Analyzing Crime Rate Prediction Using Data Mining Techniques in UK

This report explores the application of data mining techniques in crime rate prediction, focusing on its methodologies, advantages, and disadvantages, particularly within the context of the UK. It begins by defining data mining as a process of identifying patterns and correlations within large datasets to predict outcomes, emphasizing its role in enhancing revenues, reducing costs, and improving risk management. The research objectives include determining the concept of data mining and its types, analyzing its necessity in crime rate prediction, identifying its pros and cons, and evaluating its impact on crime rate prediction in the UK. Various data mining types such as clustering, association, and classification are discussed, highlighting their utility in managing and improving data quality. The report also addresses the importance of data mining in solving crimes effectively, detecting crime patterns, and aiding police forces in making informed decisions, while acknowledging limitations such as data bias and privacy concerns. Ultimately, the research underscores the significance of data mining in criminology for predicting future activities based on historical data analysis.

Application of data
mining in crime rate
prediction

Contents
INTRODUCTION...........................................................................................................................3
Research Objectives...............................................................................................................3
Research Questions................................................................................................................3
Rationale for the research.......................................................................................................4
LITERATURE REVIEW................................................................................................................4
What is the concept of data mining along with its types?......................................................4
Why it is necessary to use data mining technique in crime rate prediction?..........................5
What are the advantages and disadvantages of data mining in crime rate prediction?..........7
How data mining helps in prediction of crime rate in UK?...................................................9
Methodology..................................................................................................................................12
Discussion and Findings................................................................................................................14
Conclusion and Recommendations................................................................................................20
Limitations of the research.............................................................................................................22
REFERENCES..............................................................................................................................23

INTRODUCTION
Data mining is the method of finding anomalies, styles and correlations within massive facts
units to are expecting outcomes (Agarwal and et.al., 2020). Using a vast variety of techniques,
one could use this records to increase revenues, reduce fees, improve client relationships, lessen
risks and more. Data mining is the manner of information information via cleaning raw facts,
locating styles, developing fashions, and testing those fashions. It includes records, device
learning, and database structures. Data mining is a manner to extract know-how out of
commonly large statistics units; in other words it is an method to discover hidden relationships
amongst facts through using synthetic intelligence methods. The information which is availed
through data mining helps in reducing crime as it helps in identifying the culprits faster and also
find the areas that are affected by crime. Criminology is one of the maximum important fields for
applying information mining. It encompasses the system of coming across hidden styles and
relationships in large amounts of information. This allows for making accurate and dependable
predictions of destiny activities, primarily based on the identity and characterization of these
styles and trends in historical records. The respective research will explain the concept of data
mining along with its types, why it is necessary to use data mining technique in crime rate
prediction and the advantages and disadvantages of data mining in crime rate prediction.
Research Objectives
 To determine the concept of data mining along with its types.
 To analyze the necessity to use data mining technique in crime rate prediction.
 To identify the advantages and disadvantages of data mining in crime rate prediction.
 To analyze how data mining helps in prediction of crime rate in UK.
Research Questions
 What is the concept of data mining along with its types?
 Why it is necessary to use data mining technique in crime rate prediction?
 What are the advantages and disadvantages of data mining in crime rate prediction?
 How data mining helps in prediction of crime rate in UK?
Rationale for the research
The primary rationale for the respective research is to enhance my overall knowledge about
the concept of application of data mining in crime rate prediction. This is because I will be able

to develop new skills as well as implement the same in any further researches that I undertake.
This will help in conducting the research effectively as well as gathering appropriate data in an
effective manner. Apart from this, another reason for choosing this research is that it is linked
with the course that I am currently undertaking. Hence, I want to ensure that I implement all the
learnings from that I gain from the respective research, I implement in the future. This is because
it will not only help me in completing the respective research in an effective manner, but also, I
will be able to ensure that the findings as well as conclusions of the research are relevant as well
as valid and reliable.
LITERATURE REVIEW
What is the concept of data mining along with its types?
According to Lin, Chen and Yu (2017), data mining is an activity of identifying patterns,
correlation and anomalies within wide range of data sets in order to predict results. Through
using data mining technique, businesses can avail information for reducing crime, increasing
revenue, reducing risk and improving customer relationship. Police department mainly use data
mining tools in crime rate prediction and understand data through cleaning raw data, creating
models and finding patterns (Alam, and et.al., 2021). Data mining mainly includes machine
learning, statistics and database system. There are various types of data mining such as
clustering, association, data cleaning, data visualization, classification, machine learning,
prediction and neural networks. These types of data mining helps in managing the data, improve
data quality, make data readably understand to individuals, classify crime on the basis of crime
and also help in identifying all sorts of data patterns. Each types of data mining have its own
method and ways for managing the data.
Criminology is a method that pursuits to identify crime characteristics (Brayne, 2020). Actually
crime evaluation consists of exploring and detecting crimes and their relationships with
criminals. The excessive extent of crime datasets and additionally the complexity of relationships
between these types of records have made criminology the perfect area for making use of records
mining techniques. Identifying crime characteristics is step one for developing in addition
evaluation. The expertise this is gained from facts mining processes is a very useful device that
can help and guide police forces. Data mining is the method of coming across actionable data

from massive sets of information. Data mining makes use of mathematical analysis to derive
styles and developments that exist in records.
Why it is necessary to use data mining technique in crime rate prediction?
As per the opinion of Sangani, Sampat and Pinjarkar (2019), it is important to use data
mining techniques in crime rate prediction as it helps in solving the crime in effective and
efficient manner. Through data mining technique, the crime department can also avail productive
results when applied on crime datasets (Chamikara and et.al., 2020). The information which is
availed by data mining technique helps in producing significant outcomes from crime report
datasets. It is used in crime rate prediction for exploring and detecting crime and their
relationship with criminals. The approaches of data mining are very useful for police forces.
Process of data mining is based on algorithms to analyse and extract information due to which
the hidden pattern and relationship within the data is automatically discovers. By using data
mining technique, police department can gather crude information and separate data which can
be applied to make expectation in numerous true circumstances. The methods of data mining are
used to estimate the future wrongdoing patterns of UK. It also helps in assuming the significant
part in criminal concealment and wrongdoing control in various nations. Data mining also helps
in extracting valuable data from enormous datasets and also maintains the connection between
the traits of data.
Data mining helps in fixing the crimes quicker and this technique gives exact effects while
carried out on crime dataset, the records acquired from the facts mining strategies can assist the
police branch. Produce crucial outcomes from crime file datasets. The very step in examine of
crime is crime analysis (Fouad and et.al., 2020). Crime analysis and prediction is a scientific
approach for figuring out the crime. This machine can expect area that have high chance for
crime occurrences and visualize crime prone place. Using the concept of information mining we
are able to extract previously unknown, useful records from an unstructured statistics. Businesses
in recent times are amassing information at a completely putting rate. The sources of this big
records circulate are various. It could come from credit card transactions, publicly available
client records, data from banks and monetary establishments, as well as the statistics that
customers should provide simply to apply and down load an software on their laptops, cell
telephones, capsules, and computers. It isn't always easy to store such big quantities of statistics.
So, many relational database servers are being continuously constructed for this purpose. Online

transactional protocol or OLTP systems also are being advanced to keep all that into special
database servers. OLTP structures play a crucial role in assisting groups feature smoothly.
The Descriptive Data-Mining Tasks can also be further divided into four types that are as
follows: Clustering Analysis (Islam and et.al., 2021). Summarization Analysis. Association
Rules Analysis. Sequence Discovery Analysis. Data mining is the process of searching large sets
of data to look out for patterns and trends that can't be found using simple analysis techniques.
Data mining has several types, including pictorial data mining, text mining, social media mining,
web mining, and audio and video mining amongst others. Data mining is the process of
discovering actionable information from large sets of data. Data mining uses mathematical
analysis to derive patterns and trends that exist in data. Businesses these days are collecting data
at a very striking rate. The sources of this enormous data stream are varied. It could come from
credit card transactions, publicly available customer data, data from banks and financial
institutions, as well as the data that users have to provide just to use and download an application
on their laptops, mobile phones, tablets, and desktops (Marmo, 2021). It is not easy to store such
massive amounts of data. So, many relational database servers are being continuously built for
this purpose. Online transactional protocol or OLTP systems are also being developed to store all
that into different database servers. OLTP systems play a vital role in helping businesses
function smoothly. Data mining is the method of discovering actionable records from large sets
of records. Data mining uses mathematical evaluation to derive styles and tendencies that exist in
information. Businesses these days are amassing statistics at a completely putting rate. The
sources of this good sized data stream are numerous. It ought to come from credit card
transactions, publicly available client facts, records from banks and financial institutions, as well
as the statistics that customers need to provide just to apply and down load an utility on their
laptops, cellular phones, capsules, and desktops.
It is these systems that are responsible for storing data that comes out of the smallest of
transactions into the database. So, data related to sale, purchase, human capital management, and
other transactions are stored in database servers by OLTP systems. Now, top executives need
access to facts based on data to base their decisions on. This is where online analytical
processing or OLAP systems enter the picture. Data warehouses and other OLAP systems are
built more and more because of this very need of or top executives. We don’t only need data but

also the analytics associated with it to make better and more profitable decisions. OLTP and
OLAP systems work in tandem.
Data mining is the process of coming across actionable facts from big sets of facts. Data
mining uses mathematical evaluation to derive patterns and tendencies that exist in information.
Typically, those styles can not be discovered by using traditional information exploration
because the relationships are too complex or due to the fact there is too much statistics. The idea
of data mining has been with us seeing that lengthy before the digital age. The idea of making
use of records to information discovery has been round for centuries, beginning with guide
formulation for statistical modeling and regression analysis. In the Nineteen Thirties, Alan
Turing delivered the idea of a normal computing system that might carry out complicated
computations. This marked the rise of the electromechanical laptop and with it, the ever-
increasing explosion of virtual information that keeps to this very day.
Data has end up part of every side of business and lifestyles (Usman and et.al., 2021).
Companies today can harness facts mining programs and system getting to know for everything
from enhancing their income procedures to decoding financials for funding purposes. As a result,
information scientists have end up essential to corporations all around the global as corporations
are searching for to reap bigger dreams than ever earlier than. Data mining can solution business
questions that had been traditionally not possible to reply due to the fact they have been too time-
eating to remedy manually. Using effective computers and algorithms to execute various
statistical techniques that analyze information in distinctive methods, customers can identify
patterns, developments, and relationships they could in any other case omit. They can then
practice these findings to expect what is possibly to take place inside the destiny and take
movement to influence commercial enterprise effects. Data mining is used in lots of areas of
commercial enterprise and studies, together with sales and advertising and marketing, product
improvement, healthcare, and education (Virtanen, 2021). When used efficiently, statistics
mining can come up with an advantage over competitors through making it feasible to examine
extra about clients, develop effective advertising strategies, increase sales, and decrease prices.
What are the advantages and disadvantages of data mining in crime rate prediction?
As per the viewpoint of Chauhan and Sehgal (2017), there are various advantages and
disadvantages of data mining in crime rate prediction. The advantages which can be gained
through data mining in crime rate prediction is that it helps in gathering reliable information, cost

effective solution as compared to other data prediction, detect crime, analyse enormous amount
of data quickly, predict the future crime and discover hidden patterns. These advantages help to
make informed decisions and also detect risks and fraud. Data mining helps in uncovering the
hidden facts and information from large database of criminal records for controlling, preventing
and investigating the crime for particular case. Crime data is useful for police department as it
can reduce their workload. The information gathered through data mining can also be used for
local crime pattern analysis and also provide insight view of well reported and well recorded
crime. There are several disadvantages of data mining in crime rate prediction which can
influence the performance of police department. It is important for police force to identify the
challenges faced by using data mining techniques in crime rate prediction. The disadvantage of
data mining tools are its costly process, need to conduct accurately, data can be misused,
complex and challenging to use and also it requires large database.
Predictive policing refers to the use of predictive analytics primarily based on mathematical
fashions, and different analytical strategies in law enforcement to become aware of potential
crook hobby. Predictive policing consequently makes use of pc structures to research massive
sets of records to assist decide where to install police or to pick out folks that are purportedly
more likely to commit or be a victim of against the law. Predictive policing has the potential to
make policing fairer (Xu and et.al., 2021). By promoting selection-making based on objective
proof, predictive policing should probably alleviate positive discrepancies within the
enforcement of the regulation. When the set of rules compiled the crime hot spot map within the
above-referred to LA have a look at, it did now not directly rely on prejudice. By contrast, the
traditional LAPD hot spot maps were produced via (inevitably prejudiced) people. As a end
result, algorithms is probably capable of help law enforcement officials to higher predict
dangers, determine the identification of offenders, and identify the vulnerabilities of a
community and its contributors. However, this capability can simplest be realised if the set of
rules is indeed freed from bias, which isn't always the case. Crime facts is useful for police
branch as it is able to lessen their workload. The statistics amassed through information mining
also can be used for local crime pattern analysis and additionally provide perception view of
properly reported and nicely recorded crime. There are several hazards of information mining in
crime fee prediction which can affect the performance of police branch. It is crucial for police
pressure to identify the challenges confronted through using information mining strategies in

crime price prediction. The disadvantage of records mining gear are its high priced manner, want
to behavior as it should be, information can be misused, complicated and hard to apply and also
it calls for large database. Predictive policing refers to using predictive analytics primarily based
on mathematical models, and distinct analytical strategies in regulation enforcement to emerge as
privy to capacity crook interest.
When it involves the use of records to predict crime warm spots, many years of
criminology studies show that crime reviews and different data amassed by using the police
frequently report law enforcement’s response to the reviews they receive and situations they
come upon, in preference to imparting an objective or complete record of all of the crimes that
occur. Put in any other case, the databases inform us where crime has been spotted by way of the
police in the past. But we recognise that in many nations the police devote disproportionate
assets concentrated on sure minority organizations. The end result is that racist practices get
baked into the statistics. Predictive policing then becomes a vicious circle. Crime records
gathered on the idea of a racist coverage will create racist predictions, leading to over-policing so
that you can hold to generate misleading records and racist predictions. Predictive policing
structures exacerbate this trend by making policing merely approximately the numeric discount
of the fees of detected, rather than actual, crime.
How data mining helps in prediction of crime rate in UK?
As per the viewpoint of Yerpude (2020), data mining helps in predicting the crime rate in
UK by increasing advent of computerized system. Through using data mining, crime data analyst
can also help law enforcement officers to solve the crimes in effective and efficient manner. By
implementing the concept of data mining, the police department can gain useful and unknown
information from unstructured data. Data mining is used for predicting the crime rate in UK by
using artificial intelligence methods due to which the crime data can be automatically entered
into database. Classification is one of the techniques of data mining that helps in predicting about
the values of data (Farsi and et. al., 2018). The crime is predicted through field research and
content analysis. In field research, police forces focus on observing characteristics of locations
whereas content analysis is used for examining the police report narratives. Link analysis is
known as data mining technique which is useful in the detection of valid and useful patterns. The
main framework of this analysis totally depends upon the fact of linking one activity with
another. Data mining is a manner to extract information out of normally big information units; in

other phrases it's far an technique to find out hidden relationships amongst statistics via using
artificial intelligence methods. By the usage of this technique, crime facts can be mechanically
entered into a database, in law enforcement companies.
Data mining techniques are used to operate on large amount of data to discover hidden
patterns and relationships helpful in decision making. Classification is a predictive data mining
technique, makes prediction about values of data using known results found from different data.
The five basic steps in the crime analysis process include the collection, categorization, analysis,
dissemination, and evaluation of information. The reports and forms for use in each step of
information-gathering and analysis are explained and illustrated. Crime forecasting refers to the
basic process of predicting crimes before they occur. Tools are needed to predict a crime before
it occurs. Currently, there are tools used by police to assist in specific tasks such as listening in
on a suspect's phone call or using a body cam to record some unusual illegal activity. Predicting
Crime and Other Uses of Neural Networks in Police Decision Making. Neural networks are a
machine learning method that excel in solving classification and forecasting problems. They
have also been shown to be a useful tool for working with big data oriented environments such
as law enforcement.
Data mining is a way to extract expertise out of usually massive facts sets; in different words it's
miles an technique to find out hidden relationships among facts with the aid of the use of
synthetic intelligence methods. By the use of this method, crime facts may be automatically
entered right into a database, in regulation enforcement organizations. Crime analysts use
computer non-public computers to finish most in their work and use laptop computer systems for
fieldwork and shows. In many cases, police departments' crime analysis gadgets have their very
own servers to house tabular data, geographic facts, and software program packages which are
shared by means of customers. Broadly speaking, there are packages of predictive policing.
First, the use of arrest facts to predict geographical crime hotspots. Second, mined social media
facts that can be used to determine how probable it's far that a person would possibly dedicate
against the law. The prediction problem is based totally on an emphasis in discretionary choice
making by judges relating sentencing (Yen and Hung, 2021). The criminal justice device is based
off of discretionary regular choices that involve predictions about destiny conduct. Predicting
Crime and Other Uses of Neural Networks in Police Decision Making. Neural networks are a
device mastering approach that excel in solving category and forecasting problems. They have

additionally been proven to be a useful tool for working with huge information oriented
environments which include regulation enforcement.
Crime is one among transcendent and demanding a part of our general public. Ordinary top notch
number of violations are finished, these successive wrongdoings have made the existences of
ordinary citizens anxious. Thus, preserving the crime from occurring is a essential project. In the
new time, it's miles seen that automated reasoning has proven its significance in almost all the
field and wrongdoing expectation is one in every of them. However, it's miles expected to
preserve the best data set of the wrongdoing that has befell as this facts may be utilized for
destiny reference (Yen and Hung, 2021). The potential to foresee the wrongdoing which could
take place in destiny can assist the regulation authorization workplaces in forestalling the
wrongdoing earlier than it happens. The potential to count on any wrongdoing based totally on
time table, region, and so forth can help in giving valuable records to law authorization from
strategical standpoint. In any case, looking forward to the wrongdoing precisely is a tough errand
due to the fact violations are increasing at a demanding charge. Hence, the wrongdoing
expectation and research strategies are essential to understand the future violations and lessen
them. In Late time, severa experts have led analyses to expect the violations making use of
distinctive AI strategies and specific facts assets. For crime expectation, KNN, help vector
device and linear regression and a few extraordinary calculations are The website online is
slithered utilizing a creeping software written in python language, and the records is placed away
in an impermanent information set. Utilizing 3 particular classifiers, the statistics is grouped into
wrongdoing related statistics and non-wrongdoing related facts. The eventual outcome suggests
that fundamental calculations can be powerful inside the undertaking of wrongdoing forecast.
Crime is certainly one of transcendent and disturbing part of our preferred public.
Ordinary first-rate variety of violations are completed, those successive wrongdoings have made
the existences of ordinary citizens worrying. Thus, keeping the crime from taking place is a
essential challenge. In the new time, it is visible that automated reasoning has tested its
significance in nearly all of the subject and wrongdoing expectation is one in every of them.
However, it's far expected to keep the exceptional information set of the wrongdoing that has
happened as this information may be utilized for destiny reference. The potential to foresee the
wrongdoing that may take region in future can assist the regulation authorization offices in
forestalling the wrongdoing earlier than it takes place. The capacity to assume any wrongdoing

primarily based totally on time desk, location, and so on can help in giving treasured information
to law authorization from strategical point of view. In any case, looking ahead to the wrongdoing
exactly is a hard errand due to the reality violations are growing at a disturbing fee.
Crimes are normal social issues that impact the non-public delight, economic
development and notoriety of a state. Information mining offers fantastic techniques and
calculations to break down records and concentrate large information from it. Analysing this
statistics no longer just aides in perceiving an detail in rate of horror price yet similarly enables
in taking critical activities for aversion of violations. Wrongdoing is an indication that is culpable
and which makes harm other guiltless people and concrete regions. Violations are of numerous
kinds like robbery, murder, attack, assault, rape sexual, mind torture, bogus detainment,
hijacking, crime and a few extra. An investigation is the method for profoundly assessing the
constituent components or constructions of the item or subjects viable. The exam is done with
the purpose of obtaining an exhaustive comprehension of a field.
Methodology
Research methodology is defined as procedure or tool that is used by investigator and researcher
for determining, selecting and analyzing data or information associated with the research topic
(Yen and Hung, 2021). In context of present research, this is conducted using valid and reliable
sources. Various type of research methods used in conducting this study are mentioned below –