IRJET- Crime Analysis using Data Mining and Data Analytics

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 05 | May 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 7679
Crime Analysis using Data Mining and Data Analytics
S. Bodare1, S. Kurkute2, M. Akash3, R. Pawar4
1,2,3,4Student at Svpm College of Engineerin, Malegaon(BK)
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Abstract - Crime analysis and prevention is a systematic
approach for identifying and analyzing patterns andtrendsin
crime. Our system can predict regions which have high
probability for crimeoccurrenceandcanvisualizecrimeprone
areas. With the increasing advent of computerized systems,
crime data analysts can help the Law enforcement officers to
speed up the process of solving crimes. Using the concept of
data mining we can extract previously unknown, useful
information from an unstructured data. Here we have an
approach between computer science and criminal justice to
develop a data mining procedure the can help solve crimes
faster. Instead of focusing on causes of crime occurrence like
criminal background of offender, political enmity etc. We are
focusing mainly on crime factors of each day. Crime is one of
the most important social problems in the country, affecting
public safety, children development, and adult socioeconomic
status. Understanding what factors cause higher crime is
critical for policy makers in their efforts to reduce crime and
increase citizens life quality. Wetackleafundamentalproblem
in our paper: crime rate inference at the neighborhood level.
Traditional approaches have used demographics and
geographical influences to estimate crime rates in a region.
Key Words: Data Mining, Linear regression, Crime rate
analysis,Data Analytics
1. INTRODUCTION
The built environment based crime research until now has
rarely focused on robbery. The reason for this could be the
difficulty in knowing exactly where a robbery happened, or
the precautions against robbery seemedlessapplicablethan
other crimes such as burglary and thus the research being
perceived more worthwhile. However, street robbery is a
daily fear in urban life and reducing the type of settings that
is most likely to take place should be the aim for achieving
urban environments that are used effectively, day andnight,
for a sustainable urban development[3].
Regression is one of a powerful tool for predicting the
future values based on previous data sets. The use of
prediction in crime analysis can make crime free region.The
proposed technique can predict those regions which are
sensitive to criminal activities like theft, murder, rape, Anti-
social behavior, Domestic violence etc. The scheme uses the
data sets taken from the Indian governmentwebsiteand can
be used to predict the number of criminal activitiesinfuture.
The data will then supplied with other factors like criminal
type, age, month and year to the regressionmodel whichwill
predict the future value for the criminal activities. . This
predicted number of activities will indicate thattheregion is
high sensitive or low sensitive. If the predicted number will
be high then the region is high sensitive and if low then the
region is low sensitive. Understanding how to control crime
is important because exposures to violence and crime have
been unusually high in the India for several decades and,
while declining, they remain high.
Sensing technologies and large-scale computing
infrastructures have produced a variety of big data in urban
spaces. These heterogeneous data convey rich knowledge
about city dynamics and enable us to address many urban
challenges. For example, human mobility data could help
improve the efficiency of transportation systems such as
estimating real-time traffic flow and forecasting travel time
for road segments. With the similar motivation, we employ
such modern urban data for crime rate inference. In the
criminology literature, researchers have studied the
relationship between crime and various features (social,
demographics, and geographic factors). Examples are
historical crime records, education, ethnicity, income level,
unemployment, and spatial proximity.
In data mining, newer types of data are used. For
example, studies use twitter to predict crime, and cellphone
data to evaluate crime and social theories at scale. Overall,
existing work on crime prediction can be categorized into
three paradigms. Time-centric paradigm. This line of work
focuses on the temporal dimension of crime incidents. For
example, Mohler et. al. propose to useaself-exciting point
process to model the crime and gain insights into the
temporal trends in the rate of burglary. Existingworksin the
fields of criminology, sociology, psychology and economics
tend to mainly explore relationships between criminal
activity and socio-economic variables such as education,
ethnicity, income level, and unemployment [5]. In another
study, Ratcliffe investigates the temporal constraints on
crime, and propose an offender travel and opportunity
model. His findings suggest that a proportion of offending is
driven by the availability of opportunities presented in the
routine lives of offenders. Place-centric paradigm. Theories
of crime can be divided into those that seek to explain the
development of criminal offenders, and those that seek to
explain the development of criminal events[4].
Crime rate is increasing now-a-days in many countries.
In today’s world with such higher crime rate and brutal
crime happening, there must be someprotectionagainstthis
crime. Here we introduced a system by which crime ratecan
be predict. Crime data must feed into the system. We
introduced data mining algorithm to predict crime.

Regression Analysis, plays an important role in analyzing
and predicting crimes. Regression Analysis will cluster co-
offenders, collaboration and dissolution of organized crime
groups, identifying various relevant crime patterns, hidden
links, link prediction and statistical analysis of crime data.
This system will prevent crime occurring in society.
Crime data is analyzed which is stored in the database.
Data mining algorithm will extract information andpatterns
from database. System will groupcrime.RegressionAnalysis
will be done based on places where crime occurred, type of
crime and the timing crime took place. This wil help to
predict crime. Admin will enter crime detailsintothesystem
which is required for prediction. Admin can view criminal
historical data. Crime incident predictiondependsmainlyon
the historical crime record and various geospatial and
demographic information.
1.1 System Architecture
A description of the program architecture is presented.
Subsystem design or Block diagram, Package Diagram,
Deployment diagram with description is to be presented.
Fig.System Architecture
1.2 Dataset
A data set is a collection of data. Most commonly a data
set corresponds to the contents of a single database table, or
a single statistical data matrix, where every column of the
table represents a particular variable, and each row
corresponds to a given member of the data set in question.
Several characteristics define a data set’s structure and
properties. These include the number and types of the
attributes or variables, and various statistical measures.
Firstly, We collect data set from Indian government website
as data.gov.in.[1] The given dataset is in well trained
manner. the data file is in CSV format. The dataset contains
different types of fields such as state, crime head, and crime
taken by gender in different age as Femaleagebelow18,
Maleagebelow18, female age between 20-25 and so on.
1.3 Data Analysis
In today’s business, data analysis is playing a role in
making decisions more scientific and helping the business
achieve effective operation. Data mining is a particular data
analysis technique that focuses on modeling and knowledge
discovery for predictive rather than purely descriptive
purposes, while business intelligence covers data analysis
that relies heavily on aggregation, focusing mainly on
business information. Here, We analysis the given dataset.
Different Operations are performed in data analysis such as
Filter data according requirement, Fill missing values,
Remove noise. So well Data analysis is a process of
inspecting, cleansing, transforming, and modeling data with
the goal of discovering useful information, informing
conclusions, and supporting decision making.[3]
1.3 Linear Regression
In statistics, linear regression is a linear approach to
modeling the relationship between a scalar response (or
dependent variable) and one or more explanatory variables
(or independent variables). The case of one explanatory
variable is called simple linearregression.Formorethan one
explanatory variable, the process is called multiple linear
regressions. Linear regression is mostly usedas, Ifthegoal is
prediction, or forecasting, or error reduction,[clarification
needed] linear regression can be used to fit a predictive
model to an observed data set of values of the response and
explanatory variables. After developing such a model, if
additional values of the explanatory variables are collected
without an accompanying response value, the fitted model
can be used to make a prediction of theresponse.Regression
analysis is also used to understand which among the
independent variables are relatedtothedependentvariable,
and to explore the forms of these relationships.
1.4 Prediction Record
Here, final output is predicted which is in the form of pie
chart.
2. Inference Model
Linear regression is a basic and commonly used type of
predictive analysis.
2.1 Math and Equations
In the regression there is simple Mathematics equations

which is useful to solve our algorithm:
2.2 Algorithm
The algorithm as below Steps:-
1. SUB Regress(x,y,n,al,a0,syx,r2)
2. sumx=0:sumxy=0:st=0
3. sumy=0:sumx2=0:sr=0
4. DOFOR i=1,n
5. sumx=sumx+xi
6. sumy=sumy+yi
7. sumxy=sumxy+xi*yi
8. sumx2=sumx2+xi*xi
9. END DO
10. xm=sumx/n
11. ym=sumy/n
12.a1=(n*sumxy-sumx*sumy)/(n*sumx2-sumx*sumx)
13. a0=ym-a1*xm
14. DOFOR i=1,n
15. st=st+(yi−ym)2
16. sr=sr+(yi−al∗xi−a0)2
17. END DO 18. syx (sr/(n−2))1/2
19. r2=(st-sr)/st
20. END Regress
3. Result
In this project we predict different states in India. We
know that India is one of biggest country in world which
have 7th largest country. In that India has 1.35 billion
population. It take second largest population in world. Very
big population make many problems in that Crime is one of
biggest problem for India. We contribute little bit help for
Indian peoples. From our project people can understand
which state have highest crime and take awareness
campaign in particular area.
3.1 Different State fine from Database
In this topic we going to collect dataset from
www.data.gov.in[1]. In that dataset we got all states in India
and number of male, females which have their age ex. Male
below 18 year, Male between 18_30 year etc. Separate the
state with different crime like Rape, Assault, Dowry etc.
3.2 Graphs For Indication
Peoples can not understand prediction easily for that we
include graphs for every attributes.Followinggraphindicate
that Rape cases in India which conducted by different age
group and different colours shows different states.
3.3 Final Table For Prediction
CONCLUSION
We handle different attribute relates to crime dataset.
With the help of that attribute, we predict which attribute
conducted crime with respect to states. So with the help of
predicted output, we take different precautions where the
maximum amount of crime committed.

REFERENCES
[1] Indian Government Portal –https://data.gov.in
[2] Hongjian Wang, Huaxiu Yao, Daniel Kifer, Corina Graif,
Zhenhui Li,Non-Stationary Model for crime rate
inference using modernurbandata,2332-7790(c)2017
IEEE.1-13
[3] Wikipedia-http://Wikipedia.com
[4] S. D. Johnson,”A brief history of the analysis of crime
concentration”, European Journal of Applied
Mathematics, 21 (4-5) (2010) 349–370,
http://journals.cambridge.org/article_S095679251000
0082
[5] Chhaya Chauhan, Smriti Sehgal, ”A REVIEW: CRIME
ANALYSIS USING DATA MINING TECHNIQUES AND
ALGORITHMS” 2017 IEEE

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