Visualization of noise pollution for the entire world based on top of OSM data. Search for your city and check for the noisy places. Using global street, landuse and building data from OpenStreetMap we can approximate where noise pollution might happen. We use a very simple noise model inspired by noise pollution concept of Cities Skylines.

Take a look at the full screen map and explore some cities and their noise visualizations.

How does it work?

Check out the GitHub repository for the code and styles. You can download the vector tiles from GitHub as well.

In the model we add a buffer to noisy objects. This is the area that is probably affected by noise. Very noisy objects get a high buffer and less noisy objects a smaller buffer.

In order for this to work we make several assumptions:

Highways, trunks, primary and secondary roads are noisy, normal street or service roads are not. Railways are noisy. Retail and industrial zones always have a noise base limit. All shops and food places (especially restaurants) are noisy. Most party and event buildings are noisy (except some shady places). Most leisure buildings are noisy. Some sport buildings are noisy. Some tourism buildings are noisy.

For OSM features that match these criterias we assign a buffer and remove the overlapping parts (otherwise the vector tile size would explode). This results in a simple approximation of noise pollution for cool data visualizations.

Cities

Let’s take a look at some specific cities because they are the most interesting with lots of OSM data. Take a look at your city in the map and tell me whether the data visualization it resembles some reality.

Paris

Paris has a lot of shops in the center that emit a medium level of noise. The rails leading to the different rail stations are all hubs of noise in the city. The motorways leading around and into the city are also especially visible.

Zurich

The railway stations at Hardbrücke and Hauptbahnhof are the main source of high noise in the city. You can also see the many shops that make Zurich a noisy place. The clubbing scene at Langstrasse and Limmatquai is also clearly visible.

New York

The subway is not responsible for any noise because it is underground. It is really cool how Central Park seems to be a noise free zone. Most noise is coming from the big roads in the center and the shops around it. There is also a lot of industrial noise around the Brookylin Navy Yard, Steinway and Hunts Point.

Stockholm

Stockholm has a lot of noise from the railways (like Arlanda Express). The old town and vicinity around the Stockholm Central rail station contain a lot of small noisy shops. South of Humlegården park there seem to be many clubs lighting up the noise map. Industrial noise is mostly coming from Djurgårdsstaden and especially Hjorthagen.

Details

The noise pollution areas are divided into three noise level.

Zone dB L1 ≥ 65 L2 55 - 64.9 L3 45 - 54.9

Each OSM feature emits a custom buffer for each noise level. You are very welcome to suggest different values, they are only educated guesses derived from the Swiss sonBASE noise map. Of course this approximation does not include damping through buildings, traffic volume and all the other fancy stuff - but it is simple enough to be applied globally.

Roads

Tag L1 L2 L3 highway=motorway 60m 220m 500m highway=trunk 50m 190m 400m highway=primary 35m 160m 300m highway=secondary 80m 125m highway=tertiary 35m 65m

Railways

Tag L1 L2 L3 rail=[rail, narrow_gauge, preserved] 30m 60m 100m rail=[light_rail, tram, funicular, monorail] 30m 60m

Industrial and Retail Zones

Tag L1 L2 L3 landuse=industrial 50m 100m landuse=retail 70m 180m

Shops and Food

Tag L1 L2 L3 shop=[any] 30m 65m amenity=[bar, bbq, cafe, biergarten, fast_food, food_court, ice_cream, pub, restaurant] 35m 75m

Party

Tag L1 L2 L3 amenity=[cinema, casino, nightclub, gambling, stripclub, theatre, community_centre 40m 70m 150m

Leisure

Tag L1 L2 L3 leisure=[beach_resort, swimming_area, water_park 35m 55m 75m tourism=[camp_site, museum, picnic_site, theme_park, zoo 35m 55m 75m

Sport

Tag L1 L2 L3 sport=[american_football, baseball, beachvolleyball, bmx, canadian_football, cockfighting, cricket, dog_racing, field_hockey, horse_racing, ice_hockey, ice_skating, obstacle_course, rc_car, rugby_league, rugby_union, shooting, soccer, volleyball, tennis, water_ski] 40m 60m 80m

Conclusion

It is astonishing what you can do with OpenStreetMap. While this noise approximation is just a hacky visualization and not a real analysis it demonstrates how you can create global data visualizations on top of OSM and Mapbox GL.