As we develop our crime analysis software, HunchLab, we are always on the look out for ways of examining and improving data quality as well as new academic research that shows promise to enhance crime analysis.
In this one-hour webinar, we first explain some of the ways we examine data quality when we utilize historic incident datasets for research and analysis and how you can use these techniques in your department. Then, we walk through a series of analytic techniques and practices that can help your department improve your crime analysis processes.
Data Philly Meetup for 2/19/2013 on geospatial data science with crime data and applications of GeoTrellis to solve challenges related to large data sets.
This was a presentation on Azavea's high performance server geoprocessing work at the PlanningTech@DUSP conference hosting by the MIT Department of Urban Studies and Planning (DUSP).
Order Fulfillment Forecasting at John Deere: How R Facilitates Creativity and...Revolution Analytics
Statistical analysis has been known to be invaluable to any manufactory’s quality assurance for decades. Recently the value of valid statistical analysis has also been demonstrated to radically improve the ability of a company’s ability to weather extreme peaks and valley in customer demand. John Deere has been able to adjust to commodity spikes and housing downturns much better than its competitors have. This is in part due to the implementation of statistical analysis and the use of R software in the order fulfillment function of John Deere.
This presentation was given at Orange County's Infill and Redevelopment Conference and it explained the tools and techniques used to identify potential locations for infill development.
As we develop our crime analysis software, HunchLab, we are always on the look out for ways of examining and improving data quality as well as new academic research that shows promise to enhance crime analysis.
In this one-hour webinar, we first explain some of the ways we examine data quality when we utilize historic incident datasets for research and analysis and how you can use these techniques in your department. Then, we walk through a series of analytic techniques and practices that can help your department improve your crime analysis processes.
Data Philly Meetup for 2/19/2013 on geospatial data science with crime data and applications of GeoTrellis to solve challenges related to large data sets.
This was a presentation on Azavea's high performance server geoprocessing work at the PlanningTech@DUSP conference hosting by the MIT Department of Urban Studies and Planning (DUSP).
Order Fulfillment Forecasting at John Deere: How R Facilitates Creativity and...Revolution Analytics
Statistical analysis has been known to be invaluable to any manufactory’s quality assurance for decades. Recently the value of valid statistical analysis has also been demonstrated to radically improve the ability of a company’s ability to weather extreme peaks and valley in customer demand. John Deere has been able to adjust to commodity spikes and housing downturns much better than its competitors have. This is in part due to the implementation of statistical analysis and the use of R software in the order fulfillment function of John Deere.
This presentation was given at Orange County's Infill and Redevelopment Conference and it explained the tools and techniques used to identify potential locations for infill development.
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.
Crime Risk Forecasting and Predictive Analytics - Esri UCAzavea
Presentation at the 2011 Esri User Conference that included an overview of HunchLab features related to forecasting, specifically near repeat forecasts and load forecasts.
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.
Analysis of Crime Big Data using MapReduceKaushik Rajan
Analyzed Crime Big data of Washington DC to solve the following business queries:
> Which hour has the highest crime count?
> Which shift has the highest crime count?
> Year wise crime count
> Hour wise crime count
> Crime count by an offense
> Average of Shift wise crime count
The data was initially stored in MySql which was then moved to HDFS using SQOOP, from where 4 MapReduce operations are doing using JAVA in Eclipse IDE. The outputs of the queries are then moved to HBase using SQOOP. Two more MapReduce operations are done using PIG, the output of which is also moved to HBase using SQOOP. All the outputs were then moved to the local system and are visualized using RStudio and Tableau.
Tools used:
> MySQL, HDFS and HBase to store the data
> SCOOP to move the data from one database to another
> JAVA (Eclipse IDE) and PIG to run the MapReduce queries
> RStudio for data pre-processing and visualization
> Tableau for visualization
> LATEX for Documentation
Crime Early Warning: Automated Data Mining of CAD and RMSAzavea
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/
A Survey on Data Mining Techniques for Crime Hotspots PredictionIJSRD
A crime is an act which is against the laws of a country or region. The technique which is used to find areas on a map which have high crime intensity is known as crime hotspot prediction. The technique uses the crime data which includes the area with crime rate and predict the future location with high crime intensity. The motivation of crime hotspot prediction is to raise people’s awareness regarding the dangerous location in certain time period. It can help for police resource allocation for creating a safe environment. The paper presents survey of different types of data mining techniques for crime hotspots prediction.
What is data-driven government for public safety?IBM Analytics
How can governments become data-driven and capitalize on the ton of valuable insight hidden in the flood of data we generate every day? Where has this already been implemented, and what are the effects? Get the big picture on public safety and incident and emergency management at http://ibm.co/saferplanet
CEP: Event-Decision Architecture for PredictiveBusiness, July 2006Tim Bass
CEP: Event-Decision Architecture for PredictiveBusiness, Centre for Strategic Infocomm Technologies (CSIT), Singapore July 26, 2006, Tim Bass, CISSP, Principal Global Architect, Director, TIBCO Software Inc.
Dataset from National Institute of justice about the crimes of San Francisco. Apply Network Analysis after calculation of distances between different crime points as nodes of a city and then put the approach.
Using New Tools to Analyze and Plan Your Urban Forest Azavea
Planting locations are often determined by organization goals, available funding, practical logistics that influence the number of trees you can plant and where you can plant them, and dozens of other factors. With the new toolkit from OpenTreeMap you can use existing sociodemographic and land-use data to make more informed planting decisions, and estimate the future environmental and economic benefits of your trees.
7 misconceptions about predictive policing webinarAzavea
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.
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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.
Crime Risk Forecasting and Predictive Analytics - Esri UCAzavea
Presentation at the 2011 Esri User Conference that included an overview of HunchLab features related to forecasting, specifically near repeat forecasts and load forecasts.
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.
Analysis of Crime Big Data using MapReduceKaushik Rajan
Analyzed Crime Big data of Washington DC to solve the following business queries:
> Which hour has the highest crime count?
> Which shift has the highest crime count?
> Year wise crime count
> Hour wise crime count
> Crime count by an offense
> Average of Shift wise crime count
The data was initially stored in MySql which was then moved to HDFS using SQOOP, from where 4 MapReduce operations are doing using JAVA in Eclipse IDE. The outputs of the queries are then moved to HBase using SQOOP. Two more MapReduce operations are done using PIG, the output of which is also moved to HBase using SQOOP. All the outputs were then moved to the local system and are visualized using RStudio and Tableau.
Tools used:
> MySQL, HDFS and HBase to store the data
> SCOOP to move the data from one database to another
> JAVA (Eclipse IDE) and PIG to run the MapReduce queries
> RStudio for data pre-processing and visualization
> Tableau for visualization
> LATEX for Documentation
Crime Early Warning: Automated Data Mining of CAD and RMSAzavea
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/
A Survey on Data Mining Techniques for Crime Hotspots PredictionIJSRD
A crime is an act which is against the laws of a country or region. The technique which is used to find areas on a map which have high crime intensity is known as crime hotspot prediction. The technique uses the crime data which includes the area with crime rate and predict the future location with high crime intensity. The motivation of crime hotspot prediction is to raise people’s awareness regarding the dangerous location in certain time period. It can help for police resource allocation for creating a safe environment. The paper presents survey of different types of data mining techniques for crime hotspots prediction.
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How can governments become data-driven and capitalize on the ton of valuable insight hidden in the flood of data we generate every day? Where has this already been implemented, and what are the effects? Get the big picture on public safety and incident and emergency management at http://ibm.co/saferplanet
CEP: Event-Decision Architecture for PredictiveBusiness, July 2006Tim Bass
CEP: Event-Decision Architecture for PredictiveBusiness, Centre for Strategic Infocomm Technologies (CSIT), Singapore July 26, 2006, Tim Bass, CISSP, Principal Global Architect, Director, TIBCO Software Inc.
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Planting locations are often determined by organization goals, available funding, practical logistics that influence the number of trees you can plant and where you can plant them, and dozens of other factors. With the new toolkit from OpenTreeMap you can use existing sociodemographic and land-use data to make more informed planting decisions, and estimate the future environmental and economic benefits of your trees.
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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.
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2011 NIJ Crime Mapping Conference - Data Mining and Risk Forecasting in Web-based Analysis Tools
1. 340 N 12 th St, Suite 402 Philadelphia, PA 19107 215.925.2600 [email_address] www.azavea.com/hunchlab Data Mining and Risk Forecasting in Web-Based Analysis Tools
2.
3.
4.
5. HunchLab was developed, in part, based upon work supported by the National Science Foundation under Grant Nos. IIP-0637589 and IIP-0750507.
8. Complainant 911 Operator Radio Dispatcher Police Officer District 48 Desk Daily download & Geocoding Routines Incident Report Completed by Officer Maps distributed Through Intranet, Printing, CompStat INCT & PARS – main database sources over 5,000 incidents daily, over 2 million annually CAD Verizon 911 INCT District X District Y District Z PARS