Geospatial Risk Analysis Platform Development

Our primary goal was to enhance this risk intelligence platform with added insights into climate scenarios for risk-free investments.

However, what was challenging was to accumulate and structure massive EO datasets for easy navigation and viewing. Moreover, we had to include a data tree hierarchy coupled with gridded and imagery information for quick assessment of environmental conditions. Also, we had to develop a workable solution or an algorithm for storing, retrieving, and querying large amounts of heterogeneous data.

In short, we aimed to step up geospatial visualization to forecast catastrophic events and de-risk future investments.

Capital Numbers’ team went ahead with this project by adding custom features that can empower investors with decision-ready risk analysis. Our project managers and developers started working in parallel to upgrade the platform and ensure quick delivery with continuous innovation.

The tech stack we used included the following:

We selected Python for designing the web interface. We chose Python because it comes with a clean object-oriented design, enhanced process control capabilities, and a unit-testing framework that improves app speed and productivity. Plus, its extensive support libraries and third-party modules help create well-structured websites.

We picked Flask for the frontend because of its modularity. Besides, it comes with nicely-documented APIs and is WSGI (Web Server Gateway Interface) compliant, which promises excellent interoperability and flexibility.

To add interactive visualizations, we used Plotly’s Dash framework. It helped us create scatter plots, line charts, bar graphs, and hover-overs with ease. Moreover, Dash allowed us to add out-of-the-box components like multi-select drop-downs, radio buttons, and checkboxes smoothly.

To offer a flexible map experience, we used Dash Leaflet. It helped us include beautiful maps containing hundreds of georeferences.

To enable high-performance data processing, our experts used the NumPy (Numerical Python) package. It merged heterogeneous datasets in a streamlined way. Besides, we leveraged Pandas for data gathering and data munging.

By combining the above stack with location intelligence, Capital Numbers successfully offered a powerful solution that offers deep visibility into EO data (e.g., geographically-distributed assets, companies, portfolios, and water bodies).

Our engineers used AI best practices to process raw information and predict risks/stabilities at various spots worldwide.

Our critical contributions included: