Cherre’s accurate and up-to-date building footprint database helps the company understand the context, shape and orientation of built-up areas. However, one of Cherre’s biggest challenges is to rapidly and accurately resolve the identity of buildings, in order to link and connect disparate datasets from multiple sources that are destined to be associated with these buildings. Cherre utilizes street addresses to identify the buildings and provide them with an identifying lat/long coordinate, also known as a geocode. The better the identification of the buildings is, the more accurate the geocodes are, and hence the dataset linkage is more successful. To undertake this process, Cherre utilizes the Google Maps Geocoding API and Places API to analyze and convert millions of street addresses into geocodes.
As the number and size of the associated datasets grew, Cherre had to ensure it stayed within its service level agreement (SLA) and could resolve the addressing and geocoding process quickly and accurately. DoiT assisted Cherre in providing Google with the detailed information required to enable Cherre to increase the rate of its address resolution procedures. This allows Cherre to undertake massive amounts of geocoding in a short space of time and provide its customers with data insights within the framework of the Cherre SLA.
Once all the datasets have been geocoded, Cherre utilizes the powerful spatial analysis functionality of BigQuery Geospatial to rapidly link and integrate the datasets. Overall, DoiT’s consultancy and technology have been instrumental in helping Cherre plan, manage and optimize its BigQuery activities. Via the DoiT Console, Cherre can observe and control BigQuery costs with the multi-report breakdown capabilities, utilize the BigQuery Lens functionality, and share optimization insights with the rest of the organization.
