OSMCha has this great feature of watching edits around your neighborhood using the bounding box (bbox) filter. Today, I reviewed several edits using the combination of bbox and the new mapper flag.
</br>New mappers in the Philippines in the last couple of days
Note: Being a new mapper does not mean making bad edits per se, but by being new, we can commit errors because of unfamiliarity with the best practices of editing. Admit it old mappers, we made mistakes before. ;)
As I review each changeset, I often fix common errors based on my familiarity with the area. After fixing, I usually add a comment to the user’s changeset explaining my edits and encouraging them to continue contributing to the project.
</br>My changeset comments
While re-reading my comments, I realized I’m doing something wrong! My comments were peppered with OSM technical jargon new mappers may not even know of. I’m imagining this new user’s reaction 🤔 to my comment.
Next time, I’ll try to simplify my comments and avoid hard to understand terminologies. It is challenging to simplify without losing the substance of the comment, but, it is equally important to engage new users as first step in engaging in a conversation.
How do you welcome new users in your neighborhood?
Back in July, we talked about the Los Angeles building import hitting > 1 million mark for LA City and last month, we already imported 2 million buildings covering the rest of the county. We are not finished yet ;)
Read on for what we are doing in the last couple of months.
Downtown LA City in 3d
The local community through @MaptimeLA has also been busy validating the data on the ground. A few of them took a ferry to Catalina Island for an awesome mapping weekend! Aside from validating the imported buildings, they added local features and Mapillary street/trail level photos across the island.
MaptimeLA explorers in Catalina Island, photo by J. Regan Hutson
Also last month, LA County GIS released an update to the building data covering changes from 2008-2014. The team decided to pause importing buildings to check the new dataset. The data is now ready and mappers can continue the import starting with Malibu & Santa Monica Mountains.
New buildings (2008-2014) are now available for import
We are reviewing the best strategy to update areas with already imported buildings. Conflation is challenging and we need to make sure that data already improved by mappers won’t be damaged unnecessarily. If you have thoughts on this, hit us on gitter or the project’s repo.
Last time, we talked about how we imported over 1 million buildings in LA.
Watch this video from our SOTM-US talk. In this post, we’ll talk about our ongoing cleanup.
No data is perfect, the quality of what we imported in OpenStreetMap was generally good, but in all things data, there will always be unexpected cases.
During the import trials, we discovered that the LA City data was split to the parcel boundaries resulting to small
polygons that should be part of the larger building (see: #71).
We fix this during the import by using the Auto-tools plugin in JOSM but
there were cases when it wasn’t fixed.
Detecting split buildings
We ran a detection for split buildings by analyzing size and shapes
of buildings using OSM-QA-tiles, turf and tilereduce. A sample output looks like this:
The general idea is that:
The reverse is true for invalid/split buildings.
After several trials, we came up with an acceptable threshold for split buildings in LA.
Here are some examples of valid detection:
Fixing with Maproulette
The script detected ~4K buildings and is available as a task in Maproulette: http://maproulette.org/map/419/460642
Workflow for JOSM
For ideas how to do this in iD, let me know.
We are continuously improving how we detect split buildings, if you have ideas, comment here or directly in the ticket.
Thank you for fixing!
Part of our series of diaries sharing experiences on the ongoing LA Building Import into OpenStreetMap.
Last month, we talked about the tools. Today, it’s all about the data.
Just last week, we’ve hit 1.1 million buildings imported in LA City!
A great milestone as we are in the final stretch of import, validation and clean-up for LA City.
Here’s a few map-shots on what happened in the last couple of months.
We made sure that the import process followed the community accepted guidelines. Instead of importing everything with scripts/bots, we used the Tasking Manager to allow
volunteers to take part. We divided LA City into four TM projects and organized
mapathons within LA City to kick start the process. The animation below shows the weekly progress starting from
Southside all the way to SF Valley. Large chunks of buildings were added during and after every mapathon.
Weekly progress, March - July 2016.
We asked volunteers to use a dedicated account and over
100 usernames participated. You can see this checkerboard pattern when you color the buildings by username/id.
Colored by user ID, > 100 usernames.
Before the import, a lot of buildings already exist. In recognition and respect to the mappers
before us, we made sure that existing buildings
were properly merged/conflated (in JOSM, we use the Replace Geometry tool). This is a tedious
process but this is how it should be done. By doing this, we are keeping the editing history of existing
Feature version. Blue = v1 to Yellow > v4.
Aside from the good quality of building footprint, we included several tags that describes every building.
This is not visible in our default map rendering, but these tags exist in most of the data we imported.
We’ve included building types based on LA Country’s Assessors information.
Colored by building type.
Year the building was originally constructed
There’s a lot of history in the urban expansion of Los Angeles starting from the pueblo in 1700s up to today.
The import data has a year_built attribute which we included in the import. You can now see the city’s settlement
history in OpenStreetMap.
Colored by year built, 1800 - 2015.
Rendered in Mapbox gl-js
These are a few of the tags we’ve added. I’m excited to see what the community creates out of this data.
Our friends at MaptimeLA started experimenting with the data already!
Photo by MaptimeLA.
Are you planning on using this data in your own map? Catch the team this weekend at the SOTM-US and show us what you created!
More info about the import is available in the following links:
A typical living_street in Bangalore.
It’s been a couple of months since I moved to Bangalore.
Naturally, with a new neighborhood comes an opportunity to map and improve the data!
When I first came to this side of the city, OSM coverage was fairly good. Streets were marked and
buildings were traced, but no other detail was there. It lacked street names, POIs and neighborhood names.
So during my daily commute, I try to add a few details to make it much more updated and useful.
Mapping a totally different place comes with challenges that break many of your assumptions.
Here’s a few things I found amusing, interesting and at times confusing as I navigated around this Indian city.
Everywhere you go, you see colours. From the temples, to the rangolis, to how vegetables are stacked like a
treemap infographic. It seems that colour defines the city’s daily lives.
The last time I saw a working charcoal iron was when my grandmother was using it. Here, there are roadside shops in carts that iron your clothes while you wait.
Different beliefs can always get along
Roadside place_of_worship, left-side: Jesus and Mary, right-side: Lord Shiva.
Chai shops everywhere
You don’t need to worry about refreshments, chai shops are everywhere. This one even sells fresh coconuts.
Signs maybe confusing
This got me confused for a while, it turned out, not all hotels in India are for sleeping. They are eating hotels (amenity=fast_food) NOT sleeping hotels (tourism=hotel)!
There are exceptions to the rule!
Just as when I’m getting used to cycling on the left hand side, I stumbled into Commissariat. I initially thought the data was wrong but apparently, you drive on right at Commissariat.
Mapping is still a work in progress, I don’t have much of a plan. In my free time, I simply walk and mark anything I find. But the map is now beyond just streets and buildings. Check it out and let me know what’s missing.
work in progress
This is a part of a series of diaries sharing our experience on the ongoing LA Building Import into OpenStreetMap.
In the last 2.5 months we started importing building footprints over Los Angeles from open data available in LA county. Discussions about this import started early last year, after several
discussions, planning and trial runs, we finally started the import this April. From its start, the import team agreed that this will be a community managed import. The goal is not only to improve building coverage of OpenStreetMap within the county but also to invite local mappers to actively participate in the whole process.
In this post, I will talk about the tools we built to coordinate this massive import. Many of the processes were based on an earlier buildings and addresses import in New York City with modifications needed due to the difference of data and the context of the local community.
3 million buildings in LA County.
The data came from several open data sources provided by the Government of Los Angeles:
We combined the building geometry and parcel information using the usual join by attribute GIS operation. This creates a single shapefile of buildings with all the parcel information for each building.
join by attribute
In total, we have >3 million buildings that needs to be checked for quality and a workflow to coordinate a massive community import process.
The Assessor’s parcel data contains detailed usage of each property. We used this data to identify the type of building. To do this, we compared each attribute to taginfo and adopted tags already used by the community. For all other attributes that didn’t have corresponding tags we used the generic building=yes. A CSV was created as a lookup to convert the shapefile attribute to OpenStreetMap key/value pairs.
Like the NYC building import, we scripted the entire process pipeline so that we can execute in a single command. In general, the script performs the following steps:
This automated process allowed us to easily re-run the conversion if we need to.
For example, when we discovered data issues specific to buildings in Pasadena,
we were able to exclude Pasadena in the next run of the script.
To manage the coordination of import by the community, we used a separate instance of the OSM Tasking Manager.
By using the Tasking Manager we can:
But the current Tasking Manager does not allow downloading of import data from arbitrary polygons. To make this work,
we implemented a new feature in the Tasking Manager that allows it to:
These changes were merged into the main Tasking Manager code so anyone using the latest TM codebase can use this feature as well.
Once we had everything ready, we did a couple of trial runs to evaluate the mapping workflow.
During one of our trial runs, we discovered that since we are using arbitrary polygons instead of the usual square tasks, mappers find it difficult to visualize the edges of the task they are working on.
This can introduce data conflicts especially when the imported data is now merged to the current data within OpenStreetMap. To solve this issue we added a background layer of census block boundaries that is automatically loaded when a user downloads the data.
Downloading data for import.
The trials also encountered cases where a small sliver of building was split from the main building.
Upon further investigation, these buildings are cut by the property lines of the assessor, during the LARIAC mapping.
Since this came directly from the source data, we have no way of fixing this during the data conversion process.
This required the user to manually merge the split buildings during the importing process.
While combining overlapping buildings in JOSM works, it requires several mouse clicks to merge two buildings.
To make this process faster, we built a plugin in JOSM that merges two buildings and automatically assign the correct tags.
Merge LA buildings in 3 clicks with auto-tools plugin in JOSM.
As of this writing, 105 usernames added ~580K buildings. This is more than half for LA City.
500K buildings added. Map by F4map.
As we go along, we continue to improve the workflow and tooling, let us know how we can make this import better!
Head over to labuildingsimport.com/ and grab any task available.
If you’re a local, join our mapathons happening through MaptimeLA.
MaptimeLA building import mapathon. Photo by MaptimeLA.
In the next post, we will talk about how we interacted with the mapping community and the response by the local LA mappers on this import.
If you’re in Seattle next month, catch the team at the SOTM-US where we talk more about this import.
Over the years, people ask me how I started and what have I done so far in OpenStreetMap.
I tried to capture this in an earlier post.
Wouldn’t it be nice to have a template for your
OpenStreetMap elevator pitch? Inspired by an old discussion and The Open Source Report Card, I looked at
Pascale’s HDYC numbers to tell a story about your OSM journey.
Stories unlike numbers, are kind of imprecise, fuzzy and sometimes exaggerated. ;-)
This is the challenge of this exercise. How can you tell your OpenStreetMap story this way?
So I made something out of OpenStreetMap data, and, it’s not a map.
Click the image to see video
Try it out! Use this as a boilerplate and fill in the details of your OpenStreetMap story.
The code is messy/buggy but somehow works. Don’t take the numbers seriously, this was just a fun hack. :)
Using OSMLint and vector tiles, I made this simple dashboard to display and fix common errors.
Dashboard is based on Fulcrum Map’s Geojson Dashboard. I hope the OSM-PH community can find it useful.
You too can build your own for your local mapping community.
Basic ingredients are:
Keep on fixing the map!
Our Mapbox-Bengaluru office celebrated International Open Access Week last month with a data gallery and a mapping workshop. I co-lead the mapping workshop as part of the Missing Maps project. I penned these notes to remind me in the future how to run our workshops even better. I figured, why not share to everyone?
But, we should map not only during/after a crisis but even before a crisis happens. Preparedness is key.
Here’s how OSM works (show basic tagging but do not go into complex technical details).