This paper empirically examines the impact of Virginia's 1995 abolition of parole on criminal activity using time series analysis. Descriptive statistics show that most crime rates declined slightly after 1995, except for assault, robbery, and auto theft rates. Regression models with seasonal effects find the policy intervention was not a statistically significant predictor of any crime rate. Adding unemployment and consumer price index variables did not change these insignificant results. While the models found an initial drop in crime rates at the time of intervention, rates quickly rebounded and remained largely stable and unaffected by the stricter sentencing policy. The findings do not support the hypothesis that increasing sentencing costs deters criminal behavior.
Crime Data Analysis and Prediction for city of Los AngelesHeta Parekh
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
1) If criminal justice policy was based on evidence of cost-effectiveness, there would be less spending on prisons and more on police, resulting in fewer serious crimes and less total harm to society while also reducing threats of state bankruptcy.
2) Evidence shows prisons have unknown effects on deterrence and rehabilitation but waste resources by incarcerating many low-risk offenders, while policing high-crime areas and high-risk offenders and individuals can more cost-effectively reduce crime.
3) Adopting risk-based policies could make the criminal justice system more cost-effective by allocating prison, police, and probation/parole resources according to offenders' risks of committing serious or frequent crimes.
Using Narcotics Arrest Data To Predict Violent Crimechad_e_smith
Dallas has a high crime rate, with over 15,000 gun crimes per year. The Dallas Police Department (DPD) uses basic crime analysis methods to track crime trends over time. DPD targets crime hot spots but needs better tools to predict exact crime locations to lower the overall crime rate. This study will use narcotics arrest data from Dallas PD records to build a Geographically Weighted Regression (GWR) model to predict locations of violent crime. The model will be evaluated by comparing its predictions to actual violent crime data, with the goal of helping DPD deploy resources more efficiently and develop improved crime prevention strategies.
Predicting deaths from COVID-19 using Machine LearningIdanGalShohet
The document summarizes research to predict the impact of COVID-19 in US counties using census and COVID-19 case data. Key points:
- Researchers combined 2017 US Census data with COVID-19 case data to build a machine learning model to predict virus impact without relying solely on testing data.
- A logistic regression model was created and optimized to minimize false negatives by focusing on recall. The random selection algorithm on unbalanced data performed best with 85.17% weighted accuracy.
- The best model used unemployment rate, median income, percentage Asian population, family work percentage, and mean commute time as attributes to predict if a county would have 1 or more COVID-19 deaths.
Unit 8 project identifying crime patterns e hallElizabeth Hall
The crime data from the Coeur D'Alene Police Department shows an overall increase in Part I crimes from 2003 to 2004, with robberies, aggravated assaults, and homicides increasing significantly. The only crime that did not change was motor vehicle theft. More information on population changes and location data could provide context for the increases. Strategic analysis of robbery, homicide, and aggravated assault trends may help identify patterns.
Using Data Mining Techniques to Analyze Crime PatternZakaria Zubi
Our proposed model will be able to extract crime patterns by using association rule mining and clustering to classify crime records on the basis of the values of crime attributes.
AVAILABILITY OF THE FIRST SET OF INDICATORS RECOMMENDED BY THE STATISTICAL CO...Víctor Barragán
This document summarizes the availability of indicators on violence against women recommended by the UN Statistical Commission from 59 nationally representative surveys. It finds:
- Total rates of physical/sexual violence were available in a minority of surveys, pointing to methodological obstacles. Age-specific rates were available in about a third.
- Relationship to the perpetrator was the main focus, but classifications varied significantly between surveys.
- Last two indicators on intimate partner violence need reformulating to include both physical and sexual violence using "and/or". Their denominators could also just use ever-partnered women like other indicators.
- Frequency was largely missing across all indicators, being available in only a small number of surveys. Standard
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.
Crime Data Analysis and Prediction for city of Los AngelesHeta Parekh
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
1) If criminal justice policy was based on evidence of cost-effectiveness, there would be less spending on prisons and more on police, resulting in fewer serious crimes and less total harm to society while also reducing threats of state bankruptcy.
2) Evidence shows prisons have unknown effects on deterrence and rehabilitation but waste resources by incarcerating many low-risk offenders, while policing high-crime areas and high-risk offenders and individuals can more cost-effectively reduce crime.
3) Adopting risk-based policies could make the criminal justice system more cost-effective by allocating prison, police, and probation/parole resources according to offenders' risks of committing serious or frequent crimes.
Using Narcotics Arrest Data To Predict Violent Crimechad_e_smith
Dallas has a high crime rate, with over 15,000 gun crimes per year. The Dallas Police Department (DPD) uses basic crime analysis methods to track crime trends over time. DPD targets crime hot spots but needs better tools to predict exact crime locations to lower the overall crime rate. This study will use narcotics arrest data from Dallas PD records to build a Geographically Weighted Regression (GWR) model to predict locations of violent crime. The model will be evaluated by comparing its predictions to actual violent crime data, with the goal of helping DPD deploy resources more efficiently and develop improved crime prevention strategies.
Predicting deaths from COVID-19 using Machine LearningIdanGalShohet
The document summarizes research to predict the impact of COVID-19 in US counties using census and COVID-19 case data. Key points:
- Researchers combined 2017 US Census data with COVID-19 case data to build a machine learning model to predict virus impact without relying solely on testing data.
- A logistic regression model was created and optimized to minimize false negatives by focusing on recall. The random selection algorithm on unbalanced data performed best with 85.17% weighted accuracy.
- The best model used unemployment rate, median income, percentage Asian population, family work percentage, and mean commute time as attributes to predict if a county would have 1 or more COVID-19 deaths.
Unit 8 project identifying crime patterns e hallElizabeth Hall
The crime data from the Coeur D'Alene Police Department shows an overall increase in Part I crimes from 2003 to 2004, with robberies, aggravated assaults, and homicides increasing significantly. The only crime that did not change was motor vehicle theft. More information on population changes and location data could provide context for the increases. Strategic analysis of robbery, homicide, and aggravated assault trends may help identify patterns.
Using Data Mining Techniques to Analyze Crime PatternZakaria Zubi
Our proposed model will be able to extract crime patterns by using association rule mining and clustering to classify crime records on the basis of the values of crime attributes.
AVAILABILITY OF THE FIRST SET OF INDICATORS RECOMMENDED BY THE STATISTICAL CO...Víctor Barragán
This document summarizes the availability of indicators on violence against women recommended by the UN Statistical Commission from 59 nationally representative surveys. It finds:
- Total rates of physical/sexual violence were available in a minority of surveys, pointing to methodological obstacles. Age-specific rates were available in about a third.
- Relationship to the perpetrator was the main focus, but classifications varied significantly between surveys.
- Last two indicators on intimate partner violence need reformulating to include both physical and sexual violence using "and/or". Their denominators could also just use ever-partnered women like other indicators.
- Frequency was largely missing across all indicators, being available in only a small number of surveys. Standard
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.
Este documento resume el análisis financiero del sistema de cooperativas de ahorro y crédito en Ecuador entre diciembre de 2011 y diciembre de 2012. Los principales hallazgos son: 1) Los activos del sistema alcanzaron los $3.832 millones, un incremento del 20.4%; 2) La cartera neta llegó a $2.940 millones, un aumento del 21.6%; 3) La morosidad de la cartera subió a 4.1%.
The document appears to be a Haiku Deck presentation containing photo credits attributed to various photographers. Each line lists the name of a different photographer who contributed photos to the presentation. The presentation encourages the viewer to get started creating their own Haiku Deck presentation on SlideShare.
An industry-level strategy for addressing the runaway complexity costs and safety risks in the software supply chain and a strategy to get from A to B.
After seeing Josh Corman's rather frightening presentation on the trajectory of our industry, and realizing that nobody could solve these problems alone -- I decided to take the initiative.
I put together a rough strategy that could potentially be game-changing, but I need help from the community to figure out how to make it work. If you're up for trying to conquer some of the biggest challenges of our age, please don't hesitate to reach out!
O documento discute a revisão da norma ISO 9001, que está prevista para ser lançada em setembro de 2015. Algumas das principais mudanças incluem uma estrutura de alto nível padronizada para normas de sistemas de gestão, maior ênfase no pensamento baseado em riscos e na liderança da alta administração, e a remoção do requisito de designar um representante da direção. A revisão exigirá que as organizações certificadas realizem uma transição de três anos para se adequarem aos novos requ
This document provides an overview of the literature related to balanced scorecards and performance measurement in organizations. It begins by discussing the importance of strategy and performance measurement for organizations. It then introduces the balanced scorecard concept developed by Kaplan and Norton, which uses four perspectives - financial, customer, internal processes, and learning and growth - to translate strategy into objectives and measures. The literature highlighted that balanced scorecards can help improve communications, align business activities with strategy, and monitor overall performance. It also discussed each of the four perspectives in more detail and how they work together. The document provides relevant background information and establishes a framework for understanding balanced scorecards and how they can be used to measure organizational performance.
This short story is a Father's Day message from children to their father. They express gratitude for all he does in looking after them, helping them, and supporting them. They say he is the reason for their smiles and that he is their hero. On Father's Day, they want him to relax while they do things for him to show their appreciation for everything he does as their father every day.
Este documento describe conceptos fundamentales de bases de datos, incluyendo tipos de bases de datos como operativas, distribuidas y externas. También explica el procesamiento tradicional de archivos, el enfoque de administración de bases de datos y funciones de un sistema de administración de bases de datos como crear, mantener y utilizar bases de datos. Finalmente, cubre principios técnicos como estructuras de bases de datos relacionales, multidimensionales y orientadas a objetos.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Este documento describe la evolución de la Web desde sus inicios hasta la actualidad. Explica que la Web 1.0 consistía en páginas estáticas con poca interacción de los usuarios, mientras que la Web 2.0 permite la colaboración y contenido generado por los usuarios. Finalmente, anticipa que la Web 3.0 podría consistir en aplicaciones más pequeñas y personalizadas que gestionan datos en la nube y pueden usarse en cualquier dispositivo.
Mark Widdowson is an experienced Centre Manager seeking new opportunities. He has over 10 years of experience managing logistics centers for G4S, with responsibility for multi-million pound budgets, large teams, and customer contracts. He has a strong track record of improving safety, reducing costs and attrition, and achieving targets through process improvements and employee engagement. Currently studying for additional qualifications to further his career in transport and logistics management.
Convegno 29 Novvembre Bologna Cultura d’Europa bene comune: scuola, università, ricerca – Il futuro abita qui.
Intervento Carlo Salmaso
Confronto tra Legge iniziativa popolare per una buona scuola per la repubblica e proposta Renzi
www.lipscuola.it
Gordon the goldfish was lonely being the only fish in his bowl. He saw other goldfish swimming together in the nearby bowl and wanted to join them. Gordon came up with a plan to launch himself from his bowl into the other bowl by swimming fast enough. After building up speed by swimming laps, Gordon pushed off from the bottom of his bowl and flew through the air, landing successfully in the other bowl. However, the other fish were not very welcoming to Gordon and grumbled about his arrival. Still, Gordon was pleased that he achieved his goal of no longer being alone, even if the outcome was not ideal.
Creatividad y tic un complemento para la educaciónTRABAJOSTIC
El documento describe un proyecto que busca implementar el uso de las tecnologías de la información y la comunicación (TIC) para mejorar el desempeño de los estudiantes en el área de educación artística y potenciar su creatividad. El proyecto consiste en tres actividades donde los estudiantes exploran sitios web sobre creatividad y educación artística, plasman ideas artísticas usando medios digitales, y presentan una exhibición multimedia mostrando cómo las TIC pueden mejorar su capacidad creativa.
Anna Mary Robertson, known as Grandma Moses, took up painting in her late 70s after arthritis made needlework difficult. As a self-taught folk artist, she painted simple scenes from her rural life in the Hoosick Valley of New York. Grandma Moses found success late in life, selling her paintings for prices she could not imagine. She enjoyed fame and honors from presidents and governors for her artwork that depicted the virtues of honesty and hard work in rural America. Grandma Moses painted into her 100s, creating landscapes that captured life in the winter seasons until her death at age 101.
Este documento presenta los elementos clave para el desarrollo de un plan estratégico de negocios, incluyendo un análisis interno y externo, el establecimiento de una visión, objetivos y estrategias, así como la investigación y definición del mercado objetivo.
Convegno 29 Novvembre Bologna Cultura d’Europa bene comune: scuola, università, ricerca – Il futuro abita qui.
Intervento Carlo Salmaso
Confronto tra Legge iniziativa popolare per una buona scuola per la repubblica e proposta Renzi
www.lipscuola.it
Predictive analysis of crime forecastingFrank Smilda
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.
IRJET- Crime Analysis using Data Mining and Data AnalyticsIRJET Journal
This document discusses using data mining and analytics techniques to analyze crime data and predict crime rates. It proposes using linear regression on crime data from the Indian government to predict future crime occurrences and identify high-risk regions. The system would analyze factors like crime type, offender age, month, and year to build a regression model. This model could then predict crime rates and indicate whether a region is high or low risk for criminal activity. Graphs and tables would visualize the predictions to help law enforcement allocate resources. The goal is to help reduce crime and increase public safety by identifying patterns in historical crime data.
Este documento resume el análisis financiero del sistema de cooperativas de ahorro y crédito en Ecuador entre diciembre de 2011 y diciembre de 2012. Los principales hallazgos son: 1) Los activos del sistema alcanzaron los $3.832 millones, un incremento del 20.4%; 2) La cartera neta llegó a $2.940 millones, un aumento del 21.6%; 3) La morosidad de la cartera subió a 4.1%.
The document appears to be a Haiku Deck presentation containing photo credits attributed to various photographers. Each line lists the name of a different photographer who contributed photos to the presentation. The presentation encourages the viewer to get started creating their own Haiku Deck presentation on SlideShare.
An industry-level strategy for addressing the runaway complexity costs and safety risks in the software supply chain and a strategy to get from A to B.
After seeing Josh Corman's rather frightening presentation on the trajectory of our industry, and realizing that nobody could solve these problems alone -- I decided to take the initiative.
I put together a rough strategy that could potentially be game-changing, but I need help from the community to figure out how to make it work. If you're up for trying to conquer some of the biggest challenges of our age, please don't hesitate to reach out!
O documento discute a revisão da norma ISO 9001, que está prevista para ser lançada em setembro de 2015. Algumas das principais mudanças incluem uma estrutura de alto nível padronizada para normas de sistemas de gestão, maior ênfase no pensamento baseado em riscos e na liderança da alta administração, e a remoção do requisito de designar um representante da direção. A revisão exigirá que as organizações certificadas realizem uma transição de três anos para se adequarem aos novos requ
This document provides an overview of the literature related to balanced scorecards and performance measurement in organizations. It begins by discussing the importance of strategy and performance measurement for organizations. It then introduces the balanced scorecard concept developed by Kaplan and Norton, which uses four perspectives - financial, customer, internal processes, and learning and growth - to translate strategy into objectives and measures. The literature highlighted that balanced scorecards can help improve communications, align business activities with strategy, and monitor overall performance. It also discussed each of the four perspectives in more detail and how they work together. The document provides relevant background information and establishes a framework for understanding balanced scorecards and how they can be used to measure organizational performance.
This short story is a Father's Day message from children to their father. They express gratitude for all he does in looking after them, helping them, and supporting them. They say he is the reason for their smiles and that he is their hero. On Father's Day, they want him to relax while they do things for him to show their appreciation for everything he does as their father every day.
Este documento describe conceptos fundamentales de bases de datos, incluyendo tipos de bases de datos como operativas, distribuidas y externas. También explica el procesamiento tradicional de archivos, el enfoque de administración de bases de datos y funciones de un sistema de administración de bases de datos como crear, mantener y utilizar bases de datos. Finalmente, cubre principios técnicos como estructuras de bases de datos relacionales, multidimensionales y orientadas a objetos.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Este documento describe la evolución de la Web desde sus inicios hasta la actualidad. Explica que la Web 1.0 consistía en páginas estáticas con poca interacción de los usuarios, mientras que la Web 2.0 permite la colaboración y contenido generado por los usuarios. Finalmente, anticipa que la Web 3.0 podría consistir en aplicaciones más pequeñas y personalizadas que gestionan datos en la nube y pueden usarse en cualquier dispositivo.
Mark Widdowson is an experienced Centre Manager seeking new opportunities. He has over 10 years of experience managing logistics centers for G4S, with responsibility for multi-million pound budgets, large teams, and customer contracts. He has a strong track record of improving safety, reducing costs and attrition, and achieving targets through process improvements and employee engagement. Currently studying for additional qualifications to further his career in transport and logistics management.
Convegno 29 Novvembre Bologna Cultura d’Europa bene comune: scuola, università, ricerca – Il futuro abita qui.
Intervento Carlo Salmaso
Confronto tra Legge iniziativa popolare per una buona scuola per la repubblica e proposta Renzi
www.lipscuola.it
Gordon the goldfish was lonely being the only fish in his bowl. He saw other goldfish swimming together in the nearby bowl and wanted to join them. Gordon came up with a plan to launch himself from his bowl into the other bowl by swimming fast enough. After building up speed by swimming laps, Gordon pushed off from the bottom of his bowl and flew through the air, landing successfully in the other bowl. However, the other fish were not very welcoming to Gordon and grumbled about his arrival. Still, Gordon was pleased that he achieved his goal of no longer being alone, even if the outcome was not ideal.
Creatividad y tic un complemento para la educaciónTRABAJOSTIC
El documento describe un proyecto que busca implementar el uso de las tecnologías de la información y la comunicación (TIC) para mejorar el desempeño de los estudiantes en el área de educación artística y potenciar su creatividad. El proyecto consiste en tres actividades donde los estudiantes exploran sitios web sobre creatividad y educación artística, plasman ideas artísticas usando medios digitales, y presentan una exhibición multimedia mostrando cómo las TIC pueden mejorar su capacidad creativa.
Anna Mary Robertson, known as Grandma Moses, took up painting in her late 70s after arthritis made needlework difficult. As a self-taught folk artist, she painted simple scenes from her rural life in the Hoosick Valley of New York. Grandma Moses found success late in life, selling her paintings for prices she could not imagine. She enjoyed fame and honors from presidents and governors for her artwork that depicted the virtues of honesty and hard work in rural America. Grandma Moses painted into her 100s, creating landscapes that captured life in the winter seasons until her death at age 101.
Este documento presenta los elementos clave para el desarrollo de un plan estratégico de negocios, incluyendo un análisis interno y externo, el establecimiento de una visión, objetivos y estrategias, así como la investigación y definición del mercado objetivo.
Convegno 29 Novvembre Bologna Cultura d’Europa bene comune: scuola, università, ricerca – Il futuro abita qui.
Intervento Carlo Salmaso
Confronto tra Legge iniziativa popolare per una buona scuola per la repubblica e proposta Renzi
www.lipscuola.it
Predictive analysis of crime forecastingFrank Smilda
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.
IRJET- Crime Analysis using Data Mining and Data AnalyticsIRJET Journal
This document discusses using data mining and analytics techniques to analyze crime data and predict crime rates. It proposes using linear regression on crime data from the Indian government to predict future crime occurrences and identify high-risk regions. The system would analyze factors like crime type, offender age, month, and year to build a regression model. This model could then predict crime rates and indicate whether a region is high or low risk for criminal activity. Graphs and tables would visualize the predictions to help law enforcement allocate resources. The goal is to help reduce crime and increase public safety by identifying patterns in historical crime data.
WhitePaper-How Data Can Support Implementation of Good Practices for ReentryAndrew Dennison
- On average, 10,000 prisoners are released each week from U.S. prisons, totaling 520,000 annually. Recidivism rates are high, with 67% rearrested within 3 years and 77% within 5 years.
- The U.S. spends $80 billion annually on incarceration. In California, it costs $62,396 annually to imprison a nonviolent offender, which is $2,000 more than the state's median income.
- Many states are implementing reentry programs to reduce prison populations and costs. Performance measurement is important to determine if programs are effective in reducing recidivism and increasing public safety.
Crime Analysis based on Historical and Transportation DataValerii Klymchuk
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.
Abstract : Crime prediction is a topic of significant research across the fields of criminology, data mining, city planning, law enforcement, and political science. Crime patterns exist on a spatial level; these patterns can be grouped geographically by physical location, and analyzed contextually based on the region
in which crime occurs. This paper proposes a mechanism to parameterize street-level crime, localize crime hotspots, identify correlations between spatiotemporal crime patterns and social trends, and analyze the resulting data for the purposes of knowledge discovery and anomaly detection. The subject of this study is the county of Merseyside in the United Kingdom, over a span of 21 months beginning in December 2010 (monthly) through August 2012. Several types of crime are analyzed in this dataset, including Burglary and Antisocial Behavior. Through this analysis, several interesting findings are drawn about crime in Merseyside, including: hotspots with steadily increasing crime levels, hotspots with unstable crime levels, synchronous changes in crime trends throughout Merseyside as a whole, individual months in which certain hotspots behaved anomalously, and a strong correlation between crime hotspot locations and borough/postal code locations. We believe that this type of statistical and correlative analysis of crime patterns will help law enforcement agencies predict criminal activity, allocate resources, and promote community awareness to reduce overall crime rates.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
This paper focuses on finding spatial and temporal criminal hotspots. It analyses two different real-world crimes datasets for Denver, CO and Los Angeles, CA and provides a comparison between the two datasets through a statistical analysis supported by several graphs. Then, it clarifies how we conducted Apriori algorithm to produce interesting frequent patterns for criminal hotspots. In addition, the paper shows how we used Decision Tree classifier and Naïve Bayesian classifier in order to predict potential crime types. To further analyse crimes’ datasets, the paper introduces an analysis study by combining our findings of Denver crimes’ dataset with its demographics information in order to capture the factors that might affect the safety of neighborhoods. The results of this solution could be used to raise people’s awareness regarding the dangerous locations and to help agencies to predict future crimes in a specific location within
a particular time.
The document discusses the benefits of crime statistics. It begins by defining crime statistics as numerical data on crime incidents obtained through systematic collection and analysis of raw crime data. It then lists 12 benefits of crime statistics, including that they define a society's moral values and law and order; illustrate the efficacy of the criminal justice system; help identify crime-prone areas; inform the magnitude of crime rises; and are useful for social research. Specific examples are provided for some benefits.
Review Journal 1A simplified mathematical-computational model of .docxmichael591
Review Journal 1
A simplified mathematical-computational model of the immune response to the yellow fever vaccine
1. This model can be improved in a way if there are more test subjects and more variables and parameters with test data is added. Plus the mathematical process is always improvable so if there is an equation which is more better for this experiments then it’s can improve the model and experiment with development. Another try is to improve the qualitative results obtained from our model. Additional computational experiments, such as the effects of a) a booster dose and b) a reduction in the population of TCD8+ naive. Also, a sensitivity analysis will be performed to identify sensitive parameters and to identify connections between change in parameters values and computational results.
2. if more cases or more experiments were added then it could be more expanded research and could improve the research more but the similar results are achieved by the shorter experiments so we can say this number of experiments were enough. But there is always a room left for improvements. The second difference between the two models is that this work reduces the amount of equations from 19 to 10. The reduced model described in this work considers only the main populations of cells and molecules involved in the response to the vaccine, and abstracts some details that are not crucial to represent the behavior of the immune response. For example, the distinct compartments are not represented here. Also, some populations were not considered because no experimental data is available to validate the simulations, such as the CD4+ T cells. In future, more cell or molecule can be included in the model again, if its role is important to explain or represent some behavior that the reduced model presented in this section could not represent. That was something in the first paper journal which was not satisfactory for me.
3. This model can be applied to the numerous medical applications like cancer immunity and other immune vaccinations for the diseases or viruses in the open environment which are lethal and are possible to cure in the future. The virus cannot proliferate itself, it needs to infect a cell and use it as a factory for new viruses. This is implicitly considered in the term πvV , which represents the multiplication of the virus in the body, with a production rate πv. The term cv1V cv2+V denotes a non-specific viral clearance made by the innate immune system
4. The main problem which was to solve is that the author needs something authentic calculations to perform the experiment to show the results of that experiment as negative and for that only programming and developing an algorithm or a program is not enough. It needs the proper calculations of the human body and cells about each and every details in depth. So that’s why it was necessary to include the calculations in the immune system development. Previously the author wasn’t including proper cal.
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.
Supervised and Unsupervised Machine Learning Methodologies for Crime Pattern ...gerogepatton
Crime is a grave problem that affects all countries in the world. The level of crime in a country has a big
impact on its economic growth and quality of life of citizens. In this paper, we provide a survey of trends of
supervised and unsupervised machine learning methods used for crime pattern analysis. We use a spatiotemporal dataset of crimes in San Francisco, CA to demonstrate some of these strategies for crime
analysis. We use classification models, namely, Logistic Regression, Random Forest, Gradient Boosting
and Naive Bayes to predict crime types such as Larceny, Theft, etc. and propose model optimization
strategies. Further, we use a graph based unsupervised machine learning technique called core periphery
structures to analyze how crime behavior evolves over time. These methods can be generalized to use for
different counties and can be greatly helpful in planning police task forces for law enforcement and crime
prevention.
SUPERVISED AND UNSUPERVISED MACHINE LEARNING METHODOLOGIES FOR CRIME PATTERN ...ijaia
Crime is a grave problem that affects all countries in the world. The level of crime in a country has a big
impact on its economic growth and quality of life of citizens. In this paper, we provide a survey of trends of
supervised and unsupervised machine learning methods used for crime pattern analysis. We use a spatiotemporal dataset of crimes in San Francisco, CA to demonstrate some of these strategies for crime
analysis. We use classification models, namely, Logistic Regression, Random Forest, Gradient Boosting
and Naive Bayes to predict crime types such as Larceny, Theft, etc. and propose model optimization
strategies. Further, we use a graph based unsupervised machine learning technique called core periphery
structures to analyze how crime behavior evolves over time. These methods can be generalized to use for
different counties and can be greatly helpful in planning police task forces for law enforcement and crime
prevention.
CJ 301CHAPTER 1NOTESIt is important for everyone to become f.docxmonicafrancis71118
CJ 301
CHAPTER 1
NOTES
It is important for everyone to become familiar with the language of research as we begin this statistics course, so follow along and try to absorb the information below. This module is intended to accompany Chapter 1 of the Adventures in Criminal Justice Research text (4th edition):
This textbook introduces you to the logic of theory, research, and practice in criminal justice and gives you some practical experience through the use of the SPSS for Windows computer program.
WHAT DO WE MEAN WHEN WE SAY STATISTICS?
The word statistics can have a variety of meanings.
1. It can refer to fragments of data or information.
2. To some it may mean the theories and procedures that are used for understanding data.
3. Statistics may also be defined as collections of facts expressed as numbers.
-For example, in 1990, 341,387 full-time law enforcement officers were working in the U.S.; this is a statistic. So is the fact that 7,830 bank robberies occurred in the same year (Crime in the U.S. 1990) and that four million offenders were under some form of correctional supervision (Sourcebook of Criminal Justice Statistics 1992). The list of statistical facts about the criminal justice system may be regarded as statistics. Your age, your shoe size, your height, your sex, your ethnicity are all statistics. Indeed, any fact that can be expressed as a number, whether it is important or not, is a statistic.
4. For this class, we are going to use the following working definition of the term statistics. Statistics is a set of problem-solving procedures that are used to analyze and interpret aggregate data.
WHAT ARE SOME PRACTICAL APPLICATIONS OF STATISTICS?
Criminal justice practitioners deal with a wide variety of problems that statistics can help solve.
1. Statistics can be used to describe crime in a city, or the composition of a police department or the overcrowding problem in a county jail, or the case processing rate of a criminal court.
2. Other problems that arise in criminal justice go beyond simple description and into areas such as prediction and evaluation.
- For example, if a police department were to add 60 officers to its force, what would be the predicted impact on crime rates, staff morale, or gasoline consumption? How many new probation officer positions would be required in the next five years to keep up with the current growth in caseloads? If sentences to prison continue at their present rate, for how many new prison beds must the state plan? Is probation more effective than prison? Which community-based corrections programs work better than others? How successful is community-oriented policing or judicial training or the public defender’s office? Answering these types of question involves various statistical techniques that we will learn about during this semester.
WHAT ARE THE TYPES OF STATISTICS?
1. Descriptive Statistics – These are statistics whose function it is to describe what data looks l.
This document discusses problem-oriented policing and the SARA model. SARA is an acronym that stands for scanning, analysis, response, and assessment, which are the four steps used to identify, analyze, and select problems. The document examines a problem-oriented policing guide about liquor store robberies. It describes factors that can contribute to liquor store robberies, such as cash transactions and lone employees. The guide offers suggestions for responses like improving lighting and visibility to address the problem.
This thesis examines various variable selection methods for predicting crime rates using a 1990 US crime database. The author applies stepwise selection, lasso, elastic net, principal component regression, best random subset selection, and regression trees to select predictive variables and compare model performance. Variables consistently associated with crime rates include measures of employment, urbanization, income, poverty, ethnicity, and family structure. The best random subset selection method produced the highest out-of-sample R^2 and an unbiased assessment of model fit when applied to test data, performing better than other methods like lasso and elastic net. Predicting crime is difficult but variable selection techniques help identify important predictive factors and evaluate model performance.
INFLUENCE OF THE EVENT RATE ON DISCRIMINATION ABILITIES OF BANKRUPTCY PREDICT...ijdms
In bankruptcy prediction, the proportion of events is very low, which is often oversampled to eliminate this
bias. In this paper, we study the influence of the event rate on discrimination abilities of bankruptcy
prediction models. First the statistical association and significance of public records and firmographics
indicators with the bankruptcy were explored. Then the event rate was oversampled from 0.12% to 10%,
20%, 30%, 40%, and 50%, respectively. Seven models were developed, including Logistic Regression,
Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and
Neural Network. Under different event rates, models were comprehensively evaluated and compared based
on Kolmogorov-Smirnov Statistic, accuracy,F1 score, Type I error, Type II error, and ROC curve on the
hold-out dataset with their best probability cut-offs. Results show that Bayesian Network is the most
insensitive to the event rate, while Support Vector Machine is the most sensitive.
This article analyzes the effect of "Pay-to-Stay" jail policies, which require inmates to pay for some of their incarceration costs, on crime rates, incarceration rates, and jail expenditures using data from 11 counties in Michigan from 2000-2014. Regression analyses found that Pay-to-Stay significantly increased both crime and incarceration rates but did not significantly reduce jail expenditures. While Pay-to-Stay was intended to offset jail budgets, it may have instead increased costs by raising crime and incarceration.
Combating Gun ViolenceWhat is Gun ViolenceGu.docxrobert345678
Combating Gun Violence
What is Gun Violence
Gun violence includes homicide, violent crime, attempted murder, suicide, school/mass shootings, armed robbery, etc
Gun violence is violence committed with the use of firearms, for example pistols, shotguns, assault rifles or machine guns.
Gun violence can also have a mental impact on people
The Impact of Gun Violence
Item 3
In just one year 45,222 people died from gun related injuries in the US
Firearms cause 85,000 injuries
With more than 25% of children witnessing an act of violence in their homes, schools, or community over the past year, and more than 5% witnessing a shooting, it becomes not just an issue of gun regulation, but also of addressing the impact on those who have been traumatized by such violence (Finkelhor et al., 2009).
<a href="https://www.pewresearch.org/fact-tank/2022/02/03/what-the-data-says-about-gun-deaths-in-the-u-s/ft_22-01-26_gundeaths_1/"><img src="https://www.pewresearch.org/wp-content/uploads/2022/02/FT_22.01.26_GunDeaths_1.png?w=400"></a>
Program: Pulling Levers
Pulling Levers is program that attacks the problem of gun violence head on
Pulling Levers includes problem-oriented policing strategies that follow the core principles of deterrence theory. The strategies target specific criminal behavior committed by a small number of chronic offenders, such as youth gang members or repeat violent offenders, who are vulnerable to sanctions and punishment. The Pulling Levers Program is very Promising for reducing crime.
Corsaro and colleagues (2012) found that the High Point (N.C.) Pulling Levers had a statistically significant impact on reducing violent incidents in the target areas. Targeted census blocks (treatment group) experienced a 7.9 percent decrease in violent crime
Analysis
In the US this program is currently implemented in the following cities: Oakland, Chicago, Rockford, Indianapolis, Boston, Philadelphia, Nashville and the closest being in High Point/NC
This program has been proven to work in all of these cities having an impact on gun violence and drug related crimes
So all though Pulling levers is implemented in High Point, why is it not in our big cities where crime rates are high as well?
One of the highest rated cities for crime in NC is Durham and so far they do not have a program like Pulling Levers to combat gun violence and other crime that stems from impoverished areas.
Why Durham
Durham is the 2nd most dangerous city in NC
Durham had a murder captia of 21.5 per 100,000 which is double the national average
Why Pulling Levers?
Pulling Levers has been used in multiple cities around the nation and has been successful in targeting crime
It is also a non violent way of dealing with criminals and changing their mindset so they don’t get more involved in gang life and commit more serious offenses such as homicide or drive-bys.
If the offender chooses to be compliant with law enforcement then they will get help and tr.
1. UNIVERSITY OF CINCINNATI, CARL H LINDNER COLLEGE OF BUSINESS, DEPARTMENT OF
ECONOMICS
An Empirical Examination
of the Economic Model of
Crime Using a Time Series
Approach
The Effects of Prison Sentencing Policy Changes
in Virginia on Criminal Activity
Scott Littrell
July, 2015
2. L i t t r e l l | 1
1. Introduction
In Gary Becker’sinnovative magnumopus, Crimeand Punishment:An EconomicApproach,he
positedthatindividualsengagingincriminalactivityare rational economicagentsseekingto
maximize theirprofit.Since then,scholarsacrossmultiple disciplineshave usedthe premisesof
thismodel oncriminal behaviortogaina more robustunderstandingbehind the motivationof
criminalsandthe role punishmentplaysinenforcinglawsand deterringcriminalbehavior.Much
of thisresearchhasfocuseduponvalidatingBecker’smodelthroughempirical research.Utilizing
the economictheorypresentedby Beckerwhichstates thatcriminalsare rational economic
agentsthat respondtoincentives andchangestocostsstructures,publicpolicyhasemerged
aimed,inpart,at deterringcriminalbehavior.Namely,muchpolicyisbuiltuponthe theorythat
greatereconomiccostswill reduce criminal behaviors;forinstance,increasingpolicingpatrolsor
creatingstrictersentencing(i.e.the costsof gettingcaught) will leadtoreductionsincrimes.
To testBecker’stheory,thispaperbuildsuponthe researchconductedinthree previous
academicstudies:Testing theEconomicModel of Crime: The NationalHockey League’sTwo-
Referee Experiment(Levitt2002), Intervention TimeSeries Analysisof Crime Rates (Sridharan,et.
Al.,2003), andTime Series Analysisof Crime Rates (Greenberg,2001). Levittexaminesthe NHL’s
attemptto decrease the number of penalties byincreasingthe numberof refereesfromone to
two. The approachesutilizedcompare the numberof penaltieswithone refereetothe number
of penaltieswithtworeferees. Sridharan,et.al.utilizedatime seriesapproachtoexamine the
effectsof Virginia’sabolishmentof parole in1995 by usingdata from1984 through1998. The
modelsusedwere anARIMA and a structural VARto compare the pre-interventionperiodtothe
post-interventionperiod.GreenbergtestedBecker’stheory byexaminingthe relationship
3. L i t t r e l l | 2
between homicideandrobbery ratesandunemploymentrates overtime byusinganerror
correctionmodel (ECM).
The goal of thispaper isto make a contributiontothe previousresearchby empiricallytesting
Becker’stheory bywayof a time seriesapproach. Byexpandinguponthe workof Sridharanet
al.(2003), thisstudywill analyze the Virginialegislature’sdecisiontoabolishparole forfelony
offendersin1995. Thisstrictersentencingpolicyisanattempttodetercriminalsfrom
committingcrimesbyincreasingthe costof gettingcaught,intermsof time servedinprison.
The economictheorywill be tolookat criminal behaviorthroughanexpectedutilityfunction.
The empirical frameworkwill be tomake a predictionfromthe beginningof the intervention
and compare it tothe actual trend. ARIMA modelswill be usedalongwithastandard ordinary
leastsquares (OLS) approachforpreliminaryanalysis.Afterthe statistical analysiswe will revisit
the expectedutilityfunctiontojustifythe empirical resultsthroughacriminal’sresponseto
economicincentives.
2. Data
The data utilizedinthispaperisdrawnfromthe UniformCrime ReportingStatistics(UCRData
Online) whichcollectsandreportstime seriesdataona monthlybasis.The analysiswillutilize
the followingcrime rates asthe dependentvariables:burglary,rape,theft,larceny,autotheft,
murder,assault,andpropertycrime. The unemploymentrate forVirginiaandthe Consumer
Price Index (CPI) statisticswere collectedfromthe Federal Reserve EconomicData(Economic
ResearchDivision). See Table 1fora full descriptionof the variables.
4. L i t t r e l l | 3
Table 1: Variable definitions
Definition of Variables
Murder Rate Monthlymurderrate inVirginiabetween1980 and2013
Rape Rate Monthlyrate of rapesin Virginiabetween1980 and 2013
Robbery Rate Monthlyrobberyrate in Virginiabetween1980 and 2013
Assault Rate Monthlyassaultrate in Virginiabetween 1980 and 2013
Property Crime Rate Monthlypropertycrime rate in Virginiabetween1980 and 2013
Burglary Rate Monthlyburglaryrate inVirginiabetween1980 and2013
Larceny Rate Monthlylarcenyrate in Virginiabetween1980 and 2013
GTA Rate Monthly rate of auto theftsinVirginiabetween1980 and 2013
UnemploymentRate Virginianunemploymentrate
CPI National ConsumerPrice Index
3. EconomicFramework
Thispaperis builtfromthe theoretical frameworkof Becker(1968),specificallythe expected
utilityfunction asitrelatestocriminal behavior. A utility-maximizingcriminal isexpectedto
commitcriminal acts until theirmarginal benefit(the probabilityof profitingoff of the crime)
equalshismarginal cost(the probabilityof gettingcaughtmultipliedbythe expectedcostof the
penalty).Thisisshowninequation1.
Equation (1) pC = B
Where p isthe probabilityof gettingcaught,Cisthe expectedcostof the penalty,andBis the
immediate benefitof committingthe crime whethertheyare monetaryornon-monetary. From
this,a criminal’sexpectedutilitycanbe derivedasthe probabilityof benefitingplusthe
probabilityof gettingcaughtandpayingthe penalty.Thisisillustratedinequation2.
Equation(2) EU = pU(W+ B) + pU(W + B – C)
5. L i t t r e l l | 4
Where U isthe criminal’sutilityfunctionandWisthe criminal’sinitialwealth. Thismeansthatif
the payoff of committingthe crime isgreaterthanthe criminal’sinitial wealth minusthe costof
gettingcaught, thanit isworth committingthe crime.
A shortcomingof thisapproachis thatnot all criminalsare rational whenweighingtheir
marginal benefitversustheirmarginal cost.However,intheirmindtheymaybe behaving
perfectlyrational.If theyperceivethat theyare notlikely togetcaught thentheywill commit
the crime eventhoughthere are increasedpolice patrols.If theyperceive thatevenif theyget
caught the penaltywill notbe toosevere,thentheyare likelytocommitthe crime eventhough
the penaltymay be severe. Differencesinhow acriminal definestheirutilityfunctioncompared
withhowpolicymakersmightexpectutilityfunctionstobe definedamongcriminalslikely
affectsthe effectivenessof suchpolicy.The keyprincipletoconsideris how one definesutility
and weightstheirexpectedutility.If the resultsof this paperdonotsupporta stricter
sentencingpolicy, itmeansthe criminalsdonotperceivethispolicyasbeingmajorfactorin
whethertheycommitacrime or not. Perhapstheyare unaware of the increasedsentencing
policywhenmakingtheirdecisiontocommitacriminal act.
Anotherpotential drawbackof Becker(1968) liesinthe satisfactionsome criminalsreceive by
committingcrimesandare not doingitfor any monetarygain.Rape,murder,andassaultare
examplesof crimesthatcriminalsmayindulge evenwhenthere isnoincrease inmonetary
wealth.The utilitytheygainis implicitandtheircompulsionsmaybe an evenbiggerincentive
than increasingmonetarywealth. Assumingthata percentage of the criminal populationfeelsa
sense of enjoymentfrom committingcrimes,itcanbe difficulttocontrol because of the
compulsive nature of the criminals.
6. L i t t r e l l | 5
4. Empirical Methodology
To empiricallyexamine the impactof Virginia’sabolitionof parole on criminal activity,the
utilizationof various timeseriesapproachesare necessary.Greenberg(2001) proposedthatthe
rate at whichindividual i commitscrimes yi isa functionof motivationandopportunityattime t.
Thisrelationshipcanbe showninthe formof a regressioninequation3.
Equation (3) yit = α + β1(opportunity) +β2(motivationit) +eit
Greenberg(2001) recognizedthatpeople have savings,unemploymentbenefits andcanreceive
governmentassistancewhichwill affectthe motivation.Additionally,if peopleare home more,
thenthat takesawaythe opportunityof apotential burglary,whichcandecrease crime.
Greenbergsuggeststhe motivationeffectof unemploymentis laggedforayear at t – 1 whichis
representedbyUt-1.Thisisrepresentedinequation4.
Equation (4) yit = α + β1Ut + β2Ut-1 + et
Sridharan etal. (2003) contributedtothe methodological debate of testingcrime ratesover
time throughthe contextof interventionanalysis.Aninterventiondummyvariable wasutilized
to testthe impact of the policy,represented by Ιt,whichequalszerobefore the fixedtime
periodand1 after.The regressioncoefficient δmeasuresthe change inthe meanof crime rates
afterthe intervention. The variable yt isthe time seriesof crime rates, x̕t isa k Χ 1 vectorof
explanatoryvariablesandβisa k Χ 1 vectorof the regression coefficients.Thisisillustratedin
equation 5.
Equation (5) yt = α + x̕t β + δ Ιt + et
Since time series datatendtohave serial correlationproblems, the ARIMA methodandthe VAR
methodwasutilized totestthe crime rate trendsbefore andafterthe intervention. Forthe
7. L i t t r e l l | 6
purposesof this paper,the OLS andthe ARIMA techniquesusedby Sridharanetal.(2003) will
be utilized before the legislationandaftertotest the effectiveness. Before anytechniqueisto
be implemented,the orderof integrationmustbe discovered. Bylookingata line graphof the
time series,itisapparentthatthere isa downwardtrendforeverycrime variable. Thiswill
require differencingtomake the variablesstationary. Afterthe variableshave beendifferenced
appropriately,the modelscanbe built andthe significance of the policyinterventioncanbe
examined.Sirdharanetal.(2003) onlyuseddata up until 1999 because of availability,sothis
study will use datathrough2013.
5. Descriptive Analysis
To beginitis useful tocompare the meansof the pre-interventionperiod(1980-1995) to the
post-interventionperiod(1995-2013). Fromthe results,all of the crime rates have declined
post-intervention,exceptforthe assaultrate,robberyrate,andthe autotheftrate whichhad
small gains.The magnitude of the interventioncoefficient,whetherpositiveornegative,is
small,meaningthateffectsare notmajor. The strongestreductionappearstobe burglary and
propertycrime.Table 2 showsthe descriptive statisticsof the entire timeseries,the pre-
interventionperiod,andthe post-interventionperiod.
8. L i t t r e l l | 7
Table 2: Descriptive Statistics
Descriptive Statistics
Variable Period N Mean St. Dev. Min. Max.
Murder Rate
1980 to 2013 408 0.55 0.17 0.21 1.08
1980 to 1995 181 0.67 0.14 0.33 1.08
1995 to 2013 229 0.65 0.13 0.33 1.08
Rape Rate
1980 to 2013 408 2.13 0.48 0.91 3.25
1980 to 1995 181 2.32 0.50 0.91 3.25
1995 to 2013 229 2.30 0.50 0.91 3.25
Robbery Rate
1980 to 2013 408 8.74 2.15 3.49 14.34
1980 to 1995 181 9.90 1.78 6.28 14.34
1995 to 2013 229 9.97 1.70 6.28 14.34
Assault Rate
1980 to 2013 408 13.32 2.66 7.00 21.00
1980 to 1995 181 14.02 2.26 9.00 21.00
1995 to 2013 229 14.40 2.28 9.00 21.00
Property Crime Rate
1980 to 2013 408 269.70 58.86 137.80 420.50
1980 to 1995 181 318.70 32.50 256.50 420.50
1995 to 2013 229 313.80 32.81 246.00 420.50
Burglary Rate
1980 to 2013 408 51.30 19.29 21.91 118.60
1980 to 1995 181 69.36 13.52 44.22 118.60
1995 to 2013 229 64.95 14.88 39.77 118.60
Larceny Rate
1980 to 2013 408 199.40 39.15 109.70 303.50
1980 to 1995 181 228.40 24.95 174.80 303.50
1995 to 2013 229 227.40 24.40 174.80 303.50
GTA Rate
1980 to 2013 408 19.04 5.74 6.25 33.68
1980 to 1995 181 20.90 5.44 11.96 33.68
1995 to 2013 229 21.47 5.08 11.96 33.68
Consumer Price Index 1980 to 2013 408 158.40 43.99 78.00 234.70
Virginai Unemployment Rate 1980 to 2013 408 4.75 1.34 2.10 7.90
*UniformCrime ReportingStatistics - UCRData Online
*Federal Reserve EconomicData - EconomicResearchDivision
6. Regression Analysis
To understandthe impactof the intervention,itisnecessarytomodifyequation(5) toaccount
for seasonalityandmultiple explanatoryvariablestoforma regression model withseasonal
effects.Thisis illustratedin equation6:
9. L i t t r e l l | 8
Equation(6) yt = α + δ Ι t + ϒ st + β1 x̕t + β2 x̕t + et
The dependentvariable yt will be representedbythe particularcrime rate beingexamined.
Seasonalityisrepresentedby st andϒ representsthe coefficientsof the seasonaldummy
variables. The explanatoryvariablesrepresentedby x̕t will be representedbyak Χ 1 vectorof
the unemploymentratesinVirginiaanda k Χ1 vectorof the CPI foreach monthfrom1980 to
2013. The interventionbeginninginJanuary1995 will be representedby Ιt.
Since the variablesare notstationary,there couldbe spuriousregressionproblems whichcan
overstate the results.Eachvariable wasdifferencedonce andbecame stationary. The approach
utilizedinthisanalysisistobeginwithalinearregression model of the crime rate againstthe
interventionvariable andseasonal dummyvariables totestthe significance of the intervention.
Next,unemploymentisaddedtothe equationandthenthe CPIisaddedto see if otherfactors
affectcrime and whatimpacttheyhave on the significance of the intervention. The coefficients
for the interventionhadasmall negative relationshipto assaultrate,robberyrate,andthe auto
theftrate,the remainingcrime rateshave a positive relationship. The p-valueswere not
significantin anyof the crime rates. Whenunemploymentandthe CPIwere addedtothe
model,the intervention remainedstatisticallyinsignificantineachof the crime rates.The
coefficientsandthe R-square onlyslightlychangedasmore explanatoryvariableswere added.
In summary, the resultsdonotindicate thatany of the crime ratesare significantlyaffectedby
the intervention.The additionof the unemploymentrate andthe CPIalsohad no effectonthe
intervention.Table 3illustrates the regressionresultsasmore explanatoryvariablesare added.
Figure 1 showsa graphical illustrationof the regressionresults.Inthe graphs,notice how the
murderrate hada spike atthe interventioninsteadof the expecteddrop.Eachof the other
crime ratesdroppedsharplyinlieuof the start of the interventionandthensharplyincreases
10. L i t t r e l l | 9
immediatelyafterthe initial shock.Despitethe initial shock, all of the crime ratesremained
stable andlargelyunaffectedbythe stiffersentencingpolicy.Serialcorrelationissues canmake
OLS results lookmore significantthantheyactuallyare andtherefore these findings maybe
misleading.
Table 3: Regressionresults
Figure 1: Graphic representationof the effectivenessof the intervention.
Coefficient Std. Error t-stat p-value R2
int + seas 0.005 0.036 0.132 0.896 0.346
int + seas + unemp -0.007 0.039 -0.184 0.855 0.359
int + seas + unemp + cpi -0.008 0.040 -0.188 0.852 0.359
int + seas 0.026 0.132 0.195 0.847 0.376
int + seas + umemp 0.035 0.145 0.244 0.809 0.376
int + seas + unemp + cpi -0.008 0.146 -0.054 0.957 0.414
int + seas -0.065 0.290 -0.223 0.825 0.576
int + seas + umemp -0.102 0.319 -0.319 0.752 0.577
int + seas + unemp + cpi -0.097 0.331 -0.293 0.771 0.577
int + seas -0.006 0.025 -0.248 0.805 0.642
int + seas + umemp -0.017 0.027 -0.620 0.540 0.653
int + seas + unemp + cpi -0.019 0.028 -0.684 0.499 0.655
int + seas 0.363 3.004 0.121 0.905 0.885
int + seas + umemp -0.017 3.303 -0.005 0.996 0.886
int + seas + unemp + cpi -0.576 3.397 -0.170 0.867 0.888
int + seas 0.339 0.873 0.389 0.700 0.721
int + seas + umemp 0.361 0.961 0.376 0.710 0.721
int + seas + unemp + cpi 0.137 0.979 0.140 0.889 0.731
int + seas 0.285 2.288 0.125 0.902 0.889
int + seas + umemp 0.152 2.518 0.060 0.952 0.889
int + seas + unemp + cpi -0.260 2.592 -0.100 0.921 0.891
int + seas -0.262 0.432 -0.606 0.549 0.739
int + seas + umemp -0.530 0.460 -1.153 0.257 0.756
int + seas + unemp + cpi -0.453 0.473 -0.959 0.345 0.761
Burglary Rate
Larceny Rate
Auto Theft Rate
Estimated Interventions for Regression Models
Murder Rate
Rape Rate
Robbery Rate
Assault Rate
Property Crime Rate
12. L i t t r e l l | 11
7. Seasonal ARIMA model
The ARIMA model utilizedisdevelopedbygoingbackto the start of the interventionperiodto
make a predictionandthencomparingitto the actual resultsof the time seriesmodel. Since
seasonalityisincludedinthisstudy,the ARIMA (p,d,q)(P,D,Q) model wasutilized. The firstthing
to do whenbuildinganARIMA model isto checkthe orderof integration.Everycrime rate was
integratedatorderI(1) exceptforthe murderrate, whichwasstationarypriorto the
intervention. The ACFandPACFtestswere usedtodeterminethe appropriate ARandMA
terms,whichservedasa startingpoint. Sridharanetal.(2003) usedthe “airline model”whichis
ARIMA(0,0,1)(0,0,1) foreverycrime rate. The approach for thisanalysiswastopickthe best
model foreach of the crime rates.Using the resultsof the ACFand PACF testsas a starting
point, the lagselectionprocesswasotherwise donebyatrial anderror method.Several
variationsof (p,d,q)(P,D,Q) weretestedandthe bestmodel waschosenbylookingatthe AIC
and BIC.
Once the appropriate ARIMA modelswere built,the timeperiod wasnarrowedto between
1990 and 2000 inorder to examine the trendandpredictionmore closely.A predictionof 36
periods(3years) wasimplemented atthe startof the interventionandcomparedtothe actual
crime rate trend.Thisprocesswasrepeatedforeachof the crime rates.
The resultsshowthat the murderrate was the onlycrime that beganto decrease significantly at
the start of the intervention. The trendsof the remainingcrime rates are unaffectedbythe
intervention. Withthe exceptionof the rape rate and the assault rate,each of the crime rates
appearto be alreadyona downwardtrendbefore the interventionbegan. The murderrate is
the onlyvariable significantlyaffected atthe startof the intervention.There isnoevidence of
the interventionbeingeffective for anyof the othercrime rates. Whenthe interventiondummy
13. L i t t r e l l | 12
variable wasremovedfromthe model,the predictionsdidnotsignificantlychange. Fromthis,it
can be concluded thatthe interventionisnot highly significant.Figure 2illustratesthe prediction
versusthe actual trendfor each of the crime rates.
Figure 2: ARIMA predictions
14. L i t t r e l l | 13
8. Conclusion
Whenexaminingthe change inmeansfromthe periodbeforethe intervention(1980-1995) to
the periodafterthe intervention(1995-2013), the policyseemstobe effective.Withthe
exceptionof the assaultrate,eachof the crime rates decreasedafterthe intervention. When
studyingthe effectivenessof the interventiononthe crime rates,aregressionmodel with
seasonal effectswasapplied.The interventionhad nosignificanteffecton anyof the crime
rates. Afteraddingthe CPIand the Virginiaunemploymentrate asexplanatoryvariables,the
interventionstill wasnotsignificant.Fromthese findings,itcanbe concluded thatthere are
otherfactors that affectreportedcrimesmore thanthe intervention.Since timeserieshasa
tendencytohave serial correlation, the OLSmethodmayhave misleadingresults.Soa seasonal
ARIMA model isanappropriate methodtoevaluate the policyintervention.Aftermakinga
predictionfromthe pre-interventionperiodandcomparingtothe actual trends,the murder
rate appearsto be the onlytype of crime that was significantlyaffectedbythe intervention.
Whenremovingthe interventiondummyvariable,the resultsremainedalmostidentical.
In lightof the findingsinthisanalysis,itappearsthat Virginia’sdecisionto eliminate paroledid
not have a significanteffectonreducingreported criminal activity.The only crime rate thatwas
significantwasthe murderrate inthe ARIMA model.Whenexaminingacriminal’sexpected
utilityfunction, itcanbe concluded thatthe cost of gettingcaughtis notenoughof a deterrent
for criminals tostopcommittingcrimes orthe criminalsdonothave enoughinformationto
change theirbehavior.Hence acriminal’smarginal benefitisgreaterthanthe marginal costs.
The evidence of thispolicy analysisleadstothe conclusionthat strictersentencingisnotan
effectivestrategy forreduce criminal activity.
15. L i t t r e l l | 14
9. References
Becker,Gary S. "Crime andPunishment:AnEconomicApproach." Journalof PoliticalEconomy J POLIT
ECON 76.2 (1968): 169. NationalBureau of EconomicResearch.Web.8 June 2015.
Greenberg,DavidF."Time SeriesAnalysisof Crime Rates." Journalof QuantitativeCriminology 4thser.17
(2001): 291-327. ResearchGate.Web.8 June 2015.
Sridharan,Sanjeev, SuncicaVujic,andS.j.Koopman."InterventionTime SeriesAnalysisof Crime
Rates."Tinbergen InstituteDiscussion Paper 2003-040/4 (2003): 1-33. SSRN JournalSSRN Electronic
Journal.
Levitt,StevenD."Testingthe EconomicModel of Crime:The National HockeyLeague'sTwo-Referee
Experiment."Contributionsin EconomicAnalysis& Policy 1.1 (2002): n.
pag. Http://bfi.uchicago.edu/price-theory.GaryBeckerMiltonFriedmanInstituteforResearchin
Economicsat the Universityof Chicago,2002. Web.26 July2015.
US. Bureauof Labor Statistics, UnemploymentRatein Virginia [VAURN],retrievedfromFRED,Federal Reserve
Bank of St. Louishttps://research.stlouisfed.org/fred2/series/VAURN/,August2,2015.
US. Bureauof Labor Statistics, ConsumerPriceIndex forAll Urban Consumers:AllItems[CPIAUCNS],retrieved
fromFRED, Federal Reserve Bankof St.Louishttps://research.stlouisfed.org/fred2/series/CPIAUCNS/,
August2, 2015.
"UniformCrime ReportingStatistics." UniformCrimeReporting Statistics.N.p.,n.d.Web.02 Aug.2015.
<http://www.ucrdatatool.gov/Search/Crime/State/StatebyState.cfm>.