This visualization gives a clear image of offenses in multiple types such as “Crimes against the person”, “Property and deception offences”, “Drug offences”, “Public order and security offences”, “Justice procedure offences” and “others”. From the visualization the matter can be stated that offense of “Crimes against the person” is the higher stack in between the year of “2015” to “2017”.
This visualization the number of offences that have taken place per type of location. Here in the visualization process several location types have been visualized such as “Residential”, “Community” and “Others”. From the visualization the matter can be stated that the count of “Justice procedure offences” is the highest stack of offense in the “Residential” type location.
Here in this segment the visualization of the data of “Family vs. Non-Family Incidents” has been done for the time span of “2019” to “2024”. The visualization shows that the count of offence is higher in the segment of “Property and deception offences” and the type of incident is high in the segment of “Not Family Related Incident”.
In this segment the data visualization has been done based on the
“Family Incident Rate per 100,000 Population”. The dataset has been
collected in between the time span of “2019” to “2024”. From the
visualization the matter can be stated that the incident rate is highest
in the year of “2020” and at the same time “2024” is having the second
highest count of the “Family Incident Rate” per “100,000
population”.
In this segment the visualization has been done based on the data of “Investigation Status by Offence Type”. Here in this segment the “Offence Count” has been visualized based on the “Investigation Status”. The visualization shows that the count of “Property and Deception Offence” is the highest type of offense that occurs based on the “Investigation Status”. “Public order and security offences” is having the second highest “Offence Count” based on the “Investigation Status”.
In this segment the data has been visualized based on the segment of “Drug Offences by Drug Type”. Here in this scenario multiple offence groups have been visualized such as “Drug Dealing”, “Drug Trafficking”, “Cultivate Drugs”, “Manufacture Drugs”, “Possess Drug Manufacturing Equipment or Precursor”, “Drug Use”, “Drug Possession” and “Other Drug Offences”.The visualization shows that “Drug Possession” is having the highest count in every year among the several group of offences that related to drug.
The vizualization shows that the visualization has been done based on several recommendations such as “Enhance Drug Rehabilitation”, “Implement Youth Programs”, “Increase Community Policing” and “Strengthen Family Support Services”. From the vizualization the matter can be justified that the “Increase Community Policing” has the highest importance as per the evaluated data.
The vizualization shows that offences are having a curve pattern among this calculated “10 years”. However, the trend seems to be high in between the time span of “2015” to “2017”. In the present scenario and time stage the trend is also in an increasing pattern as per the evaluated data. In conclusion the matter can be justified that the visualization process has helped a lot in terms of understanding the behavioral pattern of the “Crime Statistics Agency” based on the evaluated data. This evaluation process helps in understanding several segments of data such as “Offence Rates by Type”, “Offences by Location Type”, “Family vs. Non-Family Incidents”, “Family Incident Rate per 100,000”, “Investigation Status by Offence Type”, “Top Offences by Code”, “Offences and Legal References”, “Summary of Trends”, “Drug Offences by Drug Type” and lastly “Recommendations for Policy Changes”.