This document summarizes a project aimed at understanding crime patterns in Chandigarh, India. Key factors that could influence crime rates, such as population density, sex ratio, and economic indicators, were identified. Crime data from 1993-2010 was analyzed using regression models. The autoregressive model, which predicts crime based on previous years' crime rates and other factors, achieved the highest accuracy at 90%. Future work will involve applying these models to other Indian cities to improve crime forecasting.

1. NITK—CMU WINTER SCHOOL 2014
24 December 2014
UNDERSTANDING CRIME TRENDS Selva Priya S(MSRIT,Bangalore), Lavanya Gupta(DA-IICT,Gandhinagar)
Introduction
Crime in India has been increasing
at an alarming rate. This project
aims at understanding crime
patterns, the factors affecting
them and future forecasts to make
our country a safer place.
Developing a generic model that
can be applied across various
geographic locations and time is
the need of the hour.
The city under consideration is Chandigarh. Chandigarh is divided in-
to about 60 sectors of which every 4-5 sectors come under one po-
lice station jurisdiction. There are 11 police stations in Chandigarh.
We are predicting the annual crime rate at city level. Sector-wise an-
nual crime rate prediction has also been computed.
There may be a lot of factors that determines the
crime rate of a place. We shortlisted a few of the
features which we think are majorly dependent on
the crime rate.
 Population Density
 Sex Ratio
 Gross Domestic Product
 Per Capita Income
 Poverty Estimates
 Literacy Rate
 Crime rate of neighboring sectors
 Upcoming festivals
 Number of tourist places
 Religious Composition
Identification of features
FEATURE VALUE
Sex ratio 0.724
Population 0.5186
Educational Institutes 0.0803
Tourist Places -0.2799
Temples -0.1707
Church 0.0435
Mosques 0.0803
FEATURE VALUE INFERENCE
Sex ratio 0.4091 weak positive
Population 0.3818 weak positive
Educational Institutes 0.1227 weak positive
Tourist Places 0.0932 weak positive
Temples 0.0614 weak positive
Church 0.4295 weak positive
Mosque 0.2318 weak positive
Figure 1: Frequency of Violent Crimes in Chandigarh for the years 1993-2010
Significance of correlations
Figure 3: Significance Levels of correlation of crime rate versus month of occurrence
Regression Modelling
We have developed two standard Regression Models based on the concept
of Linear Regression. One is for the whole city of Chandigarh, another exclu-
sive for the Sector-wise prediction.
 Whole of Chandigarh
 Sector-wise
The correlation between crime rate and sex ratio was found to be significant with confi-
dence level 0.0156 for Significance Level of 5%.
The crime rate in the months of November and December are found to be lower than
the crime rates of other months.
Predictor 1
Crime rate at a particular year is same as the
crime rate of the previous year.
Crime (t) = Crime(t-1)
Result: The percentage of prediction accuracy is
computed to be 62.73%
Predictor 2
Crime rate at a particular year is equal to the mean
of the crime rate of the past ‘k’ years.
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
PercentageAccuracy
K
PercentageAccuracy
Percentage Accuracy
Predictor 3
AR (Autoregressive) Model: This prediction claims that the
output depends linearly on its previous input values when do-
ing the time-series analysis. Crime at year ‘t’ and sector ‘j’,
Results: The average accuracy is computed to be 88.681%.
Predictor 4
AR (Autoregressive) including other features: This prediction is
based on the predictor 3 that uses AR model, but takes into con-
sideration other factors like Sex Ratio, GDP, per capita income etc.
The average accuracy is found to be 90.116%.
Future research
We plan to extend this model and apply to different cities of
India and test the results. If required, we will research into oth-
er regression techniques that can help us further improve our
results.
 Pearson Correlation
 Spearman Correlation
Correlation of features
Workflow
Figure 2: Work plan flowchart for the project
Figure 4: Accuracy of Predictor 2 for Historical Order, k
Acknowledgement References
We take this opportunity to express our gratitude and deep
regards to our guides, Dr. Bhiksha Raj and Dr. Rita Singh for
their constant monitoring and encouragement throughout
the course of this project. We would also like to extend our
thanks to our mentors, Mr. Pulkit Agrawal and Rajat.
1. http://chandigarhpolice.gov.in/
2. http://www.indiastat.com/
demographics/7/stats.aspx
3. http://www.census2011.co.in/