The city of New York has long been a subject of fascination and scrutiny for its high crime rates. With the rise of technology and data-driven analysis, it has become easier to understand the patterns and trends in crime complaints across the five boroughs. This study aims to explore the relationship between crime rates and the boroughs of New York City from 2017 to 2023, with a specific focus on the types of crime reported. To identify if there is a statistically significant difference in the number of complaints among different boroughs

By analyzing and interpreting crime data from various sources, this research aims to shed light on the complex dynamics of crime in the city and inform policy decisions to improve public safety.

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##    CMPLNT_NUM ADDR_PCT_CD       BORO_NM CMPLNT_FR_DT CMPLNT_FR_TM CMPLNT_TO_DT
## 1   246893603          44         BRONX   06/20/2022     00:00:00             
## 2   247202657          49         BRONX   06/27/2022     05:15:00             
## 3   247759365          44         BRONX   07/08/2022     20:07:00             
## 4 248104661H1          44         BRONX   07/15/2022     23:50:00             
## 5   246220488         123 STATEN ISLAND   06/06/2020     18:04:00   06/06/2022
##   CMPLNT_TO_TM CRM_ATPT_CPTD_CD HADEVELOPT HOUSING_PSA JURISDICTION_CODE
## 1       (null)        COMPLETED     (null)          NA                 0
## 2       (null)        COMPLETED     (null)          NA                 0
## 3       (null)        COMPLETED     (null)          NA                 0
## 4       (null)        COMPLETED     (null)          NA                 0
## 5     18:15:00        COMPLETED     (null)          NA                 0
##         JURIS_DESC KY_CD LAW_CAT_CD LOC_OF_OCCUR_DESC
## 1 N.Y. POLICE DEPT   105     FELONY          FRONT OF
## 2 N.Y. POLICE DEPT   105     FELONY          FRONT OF
## 3 N.Y. POLICE DEPT   113     FELONY            (null)
## 4 N.Y. POLICE DEPT   101     FELONY           OUTSIDE
## 5 N.Y. POLICE DEPT   578  VIOLATION            INSIDE
##                         OFNS_DESC PARKS_NM               PATROL_BORO PD_CD
## 1                         ROBBERY   (null)         PATROL BORO BRONX   389
## 2                         ROBBERY   (null)         PATROL BORO BRONX   380
## 3                         FORGERY   (null)         PATROL BORO BRONX   725
## 4 MURDER & NON-NEGL. MANSLAUGHTER   (null)         PATROL BORO BRONX    NA
## 5                   HARRASSMENT 2   (null) PATROL BORO STATEN ISLAND   637
##                      PD_DESC   PREM_TYP_DESC     RPT_DT STATION_NAME
## 1           ROBBERY,DWELLING          STREET 06/20/2022       (null)
## 2        ROBBERY,CAR JACKING          CHURCH 06/27/2022       (null)
## 3  FORGERY,M.V. REGISTRATION          STREET 07/08/2022       (null)
## 4                     (null)          (null) 07/15/2022       (null)
## 5 HARASSMENT,SUBD 1,CIVILIAN RESIDENCE-HOUSE 06/06/2022       (null)
##   SUSP_AGE_GROUP SUSP_RACE SUSP_SEX TRANSIT_DISTRICT VIC_AGE_GROUP VIC_RACE
## 1        UNKNOWN   UNKNOWN        U               NA         25-44    BLACK
## 2        UNKNOWN     BLACK        M               NA         45-64    WHITE
## 3          25-44     BLACK        M               NA       UNKNOWN  UNKNOWN
## 4          25-44     BLACK        M               NA         25-44    BLACK
## 5          25-44   UNKNOWN        M               NA         25-44    WHITE
##   VIC_SEX X_COORD_CD Y_COORD_CD Latitude Longitude                     Lat_Lon
## 1       F    1007951     241445 40.82935 -73.91436     (40.829355, -73.914355)
## 2       M    1026285     246465 40.84307 -73.84807     (40.843067, -73.848075)
## 3       E    1008526     244666 40.83819 -73.91227 (40.83819352, -73.91226789)
## 4       M    1007600     240213 40.82597 -73.91563  (40.82597381, -73.9156299)
## 5       F     915395     125939 40.51208 -74.24762     (40.512082, -74.247623)
##           New.Georeferenced.Column
## 1     POINT (-73.914355 40.829355)
## 2     POINT (-73.848075 40.843067)
## 3 POINT (-73.91226789 40.83819352)
## 4  POINT (-73.9156299 40.82597381)
## 5     POINT (-74.247623 40.512082)

## Rows: 531,768
## Columns: 36
## $ CMPLNT_NUM               <chr> "246893603", "247202657", "247759365", "24810…
## $ ADDR_PCT_CD              <int> 44, 49, 44, 44, 123, 5, 84, 43, 75, 14, 111, …
## $ BORO_NM                  <chr> "BRONX", "BRONX", "BRONX", "BRONX", "STATEN I…
## $ CMPLNT_FR_DT             <chr> "06/20/2022", "06/27/2022", "07/08/2022", "07…
## $ CMPLNT_FR_TM             <chr> "00:00:00", "05:15:00", "20:07:00", "23:50:00…
## $ CMPLNT_TO_DT             <chr> "", "", "", "", "06/06/2022", "02/03/2022", "…
## $ CMPLNT_TO_TM             <chr> "(null)", "(null)", "(null)", "(null)", "18:1…
## $ CRM_ATPT_CPTD_CD         <chr> "COMPLETED", "COMPLETED", "COMPLETED", "COMPL…
## $ HADEVELOPT               <chr> "(null)", "(null)", "(null)", "(null)", "(nul…
## $ HOUSING_PSA              <int> NA, NA, NA, NA, NA, NA, 463, NA, NA, NA, NA, …
## $ JURISDICTION_CODE        <int> 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ JURIS_DESC               <chr> "N.Y. POLICE DEPT", "N.Y. POLICE DEPT", "N.Y.…
## $ KY_CD                    <int> 105, 105, 113, 101, 578, 361, 233, 233, 116, …
## $ LAW_CAT_CD               <chr> "FELONY", "FELONY", "FELONY", "FELONY", "VIOL…
## $ LOC_OF_OCCUR_DESC        <chr> "FRONT OF", "FRONT OF", "(null)", "OUTSIDE", …
## $ OFNS_DESC                <chr> "ROBBERY", "ROBBERY", "FORGERY", "MURDER & NO…
## $ PARKS_NM                 <chr> "(null)", "(null)", "(null)", "(null)", "(nul…
## $ PATROL_BORO              <chr> "PATROL BORO BRONX", "PATROL BORO BRONX", "PA…
## $ PD_CD                    <int> 389, 380, 725, NA, 637, 639, 175, 175, 177, 4…
## $ PD_DESC                  <chr> "ROBBERY,DWELLING", "ROBBERY,CAR JACKING", "F…
## $ PREM_TYP_DESC            <chr> "STREET", "CHURCH", "STREET", "(null)", "RESI…
## $ RPT_DT                   <chr> "06/20/2022", "06/27/2022", "07/08/2022", "07…
## $ STATION_NAME             <chr> "(null)", "(null)", "(null)", "(null)", "(nul…
## $ SUSP_AGE_GROUP           <chr> "UNKNOWN", "UNKNOWN", "25-44", "25-44", "25-4…
## $ SUSP_RACE                <chr> "UNKNOWN", "BLACK", "BLACK", "BLACK", "UNKNOW…
## $ SUSP_SEX                 <chr> "U", "M", "M", "M", "M", "M", "(null)", "M", …
## $ TRANSIT_DISTRICT         <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ VIC_AGE_GROUP            <chr> "25-44", "45-64", "UNKNOWN", "25-44", "25-44"…
## $ VIC_RACE                 <chr> "BLACK", "WHITE", "UNKNOWN", "BLACK", "WHITE"…
## $ VIC_SEX                  <chr> "F", "M", "E", "M", "F", "F", "F", "F", "F", …
## $ X_COORD_CD               <int> 1007951, 1026285, 1008526, 1007600, 915395, 9…
## $ Y_COORD_CD               <int> 241445, 246465, 244666, 240213, 125939, 20129…
## $ Latitude                 <dbl> 40.82935, 40.84307, 40.83819, 40.82597, 40.51…
## $ Longitude                <dbl> -73.91436, -73.84807, -73.91227, -73.91563, -…
## $ Lat_Lon                  <chr> "(40.829355, -73.914355)", "(40.843067, -73.8…
## $ New.Georeferenced.Column <chr> "POINT (-73.914355 40.829355)", "POINT (-73.8…

##   CMPLNT_NUM         ADDR_PCT_CD       BORO_NM          CMPLNT_FR_DT      
##  Length:531768      Min.   :  1.00   Length:531768      Length:531768     
##  Class :character   1st Qu.: 34.00   Class :character   Class :character  
##  Mode  :character   Median : 63.00   Mode  :character   Mode  :character  
##                     Mean   : 63.13                                        
##                     3rd Qu.:101.00                                        
##                     Max.   :123.00                                        
##                     NA's   :39                                            
##  CMPLNT_FR_TM       CMPLNT_TO_DT       CMPLNT_TO_TM       CRM_ATPT_CPTD_CD  
##  Length:531768      Length:531768      Length:531768      Length:531768     
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##   HADEVELOPT         HOUSING_PSA     JURISDICTION_CODE  JURIS_DESC       
##  Length:531768      Min.   :  218    Min.   : 0.0000   Length:531768     
##  Class :character   1st Qu.:  477    1st Qu.: 0.0000   Class :character  
##  Mode  :character   Median :  696    Median : 0.0000   Mode  :character  
##                     Mean   : 6799    Mean   : 0.4613                     
##                     3rd Qu.: 1251    3rd Qu.: 0.0000                     
##                     Max.   :72405    Max.   :97.0000                     
##                     NA's   :497825                                       
##      KY_CD        LAW_CAT_CD        LOC_OF_OCCUR_DESC   OFNS_DESC        
##  Min.   :101.0   Length:531768      Length:531768      Length:531768     
##  1st Qu.:113.0   Class :character   Class :character   Class :character  
##  Median :341.0   Mode  :character   Mode  :character   Mode  :character  
##  Mean   :300.9                                                           
##  3rd Qu.:351.0                                                           
##  Max.   :678.0                                                           
##                                                                          
##    PARKS_NM         PATROL_BORO            PD_CD         PD_DESC         
##  Length:531768      Length:531768      Min.   :100.0   Length:531768     
##  Class :character   Class :character   1st Qu.:256.0   Class :character  
##  Mode  :character   Mode  :character   Median :352.0   Mode  :character  
##                                        Mean   :403.5                     
##                                        3rd Qu.:637.0                     
##                                        Max.   :922.0                     
##                                        NA's   :578                       
##  PREM_TYP_DESC         RPT_DT          STATION_NAME       SUSP_AGE_GROUP    
##  Length:531768      Length:531768      Length:531768      Length:531768     
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##   SUSP_RACE           SUSP_SEX         TRANSIT_DISTRICT VIC_AGE_GROUP     
##  Length:531768      Length:531768      Min.   : 1.0     Length:531768     
##  Class :character   Class :character   1st Qu.: 3.0     Class :character  
##  Mode  :character   Mode  :character   Median :11.0     Mode  :character  
##                                        Mean   :14.8                       
##                                        3rd Qu.:30.0                       
##                                        Max.   :34.0                       
##                                        NA's   :520479                     
##    VIC_RACE           VIC_SEX            X_COORD_CD        Y_COORD_CD    
##  Length:531768      Length:531768      Min.   : 913411   Min.   :121312  
##  Class :character   Class :character   1st Qu.: 991330   1st Qu.:185702  
##  Mode  :character   Mode  :character   Median :1004395   Median :207065  
##                                        Mean   :1005187   Mean   :207765  
##                                        3rd Qu.:1017411   3rd Qu.:234516  
##                                        Max.   :1067306   Max.   :271819  
##                                        NA's   :9         NA's   :9       
##     Latitude       Longitude        Lat_Lon          New.Georeferenced.Column
##  Min.   :40.50   Min.   :-74.25   Length:531768      Length:531768           
##  1st Qu.:40.68   1st Qu.:-73.97   Class :character   Class :character        
##  Median :40.73   Median :-73.93   Mode  :character   Mode  :character        
##  Mean   :40.74   Mean   :-73.92                                              
##  3rd Qu.:40.81   3rd Qu.:-73.88                                              
##  Max.   :40.91   Max.   :-73.70                                              
##  NA's   :9       NA's   :9

It appears that there were only 7 complaints in 2023, which may not be a representative sample since it is only a few months into the year. However, the data shows that there were 486,787 complaints in 2022, which accounts for the majority of the data.

## Warning: Removed 35 rows containing missing values (`position_stack()`).

## Warning: Removed 2 rows containing missing values (`geom_bar()`).

Prosess to Find summary yearly Values by the borough

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

Because we have a lot of complainsduring the year unable us to draw them perfectly we will focus on 2015 and 2022

Prosess to Find summary monthly Values by the borough

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

Procedure to Find summary daily Values by the borough

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

## # A tibble: 10 × 2
##    OFNS_DESC                      total_complaints
##    <chr>                                     <dbl>
##  1 PETIT LARCENY                             36786
##  2 GRAND LARCENY                             18377
##  3 HARRASSMENT 2                             16814
##  4 ASSAULT 3 & RELATED OFFENSES              11864
##  5 CRIMINAL MISCHIEF & RELATED OF             9964
##  6 FELONY ASSAULT                             5142
##  7 BURGLARY                                   4699
##  8 ROBBERY                                    4168
##  9 OFF. AGNST PUB ORD SENSBLTY &              3798
## 10 DANGEROUS DRUGS                            3223

Display the top 10 crime complaints in Bronx

## # A tibble: 10 × 2
##    OFNS_DESC                      total_complaints
##    <chr>                                     <dbl>
##  1 HARRASSMENT 2                             18951
##  2 PETIT LARCENY                             18536
##  3 ASSAULT 3 & RELATED OFFENSES              13907
##  4 CRIMINAL MISCHIEF & RELATED OF             8878
##  5 GRAND LARCENY                              8073
##  6 FELONY ASSAULT                             7512
##  7 ROBBERY                                    5161
##  8 OFF. AGNST PUB ORD SENSBLTY &              4391
##  9 GRAND LARCENY OF MOTOR VEHICLE             4010
## 10 VEHICLE AND TRAFFIC LAWS                   3113

Display the top 10 crime complaints in Staten island

## # A tibble: 10 × 2
##    OFNS_DESC                      total_complaints
##    <chr>                                     <dbl>
##  1 HARRASSMENT 2                              4704
##  2 PETIT LARCENY                              4006
##  3 CRIMINAL MISCHIEF & RELATED OF             2392
##  4 ASSAULT 3 & RELATED OFFENSES               2218
##  5 GRAND LARCENY                              1378
##  6 OFF. AGNST PUB ORD SENSBLTY &              1128
##  7 MISCELLANEOUS PENAL LAW                    1057
##  8 FELONY ASSAULT                              783
##  9 VEHICLE AND TRAFFIC LAWS                    699
## 10 DANGEROUS DRUGS                             514

Display the top 10 crime complaints in Brooklyn

## # A tibble: 10 × 2
##    OFNS_DESC                      total_complaints
##    <chr>                                     <dbl>
##  1 PETIT LARCENY                             29834
##  2 HARRASSMENT 2                             24859
##  3 ASSAULT 3 & RELATED OFFENSES              15267
##  4 GRAND LARCENY                             13062
##  5 CRIMINAL MISCHIEF & RELATED OF            12801
##  6 FELONY ASSAULT                             7501
##  7 OFF. AGNST PUB ORD SENSBLTY &              5800
##  8 MISCELLANEOUS PENAL LAW                    5529
##  9 BURGLARY                                   4514
## 10 ROBBERY                                    4474

Display the top 10 crime complaints in Queens

## # A tibble: 10 × 2
##    OFNS_DESC                      total_complaints
##    <chr>                                     <dbl>
##  1 PETIT LARCENY                             25407
##  2 HARRASSMENT 2                             17664
##  3 ASSAULT 3 & RELATED OFFENSES              11716
##  4 GRAND LARCENY                             10677
##  5 CRIMINAL MISCHIEF & RELATED OF            10671
##  6 FELONY ASSAULT                             5141
##  7 MISCELLANEOUS PENAL LAW                    4123
##  8 GRAND LARCENY OF MOTOR VEHICLE             3618
##  9 OFF. AGNST PUB ORD SENSBLTY &              3446
## 10 ROBBERY                                    3317

## Summary statistics of number of complaints by borough:

## # A tibble: 1 × 3
##   mean_complaints median_complaints sd_complaints
##             <dbl>             <dbl>         <dbl>
## 1           88628            113650        61245.

The summary statistics table shows the mean, median, and standard deviation of the number of complaints across all boroughs in the NYPD_Data dataset.

mean_complaints: The average number of complaints across all boroughs is 88,628. median_complaints: The median number of complaints across all boroughs is 113,650, which means that half of the boroughs have fewer complaints and half have more. sd_complaints: The standard deviation of the number of complaints across all boroughs is 61,245, which indicates that there is a wide variation in the number of complaints between the boroughs.

Display the top 10 crime complaints

## # A tibble: 10 × 2
##    OFNS_DESC                      total_complaints
##    <chr>                                     <dbl>
##  1 PETIT LARCENY                            115477
##  2 HARRASSMENT 2                             83003
##  3 ASSAULT 3 & RELATED OFFENSES              54988
##  4 GRAND LARCENY                             51575
##  5 CRIMINAL MISCHIEF & RELATED OF            44716
##  6 FELONY ASSAULT                            26081
##  7 OFF. AGNST PUB ORD SENSBLTY &             18564
##  8 ROBBERY                                   17411
##  9 MISCELLANEOUS PENAL LAW                   15851
## 10 BURGLARY                                  15759

Since our research question is focused on identifying if there is a statistically significant difference in the number of complaints among different boroughs, a two-sample t-test would be appropriate. We can compare the mean number of complaints per year between two boroughs and test whether the difference is statistically significant.

To perform a t-test , we will first need to extract the necessary data. We will extract the number of complaints for two boroughs (Brooklyn and Manhattan)

The null hypothesis

H0: μ1 = μ2 (The mean number of complaints in Brooklyn is equal to the mean number of complaints in Manhattan)

The alternative hypothesis

HA: μ1 ≠ μ2 (The mean number of complaints in Brooklyn is not equal to the mean number of complaints in Manhattan)

Here, μ1 represents the population mean of the number of complaints in Brooklyn, and μ2 represents the population mean of the number of complaints in Manhattan

## 
##  Welch Two Sample t-test
## 
## data:  BROOKLYN_data$`No. complaint` and MANHATTAN_data$`No. complaint`
## t = 0.044592, df = 406.74, p-value = 0.9645
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -514.7782  538.6747
## sample estimates:
## mean of x mean of y 
##  692.3615  680.4133

conduct a linear regression analysis for the years 2015 and 2022

## # A tibble: 5 × 6
## # Groups:   BORO_NM [5]
##   BORO_NM        Year `No. complaint` Percentage `Monthly Average` Daily Avera…¹
##   <chr>         <dbl>           <dbl> <chr>                  <dbl>         <dbl>
## 1 STATEN ISLAND  2015               4 0%                         0             0
## 2 QUEENS         2015              28 0.01%                      2             0
## 3 MANHATTAN      2015              25 0%                         2             0
## 4 BROOKLYN       2015              30 0.01%                      2             0
## 5 BRONX          2015              21 0%                         2             0
## # … with abbreviated variable name ¹​`Daily Average`

## # A tibble: 6 × 3
## # Groups:   BORO_NM [6]
##   BORO_NM        Year `No. complaint`
##   <chr>         <dbl>           <dbl>
## 1 (null)         2022             949
## 2 BRONX          2022          110055
## 3 BROOKLYN       2022          144319
## 4 MANHATTAN      2022          130973
## 5 QUEENS         2022          112244
## 6 STATEN ISLAND  2022           22055

## 
## Call:
## lm(formula = `No. complaint` ~ Year, data = rbind(boroughyearlycomplaintdata_2015, 
##     boroughyearlycomplaintdata_2022))
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -85817     -9      6  24384  57553 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -24969926    7812200  -3.196   0.0109 *
## Year            12392       3870   3.202   0.0108 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 44730 on 9 degrees of freedom
## Multiple R-squared:  0.5326, Adjusted R-squared:  0.4807 
## F-statistic: 10.25 on 1 and 9 DF,  p-value: 0.01079

The linear regression model that we fitted estimates that the number of complaints has increased by approximately 12,392 per year since 2015. The intercept of the model (-24,969,926) is not meaningful in this context as it represents the number of complaints in the year 0 which is an arbitrary point of reference.

The p-value for the Year variable in the model is less than the typical threshold of 0.05, which indicates that the Year variable is statistically significant in predicting the number of complaints. In other words, we can say that there is a significant linear relationship between the year and the number of complaints.

The adjusted R-squared value of the model is 0.4807. This means that the Year variable explains about 48% of the variation in the number of complaints. This suggests that there are other factors besides time (Year) that are influencing the number of complaints.

Finally, the F-statistic and its associated p-value indicate that the regression line fits the data significantly better than a horizontal line, providing evidence that there is indeed a significant linear relationship between Year and the number of complaints.

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

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

Our analysis of the NYPD complaints dataset revealed that Brooklyn had the highest number of complaints overall, followed by Manhattan and Queens . We also observed an overall decrease in the number of complaints in recent years. Our statistical analysis using a two-sample t-test showed that there was no significant difference in the mean number of complaints per year between Brooklyn and Manhattan. Our results can inform policy decisions and community engagement strategies aimed at improving police accountability and trust in the city.

we also performed a linear regression analysis on the data from 2015 to 2022. The analysis showed that there was a significant positive correlation between the year and the number of complaints. The regression equation showed that for every one year increase, the number of complaints increased by an average of 12,392. This finding suggests that although there has been an overall decrease in the number of complaints over recent years, this decrease is not linear and complaints may continue to increase in the future. Therefore, it is important to continue monitoring and addressing concerns related to police-community relations in New York City.