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To conduct a comprehensive data analysis of the 'USArrests' dataset in R, with the primary goal of exploring the relationship between urbanization and crime rates across US states. The project will leverage foundational R functionalities to perform data exploration, visualization, and hypothesis testing.

I started to analysed this project keeping one thing in my mind. I lived in Boston City for past 10 years but how safe is it compared to any other big cities around the United States? It’s no secret here that Boston is greater than most places on the planet (especially if you ask any Bostonian who is literally comparing it to New York). That’s not just their stance/attitude/ego talking; but in our analysis I have some serious data just to prove it. According to most of the recent polls and studies published as of today 12/21/2018, from tasty burger/water, shack shake to pedestrian safety(Vision Zero Boston) Boston seems to get better year after year. Vision Zero Boston is Cities commitment to focus the City’s resources on proven strategies to eliminate fatal and serious traffic crashes in the City by 2030, that rate Boston as tops in the nation.When I get a chance to look at Beantown’s incidence of rape (39 per 100,000, putting it third on the list of cities in the survey) and assault (483 per 100,000, fifth on the list of cities in the survey) [**external data Source**] it might seem like a surprisingly dangerous big city.Surprising then is that only 27% of those surveyed as of end of 2018 said they considered Boston to fairly or very unsafe. According to recent surveys the city’s low murder rate per capita (9 per 100,000) could likely be a contributing factor to this perception.The city also has the second-lowest incidence of vehicle theft at a rate of 258 per 100,000 and fares well on the other crimes rates. Is it true to say that Boston was more violent than New York and Seattle, but less violent than Chicago and Las Vegas, according to numbers from the FBI, based on crimes committed back in 2015. As of Today 12/21/18 Nationally, Boston ranked 14 out of 50 according to Us News.After digging up and analysing the data what we found out that in recent numbers from the Boston Police Department, or BPD, show that violent crime, as well as property crime, has continued to drop, and has been steadily dropping for past years. My Goal was to build R shiny app to see the results without digging too much in data. Took a lot of help from Online Community to do EDA as well as to built the R Shiny App. Hope you guys enjoy reading these and if you like please Upvote. I have successfully built R shiny app which covers crime map based on murders, aggravated assaults, and robberies, as well as property crimes such as burglaries, auto thefts, larcenies, and arson for data of date between August 2015 till today. Crimes such as sexual assaults and rapes have not been included because of a lack of data. ## Executive Summary Public safety is vital to public health and happiness, and a state's safety can be a crucial factor in deciding where to relocate your family.The map shows that Mattapan,Roxbury, and Dorchester have had more aggravated assaults than other neighborhoods. Roxbury, downtown, and Dorchester had more robberies than other sections of the city. Back Bay, the South End, and downtown suffered more larcenies. In 2017 the rate of violent crime in Massachusetts fell for the sixth year in a row and dropped below national levels for the second time since 2016, according to FBI statistics. In 2017, there were 358 incidents of violent crime per 100,000 people in Massachusetts, compared to the national rate of 394 per 100,000 people, according to data published in late September.The FBI defines violent crime as homicide, rape, robbery, and aggravated assault. The bureau said it calculates crime rates using figures voluntarily provided by law enforcement agencies, and tallies estimates when it doesn’t receive a complete year’s worth of data. > The overall crime rate in Boston is equal to the national average. > For every 100,000 people, there are 7.56 daily crimes that occur in Boston. > Boston is safer than 14% of the cities in the United States. > In Boston you have a 1 in 37 chance of becoming a victim of any crime. > The number of total year over year crimes in Boston has decreased by 9%. I want to highlight what the City of Boston says about the data they provide: “Although the City has made reasonable efforts to provide accurate data, the City makes no representations or guarantees about the accuracy, completeness, or currency of the information provided. The City of Boston provides this data as is and with all faults, and makes no warranty of any kind. Each user is responsible for determining the suitability of the data for their intended use or purpose.”

An analysis of US State Crime Rate data

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Human Scream Detection and Analysis for Controlling Crime Rate using Machine Learning and Deep Learning

This file is aimed to find influencing factors and patterns of the occurrences of crime in Los Angeles

Area-wise crime analysis of London to predict the safest place for residence and places with most crime rates so that local police can control criminal activities.

Time Series - ARIMA - 1D Convolution - LSTM - ADF KPSS Test - PACF and ACF - Map Visualization - Random Forest - Chicago Crime Data - Data Analysis - Crime Category and Crime Rate Prediction - ML Forecast

Principles of Data Science Part I. Fivethirtyeight data graphics An R package that provides access to the code and data sets published by FiveThirtyEight https://github.com/fivethirtyeight/data, was just made available to public. The developers, Albert Kim and his colleagues, maintains a webpage for the package fivethirtyeight: https://rudeboybert.github.io/fivethirtyeight/ The data sets included are massive. You can find a list of these, including the URLs to the original fivethirtyeight.com articles, at https://rudeboybert.github.io/fivethirtyeight/articles/fivethirtyeight.html. The task (Part I) is to choose one of the articles with data graphics, and recreate one or more of the data graphics found in the article. Examples of such report can be found at https://rudeboybert.github.io/fivethirtyeight/articles/ The report will consist of 1. A technical discussion of your data wrangling-visualization statements; 2. A brief paragraph explaining the context of the data graphic you created, and be prepared by R markdown. Part II. Retreive, explore, and analyze This part of the task is to retreive, explore, and analyze data in one of the topic areas. You will need to choose one from American Time Use Survey Data and Economic Mobility data (see below). Scope of the work The final product will consist of 1. visualization or tabulation of the data (from either exploring or modeling), 2. results of statistic tests for your hypothesis, 3. and modeling and predictions from statistical learning methods. report The report consists of 1. Proposed goals in your progress report, 2. Analysis (both code chunks and results), 3. Interpretation, 1. Economic Mobility data We will look at economic mobility across generations in the contemporary USA. The data come from a large study1, based on tax records, which allowed researchers to link the income of adults to the income of their parents several decades previously. For privacy reasons, we don’t have that individual-level data, but we do have aggregate statistics about economic mobility for several hundred communities, containing most of the American population, and covariate information about those communities. We are interested in predicting economic mobility from the characteristics of communities. Data can be read using the following R code. There are 741 communities (observations) and 43 variables. dat <- read.csv("mobility.csv") The variable we want to predict is economic mobility; the rest are predictor variables or covariates. 1. Mobility: The probability that a child born in 1980–1982 into the lowest quintile (20%) of household income will be in the top quintile at age 30. Individuals are assigned to the community they grew up in, not the one they were in as adults. (가계 소득의 최저 5 분위수 (20 %)에 속해 있는 1980-1982 년 출생한 아이가 30세에 되었을 때 상위 1 분위에 속할 확률)
 2. Population in 2000. (2000년 기준 인구)
 3. Is the community primarily urban or rural? (커뮤니티가 도시인가 시골인가?)
 4. Black: percentage of individuals who marked black (and nothing else) on census forms. (흑인의 비율)
 5. Racial segregation: a measure of residential segregation by race. (인종별 주거지 분리의 정도)
 6. Income segregation: Similarly but for income. (소득별 주거지 분리의 정도)
 7. Segregation of poverty: Specifically a measure of residential segregation for those in the bottom quarter of the national income distribution. (저소득층과 중상류층의 주거지 분리의 정도)
 8. Segregation of affluence: Residential segregation for those in the top qarter. (상류층과 중하층의 주거지 분리의 정도)
 9. Commute: Fraction of workers with a commute of less than 15 minutes. (15 분 미만 통근하는 주민의 비율)
 10. Mean income: Average income per capita in 2000. (평균 소득 )
 11. Gini: A measure of income inequality, which would be 0 if all incomes were perfectly equal, and tends towards 100 as all the income is concentrated among the richest individuals. ( 지니 계수)
 12. Share 1%: Share of the total income of a community going to its richest 1%. (상위 1% 가 차지하는 수입의 비율)
 13. Gini bottom 99%: Gini coefficient among the lower 99% of that community. (상위 1 %를 제외한 나머지의 지니 계수)
 14. Fraction middle class: Fraction of parents whose income is between the national 25th and 75th percentiles. ( 중산층 비율 )
 15. Local tax rate: Fraction of all income going to local taxes. ( 지방세율 )
 16. Local government spending: per capita. ( 1 인당 지방정부 지출 )
 17. Progressivity: Measure of how much state income tax rates increase with income. ( 세금 가중의 정도 )
 18. EITC: Measure of how much the state contributed to the Earned Income Tax Credit (a sort of negative income tax for very low-paid wage earners). ( 저소득층을 위한 세금 공제의 정도 )
 19. School expenditures: Average spending per pupil in public schools. ( 공립학교의 학생 1 인당 평균 지출. )
 20. Student/teacher ratio: Number of students in public schools divided by number of teachers.( 학생 / 교사 비율 )
 21. Test scores: Residuals from a linear regression of mean math and English test scores on household income per capita. ( 시험 점수: 언어+수학 점수를 평균 가정 소득에 회귀한 잔차 )
 22. High school dropout rate: Also, residuals from a linear regression of the dropout rate on per-capita income. ( 고등학교 중퇴율 : 실제 중퇴율를 평균 가정 소득에 회귀한 잔차 )
 23. Colleges per capita ( 1 인당 대학의 갯수 )
 24. College tuition: in-state, for full-time students ( 대학 등록금 )
 25. College graduation rate: Again, residuals from a linear regression of the actual graduation rate on household income per capita. ( 대학 졸업율: 실제 졸업율를 평균 가정 소득에 회귀한 잔차 )
 26. Labor force participation: Fraction of adults in the workforce. ( 노동인구 중 성인의 비율 )
 27. Manufacturing: Fraction of workers in manufacturing. ( 제조업 근로자의 비율 )
 28. Chinese imports: Growth rate in imports from China per worker between 1990 and 2000. ( 중국산 수입 증가율 )
 29. Teenage labor: fraction of those age 14–16 who were in the labor force. ( 노동인구 중 10 대의 비율 )
 30. Migration in: Migration into the community from elsewhere, as a fraction of 2000 population. ( 이사오는 비율 )
 31. Migration out: Ditto for migration into other communities. ( 이사 나가는 비율 )
 32. Foreign: fraction of residents born outside the US. ( 외국 태생 인구 비율 )
 33. Social capital: Index combining voter turnout, participation in the census, and participation in community organizations. ( 사회 참여의 정도 )
 34. Religious: Share of the population claiming to belong to an organized religious body. ( 종교 생활 참여의 정도 )
 35. Violent crime: Arrests per person per year for violent crimes. ( 폭력 범죄율 )
 36. Single motherhood: Number of single female households with children divided by the total number of households with children. ( 전체 아이가 있는 가정 중 엄마 혼자 아이 키우는 집의 비율 )
 37. Divorced: Fraction of adults who are divorced. (이혼한 비율 )
 38. Married: Ditto. ( 결혼한 비율 )
 39. Longitude: Geographic coordinate for the center of the community (경도: 동서 )
 40. Latitude: Ditto ( 위도: 남북 )
 41. ID: A numerical code, identifying the community. ( 커뮤니티 식별 코드 )
 42. Name: the name of principal city or town. ( 동네 이름 )
 43. State: the state of the principal city or town of the community. ( 동네가 속한 미국의 주)
 1. Chetty, Raj, Nathaniel Hendren, Patrick Kline and Emmanuel Saez (2014). “Where is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States.” Quarterly Journal of Economics, 129: 1553– 1623. Finding and reading this paper does not actually help you↩


Using different techniques such as clustering and data analysis, analyzed the dataset and predicted the future crime patterns under different categories.

Micro analysis project of LA Crimes Rate data using Python & Pandas Data Frame

Python and Pandas Jupyter Notebook where users can see analysis of Seattle crime rates over time.

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Tools used: Rstudio This research project was commissioned by the consulting group, Consulting Group Room 208, for the state of North Carolina in order for its policy makers and the state’s business managers to make better informed decisions regarding crime rate and the factors that influence it. • Cleaned and Organized the data-set on R making it appropriate for statistical analysis to study trends. • Analyzed and Represented graph for uni-variate and bi-variate relationships between variables. • Performed Statistical analysis, Linear Regression & Graphical Representation using R.

Crime Rate Analysis using Linear Regression in Python

An analysis of the crime rates of various cities and their correlations to median income, graduation rate, and poverty rate.

This project involves the prediction of house prices in Boston using Lasso Regression in Jupyter Notebook. The dataset contains features such as average number of rooms per dwelling, crime rate, and more. Through this analysis, we aim to build a regression model that accurately predicts house prices based on the given input features.

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CrimeScope India is an end-to-end data analysis project analyzing property crime trends across Indian states from 2001 to 2019. I utilized Python, SQL, and EDA for data preprocessing and insights, and built an interactive Power BI dashboard for visual storytelling.

College Project

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A website used for visualizing statistically analyzed data of crimes in the state, with machine learning algorithms displayed over a map with GIS (Geographical Information System). Technologies used: python flask framework, ML libraries, GIS API’s. Still under development :)

ARIMA, Prophet and LSTM models to predict/forecast the crime rates.

I used 911 calls data of New Orleans of 2011,2012,2013,2014 and did a time series analysis using ARIMA model and predicted crime rate for future.

The project is statistical data analysis project. In which, we are going to analyse the Crime-Data about San-Francisco to predict the different level of crime rates over the city. The project will provide different types of analysis from the data like statistical analysis of each type of crime, individual Geographic presentation of each type of crime etc. This analysis will definitely help the police department with many different security aspects. The statistical analysis helps to know the crime rates. As well as, Geographic analysis will help them to develop and maintain security to make the city more secure.