Interactive Visualization and Spatial Data Science

The Jupyter environment allows for extensive customization and deep analysis through spatial data science.

The example iframe below is the product of village/campus interactiveOnly.ipynb. Building stock is differentiated through color. A school, housing, retail, healthcare and community focused facilities are easily identified while the tooltips highlight the underlying data. Additional features unique to an aoi can also be included. Here farmland, streams, recreational spaces and bus rapid transit routes have been added - you are thus limited only through data and your imagination.

To navigate on a laptop without a mouse:

• trackpad left-click drag-left and -right;
• Ctrl left-click drag-up, -down, -left and -right to rotate and so-on and
• + next to Backspace zoom-in and - next to + zoom-out.

The visualisation above employs the default Carto Dark Matter basemap. Pydeck supports a number of map_styles including the extensive mapbox gallery and Maptiler urls (e.g.: https://api.maptiler.com/maps/{style}/style.json?key={your API key}).

Spatial Data Science

To highlight a real-world application of an ISO 19107 compliant city model; CityJSONspatialDataScience.ipynb illustrates a example of population estimation and the calculation of Building Volume per Capita (Ghosh, T.; et. al.).

While the prefered process would proceed osm_LoD1_3DCityModel -> Spatial Data Science -> Interactive visualization; an alternate does exist.
interactiveOnly.ipynb will create a basic 3D model visualisation followed by population estimation and the calculation of Building Volume per Capita (Ghosh, T.; et. al.).

Please consider your needs before executing the solution. We do not want to burden the OpenStreetMap server with repeat calls for data.