Management Information Systems Assessment 1: Optimizing Crime Management and Police Resource Allocation in Springfield

IFN558

Management Information Systems

Assessment 1

Student Name

Student ID

Table of Figure

Business Problem: Optimizing Crime Management and Police Resource Allocation in Springfield

Current threats for the Springfield police force include growing crime rates and the declining confidence of citizens. The central business problem is: To what extent and how can the PD use crime data: to manage resources, decrease crime rates, and increase perceptions of safety in three main suburbs: Central, North, and West Springfield

This problem stems from:

• Fear and insecurity because of the occurrence of crime, more so when the media broadcasts the incidents.

• A brief of the police chief’s challenges on how to rationally plan and distribute resources using data collected.

• The dissection of factors such as situation, target, and terrain, to be applied in enhancing the local variable response of officers in the field of deployment.

Dashboards Overview: Knowledge-Based Solution for Crime Control

The solution to this problem lies in three dashboards tailored for different stakeholders: What we have is Mayor Jones, Chief Odinson, and Sergeant Rogers. These dashboards give different views on the crime data to enhance the decision-making plan of the stakeholders (Beard, and Aghassibake, 2021).

Each dashboard contains three main charts: a line chart, a bar chart, and a bubble chart, each of which will be used to correspond to the needs and goals of the stakeholder while maintaining the general theme of crime patterns, resource allocation, and public safety.

Dashboard 1: Review of Mayor Jones’ city-wide crime report

This is why, Mayor Jones’ concern is with the safety of the citizens of Springfield and she requires insights into social issues affecting this city. Her dashboard provides her with a comprehensive view of crime incidence by suburbs for her to identify overriding causes of crime and outcomes to formulate city policies or preventions.

Figure 1: Dashboard 1

Chart 1: It contains Linear Graph –City Wide Crime Rates Over the Years (1998-2019)

This chart represents the total crime rate for Springfield with the crime rate trend line illustrating the year-to-year change. It includes major crime types such as:

• Theft

• Assault

• Verbal Threats

Interpretation: If the line chart is upward showing that there has been an increase in thefts for the past five years, then it is time for the city to embark on a campaign where this crime type is targeted and actions, chief of which may include use of police patrols, public sensitization, or surveillance technology.

Chart 2: Bar Chart – Crime Breakdown by Crime Type and Rate

This chart gives the total number of crimes, thus raising the question of which crimes are frequent in the whole of the city. It could include categories like:

• Assault

• Theft

• Drug-Related Crimes

• Verbal Threats

Interpretation:

It might be seen from the bar chart that the two most recurring criminal activities in all the suburbs are assault and theft, which may compel the Mayor to concentrate a lot on minimizing these related incidents. For example, more funding could be channelled to publicity or society-orientated programs that seek to prevent such crimes (Batt, et al., 2020).

Chart 3: Bubble Chart – Crime Type by Gender

The bubble chart on Mayor Jones’ dashboard shows how different crime type is associated with gender in the city. The size of circles/ bubbles present corresponds to the number of crimes committed and colour may depict gender; red for male and green for female.

Interpretation:

If the bubble chart indicates that theft is more likely to be perpetrated by males in Central Springfield, the Mayor should use his influence to work with community groups to introduce Youth engagement programs to combat youth delinquency, and male youth in particular due to the antisocial effect they have on society.

Dashboard 2: In Chief Odinson’s Comparative Suburb Analysis

There is no doubt that Chief Odinson’s position is to total up to the efficient administration of Springfield’s police force and, therefore, force comparison on crime data from the three key suburbs that include; central, North, and west Springfield. He uses his dashboard based on the analysis of the crime statistics in these suburbs.

Figure 2: Dashboard 2

Chart 1: Bar Chart – Crime Rates by Suburb

This bar chart illustrates the crime indicators in Central, North, and West Springfield and divides crime rates by major crime categories including theft, assault, and drug offenses among others. All of them are Suburb bars which make it easier for the Chief to figure out where crime is rife.

Interpretation:

With the help of the chart, it is possible to understand that if North Springfield has a higher crime rate than Central or West, then Chief Odinson can hire more police officers or install more cameras (as other resources) in North Springfield. This means the response to the needs of the area is well informed by data obtained from implementing knowledge management processes (Balaji, et al., 2021).

Chart 2: Line Chart – Crime Trends by Suburb

The line chart presents the dynamics of crime rates in each of the suburbs, thus allowing us to compare the changes in the crime rates in Central, North, and West Springfield to 1998. The Chief can notice if the crime rate is rising or falling in regions that he or she is investigating.

Interpretation:

If there is a year that Central Springfield experienced a decrease in crime rates while West Springfield had an increase in the crime rates, then there is something that Chief Odinson can look at in Central Springfield policies or strategies that can apply in West Springfield. This is to mean that it is intended to redeploy other officers or specific crime-fighting measures that have been effective in other regions.

Chart 3: Bubble Chart – Crime Type and Gender and Suburb

The bubble chart displayed on the Chief’s dashboard gives information about crime types and gender for each suburb. The size of a bubble augments the number of incidences of a certain type of crime in a suburb while different color depicts sex.

Interpretation:

For instance, it may be observed from the bubble chart that drug-related offenses are most common in West Springfield; more so, a higher number of males counts. This ability helps Chief Odinson to order special drug task forces in West Springfield and leave other areas patrolling by other concerns.

Dashboard 3: Sergeant Rogers’ Local Crime Insights

Sergeant Rogers is required to know the types of criminal activities concerning a particular suburb, for instance, Central Springfield. His dashboard is meant to present specific, by region, details of crime statistics to enable him to assign officers and tackle new trends of crime proficiently.

Figure 3: Dashboard 3

Chart 1: Line Chart – Crime Trends in Central Springfield

This chart is particularly of interest to crime trends in Central Springfield over the years, so that Sergeant Rogers can discover which crimes are growing or reducing in the area. It divides the crime types into different categories, which include; assault, theft, and threats.

Interpretation:

If the line chart reveals the increase in theft within the last five years, based on this data Sergeant Rogers can launch targeted patrols in areas with increased incidence of theft in Central Springfield or can help communities to conduct the events aimed at crime prevention.

Chart 2: Bar chart – crime type for central Springfield

The bar chart gives Sergeant Rogers a look at the incidences of the most common occurrences in Central Springfield and which of those issues require his attention in the short term.

Interpretation:

Sergeant Rogers can give priority to training officers on handling individuals who make threatening comments and post-images as a way of curtailing part of endangering lives in Central Springfield If verbal threats and assaults are the most common incidents in Central Springfield, then, Sergeant Rogers can train the officers to handle those in cases of confrontation or use community interaction to discourage such acts (Vasundhara, 2021).

Chart 3: Bubble Chart – Gender Disparities in Crime for Central Springfield

The bubble diagram shows the distribution of different types of crimes and the gender of criminals in Central Springfield the area of the bubble points to how often crimes are committed by men or women.

Interpretation:

For instance, when using the bubble chart and findings reveal that males are the main culprits of theft crimes, he should coordinate awareness creation activities with male youth for future criminal prevention. It also enables him to make general policing strategies something specific that would suit his jurisdiction’s demographics that are most involved in the crimes.

Conclusion

Actionable Insights from Dashboards

• Mayor Jones can be able to work towards the overall crime rates within the city of Sunnydale embracing perspectives that consider theft and assault major types of crime.

• Chief Odinson could easily justify where he would be putting more of his formations because the crime rates and trends in the three suburbs are well portrayed.

• Any sort of statistics delivered down to a local level will allow Sergeant Rogers to put his officers in the right places depending on the most common crime areas in Central Springfield and the kinds of crimes.

These dashboards allow for the responsible, informed decisions of crime fighting to be made by each stakeholder so that crime can be fought and public safety in Springfield can be increased.

References

Balaji, N., Pai, B.K., Bhat, B. and Praveen, B., 2021, February. Data visualization in Splunk and Tableau: a case study demonstration. In Journal of Physics: Conference Series (Vol. 1767, No. 1, p. 012008). IOP Publishing, https://iopscience.iop.org/article/10.1088/1742-6596/1767/1/012008/pdf

Batt, S., Grealis, T., Harmon, O. and Tomolonis, P., 2020. Learning Tableau: A data visualization tool. The Journal of Economic Education, 51(3-4), pp.317-328, https://www.tandfonline.com/doi/pdf/10.1080/00220485.2020.1804503

Beard, L. and Aghassibake, N., 2021. Tableau (version 2020.3). Journal of the Medical Library Association: JMLA, 109(1), p.159, https://www.theseus.fi/bitstream/handle/10024/652129/Patel_Ashwin.pdf?sequence=4

Vasundhara, S., 2021. Data Visualization View with Tableau. Stochastic Modeling and Applications, 25(1), pp.178-187, https://www.mukpublications.com/resources/sma%20v25-1-18-final.pdf