Introduction

Crime is a significant concern for cities worldwide, impacting public safety, quality of life, and socioeconomic development. In Buenos Aires, Argentina, addressing crime and enhancing public safety are critical priorities for local authorities and communities. To gain insights into crime dynamics and develop effective strategies for crime prevention and intervention, we embark on an extensive exploration and analysis of crime data reported in Buenos Aires for the year 2022.

This project combines traditional exploratory data analysis (EDA) techniques with predictive modeling to delve deep into the patterns, trends, and underlying factors associated with reported crime incidents. By leveraging data-driven approaches, we aim to uncover actionable insights that can inform decision-making processes and contribute to the development of evidence-based crime reduction strategies.

Dataset Overview

The dataset “delitos_2022.csv” provides a comprehensive record of crime incidents reported in Buenos Aires throughout the year 2022. It encompasses various attributes associated with each reported crime, including the type of crime, location, date, demographic information, and additional contextual details.

Our analysis will focus on exploring the temporal, spatial, and demographic dimensions of crime in Buenos Aires. Additionally, we will investigate the relationship between different crime types, examine crime hotspots across neighborhoods and districts, and analyze the demographic characteristics of both perpetrators and victims.

Furthermore, we will employ predictive modeling techniques to develop machine learning models capable of forecasting crime occurrences and identifying factors that contribute to the likelihood of specific types of crimes. By building predictive models, we aim to enhance our understanding of the complex dynamics underlying criminal activities and improve the accuracy of future crime predictions.

Throughout this analysis, we emphasize the importance of data-driven decision-making and the potential of data analytics to support policymakers, law enforcement agencies, and community stakeholders in their efforts to combat crime and promote public safety in Buenos Aires.

Data Source

The dataset “delitos_2022.csv” is sourced from the Buenos Aires Open Data Portal and represents a valuable resource for studying crime trends and patterns in the city. The dataset contains anonymized information about reported crime incidents and is publicly available for research and analysis.

Variable Description
id.mapa Identifier map
anio Year of the crime occurrence
mes Month of the crime occurrence
dia Day of the crime occurrence
fecha Date of the crime occurrence
franja Hour of crime
tipo Type of crime
subtipo Subtype of crime
uso_arma Whether weapons were used in the crime (Yes/No)
uso_moto Whether motorcycles were involved in the crime (Yes/No)
barrio Neighborhood where the crime occurred
comuna District where the crime occurred
latitud Latitude coordinate of the crime location
longitud Longitude coordinate of the crime location
cantidad Quantity or count of crimes reported

Buenos Aires Open Data Portal

Prepare packages and workspace

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Upload crime buenos aires 2022 Dataset

Data Cleaning and Preprocessing

Quick View of Data and Structure

##   id.mapa anio       mes       dia      fecha franja tipo    subtipo uso_arma
## 1       1 2022   OCTUBRE   VIERNES 2022-10-14      3 Robo Robo total       NO
## 2       2 2022   OCTUBRE    JUEVES 2022-10-27      5 Robo Robo total       NO
## 3       3 2022 NOVIEMBRE    MARTES 2022-11-29      0 Robo Robo total       NO
## 4       4 2022 NOVIEMBRE     LUNES 2022-11-28      0 Robo Robo total       NO
## 5       5 2022 NOVIEMBRE MIERCOLES 2022-11-30      3 Robo Robo total       NO
## 6       6 2022      MAYO    MARTES 2022-05-17      5 Robo Robo total       NO
##   uso_moto            barrio comuna    latitud   longitud cantidad
## 1       NO         CHACARITA     15 -34.584136 -58.454704        1
## 2       NO          BARRACAS      4 -34.645043 -58.373194        1
## 3       NO         CHACARITA     15 -34.589982 -58.446471        1
## 4       NO         CHACARITA     15  -34.58832 -58.441232        1
## 5       NO          RECOLETA      2 -34.596748 -58.413609        1
## 6       NO PARQUE AVELLANEDA      9 -34.640978 -58.480254        1
## 'data.frame':    140918 obs. of  15 variables:
##  $ id.mapa : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ anio    : int  2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 ...
##  $ mes     : chr  "OCTUBRE" "OCTUBRE" "NOVIEMBRE" "NOVIEMBRE" ...
##  $ dia     : chr  "VIERNES" "JUEVES" "MARTES" "LUNES" ...
##  $ fecha   : chr  "2022-10-14" "2022-10-27" "2022-11-29" "2022-11-28" ...
##  $ franja  : chr  "3" "5" "0" "0" ...
##  $ tipo    : chr  "Robo" "Robo" "Robo" "Robo" ...
##  $ subtipo : chr  "Robo total" "Robo total" "Robo total" "Robo total" ...
##  $ uso_arma: chr  "NO" "NO" "NO" "NO" ...
##  $ uso_moto: chr  "NO" "NO" "NO" "NO" ...
##  $ barrio  : chr  "CHACARITA" "BARRACAS" "CHACARITA" "CHACARITA" ...
##  $ comuna  : chr  "15" "4" "15" "15" ...
##  $ latitud : chr  "-34.584136" "-34.645043" "-34.589982" "-34.58832" ...
##  $ longitud: chr  "-58.454704" "-58.373194" "-58.446471" "-58.441232" ...
##  $ cantidad: int  1 1 1 1 1 1 1 1 1 1 ...

Our dataset comprises a total of 140918 observations and 15 columns, featuring variables of only two types :

Int (Integer) : Comprising 3 variables

Ch (Character) : Comprising 12 variables

Handling Missing Values

##  id.mapa     anio      mes      dia    fecha   franja     tipo  subtipo 
##        0        0        0        0        0       47        0        0 
## uso_arma uso_moto   barrio   comuna  latitud longitud cantidad 
##        0        0     2391     2393     2385     2385        0

Several columns contain empty cells and “null” or “na” values. The columns barrio, comuna, latitud, and longitud are interrelated, so removing null values from the comuna column will suffice. Additionally, we’ll remove null values from the franja column.

##  id.mapa     anio      mes      dia    fecha   franja     tipo  subtipo 
##        0        0        0        0        0        0        0        0 
## uso_arma uso_moto   barrio   comuna  latitud longitud cantidad 
##        0        0        0        0        0        0        0

Great! Now our dataset is clean and free from any empty or null values that could potentially impact our subsequent analysis.

Featuring Transformation

Debido al idioma del pais de origen de nuestra base de datos, las columnas estan en español, esto puede complicar la lectura para algunos analistas por lo que intentaremos traducir la base para que sea mas legible para los demas.

Great! Now all columns and values are translated. Now we gonna create some columns for see better the hour of crimes in Argentina

##   id year season    month       day       date hour hour_interval    type
## 1  1 2022 Autumn  october    friday 2022-10-14    4         Night Robbery
## 2  2 2022 Autumn  october  thursday 2022-10-27    6         Night Robbery
## 3  3 2022 Autumn november   tuesday 2022-11-29    1         Night Robbery
## 4  4 2022 Autumn november    monday 2022-11-28    1         Night Robbery
## 5  5 2022 Autumn november wednesday 2022-11-30    4         Night Robbery
## 6  6 2022 Spring      may   tuesday 2022-05-17    6         Night Robbery
##         subtype weapon_use motorcycle_use      neighborhood district   latitude
## 1 Total robbery         No             No         CHACARITA       15 -34.584136
## 2 Total robbery         No             No          BARRACAS        4 -34.645043
## 3 Total robbery         No             No         CHACARITA       15 -34.589982
## 4 Total robbery         No             No         CHACARITA       15  -34.58832
## 5 Total robbery         No             No          RECOLETA        2 -34.596748
## 6 Total robbery         No             No PARQUE AVELLANEDA        9 -34.640978
##    longitude quantity
## 1 -58.454704        1
## 2 -58.373194        1
## 3 -58.446471        1
## 4 -58.441232        1
## 5 -58.413609        1
## 6 -58.480254        1

Exploratory Data Analysis (EDA)

## `summarise()` has grouped output by 'season'. You can override using the
## `.groups` argument.
## `geom_smooth()` using formula = 'y ~ x'

## `summarise()` has grouped output by 'day'. You can override using the `.groups`
## argument.

## `summarise()` has grouped output by 'hour_interval'. You can override using the
## `.groups` argument.

## `summarise()` has grouped output by 'hour_interval'. You can override using the
## `.groups` argument.

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## `summarise()` has grouped output by 'hour', 'neighborhood'. You can override
## using the `.groups` argument.

Model Selection

Linear Model

## `summarise()` has grouped output by 'hour'. You can override using the
## `.groups` argument.

## 
## Call:
## lm(formula = crime_count ~ hour + neighborhood + 0, data = regresion_data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -205.795  -19.503   -2.837   18.043  193.476 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## hour1                           60.858     10.021   6.073 1.74e-09 ***
## hour2                          -41.017     10.021  -4.093 4.57e-05 ***
## hour3                          -50.872     10.021  -5.076 4.53e-07 ***
## hour4                          -52.038     10.021  -5.193 2.47e-07 ***
## hour5                          -50.872     10.021  -5.076 4.53e-07 ***
## hour6                          -33.663     10.021  -3.359 0.000809 ***
## hour7                          -10.997     10.021  -1.097 0.272743    
## hour8                           31.003     10.021   3.094 0.002027 ** 
## hour9                           46.920     10.021   4.682 3.20e-06 ***
## hour10                          30.024     10.021   2.996 0.002797 ** 
## hour11                          33.566     10.021   3.349 0.000838 ***
## hour12                          23.274     10.021   2.323 0.020391 *  
## hour13                          48.170     10.021   4.807 1.75e-06 ***
## hour14                          28.524     10.021   2.846 0.004505 ** 
## hour15                          32.795     10.021   3.273 0.001099 ** 
## hour16                          35.691     10.021   3.562 0.000385 ***
## hour17                          41.733     10.021   4.164 3.37e-05 ***
## hour18                          53.337     10.021   5.322 1.24e-07 ***
## hour19                          63.024     10.021   6.289 4.63e-10 ***
## hour20                          52.316     10.021   5.221 2.14e-07 ***
## hour21                          55.337     10.021   5.522 4.20e-08 ***
## hour22                          31.024     10.021   3.096 0.002013 ** 
## hour23                          21.587     10.021   2.154 0.031452 *  
## hour24                           4.274     10.021   0.427 0.669809    
## neighborhoodALMAGRO            199.375     11.653  17.110  < 2e-16 ***
## neighborhoodBALVANERA          373.833     11.653  32.081  < 2e-16 ***
## neighborhoodBARRACAS           172.333     11.653  14.789  < 2e-16 ***
## neighborhoodBELGRANO           155.917     11.653  13.380  < 2e-16 ***
## neighborhoodBOCA                77.542     11.653   6.654 4.51e-11 ***
## neighborhoodBOEDO               64.458     11.653   5.532 3.98e-08 ***
## neighborhoodCABALLITO          232.417     11.653  19.945  < 2e-16 ***
## neighborhoodCHACARITA           57.792     11.653   4.960 8.20e-07 ***
## neighborhoodCOGHLAN              4.292     11.653   0.368 0.712723    
## neighborhoodCOLEGIALES          49.333     11.653   4.234 2.49e-05 ***
## neighborhoodCONSTITUCION       170.833     11.653  14.660  < 2e-16 ***
## neighborhoodFLORES             307.833     11.653  26.417  < 2e-16 ***
## neighborhoodFLORESTA            53.250     11.653   4.570 5.45e-06 ***
## neighborhoodLINIERS             82.958     11.653   7.119 1.98e-12 ***
## neighborhoodMATADEROS          117.083     11.653  10.048  < 2e-16 ***
## neighborhoodMONSERRAT          116.750     11.653  10.019  < 2e-16 ***
## neighborhoodMONTE CASTRO        29.833     11.653   2.560 0.010596 *  
## neighborhoodNUEVA POMPEYA      109.917     11.653   9.433  < 2e-16 ***
## neighborhoodNUÑEZ               70.250     11.653   6.029 2.27e-09 ***
## neighborhoodPALERMO            476.292     11.653  40.874  < 2e-16 ***
## neighborhoodPARQUE AVELLANEDA   79.000     11.653   6.780 1.98e-11 ***
## neighborhoodPARQUE CHACABUCO   109.375     11.653   9.386  < 2e-16 ***
## neighborhoodPARQUE CHAS          4.000     11.653   0.343 0.731463    
## neighborhoodPARQUE PATRICIOS    72.458     11.653   6.218 7.17e-10 ***
## neighborhoodPATERNAL            17.292     11.653   1.484 0.138122    
## neighborhoodPUERTO MADERO        2.208     11.653   0.190 0.849727    
## neighborhoodRECOLETA           244.708     11.653  21.000  < 2e-16 ***
## neighborhoodRETIRO             132.625     11.653  11.381  < 2e-16 ***
## neighborhoodSAAVEDRA            80.458     11.653   6.905 8.57e-12 ***
## neighborhoodSAN CRISTOBAL       80.583     11.653   6.915 7.97e-12 ***
## neighborhoodSAN NICOLAS        191.833     11.653  16.463  < 2e-16 ***
## neighborhoodSAN TELMO           60.833     11.653   5.221 2.14e-07 ***
## neighborhoodVELEZ SARSFIELD     32.833     11.653   2.818 0.004926 ** 
## neighborhoodVERSALLES            6.000     11.653   0.515 0.606727    
## neighborhoodVILLA CRESPO       140.375     11.653  12.047  < 2e-16 ***
## neighborhoodVILLA DEL PARQUE    46.625     11.653   4.001 6.73e-05 ***
## neighborhoodVILLA DEVOTO        82.917     11.653   7.116 2.02e-12 ***
## neighborhoodVILLA GRAL. MITRE   30.833     11.653   2.646 0.008263 ** 
## neighborhoodVILLA LUGANO       217.833     11.653  18.694  < 2e-16 ***
## neighborhoodVILLA LURO          29.750     11.653   2.553 0.010815 *  
## neighborhoodVILLA ORTUZAR        9.625     11.653   0.826 0.408993    
## neighborhoodVILLA PUEYRREDON    32.000     11.653   2.746 0.006130 ** 
## neighborhoodVILLA REAL           4.708     11.653   0.404 0.686252    
## neighborhoodVILLA RIACHUELO     10.625     11.653   0.912 0.362074    
## neighborhoodVILLA SANTA RITA    27.208     11.653   2.335 0.019729 *  
## neighborhoodVILLA SOLDATI       97.708     11.653   8.385  < 2e-16 ***
## neighborhoodVILLA URQUIZA       97.125     11.653   8.335 2.33e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 40.37 on 1081 degrees of freedom
## Multiple R-squared:  0.9437, Adjusted R-squared:   0.94 
## F-statistic: 255.4 on 71 and 1081 DF,  p-value: < 2.2e-16

Model Fit

Observations 1152
Dependent variable crime_count
Type OLS linear regression
F(71,1081) 255.41
R² 0.94
Adj. R² 0.94 

## `geom_smooth()` using formula = 'y ~ x'

## # A tibble: 1,152 × 9
##    crime_count hour  neighborhood .fitted .resid   .hat .sigma .cooksd
##          <int> <fct> <fct>          <dbl>  <dbl>  <dbl>  <dbl>   <dbl>
##  1         116 4     BALVANERA       322.  -206. 0.0616   39.9  0.0256
##  2         127 5     BALVANERA       323.  -196. 0.0616   39.9  0.0232
##  3         603 16    BALVANERA       410.   193. 0.0616   39.9  0.0226
##  4         630 19    BALVANERA       437.   193. 0.0616   39.9  0.0226
##  5         719 20    PALERMO         529.   190. 0.0616   39.9  0.0219
##  6         589 14    BALVANERA       402.   187. 0.0616   40.0  0.0211
##  7         138 3     BALVANERA       323.  -185. 0.0616   40.0  0.0207
##  8         584 15    BALVANERA       407.   177. 0.0616   40.0  0.0190
##  9         163 2     BALVANERA       333.  -170. 0.0616   40.0  0.0174
## 10         706 1     PALERMO         537.   169. 0.0616   40.0  0.0172
## # ℹ 1,142 more rows
## # ℹ 1 more variable: .std.resid <dbl>
## 
##  Jarque Bera Test
## 
## data:  linear_model$residuals
## X-squared = 1382.3, df = 2, p-value < 2.2e-16
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -205.795  -19.503   -2.837    0.000   18.043  193.476
## 
##  studentized Breusch-Pagan test
## 
## data:  linear_model
## BP = 606.98, df = 70, p-value < 2.2e-16
## 
## t test of coefficients:
## 
##                               Estimate Std. Error t value  Pr(>|t|)    
## hour1                          60.8576     9.2180  6.6021 6.343e-11 ***
## hour2                         -41.0174     9.7753 -4.1960 2.939e-05 ***
## hour3                         -50.8715    10.2401 -4.9679 7.864e-07 ***
## hour4                         -52.0382    10.6324 -4.8943 1.137e-06 ***
## hour5                         -50.8715    10.2913 -4.9431 8.905e-07 ***
## hour6                         -33.6632     9.3564 -3.5979 0.0003353 ***
## hour7                         -10.9965     9.0216 -1.2189 0.2231419    
## hour8                          31.0035     8.5771  3.6147 0.0003146 ***
## hour9                          46.9201     8.4373  5.5610 3.379e-08 ***
## hour10                         30.0243     7.8250  3.8370 0.0001318 ***
## hour11                         33.5660     7.1894  4.6688 3.410e-06 ***
## hour12                         23.2743     7.1059  3.2754 0.0010888 ** 
## hour13                         48.1701     8.0753  5.9651 3.308e-09 ***
## hour14                         28.5243     8.4856  3.3615 0.0008023 ***
## hour15                         32.7951     8.3112  3.9459 8.463e-05 ***
## hour16                         35.6910     8.4682  4.2147 2.710e-05 ***
## hour17                         41.7326     8.1391  5.1274 3.479e-07 ***
## hour18                         53.3368     9.1211  5.8476 6.599e-09 ***
## hour19                         63.0243     9.9418  6.3394 3.383e-10 ***
## hour20                         52.3160     9.0384  5.7882 9.313e-09 ***
## hour21                         55.3368     8.5743  6.4538 1.644e-10 ***
## hour22                         31.0243     7.4980  4.1377 3.780e-05 ***
## hour23                         21.5868     7.1751  3.0086 0.0026857 ** 
## hour24                          4.2743     7.5657  0.5650 0.5722170    
## neighborhoodALMAGRO           199.3750    10.6840 18.6611 < 2.2e-16 ***
## neighborhoodBALVANERA         373.8333    29.2184 12.7945 < 2.2e-16 ***
## neighborhoodBARRACAS          172.3333     8.2689 20.8413 < 2.2e-16 ***
## neighborhoodBELGRANO          155.9167    12.1081 12.8771 < 2.2e-16 ***
## neighborhoodBOCA               77.5417     7.6456 10.1419 < 2.2e-16 ***
## neighborhoodBOEDO              64.4583     7.4505  8.6516 < 2.2e-16 ***
## neighborhoodCABALLITO         232.4167    15.3133 15.1774 < 2.2e-16 ***
## neighborhoodCHACARITA          57.7917     8.2300  7.0221 3.858e-12 ***
## neighborhoodCOGHLAN             4.2917     8.9802  0.4779 0.6328139    
## neighborhoodCOLEGIALES         49.3333     7.5514  6.5330 9.907e-11 ***
## neighborhoodCONSTITUCION      170.8333     8.0043 21.3428 < 2.2e-16 ***
## neighborhoodFLORES            307.8333    18.6055 16.5453 < 2.2e-16 ***
## neighborhoodFLORESTA           53.2500     8.2171  6.4804 1.387e-10 ***
## neighborhoodLINIERS            82.9583     6.9949 11.8598 < 2.2e-16 ***
## neighborhoodMATADEROS         117.0833     8.4425 13.8683 < 2.2e-16 ***
## neighborhoodMONSERRAT         116.7500     8.2814 14.0978 < 2.2e-16 ***
## neighborhoodMONTE CASTRO       29.8333     8.2341  3.6232 0.0003045 ***
## neighborhoodNUEVA POMPEYA     109.9167     8.0976 13.5740 < 2.2e-16 ***
## neighborhoodNUÑEZ              70.2500     7.4311  9.4535 < 2.2e-16 ***
## neighborhoodPALERMO           476.2917    19.3987 24.5527 < 2.2e-16 ***
## neighborhoodPARQUE AVELLANEDA  79.0000     8.2526  9.5728 < 2.2e-16 ***
## neighborhoodPARQUE CHACABUCO  109.3750     8.5059 12.8587 < 2.2e-16 ***
## neighborhoodPARQUE CHAS         4.0000     9.1144  0.4389 0.6608452    
## neighborhoodPARQUE PATRICIOS   72.4583     7.5764  9.5637 < 2.2e-16 ***
## neighborhoodPATERNAL           17.2917     8.9199  1.9386 0.0528157 .  
## neighborhoodPUERTO MADERO       2.2083     9.2106  0.2398 0.8105622    
## neighborhoodRECOLETA          244.7083    15.6320 15.6543 < 2.2e-16 ***
## neighborhoodRETIRO            132.6250     8.3186 15.9432 < 2.2e-16 ***
## neighborhoodSAAVEDRA           80.4583     7.4740 10.7651 < 2.2e-16 ***
## neighborhoodSAN CRISTOBAL      80.5833     7.5991 10.6043 < 2.2e-16 ***
## neighborhoodSAN NICOLAS       191.8333    15.3238 12.5187 < 2.2e-16 ***
## neighborhoodSAN TELMO          60.8333     8.9326  6.8103 1.613e-11 ***
## neighborhoodVELEZ SARSFIELD    32.8333     8.1905  4.0087 6.524e-05 ***
## neighborhoodVERSALLES           6.0000     9.0498  0.6630 0.5074755    
## neighborhoodVILLA CRESPO      140.3750     8.9150 15.7459 < 2.2e-16 ***
## neighborhoodVILLA DEL PARQUE   46.6250     8.2430  5.6563 1.979e-08 ***
## neighborhoodVILLA DEVOTO       82.9167     7.4814 11.0831 < 2.2e-16 ***
## neighborhoodVILLA GRAL. MITRE  30.8333     8.4176  3.6630 0.0002614 ***
## neighborhoodVILLA LUGANO      217.8333    11.0599 19.6958 < 2.2e-16 ***
## neighborhoodVILLA LURO         29.7500     8.5145  3.4941 0.0004950 ***
## neighborhoodVILLA ORTUZAR       9.6250     8.6628  1.1111 0.2667859    
## neighborhoodVILLA PUEYRREDON   32.0000     8.0252  3.9874 7.128e-05 ***
## neighborhoodVILLA REAL          4.7083     9.1206  0.5162 0.6058000    
## neighborhoodVILLA RIACHUELO    10.6250     9.0390  1.1755 0.2400675    
## neighborhoodVILLA SANTA RITA   27.2083     8.7651  3.1042 0.0019577 ** 
## neighborhoodVILLA SOLDATI      97.7083     7.4155 13.1762 < 2.2e-16 ***
## neighborhoodVILLA URQUIZA      97.1250     7.5064 12.9390 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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