Predictive policing computational thinking show and tell
•Download as PPTX, PDF•
1 like•1,023 views
Predictive policing uses advanced data analysis and technology to predict where and when crimes are likely to occur based on patterns in historical crime data. The predictions are used to more efficiently deploy law enforcement resources to targeted areas in an effort to prevent crimes before they happen. Predictive policing algorithms analyze large datasets on past crimes, including details like type of crime, location, and timing, to identify patterns and assign probabilities of future criminal activity to specific regions.
Report
Share
Report
Share
Recommended
Predictive Policing on Gun Violence Using Open Data
This presentation is an abstract of a 2013 whitepaper published by PredPol.
PredPol delivers the same predictive accuracy for gun violence using unique mathematical methods. A study of Chicago data shows that PredPol successfully predicts 50% of gun homicides by flagging in real-time only 10.3% of city locations. Knowing where and when gun homicides are most likely to occur empowers law enforcement to use their knowledge, skills and experience to disrupt gun crime before it happens.
The study uses open government data from Chicago and predictive crime analysis.
For the full whitepaper, visit predpol.com & request information.
Predictive Policing - How Emerging Technologies Are Helping Prevent Crimes?
Futuristic technologies like AI, BigData, Analytics, etc., can aid in preventing and predicting crimes big time if implemented optimally.
PredPol: How Predictive Policing Works
PredPol’s cloud-based predictive policing software enables law enforcement agencies to better prevent crime in their communities by generating predictions on the places and times that future crimes are most likely to occur.
PredPol’s technology has been helping law enforcement agencies to dramatically reduce crime in jurisdictions of all types and sizes, across the U.S. and overseas. Over the past year, Atlanta and Los Angeles have reduced specific crimes in targeted areas at rates ranging from nearly 20% to over 40%. Smaller jurisdictions, such as Norcross, Georgia, have seen nearly a 30% reduction in burglaries and robberies; in Alhambra, California, car burglaries have dropped 20% since the software technology was deployed.
Using advanced mathematics and computer learning, PredPol’s algorithms predict many types of crime, including property crimes, drug incidents, gang activity, and gun violence as well as traffic accidents.
Only three pieces of data are used to make predictions – type of crime, place of crime, and time of crime. No personal data is utilized in making these predictions.
Crime analysts and command staff using PredPol are 100% more effective than they are with traditional hotspot mapping at predicting where and when crimes are likely to occur. That means police have twice as many opportunities to deter and reduce crime.
Predictive Policing
Predictive policing uses statistical analysis and data to predict criminal activity and identify crime patterns in order to prevent future crimes and solve past cases. It relies on the idea that criminals tend to operate within a "comfort zone" and commit similar crimes in similar locations. Predictive policing involves collecting large data sets, analyzing the data to identify crime hot spots or individuals at risk of offending, intervening through police operations, and assessing the results to continue refining predictions. While predictive policing shows promise, its effectiveness depends on proper implementation and action based on predictions, and it has certain limitations in predicting some types of crimes.
Analytics-Based Crime Prediction
As per studies conducted by the University of California, it is observed that crime in any area follows the same pattern as that of earthquake aftershocks. It is difficult to predict an earthquake, but once it happens the aftershocks following it are quite predictable. Same is true for the crimes happening in a geographical area.
7 misconceptions about predictive policing webinar
Over the last few years, Predictive Policing has become more common in police departments around the world. With the rising interest in crime forecasting tools, important questions concerning ethics, privacy and fairness have been raised. We know that there are some misconceptions when it comes to the topic, and we want to dispel some of the common myths about Predictive Policing.
We invite you to join us as we walk through 7 Misconceptions of Predictive Policing. In this webinar, we aim to discuss some of the charged rhetoric and beliefs that surround the term. Also, we will highlight the some of the diverse crime modeling concepts that are used to make robust, predictions when forecasting crime.
Application of GIS in Criminology and Defence Intelligence
This document discusses the application of geographic information systems (GIS) in criminology and defense intelligence. It provides examples of how GIS has been used to map crime rates and identify spatial patterns in criminal behavior. GIS allows crime analysis to identify crime hotspots, support investigative leads, and help allocate law enforcement resources more efficiently. The document also outlines how GIS aids tactical crime analysis and criminal investigations through geographic profiling. Finally, it notes that GIS is increasingly important for military applications by helping commanders understand terrain influences on operations.
report
This document summarizes research analyzing crime data from Atlanta and Georgia Tech. It discusses using patrol analysis and identifying hot spots to optimize police patrol routes. Time series analysis of crime data revealed seasonal patterns, with some crime types peaking in September. Hot spot analysis identified concentrated areas of crime in Atlanta using statistical tests, with the nearest neighbor index method most accurately representing hot spots. In conclusion, optimizing patrol routes based on crime patterns and hot spots could lower crime rates and improve police efficiency.
Recommended
Predictive Policing on Gun Violence Using Open Data
This presentation is an abstract of a 2013 whitepaper published by PredPol.
PredPol delivers the same predictive accuracy for gun violence using unique mathematical methods. A study of Chicago data shows that PredPol successfully predicts 50% of gun homicides by flagging in real-time only 10.3% of city locations. Knowing where and when gun homicides are most likely to occur empowers law enforcement to use their knowledge, skills and experience to disrupt gun crime before it happens.
The study uses open government data from Chicago and predictive crime analysis.
For the full whitepaper, visit predpol.com & request information.
Predictive Policing - How Emerging Technologies Are Helping Prevent Crimes?
Futuristic technologies like AI, BigData, Analytics, etc., can aid in preventing and predicting crimes big time if implemented optimally.
PredPol: How Predictive Policing Works
PredPol’s cloud-based predictive policing software enables law enforcement agencies to better prevent crime in their communities by generating predictions on the places and times that future crimes are most likely to occur.
PredPol’s technology has been helping law enforcement agencies to dramatically reduce crime in jurisdictions of all types and sizes, across the U.S. and overseas. Over the past year, Atlanta and Los Angeles have reduced specific crimes in targeted areas at rates ranging from nearly 20% to over 40%. Smaller jurisdictions, such as Norcross, Georgia, have seen nearly a 30% reduction in burglaries and robberies; in Alhambra, California, car burglaries have dropped 20% since the software technology was deployed.
Using advanced mathematics and computer learning, PredPol’s algorithms predict many types of crime, including property crimes, drug incidents, gang activity, and gun violence as well as traffic accidents.
Only three pieces of data are used to make predictions – type of crime, place of crime, and time of crime. No personal data is utilized in making these predictions.
Crime analysts and command staff using PredPol are 100% more effective than they are with traditional hotspot mapping at predicting where and when crimes are likely to occur. That means police have twice as many opportunities to deter and reduce crime.
Predictive Policing
Predictive policing uses statistical analysis and data to predict criminal activity and identify crime patterns in order to prevent future crimes and solve past cases. It relies on the idea that criminals tend to operate within a "comfort zone" and commit similar crimes in similar locations. Predictive policing involves collecting large data sets, analyzing the data to identify crime hot spots or individuals at risk of offending, intervening through police operations, and assessing the results to continue refining predictions. While predictive policing shows promise, its effectiveness depends on proper implementation and action based on predictions, and it has certain limitations in predicting some types of crimes.
Analytics-Based Crime Prediction
As per studies conducted by the University of California, it is observed that crime in any area follows the same pattern as that of earthquake aftershocks. It is difficult to predict an earthquake, but once it happens the aftershocks following it are quite predictable. Same is true for the crimes happening in a geographical area.
7 misconceptions about predictive policing webinar
Over the last few years, Predictive Policing has become more common in police departments around the world. With the rising interest in crime forecasting tools, important questions concerning ethics, privacy and fairness have been raised. We know that there are some misconceptions when it comes to the topic, and we want to dispel some of the common myths about Predictive Policing.
We invite you to join us as we walk through 7 Misconceptions of Predictive Policing. In this webinar, we aim to discuss some of the charged rhetoric and beliefs that surround the term. Also, we will highlight the some of the diverse crime modeling concepts that are used to make robust, predictions when forecasting crime.
Application of GIS in Criminology and Defence Intelligence
This document discusses the application of geographic information systems (GIS) in criminology and defense intelligence. It provides examples of how GIS has been used to map crime rates and identify spatial patterns in criminal behavior. GIS allows crime analysis to identify crime hotspots, support investigative leads, and help allocate law enforcement resources more efficiently. The document also outlines how GIS aids tactical crime analysis and criminal investigations through geographic profiling. Finally, it notes that GIS is increasingly important for military applications by helping commanders understand terrain influences on operations.
report
This document summarizes research analyzing crime data from Atlanta and Georgia Tech. It discusses using patrol analysis and identifying hot spots to optimize police patrol routes. Time series analysis of crime data revealed seasonal patterns, with some crime types peaking in September. Hot spot analysis identified concentrated areas of crime in Atlanta using statistical tests, with the nearest neighbor index method most accurately representing hot spots. In conclusion, optimizing patrol routes based on crime patterns and hot spots could lower crime rates and improve police efficiency.
Crime analysis of different situations
It helps in analyzing the similar types of crimes happening and also for predicting the future crimes
06 analysis of crime
Crime analysis involves the systematic study of crime and disorder problems using qualitative and quantitative data. It examines sociodemographic, spatial, and temporal factors to assist police in apprehension, reduction, prevention, and evaluation. Crime analysis developed from early uses of pin maps and began professionalizing in the 1980s. It includes administrative, investigative, tactical, and strategic types. Ratcliffe's Hotspot Matrix examines crime concentrations spatially as dispersed, clustered, or a hotpoint and temporally as diffused, focused, or acute to determine appropriate police tactics. Strategic crime analysis uses tactical analysis to identify long-term community problems and generate innovative solutions in partnership with community policing.
Crime analysis
GIS aids crime analysis by identifying patterns and trends, supporting intelligence-led policing strategies, and integrating diverse data sources. It enhances crime analysis by highlighting suspicious incidents, supporting cross-jurisdictional pattern analysis, and educating the public. GIS provides tools to capture crime series, forecast crime, and optimize resource allocation to reduce crime and disorder.
Predictive analysis of crime forecasting
This document discusses various methods for predictive crime mapping, beginning with simply using past crime "hot spots" as a predictor of future hot spots. While this approach has limited accuracy over short periods, past hot spots can predict up to 90% of future crime over longer periods like a year. The document then reviews more sophisticated predictive modeling methods and the role of geographic information systems in developing spatial models to predict crime.
kyung_kim_pura
This report details applying the PPAC (Police Patrol Allocation and Coverage) model to crime data from the Georgia Tech Police Department from 2011-2014. The report cleans the data, formulates the PPAC optimization model to maximize police coverage of areas, particularly high crime "hot spots." The model generates optimal patrol zones. Future work involves re-zoning patrol areas based on the results and incorporating a time component to optimize patrol zones over time.
HunchLab 2.0 Predictive Missions: Under the Hood
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.
Crime analysis
This document outlines a project to analyze crime and census data in London. It describes a multi-phase approach including: 1) loading and visualizing crime data, 2) adding census data to the model and performing clustering and regression analysis, and 3) using the results to inform data mining. Key analysis techniques include k-means clustering of census variables to categorize areas, linear regression of census factors on crime types, and decision tree analysis using both crime and census data. The goal is to understand how socioeconomic factors relate to crime levels and types in different parts of London.
HunchLab 2.0 Preview Webinar - Place
This document presents a preview of the new version of HunchLab with a focus on geographic modeling. HunchLab 2.0 allows police departments to prioritize resource deployments by using predictive analytics that take into account many data sets and reflect the priorities of the police department. The webinar was recorded on September 25th, 2013.
Machine Learning Approaches for Crime Pattern Detection
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.
Fundamentalsof Crime Mapping Tactical Analysis Concepts
This document discusses various spatial analysis techniques for predicting the next location of offenses in a crime series, including standard deviation rectangles and ellipses, convex hull polygons, correlated walk analysis, and analyzing distance between hits, target locations, and journey to crime data. It provides examples of analyses of past crime series where these techniques successfully predicted over 50% of next hits. The document advocates combining multiple analytical methods and data sources to refine location predictions.
Forecasting Space-Time Events - Strata + Hadoop World 2015 San Jose
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 Mapping & Analysis – Georgia Tech
This document summarizes a crime mapping and analysis project conducted for the Georgia Tech Police Department. The objectives were to map crime incidents from 2010-2015, identify crime hot spots, and direct police resources. Crime data was cleaned, geocoded and analyzed in ArcGIS. Point density analysis identified the most crime-heavy grids, with the area around Student Center, Ferst Drive, and North Avenue Apartments among the highest. The analysis can help GTPD better deploy patrols and resources to reduce crime in these locations.
Technical Seminar
Presentation on Big data analytics and mining for effective visualization and trends forecasting of crime data.
The Real-time Police Force: Publishing Analytic Information to the Field with...
http://www.azavea.com/products/hunchlab
Police agencies collect a wealth of data. Every call for services and every incident of crime is captured and logged (and often automatically geocoded to a point in space). Making sense of this wealth of data is critical to police agencies being led by intelligence and analysis and not simply putting cops out into the field haphazardly.
Most police forces have a process whereby this raw information is groomed into maps by a central crime analysis unit. Determining where hotspots are present and describing recent events is definitely useful, but how can we accelerate this process to adapt our analytic output in nearly real-time and then disseminate this information to the field?
The answer is by automating the flow of information. We see this feature as a core strength within our product, HunchLab. New information is automatically pulled into HunchLab through integration with police agencies computer aided dispatch (CAD) and records management systems (RMS). This new information is then immediately incorporated into analytic output. New incidents can trigger early warning alerts for spikes in activity or modify short-term risk assessment in a particular police district. But it's not just about consuming this information within HunchLab itself. The system provides secure access to analytic output via APIs that can be integrated into other back-end applications, further analytic tools, and even mobile applications.
http://www.azavea.com/products/hunchlab
Crime Early Warning: Automated Data Mining of CAD and RMS
The genesis of HunchLab was the idea to mine law enforcement agencies' CAD and RMS databases to detect unusual levels of activity in particular areas and then send alerts to the appropriate police staff. While crime analysis tools often are aiming to display what has happened, the concept of a geographic early warning system, such as within HunchLab, tries to answer the question: "what is unusual that is happening?"
http://www.azavea.com/products/hunchlab/features/early-warning/
Memphis Police Dept
1. The Memphis Police Department was facing increasing crime rates and limited resources to address it.
2. They implemented an analytics solution using predictive modeling to gain insight into crime trends and hotspots.
3. This allowed them to proactively redirect patrols and prevent crimes, reducing serious crime by 30% and violent crime by 15% while increasing conviction rates fourfold with an 863% return on investment.
Hello Criminals! Meet Big Data: Preventing Crime in San Francisco by Predicti...
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.
Crime Analysis & Prediction System
Crime Analysis & Prediction System is a system to analyze & detect crime hotspots & predict crime.
It collects data from various data sources - crime data from OpenData sites, US census data, social media, traffic & weather data etc.
It leverages Microsoft's Azure Cloud and on premise technologies for back-end processing & desktop based visualization tools.
Using Data Mining Techniques to Improve Efficiency in Police Intelligence
This is presentation that was given by Dr Rick Adderley at the UK KDD Symposium on 29th September 2011.
Us Pennsylvania State Police
The Pennsylvania State Police chose ABM's Prophecy incident mapping and predictive analysis tool as part of its Records Management System solution in 2001. The implementation of Prophecy began in May 2003 and was completed within 60 days. During the first eight months of use, fatalities decreased by 7.4% and criminal offenses decreased by 1.3%. Prophecy has given the PSP improved analytical capabilities to more efficiently target resources.
Workware Systems Situational Awareness & Predictive Policing Overview
Front line and community-based Policing is a high knowledge-based activity. Exploiting organisational knowledge in dynamic front line policing activity key to improving productivity. Productivity and effectiveness can be improved by pushing information to front line officers which is:
- Relevant to their role
- Relevant to their location
- Relevant in time
Our solutions Help Patrol Officers understand their environment and take action. We do this without significant cost and complexity.
Online crime investigation
Globally, public safety organizations are under severe pressure to carry out criminal inquiries with fast turnaround times, while keeping people and property safe.
More Related Content
What's hot
Crime analysis of different situations
It helps in analyzing the similar types of crimes happening and also for predicting the future crimes
06 analysis of crime
Crime analysis involves the systematic study of crime and disorder problems using qualitative and quantitative data. It examines sociodemographic, spatial, and temporal factors to assist police in apprehension, reduction, prevention, and evaluation. Crime analysis developed from early uses of pin maps and began professionalizing in the 1980s. It includes administrative, investigative, tactical, and strategic types. Ratcliffe's Hotspot Matrix examines crime concentrations spatially as dispersed, clustered, or a hotpoint and temporally as diffused, focused, or acute to determine appropriate police tactics. Strategic crime analysis uses tactical analysis to identify long-term community problems and generate innovative solutions in partnership with community policing.
Crime analysis
GIS aids crime analysis by identifying patterns and trends, supporting intelligence-led policing strategies, and integrating diverse data sources. It enhances crime analysis by highlighting suspicious incidents, supporting cross-jurisdictional pattern analysis, and educating the public. GIS provides tools to capture crime series, forecast crime, and optimize resource allocation to reduce crime and disorder.
Predictive analysis of crime forecasting
This document discusses various methods for predictive crime mapping, beginning with simply using past crime "hot spots" as a predictor of future hot spots. While this approach has limited accuracy over short periods, past hot spots can predict up to 90% of future crime over longer periods like a year. The document then reviews more sophisticated predictive modeling methods and the role of geographic information systems in developing spatial models to predict crime.
kyung_kim_pura
This report details applying the PPAC (Police Patrol Allocation and Coverage) model to crime data from the Georgia Tech Police Department from 2011-2014. The report cleans the data, formulates the PPAC optimization model to maximize police coverage of areas, particularly high crime "hot spots." The model generates optimal patrol zones. Future work involves re-zoning patrol areas based on the results and incorporating a time component to optimize patrol zones over time.
HunchLab 2.0 Predictive Missions: Under the Hood
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.
Crime analysis
This document outlines a project to analyze crime and census data in London. It describes a multi-phase approach including: 1) loading and visualizing crime data, 2) adding census data to the model and performing clustering and regression analysis, and 3) using the results to inform data mining. Key analysis techniques include k-means clustering of census variables to categorize areas, linear regression of census factors on crime types, and decision tree analysis using both crime and census data. The goal is to understand how socioeconomic factors relate to crime levels and types in different parts of London.
HunchLab 2.0 Preview Webinar - Place
This document presents a preview of the new version of HunchLab with a focus on geographic modeling. HunchLab 2.0 allows police departments to prioritize resource deployments by using predictive analytics that take into account many data sets and reflect the priorities of the police department. The webinar was recorded on September 25th, 2013.
Machine Learning Approaches for Crime Pattern Detection
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.
Fundamentalsof Crime Mapping Tactical Analysis Concepts
This document discusses various spatial analysis techniques for predicting the next location of offenses in a crime series, including standard deviation rectangles and ellipses, convex hull polygons, correlated walk analysis, and analyzing distance between hits, target locations, and journey to crime data. It provides examples of analyses of past crime series where these techniques successfully predicted over 50% of next hits. The document advocates combining multiple analytical methods and data sources to refine location predictions.
Forecasting Space-Time Events - Strata + Hadoop World 2015 San Jose
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 Mapping & Analysis – Georgia Tech
This document summarizes a crime mapping and analysis project conducted for the Georgia Tech Police Department. The objectives were to map crime incidents from 2010-2015, identify crime hot spots, and direct police resources. Crime data was cleaned, geocoded and analyzed in ArcGIS. Point density analysis identified the most crime-heavy grids, with the area around Student Center, Ferst Drive, and North Avenue Apartments among the highest. The analysis can help GTPD better deploy patrols and resources to reduce crime in these locations.
Technical Seminar
Presentation on Big data analytics and mining for effective visualization and trends forecasting of crime data.
The Real-time Police Force: Publishing Analytic Information to the Field with...
http://www.azavea.com/products/hunchlab
Police agencies collect a wealth of data. Every call for services and every incident of crime is captured and logged (and often automatically geocoded to a point in space). Making sense of this wealth of data is critical to police agencies being led by intelligence and analysis and not simply putting cops out into the field haphazardly.
Most police forces have a process whereby this raw information is groomed into maps by a central crime analysis unit. Determining where hotspots are present and describing recent events is definitely useful, but how can we accelerate this process to adapt our analytic output in nearly real-time and then disseminate this information to the field?
The answer is by automating the flow of information. We see this feature as a core strength within our product, HunchLab. New information is automatically pulled into HunchLab through integration with police agencies computer aided dispatch (CAD) and records management systems (RMS). This new information is then immediately incorporated into analytic output. New incidents can trigger early warning alerts for spikes in activity or modify short-term risk assessment in a particular police district. But it's not just about consuming this information within HunchLab itself. The system provides secure access to analytic output via APIs that can be integrated into other back-end applications, further analytic tools, and even mobile applications.
http://www.azavea.com/products/hunchlab
Crime Early Warning: Automated Data Mining of CAD and RMS
The genesis of HunchLab was the idea to mine law enforcement agencies' CAD and RMS databases to detect unusual levels of activity in particular areas and then send alerts to the appropriate police staff. While crime analysis tools often are aiming to display what has happened, the concept of a geographic early warning system, such as within HunchLab, tries to answer the question: "what is unusual that is happening?"
http://www.azavea.com/products/hunchlab/features/early-warning/
Memphis Police Dept
1. The Memphis Police Department was facing increasing crime rates and limited resources to address it.
2. They implemented an analytics solution using predictive modeling to gain insight into crime trends and hotspots.
3. This allowed them to proactively redirect patrols and prevent crimes, reducing serious crime by 30% and violent crime by 15% while increasing conviction rates fourfold with an 863% return on investment.
Hello Criminals! Meet Big Data: Preventing Crime in San Francisco by Predicti...
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.
Crime Analysis & Prediction System
Crime Analysis & Prediction System is a system to analyze & detect crime hotspots & predict crime.
It collects data from various data sources - crime data from OpenData sites, US census data, social media, traffic & weather data etc.
It leverages Microsoft's Azure Cloud and on premise technologies for back-end processing & desktop based visualization tools.
Using Data Mining Techniques to Improve Efficiency in Police Intelligence
This is presentation that was given by Dr Rick Adderley at the UK KDD Symposium on 29th September 2011.
Us Pennsylvania State Police
The Pennsylvania State Police chose ABM's Prophecy incident mapping and predictive analysis tool as part of its Records Management System solution in 2001. The implementation of Prophecy began in May 2003 and was completed within 60 days. During the first eight months of use, fatalities decreased by 7.4% and criminal offenses decreased by 1.3%. Prophecy has given the PSP improved analytical capabilities to more efficiently target resources.
What's hot (20)
Machine Learning Approaches for Crime Pattern Detection
Machine Learning Approaches for Crime Pattern Detection
Fundamentalsof Crime Mapping Tactical Analysis Concepts
Fundamentalsof Crime Mapping Tactical Analysis Concepts
Forecasting Space-Time Events - Strata + Hadoop World 2015 San Jose
Forecasting Space-Time Events - Strata + Hadoop World 2015 San Jose
The Real-time Police Force: Publishing Analytic Information to the Field with...
The Real-time Police Force: Publishing Analytic Information to the Field with...
Crime Early Warning: Automated Data Mining of CAD and RMS
Crime Early Warning: Automated Data Mining of CAD and RMS
Hello Criminals! Meet Big Data: Preventing Crime in San Francisco by Predicti...
Hello Criminals! Meet Big Data: Preventing Crime in San Francisco by Predicti...
Using Data Mining Techniques to Improve Efficiency in Police Intelligence
Using Data Mining Techniques to Improve Efficiency in Police Intelligence
Similar to Predictive policing computational thinking show and tell
Workware Systems Situational Awareness & Predictive Policing Overview
Front line and community-based Policing is a high knowledge-based activity. Exploiting organisational knowledge in dynamic front line policing activity key to improving productivity. Productivity and effectiveness can be improved by pushing information to front line officers which is:
- Relevant to their role
- Relevant to their location
- Relevant in time
Our solutions Help Patrol Officers understand their environment and take action. We do this without significant cost and complexity.
Online crime investigation
Globally, public safety organizations are under severe pressure to carry out criminal inquiries with fast turnaround times, while keeping people and property safe.
Predective policing
Predictive policing uses data analysis to help law enforcement anticipate and prevent crime. By analyzing disparate data sources, police can identify patterns to predict future crime trends and deploy resources proactively. While some question if this is truly innovative, most see it as building on proven policing strategies like problem-oriented policing and intelligence-led policing. The National Institute of Justice is funding demonstration projects using predictive policing techniques to evaluate their effectiveness.
Propose Data Mining AR-GA Model to Advance Crime analysis
This document proposes a data mining model to advance crime analysis using association rule (AR) and genetic algorithm (GA). The model has three correlated dimensions: a crime dataset, criminal dataset, and geo-crime dataset. AR will be applied to each dataset separately to extract patterns, then GA will be used to mix the resulting ARs and exploit relationships across the three dimensions. This is intended to help detect universal crime patterns and speed up the crime solving process. The model was applied to real crime data from a sheriff's office and validated. Privacy-preserving techniques are also suggested to hide sensitive rules from appearing in the results.
Predective policingachtergrond
This document summarizes a discussion at the first Predictive Policing Symposium held in 2009. The symposium brought together researchers and practitioners to discuss using advanced analytics and predictive models to anticipate crime locations and enable proactive, targeted police strategies. While predictive policing shows promise, issues around privacy, civil liberties, and ensuring police legitimacy were raised. The document also provides an example of how statistical modeling of past crime incidents can identify locations with an increased likelihood of future crimes, allowing police to focus resources preventatively.
Crime analysis mapping, intrusion detection using data mining
This document discusses using data mining techniques like K-nearest neighbors and artificial neural networks for crime analysis mapping and intrusion detection. It proposes collecting crime data, grouping it, clustering it, and forecasting it to help reduce crime rates. The existing system relies on manual storage and analysis of crime data. The proposed system would use data mining tools like artificial neural networks and knowledge discovery in databases to automatically analyze crime data collected from police to help identify patterns and solve cases. It outlines collecting both supervised and unsupervised crime data, identifying unnecessary data, and using tools like SAM to train on supervised data and solve other cases.
Crime analysis mapping, intrusion detection using data mining
Crime analysis mapping, intrusion detection using data mining
Data Mining plays a key role in Crime Analysis. There are many different algorithms mentioned in previous research papers, among them are the virtual identifier, pruning strategy, support vector machines, and apriori algorithms. VID is to find relation between record and vid. The apriori algorithm helps the fuzzy association rules algorithm and it takes around six hundred seconds to detect a mail bomb attack. In this research paper, we identified Crime mapping analysis based on KNN (K – Nearest Neighbor) and ANN (Artificial Neural Network) algorithms to simplify this process. Crime Mapping is conducted and Funded by the Office of Community Oriented Policing Services (COPS). Evidence based research helps in analyzing the crimes. We calculate the crime rate based on the previous data using data mining techniques. Crime Analysis uses quantitative and qualitative data in combination with analytic techniques in resolving the cases. For public safety purposes, the crime mapping is an essential research area to concentrate on. We can identity the most frequently crime occurring zones with the help of data mining techniques. In Crime Analysis Mapping, we follow the following steps in order to reduce the crime rate: 1) Collect crime data 2) Group data 3) Clustering 4) Forecasting the data. Crime Analysis with crime mapping helps in understanding the concepts and practice of Crime Analysis in assisting police and helps in reduction and prevention of crimes and crime disorders.
Mr. Friend is acrime analystwith the SantaCruz, Califo.docx
Mr. Friend is a
crime analyst
with the Santa
Cruz, California,
Police
Department.
Predictive Policing: Using Technology to Reduce Crime
By Zach Friend, M.P.P.
4/9/2013
Nationwide law enforcement agencies face the problem
of doing more with less. Departments slash budgets
and implement furloughs, while management struggles
to meet the public safety needs of the community. The
Santa Cruz, California, Police Department handles the
same issues with increasing property crimes and
service calls and diminishing staff. Unable to hire more
officers, the department searched for a nontraditional
solution.
In late 2010 researchers published a paper that the
department believed might hold the answer. They
proposed that it was possible to predict certain crimes,
much like scientists forecast earthquake aftershocks.
An “aftercrime” often follows an initial crime. The time and location of previous criminal activity helps to
determine future offenses. These researchers developed an algorithm (mathematical procedure) that
calculates future crime locations.1
Equalizing Resources
The Santa Cruz Police Department has 94 sworn officers and serves a population of 60,000. A
university, amusement park, and beach push the seasonal population to 150,000. Department personnel
contacted a Santa Clara University professor to apply the algorithm, hoping that leveraging technology
would improve their efforts. The police chief indicated that the department could not hire more officers.
He felt that the program could allocate dwindling resources more efficiently.
Santa Cruz police envisioned deploying officers by shift to the most targeted locations in the city. The
predictive policing model helped to alert officers to targeted locations in real time, a significant
improvement over traditional tactics.
Making it Work
The algorithm is a culmination of anthropological and criminological behavior research. It uses complex
mathematics to estimate crime and predict future hot spots. Researchers based these studies on
In Depth
Featured Articles
- IAFIS Identifies Suspect from 1978 Murder Case
- Predictive Policing: Using Technology to Reduce
Crime
- Legal Digest Part 1 - Part 2
Search Warrant Execution: When Does Detention Rise to
Custody?
- Perspective
Public Safety Consolidation: Does it Make Sense?
- Leadership Spotlight
Leadership Lessons from Home
Archive
- Web and Print
Departments
- Bulletin Notes - Bulletin Honors
- ViCAP Alerts - Unusual Weapons
- Bulletin Reports
Topics in the News
See previous LEB content on:
- Hostage Situations - Crisis Management
- School Violence - Psychopathy
About LEB
- History - Author Guidelines (pdf)
- Editorial Staff - Editorial Release Form (pdf)
Patch Call
Known locally as the
“Gateway to the Summit,”
which references the city’s
proximity to the Bechtel Family
National Scout Reserve. More
The patch of the Miamisburg,
Ohio, Police Department
prominently displays the city
seal surroun.
Mr. Friend is acrime analystwith the SantaCruz, Califo.docx
Mr. Friend is a
crime analyst
with the Santa
Cruz, California,
Police
Department.
Predictive Policing: Using Technology to Reduce Crime
By Zach Friend, M.P.P.
4/9/2013
Nationwide law enforcement agencies face the problem
of doing more with less. Departments slash budgets
and implement furloughs, while management struggles
to meet the public safety needs of the community. The
Santa Cruz, California, Police Department handles the
same issues with increasing property crimes and
service calls and diminishing staff. Unable to hire more
officers, the department searched for a nontraditional
solution.
In late 2010 researchers published a paper that the
department believed might hold the answer. They
proposed that it was possible to predict certain crimes,
much like scientists forecast earthquake aftershocks.
An “aftercrime” often follows an initial crime. The time and location of previous criminal activity helps to
determine future offenses. These researchers developed an algorithm (mathematical procedure) that
calculates future crime locations.1
Equalizing Resources
The Santa Cruz Police Department has 94 sworn officers and serves a population of 60,000. A
university, amusement park, and beach push the seasonal population to 150,000. Department personnel
contacted a Santa Clara University professor to apply the algorithm, hoping that leveraging technology
would improve their efforts. The police chief indicated that the department could not hire more officers.
He felt that the program could allocate dwindling resources more efficiently.
Santa Cruz police envisioned deploying officers by shift to the most targeted locations in the city. The
predictive policing model helped to alert officers to targeted locations in real time, a significant
improvement over traditional tactics.
Making it Work
The algorithm is a culmination of anthropological and criminological behavior research. It uses complex
mathematics to estimate crime and predict future hot spots. Researchers based these studies on
In Depth
Featured Articles
- IAFIS Identifies Suspect from 1978 Murder Case
- Predictive Policing: Using Technology to Reduce
Crime
- Legal Digest Part 1 - Part 2
Search Warrant Execution: When Does Detention Rise to
Custody?
- Perspective
Public Safety Consolidation: Does it Make Sense?
- Leadership Spotlight
Leadership Lessons from Home
Archive
- Web and Print
Departments
- Bulletin Notes - Bulletin Honors
- ViCAP Alerts - Unusual Weapons
- Bulletin Reports
Topics in the News
See previous LEB content on:
- Hostage Situations - Crisis Management
- School Violence - Psychopathy
About LEB
- History - Author Guidelines (pdf)
- Editorial Staff - Editorial Release Form (pdf)
Patch Call
Known locally as the
“Gateway to the Summit,”
which references the city’s
proximity to the Bechtel Family
National Scout Reserve. More
The patch of the Miamisburg,
Ohio, Police Department
prominently displays the city
seal surroun ...
Crime Analysis based on Historical and Transportation Data
Contains experimental results based on real crime data from an urban city. Our set of statistics reveals seasonality in crime patterns to accompany predictive machine learning models assessing the risks of crime. Moreover, this work provides a discussion on implementation, design for a prototype of cloud based crime analytics dashboard.
A predictive model for mapping crime using big data analytics
Abstract Crime reduction and prevention challenges in today’s world are becoming increasingly complex and are in need of a new technique that can handle the vast amount of information that is being generated. Traditional police capabilities mostly fall short in depicting the original division of criminal activities, thus contribute less in the suitable allocation of police services. In this paper methods are described for crime event forecasting, using Hadoop, by studying the geographical areas which are at greater risk and outside the traditional policing limits. The developed method makes the use of a geographical crime mapping algorithm to identify areas that have relatively high cases of crime. The term used for such places is hot spots. The identified hotspot clusters give valuable data that can be used to train the artificial neural network which further can model the trends of crime. The artificial neural network specification and estimation approach is enhanced by processing capability of Hadoop platform. Keywords— Crime forecasting; Cluster analysis; artificial neural networks; Patrolling; Big data; Hadoop; Gamma test.
Crime Data Analysis and Prediction for city of Los Angeles
This document analyzes crime data from Los Angeles from 2010-2020 to identify trends, predict future crime rates, and make recommendations to law enforcement. Key findings include:
- Crime rates have generally declined over the past decade but dropped significantly in 2020 due to the pandemic.
- Robbery, burglary, and vandalism are the most common crimes.
- Areas with lower median household incomes tend to have higher crime rates.
- Females are consistently the most impacted victims of crime over the past 10 years.
- Southwest LA and other areas have been identified as "hot spots" for criminal activity.
Predictive analysis indicates crime rates will continue increasing post-lockdown in
The International Journal of Engineering and Science (IJES)
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Application of data science in predictive policing
This document discusses the transformative role of data science and predictive policing in law enforcement. Predictive policing uses data science methods like crime mapping, predictive analytics, and behavioral analysis to anticipate criminal activity and optimize resource allocation. While this proactive approach enhances situational awareness and prevention, challenges regarding data quality, bias, privacy, and transparency must be addressed. Effective predictive policing requires training law enforcement in data science and supporting technology implementation, with the goal of moving from reactive to proactive crime strategies through ethical and collaborative practices.
Crime
This document discusses using data mining techniques like clustering to detect crime patterns from crime data. It proposes using a k-means clustering algorithm with attribute weighting to group similar crimes. Testing on real crime data from a sheriff's office, it was able to identify crime patterns that detectives could validate matched actual crime sprees. The method provides an automated way to detect patterns and help detectives solve crimes faster by focusing on clustered groups of related incidents.
Case.pptx
This document proposes a crime analysis system using data mining and machine learning techniques. It involves collecting large amounts of unstructured data from various online sources, storing it in a NoSQL database for flexibility, classifying crimes using naive Bayes, identifying patterns using clustering, and potentially adding criminal profiling capabilities in the future. The goal is to develop a more efficient tool for law enforcement to detect crime patterns and help solve cases faster.
Physical and Cyber Crime Detection using Digital Forensic Approach: A Complet...
Criminalization may be a general development that has significantly extended in previous few years. In
order, to create the activity of the work businesses easy, use of technology is important. Crime investigation analysis
is a section records in data mining plays a crucial role in terms of predicting and learning the criminals. In our
paper, we've got planned an incorporated version for physical crime as well as cybercrime analysis. Our approach
uses data mining techniques for crime detection and criminal identity for physical crimes and digitized forensic tools
(DFT) for evaluating cybercrimes. The presented tool named as Comparative Digital Forensic Process tool
(CDFPT) is entirely based on digital forensic model and its stages named as Comparative Digital Forensic Process
Model (CDFPM). The primary step includes accepting the case details, categorizing the crime case as physical crime
or cybercrime and sooner or later storing the data in particular databases. For physical crime analysis we've used kmeans
approach cluster set of rules to make crime clusters. The k-means method effects are a lot advantageous by the
utilization of GMAPI generation. This provides advanced and consumer-friendly visual-aid to k-means approach for
tracing the region of the crime. we have applied KNN for criminal identification with the
help of observing beyond crimes and finding similar ones that suit this crime, if no past document is discovered then
the new crime sample are introduced to the crime data-set. With the advancements of web, the network form has
become much more complicated and attacking methods are further more than that as well. For crime analysis
we're detecting the attacks executed on host system through an outsider the usage of
assorted digitized forensic tools to produce information security with the help of generating reports for an
event which could need any investigation. Our digitized technique aids the development of the society
by helping the investigation businesses to follow a custom-built investigative technique in crime analysis and criminal
identification as opposed to manually looking the database to analyze criminal activities, and as a
result facilitate them in combating crimes.
Crime forecasting system for soic final
The document proposes developing a Crime Forecasting System (CFS) to help reduce crime rates in Karachi, Pakistan. The CFS would collect and analyze crime incident data to help law enforcement agencies predict crime patterns and prevent crimes. It would provide a user-friendly interface with crime reports, maps showing crime locations, and statistical analysis. The goal is to address current issues like a lack of data sharing between agencies, inefficient resource allocation, inconsistent data storage, and slow response times. The CFS could help cut crime rates by enabling more proactive policing strategies.
Crime Analysis
This document discusses crime analysis and its applications in community-oriented policing. Crime analysis involves understanding crime patterns through statistical analysis and crime mapping to identify problems and potential solutions. It helps police departments target areas with high crime rates or unusual increases in crime. Crime analysis also examines relationships between crimes in terms of time, location, offender characteristics, and causal factors to aid investigations of serial crimes and displacement. The core functions of law enforcement like prevention, investigation, and apprehension can be enhanced through crime analysis.
The Importance Of Intelligence-Led Policing
The document discusses the importance of intelligence-led policing for modern law enforcement agencies. As this concept has shaped policing, the need for intelligence analysts has grown. Analysts must have research, analytical, critical thinking, and communication skills to effectively interpret data and disseminate timely intelligence. Community policing and intelligence-led policing can complement each other by helping gather more intelligence than using just one strategy alone. Gathering intelligence about communities is important for productive community relations and counter-terrorism efforts.
Similar to Predictive policing computational thinking show and tell (20)
Workware Systems Situational Awareness & Predictive Policing Overview
Workware Systems Situational Awareness & Predictive Policing Overview
Propose Data Mining AR-GA Model to Advance Crime analysis
Propose Data Mining AR-GA Model to Advance Crime analysis
Crime analysis mapping, intrusion detection using data mining
Crime analysis mapping, intrusion detection using data mining
Crime analysis mapping, intrusion detection using data mining
Crime analysis mapping, intrusion detection using data mining
Mr. Friend is acrime analystwith the SantaCruz, Califo.docx
Mr. Friend is acrime analystwith the SantaCruz, Califo.docx
Mr. Friend is acrime analystwith the SantaCruz, Califo.docx
Mr. Friend is acrime analystwith the SantaCruz, Califo.docx
Crime Analysis based on Historical and Transportation Data
Crime Analysis based on Historical and Transportation Data
A predictive model for mapping crime using big data analytics
A predictive model for mapping crime using big data analytics
Crime Data Analysis and Prediction for city of Los Angeles
Crime Data Analysis and Prediction for city of Los Angeles
The International Journal of Engineering and Science (IJES)
The International Journal of Engineering and Science (IJES)
Application of data science in predictive policing
Application of data science in predictive policing
Physical and Cyber Crime Detection using Digital Forensic Approach: A Complet...
Physical and Cyber Crime Detection using Digital Forensic Approach: A Complet...
Recently uploaded
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
How to Get CNIC Information System with Paksim Ga.pptx
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
Building Production Ready Search Pipelines with Spark and Milvus
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Programming Foundation Models with DSPy - Meetup Slides
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
UiPath Test Automation using UiPath Test Suite series, part 5
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Pushing the limits of ePRTC: 100ns holdover for 100 days
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
Microsoft - Power Platform_G.Aspiotis.pdf
Revolutionizing Application Development
with AI-powered low-code, presentation by George Aspiotis, Sr. Partner Development Manager, Microsoft
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Recently uploaded (20)
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5
Pushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 days
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Predictive policing computational thinking show and tell
- 1. + Predictive Policing “Predicting a crime even before a criminal’s thought of it”
- 2. + Welcome to the future of policing
- 3. + What is it? Predictive policing, with roots in business analytics, relies on using advanced technological tools and data analysis to take proactive measures to “pre-empt” crime. Its big change from the focus of re-active system to the pre-emptive system. Time Magazine named predictive policing one of 50 best inventions for 2011
- 4. + Aims of a Predictive Policing System Assign probabilities of future crime events to regions of space and time Present estimated crime risk in a useable framework to law enforcement decision makers Lead to more efficient & more accurate resource deployment by local law enforcement agencies
- 5. + How it works? A Predictive policing uses complex mathematics to estimate crime and predict future hot spots. Researchers based these studies on information that officers inherently know. For example, when people are victims, the chance that they or their neighbors will be victimized again increases. Offenders criminalize familiar areas. There are detectable patterns associated with the times and locations of their crimes.
- 6. +
- 7. + Algorithm The algorithm for Predictive Policing is based on an Earthquake after shock prediction algorithm which is essentially an pattern detection algorithm. The algorithm uses historical information combined with current data to determine patterns. The system needs between 1,200 and 2,000 data points, including burglaries, batteries, assaults, or other crimes . Then it will break down this list into individual elements, and join them one by one to form a detectable pattern.
- 9. +