Inspiration

We all have searched for a flat, but we all have different requests. Someone need access to amenities like cinema, theater, yet all want to live near park. Someone is ok with car, others need public transport.

What it does

How we built it

We have created Prisma Schema for Geo data (inspired by geoJson) coupled tohether with additional parameters like "district" to better identify the Prague district (polygon or point to polygon intersection) This exposes GraphQL interface. Frontend is ServerRendered by NextJS and Apollo SSR so only small js is transferred to client. Data are generated by typescript interface to the database or taken directly from the datasource if it uses webql and is not needed for filtering

React is handling the whole frontend.

Challenges we ran into

Multiple WebQL data sources (internal flatzone and realities) Prisma not able to consume and create [[Float]] so neccesary to re-parse inputs

Accomplishments that we're proud of

Running the whole stack, finding great data about malls, natures, fitCentres which might be great addition to standard queries like public transport etc.

What we learned

Prisma, cooperate with new people, we have learned a lot about geoJson and geoData generally

What's next for PavCon flat selector

rule the world

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