Data Analytics for Crime Incidents and Police Funding
Added on 2023-04-25
12 Pages1290 Words388 Views
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RUNNING HEADER: DATA ANALYTICS 1
Data Analytics
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Data Analytics
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Data Analytics 2
Summary
The following study aims at determining whether the police department needs to be funded. The
basis of determining this was by analyzing the crime incidents with respect to sectors, time and
type. The association between the incidents of crime and the number of officers at the crime
scene was analyzed using linear regression analysis.
Data Preparation
The first step was removing potential errors and outliers. An example of an error is in the
District/Sector column which had blank fields. A value for the field was provided by imputing
missing values from another nearby column in that row. Imputation was used since it the
problem of bias will be small relative to the benefits that would be derived. Thus, it was helpful
in reducing omitted bias of variables. On the other hand, a duplicate was not present as seen in
the column “CAD CDW ID”. However, the longitude and the latitude columns were duplicates
since the incident location contains both longitude and latitude. Hence, they were removed.
Data Analysis
A number of columns were also removed since they were not necessary for the purpose of this
project. They include Event Clearance Code, CAD CDW ID, General Offense Number, CAD
Event Number, Event Clearance SubGroup, Initial Type Description, Hundred Block Location,
Initial Type Subgroup, Census Tract, At Scene Time, Initial Type Group, and Incident Locations.
Once the values were imputed, the errors, duplicate rows and the unnecessary columns removed,
the remaining dataset was presented under the worksheet Clean Data and used for analysis.
Summary
The following study aims at determining whether the police department needs to be funded. The
basis of determining this was by analyzing the crime incidents with respect to sectors, time and
type. The association between the incidents of crime and the number of officers at the crime
scene was analyzed using linear regression analysis.
Data Preparation
The first step was removing potential errors and outliers. An example of an error is in the
District/Sector column which had blank fields. A value for the field was provided by imputing
missing values from another nearby column in that row. Imputation was used since it the
problem of bias will be small relative to the benefits that would be derived. Thus, it was helpful
in reducing omitted bias of variables. On the other hand, a duplicate was not present as seen in
the column “CAD CDW ID”. However, the longitude and the latitude columns were duplicates
since the incident location contains both longitude and latitude. Hence, they were removed.
Data Analysis
A number of columns were also removed since they were not necessary for the purpose of this
project. They include Event Clearance Code, CAD CDW ID, General Offense Number, CAD
Event Number, Event Clearance SubGroup, Initial Type Description, Hundred Block Location,
Initial Type Subgroup, Census Tract, At Scene Time, Initial Type Group, and Incident Locations.
Once the values were imputed, the errors, duplicate rows and the unnecessary columns removed,
the remaining dataset was presented under the worksheet Clean Data and used for analysis.
Data Analytics 3
Table 1: Events by date
Total
244
583
219
Events by Date
26-Mar 27-Mar 28-Mar
Figure 1: Events by date
From table and figure 1 above, it can be seen that most of the crimes occurred on the 27th of
March (583 crimes) while the 26th and 28th of March had the least with 244 and 219 crimes
respectively.
To know the type of group of crimes which occur the most is as shown below.
Table 1: Events by date
Total
244
583
219
Events by Date
26-Mar 27-Mar 28-Mar
Figure 1: Events by date
From table and figure 1 above, it can be seen that most of the crimes occurred on the 27th of
March (583 crimes) while the 26th and 28th of March had the least with 244 and 219 crimes
respectively.
To know the type of group of crimes which occur the most is as shown below.
Data Analytics 4
Table 2: Events by type
Table 2: Events by type
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