#30mapsinamonth — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #30mapsinamonth, aggregated by home.social.
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For the twenty ninth #30MapsInAMonth are two visualisations based on the @WorldPopProject 2030 projected population as 100m² raster data. The first shows the population of the Islands of Northern Europe and the second population change.
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#Raster #Population #Beethoven #GBR #IRE #OpenData #30dayMapChallenge
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For the nineteenth #30MapsInAMonth are a set of map using different coordinate system projections of Natural Earth railway data https://www.naturalearthdata.com/
The four maps represent points on the oblate-spheroid that is the Earth on a surface. The first uses latitude and longitude in °, the rest are in meters. The second is commonly used on the web, the third a projection for Europe and the final is the UK Ordnance Survey map projection.
#rail #WorldMap #NaturalEarth #Coordinates #30dayMapChallenge
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For the eleventh #30MapsInAMonth is an animated map of the British rail network showing track occupancy count for passenger train services in hour slices for the week of 18 August 2025.
This is based on the Network Rail centre-line track- or network-model and Common Interface File (CIF) format timetable file published on the Rail Data Marketplace combined with ORR and @openstreetmap location data for timetable points.
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For the fourth #30MapsInAMonth is a map also based on population distribution and the shortest walking route to routes from the 2021 Office for National Statistics census Output Area locations to the stations centred on Sheffield. The line width is proportional to the aggregated population and the routes are based on based on @openstreetmap path data, and the station locations on Office of Rail and Road (ORR) active station list
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For the third #30MapsInAMonth are three theoretical European full-automated luxury high-speed heavy-rail network maps based on population distribution. This uses H3 hierarchical hexagon library to aggregate @WorldPopProject population data into #polygons, creating a maximum spanning tree network using population-to-population edge-weights and names for major urban centres added using @EUCommission Global Human Settlement Layer data.