This document outlines a business analytics project analyzing crime data from San Francisco over a 5 year period from 2010-2015. The project aims to identify factors that promote criminal behavior to more accurately predict crime. Variables like weather, demographics, and transportation are analyzed to build regression models correlating these factors with crime occurrences. The top 10 most frequent and severe crimes are also identified based on offense data. Maps and predictive modeling are used to understand crime patterns and help deploy police resources more efficiently.
Crime Mapping & Analysis – Georgia Tech
Crime analysis is a law enforcement function that involves systematic analysis for identifying and analyzing patterns and trends in crime and disorder. Information on patterns can help law enforcement agencies deploy resources in a more effective manner, and assist detectives in identifying and apprehending suspects.
Crime Mapping & Analysis – Georgia Tech
Crime analysis is a law enforcement function that involves systematic analysis for identifying and analyzing patterns and trends in crime and disorder. Information on patterns can help law enforcement agencies deploy resources in a more effective manner, and assist detectives in identifying and apprehending suspects.
Police
CRJ630 San Diego Police Department
San Diego Police Department. (2013). City of San Diego Fiscal Year 2013 Proposed Budget. Retrieved from:
http://www.sandiego.gov/fm/proposed/pdf/2013/vol2/v2police.pdf
Page Intentionally Left Blank
Police
Department Description
For 123 years, the San Diego Police Department (SDPD) has served the residents of this City with professionalism,
dependability, and integrity. In addition to the full-service headquarters building, the City is represented by nine area
commands divided into 19 service areas, policing 123 neighborhoods. The Department provides patrol, traffic,
investigative, records, permits and licensing, laboratory, and support services.
The mission of the Department is accomplished through the practice of community-based policing and problem
solving known as Neighborhood Policing. This approach requires a shared responsibility between the Police
Department and the residents of San Diego for addressing underlying problems contributing to crime and the fear of
crime. The men and women of the SDPD work together in a problem-solving partnership with communities,
government agencies, private groups, and individuals to fight crime and improve the quality of life for the residents
and visitors of San Diego.
The Department's mission is:
To maintain peace and order by providing the highest quality police services
Goals and Objectives
The following goals and objectives represent the action plan for the Department:
Goal 1: Improve quality of life for all
The Police Department’s highest priority is to ensure that San Diego is safe for all of its residents. The Department
will move toward accomplishing this goal by focusing on the following objectives:
• Reduce violent crime through the prevention, identification, and apprehension of criminal offenders
• Maintain priority call response times
• Ensure effective policing by addressing command and community priorities
- 419 - City of San Diego
Fiscal Year 2013 Proposed Budget
Police
Goal 2: Strive for continuous improvement in efficiency and effectiveness
In the pursuit of operational excellence, it is important to continuously seek ways in which to operate as efficiently
and effectively as possible. The Department will move toward accomplishing this goal by focusing on the following
objectives:
• Effectively utilize and manage resources
• Efficiently manage staffing levels
• Ensure continuous improvement of operations by identifying best practices in policing
• Pursue funding sources for new technology and equipment
Goal 3: Ensure accountability to high standards of performance, ethics, and professional conduct
High standards of integrity, professional conduct, and performance are vital to the success of the Police Department.
The Department will move toward accomplishing this goal by focusing on the following objectives:
• Empower and develop the wor.
We are pleased to invite you to this presentation, that will let you discover how Location Intelligence and Analytics helps Polices, Public Safety and Law Enforcement Agencies to overcome their challenges, such as:
- Better find crime patterns and trends to predict future risk areas and hot spots
- Uncover criminal networks and understand suspect activity patterns, by correlating the right data such as cell phone records or financial transactions
- Empower operations staff with location-driven dashboards to visualize workforce locations in real time, and allocate resources based on ongoing events
- Promote workforce cohesion and diversity, and engage with communities by sharing spatial analysis reports
- All backuped by a demonstration where we will build together a location-driven report from scratch
Take care.
Galigeo Team
DWBI - Criminalytics: Entities affecting the Rate of Crime in Republic of IrelandShrikant Samarth
Task: To develop a data warehouse from multiple structured and unstructured sources of data and implement a minimum of three non-trivial business intelligence queries on the data warehouse with the help of visualizations.
Approach: Created Data warehousing project for Data Warehousing and Business Intelligence module based on entities affecting the rate of crime in the Republic of Ireland. Created Data warehouse and build automated cube to fetch proper data periodically. Used R programming language to clean data, to store data used SQL Server as Database, SSAS for creating Data Cube so the user gets a proper insight of various accident conditions, also used Tableau for various Reports.
Tools: RStudio, SQLServer, SSIS, SSAS, Tableau
VIDEO Description: https://www.youtube.com/watch?v=uRdyZQja66M&t=134s
Hello Criminals! Meet Big Data: Preventing Crime in San Francisco by Predicti...Tarun Amarnath
Throughout the world, people look to San Francisco as a hub for technology; however, this hides a hidden undercurrent of crime in the City by the Bay. My experiment uses Azure ML and Python to predict without bias the category of crime likeliest to occur at a certain time and location in San Francisco.
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.
Peelian Principle in a Data-Driven World, by The R Simmons GroupThe R Simmons Group
Police executives can better engage with the community, and manage crisis with the use of data-driven solutions. We present analysis on how identify community needs, and align them with tactical initiatives. The paper also, presents a detail guide on how to roll our an effective technology strategy that achieves buy in.
Peelian Principle in a Data-Driven World, By The R Simmons groupRufus Simmons III,MBA
How police executives can use data-driven solutions to engage the community, manage crisis, and increase operational efficiency. This white paper present how to analyze community needs, and internal deficits that require data-driven solutions.
4.1
Updated April-09
Lecture Notes
Chapter 4
Enterprise Excellence
Implementation
ENTERPRISE EXCELLENCE
4.2
Updated April-09
Learning Objectives
• Management & Operations Plans
• Enterprise Excellence Projects
• Enterprise Excellence Project decision Process
• Planning the Enterprise Excellence Project
• Tollgate Reviews
• Project Notebook
4.3
Updated April-09
MANAGEMENT AND OPERATIONS PLANS
• The scope and complexity of the
implementation projects will vary from the
executive level, to the management level, to
the operational level
• Each plan, as it is developed and deployed,
will include projects to be accomplished
• Conflicts typically will occur amongst
requirements of quality, cost, and schedule
when executing a project
4.4
Updated April-09
ENTERPRISE EXCELLENCE PROJECTS
• An Enterprise Excellence project will be one of three
types:
1. Technology invention or innovation
2. New product, service, or process development
3. Product, service, or process improvement
• Enterprise Excellence uses the scientific method
• The scientific method is a process of organizing
empirical facts and their interrelationships in a
manner that allows a hypothesis to be developed and
tested
4.5
Updated April-09
ENTERPRISE EXCELLENCE PROJECTS
• The scientific method consists of the
following steps:
1. Observe and describe the situation
2. Formulate a hypothesis
3. Use the hypothesis to predict results
4. Perform controlled tests to confirm the hypothesis
4.6
Updated April-09
ENTERPRISE EXCELLENCE PROJECTS
• Figure 4.1 shows the project decision process
4.7
Updated April-09
ENTERPRISE EXCELLENCE PROJECT
DECISION PROCESS
• Inventing/Innovating Technology:
Technology development is accomplished using
system engineering
This system approach enables critical functional
parameters and responses to be quickly transferred
into now products, services, and processes
The process is a four-phase process (I2DOV):
Invention & Innovation – Develop – Optimize – Verify
4.8
Updated April-09
ENTERPRISE EXCELLENCE PROJECT
DECISION PROCESS
• Development of Products, Services, and
Processes
The Enterprise Excellence approach for developing
products, services, and processes is the Design for
Lean Six Sigma strategy.
This strategy helps to incorporate customer
requirements and expectations into the product
and/or service.
Concept – Design – Optimize - Verify (CDOV) is a
specific sequential design & development process
used to execute the design strategy.
4.9
Updated April-09
ENTERPRISE EXCELLENCE PROJECT
DECISION PROCESS
• Improving Products, Services, and Processes:
Improving products, services and processes usually
involves the effectiveness and efficiency of operations.
A product or service is said to be effective when it meets
all of its customer requirements.
Effectiveness can be simply expressed as "doing the
right things the first time ...
Police
CRJ630 San Diego Police Department
San Diego Police Department. (2013). City of San Diego Fiscal Year 2013 Proposed Budget. Retrieved from:
http://www.sandiego.gov/fm/proposed/pdf/2013/vol2/v2police.pdf
Page Intentionally Left Blank
Police
Department Description
For 123 years, the San Diego Police Department (SDPD) has served the residents of this City with professionalism,
dependability, and integrity. In addition to the full-service headquarters building, the City is represented by nine area
commands divided into 19 service areas, policing 123 neighborhoods. The Department provides patrol, traffic,
investigative, records, permits and licensing, laboratory, and support services.
The mission of the Department is accomplished through the practice of community-based policing and problem
solving known as Neighborhood Policing. This approach requires a shared responsibility between the Police
Department and the residents of San Diego for addressing underlying problems contributing to crime and the fear of
crime. The men and women of the SDPD work together in a problem-solving partnership with communities,
government agencies, private groups, and individuals to fight crime and improve the quality of life for the residents
and visitors of San Diego.
The Department's mission is:
To maintain peace and order by providing the highest quality police services
Goals and Objectives
The following goals and objectives represent the action plan for the Department:
Goal 1: Improve quality of life for all
The Police Department’s highest priority is to ensure that San Diego is safe for all of its residents. The Department
will move toward accomplishing this goal by focusing on the following objectives:
• Reduce violent crime through the prevention, identification, and apprehension of criminal offenders
• Maintain priority call response times
• Ensure effective policing by addressing command and community priorities
- 419 - City of San Diego
Fiscal Year 2013 Proposed Budget
Police
Goal 2: Strive for continuous improvement in efficiency and effectiveness
In the pursuit of operational excellence, it is important to continuously seek ways in which to operate as efficiently
and effectively as possible. The Department will move toward accomplishing this goal by focusing on the following
objectives:
• Effectively utilize and manage resources
• Efficiently manage staffing levels
• Ensure continuous improvement of operations by identifying best practices in policing
• Pursue funding sources for new technology and equipment
Goal 3: Ensure accountability to high standards of performance, ethics, and professional conduct
High standards of integrity, professional conduct, and performance are vital to the success of the Police Department.
The Department will move toward accomplishing this goal by focusing on the following objectives:
• Empower and develop the wor.
We are pleased to invite you to this presentation, that will let you discover how Location Intelligence and Analytics helps Polices, Public Safety and Law Enforcement Agencies to overcome their challenges, such as:
- Better find crime patterns and trends to predict future risk areas and hot spots
- Uncover criminal networks and understand suspect activity patterns, by correlating the right data such as cell phone records or financial transactions
- Empower operations staff with location-driven dashboards to visualize workforce locations in real time, and allocate resources based on ongoing events
- Promote workforce cohesion and diversity, and engage with communities by sharing spatial analysis reports
- All backuped by a demonstration where we will build together a location-driven report from scratch
Take care.
Galigeo Team
DWBI - Criminalytics: Entities affecting the Rate of Crime in Republic of IrelandShrikant Samarth
Task: To develop a data warehouse from multiple structured and unstructured sources of data and implement a minimum of three non-trivial business intelligence queries on the data warehouse with the help of visualizations.
Approach: Created Data warehousing project for Data Warehousing and Business Intelligence module based on entities affecting the rate of crime in the Republic of Ireland. Created Data warehouse and build automated cube to fetch proper data periodically. Used R programming language to clean data, to store data used SQL Server as Database, SSAS for creating Data Cube so the user gets a proper insight of various accident conditions, also used Tableau for various Reports.
Tools: RStudio, SQLServer, SSIS, SSAS, Tableau
VIDEO Description: https://www.youtube.com/watch?v=uRdyZQja66M&t=134s
Hello Criminals! Meet Big Data: Preventing Crime in San Francisco by Predicti...Tarun Amarnath
Throughout the world, people look to San Francisco as a hub for technology; however, this hides a hidden undercurrent of crime in the City by the Bay. My experiment uses Azure ML and Python to predict without bias the category of crime likeliest to occur at a certain time and location in San Francisco.
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.
Peelian Principle in a Data-Driven World, by The R Simmons GroupThe R Simmons Group
Police executives can better engage with the community, and manage crisis with the use of data-driven solutions. We present analysis on how identify community needs, and align them with tactical initiatives. The paper also, presents a detail guide on how to roll our an effective technology strategy that achieves buy in.
Peelian Principle in a Data-Driven World, By The R Simmons groupRufus Simmons III,MBA
How police executives can use data-driven solutions to engage the community, manage crisis, and increase operational efficiency. This white paper present how to analyze community needs, and internal deficits that require data-driven solutions.
4.1
Updated April-09
Lecture Notes
Chapter 4
Enterprise Excellence
Implementation
ENTERPRISE EXCELLENCE
4.2
Updated April-09
Learning Objectives
• Management & Operations Plans
• Enterprise Excellence Projects
• Enterprise Excellence Project decision Process
• Planning the Enterprise Excellence Project
• Tollgate Reviews
• Project Notebook
4.3
Updated April-09
MANAGEMENT AND OPERATIONS PLANS
• The scope and complexity of the
implementation projects will vary from the
executive level, to the management level, to
the operational level
• Each plan, as it is developed and deployed,
will include projects to be accomplished
• Conflicts typically will occur amongst
requirements of quality, cost, and schedule
when executing a project
4.4
Updated April-09
ENTERPRISE EXCELLENCE PROJECTS
• An Enterprise Excellence project will be one of three
types:
1. Technology invention or innovation
2. New product, service, or process development
3. Product, service, or process improvement
• Enterprise Excellence uses the scientific method
• The scientific method is a process of organizing
empirical facts and their interrelationships in a
manner that allows a hypothesis to be developed and
tested
4.5
Updated April-09
ENTERPRISE EXCELLENCE PROJECTS
• The scientific method consists of the
following steps:
1. Observe and describe the situation
2. Formulate a hypothesis
3. Use the hypothesis to predict results
4. Perform controlled tests to confirm the hypothesis
4.6
Updated April-09
ENTERPRISE EXCELLENCE PROJECTS
• Figure 4.1 shows the project decision process
4.7
Updated April-09
ENTERPRISE EXCELLENCE PROJECT
DECISION PROCESS
• Inventing/Innovating Technology:
Technology development is accomplished using
system engineering
This system approach enables critical functional
parameters and responses to be quickly transferred
into now products, services, and processes
The process is a four-phase process (I2DOV):
Invention & Innovation – Develop – Optimize – Verify
4.8
Updated April-09
ENTERPRISE EXCELLENCE PROJECT
DECISION PROCESS
• Development of Products, Services, and
Processes
The Enterprise Excellence approach for developing
products, services, and processes is the Design for
Lean Six Sigma strategy.
This strategy helps to incorporate customer
requirements and expectations into the product
and/or service.
Concept – Design – Optimize - Verify (CDOV) is a
specific sequential design & development process
used to execute the design strategy.
4.9
Updated April-09
ENTERPRISE EXCELLENCE PROJECT
DECISION PROCESS
• Improving Products, Services, and Processes:
Improving products, services and processes usually
involves the effectiveness and efficiency of operations.
A product or service is said to be effective when it meets
all of its customer requirements.
Effectiveness can be simply expressed as "doing the
right things the first time ...
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
1. DSO 510 Business Analytics | Group Project
1
Crime in San Francisco
DSO 510 Business Analytics | Group Project
Phase 4 Presentation
Andrew Chen | Yile Wu | Chi Zhang | Chulsoon Pak
3. DSO 510 Business Analytics | Group Project
3
PHASE I
Define business analytics proposal, data required, data analysis approach,
and decision making and innovation framework
4. DSO 510 Business Analytics | Group Project
4
• 2014 Population: 852,4691
• 13th most populous city in the nation
• Separated into 10 districts
• Top global innovation center, with
highest concentration of technology-
related jobs in the U.S.
• Crime in San Francisco has historically
been higher than the U.S. average
• Crime Index of SF is rated 3 out of 100
(safer than 3% of other U.S. cities)2
BACKGROUND: SAN FRANCISCO
1. United States Census Bureau, July 2014
2. "Crime rates for San Francisco, CA", NeighborhoodScout, 2013
5. DSO 510 Business Analytics | Group Project
5
In order to make San Francisco a safer place,
we aim identify factors that promote criminal
behavior to predict crime more accurately.
GOAL DEFINITION
6. DSO 510 Business Analytics | Group Project
6
DEFINING OUR VARIABLES
Dependent Variables
1. Number of Crimes
2. Time of Crime
3. Date of Crime
4. Severity of Crime
5. Type of Crime
6. Location of Crime
Independent Variables
1. Day of Week
2. Season of Year
3. Weather
4. Daylight
5. Income Level of District
6. Average Housing Price of District
7. Age Composition of District
8. Population density
9. Degree of Urbanization
10. Modes of Transportation
11. Level of Education
12. Divorce rate of Families
7. DSO 510 Business Analytics | Group Project
7
• San Francisco Crime Data:
• https://data.sfgov.org/Public-Safety/
• 700,000+ data points (5 years of data)
• San Francisco Weather, Population,
Housing, Hazard Risk, and Demographics
• www.sfclimatehealth.org/
• http://aa.usno.navy.mil/data/docs/RS_
OneYear.php
• San Francisco Housing, Income Level,
Employment, and Transportation by
District
• www.sf-planning.org
DATA COLLECTION
8. DSO 510 Business Analytics | Group Project
8
INTERPRETATION & ACTION
1. Identify to what degree different variables contribute to crime
2. Predict probability and severity of crimes in terms of time and location
Implementation
1. Assist SFPD in efficient deployment of its police force
2. Integrate data with mapping algorithms to provide the safest real-time routes
3. Organize anti-crime education in high crime areas (how to handle crimes under different
situations)
4. Utilize data in product development and marketing of security-related products
5. Enhance San Francisco city-planning to reduce crimes
10. DSO 510 Business Analytics | Group Project
10
In order to make San Francisco a safer place,
we aim identify factors that promote criminal
behavior to predict crime more accurately.
GOAL DEFINITION
11. DSO 510 Business Analytics | Group Project
11
DEFINING OUR VARIABLES
Dependent Variables
1. Number of Crimes per Day
2. Number of Crimes per Month
3. Time Slot of Crime
4. Date of Crime
5. Severity of Crime
6. Location of Crime
Independent Variables
1. Day of Week
2. Month
3. Weather
4. Daylight
5. Income Level of District
6. Age Composition of District
7. Modes of Transportation
8. Level of Education
9. Employment of District
12. DSO 510 Business Analytics | Group Project
12
SUMMARY STATISTICS
• 5 Years of Data
• From August 2010
• To August 2015
• 726,245 Crimes
Reported
Monthly Statistics
13. DSO 510 Business Analytics | Group Project
13
MONTHLY CRIME DATA (2010 – 2015)
14. DSO 510 Business Analytics | Group Project
14
CRIME PATTERNS BY MONTH OF YEAR
15. DSO 510 Business Analytics | Group Project
15
WHICH CRIMES ARE MOST FREQUENTLY
COMMITTED?
Top 5 Crimes*
1. Theft
2. Assault
3. Vandalism
4. Drug Violation
5. Vehicle Theft
*Other Offenses, Non-Criminal
Offenses, and Warrants are excluded
16. DSO 510 Business Analytics | Group Project
16
CRIMES THAT DEMAND GREATER ATTENTION
Assault,
Robbery,
Missing Person
Theft,
Vandalism
Forcible Sex Offenses,
Murder,
Kidnapping
Disorderly Conduct,
Gambling,
Loitering
HIGH FREQUENCY
LOW FREQUENCY
HIGH
SEVERITY
LOW
SEVERITY
17. DSO 510 Business Analytics | Group Project
17
CRIMES PER DAY OF THE WEEK
• Friday and Saturday’s have
the most crimes committed
– Late night parties/Events
• Sunday and Monday’s have
the least crimes committed
– Church, Family gatherings
– Back to Work/School
18. DSO 510 Business Analytics | Group Project
18
CRIME PER DISTRICT
• Number of Crimes per District
• Some districts have significantly higher crime than others
• A good indicator to help SFPD deploy police forces by districts
19. DSO 510 Business Analytics | Group Project
19
INNER JOIN WITH DAYLIGHT DATA
Crime Data Sunrise and Sunset Data
Inner join by date
20. DSO 510 Business Analytics | Group Project
20
DAYLIGHT AFFECTS SOME TYPES OF CRIMES
• Crime breakdown based on day or nighttime (in percentages)
– Data eliminated our initial hypothesis that crimes are more likely committed during the night
21. DSO 510 Business Analytics | Group Project
21
LOOKING AHEAD
Data Manipulation
• Clean up and join other demographic data to existing data
• Categorize meaningful variables into numeric values in order to
run further statistical models
• Assign values for severity and frequency of each crime
Further Insights
• Dig deeper into crimes by district, day of week, and time of day
• Produce a spatial map of crime
23. DSO 510 Business Analytics | Group Project
23
DEFINING OUR VARIABLES
Dependent Variables
1. Number of Crimes per Day
2. Number of Crimes during the Day
3. Number of Crimes during the Night
4. Number of Crimes per Month
5. Time Slot of Crime
Independent Variables
1. Day of Week
2. Month
3. Average Temperature
4. Precipitation
5. Daylight
6. Income Level of District
7. Age Composition of District
8. Modes of Transportation
9. Level of Education
10. Employment of District
24. DSO 510 Business Analytics | Group Project
24
TEN CRIMES TO FOCUS ON
• Weighted based on frequency and severity of crime sentence
Frequency Low.yr High.yr Avg.yr Weight
LARCENY/THEFT 168,901 0 25 13 2,136,598
ASSAULT 62,449 1 25 13 811,837
DRUG/NARCOTIC 31,180 1 40 20 631,395
ROBBERY 18,652 15 30 23 419,670
BURGLARY 29,020 3 20 12 333,730
SEX OFFENSES,
FORCIBLE
3,927 20 100 60 235,620
FRAUD 14,237 1 25 13 185,081
VEHICLE THEFT 31,002 5 5 5 155,010
KIDNAPPING 2,162 0 100 50 108,208
WEAPON LAWS 7,444 0 20 10 74,812
26. DSO 510 Business Analytics | Group Project
26
LINEAR REGRESSION MODEL
27. DSO 510 Business Analytics | Group Project
27
LINEAR REGRESSION MODEL
• Dependent Variable:
• Total Daily Crime
• Independent Variables:
• Day of Week
• Average Temperature
• Precipitation
• Significance level: <.0001
• R-Squared Value: 0.1956
28. DSO 510 Business Analytics | Group Project
28
RESIDUALS ANALYSIS
32. DSO 510 Business Analytics | Group Project
32
BINARY LOGISTIC REGRESSION
33. DSO 510 Business Analytics | Group Project
33
BINARY LOGISTIC REGRESSION
34. DSO 510 Business Analytics | Group Project
34
BINARY LOGISTIC REGRESSION
35. DSO 510 Business Analytics | Group Project
35
PREDICTIVE MODELING
36. DSO 510 Business Analytics | Group Project
36
PREDICTIVE MODELING
37. DSO 510 Business Analytics | Group Project
37
PREDICTIVE MODELING
38. DSO 510 Business Analytics | Group Project
38
PREDICTIVE MODELING
39. DSO 510 Business Analytics | Group Project
39
PREDICTIVE MODELING
Editor's Notes
10 districts
BAYVIEW CENTRAL INGLESIDE MISSION NORTHERN PARK RICHMOND SOUTHERN TARAVAL TENDERLOIN
10 districts
BAYVIEW CENTRAL INGLESIDE MISSION NORTHERN PARK RICHMOND SOUTHERN TARAVAL TENDERLOIN
Interpretation, Action, and Feedback
Describe interpretation of the data analysis and modeling in the context of the business goal
Outline the possible options and/or decisions available to the business based on the data analysis and modeling
- Adjustments/Improvements to SFPD police deployment
- Connect with map/navigation tools to provide safest routes in real-time
- Contribute to city-planning of SF municipal (by identifying factors that add to- vs. reduce- chances of crime)
1. the analysis could help San Francisco city to launch anticrime education in high crime rate areas. Citizens would realize how to handle crime under different situation.
2. the analysis could help certain anticrime products company to locate their potential customers. According to different crime type, companies could sell products related to "house security", "vehicle alarm", and "self-defense weapon". (aka Amazon)
John
John
Andrew
Charles
These crimes would be weighted higher in terms of our model.
Charles
Dylan
Dylan
Types of Crime to Analyze
Charles
Types of Crime to Analyze
Charles
MISSING – TESTING global null hypothesis
Analysis of Maximum Likelihood Estimates
Only significant for Friday, Saturday, and Sunday
MISSING Association of Predicted Probabilities and Observed Responses
ROC Plot
MISSING
Cumulative Lift is Missing and Bottom Two Statistics are missing as well
ROC Plot
Cumulative Lift is Missing and Bottom Two Statistics are missing as well
ROC Plot
