Slide 1

Slide 1 text

Validating the map State of the Map 2017, Japan user:PlaneMad | Arun Ganesh

Slide 2

Slide 2 text

Crowdsourced data projects

Slide 3

Slide 3 text

Cities can become shops

Slide 4

Slide 4 text

Or an office

Slide 5

Slide 5 text

Or just get a bad name

Slide 6

Slide 6 text

Why does it happen?

Slide 7

Slide 7 text

My mouse moved

Slide 8

Slide 8 text

It’s time to rename my city

Slide 9

Slide 9 text

This looks prettier

Slide 10

Slide 10 text

The virus has caught on

Slide 11

Slide 11 text

This description tag looks useful

Slide 12

Slide 12 text

Need PokeStop now

Slide 13

Slide 13 text

This is a nice place to draw

Slide 14

Slide 14 text

From where? Contributors or Software

Slide 15

Slide 15 text

60% of harmful changes are from New contributors Source: OSMCha

Slide 16

Slide 16 text

New contributors Naturally make mistakes Not aware of OpenStreetMap Not aware of contribution guidelines Not familiar with editing tools Not familiar with tagging system Not aware of community

Slide 17

Slide 17 text

Experienced contributors Make large scale edits Use powerful tools Edit important and very complex features Not scared of making a mistake But mistakes happen

Slide 18

Slide 18 text

More new and active contributors than ever before

Slide 19

Slide 19 text

Software Simple tools attracts new users Power tools allows large scale editing Don’t give powerful tools to new users

Slide 20

Slide 20 text

Everything breaks

Slide 21

Slide 21 text

Protecting the map No single strategy can be foolproof We need as many eyes on the data as possible

Slide 22

Slide 22 text

We need to see the data, not the map

Slide 23

Slide 23 text

Foolproof data validation requires local context

Slide 24

Slide 24 text

We need accessible tools for community to peer review data Over 30 tools listed on OSM Wiki None on the OSM Homepage

Slide 25

Slide 25 text

30,000 changesets by 4,000 users everyday. 2000 new users/day

Slide 26

Slide 26 text

OSMChangesetAnalyzer

Slide 27

Slide 27 text

Filter changesets by location, date, category..

Slide 28

Slide 28 text

Visualize a changeset like Achavi

Slide 29

Slide 29 text

Peer review and tag changesets

Slide 30

Slide 30 text

103,680 changesets reviewed 14,133 found with bad quality Use for machine learning?

Slide 31

Slide 31 text

Contribute Source code https://github.com/mapbox/osmcha-frontend/issues Write detectors https://github.com/mapbox/osm-compare

Slide 32

Slide 32 text

Get in touch [email protected] Join the OSMCha Workshop on Sunday 1.30pm