A social media and crowd-sourcing data mining system for crime prevention during and post-crisis situations

A number of large crisis situations, such as natural disasters have affected the planet over the last decade. The outcomes of such disasters are catastrophic for the infrastructures of modern societies. Furthermore, after large disasters, societies come face-to-face with important issues, such as the loss of human lives, people who are missing and the increment of the criminality rate. In many occasions, they seem unprepared to face such issues. This paper aims to present an automated system for the synchronization of the police and Law Enforcement Agencies (LEAs) for the prevention of criminal activities during and post a large crisis situation. The paper presents a review of the literature focusing on the necessity of using data mining in combination with advanced web technologies, such as social media and crowd-sourcing, for the resolution of the problems related to criminal activities caused during and post-crisis situations. The paper provides an introduction to examples of different techniques and algorithms used for social media and crowd-sourcing scanning, such as sentiment analysis and link analysis. The main focus of the paper is the ATHENA Crisis Management system. The function of the ATHENA system is based on the use of social media and crowd-sourcing for collecting crisis-related information. The system uses a number of data mining techniques to collect and analyze data from the social media for the purpose of crime prevention. A number of conclusions are drawn on the significance of social media and crowd-sourcing data mining techniques for the resolution of problems related to large crisis situations with emphasis to the ATHENA system

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

The Impact of Social Media and Digital Technology on Electoral Violence in Kenya

Electoral violence has become synonymous with Kenya’s elections. This acquired deadly proportions during the 2007 elections. However, it was also during this time that social media and digital technology was first used for political reasons including campaigning and polling. Social media and digital technology had mixed uses where it was not only used to propagate hate speech and mobilise for violence, but also to identify and map out violence hotspots. Since then, they have increasingly become an indispensable tool in Kenya’s politics and governance, used by political leaders to spread information, campaign and mobilise. However, the widespread reach of social media has also been a major challenge to security, peace and peacebuilding since it has been used to incite hatred and violence. This paper identifies the specific threats that social media and digital technology pose and opportunities they present for violence prevention. Ultimately, the paper seeks to present the opportunities that exist for partnerships between state and non-state actors to effectively prevent political and electoral violence.ESR

The Rise of Mobile and the Diffusion of Technology-Facilitated Trafficking

In this report, researchers at the USC Annenberg Center on Communication Leadership & Policy (CCLP) reveal how those involved in human trafficking have been quick to adapt to the 21st-century global landscape. While the rapid diffusion of digital technologies such as mobile phones, social networking sites, and the Internet has provided significant benefits to society, new channels and opportunities for exploitation have also emerged. Increasingly, the business of human trafficking is taking place online and over mobile phones. But the same technologies that are being used for trafficking can become a powerful tool to combat trafficking. The precise role that digital technologies play in human trafficking still remains unclear, however, and a closer examination of the phenomenon is vital to identify and respond to new threats and opportunities.This investigation indicates that mobile devices and networks have risen in prominence and are now of central importance to the sex trafficking of minors in the United States. While online platforms such as online classifieds and social networking sites remain a potential venue for exploitation, this research suggests that technology facilitated trafficking is more diffuse and adaptive than initially thought. This report presents a review of current literature, trends, and policies; primary research based on mobile phone data collected from online classified sites; a series of firsthand interviews with law enforcement; and key recommendations to policymakers and stakeholders moving forward

Social Media Role in Relieving the Rohingya Humanitarian Crisis

This research explores the possibilities and limitations associated with utilizing new media technologies in relieving humanitarian crises by focusing on the Rohingyan case. The main interest is to approach a conceptual communication framework based on the current Rohingya refugees’ perceptions about social media networks and mobile apps and the potential suggestions to optimize its usefulness in relieving their crisis. Addressing the obstructive challenges that interrupt the new media technologies functionality is another objective of the study. The mixed methodology interlaces the qualitative findings of the questionnaire with the qualitative outcomes of the semi-structured interviews to reach an inclusive investigation to the research questions. An examination to the significance of relationships between Rohingya demographic attributes and their preferences and perceptions toward social media platforms and mobile apps is substantial to explore the dominant factors that may influence the relationship between the Rohingya and different media platforms. Keywords: social media, mixed methods, Rohingya, humanitarian crises DOI: 10.7176/NMMC/87-04 Publication date: January 31st 202

The Digital Life of Walkable Streets

Walkability has many health, environmental, and economic benefits. That is why web and mobile services have been offering ways of computing walkability scores of individual street segments. Those scores are generally computed from survey data and manual counting (of even trees). However, that is costly, owing to the high time, effort, and financial costs. To partly automate the computation of those scores, we explore the possibility of using the social media data of Flickr and Foursquare to automatically identify safe and walkable streets. We find that unsafe streets tend to be photographed during the day, while walkable streets are tagged with walkability-related keywords. These results open up practical opportunities (for, e.g., room booking services, urban route recommenders, and real-estate sites) and have theoretical implications for researchers who might resort to the use social media data to tackle previously unanswered questions in the area of walkability.Comment: 10 pages, 7 figures, Proceedings of International World Wide Web Conference (WWW 2015

Crowdsourcing Crisis Management Platforms: A Privacy and Data Protection Risk Assessment and Recommendations

Over the last few years, crowdsourcing have expanded rapidly allowing citizens to connect with each other, governments to connect with common mass, to coordinate disaster response work, to map political conflicts, acquiring information quickly and participating in issues that affect day-to- day life of citizens. As emerging tools and technologies offer huge potential to response quickly and on time during crisis, crisis responders do take support from these tools and techniques. The ‘Guiding Principles’ of the Sendai Framework for Disaster Risk Reduction 2015-2030 identifies that ‘disaster risk reduction requires a multi-hazard approach and inclusive risk-informed decision-making (RIDM) based on the open exchange and dissemination of disaggregated data, including by sex, age and disability, as well as on easily accessible, up-to-date, comprehensible, science-based, non-sensitive risk information, complemented by traditional knowledge. Addressing the ‘Priority Action’ 1 & 2, this PhD research aims to identify various risks and present recommendations for ‘RIDM Process’ in form of a general Privacy and Data Protection Risk Assessment and Recommendations for crowdsourcing crisis management. It includes legal, ethical and technical recommendations

Measuring objective and subjective well-being: dimensions and data sources

AbstractWell-being is an important value for people's lives, and it could be considered as an index of societal progress. Researchers have suggested two main approaches for the overall measurement of well-being, the objective and the subjective well-being. Both approaches, as well as their relevant dimensions, have been traditionally captured with surveys. During the last decades, new data sources have been suggested as an alternative or complement to traditional data. This paper aims to present the theoretical background of well-being, by distinguishing between objective and subjective approaches, their relevant dimensions, the new data sources used for their measurement and relevant studies. We also intend to shed light on still barely unexplored dimensions and data sources that could potentially contribute as a key for public policing and social development