Data visualization in public safety

Data visualization in public safety

Data visualization aids greatly in the ease of data representation for public safety agencies and understanding its implications. Charts, graphs, maps, and tables are commonly used to represent statistical data. Among the examples of data visualization in use are mapping tools used by the public safety agencies in the cities (Sarker, Wu, & Hossin, 2018). Four exemplary examples include ArcGIS, Hopper, ODMap, and CrimeMapping.com.

ArcGIS is an Esri’s mapping technique used by local and state agencies for tracking and mapping diverse datasets. For instance, Los Angeles uses it to rank the sanitary conditions of streets and construction projects while California uses it to track emergencies and disasters for quick and effective deployment of resources (Sarker, Wu, & Hossin, 2018). Hopper is a prediction tool for risk and danger used to avoid safety issues forehand. Therefore, it incorporates machine learning techniques with data given in the prediction of possible areas of traffic accidents on roads.

The ODMap avails data mapping on drug overdose surveillance across regions that alert to a local overdose increase. According to states using the program, it allows the agencies to focus on the region requiring an urgent response. Lastly, CrimeMapping.com is a web-based tool that comprises a compilation of crime statistics from different areas to assist in crime reduction by availing information and community policing. The system feeds data from local crime and is accessible to the public.  Hence, users can identify risk areas and incidences of crime.

Data visualization tools vary depending on the need of the data being displayed; however, all tools are essential in some ways. To begin with, the graphical analysis of data helps in understanding the patterns in the variables portrayed by the data. Moreover, public safety agencies can compare and contrast the data features that assist in identifying the progress in their respective fields. Additionally, data visualization helps to identify new trends and implications of system changes that are implemented in public safety (Nourjou & Gelernter, 2015).

References

Nourjou, R., & Gelernter, J. (2015, November). Distributed autonomous GIS to form teams for public safety. In Proceedings of the Fourth ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems (pp. 59-62).

Sarker, M. N. I., Wu, M., & Hossin, M. A. (2018, May). Smart governance through bigdata: Digital transformation of public agencies. In 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD) (pp. 62-70). IEEE.