistical characteristics that crime damage experience is statistically meaningful of its difference of each group are age, status of marriage, final education status and residential area and for the fragility precision of crime damage was sex and status of marriage and for the interest about the crime relating information has meaningful difference statistically of each group depends on sex, age, final education status, income of the house and location of residential area. Second, after processing correlation analysis on individual characteristic primary factor and crime fear, the result of 3 primary factor independent variable all shows statistically meaningful correlation with crime fear and especially fragility primary factor on crime damage showed the most high correlation with crime fear. Lastly, fragility of crime damage, interest on crime information and crime damage experience has effected as characteristics of individual and especially fragility of crime damage which the person thought to be the most fragility on crime damage out of these individual characteristic primary factor showed to have the most effecting primary factor.

• Journal of the Korean housing association
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• v.25 no.3
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• pp.85-92
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• 2014

The purpose of this study is to explore the relationship between housing types and crime and to suggest the appropriate strategies and interventions of housing policies for crime prevention. For spatial analysis of crime data, spatial autocorrelation is tested by Moran's I Test. A Ordinary Least Squares-based regression model is employed because crime data used in this study fails to show spatial autocorrelation. Results show that housing type variables except non-residential housing type are not associated with crime. Among land-use characteristics, the percentage of commercial areas is likely to better explain crime occurrence rather than housing types. It is surprising that residents' satisfaction to housing environment has a positive direction in its relationship with crime even though it cannot have a statistical significance. However, fear of crime shows a negative direction with crime although it fails to have a statistical significance. The findings of this study can contribute to understand the association between housing types and crime when setting housing policies for crime prevention.

• Journal of Distribution Science
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• v.16 no.7
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• pp.47-56
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• 2018

Purpose - This study aims to not only investigate spatial pattern of immigrants' residence and crime occurrences in South Korea, but shed light on how geographic distribution of immigrants and immigrant segregation affect crime rates. Research design, data, and methodology - Th unit of analysis is Si-Gun-Gu municipal level entities of South Korea. The crime data was obtained by Korea National Police Agency and two major types(violence and property) of crime were measured. Most demographic, social, and economic variables were derived from Korean Census Data in 2015. In order to examine spatial patterns of immigrants' distribution and crime rates in South Korea, the present study utilized GIS mapping technique and Exploratory Spatial Data Analysis(ESDA) tools. The causal linkage was investigated by a series of regression models using STATA. Results - Spatial inequality between urban metropolitan vs rural areas was visualized by mapping. Assuming large Moran's I value, spatial autocorrelation appeared to be quite strong. Several neighborhood characteristics such as residential stability and economic prosperity were found to be important factors leading to crime rate change. Residential distribution and segregation for immigrants were negatively significant in the regression models. Conclusions - Unlike the traditional arguments of social disorganization theory, immigrant segregation appeared to reduce violent crime rate and the high proportion of immigrants also turned out to be a crime prevention factor.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.57-62
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• 2014

In this paper, we proposed and implemented a crime pattern analysis algorithm using big data. The proposed algorithm uses crime-related big data collected and published in the supreme prosecutors' office. The algorithm analyzed crime patterns in Seoul city from 2011 to 2013 using the spatial statistics analysis like the standard deviational ellipse and spatial density analysis. Using crime frequency, We calculated the crime probability and danger factors of crime areas, time, date, and places. Through a result we analyzed spatial statistics. As the result of the proposed algorithm, we could grasp differences in crime patterns of Seoul city, and we calculated degree of risk through analysis of crime pattern and danger factor.