InetSoft BI Webcast: A Case Study of Geographic Business Intelligence

This is a continuation of the transcript of a Webinar hosted by InetSoft entitled "Geographic Business Intelligence Trends." The speaker is Mark Flaherty, CMO at InetSoft.

Mark Flaherty (MF): Information workers at a county seat’s administrative offices are detecting a rise in juvenile crime. Costs of policing are increasing, and insurance premiums and retail confidence are being affected. The staff is asked to understand the trends and try to discern the causes.

Specifically the arrest rates have risen among 11 to 15 year-olds. Now they need to understand when did the problem start? What could be the contributing factors for this rise in juvenile crime? And what possible solutions are available? Should policing be increased further? Or could youth services be improved?

They started with a dashboard view that shows the statistics related to the rise in juvenile crime among 11 to 15 year-olds. The chart shows a subset of county townships and the number of crimes committed weekly over the last three years and the associated police costs. With a quick glance the chart shows the crime levels were relatively static over the first 2 years, and many towns experienced a drop off in the current year. All except for one town. There was a huge spike. Police costs mirrored the pattern.

 

Using a Heat Map Visualization

Now the manager clicks on the spike in the bar chart and the map on the right hand side updates to show that town with crime reports marked on the map using a heat map visualization. The hot red shows the problem areas. Also depicted on the map are the locations of youth services such as clubs. At first glance it seemed that in the areas not affected by juvenile crime, there are a scattering of youth services. Within the problem area, there are already a number of youth organizations. At first glance, it doesn’t appear that the number of youth service locations is a key issue.

Further analysis is necessary. Drilling into a related report lists each of the youth centers, their addresses and the number of activities currently offered. A quick glance at the report shows that there are differing numbers of activities among the centers offered for this age group, ranging from 1 to 5. So perhaps this is an issue. So the manager just needs to add that dimension to the map, using a sizing dimension, for each center, sized by number of activities offered. Lo and behold, more of the smaller circles are in the problem areas.

Further drilling into the details for the centers located in the problem area shows there used to be more activities, but indeed many activities were cancelled at the end of last year due to budget constraints. The costs savings of the budget cut can be compared to the cost increases of the policing. The cuts saved $142,000 but led to an increase in $894,000 in the police budget.

Case Study: Leveraging Geographic Business Intelligence in a Police Department

Modern policing requires the efficient use of data to address complex challenges such as crime prevention, resource allocation, and public safety management. Geographic Business Intelligence (GBI) combines Geographic Information Systems (GIS) and Business Intelligence (BI) to provide police departments with powerful tools for data-driven decision-making. This case study explores how a mid-sized city's Police Department implemented GBI to enhance operational efficiency, improve crime prevention strategies, and strengthen community relations.

Background

The Police Department in question faced several challenges, including rising crime rates, inefficient resource allocation, and a need for better community engagement. Traditional methods of data collection and analysis were insufficient to address these issues effectively. To overcome these challenges, the department decided to adopt a GBI solution, integrating spatial and business intelligence to provide a comprehensive view of crime patterns, resource utilization, and public feedback.

Implementation of Geographic Business Intelligence

Geographic Information Systems (GIS)

GIS technology enables the Police Department to visualize and analyze spatial data, offering insights into crime patterns, hotspot areas, and patrol routes. Key applications of GIS in the department include:

1. Crime Mapping and Analysis:
   • GIS is used to create detailed maps showing the distribution and frequency of various types of crimes.
   • Spatial analysis helps identify crime hotspots and temporal patterns, enabling targeted interventions.
2. Patrol Route Optimization:
   • GIS tools assist in designing optimal patrol routes based on crime data, ensuring efficient coverage of high-risk areas.
   • Real-time tracking of patrol units improves response times and resource allocation.
3. Incident Response and Management:
   • GIS provides real-time mapping of incidents, helping dispatchers deploy the nearest available units.
   • Integration with emergency response systems ensures swift coordination during critical situations.

Business Intelligence (BI)

BI tools allow the Police Department to aggregate, analyze, and visualize data from various sources, providing actionable insights for strategic and operational decisions. Key applications of BI in the department include:

1. Performance Monitoring:
   • BI dashboards display key performance indicators (KPIs) such as response times, crime resolution rates, and officer productivity.
   • Historical data analysis helps evaluate the effectiveness of policing strategies and identify areas for improvement.
2. Predictive Analytics:
   • BI tools enable predictive modeling to forecast crime trends based on historical data and current conditions.
   • This allows the department to proactively deploy resources and implement preventive measures.
3. Community Engagement:
   • BI platforms facilitate the sharing of crime statistics, safety tips, and policing activities with the public through interactive dashboards and mobile apps.
   • Engaging the community enhances transparency and builds trust between the police and the public.

Case Example: Reducing Crime through Geographic Business Intelligence

A notable example of the Police Department's use of GBI is in its efforts to reduce property crime in residential neighborhoods. The project aimed to identify crime hotspots, optimize patrol routes, and engage the community in crime prevention efforts.

Project Overview

The project involved the implementation of a comprehensive GBI solution to monitor crime trends, optimize resource allocation, and enhance community engagement. Key components of the project included:

1. Data Integration:
   • The department integrated data from crime reports, emergency calls, patrol logs, and community feedback.
   • This data was processed and analyzed using BI tools to identify crime patterns, hotspot areas, and response times.
2. Crime Analysis and Prevention:
   • GIS technology was used to map crime incidents, highlighting areas with high rates of property crime.
   • Predictive analytics helped forecast potential crime hotspots, allowing for proactive deployment of patrol units.
3. Patrol Optimization:
   • Real-time data from patrol units and GIS-based tools facilitated the design of optimal patrol routes covering high-risk areas.
   • Continuous monitoring and adjustment of routes ensured efficient use of resources and improved response times.
4. Community Engagement:
   • The department developed a mobile app and online dashboards to share crime data, safety tips, and policing activities with residents.
   • Community feedback was collected through the app, enhancing communication and trust between the police and the public.

Outcomes

The implementation of GBI resulted in significant improvements in crime reduction and police operations:

• Reduced Property Crime: Targeted patrols and proactive measures led to a noticeable reduction in property crime rates in high-risk neighborhoods.
• Improved Response Times: Optimized patrol routes and real-time incident mapping significantly reduced response times, enhancing public safety.
• Enhanced Community Trust: Increased transparency and engagement through the mobile app and online dashboards fostered better relationships with the community.
• Data-Driven Decision Making: The use of GBI enabled the department to make informed decisions, optimize resource allocation, and implement effective crime prevention strategies.

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