{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Hazel Baker-harvey\n", "## Ting Lin\n", "## Pratyusha Meka" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction and Background" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Media is deeply coupled with American culture; in 2017 alone almost 600 million tickets were sold at movie theaters in the US (http://www.the-numbers.com/market/). How is the media consumed affecting the public?\n", "\n", "One theory presents media as a possibly negative influencer of culture. Dating back even to the 1950's, media has often been villainized as an inciter of violence. More recently in 1999, a Gallup poll found that 62% of the poulation believed violent media was a major cause of violent actions (http://www.gallup.com/poll/5626/blame-game-youth-media-violence.aspx). Often mass shootings are immeditaley tied to the culprit's media consumption habits; this happenned famously with Sandy Hook, and also with San Bernardino (https://www.thea-blast.org/top-stories/2016/01/14/is-violence-in-the-media-to-blame-for-mass-shootings/).\n", "Society clearly concedes that movies should be content restricted, and age restricted as proved with our TV censor laws as well as movie rating system (https://en.wikipedia.org/wiki/Censorship_in_the_United_States https://en.wikipedia.org/wiki/Motion_Picture_Association_of_America_film_rating_system). Violent films are screened, and children are prevented from viewing them. Yet common sense tells us that with media being such a hallmark of our culture, media cannot create violent people. People can enjoy violent films without being incited to commit violence.\n", "\n", "Though there is an immediate desire to blame media for violence, research actually often states the opposite. There was a study done by the National Bureau of Economic Research in which violent films were shown to reduce violent crime and drug consumption. Specifically “estimates suggest that in the short-run violent movies deter almost 1,000 assaults on an average weekend” (http://www.nber.org/papers/w13718). This study claims that the violent nature of the films serves as an outlet for violent energy, thus leading to less committed acts. Our team wonders if there is an effect of violent media in the long run. We also wonder about the validity of their claim that the violent content itself is what influences the deterance of violent acts in the short run?\n", "\n", "Other researchers disagree that violent films do promote aggression (http://www.nytimes.com/2008/01/07/business/media/07violence.html). This study argues that exposure to violence increases over all agressive behavior. This article however fails to answer to the fact that there is a decrease in violent crime in the short run in conjunction with violent films.\n", "\n", "Another study, asnwering to the decrease in violent crime, says it is not the content of the films that cause that reduced crime. The paper asserts that it is not a result of genre, but a result of occupying young men (those most likely to commit violent crime). This article also offers that specific actors could contribute to crime rate as well. (https://eml.berkeley.edu/~sdellavi/wp/moviescrimeQJEProofs2009.pdf).\n", "\n", "There is clearly a lot of controversy and differing opinions surrounding the topic of violent media's effects on the public. Does violent media have long term effects? Will the effect of violent media be seen through a larger time scale? Is violent film the only genre which will influence crime rate? Do other genres that attract young male audiences effect crime similarly to violent films?\n", "\n", "We hope to combine these questions in one project looking at genre specifically and enlarging the time frame. This will give us a better, more complete, picture on violent crime and its interaction with media.\n", "Our question stands: How does the number of each genre of film produced per year affect crime rate in San Diego? Is there a large scale, large time frame, influence deriving from the type of media consumed?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Description" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We originally started with three data sets, two relating to crime and one relating to movies.\n", "\n", "Our movie data set was taken from Kaggle and was already pre scraped for us off of IMDB. This data set contained movies dating back 100 years, and each entry had information about title, main actor, genre, director, budget, year released, rating, and more. The data set can be found at : https://www.kaggle.com/deepmatrix/imdb-5000-movie-dataset. In total the data contains 5044 entries. We hope to use this data to make new dataframes that isolate movies by year released and by genre.\n", "\n", "When we load this data we call it \"moviedata\" and the file is called \"movie_metadata.csv.\"\n", "\n", "We then had two data sets on crime, one national and one local to the San Diego region. Our local data set spanned from 2008 to 2012 and had detailed information on time, location, and type of each crime in the San Diego region. This data can be found at : http://data.sandiegodata.org/dataset/clarinova_com-crime-incidents-casnd-7ba4-extract and has 797979 entries. This data will be used initally in our analysis just of the years 2008 - 2012 to see if there are local large scale effects to media consumption.\n", "\n", "When we load this data we call it \"incidents\" and the file is called \"incidents-5y.csv.\"\n", "\n", "Our national dataset has data dating back to 1960 which reported number of crime by types (murder, robbery etc) corresponding to year. The national data set can be found at : https://data.world/government/uniform-crime-reports and had 53 entries. This data set will be used after our preliminary tests to enlarge our timescale and frame.\n", "\n", "When we load this data we call it \"reportNational\" and the file is called \"CrimeReports.csv.\"" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Import all the packages we needed\n", "%matplotlib inline\n", "%config Inlinebackend.figure_format = 'retina'\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import pandas as pd\n", "import patsy\n", "import statsmodels.api as sm\n", "from scipy.stats import chi2_contingency\n", "from sklearn import datasets" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Here we load the data\n", "moviedata = pd.read_csv(\"movie_metadata.csv\") \n", "incidents = pd.read_csv(\"incidents-5y.csv\")\n", "reportNational = pd.read_csv(\"Uniform crime reports.csv\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Cleaning/Pre-processing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are only interested in certain parts of the dataframe so we're going to select the year, the title of the movie, the genre, the keywords, the country and the rating of the movie. We remove any missing data points. And then lastly we exclude any movies not released in the US. We chose to do this because we assume that only movie played regularly in the US will affect US crime rates." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Data Clean for the moviedata \n", "#Select: \n", "# title_year, movie_title, genres, plot_keywords, country, content_rating\n", "#Methods: \n", "# drop all the NAN and extract \"USA\" movies\n", "\n", "movie1 = moviedata[['title_year','movie_title','genres','plot_keywords','country','content_rating']]\n", "movie2 = movie1.dropna()\n", "movieUSA = movie2[movie2['country'] == \"USA\"]\n", "\n", "USASort = movieUSA.sort_values(['title_year'], ascending = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At the end of the above box, we sort the movies by year released. We will only be focusing on movies released from 2008 to 2012 because those dated correspond to our crime data set. In the next box we exclude any movie not released in the time period 200-2012; having the data sorted made this step much easier." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#sort values in ascending year from 2008 to 2012\n", "Myear0812 = USASort[(USASort['title_year'] >= 2008) & (USASort['title_year'] <= 2012)].reset_index(drop=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because 2008-2012 is a fairly small time frame, we assume there will be no large differences in total number of movies released each year. Below we visualize our data to check this prediction." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Movie production in each year from 2008-2012 approximate the same\n", "plt.figure(figsize = (15,3))\n", "year = Myear0812.loc[:, 'title_year'].value_counts().sort_index()\n", "\n", "year.plot.bar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our assumption was correct, each year from 2008 to 2012 has approximatley the same number of movies released." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we want to split our main movie dataframe up by year, to further analyze each year in our time frame." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Sort data into df by year\n", "Mgy08 = USASort[USASort['title_year'] == 2008.0]\n", "Mgy09 = USASort[USASort['title_year'] == 2009.0]\n", "Mgy10 = USASort[USASort['title_year'] == 2010.0]\n", "Mgy11 = USASort[USASort['title_year'] == 2011.0]\n", "Mgy12 = USASort[USASort['title_year'] == 2012.0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The genres that we have identified are : action, animation, comedy, documentary ... etc Below we initialize a series containing all the genres. This will help us if we need a list of genres later, and will also allow us to know how many movies of each genre was produced in each year. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Set Variable for all movie genres \n", "ser_genres = ['Action','Animation','Comedy','Documentary','Family','Horror','Musical','Romance', 'Sport','War','Adventure','Biography','Crime','Drama','Fantasy','History','Music','Mystery','Sci-Fi','Thriller','Western']\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we initialize data frames that will eventually be populated with the counts of each genre by year." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Creating a new data frame for 2008 to 2012\n", "df08 = pd.DataFrame(ser_genres)\n", "df08['n'] = 0\n", "df08.columns =['genres','n']\n", "df09 = pd.DataFrame(ser_genres)\n", "df09['n'] = 0\n", "df09.columns =['genres','n']\n", "df10 = pd.DataFrame(ser_genres)\n", "df10['n'] = 0\n", "df10.columns =['genres','n']\n", "df11 = pd.DataFrame(ser_genres)\n", "df11['n'] = 0\n", "df11.columns =['genres','n']\n", "df12 = pd.DataFrame(ser_genres)\n", "df12['n'] = 0\n", "df12.columns =['genres','n']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because we will need to repeat the process of searching our movie dataset for genre name, and then populating the dataframes with the counts, we made three methods." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Function: get a substring from the Series and return number x in series, y in string\n", "def countString(x, y):\n", " num = x.str.contains(y).value_counts() \n", " if num[False] == len(x):\n", " return 0\n", " else:\n", " num = num[True]\n", " return num" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Function: get data and store in df_temp\n", "def fillDataFramesMov(database,df_temp):\n", " for i in range (0,len(df_temp)):\n", " n = countString(database['genres'],df_temp.ix[i,'genres'])\n", " df_temp.ix[i,'n'] = n \n", " return df_temp" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def fillDataFramesCrime(database,df_temp):\n", " for i in range (0,len(df_temp)):\n", " n = countString(database['type'],df_temp.ix[i,'crime'])\n", " df_temp.ix[i,'n'] = n \n", " return df_temp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With out methods, we populate each movie dataset with the counts of movie genres by year." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "#Assign frequency to the movie genres in 2008\n", "df08 = fillDataFramesMov(Mgy08,df08)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Assign frequency to the movie genres in 2009\n", "df09 = fillDataFramesMov(Mgy09,df09)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Assign frequency to the movie genres in 2010\n", "df10 = fillDataFramesMov(Mgy10,df10)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Assign frequency to the movie genres in 2011\n", "df11 = fillDataFramesMov(Mgy11,df11)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Assign frequency to the movie genres in 2012\n", "df12 = fillDataFramesMov(Mgy12,df12)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With our movie data cleaned and organized, we now move onto our crime data. We drop data points that are irrelevant to our experiement, only keeping year, the kind of crime, and the location of the crime. We then focus the neighborhood on those most proximal to theaters, and therefore we assume most influenced by movies. Because the data set is so large (797979 entries), we randomly sample 1000 data points. This will provide a representative sample without being overwhelming to work with." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Clean for the incidents data\n", "#Select: year, is_night, type, city, nbrhood, asr_zone, lat, lon\n", "#Method: assuming incidents happened in commercial area (asr_zone = 6.0)\n", "# we want population close to the theather(commmerical) area \n", "# drop all NAN.\n", "# taking 1000 samples randomly\n", "\n", "incidents1 = incidents[['year', 'is_night', 'type', 'city', 'nbrhood', 'asr_zone', 'lat', 'lon']]\n", "incidentsN = incidents1.dropna()\n", "\n", "commercial = incidentsN[incidentsN['asr_zone'] == 6]\n", "\n", "c = commercial.sample(n=1000).reset_index(drop=True)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want to know which crimes occur more often than others. We don't think that each type of crime occurs as often as others. Let's visualize this in the form of a bar graph." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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Dhg34+voa5zds2EBISAh2dpdvo0GDBgQGBrJu3boy+zx9+jTffPMNVqu12DhP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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Categorization of crime from our 1000 samples\n", "plt.figure(figsize = (10,3))\n", "x = c.loc[:, 'type'].value_counts() \n", "x.plot.barh().invert_yaxis()\n", "\n", "plt.xlabel('Event Count', color = \"Blue\")\n", "\n", "f1 = plt.gcf()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that drug and alcohol violations are the most popular in San Diego but the number of theft/larceny crimes are much higher than the number of sex crimes and crimes regarding weapons. This could be not representative of the actual data, but we have confidence that a sample of 1000 is robust to chance effects." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we will repeat similar steps to clean our crime data as performed for our movie data. We isolate crime by year within our time frame of 2008-2012. We create a series describing type of crime. We then initialize and populate our datasets that contain counts of kind of crime by year." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Get each title year in crime\n", "c08 = c[c['year'] == 2008]\n", "c09 = c[c['year'] == 2009]\n", "c10 = c[c['year'] == 2010]\n", "c11 = c[c['year'] == 2011]\n", "c12 = c[c['year'] == 2012]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Crime by category\n", "ser_crimes = ['DRUGS','LARCENY','VEHICLE','MOTOR','ASSAULT','BURGLARY','VANDALISM','DUI','ROBBERY','FRAUD','SEX','WEAPONS','ARSON','HOMICIDE']\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Creating a new data frame for 2008 to 2012\n", "dfc08 = pd.DataFrame(ser_crimes)\n", "dfc08['n'] = 0\n", "dfc08.columns =['crime','n']\n", "dfc09 = pd.DataFrame(ser_crimes)\n", "dfc09['n'] = 0\n", "dfc09.columns =['crime','n']\n", "dfc10 = pd.DataFrame(ser_crimes)\n", "dfc10['n'] = 0\n", "dfc10.columns =['crime','n']\n", "dfc11 = pd.DataFrame(ser_crimes)\n", "dfc11['n'] = 0\n", "dfc11.columns =['crime','n']\n", "dfc12 = pd.DataFrame(ser_crimes)\n", "dfc12['n'] = 0\n", "dfc12.columns =['crime','n']" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Assign frequency to the movie genres in 2008\n", "dfc08 = fillDataFramesCrime(c08,dfc08)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Assign frequency to the movie genres in 2009\n", "dfc09 = fillDataFramesCrime(c09,dfc09)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Assign frequency to the movie genres in 2010\n", "dfc10 = fillDataFramesCrime(c10,dfc10)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Assign frequency to the movie genres in 2011\n", "dfc11 = fillDataFramesCrime(c11,dfc11)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Assign frequency to the movie genres in 2012\n", "dfc12 = fillDataFramesCrime(c12,dfc12)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "We now have two sets of five data sets. Movie genres by year are described by the data sets df08-df12. Crime type by year is described in the data sets dfc08-dfc12." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To be better able to work with our datasets, we combined the two sets of five to make one master dataset for crime, and one for genre. Below each chunk of code is the displayed new dataset." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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20082009201020112012
Genres
Action3333414039
Adventure2831293230
Animation7119129
Biography74758
Comedy6872716770
Crime2128241826
Documentary63464
Drama7984766967
Family2121282116
Fantasy2027271828
History52314
Horror1722161722
Music13127810
Musical53344
Mystery1320111812
Romance4345512733
Sci-Fi2028132319
Sport94565
Thriller3748454145
War73223
Western10322
\n", "
" ], "text/plain": [ " 2008 2009 2010 2011 2012\n", "Genres \n", "Action 33 33 41 40 39\n", "Adventure 28 31 29 32 30\n", "Animation 7 11 9 12 9\n", "Biography 7 4 7 5 8\n", "Comedy 68 72 71 67 70\n", "Crime 21 28 24 18 26\n", "Documentary 6 3 4 6 4\n", "Drama 79 84 76 69 67\n", "Family 21 21 28 21 16\n", "Fantasy 20 27 27 18 28\n", "History 5 2 3 1 4\n", "Horror 17 22 16 17 22\n", "Music 13 12 7 8 10\n", "Musical 5 3 3 4 4\n", "Mystery 13 20 11 18 12\n", "Romance 43 45 51 27 33\n", "Sci-Fi 20 28 13 23 19\n", "Sport 9 4 5 6 5\n", "Thriller 37 48 45 41 45\n", "War 7 3 2 2 3\n", "Western 1 0 3 2 2" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Merge all Genres by Year\n", "df08.columns = ['Genres','2008']\n", "df09.columns = ['Genres','2009']\n", "df10.columns = ['Genres','2010']\n", "df11.columns = ['Genres','2011']\n", "df12.columns = ['Genres','2012']\n", "\n", "genresY = df08.merge(df09, on='Genres').merge(df10, on='Genres').merge(df11, on='Genres').merge(df12, on='Genres')\n", "genresY = pd.pivot_table(genresY, index = 'Genres')\n", "genresY" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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20082009201020112012
Crime
ARSON00100
ASSAULT212291922
BURGLARY14157169
DRUGS6259756327
DUI1917221517
FRAUD72867
HOMICIDE00000
LARCENY2922363936
MOTOR25198813
ROBBERY107735
SEX40355
VANDALISM121113128
VEHICLE5441211831
WEAPONS31120
\n", "
" ], "text/plain": [ " 2008 2009 2010 2011 2012\n", "Crime \n", "ARSON 0 0 1 0 0\n", "ASSAULT 21 22 9 19 22\n", "BURGLARY 14 15 7 16 9\n", "DRUGS 62 59 75 63 27\n", "DUI 19 17 22 15 17\n", "FRAUD 7 2 8 6 7\n", "HOMICIDE 0 0 0 0 0\n", "LARCENY 29 22 36 39 36\n", "MOTOR 25 19 8 8 13\n", "ROBBERY 10 7 7 3 5\n", "SEX 4 0 3 5 5\n", "VANDALISM 12 11 13 12 8\n", "VEHICLE 54 41 21 18 31\n", "WEAPONS 3 1 1 2 0" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Merge all Crime by Year\n", "dfc08.columns = ['Crime','2008']\n", "dfc09.columns = ['Crime','2009']\n", "dfc10.columns = ['Crime','2010']\n", "dfc11.columns = ['Crime','2011']\n", "dfc12.columns = ['Crime','2012']\n", "\n", "crimeY = dfc08.merge(dfc09, on='Crime').merge(dfc10, on='Crime').merge(dfc11, on='Crime').merge(dfc12, on='Crime')\n", "\n", "#removes rows and columns with only 0s\n", "#used later for chi-squared test\n", "crime_no_zero = crimeY[:12]\n", "crime_no_zero = crime_no_zero.drop('2012', 1)\n", "crime_no_zero = pd.pivot_table(crime_no_zero, index = \"Crime\")\n", "#crime_no_zero\n", "\n", "crime_pivot = pd.pivot_table(crimeY, index = \"Crime\")\n", "crime_pivot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also wanted the same data set, but rotated for easier graphing. Below is the code that rotates the data sets, as well as the printed new datasets." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ActionAnimationComedyDocumentaryFamilyHorrorMusicalRomanceSportWar...BiographyCrimeDramaFantasyHistoryMusicMysterySci-FiThrillerWestern
Year
200833287768216792120...1713513432093771
2009333111472283842127...2212320452844830
201041299771244762827...167311511354523
2011403212567186692118...178418272364122
201239309870264671628...2210412331954532
\n", "

5 rows × 21 columns

\n", "
" ], "text/plain": [ " Action Animation Comedy Documentary Family Horror Musical \\\n", "Year \n", "2008 33 28 7 7 68 21 6 \n", "2009 33 31 11 4 72 28 3 \n", "2010 41 29 9 7 71 24 4 \n", "2011 40 32 12 5 67 18 6 \n", "2012 39 30 9 8 70 26 4 \n", "\n", " Romance Sport War ... Biography Crime Drama Fantasy History \\\n", "Year ... \n", "2008 79 21 20 ... 17 13 5 13 43 \n", "2009 84 21 27 ... 22 12 3 20 45 \n", "2010 76 28 27 ... 16 7 3 11 51 \n", "2011 69 21 18 ... 17 8 4 18 27 \n", "2012 67 16 28 ... 22 10 4 12 33 \n", "\n", " Music Mystery Sci-Fi Thriller Western \n", "Year \n", "2008 20 9 37 7 1 \n", "2009 28 4 48 3 0 \n", "2010 13 5 45 2 3 \n", "2011 23 6 41 2 2 \n", "2012 19 5 45 3 2 \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Creates data frame with columns being genres\n", "genByGen = genresY.transpose()\n", "genByGen.columns = ser_genres\n", "#genByGen = genByGen.drop(genByGen.index[[0]])\n", "genByGen.index.name = 'Year'\n", "genByGen" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DRUGSLARCENYVEHICLEMOTORASSAULTBURGLARYVANDALISMDUIROBBERYFRAUDSEXWEAPONSARSONHOMICIDE
Year
200862295425211412191074300
20095922411922151117720100
20107536218971322783110
2011633918819161215365200
201227363113229817575000
\n", "
" ], "text/plain": [ " DRUGS LARCENY VEHICLE MOTOR ASSAULT BURGLARY VANDALISM DUI ROBBERY FRAUD \\\n", "Year \n", "2008 62 29 54 25 21 14 12 19 10 7 \n", "2009 59 22 41 19 22 15 11 17 7 2 \n", "2010 75 36 21 8 9 7 13 22 7 8 \n", "2011 63 39 18 8 19 16 12 15 3 6 \n", "2012 27 36 31 13 22 9 8 17 5 7 \n", "\n", " SEX WEAPONS ARSON HOMICIDE \n", "Year \n", "2008 4 3 0 0 \n", "2009 0 1 0 0 \n", "2010 3 1 1 0 \n", "2011 5 2 0 0 \n", "2012 5 0 0 0 " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Creates data frame with columns being crime genres\n", "criByCri = crimeY.transpose()\n", "criByCri.columns = ser_crimes\n", "criByCri = criByCri.drop(criByCri.index[[0]])\n", "criByCri.index.name = 'Year'\n", "criByCri" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Visualization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data visualization is key, and is vital to communicating scientific results to the public. We use multiple different methods of visualizations below. We mainly focus on bar graphs because our data is comparing multipe groups and also tracking changes over time. All of our graphs have color legends to explain what each genre or crime type is presented as. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These first graphs show each inividual year as a singualr bar, then each genre is colored to demonstrate what proportion of the year's production it represents." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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AgYG96N37HS5dukR09A7efbc7vXu/w3ffrbXyLAqPHvmJiIjILby9q3PiRAxm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\nd96ZefPmZdasWRkZGcmBAwfypS99KR/84AfbPR4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w8LR2KfeVm5sp32t3fOQ3ceJEXn/9dcqXLw/AoUOHaNasGQCtW7fm119/5cCB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\n82B9fX258cYb85vf/CaNjY256aabcswxx/TnKQA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P+x7v2LEjbNu2lW7detKmzYusXfv9fffxOFOgEhERkRx5eFRkxox5Fvfj6VkFT88q926Y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\ncDC9vb359Kc/nWXLlnm5J3DCWKEC2tott9ySsbG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HO3hwPxs3bqBbt560a/ccy5f/eMd9PMgKFaiSk5N55ZVXiImJwcHBgUGDBmEwGPD19SUs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\n2sOUKFECAHv7f6OEl5d3QbfkOXLkEPP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buyYnTsRiNpvZvXsngYF9aNy4KTt2bOPo0SM0bdqcU6dOMmHCVBwcHBg/fjTb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\nzWZrDR4fn2ytoaUYKuqfDIuIQOG2DxA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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot each year with proportional genres\n", "g1 = genByGen.groupby(['Action','Animation','Comedy','Documentary','Family','Horror','Musical','Romance', 'Sport','War','Adventure','Biography','Crime','Drama',\n", " 'Fantasy','History','Music','Mystery','Sci-Fi','Thriller','Western'])\n", "\n", "fig = g1.plot(kind='bar',figsize = (10,10),stacked=True,sharex =True)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next boxes demonstrate our ambition to come up with a line of regression that could predict both number of crime type and number of movies within a genre by year of production. However we decided this was not useful because the number of any kind of movie or any kind of crime did not change much year to year." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#returns genre based on index\n", "def getGenre(num):\n", " genrez = ['Action','Animation','Comedy','Documentary','Family','Horror','Musical','Romance', 'Sport','War','Adventure','Biography','Crime','Drama',\n", " 'Fantasy','History','Music','Mystery','Sci-Fi','Thriller','Western']\n", " return genrez[num]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#will make lines\n", "linesMov = pd.DataFrame(columns = ['Genres','a','b'])\n", "linesMov['Genres'] = ['Action','Animation','Comedy','Documentary','Family','Horror','Musical','Romance', 'Sport','War','Adventure','Biography','Crime','Drama',\n", " 'Fantasy','History','Music','Mystery','Sci-Fi','Thriller','Western']" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#x = np.array(genByGen[tempGen])\n", "#y = np.array(genByGen.index)\n", "#for i in range (0,(len(genByGen.columns))-1):\n", "# tempGen = getGenre(i)\n", "# a,b = np.polyfit(x,y,1)\n", "# linesMov.ix[i,'a'] = a\n", "# linesMov.ix[i,'b'] = b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the next plots we have a combination of bar graph and histogram. This graph shows the number of movies produced in a genre, but for each genre we plot data from 2008-2012. This graph helps visualize the unequal distribution of genre type (more dramas than sport films). But the graph also shows the relative repetition of genres by year." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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LKF7fW7N6vozgtb018mUO+TLHyvmsnO166WXks+/WrJ4vo3h9by0r86VV3KXb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\n+R0AAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Plot line graph for each year x = 'genres', y = 'amounts'\n", "#each year is identical\n", "genresY.plot.bar(figsize = (15,5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we plot the same data, but as a function of year with each bar representing a genre. " ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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8eZNKlSqjnj27KD09XcHBT6tSpco3PL927bqKiBiqPXt+lqenp8qWLaf4+Lgc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\nuTl77LFH7r///nzxi1/MH//4x9x0001tMWa70qNH3dbf1M6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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Plot line graph for each year x = 'genres', y = 'amounts'\n", "#each year is identical\n", "genByGen.plot.barh(figsize = (15,10));plt.legend(loc='best')\n", "\n", "#genresY.plot.line(figsize = (15,5))\n", "#genresY['Action'].plot(kind='line')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now repeat the above graphs, but with our crime data. The first graph shows number of each type of crime. Each bar represents a specfiic year during 2008-2012. Again we can see that relative number of crimes are unchanging year to year, but certain crimes are always committed more than other (more drug use than arson). " ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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OHXL8bLfbZbPZJEkBAQFKTU297J2ULOkvHx/v64h57cqUKWbkft3l7/271v4W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\nRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Plot line graph for each year x = 'crime type', y = 'amounts'\n", "#each year is identical\n", "crime_pivot.plot.bar(figsize = (15,5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The graph below shows number of crime by year, with each bar representing a particular type of crime." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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8fasqJib2ru3o1i1YHToESZJu3rypHj06q127jho1aowiIsJUt249eXmV1pQp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\nDz8sFu7sgfzsi+zsjfzsi+wCUZxC5HA4lJCQIEm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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Plot line graph for each year x = 'crimes', y = 'amounts'\n", "#each year is identical\n", "criByCri.plot.bar(figsize = (10,10));plt.legend(loc='best')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To further emphasize our point that there is not much change from year to year, we have plotted each year in another program. By using seaborn we made graphs that are more pleasing to the eye. Unfortunately, these graphs repeat our above findings, that some crimes routinely occur more than others. But, there is no significant variance in crime from year to year." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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tzfz585k2bRpms5ns7GyeeOIJfH19HV2uiIhIsVRg2AgNDeWZZ57h3XffpVmz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\nxFAKGyIiImKo/wPj4Yki623GSQAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#crime 08\n", "sns.set(style=\"whitegrid\", color_codes=True)\n", "sns.barplot(x='2008', y='Crime', data=crimeY, orient = \"h\");" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": 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M1WrlqaeewtfX19HlioiIFEsFho3Q0FCef/553n//fZo0aYLNZuPIkSNUqlSJ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\nSYC+NaePvwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#crime 09\n", "sns.set(style=\"whitegrid\", color_codes=True)\n", "sns.barplot(x='2009', y='Crime', data=crimeY, orient = \"h\");" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": 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RdMewERISwosvvshHH31EgwYNsFgsHD58mLJly7Jo0aLCqtEQTZs25csvvyzw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\nDPX/sowthZnlrW0AAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#crime 10\n", "sns.set(style=\"whitegrid\", color_codes=True)\n", "sns.barplot(x='2010', y='Crime', data=crimeY, orient = \"h\");" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "image/png": 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BgYH2LldERMQhFRk2IiIieO6553j//fdp2rQpFouFw4cPU7lyZRYvXlxcNRqi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\nIvJn6MqGiIiIGEphQ0RERAy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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#crime 11\n", "sns.set(style=\"whitegrid\", color_codes=True)\n", "sns.barplot(x='2011', y='Crime', data=crimeY, orient = \"h\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After visualizing our data and performing a sniff test, we think it is clear that there will be no significant result comparing genres of movies to types of crime. Because both the genre distribution and crime distribution do not change year to year, we decided to investigate another facet of our data set..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We went back to our original data set of movies, and again selected on movies in the US. But this time we kept all movies from 1960-2012. We decided to to this so we can compare it to our larger dataset of crimes going back to 1960. We hope by expanding our timeframe larger than 5 years we will see more patters." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Take year, genres, country from USASort and select movies from 1960-2012\n", "m1 = USASort[['title_year', 'genres', 'country']]\n", "m1 = m1[(m1.title_year > 1959) & (m1.title_year < 2013)]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also suspect that focusing on all genres is too big of a focus. Because our experiement is based off of previous work focusing on violent films and violent crimes, we decide to follow in their footsteps. We isolate only movies that probably have violent themes from the Crime, War, Horror, and Action genres." ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Get Crime, War, Horror, Action movies from Genres\n", "c = 'Crime'\n", "w = 'War'\n", "h = 'Horror'\n", "a = 'Action'\n", "\n", "for index, row in m1.iterrows():\n", " if row['genres'].find(c or w or h or a) == -1 :\n", " m1.drop(index, inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then group together and count all the films we suspect as having violent content, meaning have a possibly violent genre." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Group and count genres which contains keyword 'crime','war','horror','action' in response to the year\n", "m1 = m1[['title_year', 'genres']]\n", "m1 = m1.groupby('title_year').count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We struggled to make a method to count the number movies in violent genres by year, so we manually counted and placed results in an array representing number of war, crime, action, or horror movie by year." ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Creating a vector that represents the genres in each year\n", "vector = np.array([0, 1,1,1,0,0,1,2, 0,1,0,4,2,2,1,0,1, 0,0,2,1,3,1,3,3,2,2,5,4,4,5,9,8,8,16,17,23,25,20,23,26,41,35,31,22,24,21,27,22,29,24,19,\n", "27])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we prune our dataset on national crime back to 1960 for relevent data. We only care about violent crime, so for the new data set we only care about the year, and total number of violent crime." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearPopulationViolent crime totalMurder and nonnegligent ManslaughterForcible rapeRobberyAggravated assaultProperty crime totalBurglaryLarceny-theftMotor vehicle theft
0196017932317528846091101719010784015432030957009121001855400328200
1196118299200028939087401722010667015676031986009496001913000336000
2196218577100030151085301755011086016457034507009943002089600366800
31963188483000316970864017650116470174210379250010864002297800408300
41964191141000364220936021420130390203050420040012132002514400472800
51965193526000387390996023410138690215330435200012825002572600496900
619661955760004301801104025820157990235330479330014101002822000561200
719671974570004999301224027620202910257160540350016321003111600659800
819681993990005950101380031670262840286700612520018589003482700783600
919692013850006618701476037170298850311090674900019819003888600878500
1019702032352987388201600037990349860334970735920022050004225800928400
1119712062120008165001778042260387700368760777170023993004424200948200
1219722082300008349001867046850376290393090741390023755004151200887200
1319732098510008759101964051400384220420650784220025655004347900928800
1419742113920009747202071055400442400456210927870030392005262500977100
1519752131240001039710205105609047050049262010252700326530059777001009600
161976214659000100421018780570804278105005301034550031087006270800966000
17197721633200010295801912063500412610534350995500030715005905700977700
1819782180590001085550195606761042693057146010123400312830059910001004100
1919792200990001208030214607639048070062948011041500332770066010001112800
2019802253492641344520230408299056584067265012063700379520071369001131700
2119812294657141361820225208250059291066390012061900377970071944001087800
2219822316644581322390210107877055313066948011652000344710071425001062400
2319832337919941258087193087891850656765329410850543312985167127591007933
2419842358249021273282186928423348500868534910608473298443465918741032165
2519852379237951327767189768767149787472324611102590307334869263801102862
2619862401328871489169206139145954277583432211722700324141072571531224137
2719872422889181483999200969111151770485508812024709323618474998511288674
2819882444989821566221206759248654296891009212356865321807777058721432916
2919892468192301646037215009450457832695170712605412316817078724421564800
301990249464396182012723438102555639271105486312655486307390979456701635907
311991252153092191176724703106593687732109273912961116315715081422281661738
321992255029699193227423760109062672478112697412505917297988479151991610834
331993257782608192601724526106014659870113560712218777283480878209091563060
341994260327021185767023326102216618949111317912131873271277478798121539287
35199526280327617987922160697470580509109920712063935259378479977101472441
36199626522857216885401964596252535594103704911805323250640079046851394238
37199726778360716360961820896153498534102320111558475246052677437601354189
3819982702480031533887169749314444718697658310951827233273573763111242781
3919992726908131426044155228941140937191174010208334210073969555201152075
4020002814219061425486155869017840801691170610182584205099269715901160002
4120012853175591439480160379086342355790902310437189211653170922671228391
4220022879739241423677162299523542080689140710455277215125270573791246646
4320032907889761383676165289388341423585903010442862215483470268021261226
4420042936568421360088161489508940147084738110319386214444669370891237851
4520052965070611390745167409434741743886222010174754215544867834471235859
4620062993984841435123173099447244924687409610019601219499366263631198245
472007301621157142297017128921604473248663589882212219019865915421100472
48200830405972413944611646590750443563843683977415222288876586206959059
49200930700655013258961539989241408742812514933706022033136338095795652
50201030933021912512481472285593369089781844911262521684596204601739565
51201131158781612060311466184175354772752423905274321851406151095716508
52201231391404012144641482784376354522760739897543821037876150598721053
\n", "
" ], "text/plain": [ " Year Population Violent crime total \\\n", "0 1960 179323175 288460 \n", "1 1961 182992000 289390 \n", "2 1962 185771000 301510 \n", "3 1963 188483000 316970 \n", "4 1964 191141000 364220 \n", "5 1965 193526000 387390 \n", "6 1966 195576000 430180 \n", "7 1967 197457000 499930 \n", "8 1968 199399000 595010 \n", "9 1969 201385000 661870 \n", "10 1970 203235298 738820 \n", "11 1971 206212000 816500 \n", "12 1972 208230000 834900 \n", "13 1973 209851000 875910 \n", "14 1974 211392000 974720 \n", "15 1975 213124000 1039710 \n", "16 1976 214659000 1004210 \n", "17 1977 216332000 1029580 \n", "18 1978 218059000 1085550 \n", "19 1979 220099000 1208030 \n", "20 1980 225349264 1344520 \n", "21 1981 229465714 1361820 \n", "22 1982 231664458 1322390 \n", "23 1983 233791994 1258087 \n", "24 1984 235824902 1273282 \n", "25 1985 237923795 1327767 \n", "26 1986 240132887 1489169 \n", "27 1987 242288918 1483999 \n", "28 1988 244498982 1566221 \n", "29 1989 246819230 1646037 \n", "30 1990 249464396 1820127 \n", "31 1991 252153092 1911767 \n", "32 1992 255029699 1932274 \n", "33 1993 257782608 1926017 \n", "34 1994 260327021 1857670 \n", "35 1995 262803276 1798792 \n", "36 1996 265228572 1688540 \n", "37 1997 267783607 1636096 \n", "38 1998 270248003 1533887 \n", "39 1999 272690813 1426044 \n", "40 2000 281421906 1425486 \n", "41 2001 285317559 1439480 \n", "42 2002 287973924 1423677 \n", "43 2003 290788976 1383676 \n", "44 2004 293656842 1360088 \n", "45 2005 296507061 1390745 \n", "46 2006 299398484 1435123 \n", "47 2007 301621157 1422970 \n", "48 2008 304059724 1394461 \n", "49 2009 307006550 1325896 \n", "50 2010 309330219 1251248 \n", "51 2011 311587816 1206031 \n", "52 2012 313914040 1214464 \n", "\n", " Murder and nonnegligent Manslaughter Forcible rape Robbery \\\n", "0 9110 17190 107840 \n", "1 8740 17220 106670 \n", "2 8530 17550 110860 \n", "3 8640 17650 116470 \n", "4 9360 21420 130390 \n", "5 9960 23410 138690 \n", "6 11040 25820 157990 \n", "7 12240 27620 202910 \n", "8 13800 31670 262840 \n", "9 14760 37170 298850 \n", "10 16000 37990 349860 \n", "11 17780 42260 387700 \n", "12 18670 46850 376290 \n", "13 19640 51400 384220 \n", "14 20710 55400 442400 \n", "15 20510 56090 470500 \n", "16 18780 57080 427810 \n", "17 19120 63500 412610 \n", "18 19560 67610 426930 \n", "19 21460 76390 480700 \n", "20 23040 82990 565840 \n", "21 22520 82500 592910 \n", "22 21010 78770 553130 \n", "23 19308 78918 506567 \n", "24 18692 84233 485008 \n", "25 18976 87671 497874 \n", "26 20613 91459 542775 \n", "27 20096 91111 517704 \n", "28 20675 92486 542968 \n", "29 21500 94504 578326 \n", "30 23438 102555 639271 \n", "31 24703 106593 687732 \n", "32 23760 109062 672478 \n", "33 24526 106014 659870 \n", "34 23326 102216 618949 \n", "35 21606 97470 580509 \n", "36 19645 96252 535594 \n", "37 18208 96153 498534 \n", "38 16974 93144 447186 \n", "39 15522 89411 409371 \n", "40 15586 90178 408016 \n", "41 16037 90863 423557 \n", "42 16229 95235 420806 \n", "43 16528 93883 414235 \n", "44 16148 95089 401470 \n", "45 16740 94347 417438 \n", "46 17309 94472 449246 \n", "47 17128 92160 447324 \n", "48 16465 90750 443563 \n", "49 15399 89241 408742 \n", "50 14722 85593 369089 \n", "51 14661 84175 354772 \n", "52 14827 84376 354522 \n", "\n", " Aggravated assault Property crime total Burglary Larceny-theft \\\n", "0 154320 3095700 912100 1855400 \n", "1 156760 3198600 949600 1913000 \n", "2 164570 3450700 994300 2089600 \n", "3 174210 3792500 1086400 2297800 \n", "4 203050 4200400 1213200 2514400 \n", "5 215330 4352000 1282500 2572600 \n", "6 235330 4793300 1410100 2822000 \n", "7 257160 5403500 1632100 3111600 \n", "8 286700 6125200 1858900 3482700 \n", "9 311090 6749000 1981900 3888600 \n", "10 334970 7359200 2205000 4225800 \n", "11 368760 7771700 2399300 4424200 \n", "12 393090 7413900 2375500 4151200 \n", "13 420650 7842200 2565500 4347900 \n", "14 456210 9278700 3039200 5262500 \n", "15 492620 10252700 3265300 5977700 \n", "16 500530 10345500 3108700 6270800 \n", "17 534350 9955000 3071500 5905700 \n", "18 571460 10123400 3128300 5991000 \n", "19 629480 11041500 3327700 6601000 \n", "20 672650 12063700 3795200 7136900 \n", "21 663900 12061900 3779700 7194400 \n", "22 669480 11652000 3447100 7142500 \n", "23 653294 10850543 3129851 6712759 \n", "24 685349 10608473 2984434 6591874 \n", "25 723246 11102590 3073348 6926380 \n", "26 834322 11722700 3241410 7257153 \n", "27 855088 12024709 3236184 7499851 \n", "28 910092 12356865 3218077 7705872 \n", "29 951707 12605412 3168170 7872442 \n", "30 1054863 12655486 3073909 7945670 \n", "31 1092739 12961116 3157150 8142228 \n", "32 1126974 12505917 2979884 7915199 \n", "33 1135607 12218777 2834808 7820909 \n", "34 1113179 12131873 2712774 7879812 \n", "35 1099207 12063935 2593784 7997710 \n", "36 1037049 11805323 2506400 7904685 \n", "37 1023201 11558475 2460526 7743760 \n", "38 976583 10951827 2332735 7376311 \n", "39 911740 10208334 2100739 6955520 \n", "40 911706 10182584 2050992 6971590 \n", "41 909023 10437189 2116531 7092267 \n", "42 891407 10455277 2151252 7057379 \n", "43 859030 10442862 2154834 7026802 \n", "44 847381 10319386 2144446 6937089 \n", "45 862220 10174754 2155448 6783447 \n", "46 874096 10019601 2194993 6626363 \n", "47 866358 9882212 2190198 6591542 \n", "48 843683 9774152 2228887 6586206 \n", "49 812514 9337060 2203313 6338095 \n", "50 781844 9112625 2168459 6204601 \n", "51 752423 9052743 2185140 6151095 \n", "52 760739 8975438 2103787 6150598 \n", "\n", " Motor vehicle theft \n", "0 328200 \n", "1 336000 \n", "2 366800 \n", "3 408300 \n", "4 472800 \n", "5 496900 \n", "6 561200 \n", "7 659800 \n", "8 783600 \n", "9 878500 \n", "10 928400 \n", "11 948200 \n", "12 887200 \n", "13 928800 \n", "14 977100 \n", "15 1009600 \n", "16 966000 \n", "17 977700 \n", "18 1004100 \n", "19 1112800 \n", "20 1131700 \n", "21 1087800 \n", "22 1062400 \n", "23 1007933 \n", "24 1032165 \n", "25 1102862 \n", "26 1224137 \n", "27 1288674 \n", "28 1432916 \n", "29 1564800 \n", "30 1635907 \n", "31 1661738 \n", "32 1610834 \n", "33 1563060 \n", "34 1539287 \n", "35 1472441 \n", "36 1394238 \n", "37 1354189 \n", "38 1242781 \n", "39 1152075 \n", "40 1160002 \n", "41 1228391 \n", "42 1246646 \n", "43 1261226 \n", "44 1237851 \n", "45 1235859 \n", "46 1198245 \n", "47 1100472 \n", "48 959059 \n", "49 795652 \n", "50 739565 \n", "51 716508 \n", "52 721053 " ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Get year, violent crime total from national report\n", "selection = reportNational[['Year', 'Violent crime total']]\n", "reportNational_pivot = pd.pivot_table(reportNational, index = \"Year\")\n", "reportNational" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now construct a dataframe that goes back to 1960 from 2012, it contains the total number of violent crime by year as well as total number of movies with possible violent content (action, horror, crime, war)." ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n", "
" ], "text/plain": [ " year violent crime movies\n", "0 1960 288460 0\n", "1 1961 289390 1\n", "2 1962 301510 1\n", "3 1963 316970 1\n", "4 1964 364220 0\n", "5 1965 387390 0\n", "6 1966 430180 1\n", "7 1967 499930 2\n", "8 1968 595010 0\n", "9 1969 661870 1\n", "10 1970 738820 0\n", "11 1971 816500 4\n", "12 1972 834900 2\n", "13 1973 875910 2\n", "14 1974 974720 1\n", "15 1975 1039710 0\n", "16 1976 1004210 1\n", "17 1977 1029580 0\n", "18 1978 1085550 0\n", "19 1979 1208030 2\n", "20 1980 1344520 1\n", "21 1981 1361820 3\n", "22 1982 1322390 1\n", "23 1983 1258087 3\n", "24 1984 1273282 3\n", "25 1985 1327767 2\n", "26 1986 1489169 2\n", "27 1987 1483999 5\n", "28 1988 1566221 4\n", "29 1989 1646037 4\n", "30 1990 1820127 5\n", "31 1991 1911767 9\n", "32 1992 1932274 8\n", "33 1993 1926017 8\n", "34 1994 1857670 16\n", "35 1995 1798792 17\n", "36 1996 1688540 23\n", "37 1997 1636096 25\n", "38 1998 1533887 20\n", "39 1999 1426044 23\n", "40 2000 1425486 26\n", "41 2001 1439480 41\n", "42 2002 1423677 35\n", "43 2003 1383676 31\n", "44 2004 1360088 22\n", "45 2005 1390745 24\n", "46 2006 1435123 21\n", "47 2007 1422970 27\n", "48 2008 1394461 22\n", "49 2009 1325896 29\n", "50 2010 1251248 24\n", "51 2011 1206031 19\n", "52 2012 1214464 27" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Creating a final report that contains year, violent crime, movies(contains the violent key words) nationally\n", "col = ['year', 'violent crime', 'movies']\n", "final_report = pd.DataFrame(columns = col)\n", "final_report['year'] = selection['Year']\n", "final_report['violent crime'] = selection['Violent crime total']\n", "final_report['movies'] = vector\n", "final_report" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we visualize the data as a scatter plot. Each data point is a year as represented by the total number of violent crimes and total number of possibly violent movies." ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Fh4fLzc1NTZo0UUhIiFJSUpSTk6P09HSFhIRIkvr06aPi4mIdP35ckrR69WpNmjRJt956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g4dOvDyyy9XTEcIIUQ5M1OKWtheRcTFxeHu7l4lytTm6xn3/m7TkqbbmZvfPQkEsG9gBWZmXC/i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\nceMGdevWZ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g==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Plot the result\n", "plt.figure(figsize = (15, 10))\n", "plt.scatter(final_report['violent crime'], final_report['movies'])\n", "plt.title(\"Number of movies vs. Violent crimes\")\n", "for label, movie_count, crime_count in zip(final_report['year'], final_report['movies'], final_report['violent crime']):\n", " plt.annotate(label, xy = (crime_count, movie_count), xytext = (1, -1), textcoords = 'offset points')\n", " \n", "plt.xlabel('Number of Violent crimes')\n", "plt.ylabel('Number of Violent keywords in Movies')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Data Analysis & Results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want to see if on a national scale there is an association between the year and the number of each type of crime committed. We will run a chi squared test. This will allow us to later compare it to the results of a chi squared test between year and the number of each type of crime committed in San Diego." ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true }, "outputs": [], "source": [ "valueN, pN, dfN, expectedN = chi2_contingency(observed= reportNational_pivot)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nationally there seems to be an association between year and type of crime committed. However, our time frame is much larger nationally than our time frame for San Diego. So we will see if there is still an association on a national level between the years 2008 and 2012. " ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "part_of_national = reportNational[48:53]\n", "valuepN, pPN, dfPN, expectedPN= chi2_contingency(observed= part_of_national)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want to check if there is any relation between year and the number of movies released in each genre. We will run a chi-squared test to see if there is an association. We don't expect there to be an association, but we will run the test to be sure." ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.984870188048\n" ] } ], "source": [ "valueG, pG, dfG, expectedG = chi2_contingency(observed= genresY)\n", "print(pG)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given the high p-value, we can conclude that there is no association between the two variables. For consistency we will run another chi-squared test on year and the number of each type of crime." ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.000706387719749\n" ] } ], "source": [ "valueC, pC, dfC, expectedC = chi2_contingency(observed = crime_no_zero )\n", "print(pC)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The p-value here is high as well meaning that there is no association between the two variables. Since we don't have an association between the types of crimes and year and the types of movies and year, we will now focus our search on a more narrow scope. We will see if there is an association between the number of violent crimes committed in a year nationally, and the number of American movies released in a year under the genres Action, Crime, War and Horror. We created a scatter plot above and the relationship does not appear to be entirely linear but we will run a regression test to be sure. In this case our null hypothesis is that there is no significant relationship in the number of violent crimes committed in a year and the number of movies released in those specific genres. " ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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7/WE0nnChLlo25x2lbK56ZrF6FT9zyrmyufr6M6kectoM3imutMCCaY78dA+J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\ng8myHC2bU5bq/3aqV3MFwq6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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a1, b1 = np.polyfit(final_report['violent crime'], final_report['movies'], 1)\n", "sns.regplot(final_report['violent crime'], final_report['movies'])" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: violent crime R-squared: 0.567\n", "Model: OLS Adj. R-squared: 0.559\n", "Method: Least Squares F-statistic: 68.13\n", "Date: Tue, 13 Jun 2017 Prob (F-statistic): 5.06e-11\n", "Time: 15:32:37 Log-Likelihood: -798.18\n", "No. Observations: 53 AIC: 1598.\n", "Df Residuals: 52 BIC: 1600.\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "movies 6.196e+04 7506.389 8.254 0.000 4.69e+04 7.7e+04\n", "==============================================================================\n", "Omnibus: 2.676 Durbin-Watson: 0.091\n", "Prob(Omnibus): 0.262 Jarque-Bera (JB): 2.332\n", "Skew: -0.407 Prob(JB): 0.312\n", "Kurtosis: 2.373 Cond. No. 1.00\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "#sm.OLS()\n", "result = sm.OLS(final_report['violent crime'], final_report['movies']).fit()\n", "print(result.summary())" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "The results shown above tell us that R-squared is 0.567 which gives us an R value of 0.752 . Since the p-value is much smaller than 0.05, we can reject our null hypothesis. We find that there is a relationship between the number of violent crimes committed in a year and the number of Action, Horror, War and Crime related movies released. It is important to note that initially the conditions for inference weren't completely met since the relationship doesn't look linear. " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Conclusions and Discussion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While according to simple statistics it appears we have found an effect, this may be more an artifact than a real discovery." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We demonstrated with this project our ability to clean large data sets, deciding which variables are relevant to our question. But it is also important to remember with each assumption, we may have jeopardized our results! We chose to only look at American movies, which could make us oblivious to results informed by foreign films. We also persribed 'War', 'Crime', 'Horror', and 'Action' as violent films. This is not always true, nor exclusive. We feel it was a good estimate of content, but we could have also analyzed keywords to better understand content. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also had a very large time frame. We first focused on 2008-2012 in which we saw no effects. But then we zoomed out to focus on 1960-2012. While at this stage we could see change, this time frame is so large that the change is more likely due to outside factors and not movies. Also movies are not played continuously for a year, the release year can be not representative of when the film was shown. Also movies only last an hour, so perhaps crime should also be analyzed in terms of hour, not year." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ignoring all the possible confounds, we conclude that there is an effect on crime rate as a result of movie genre. We find a statistically significant effect that as number of violent films produced increases, so does number of violent crimes committed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A possible future direction for studies like this would be to focus on number of attendees and popularity of movies compared to crime rate. This would address previous research that postulates that crime reduction during violent movies showtimes is because people are occupied in the theater, not because of the content. Also this research could be replicated but focusing on key words, and also attempting to disentangle overall trends in crime and movie genres from our results." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This research is vital because we consume immense amounts of media, especially in the US. This media may be affecting how we think and act, and it is important to stay critical of what we consume. But this also does not mean that you should feel guilty for watching your favorite Tarantino film!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }