At A Glance Page

Crime data is complex, scattered across multiple sources, and often inaccessible to the general public. Community members, researchers, and policymakers needed a way to quickly understand crime trends without navigating technical databases or requesting custom reports.

The Glenda Gordy Research Center needed a public-facing solution that could display real-time crime statistics while protecting sensitive internal data and maintaining performance under varying traffic loads.

I designed a hybrid data-ingestion pipeline that safely bridges internal systems with public-facing displays. The architecture prioritizes security, performance, and maintainability.

The solution uses Next.js API routes as a secure gateway, implementing caching strategies to minimize database load while ensuring data freshness. Python scripts handle data aggregation from multiple sources, transforming raw crime data into user-friendly statistics.

The frontend leverages Tailwind CSS for a responsive, accessible design that works seamlessly across devices. The UI presents complex data through interactive charts and clear visualizations that make insights immediately understandable.

Why Next.js API Routes? They provide a serverless architecture that scales automatically and keeps sensitive database credentials secure. The API layer acts as a controlled gateway, sanitizing requests and implementing rate limiting.

Why Python for Data Processing? Python's data science ecosystem (pandas, NumPy) made it ideal for complex data transformations and aggregations. The scripts run on a scheduled basis, pre-computing statistics to minimize real-time processing.

The caching strategy uses a multi-tier approach: in-memory caching for frequently accessed data, database-level caching for aggregated statistics, and CDN caching for static visualizations. This reduces API response times from seconds to milliseconds.

Security was paramount. The system implements input validation, parameterized queries to prevent SQL injection, and strict data access controls. Public endpoints expose only pre-approved, aggregated data—never raw records.

Initial Challenge: Balancing data freshness with performance. Real-time queries were too slow for a public-facing page. Solution: Implemented intelligent caching with configurable TTLs based on data volatility.

Data Consistency: Multiple data sources with different update schedules created synchronization challenges. I implemented a reconciliation layer that handles discrepancies and marks data staleness clearly in the UI.

Accessibility: Making complex charts accessible required significant work. I added ARIA labels, keyboard navigation, and text alternatives for all visualizations.

Key Lesson: Public data presentation requires a completely different architecture than internal tools. What works for authenticated users with patience doesn't work for the general public expecting instant insights.

If I built this again, I'd implement a GraphQL API for more flexible data queries and consider real-time updates using WebSockets for live statistics displays.