Big Data in Public Safety
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An introductory presentation about possibilities that Big Data opens to public safety company like e.g. taking advantage of smart city grids, crime and accident databases
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Big Data in Public Safety
- 1. Image source: http://sfdata.startupweekend.org/files/2014/05/Fotolia_41498462_M1.jpg IN PUBLIC SAFETY Michal Kosinski
- 2. Winning the customer More: How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did, How Companies Learn Your Secrets Photo illustration by James Emmerman. Photo Courtesy of Shutterstock.
- 3. Predicting and influencing behavior imgsrc:https://www.seoclerk.com/pics/122043-1.jpg Imgsrc:http://www.thesleuthjournal.com/wp-content/uploads/2013/10/mind-control2.jpg More: Facebook 'likes' can reveal your secrets, study finds, Everything You Need to Know About Facebook’s Controversial Emotion Experiment Could Facebook swing an election?
- 4. Gimme moar money! Moar: How Kaggle Solves Big Problems With Big Data Contests, Improve HealthCare, win $3,000,000, NASA’s Asteroid Grand Challenge
- 5. Source: http://www.darpa.mil/uploadedImages/Content/NewsEvents/Releases/2012/cyber_144_144(1).png
- 9. Why now? „We've always had the data, but it is only now we have tools to ask a question of that data and get the answer back before we forget why we asked the question in the first place.” -- Hilary Mason More: Dirty Secrets of Data Science by Hilary Mason
- 11. Smart Grids source: http://smartgridtech.wordpress.com/smart-grid/ More: This Danish Island Powered By Renewables Is Creating Followers Worldwide
- 12. Smart Cities & IoT Big Picture
- 13. Smart Parking Trafic congestion Smart Roads City Sensors Air pollution Landslide and Avalanche Earthquake early detection Environment Sensors Chemical leakage detection Leakages detection River floods Water Sensors Smart Grids Tank level (oil, gas) Water flow Metering Sensors Explosive and Hazardous gases Radiation levels Liquid presence Security & Emergency Sensors Sensors for Smart Cities More: 50 Sensor Applications for a Smarter World
- 14. Smart Cities Now cities around the world are expected to spend $41 trillion in the next 20 years ($108 billion by 2020) on infrastructure upgrade Source: In the wake of Intel’s deal with San José, what makes a smart city?
- 15. More about Smart Cities • IBM SmarterCity Public Safety (video) • IBM Smarter Public Safety (video) • IBM Miami-Dade Police Department - IBM Smarter Planet Leadership Series Client Success Video (video) • TED: Kent Larson: Brilliant designs to fit more people in every city (video) • Miami-Dade County and IBM Establish Public-Private Partnership on Smarter Cities Initiative • Cisco will make KC a 'smart city‘ • Governments can bridge costs and services gaps with sensor networks
- 16. SMAC in Public Safety •How to best allocate limited resources to multiple events •money efficiency •Better prioritization •Spend less on IT infrastructure – more on critical resources •On-demand, real-time, scalable •Gives ability to deliver realtime data to responders in the field •Makes actions more traceable (source for data analytics) •Historical event analysis •Making improvements for the future •Source for data analysis •Additional information for responders •Sometimes easier to call for help •Collaboration between emergency agencies & public Social Mobile (Data) Analytics Cloud More: How Mobility, Analytics, Cloud and Social Media are Changing the Game in Emergency Management
- 17. „Social” in action Source: http://mashable.com/2012/02/06/social-media-public-safety-2/ Photo source: http://www.preppersworldusa.com/2014/01/25/columbia-mall-shooting-press-conference-police-confirmed-3-people-killed/
- 19. Data Science, stupid! in fact, it’s data analysis on steroids more: The Culture of Big Data Analytics, Interactive chart: Drew Conway blog, Image source: Data Science e Python
- 20. OSEMN model Obtain • From other location • Query from database or API • Extract from another file • Generate data (e.g. Sensors) Scrub • Filtering lines • Extracting columns or words • Rreplacing values • Handling missing values • Converting formats Explore • Understanding data • Deriving statistics • Creating visualizations Model • Clustering • Classification • Regression • Dimensionality reduction iNterpret • Drawing conclusion from data • Evaluating meaning of results • Communicating result Source: A Taxonomy of Data Science
- 21. Who is Data Scientist • Key skills • Math & Stats • NoSQL (& SQL) • Python, R & Java • Algorithms • Soft skills • Visualization
- 22. Data Scientist Roadmap Source: http://nirvacana.com/thoughts/becoming-a-data-scientist/
Editor's Notes
- Up until 2006 there was some data available from various web pages, but contemporary technical solutions were inadequate to process it (SQL databases, single core servers) In 1998 Google was founded In 2006 Google announced BigTable and GFS In coming years, many open source implementations of BigTable were created, many companies went into it like Facebook, Twitter, Amazon Data analytics already existed, but were limited to small amounts of data
- Customers are attached to their routines that are so ingrained in their mind, that it is almost impossible to change their habits Target found out, that if woman is in the second trimester, if she’s going to do shopping there, most likely they attach her to the brand and she will be doing more and more shopping there Entire thing was discovered when father of teenage girl in Minessota learned that his daughter received an email with coupons for baby care products. He was outraged, but then his daughter admit being pregnant.
- University of Cambridge findings from ~60k people – ability to predict based on „likes” only: Caucasian or African-American, gender, male sexuality, Democratic or Republican leanings, and detecting Christians and Muslims (80-95% of accuracy) Smoking, using drugs, drinking alcohol (~70% accuraccy) Single vs in relationship (67% accurracy) Parents divorced before age of 21 (60% accurracy) For one week in 2012, Facebook altered the algorithms it uses to determine which status updates appeared in the News Feed of 689,003 randomly selected users (about 1 of every 2,500 Facebook users). For people who had positive content reduced in their News Feed, a larger percentage of words in people’s status updates were negative and a smaller percentage were positive. When negativity was reduced, the opposite pattern occurred. These results suggest that the emotions expressed by friends, via online social networks, influence our own moods, constituting, to our knowledge, the first experimental evidence for massive-scale emotional contagion via social networks The firm announced plans to mine data of millions of American voters, just as an experiment which tried to encourage people to vote was disclosed.
- Netflix contest $1,000,000 – recommendations for new movies Heritage Provider Network - $3,000,000 - Identify patients who will be admitted to a hospital within the next year NASA contest $35,000 – spotting earth-threatening asteroids General Electric $250,000 – optimization of flight routes
- There is no one good definition Standard data storage solutions (SQL databases) are not sufficient
- Multiple sources Plethora of sources Enormous data ingestion per minute
- Smarter resource management Water Energy Green gases generation Cost optimization Disaster detection Forest fires Leakages Traffic accidents Life & health quality improvement
- Interesting sensors from public safety perspective
- 96% of public safety agencies use social networks for push/pull
- Several larger agencies have established dedicated units (often within police departments) to provide real-time intelligence. Real-time crime centers operate in several major U.S. cities — notably, New York City and Houston, TX. These centers have access to powerful data aggregation and decision support tools. The New York City Police Department has created a social media unit within its intelligence division. Criminals are stupid enough to use social media during commiting a crime Crime analysis Situational awareness Crime reduction strategies Patrol resource optimization
- 140,000 to 190,000 people with deep analytic skills as well as 1.5 million managers and analysts will be needed by 2018 to fill jobs in Big Data (Source: McKinsey)
- There are two types of data scientist: PhD level, where they invent new stuff Data Engineer level, where they process data