A Crime Data Analysis of Prediction Based on Classification Approaches

تعتبر الجرائم نشاطا غير مشروع بجميع أنواعه يعاقب عليه القانون ويؤثر على نوعية حياة المجتمع وتطوره الاقتصادي. مع الارتفاع الكبير في معدلات الجريمة على مستوى العالم، هناك ضرورة لتحليل بيانات الجريمة لخفض معدل الجريمة. وهذا يشجع الشرطة والأفراد على اتخاذ الإجراءات المطلوبة والحد بشكل أكثر فعالية من الجرائم. الغرض من هذا البحث هو تطوير نماذج تنبؤية يمكن أن تساعد في تحليل أنماط الجريمة وبالتالي دعم جهود منع الجريمة في قسم بوسطن. تم اعتماد عامل الموقع الجغرافي في نموذجنا ، ويرجع ذلك إلى كونه عاملاً مؤثرًا في عدة مواقف ، سواء كان السفر إلى منطقة معينة أو العيش فيها لمساعدة الناس في التعرف بين بيئة آمنة وغير آمنة. يمكن أن يكون الموقع الجغرافي، جنبًا إلى جنب مع الأساليب والتقنيات الجديدة، مفيدًا للغاية في التحقيق في الجرائم. يتركز الهدف على الدراسة المقارنة بين ثلاث خوارزميات تعلم تحت الإشراف. حيث يستخدم التعلم مجموعات البيانات للتدريب، واختبارها للحصول على النتائج المرجوة عليها. تم استخدام خوارزميات التعلم الآلي المختلفة في مجموعة البيانات الخاصة بجرائم مدينة بوسطن، وهي شجرة القرار ونايف بايز والانحدار اللوجستي المصنفات هنا للتنبؤ بنوع الجريمة التي تحدث في المنطقة. تتم مقارنة مخرجات هذه الطرق مع بعضها البعض للعثور على نموذج واحد يناسب هذا النوع من البيانات بأفضل أداء. من النتائج التي تم الحصول عليها، أظهرت شجرة القرار أعلى نتيجة مقارنة بـ نايف بايز والانحدار اللوجستي.Crime is considered as an unlawful activity of all kinds and it is punished by law. Crimes have an impact on a society's quality of life and economic development. With a large rise in crime globally, there is a necessity to analyze crime data to bring down the rate of crime. This encourages the police and people to occupy the required measures and more effectively restricting the crimes. The purpose of this research is to develop predictive models that can aid in crime pattern analysis and thus support the Boston department's crime prevention efforts. The geographical location factor has been adopted in our model, and this is due to its being an influential factor in several situations, whether it is traveling to a specific area or living in it to assist people in recognizing between a secured and an unsecured environment.  Geo-location, combined with new approaches and techniques, can be extremely useful in crime investigation. The aim is focused on comparative study between three supervised learning algorithms. Where learning used data sets to train and test it to get desired results on them. Various machine learning algorithms on the dataset of Boston city crime are Decision Tree, Naïve Bayes and Logistic Regression classifiers have been used here to predict the type of crime that happens in the area. The outputs of these methods are compared to each other to find the one model best fits this type of data with the best performance. From the results obtained, the Decision Tree demonstrated the highest result compared to Naïve Bayes and Logistic Regression

The use of big data and data mining in the investigation of criminal offences

The aim of this study was to determine the features and prospects of using Big Data and Data Mining in criminal proceedings. The research involved the methods of a systematic approach, descriptive analysis, systematic sampling, formal legal approach and forecasting. The object of using Big Data and Data Mining are various crimes, the common features of which are the seriousness and complexity of the investigation. The common tools of Big Data and Data Mining in crime investigation and crime forecasting as interrelated tasks were identified. The creation of databases is the result of the processing of data sources by Data Mining methods, each being distinguished by the specifics of use. The main risks of implementing Big Data and Data Mining are violations of human rights and freedoms. Improving the use of Big Data and Data Mining requires standardization of procedures with strict adherence to the fundamental ethical, organizational and procedural rules. The use of Big Data and Data Mining is a forensic innovation in the investigation of serious crimes and the creation of an evidence base for criminal justice. The prospects for widespread use of these methods involve the standardization of procedures based on ethical, organizational and procedural principles. It is appropriate to outline these procedures in framework practical recommendations, emphasizing the responsibility of officials in case of violation of the specified principles. The area of further research is the improvement of innovative technologies and legal regulation of their application

Применение методов Data Mining & Knowledge Discovery в оперативно-розыскной деятельности

Нефедов, С. Н. Применение методов Data Mining & Knowledge Discovery в оперативно-розыскной деятельности / С. Н. Нефедов, В. А. Пархименко, М. М. Татур // Актуальные вопросы оперативно-розыскной деятельности : республиканская научно-практическая конференция (Минск, 2 июня 2017 г.) : тезисы докладов. - Минск : Академия МВД, 2017. - С. 70 - 72

Investigating key attributes in experience and satisfaction of hotel customer using online review data

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. With the development of social media, customers are sharing their experiences, and it is rapidly spreading as a form of online review. That is why the online review has become a significant information source affecting customers\u27 purchase intention and behavior. Therefore, it is important to understand the customer\u27s experience shown in the online review in order to maintain sustainable customer satisfaction and loyalty. The purpose of this study is to investigate what are the key attributes and the structural relationship of those key attributes. To accomplish this purpose, a total of 6596 hotel reviews were collected from Google (google.com). A frequency analysis using text mining was performed to figure out the most frequently mentioned attributes. In addition, semantic network analysis, factor analysis, and regression analysis were applied to understand the experience and satisfaction of the hotel customer. As a result, the top 99 keywords were divided into four groups such as Intangible Service , Physical Environment , Purpose , and Location . The factor analysis reduced the dimension of the original 64 keywords to 22 keywords, and grouped them into five factors, which are Access , F&B (Food and Beverage) , Purpose , Tangibles , and Empathy . Based on these results, theoretical and practical implications for sustainable hotel marketing strategies are suggested

Predictive Policing

UAE is one of the safest countries to live in, but that does not indicate that the country does not witness crimes, During the COVID-19 pandemic, the country saw an increase in cyber and digital crimes. Apart from cybercrime, there are other types of crimes, such as street crimes and violent crimes. Data analytics aids Dubai Police to predict crimes. Criminal investigation is one of the fields that is very interesting and is taught in colleges and academies. Data analytics opens the door for studying the details of each crime. Data mining tools consist of a variety of techniques that can help solve a problem or indicate a cause or an effect of something. Data analysts use data mining tools through a lot of software that allow the user to analyze data easily and fluently. SAS (statistical analysis system) is one of the reputable software that is used especially for visualizing and analyzing data. In this capstone, we will use SAS since it is a software that is accredited from Dubai Police and we use it already in our workplace. Prediction techniques supports to interpret and facilitate Dubai Police to develop strategies to reduce the crime rate. Hence, it allows UAE to sustain its position as the “safest” country. The capstone idea will actually help us develop what we do at work and stop or reduce crime which is one of the main pillars in Dubai Police. The crime related data will be collected from CID in Dubai police. Link analysis and predictive analysis will be performed in this project to forecast any crime. We will build a predictive model using SAS to predict crime. This proposed project will help to identify the trends of historical crime data. Project timeline has been provided in this writing to have a better outline. The first step is to collect the data from the source which is in our case, the criminal investigation department in Dubai Police. Meeting with the department; they have agreed on giving us datasets of specific crimes that Dubai Police finds critical and needs further analysis from five years. Thus, the data that we will be analyzing will be from the years 2017 to the year 2021. After collecting the data ; the processing took place which is the cleaning part of the data. Since the data is in Arabic and it is old as mentioned earlier that the data of the past five years are collected; there are some missing fields, some inconsistencies and some redundant data. After cleaning the dataset which took 70% of the time working on this project. Now the dataset is ready and can be analyzed in SAS. Importing the dataset through SAS was the first step. Then, we started analyzing the criminals first as we wanted to build a portfolio of the criminals and observe of any patterns found. The highest nationality of the criminals was India. We tried to see if there are higher nationalities in certain years, but in all five years the analysis showed that India was the number one nationality in criminals. Then we wanted to observe the criminals’ education level; the highest education level was unemployed meaning they do not have any degree that supports them. The education level part was very interesting because we found out that even though university degrees did not come first in the highest education level. however there is a sample of the criminals that hold very high level degrees such as PhDs and Masters degrees and this shows us that the stereotype of how uneducated people are bad or are the only people that commit crimes should be disregarded. Next , we analyzed the criminals’ age group and the outcome was that 30 – 45 age groups are the ones that commit crimes the most in Dubai. Finally, we have analyzed the criminals’ gender to see which gender commits most crimes in Dubai and from our analysis; the outcome showed that men are the most that commit crimes in Dubai. After analyzing the criminals’ profiles ; we have moved on to analyzing the crimes in the past five years. The type of crime was the first thing we wanted to analyze to observe what is the most crime committed in Dubai in the last five years. Fraud was the most crime committed in Dubai and this was not a huge shock to us since Dubai is considered a business city and it attracts some people to do their business in it. Dubai has always been interested in building the city financially in the best , legal way possible, however there will always be people that see it as a city to commit fraud in since it has a large population and has many tourists visiting the city. Next, we analyzed the crime replotting per year. 2019 has scored the highest in crime reporting in Dubai; right before the pandemic. We analyzed the police stations that had the most reporting in the past five years in order to observe the locations that are considered crime appealing to criminals. This analysis is very important since every area has a police station assigned to it and the outcome of this analysis was that Bur Dubai police station had the highest number of incidents in the last five years. Lastly, we wanted to analyze what time was the crime committed and the result was that most crimes have been committed in the morning between 9AM and 11AM and that was very shocking and interesting to us because it is know globally that most crimes are committed at night in the dark where no one can see the criminal , but this is due to the type of crime as well , and as we have observed that fraud is the most committed crime, then the morning is the best time to commit this crime since people are awake and willing to do business with other people whether it was online or offline. Finally, the purpose of this whole project is to forecast the crime rates; thus, we built a forecasting model in SAS and it showed us that in the upcoming years, the crime rates in Dubai will decrease dramatically based on the pattern of crimes in the historical data. This is a positive result; however this does not mean that Dubai Police should neglect the surveillance and monitoring of the city due to this forecasting as it is not always accurate

PREDICTIVE POLICING: A Machine Learning Approach to Predict and Control Crimes in Metropolitan Cities

Security is the one of the basic need of human’s life and biggest challenge of history that cannot be diminished at least in metropolitan cities like Karachi. It can only be controlled by efficient resources allocation and effective strategies with forthcoming insight of criminal moves. Big data analytics with the support of machine learning algorithms makes it possible to deals with huge amount of data, extract hidden inter-connection, pattern and meaningful information. This paper, proposed the model for the predictive policing system and built test model using k-means and naïve Bayes methodologies for street crime in Karachi region. The model is then run under R and WEKA environment which produced accuracy around 70%

Digitalisation and Big Data Mining in Banking

open access articleBanking as a data-intensive subject has been progressing continuously under the promoting influences of the era of big data. Exploring the advanced big data analytic tools like Data Mining (DM) techniques is key for the banking sector, which aims to reveal valuable information from the overwhelming volume of data and achieve better strategic management and customer satisfaction. In order to provide sound direction for the future research and development, a comprehensive and most up to date review of the current research status of DM in banking will be extremely beneficial. Since existing reviews only cover the applications until 2013, this paper aims to fill this research gap and presents the significant progressions and most recent DM implementations in banking post 2013. By collecting and analyzing the trends of research focus, data resources, technological aids, and data analytical tools, this paper contributes to bringing valuable insights with regard to the future developments of both DM and the banking sector along with a comprehensive one stop reference table. Moreover, we identify the key obstacles and present a summary for all interested parties that are facing the challenges of big data

Big-Crypto: Big Data, Blockchain and Cryptocurrency

open access articleCryptocurrency has been a trending topic over the past decade, pooling tremendous technological power and attracting investments valued over trillions of dollars on a global scale. The cryptocurrency technology and its network have been endowed with many superior features due to its unique architecture, which also determined its worldwide efficiency, applicability and data-intensive characteristics. This paper introduces and summarises the interactions between two significantconceptsinthedigitalizedworld,i.e.,cryptocurrencyandBigData. Bothsubjectsareatthe forefront of technological research, and this paper focuses on their convergence and comprehensively reviews the very recent applications and developments after 2016. Accordingly, we aim to present a systematic review of the interactions between Big Data and cryptocurrency and serve as the one-stop reference directory for researchers with regard to identifying research gaps and directing future explorations

Big Data and Climate Change

open access articleClimate science as a data-intensive subject has overwhelmingly affected by the era of big data and relevant technological revolutions. The big successes of big data analytics in diverse areas over the past decade have also prompted the expectation of big data and its efficacy on the big problem—climate change. As an emerging topic, climate change has been at the forefront of the big climate data analytics implementations and exhaustive research have been carried out covering a variety of topics. This paper aims to present an outlook of big data in climate change studies over the recent years by investigating and summarising the current status of big data applications in climate change related studies. It is also expected to serve as a one-stop reference directory for researchers and stakeholders with an overview of this trending subject at a glance, which can be useful in guiding future research and improvements in the exploitation of big climate data

Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance

open access articleThe launch of the United Nations (UN) 17 Sustainable Development Goals (SDGs) in 2015 was a historic event, uniting countries around the world around the shared agenda of sustainable development with a more balanced relationship between human beings and the planet. The SDGs affect or impact almost all aspects of life, as indeed does the technological revolution, empowered by Big Data and their related technologies. It is inevitable that these two significant domains and their integration will play central roles in achieving the 2030 Agenda. This research aims to provide a comprehensive overview of how these domains are currently interacting, by illustrating the impact of Big Data on sustainable development in the context of each of the 17 UN SDGs