Predictive Modeling for Topographical Analysis of Crime Rate

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1206
Predictive Modeling for Topographical Analysis of Crime Rate
Prof. Vrushali V. Kondhalkar1, Antara More2, Bhawana Singh3, Jaspreet Singh Rahi4,
Sudipti Ranjan5
1Prof. Vrushali V. Kondhalkar, Dept. Of Computer Engineering, Pune, Maharashtra, India
2Antara More, Dept. Of Computer Engineering, Pune, Maharashtra, India
3Bhawana Singh, Dept. Of Computer Engineering, Pune, Maharashtra, India
4Jaspreet Singh Rahi, Dept. Of Computer Engineering, Pune, Maharashtra, India
5Sudipti Ranjan, Dept. Of Computer Engineering, Pune, Maharashtra, India
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Abstract - Criminal activities are increasing all over the world. It is important to reduce crime as it directly affects the
country's economic growth. Therefore, there is an urgent need for security agencies to fight and reduce crime in the
community. The proposed system helps us to detect crime and resolve criminal cases quickly based on data collected using
machine learning strategies. The system helps to predict the type of crime in a particular area based on crime patterns. In this
project, we will be using a machine learning method. Contains important information about crime reporting such as date, type
of crime, location of the crime, etc. The data is downloaded from a database called kaggle.com and is pre-processed so that we
can extract the most important natural features of crime reporting such as roads or a few places, dates, and times, and areas
with a higher crime rate than others. This data is used as an incentive to predict and resolve crime at an instant rate. This
project will help us to find a way to improve the crime detection system, the type of crime that will occur in a particular area,
and the way to improve investigative efforts of any kind of crime.
Keywords: Machine learning, Crime prediction
1. INTRODUCTION
Today crime is on the rise worldwide. It affects the quality of life and the development of economic well-being and the
dignity of the nation. It directly affects the nation's financial growth by burdening the government with the financial
burden due to the need for more police, and criminal justice courts. In terms of public safety, there is a need for more
sophisticated ways to improve crime analysis to protect their communities. Accurate forecasts of crime help to reduce
crime but remain problematic as crime relies on many complex issues. The basic pattern of crime and its relationship to
the region or region helps us to identify and predict crime in a particular area. According to a previous study, it is clear that
in every city there are fewer roads or areas with a higher crime rate than others. Crime can be predicted as criminals
become more active and active in their comfort zone. When they succeed they try to repeat the crime in the same place.
The occurrence of a crime depends on several factors such as criminal intelligence, local security, etc. Usually, Criminals
choose the same location and time to try the next crime. While it may not be true in all cases, the chances of recurrence are
high, as per the study, and this makes crime predictable. Predicting crime patterns is an important function in developing
more effective crime prevention strategies or developing investigative efforts based on the availability of prior data such
as case information, location, date, and time. Here, we use machine learning techniques to predict crime and its types in
crime hotspots. Machine Learning is a form of practical intelligence that helps us to identify patterns using data analysis.
There are three stages:
1) The dataset is extracted from the official site.
2) With the help of a machine learning algorithm, using python as core we can predict the type of crime that will occur in a
particular area.
3) The model would be trained for prediction. The training would be done using the training data set which will be
validated using the test dataset uploaded using the Kaggle website.

2. MACHINE LEARNING
For analyzing the collected data we will use a Classification algorithm i.e. Random Forest which is a Supervised Machine
Learning technique that is used to identify the category of new observations based on training data.
2.1 Algorithm:
2.1.1 Random Forest Algorithm:
It uses a tree-like graph to show the possible results. If you enter a training database with objectives and features in the
decision tree, it will create a set of rules. These rules can be used to make predictions. There are two stages in the Random
Forest algorithm, one for random forest creation, and the other for predicting from a random forest classifier created in
the first phase.
A. Random Forest Creation :
1) Randomly select "K" features from total "m" features where k << m
2) Among the "K" features, calculate the node "d" using the best split point
3) Split the node into daughter nodes using the best split
4) Repeat the a to e steps until the "l" number of nodes has been reached
5) Build a forest by repeating steps a to d for "n" number times to create "n" number of trees
B. Prediction Using Random Forest Classifier :
1) Takes the test features and uses the rules of each randomly created decision tree to predict the outcome and stores the
predicted outcome (target)
2) Calculate the votes for each predicted target
3) Consider the high voted predicted target as the final prediction from the random algorithm.
3. LITERATURE REVIEW
3.1 Crime Prediction & Monitoring Framework Based on Spatial Analysis
In this, the authors, Hitesh Kumar, Reddy Toppi Reddy, Bhavna Sardinia, and Ginika Mahajana provided a framework for
viewing criminal networks and analyzing them with various machine learning algorithms using various Google Maps and R
packages. First, raw data sets are processed and visualized based on need. Machine learning algorithms are used to extract
information from these large databases and to detect hidden connections between the data which are also used to report
and detect key crime patterns for crime analysts to analyze these criminal networks through various interactions.
detection of crime predictions
3.2. Analyzing Crime Through Machine Learning
In this Suhong Kim, Param Joshi, Parminder Singh Kalsi, and Pooya Taheri provide a crime-based model of Vancouver.
Vancouver's crime data for the past 15 years is analyzed using two different data processing methods. Guessing machine
learning models KNN and an advanced decision tree were used to determine the accuracy of crime forecasts between 39%
to 44%.
3.3 Using machine learning algorithms to analyze crime data
In this case, Lawrence McClendon and Natarajan Meghanathan used Linear Regression, Additive Regression, and Decision
Stump algorithms using the same set of limitations, communities, and uncommon crime databases to conduct comparative
studies between violent crime patterns from this database and statistical data. real estate of the state of Mississippi
provided by neighborhoodscout.com

3.4. Decision Tree Algorithm Based System for crime reporting at the University
In this model, Adewale Opeoluwa Ogunde, Gabriel Opeyemi Ogunleye, and Oluwaleke Oreoluwa proposed a program to
investigate and detect criminals of any crime committed within Redeem’s University. For crime detection at universities,
Previous details of both crime and crime were collected from the Student and Development Services (DSSD) Unit. The data
was processed in advance to obtain clean and accurate data. The Iterative Dichotomiser 3 (ID3) decision-making algorithm
derived from the WEKA mining software was used to analyze and train data. The acquired model was then used to develop
a system that demonstrated the hidden relationship between crime-related data, in the form of cutting trees. This result
was then used as a basis for information on the development of a crime forecasting system.
3.5. Criminal Prediction Analysis in India using the Hybrid Clustering method
In this process, Dr.J. Kiran, and Kaishveen proposed a crime prediction framework based on the naïve Bayes classifier. The
naïve Bayes classifier is compared to the KNN classifier. The proposed techniques are applied to Anaconda and the
simulation results show that the naïve Bayes has high accuracy and a short duration of action.
3.6. Summary of Literature Review
Title Publication and year Author Technical details
Crime
Prediction &
Monitoring
Framework
Based on
Spatial Analysis
International
Conference on
Computational
Intelligence and
Data Science
(ICCIDS 2018)
Hitesh Kumar Reddy
Toppi Reddy,
Bhavna Sardinia, Ginika
Mahajana
The author provides a framework for
visualize criminal networks
and diversity analysis
machine-learning algorithms
using Google Maps once
various packages for R.
Crime Analysis
Through
Machine
Learning
IEEE 9th
Annual
Information
Technology,
Electronics and
Mobile
Communication
Conference
(IEMCON 2018)
Suhong Kim; Param
Joshi ;
Parminder
Singh Kalsi; Pooya
Taheri
Suhong Kim et al. provide a machine-based crime
reporting model in Vancouver. Vancouver's crime data
for the past 15 years is analyzed using two different data
processing methods. Guessing machine learning models
KNN and an advanced decision tree were used to
determine the accuracy of crime forecasts between 39%
to 44%.
Using Machine Learning
algorithms to analyze
crime data
An International Journal
(MLAIJ) Vol.2, No.1, (March
2015)
Lawrence McClendon and
Natarajan Meghanathan*
The author has used Linear Regression, Additive
Regression, and Decision Stump algorithms using the
same limited set of features, communities, and
uncommon crime databases to conduct comparative
studies between violent crime patterns from this data
and actual crime statistics in the Mississippi status
provided by neighbors out.
A Decision Tree Algorithm
Based System for
Predicting Crime in the
University
Machine Learning Research
2017; 2(1): 26-34
Adewale Opeoluwa
Ogunde1, *, Gabriel
Opeyemi Ogunleye2,
Oluwaleke Oreoluwa1
The author has proposed a system to investigate and
detect criminals for any crime committed within
Redeem’s University. For crime detection at universities,
Previous details of both crime and crime were collected
from the Student and Development Services (DSSD) Unit.
The data was processed in advance to obtain clean and
accurate data. The Iterative Dichotomiser 3 (ID3)
decision-making algorithm derived from the WEKA
mining software was used to analyze and train data. The
acquired model was then used to develop a system that
demonstrated the hidden relationship between crime-
related data, in the form of cutting trees. This result was
then used as a basis for information on the development
of a crime forecasting system.
Prediction Analysis of
Crime in India Using a
Hybrid Clustering
Approach
2018 2nd International
Conference on I-SMAC (IoT in
Social, Mobile, Analytics, and
Cloud) (ISMAC)I-SMAC (IoT
in Social, Mobile, Analytics,
and Cloud) (ISMAC)
J. Kiran; K Kaishveen J. Kiran; K Kaishveen proposed a crime prediction
framework based on the naïve Bayes classifier. The naïve
Bayes classifier is compared to the KNN classifier. The
proposed techniques are applied to Anaconda and the
simulation results show that the naïve Bayes has high
accuracy and a short duration of action.

4. DRAWBACKS OF EXISTING SYSTEMS
Current strategies are based on an analysis of crime scenes or theory with integrated crime data. However, it is difficult to
quantify the likelihood of a future crime based on an accurate definition of a past crime. Therefore, existing methods are
not suitable for adapting to different environments and criminal practices.
5. CONCLUSION
Work on this project is mainly focused on predicting the type of crime and crime that may occur in the future. Using the
concept of machine learning we create a model using a set of training data that we have encountered. Predicting crime
patterns is an important function in developing more effective crime prevention strategies or developing investigative
efforts based on the availability of prior data such as case information, location, date, and time. Here, we use machine
learning techniques to predict crime and its types in crime hotspots.
6, REFERENCES
[1] Hitesh Kumar Reddy Toppi Reddy, Bhavna Sardinia, Ginika Mahajana, "Crime Prediction & Monitoring Framework
Based on Spatial Analysis ", International Conference on Computational Intelligence and Data Science (ICCIDS 2018).
[2] Suhong Kim; Param Joshi; Parminder Singh Kalsi; Pooya Taheri, "Crime Analysis Through Machine Learning", 2018
IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON).
[3] Lawrence McClendon and Natarajan Meghanathan*, "USING MACHINE LEARNING ALGORITHMS TO ANALYZE CRIME
DATA", An International Journal (MLAIJ) Vol.2, No.1, March 2015.
[4] Adewale Opeoluwa Ogunde1, *, Gabriel Opeyemi Ogunleye2, Oluwaleke Oreoluwa1, "A Decision Tree Algorithm Based
System for Predicting Crime in the University ", Machine Learning Research 2017; 2(1): 26-34.
[5] Dr.J.Kiran, Kaishveen., "Prediction Analysis of Crime in India Using a Hybrid Clustering Approach",2018 2nd
International Conference on I-SMAC (IoT in Social, Mobile, Analytics, and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile,
Analytics, and Cloud) (I-SMAC), 2018 2nd International Conference.

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