Leveraging IoT for Analyzing UK's Covid Recovery & Mobility: A Case Study on Avison Young
Applicable Industries
- Buildings
- Retail
Use Cases
- Autonomous Transport Systems
- Transportation Simulation
About The Customer
Avison Young is a global commercial real estate services firm with its headquarters in Toronto. The firm has a presence in more than 100 offices across 15 countries. Avison Young believes that commercial real estate is not just about buildings, square footage, and occupancies. They believe that at its best, commercial real estate is about creating spaces and places that improve lives and help businesses thrive. This belief extends to their employees, citizens, and communities that make an impact matter. The firm was facing a significant challenge due to the Covid-19 pandemic, which had greatly impacted the commercial real estate market.
The Challenge
Avison Young, a global commercial real estate services firm, faced a significant challenge due to the Covid-19 pandemic. The widespread retail slowdown and permanent work-from-home policies led to a drastic reduction in the demand for commercial spaces. This situation greatly impacted the commercial real estate market, which Avison Young is a part of. However, with vaccines becoming increasingly available, the commercial real estate market was expected to experience a sharp rebound. Many people were anticipated to return to shopping, traveling, training, and enjoying spaces outside their homes. Avison Young needed a way to visualize and understand these movement trends across local authority districts over time to better strategize and plan for the expected rebound.
The Solution
Avison Young turned to IoT and data analytics to address this challenge. They used data from Google Mobility Reports and the Python package CARTOframes to visualize movement trends across local authority districts over time. This solution allowed them to create a map that showed how mobility had been affected in areas of the UK that had seen tighter levels of restrictions. It highlighted the differentiation between major cities, suburban locations, and those areas which had benefitted, at least relatively, from the changes to everyday life during the pandemic. The use of a variety of analytical tools, Spatial Data Science techniques, and a range of modern real-time datasets provided greater insight into how people and businesses were likely to respond and recover from the pandemic.
Operational Impact
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