Estimation of the risk of snatching in a suburb: Case study of Soka, Saitama prefecture, Japan

In this paper, the estimation model for the risk of street crime using Space Syntax is created and shown. Crime in Japan was increasing with a peak of 2002, and crime prevention became regarded as important in the field of urban and architectural planning and design. In the scientific field, the feature of space where street crime happen easily or space where the density of street crime is high have been clarified with accumulation of previous research. For example, the regression model using them as an explained variable has been estimated. In analysis of the city level, population, land use, etc. were used as explanatory variables and in the street level, the distance to urban facilities, the spatial characterization of the street, etc. were used as explanatory variables However, there is little research on the feature of the space where the probability of encountering criminal damage is high. Then, the purpose of this research is to build the estimation model about the damage risk using base data of urban space, road data, or Space Syntax, and the factor which contributes to a damage risk is clarified. Moreover, the space where a crime happens easily was compared with the space which encounters criminal damage easily and the common feature and difference of space composition were clarified. Field-survey was done at suburban residential area in Soka city, Saitama prefecture, Japan. Firstly, passing and stopping quantity of pedestrians, cars, and etc. on streets within about 1 km from railway station were measured by the counting survey by walking through some routes. Three rules were set up in this investigation. (1) Two investigators walk along the route predetermined to the opposite directions every other hour. (2) Investigators walk at a fixed speed. (3) Investigators count the number of the person, the bike or the car which passed or stayed for every segment. This work was repeated 16 times each by all the 6 routes. The sum total data for 16 hours was used for analysis. The criminal damage risk was computed from criminal data and the observed amount of passings. Multiple linear regression analyses which made computed damage risk (or street crime density per unit meter) explained variable and made some indices from Space Syntax and spatial character the explanatory variables were performed, and the factors which contributes to a damage risk and street crime generating density and the difference was clarified.