Analysing the crime data of 3 metropolitan cities of United States to find patterns in crimes based on location, time of the day, type of crime, average income range in the area.
EssaysExperts.net is the only custom writing service that uses ultra modern approaches coupled with thorough training in providing high quality academic writing services. Our services will enable you achieve success and realize your academic dreams. At http://www.essaysexperts.net/ ,we are the best solution for your acdemic assignments
This is the analytics report of big data set of Chicago Crime. I had generated this report as a course assignment while studying Master of Technology at FedUni Australia.
This document describes an analysis of crime data from Chicago surrounding the University of Chicago campus. The author downloaded crime data from the city of Chicago covering a one year period. He extracted data for a specific area around campus, totaling 1,385 crime reports. He then performed several analyses of the data, including examining crime frequencies by time of day, day of week, month, and type of crime. Graphs and tables are included to illustrate the results.
This document analyzes gun-related crime data using big data tools like Apache Hive and Pig. It summarizes the deadliest US mass shootings from 2016 to 2015. It then outlines the tools, data specifications, and workflow used to analyze gun sales rates, gun ownership rates, and crime rates over 2014-2015. Visualizations created in Excel, Tableau and 3D maps show trends in gun crimes in different areas for those years. In conclusion, it finds higher gun crime in central LA, guns comprising 28% of total crimes in 2015, and areas with higher income reporting less gun crimes in New York. Suggestions include using financial stability to predict gun crime likelihood.
Helping Chicago Communities Identify Subjects Who Are Likely to be Involved i...Brendan Sigale
- The document describes a project to analyze Chicago Police Department data to develop a model that predicts an individual's risk level of being involved in a shooting based on various factors.
- An artificial neural network model was found to best predict the risk score, achieving an R2 of 0.9234 and mean average error of 10.583.
- K-means clustering identified 3 clusters that characterize individuals based on attributes like gender, race, age, and criminal history with drugs and weapons.
Protecting your Members: Combating Online Threats (Credit Union Conference Se...NAFCU Services Corporation
With online technology use on the rise, so is the threat of online attacks that can affect consumer’s smartphones and computers. According to the March 2012 McAfee quarterly report, over 4.5 million computers have been infected worldwide with malware. In order to launch a successful attack, social engineers leverage the vulnerability of online users by accessing their digital footprint. So what can you do to arm your credit union members against cyber security attacks? Follow this presentation and you will learn how to keep your members at bay from these threats and educate them on how to be alert, aware and safe online. For more details: www.nafcu.org/cyveillance
Heavy, Messy, Misleading: why Big Data is a human problem, not a tech onePulsar
"Big data" has been around for a few years now but for every hundred people talking about it there’s probably only one actually doing it. As a result Big Data has become the preferred vehicle for inflated expectations and misguided strategy.
As always, the seed of the issue is in the expression itself. Big Data is not so much about a quality of the data or the tools to mine it, it’s about a new approach to product, policy or business strategy design. And that’s way harder and trickier to implement than any new technology stack.
In this talk we look at where Big Data is going, what are the real opportunities, limitations and dangers and what can we do to stop talking about it and start doing it today.
EssaysExperts.net is the only custom writing service that uses ultra modern approaches coupled with thorough training in providing high quality academic writing services. Our services will enable you achieve success and realize your academic dreams. At http://www.essaysexperts.net/ ,we are the best solution for your acdemic assignments
This is the analytics report of big data set of Chicago Crime. I had generated this report as a course assignment while studying Master of Technology at FedUni Australia.
This document describes an analysis of crime data from Chicago surrounding the University of Chicago campus. The author downloaded crime data from the city of Chicago covering a one year period. He extracted data for a specific area around campus, totaling 1,385 crime reports. He then performed several analyses of the data, including examining crime frequencies by time of day, day of week, month, and type of crime. Graphs and tables are included to illustrate the results.
This document analyzes gun-related crime data using big data tools like Apache Hive and Pig. It summarizes the deadliest US mass shootings from 2016 to 2015. It then outlines the tools, data specifications, and workflow used to analyze gun sales rates, gun ownership rates, and crime rates over 2014-2015. Visualizations created in Excel, Tableau and 3D maps show trends in gun crimes in different areas for those years. In conclusion, it finds higher gun crime in central LA, guns comprising 28% of total crimes in 2015, and areas with higher income reporting less gun crimes in New York. Suggestions include using financial stability to predict gun crime likelihood.
Helping Chicago Communities Identify Subjects Who Are Likely to be Involved i...Brendan Sigale
- The document describes a project to analyze Chicago Police Department data to develop a model that predicts an individual's risk level of being involved in a shooting based on various factors.
- An artificial neural network model was found to best predict the risk score, achieving an R2 of 0.9234 and mean average error of 10.583.
- K-means clustering identified 3 clusters that characterize individuals based on attributes like gender, race, age, and criminal history with drugs and weapons.
Protecting your Members: Combating Online Threats (Credit Union Conference Se...NAFCU Services Corporation
With online technology use on the rise, so is the threat of online attacks that can affect consumer’s smartphones and computers. According to the March 2012 McAfee quarterly report, over 4.5 million computers have been infected worldwide with malware. In order to launch a successful attack, social engineers leverage the vulnerability of online users by accessing their digital footprint. So what can you do to arm your credit union members against cyber security attacks? Follow this presentation and you will learn how to keep your members at bay from these threats and educate them on how to be alert, aware and safe online. For more details: www.nafcu.org/cyveillance
Heavy, Messy, Misleading: why Big Data is a human problem, not a tech onePulsar
"Big data" has been around for a few years now but for every hundred people talking about it there’s probably only one actually doing it. As a result Big Data has become the preferred vehicle for inflated expectations and misguided strategy.
As always, the seed of the issue is in the expression itself. Big Data is not so much about a quality of the data or the tools to mine it, it’s about a new approach to product, policy or business strategy design. And that’s way harder and trickier to implement than any new technology stack.
In this talk we look at where Big Data is going, what are the real opportunities, limitations and dangers and what can we do to stop talking about it and start doing it today.
Hello Criminals! Meet Big Data: Preventing Crime in San Francisco by Predicti...Tarun Amarnath
Throughout the world, people look to San Francisco as a hub for technology; however, this hides a hidden undercurrent of crime in the City by the Bay. My experiment uses Azure ML and Python to predict without bias the category of crime likeliest to occur at a certain time and location in San Francisco.
A Comparative Study of Data Mining Methods to Analyzing Libyan National Crime...Zakaria Zubi
Our proposed model will be able to extract crime patterns by using association rule mining and clustering to classify crime records on the basis of the values of crime attributes.
Machine Learning Approaches for Crime Pattern DetectionAPNIC
This document discusses machine learning approaches for predicting crime patterns. It begins by stating the large number of violent crimes in the US and explaining that predicting crimes can help avoid them and ensure better resource allocation. It then discusses existing crime prediction systems like PredPol and the general crime prediction process of data gathering, classification/clustering, and prediction. It provides various methods for data gathering, like crime records, social media, IoT devices, and newspapers. It also discusses clustering algorithms like k-means that can be used. Finally, it notes that PredPol has achieved a 22.7% reduction in crimes in one area, but that combining additional techniques like machine learning, big data analysis, and image processing could further improve crime prediction.
This document discusses big data and techniques for working with large datasets. It introduces key concepts like what big data is, why it is important, and when big data problems arise. It then outlines some common techniques, tools, and applications for big data, including MapReduce, NoSQL databases, and analytics platforms. Specific examples are provided of using big data for personalized recommendations, network monitoring, and analyzing social networks.
This document summarizes a presentation about using big geo-data and data visualization techniques to transform raw data into useful information. It discusses using the WebGLayer JavaScript library to interactively visualize large datasets of up to 1.5 million data points in under 100ms in a web browser. Examples are given of visualizing traffic accidents in Flanders and crime data in Chicago. Potential applications discussed include analyzing criminality, traffic, parking, and water usage data. Upcoming developments include visualizing traffic flow data from Flanders and Plzeň and criminal offenses data from Plzeň.
Using Google search data, this document analyzes interest in Wikileaks over time:
- Quantitative analysis shows search volume for "Wikileaks" spiked in December 2010 and July 2008, and compares it to volume for "Obama" searches.
- Qualitative analysis examines related search terms suggested by Google in December 2010, indicating interest in people, conflicts, places, associations and legal issues around Wikileaks.
ESWC SS 2012 - Friday Keynote Marko Grobelnik: Big Data Tutorialeswcsummerschool
The document discusses big data techniques, tools, and applications. It describes how big data is enabled by increases in storage capacity, processing power, and data availability. It outlines common approaches to distributed processing, storage, and programming models for big data, including MapReduce, NoSQL databases, and cloud computing. It also provides examples of applications involving log file analysis, network alarm monitoring, media content analysis, and social network analysis.
Forecasting Space-Time Events - Strata + Hadoop World 2015 San JoseAzavea
This presentation uses the speaker’s experience in building a crime forecasting package to outline some tools and techniques useful in modeling space-time event data. While the case study focuses on modeling crime, the techniques and tools presented are applicable to a broad selection of domains.
This presentation was given at Strata + Hadoop World 2015 in San Jose by Jeremy Heffner.
Crime Risk Forecasting: Near Repeat Pattern Analysis & Load ForecastingAzavea
http://www.azavea.com/hunchlab
This is a rather technical dive into the near repeat pattern analysis and load forecasting features that we've built into HunchLab. Both of these features are aimed at helping a law enforcement agency to better predict risk levels across their jurisdictions and allocate resources according. While no application of predictive analytics will be perfect, forecasting risk based on models of the past can help officers and analysts to anticipate the appropriate next steps.
Near repeat pattern analysis helps officers quantify the risk that arises from multiple incidents happening close to one another in space and time. What we are quantifying is how the fact that your neighbor's house is burgled raises your risk of a burglary in the coming days and weeks.
With load forecasting we are looking at cyclical temporal patterns in incidents. How does the time of year, time of day, and day of week change the levels of crime incidents that we should expect across a jurisdiction? By modeling these cyclical patterns we can project crime levels into the future, helping law enforcement agencies to allocate resources appropriately as well as better manage organizational accountability.
This document describes a real-time crime analysis and alert system called CrimeX. It analyzes historical crime data and ingests real-time crime and user data to provide crime alerts to users. The system ingests large amounts of crime data from various sources, processes it using Python scripts, and indexes it in Elasticsearch. It then processes real-time crime and user location data to identify nearby crimes and alert users. The system aims to help understand criminal behavior and dynamics between criminals and law enforcement. It outlines the data flow and technical challenges around performance and security.
This document describes a real-time crime analysis and alert system called CrimeX. It collects and analyzes crime data from various sources to provide insights into criminal activity and alerts users in real-time. The system ingests raw crime data, refines it using batch processing and Python scripts, then indexes it for real-time queries in Elasticsearch. It uses the indexed data to analyze past crimes based on user locations and alert nearby users of current criminal activity. The system was optimized to reduce front-end loading times and network latency between processing components.
1) Journalists are increasingly expected to analyze data to uncover stories and trends, rather than just report on isolated events.
2) Open data from sources like governments and communities can be used with tools like Excel, Google Fusion Tables, and Tableau to clean, visualize, and analyze information.
3) Effective data stories connect different types of data to provide context and insight into issues, rather than just describing events. Personalizing stories for readers can also make data journalism more engaging.
HunchLab 2.0 Predictive Missions: Under the HoodAzavea
HunchLab is a predictive policing software that uses machine learning to analyze historical crime data and predict future crime hotspots. It represents common crime theories like risk terrain modeling and routine activity theory as variables. The modeling process involves generating training examples from years of data, enriching it with geographic and temporal variables, building and evaluating multiple models using techniques like gradient boosting and generalized additive models, and selecting the best performing model. HunchLab aims to learn from a jurisdiction's unique data to help prioritize police patrols.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Hello Criminals! Meet Big Data: Preventing Crime in San Francisco by Predicti...Tarun Amarnath
Throughout the world, people look to San Francisco as a hub for technology; however, this hides a hidden undercurrent of crime in the City by the Bay. My experiment uses Azure ML and Python to predict without bias the category of crime likeliest to occur at a certain time and location in San Francisco.
A Comparative Study of Data Mining Methods to Analyzing Libyan National Crime...Zakaria Zubi
Our proposed model will be able to extract crime patterns by using association rule mining and clustering to classify crime records on the basis of the values of crime attributes.
Machine Learning Approaches for Crime Pattern DetectionAPNIC
This document discusses machine learning approaches for predicting crime patterns. It begins by stating the large number of violent crimes in the US and explaining that predicting crimes can help avoid them and ensure better resource allocation. It then discusses existing crime prediction systems like PredPol and the general crime prediction process of data gathering, classification/clustering, and prediction. It provides various methods for data gathering, like crime records, social media, IoT devices, and newspapers. It also discusses clustering algorithms like k-means that can be used. Finally, it notes that PredPol has achieved a 22.7% reduction in crimes in one area, but that combining additional techniques like machine learning, big data analysis, and image processing could further improve crime prediction.
This document discusses big data and techniques for working with large datasets. It introduces key concepts like what big data is, why it is important, and when big data problems arise. It then outlines some common techniques, tools, and applications for big data, including MapReduce, NoSQL databases, and analytics platforms. Specific examples are provided of using big data for personalized recommendations, network monitoring, and analyzing social networks.
This document summarizes a presentation about using big geo-data and data visualization techniques to transform raw data into useful information. It discusses using the WebGLayer JavaScript library to interactively visualize large datasets of up to 1.5 million data points in under 100ms in a web browser. Examples are given of visualizing traffic accidents in Flanders and crime data in Chicago. Potential applications discussed include analyzing criminality, traffic, parking, and water usage data. Upcoming developments include visualizing traffic flow data from Flanders and Plzeň and criminal offenses data from Plzeň.
Using Google search data, this document analyzes interest in Wikileaks over time:
- Quantitative analysis shows search volume for "Wikileaks" spiked in December 2010 and July 2008, and compares it to volume for "Obama" searches.
- Qualitative analysis examines related search terms suggested by Google in December 2010, indicating interest in people, conflicts, places, associations and legal issues around Wikileaks.
ESWC SS 2012 - Friday Keynote Marko Grobelnik: Big Data Tutorialeswcsummerschool
The document discusses big data techniques, tools, and applications. It describes how big data is enabled by increases in storage capacity, processing power, and data availability. It outlines common approaches to distributed processing, storage, and programming models for big data, including MapReduce, NoSQL databases, and cloud computing. It also provides examples of applications involving log file analysis, network alarm monitoring, media content analysis, and social network analysis.
Forecasting Space-Time Events - Strata + Hadoop World 2015 San JoseAzavea
This presentation uses the speaker’s experience in building a crime forecasting package to outline some tools and techniques useful in modeling space-time event data. While the case study focuses on modeling crime, the techniques and tools presented are applicable to a broad selection of domains.
This presentation was given at Strata + Hadoop World 2015 in San Jose by Jeremy Heffner.
Crime Risk Forecasting: Near Repeat Pattern Analysis & Load ForecastingAzavea
http://www.azavea.com/hunchlab
This is a rather technical dive into the near repeat pattern analysis and load forecasting features that we've built into HunchLab. Both of these features are aimed at helping a law enforcement agency to better predict risk levels across their jurisdictions and allocate resources according. While no application of predictive analytics will be perfect, forecasting risk based on models of the past can help officers and analysts to anticipate the appropriate next steps.
Near repeat pattern analysis helps officers quantify the risk that arises from multiple incidents happening close to one another in space and time. What we are quantifying is how the fact that your neighbor's house is burgled raises your risk of a burglary in the coming days and weeks.
With load forecasting we are looking at cyclical temporal patterns in incidents. How does the time of year, time of day, and day of week change the levels of crime incidents that we should expect across a jurisdiction? By modeling these cyclical patterns we can project crime levels into the future, helping law enforcement agencies to allocate resources appropriately as well as better manage organizational accountability.
This document describes a real-time crime analysis and alert system called CrimeX. It analyzes historical crime data and ingests real-time crime and user data to provide crime alerts to users. The system ingests large amounts of crime data from various sources, processes it using Python scripts, and indexes it in Elasticsearch. It then processes real-time crime and user location data to identify nearby crimes and alert users. The system aims to help understand criminal behavior and dynamics between criminals and law enforcement. It outlines the data flow and technical challenges around performance and security.
This document describes a real-time crime analysis and alert system called CrimeX. It collects and analyzes crime data from various sources to provide insights into criminal activity and alerts users in real-time. The system ingests raw crime data, refines it using batch processing and Python scripts, then indexes it for real-time queries in Elasticsearch. It uses the indexed data to analyze past crimes based on user locations and alert nearby users of current criminal activity. The system was optimized to reduce front-end loading times and network latency between processing components.
1) Journalists are increasingly expected to analyze data to uncover stories and trends, rather than just report on isolated events.
2) Open data from sources like governments and communities can be used with tools like Excel, Google Fusion Tables, and Tableau to clean, visualize, and analyze information.
3) Effective data stories connect different types of data to provide context and insight into issues, rather than just describing events. Personalizing stories for readers can also make data journalism more engaging.
HunchLab 2.0 Predictive Missions: Under the HoodAzavea
HunchLab is a predictive policing software that uses machine learning to analyze historical crime data and predict future crime hotspots. It represents common crime theories like risk terrain modeling and routine activity theory as variables. The modeling process involves generating training examples from years of data, enriching it with geographic and temporal variables, building and evaluating multiple models using techniques like gradient boosting and generalized additive models, and selecting the best performing model. HunchLab aims to learn from a jurisdiction's unique data to help prioritize police patrols.
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Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
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The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
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Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
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Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
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2. Introduction/ Overview
• Crime situation in the United States has always been an issue
• Reason being: freedom to own weapons, poor economic situation
• Los Angeles, New York and Chicago tops all major U.S. cities in
crime issue
4. H/W Experimental Specs
Cluster Version: IBM Analytics Engine
Number of Nodes: 2
Memory Size: 16 GB x2
CPU : 4vcpu x2
HDFS Disk : 600 GB x2
5. 3.1 GB
Data size > 3GB (GigaByte):
Extra credit 1.5 out of 100% (1.5 points = 3GB x 0.5)
(Screenshot of dataset size in computer files properties)
Data Size
6. Raw Data Source: Dataset URLs
Los Angeles
https://data.lacity.org/A-Safe-City/Crime-Data-from-2010-to-Present/y8tr-7khq
New York
https://data.cityofnewyork.us/Public-Safety/NYPD-Complaint-Data-Historic/qgea-i56i
Chicago
https://www.kaggle.com/currie32/crimes-in-chicago/data
9. Facts about New York Crime Situation
• The overall crime rate in NY is 28% lower than national average
• For every 100,000 people, there are 5.58 daily crimes that occur in NY
• NY is safer than 22% of the cities in the United States
• In NY, you have a 1 in 50 chance of becoming a victim of any crime
(Provide proof/reference in term paper)
16. Facts about Los Angeles Crime Situation
• The overall crime rate in LA is 13% higher than national average
• For every 100,000 people, there are 8.75 daily crimes that occur in LA
• In LA, you have a 1 in 32 chance of becoming a victim of any crime
• The number of total year over year crimes has increased by 7%
23. Facts about Chicago Crime Situation
• The overall crime rate in Chicago is 51% higher than national average
• For every 100,000 people, there are 11.77 daily crimes that occur in
Chicago
• Chicago is safer than 4% of the cities in the United States
• In Chicago, you have a 1 in 24 chance of becoming a victim of any crime
30. In Comparison
● Crime Severity (in average):
Chicago (most dangerous) > New York > Los Angeles > (safest)
● However, theft-related crime is the common crime type among 3 cities
● Crimes can be committed anytime throughout the day
● Economic situation is undeniably one of the most important factor that
caused people to commit crimes
● Generally, crime will always present when population density is high