Web GIS platforms and flood monitoring for smart city programmes
Web GIS dashboards, IoT-adjacent spatial data platforms, and integrated urban flood and infrastructure monitoring systems for master-planned and smart-city developments.
Smart city programmes accumulate spatial data faster than they build the means to use it. Drainage assets sit in one system, flood modeling in a consultant's report, infrastructure condition in a spreadsheet, and no single view answers the question a planner actually has, which is usually where a specific risk and a specific asset intersect.
We build web GIS dashboards that bring modelled flood hazard, asset condition and infrastructure status into one interface a non-specialist can use. The platform is the deliverable, not a report about it — planning staff query it directly rather than requesting an extract from a GIS team.
What makes this sector different
- Spatial data is fragmented across departments and systems that were never designed to interoperate.
- Modeling results delivered as static reports go stale and cannot be queried.
- The people who need the answers rarely have GIS software or GIS training.
- Platforms built as one-off deliverables decay once the delivering consultant leaves.
What we deliver
- Web GIS dashboard with interactive mapping, filtering and drill-down detail
- PostgreSQL/PostGIS spatial database designed for the city's own data model
- Role-based access separating planning, finance and public-facing views
- Deployment that runs on your infrastructure, with documentation your team can maintain
Common questions from smart cities
Do we need GIS specialists on staff to use the dashboard?
No, and that is usually the point of commissioning one. The interface is built for planners and public-works staff — map, filter, drill down, export. GIS expertise is required to build and extend the platform, not to answer everyday questions with it.
Can it integrate with systems we already run?
Generally yes, through whatever the existing system exposes — a database connection, an API, or a scheduled export. What matters is agreeing where each dataset is authoritative, because a dashboard that silently duplicates a source becomes wrong the first time the two diverge.
What happens when the project ends?
You get the source, the schema and the deployment documentation, and the stack is deliberately mainstream — PostgreSQL/PostGIS and a standard web framework — so any competent developer can maintain it. A platform only your original supplier can touch is a liability rather than an asset.