Editor usage stats

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Usage statistics of OSM editors here …

Info about this page

Note that most plots on this page include at least one logarithmic scale. This may be unfamiliar to some readers (seeing usually linear scales).

Remark on updates. It will occasionally be updated, but I cannot promise to maintain regular intervals. If you think another update is past due, please remind me. See this page's history for the last update.

This page has been thoroughly redesigned in mid-2012. If you are looking for the older raw statistics, check the archive page.

Market share of various editors over time

The "market share" of an OSM editor can be defined using different weights: the number of changesets created, the number of its users, or the number of edits actually made, and various others (including combinations of the former). Each definition has its advantages and disadvantages, but each also tells something about how a given editor is (or was) used.

In the tables below, the market shares of various editors according to the aforementioned three definitions are presented, broken up into years. For the corresponding plots, the time resolution is six months.

Methodology

If you are not interested in technical details, feel free to skip this section.

All the information presented on this page is extracted from changesets-yymmdd.osm.bz2 files. An editor is identified by the created_by tag of each changeset. (This implies that 2009 data cover only the time following the startup of API 0.6, when changesets – and changeset metadata – were introduced, and that statistics for earlier years cannot be generated in this simple way.)

In all the tables, 2020 means "2020 so far". For obvious reasons, full-year statistics cannot yet be generated.

The tables and plots show, respectively, the number of changesets created using an editor (a simple counting exercise in processing the changesets file), the number of total edits made with it (computed by summing up the num_changes of each changeset), and the number of distinct user IDs (uids) seen in association with each editor. The latter is assumed to correspond to the number of actual users/mappers (though some may use more than one account, which is not considered here). The data are resolved by years for the tables and by half years for the plots. Market shares (as percentages) are calculated with respect to the overall sums.

In the "distinct uids" table, mappers editing with more than one editor will be counted once for each editor. This double-counting implies that the sum of all is not equal to the total number of mappers editing in a given period of time. The "market shares" in the corresponding plot are, however, calculated in relation to that number. Therefore, the market shares do not add up to one; instead, their interpretation is as follows: if an editor has, say, a market share of 0.5 in a given period of time, 50% of all mappers have used that particular editor at least once in that period - but some of them may have used other editors as well. For example, in the first half of 2012, Potlatch 2 and JOSM had respective market shares of roughly 85% and 36%, the sum of which already largely exceeds 100%.

The double-counting of users could in principle be remedied by "splitting" users according to some sort of weight, for example, the number of changesets created with each editor, but that would alter the definition and distort the distribution, diluting the desired information about the user basis. Therefore, such is not done here.

Observations

These are some conclusions to be drawn from the numbers below. The list is by no means exclusive.

  • It is obvious that according to the number of edits, JOSM has never been challenged as the number one editor (the only nominal exception in 2009 being a special case, when huge numbers of superfluous tags from the TIGER import were removed by woodpeck_fixbot using osmtools, and with changesets from before API 0.6 that can not be associated with any editor). JOSM's dominance is not so clear in terms of changesets created - this has to do with Potlatch 1 creating zillions of empty changesets in live editing mode, but also with the fact that Potlatch users tend to make smaller changesets on average, and iD users creating even smaller changesets than Potlatch 2 users.
  • Also, the common notion that the main online editors on the OSM website reach the largest number of users, is confirmed; with that role initially held by Potlatch 1, then Potlatch 2 and now shifting to iD. In fact, the number of actual edits made with Potlatch 1 has gradually decreased with increasing popularity of Potlatch 2, dropping to the permille level in 2012. However, the popularity of Potlatch 2 has so far been less affected by the introduction of iD than the popularity of Potlatch 1 when Potlatch 2 went live. As of early 2014, iD has twice the number of users Potlatch 2 has and created four times the number of changesets, but there are still more edits made with Potlatch 2 than with iD.
  • It turns out that Merkaartor, often regarded as one of the "big three" editors and seen as a serious competitor to JOSM, does not really fill that role. Merkaartor's market share has never exceeded 2 percent in terms of edits and 5 percent in terms of changesets; its user base has at no time been comparable to that of JOSM and has been continuously declining for years. This may be related to the editor's stalled development.
  • Most other editors have never reached percent-level market shares (the exceptions being import scripts, used by only few users but editing large amounts of data).
  • Recently, several smartphone applications have obtained a non-negligible audience (making very few actual edits per changesets, which of course reflects their goal of filling the niche for quickly editing, like entering the opening hours of some POI). These include Go Map!!, Vespucci, Pushpin, POI+, and the navigation tool OsmAnd.
  • Another mobile application takes a somewhat surprising position in the tables: MapStalt Mini by Microsoft. Quickly dumped onto the market in 2011 (not even able to write proper created_by tag, and unmaintained since the first release), MapStalt has for several years reached more users than most other mobile editors. The numbers of changesets and edits made by these users are, however, negligible; most users seem to only have tried the program once. But even the large number of (one-time) users is surprising, as the target system Windows Phone has never reached serious market shares, and one would hardly expect an above-average interest in a free/open/crowdsourced project from users of Microsoft's products.
  • JOSM's (relative) market share in terms of users has continually decreased over the past years (unlike its market shares by changesets and edits, which have remained roughly constant). The total number of all mappers has grown, but the number of JOSM users seems to have saturated. In other words: a growing percentage of users has been using exclusively one of the online editors and not been converted to JOSM users.
  • Considering the user statistics, one should keep in mind that these are fully unbiased counts. Every mapper who has made at least one changeset with an editor is counted as a user of that editor. Therefore the analysis distinguishes neither between one-time and regular mappers nor between main and secondary/auxiliary editors.

Tables and figures

These tables were generated automatically with wiki.pl from the changeset dump of 17.07.2020. The numbers for year 2020 are obviously not final.

by number of users (distinct uids)

by number of users (distinct uids)
editor 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
iD 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000056176 56 176 (43.6%) 00000125149 125 149 (80.1%) 00000133826 133 826 (81.8%) 00000148470 148 470 (56.7%) 00000194572 194 572 (61.0%) 00000214073 214 073 (69.5%) 00000203449 203 449 (72.2%) 00000136746 136 746 (77.1%)
MAPS.ME 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000096584 96 584 (36.9%) 00000102375 102 375 (32.1%) 00000071199 71 199 (23.1%) 00000055719 55 719 (19.8%) 00000021304 21 304 (12.0%)
JOSM 00000013753 13 753 (18.8%) 00000018795 18 795 (23.6%) 00000020228 20 228 (21.2%) 00000023441 23 441 (19.0%) 00000023138 23 138 (17.9%) 00000021862 21 862 (14.0%) 00000022794 22 794 (13.9%) 00000022313 22 313 (8.5%) 00000023190 23 190 (7.3%) 00000022762 22 762 (7.4%) 00000022801 22 801 (8.1%) 00000016032 16 032 (9.0%)
StreetComplete 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000007 7 (0.0%) 00000009572 9 572 (3.0%) 00000009764 9 764 (3.2%) 00000008967 8 967 (3.2%) 00000007047 7 047 (4.0%)
OsmAnd 00000000000 - 00000000192 192 (0.2%) 00000000645 645 (0.7%) 00000001129 1 129 (0.9%) 00000001653 1 653 (1.3%) 00000001927 1 927 (1.2%) 00000002354 2 354 (1.4%) 00000003538 3 538 (1.4%) 00000004862 4 862 (1.5%) 00000005982 5 982 (1.9%) 00000007063 7 063 (2.5%) 00000004274 4 274 (2.4%)
Vespucci 00000000058 58 (0.1%) 00000000237 237 (0.3%) 00000000460 460 (0.5%) 00000000957 957 (0.8%) 00000001622 1 622 (1.3%) 00000001801 1 801 (1.2%) 00000002075 2 075 (1.3%) 00000002379 2 379 (0.9%) 00000002920 2 920 (0.9%) 00000003332 3 332 (1.1%) 00000003632 3 632 (1.3%) 00000002849 2 849 (1.6%)
Potlatch 2 00000000000 - 00000003787 3 787 (4.8%) 00000066586 66 586 (69.9%) 00000101941 101 941 (82.5%) 00000072649 72 649 (56.4%) 00000029358 29 358 (18.8%) 00000024045 24 045 (14.7%) 00000014694 14 694 (5.6%) 00000010021 10 021 (3.1%) 00000006451 6 451 (2.1%) 00000004912 4 912 (1.7%) 00000002499 2 499 (1.4%)
RapiD 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000001105 1 105 (0.4%) 00000002188 2 188 (1.2%)
Go Map!! 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000002205 2 205 (1.7%) 00000002046 2 046 (1.3%) 00000001335 1 335 (0.8%) 00000001508 1 508 (0.6%) 00000001834 1 834 (0.6%) 00000002687 2 687 (0.9%) 00000003096 3 096 (1.1%) 00000002129 2 129 (1.2%)
OsmHydrant 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000001 1 (0.0%) 00000000344 344 (0.2%) 00000000665 665 (0.4%) 00000000751 751 (0.3%) 00000000866 866 (0.3%) 00000000934 934 (0.3%) 00000001166 1 166 (0.4%) 00000000832 832 (0.5%)
GNOME Maps 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000002 2 (0.0%) 00000000247 247 (0.1%) 00000000421 421 (0.1%) 00000000472 472 (0.2%) 00000000556 556 (0.2%) 00000000428 428 (0.2%)
MapContrib 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000003 3 (0.0%) 00000000097 97 (0.0%) 00000000243 243 (0.1%) 00000000243 243 (0.1%) 00000000314 314 (0.1%) 00000000306 306 (0.2%)
Osmose Editor 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000157 157 (0.1%) 00000000224 224 (0.1%) 00000000249 249 (0.1%) 00000000284 284 (0.1%) 00000000275 275 (0.1%) 00000000377 377 (0.1%) 00000000279 279 (0.2%)
Pic4Review 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000250 250 (0.1%) 00000000517 517 (0.2%) 00000000242 242 (0.1%)
Level0 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000109 109 (0.1%) 00000000174 174 (0.1%) 00000000205 205 (0.1%) 00000000222 222 (0.1%) 00000000233 233 (0.1%) 00000000252 252 (0.1%) 00000000216 216 (0.1%)
Osm Go! 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000002 2 (0.0%) 00000000002 2 (0.0%) 00000000046 46 (0.0%) 00000000194 194 (0.1%) 00000000206 206 (0.1%)
OSM Contributor 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000198 198 (0.1%) 00000000424 424 (0.1%) 00000000443 443 (0.2%) 00000000187 187 (0.1%)
Merkaartor 00000002262 2 262 (3.1%) 00000002784 2 784 (3.5%) 00000002193 2 193 (2.3%) 00000001687 1 687 (1.4%) 00000001090 1 090 (0.8%) 00000000597 597 (0.4%) 00000000456 456 (0.3%) 00000000409 409 (0.2%) 00000000332 332 (0.1%) 00000000256 256 (0.1%) 00000000241 241 (0.1%) 00000000177 177 (0.1%)
FireYak 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000021 21 (0.0%) 00000000131 131 (0.0%) 00000000158 158 (0.1%) 00000000221 221 (0.1%) 00000000133 133 (0.1%)
IsraelHiking 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000088 88 (0.0%) 00000000131 131 (0.0%) 00000000153 153 (0.1%) 00000000122 122 (0.1%)
Potlatch 0.x/1.x 00000059540 59 540 (81.3%) 00000068273 68 273 (85.7%) 00000026327 26 327 (27.6%) 00000013033 13 033 (10.5%) 00000000745 745 (0.6%) 00000000517 517 (0.3%) 00000000429 429 (0.3%) 00000000382 382 (0.1%) 00000000272 272 (0.1%) 00000000233 233 (0.1%) 00000000199 199 (0.1%) 00000000111 111 (0.1%)
RevertUI 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000023 23 (0.0%) 00000000058 58 (0.0%) 00000000076 76 (0.0%) 00000000083 83 (0.0%) 00000000106 106 (0.1%)
OSM ↔ Wikidata 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000066 66 (0.0%) 00000000082 82 (0.0%) 00000000160 160 (0.1%) 00000000071 71 (0.0%)
osmapi 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000042 42 (0.0%) 00000000048 48 (0.0%) 00000000072 72 (0.0%) 00000000069 69 (0.0%) 00000000067 67 (0.0%) 00000000047 47 (0.0%) 00000000044 44 (0.0%)
Jungle Bus 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000100 100 (0.0%) 00000000079 79 (0.0%) 00000000044 44 (0.0%) 00000000030 30 (0.0%)
rosemary 00000000000 - 00000000266 266 (0.3%) 00000001137 1 137 (1.2%) 00000001051 1 051 (0.9%) 00000001532 1 532 (1.2%) 00000001723 1 723 (1.1%) 00000002761 2 761 (1.7%) 00000002487 2 487 (0.9%) 00000002345 2 345 (0.7%) 00000001990 1 990 (0.6%) 00000000072 72 (0.0%) 00000000024 24 (0.0%)
ArcGIS 00000000000 - 00000000045 45 (0.1%) 00000000107 107 (0.1%) 00000000101 101 (0.1%) 00000000061 61 (0.0%) 00000000067 67 (0.0%) 00000000067 67 (0.0%) 00000000156 156 (0.1%) 00000000087 87 (0.0%) 00000000041 41 (0.0%) 00000000040 40 (0.0%) 00000000023 23 (0.0%)
osmtools 00000000061 61 (0.1%) 00000000108 108 (0.1%) 00000000053 53 (0.1%) 00000000042 42 (0.0%) 00000000032 32 (0.0%) 00000000030 30 (0.0%) 00000000028 28 (0.0%) 00000000034 34 (0.0%) 00000000036 36 (0.0%) 00000000025 25 (0.0%) 00000000023 23 (0.0%) 00000000014 14 (0.0%)
Other 00000000277 277 (0.4%) 00000000440 440 (0.6%) 00000000307 307 (0.3%) 00000000385 385 (0.3%) 00000000286 286 (0.2%) 00000000264 264 (0.2%) 00000000335 335 (0.2%) 00000000369 369 (0.1%) 00000000489 489 (0.2%) 00000000600 600 (0.2%) 00000000931 931 (0.3%) 00000000782 782 (0.4%)
Not Specified 00000018623 18 623 (25.4%) 00000000882 882 (1.1%) 00000000539 539 (0.6%) 00000000443 443 (0.4%) 00000000269 269 (0.2%) 00000000424 424 (0.3%) 00000000704 704 (0.4%) 00000001586 1 586 (0.6%) 00000001404 1 404 (0.4%) 00000001182 1 182 (0.4%) 00000001067 1 067 (0.4%) 00000000549 549 (0.3%)

OSM editor marketshares by distinct uids.svg

See comments above to understand why the market shares in this plot do not add up to one. (It's not a bug, it's a feature.)

See also this plot for the absolute numbers - the normalized plot above may be somewhat misleading if interpreted improperly.

by number of edits

by number of edits
editor 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
JOSM 00125615525 125 615 525 (20.8%) 00494458279 494 458 279 (77.4%) 00539126945 539 126 945 (80.5%) 00542126504 542 126 504 (74.0%) 00500018617 500 018 617 (71.0%) 00666714111 666 714 111 (76.7%) 00642694132 642 694 132 (74.4%) 00625442567 625 442 567 (70.8%) 00664173193 664 173 193 (67.1%) 00794008723 794 008 723 (68.4%) 00790709661 790 709 661 (64.1%) 00522335462 522 335 462 (65.4%)
iD 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00034430862 34 430 862 (4.9%) 00109275830 109 275 830 (12.6%) 00142199332 142 199 332 (16.5%) 00189401329 189 401 329 (21.5%) 00279192774 279 192 774 (28.2%) 00321496138 321 496 138 (27.7%) 00359714471 359 714 471 (29.2%) 00227706433 227 706 433 (28.5%)
RapiD 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00011678103 11 678 103 (0.9%) 00022244360 22 244 360 (2.8%)
Potlatch 2 00000000000 - 00007584901 7 584 901 (1.2%) 00092203498 92 203 498 (13.8%) 00146236064 146 236 064 (20.0%) 00133374376 133 374 376 (18.9%) 00077895566 77 895 566 (9.0%) 00060077122 60 077 122 (7.0%) 00042908801 42 908 801 (4.9%) 00033220441 33 220 441 (3.4%) 00025135503 25 135 503 (2.2%) 00020583332 20 583 332 (1.7%) 00009701185 9 701 185 (1.2%)
Redaction bot 00000000000 - 00000000000 - 00000000000 - 00021303266 21 303 266 (2.9%) 00000284119 284 119 (0.0%) 00000102887 102 887 (0.0%) 00000081142 81 142 (0.0%) 00000195753 195 753 (0.0%) 00000278968 278 968 (0.0%) 00000323820 323 820 (0.0%) 00000308288 308 288 (0.0%) 00002527684 2 527 684 (0.3%)
Go Map!! 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000501785 501 785 (0.1%) 00000632666 632 666 (0.1%) 00000976605 976 605 (0.1%) 00000891117 891 117 (0.1%) 00001141922 1 141 922 (0.1%) 00002535539 2 535 539 (0.2%) 00003782685 3 782 685 (0.3%) 00002470847 2 470 847 (0.3%)
osmtools 00150412240 150 412 240 (24.9%) 00008994328 8 994 328 (1.4%) 00000981921 981 921 (0.1%) 00001229689 1 229 689 (0.2%) 00002734257 2 734 257 (0.4%) 00001499370 1 499 370 (0.2%) 00006826400 6 826 400 (0.8%) 00015035558 15 035 558 (1.7%) 00002655505 2 655 505 (0.3%) 00003271989 3 271 989 (0.3%) 00007558536 7 558 536 (0.6%) 00002184126 2 184 126 (0.3%)
Vespucci 00000002087 2 087 (0.0%) 00000022298 22 298 (0.0%) 00000049455 49 455 (0.0%) 00000165722 165 722 (0.0%) 00000277566 277 566 (0.0%) 00000804178 804 178 (0.1%) 00000885787 885 787 (0.1%) 00001055056 1 055 056 (0.1%) 00001559094 1 559 094 (0.2%) 00002073140 2 073 140 (0.2%) 00002296702 2 296 702 (0.2%) 00002107619 2 107 619 (0.3%)
osmapi 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000797190 797 190 (0.1%) 00000139521 139 521 (0.0%) 00000682193 682 193 (0.1%) 00000288536 288 536 (0.0%) 00000240521 240 521 (0.0%) 00000162462 162 462 (0.0%) 00001999009 1 999 009 (0.3%)
Merkaartor 00005378663 5 378 663 (0.9%) 00010887187 10 887 187 (1.7%) 00010250026 10 250 026 (1.5%) 00009702870 9 702 870 (1.3%) 00004380406 4 380 406 (0.6%) 00003791724 3 791 724 (0.4%) 00003696590 3 696 590 (0.4%) 00002522894 2 522 894 (0.3%) 00001735350 1 735 350 (0.2%) 00002138115 2 138 115 (0.2%) 00002316287 2 316 287 (0.2%) 00001272092 1 272 092 (0.2%)
StreetComplete 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000085 85 (0.0%) 00001257149 1 257 149 (0.1%) 00001701133 1 701 133 (0.1%) 00001983723 1 983 723 (0.2%) 00001241920 1 241 920 (0.2%)
ArcGIS 00000000000 - 00000015203 15 203 (0.0%) 00000418665 418 665 (0.1%) 00000039091 39 091 (0.0%) 00000186947 186 947 (0.0%) 00001483853 1 483 853 (0.2%) 00000146144 146 144 (0.0%) 00000086338 86 338 (0.0%) 00000440235 440 235 (0.0%) 00000762971 762 971 (0.1%) 00000340745 340 745 (0.0%) 00000714099 714 099 (0.1%)
MAPS.ME 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000812010 812 010 (0.1%) 00001033627 1 033 627 (0.1%) 00000714216 714 216 (0.1%) 00000613554 613 554 (0.0%) 00000176990 176 990 (0.0%)
Osmose Editor 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000020371 20 371 (0.0%) 00000050115 50 115 (0.0%) 00000067917 67 917 (0.0%) 00000124560 124 560 (0.0%) 00000070201 70 201 (0.0%) 00000152085 152 085 (0.0%) 00000160003 160 003 (0.0%)
Level0 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000025528 25 528 (0.0%) 00000113023 113 023 (0.0%) 00000146327 146 327 (0.0%) 00000188651 188 651 (0.0%) 00000320786 320 786 (0.0%) 00000206785 206 785 (0.0%) 00000154910 154 910 (0.0%)
AutoAWS 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00002674602 2 674 602 (0.2%) 00000076127 76 127 (0.0%) 00000125735 125 735 (0.0%)
Services_OSM 00000000000 - 00000000000 - 00000000000 - 00000028736 28 736 (0.0%) 00000018942 18 942 (0.0%) 00000099656 99 656 (0.0%) 00000124012 124 012 (0.0%) 00000060583 60 583 (0.0%) 00000053450 53 450 (0.0%) 00000001704 1 704 (0.0%) 00000022260 22 260 (0.0%) 00000123102 123 102 (0.0%)
OSM ↔ Wikidata 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000083998 83 998 (0.0%) 00000095677 95 677 (0.0%) 00000114685 114 685 (0.0%) 00000117538 117 538 (0.0%)
OsmAnd 00000000000 - 00000004726 4 726 (0.0%) 00000011576 11 576 (0.0%) 00000022532 22 532 (0.0%) 00000036647 36 647 (0.0%) 00000056023 56 023 (0.0%) 00000072002 72 002 (0.0%) 00000113397 113 397 (0.0%) 00000151873 151 873 (0.0%) 00000204448 204 448 (0.0%) 00000251119 251 119 (0.0%) 00000114634 114 634 (0.0%)
OsmHydrant 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000021 21 (0.0%) 00000019935 19 935 (0.0%) 00000049350 49 350 (0.0%) 00000043986 43 986 (0.0%) 00000051439 51 439 (0.0%) 00000051988 51 988 (0.0%) 00000058993 58 993 (0.0%) 00000043677 43 677 (0.0%)
Potlatch 0.x/1.x 00044030413 44 030 413 (7.3%) 00057141365 57 141 365 (8.9%) 00008753066 8 753 066 (1.3%) 00001470315 1 470 315 (0.2%) 00000469461 469 461 (0.1%) 00000196441 196 441 (0.0%) 00000127480 127 480 (0.0%) 00000080028 80 028 (0.0%) 00000157354 157 354 (0.0%) 00000046871 46 871 (0.0%) 00000054628 54 628 (0.0%) 00000015426 15 426 (0.0%)
rosemary 00000000000 - 00000022898 22 898 (0.0%) 00000174901 174 901 (0.0%) 00000094049 94 049 (0.0%) 00000107887 107 887 (0.0%) 00000089020 89 020 (0.0%) 00000146213 146 213 (0.0%) 00000126016 126 016 (0.0%) 00000121314 121 314 (0.0%) 00000105922 105 922 (0.0%) 00000068136 68 136 (0.0%) 00000011969 11 969 (0.0%)
osmupload.py 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00018057237 18 057 237 (2.6%) 00000000000 - 00002109443 2 109 443 (0.2%) 00000000000 - 00000000000 - 00000000000 - 00000041463 41 463 (0.0%) 00000011841 11 841 (0.0%)
SviMik 00000000000 - 00000000000 - 00000000000 - 00000005569 5 569 (0.0%) 00003060161 3 060 161 (0.4%) 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00001692718 1 692 718 (0.1%) 00000008873 8 873 (0.0%)
upload.py 00031074573 31 074 573 (5.1%) 00010166002 10 166 002 (1.6%) 00002414205 2 414 205 (0.4%) 00000421238 421 238 (0.1%) 00000776590 776 590 (0.1%) 00000429871 429 871 (0.0%) 00000488194 488 194 (0.1%) 00000004827 4 827 (0.0%) 00000038997 38 997 (0.0%) 00000003021 3 021 (0.0%) 00000004195 4 195 (0.0%) 00000000229 229 (0.0%)
AND node cleaner/retagger 00003314543 3 314 543 (0.5%) 00000108633 108 633 (0.0%) 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 -
FindvejBot 00000000262 262 (0.0%) 00003797787 3 797 787 (0.6%) 00000358760 358 760 (0.1%) 00000104606 104 606 (0.0%) 00000000175 175 (0.0%) 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 -
FixKarlsruheSchema 00000122694 122 694 (0.0%) 00000271085 271 085 (0.0%) 00000426449 426 449 (0.1%) 00000003941 3 941 (0.0%) 00000000000 - 00000000000 - 00000000000 - 00000000132 132 (0.0%) 00000000000 - 00000000000 - 00000000000 - 00000000000 -
Jeff's Uploader 00000000000 - 00005128684 5 128 684 (0.8%) 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 -
Mapzen 00000033941 33 941 (0.0%) 00000447014 447 014 (0.1%) 00000102080 102 080 (0.0%) 00000006686 6 686 (0.0%) 00000001086 1 086 (0.0%) 00000000749 749 (0.0%) 00000000033 33 (0.0%) 00000000007 7 (0.0%) 00000000006 6 (0.0%) 00000000008 8 (0.0%) 00000000000 - 00000000000 -
MyUploader 00000000000 - 00002660404 2 660 404 (0.4%) 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 -
Pushpin 00000000000 - 00000000000 - 00000000000 - 00000009837 9 837 (0.0%) 00000062718 62 718 (0.0%) 00000035563 35 563 (0.0%) 00000027135 27 135 (0.0%) 00000015091 15 091 (0.0%) 00000006810 6 810 (0.0%) 00000000767 767 (0.0%) 00000000000 - 00000000000 -
PythonOsmApi 00002254174 2 254 174 (0.4%) 00000922941 922 941 (0.1%) 00009503361 9 503 361 (1.4%) 00000045963 45 963 (0.0%) 00000036616 36 616 (0.0%) 00000026018 26 018 (0.0%) 00000027225 27 225 (0.0%) 00000003343 3 343 (0.0%) 00000007432 7 432 (0.0%) 00000000000 - 00000000000 - 00000000000 -
QGIS 00000004206 4 206 (0.0%) 00000030901 30 901 (0.0%) 00000029737 29 737 (0.0%) 00000036840 36 840 (0.0%) 00000009488 9 488 (0.0%) 00000000949 949 (0.0%) 00000000000 - 00000000000 - 00000000001 1 (0.0%) 00000000000 - 00000000000 - 00000000000 -
RawEdit 00000007996 7 996 (0.0%) 00000030087 30 087 (0.0%) 00000027561 27 561 (0.0%) 00000028837 28 837 (0.0%) 00000031049 31 049 (0.0%) 00000014062 14 062 (0.0%) 00000008173 8 173 (0.0%) 00000022400 22 400 (0.0%) 00000002834 2 834 (0.0%) 00000000876 876 (0.0%) 00000000000 - 00000000000 -
Roy 00000000000 - 00000000000 - 00000000000 - 00001216065 1 216 065 (0.2%) 00002940171 2 940 171 (0.4%) 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 -
Tayberry 00000000000 - 00000164902 164 902 (0.0%) 00000763472 763 472 (0.1%) 00000005506 5 506 (0.0%) 00000141914 141 914 (0.0%) 00000000000 - 00000221472 221 472 (0.0%) 00000000000 - 00000245462 245 462 (0.0%) 00000020769 20 769 (0.0%) 00000000000 - 00000000000 -
bulk_upload.py 00064669041 64 669 041 (10.7%) 00024649625 24 649 625 (3.9%) 00001442184 1 442 184 (0.2%) 00000759090 759 090 (0.1%) 00000185033 185 033 (0.0%) 00000008089 8 089 (0.0%) 00001061001 1 061 001 (0.1%) 00001113638 1 113 638 (0.1%) 00000002398 2 398 (0.0%) 00000001036 1 036 (0.0%) 00026933301 26 933 301 (2.2%) 00000000000 -
bulk_upload_sax.py 00007781579 7 781 579 (1.3%) 00000722332 722 332 (0.1%) 00000000000 - 00000052554 52 554 (0.0%) 00000000000 - 00000035010 35 010 (0.0%) 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 -
osmapis 00000000000 - 00000000000 - 00000000000 - 00004169283 4 169 283 (0.6%) 00000343809 343 809 (0.0%) 00000327196 327 196 (0.0%) 00000137593 137 593 (0.0%) 00000307650 307 650 (0.0%) 00000095443 95 443 (0.0%) 00000064731 64 731 (0.0%) 00000000000 - 00000000000 -
osmlinzaddr.py 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000000000 - 00000229011 229 011 (0.0%) 00001597789 1 597 789 (0.1%) 00000000666 666 (0.0%) 00000000000 -
Other 00002388027 2 388 027 (0.4%) 00001325186 1 325 186 (0.2%) 00000906033 906 033 (0.1%) 00000896204 896 204 (0.1%) 00000600108 600 108 (0.1%) 00000666953 666 953 (0.1%) 00000265226 265 226 (0.0%) 00000354804 354 804 (0.0%) 00000548424 548 424 (0.1%) 00000533740 533 740 (0.0%) 00001323577 1 323 577 (0.1%) 00000956908 956 908 (0.1%)
Not Specified 00167313853 167 313 853 (27.7%) 00009066286 9 066 286 (1.4%) 00001670818 1 670 818 (0.2%) 00002752021 2 752 021 (0.4%) 00001173684 1 173 684 (0.2%) 00003873676 3 873 676 (0.4%) 00001404891 1 404 891 (0.2%) 00001434152 1 434 152 (0.2%) 00000460971 460 971 (0.0%) 00000765721 765 721 (0.1%) 00000466112 466 112 (0.0%) 00000284930 284 930 (0.0%)

OSM editor marketshares by edits.svg


More complex analysis

The tables and plots above present very simple and straightforward analyses, whose interpretation is unambiguous. On the other hand, especially the "number of users" analysis has a few weak points: For each editor, the table gives the number of users who have made at least one edit with it; there is neither any distinction between regular and one-time users nor between using some editor as one's primary tool and just trying it out once. For example, most users make their first edits using one of the online editors (Potlatch 1 in the old days, then Potlatch 2, nowadays mostly iD). They are all counted as e.g. "iD users" even if they quickly turn away from OSM or choose another editor for all of their later edits.

The plot below (made by User:Nop) constitutes an attempt to avoid these weaknesses and associate with each user a primary or preferred editor. The main differences of this analysis are:

  • Only the regularly used editors of each user are counted. Regular use means that an editor was used for at least 30% of the edits in that month.
  • The analysis is much finer. Data is examined by month (not cumulated over a whole year) so trends are more visible.
  • The plot is linear by market share of the major editors in percent and should be more easily comprehensible than logarithmic scales.
  • Only the users (unique user ids) are counted, not how many edits they performed.
Editor usage main linear.png

Editor profiles (2012)

In this section, a selection of editors are displayed at their position in a two-dimensional space spanned by various quantities which allow some characterisation of the editor. (The selection is more or less arbitrary. If your favourite editor is missing, please complain.) All figures are for edits in the year 2012 only.

Editor profiles users edits 2012.png

The first plot displays each editor by the number of its users and the total edits made using it, which may be interpreted as the editor's total impact on the OSM database. The distribution of editors across the plot is roughly diagonal: There is a mainstream region in the upper right, where editors have a large user basis and are used to create or modify large amounts of data: JOSM, Potlatch 2 and - to a lesser extent - Merkaartor and Potlatch 1. All other editors are used by relatively few mappers (down to just one), and consequently, relatively few edits are made with them. Only a few specialized tools for large-scale edits appear somewhat off-diagonal.

Editor profiles users avgEditsPerChangeset 2012.png

The second plot again has the number of each editor's users on the horizontal axis, but now the average size of each changeset is plotted on the vertical axis. This tells us something about how each editor is used - for editing only few objects, or for larger changesets. The number of edits in a changeset using one of the mainstream editors is of the order of 100. Surprisingly, the average JOSM changeset is only roughly a factor of 2 larger than the average Potlatch 2 changeset. Various other editors (often mobile applications) usually make only a few edits in each changeset. As expected, specialized tools for large-scale edits (used by only few mappers) generate large changesets. Of course, these are all just average values, which tell nothing about the distribution of changeset sizes. We will address that issue below and try to explore the distribution of changeset sizes.

Editor changeset size distribution.png

As mentioned, the average size of a changeset (i.e. the number of edits in it) provides only very little information about how an editor is used - some number appearing as an average value may result from very different distributions. To visualize the distribution of changeset sizes, quantiles are plotted for the mainstream editors in the plot to the right. Each box describes the changeset sizes which make up 50 %, 90 % etc. of all changesets created using a given editor (e.g. 50 % of all Potlatch 1 changesets contain 4 objects or less, 90 % contain no more than 58 objects). The 50 % quantile is the median; for both Potlatch versions, this is roughly identical to the average. For JOSM and Merkaartor, both measures differ significantly, hinting a larger tail of the changeset size distributions.

Note: I am aware that this plot badly needs some cosmetic improvements. Please be patient.

Changeset size histo 2012 100.png

The distribution of changeset sizes is also represented in this histogram, that is, a diagram showing how many changesets exist with 1-100 edits, 101-200 edits, and so on. (Empty changesets - 0 edits - are dropped here.) Clearly Potlatch 1 is hardly ever used for making more than a few edits, while the number of changesets made with Potlatch 2 forms a nearly constant fraction of the corresponding number of changesets made with JOSM for quite a large range of changeset sizes. Only for really large changesets with more than, say, 5000 edits, this fraction drops off significantly and goes to zero around 20 000 edits (see the corresponding histogram for the full range up to 50 000 edits). Another diagram for the low end (up to 200 edits) is also available.


Update discipline of JOSM users

Josm versions recent.svg

How regularly do JOSM users update their editor? This plot shows the JOSM versions in use in a given month, weighted by the number of changesets and clustered by the version number's leading digits. The majority of users clearly keeps their JOSM largely up-to-date, working with at least a recent "tested" release - commercial software manufacturers must envy the JOSM developers. The average version in use is roughly three months behind the current release; however, also very old versions are still being used by a small but nonvanishing and apparently die-hard number of mappers. Even the ancient release 1566 from May, 2009 is still seen occasionally.

There is also another plot which covers a longer term.

See also