University of Twente Student Theses


Crime rate prediction from street view images using convolutional neural networks and transfer learning

Kadiyam, Praneeth (2021) Crime rate prediction from street view images using convolutional neural networks and transfer learning.

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Abstract:Until recently, street view imagery is not considered a data source for scientific research. With growing interest in deep learning and computer vision, street view imagery evolved as a novel data source due to its fine resolution and rich visual scene content. They replace the tedious field surveys with virtual audits. Of late, street view imagery is used to relate visual perception of predicting non-visual attributes like building age estimation, property evaluation, walking likelihood etc. In addition to these, a few research works also used street view imagery to predict crime rates. Predicting crime rate from street view imagery is based on famous environmental theories like Broken Windows theory or Routine Activity of Places theory. They state that environmental variables influence crime occurrence. The fast-paced urbanisation and growing population can motivate criminals and encourage crime occurrences in cities. There is a need to manage the resources of the law enforcement department effectively to control the crime. This research takes the motivation from the theories mentioned above works and investigates the effect of visual variables from street view imagery on predicting crime rates. Previous research mainly concentrated on classifying crimes based on the severity or ranked the most occurred crime in each place. This work tries to predict the crime counts of four different types from the street view imagery by solving a multi-output regression problem. Greater London is selected as the study area of research, and the crime data of one year is considered. A deep learning model is built to achieve this, taking multiple inputs, and simultaneously predicting crime counts for four different crime types. ResNet18 is used as a building block for building the model. A workflow is designed to model the crime data and prepare the labelled dataset for input to the built model. Kernel Density Estimation is used to model the crime data, and the outputs are used to extract the street view imagery and label the data. Four street view images and population density are given as inputs, and the crime rates of burglary, robbery, other thefts and vehicle crimes are predicted simultaneously. Different configurations of models are trained and compared to understand the effect of visual variables in crime rate prediction. The results obtained show a considerable relationship between visual variables of the built environment and crime rate. The R-squared value for burglary is 51%, robbery is 44%, other thefts is 50%, and vehicle crimes is 49%. However, there were no significant changes in the R-squared values, excluding population density as an explanatory variable. The scatterplots of actual and predicted crime rates are interpreted to understand and evaluate the model's performance. The inclusion of additional variables like socio-economic variables might have affected the performance of the model.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
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