SVG and WGS84 https://www.w3.org/TR/SVG11/coords.html
The project consists of three parts:
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Application (native web browser)
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CouchDB and S3
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Scraper (Golang)
The developments tools are listed in flake.nix. The development environment can be start with command:
nix develop1. Application
There is a development server server.js that serves the app and static assets.
The application resides in app directory.
| Unresolved directive in <stdin> - include::./app/requirements.tsv[] |
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Run (the development server) with:
node server.jsStatic assets are deployed directly to a web server with Git. Later it could be possible to download the assets to CounchDB.
2. Scraper runner
Golang implementation fetches the data to CouchDB. The runner resides in scrape directory.
| Unresolved directive in <stdin> - include::./scrape/requirements.tsv[] |
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The running will require some cookies to be gotten from a web browser. Run with:
export OTA_TOKEN=...
export OTA_CUID=...
export OTA_LOADED=...
export PHPSESSID=...
go run main.go3. Next steps
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Implement additional map features: "koulut", "päiväkodit"
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User journey matching for features and fixes accordingly
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UTM projection for geometry
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WebGL?
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Better colors for map
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Lots of refactoring across everything. Code can be reduced by atleast 40% with smarter use
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Make touch gestures work better
4. Analysis Data processing
List of open WFS data sources are in: https://kartta.hel.fi/avoindata/dokumentit/Aineistolista_wfs_avoindata.html
WFS Capabilities can be found from: https://kartta.hel.fi/ws/geoserver/avoindata/wfs?version=2.0.0&request=GetCapabilities
The node.js script download.js downloads the material.
Description of the statistical data can be found from: https://hri.fi/data/fi/dataset/helsingin-seudun-aluesarjat-tilastotietokannan-tiedot-paikkatietona
, and the description at: https://www.hel.fi/static/avoindata/dokumentit/Aluesarjat_Avainluvut_2024.pdf