Case Study:

In the era of digital transformation, harnessing the power of open data presents both an opportunity & a challenge. For the UK Crime Open Data project, the primary issue was generating actionable insights from vast & disparate datasets.

The specific challenges included

• Data Volume & Variety: Handling large volumes of crime data from multiple sources with varying formats & structures.
• Data Integration: Integrating & transforming these diverse datasets into a cohesive format suitable for analysis.
• Insight Generation: Creating meaningful insights that can be easily accessed & utilized by stakeholders, ranging from law enforcement agencies to the general public.
• User Accessibility: Ensuring that the insights are easily accessible through an intuitive interface, catering to both technical & non-technical users.

To address these challenges, TappingToad implemented a robust & scalable solution leveraging Python & cloud technologies.

The approach was divided into three key phases:

1. Data Ingestion & Transformation

• Utilized Python Jupyter Notebooks for the initial data ingestion. This allowed for the extraction & consolidation of crime data from various sources.
• Employed Python libraries such as Pandas for data cleaning & transformation, ensuring consistency & accuracy across the dataset.
• Automated the ETL (Extract, Transform, Load) processes to handle continuous data updates efficiently.

2. Data Storage & Management

• Chose Google Firestore as the NoSQL database for storing the transformed data. This provided a flexible & scalable storage solution that could handle large datasets with ease.
• Implemented data indexing & optimization strategies to enhance query performance & data retrieval times.

3. Insight Generation & Visualization

• Developed a Python Flask app to serve as the backend for the insights application. This app managed data requests & delivered processed information to the front end.
• Leveraged Plotly for creating interactive data visualizations, enabling users to explore the data through graphs, charts & maps.
• Deployed the Flask app & Plotly visualizations on Google App Engine, ensuring a reliable & scalable platform for the insights application.
• Designed the user interface to be intuitive & accessible, providing a seamless experience for users to explore & understand crime trends & patterns.

Welcome to our prototype for visualizing UK crime data by Lower layer Super Output Areas 2021 (LSOA21)! This tool provides a dynamic view of crime statistics based on your postcode entry, displaying both the total number of reported crimes and a detailed breakdown of crime types in an intuitive bar chart format.

Please note: The data currently available is specific to Kent only. For more insights and updates, get in touch with us.

Data Sources:

1. Crime Data, data.police.uk licensed under the Open Government Licence v.3.0
2. ONS Postcode Directory (May 2024) for the UK, Office for National Statistics Open Geo Portal, licensed under the Open Government Licence v.3.0

The implementation of this solution yields significant benefits & impactful results.

• Enhanced Data Accessibility: Stakeholders could easily access & interact with the crime data through the web-based application, leading to more informed decision-making.
• Improved Data Insights: The interactive visualizations provided clear & actionable insights, helping to identify crime hotspots, trends & anomalies.
• Scalability: The use of cloud-based services ensured that the solution could scale with increasing data volumes & user’s demands without compromising the performance.
• Efficiency & Automation: The automated ETL processes reduced the manual effort required for data processing, allowing timely updates & consistent data quality.
• User Engagement: The intuitive interface & powerful visualizations increased user engagement, making the data accessible to a broader audience, including law enforcement agencies, policy makers & the public.

Interested In Working With Us?