How to extract highways and subway lines from OpenStreetMap as a shapefile

It’s possible to use Overpass Turbo to extract any object from the OpenStreetMap “planet” and convert it from a GeoJSON or KML file to a shapefile for manipulation and analysis in GIS.

Say you want the subway lines for Mexico City, and you can’t find a GTFS file that you could convert to shapefile, and you can’t find the right files on Sistema de Transporte Colectivo’s website (I didn’t look for it).

Here’s how to extract the subway lines that are shown in OpenStreetMap and save them as a GIS shapefile.

This is my second tutorial to describe using Overpass Turbo. The first extracted places of worship in Cook County. I’ve also used Overpass Turbo to extract a map of campgrounds

Extract free and open source data from OpenStreetMap

  1. Open the Overpass Turbo website and, on the map, search for the city from which you want to extract data. (The Overpass query will be generated in such a way that it’ll only search for data in the current map view.)
  2. Click the “Wizard” button in the top toolbar. (Alternatively you can copy the code below and paste it into the text area on the website and click the “Run” button.)
  3. In the Wizard dialog box, type in “railway=subway” in order to find metro, subway, or rapid transit lines. (If you want to download interstate highways, or what they call motorways in the UK, use “highway=motorway“.) Then click the “build and run query” button.
  4. In a few seconds you’ll see lines and dots (representing the metro or subway stations) on the map, and a new query in the text area. Notice that the query has looked for three kinds of objects: node (points/stations), way (the subway tracks), relation (the subway routes).
  5. If you don’t want a particular kind of object, then delete its line from the query and click the “Run” button. (You probably don’t want relation if you’re just needing GIS data for mapping purposes, and because routes are not always well-defined by OpenStreetMap contributors.)
  6. Download the data by clicking the “Export” button. Choose from one of the first three options (GeoJSON, GPX, KML). If you’re going to use a desktop GIS software, or place this data in a web map (like Leaflet), then choose GeoJSON. Now, depending on what browser you’re using, a couple things could happen after you click on GeoJSON. If you’re using Chrome then clicking it will download a file. If you’re using Safari then clicking it will open a new tab and put the GeoJSON text in there. Copy and paste this text into TextEdit and save the file as “mexico_city_subway.geojson”.
Overpass Turbo screenshot 1 of 2

Screenshot 1: After searching for the city for which you want to extract data (Mexico City in this case), click the “Wizard” button and type “railway=subway” and click run.

Overpass Turbo screenshot 2

Screenshot 2: After building and running the query from the Wizard you’ll see subway lines and stations.

Overpass Turbo screenshot 3

Screenshot 3: Click the Export button and click GeoJSON. In Chrome, a file will download. In Safari, a new tab with the GeoJSON text will open (copy and paste this into TextEdit and save it as “mexico_city_subway.geojson”).

Convert the free and open source data into a shapefile

  1. After you’ve downloaded (via Chrome) or re-saved (Safari) a GeoJSON file of subway data from OpenStreetMap, open QGIS, the free and open source GIS desktop application for Linux, Windows, and Mac.
  2. In QGIS, add the GeoJSON file to the table of contents by either dragging the file in from the Finder (Mac) or Explorer (Windows), or by clicking File>Open and browsing and selecting the file.
  3. Convert it to GeoJSON by right-clicking on the layer in the table of contents and clicking “Save As…”
  4. In the “Save As…” dialog box choose “ESRI Shapefile” from the dropdown menu. Then click “Browse” to find a place to save this file, check “Add saved file to map”, and click the “OK” button.
  5. A new layer will appear in your table of contents. In the map this new layer will be layered directly above your GeoJSON data.
Overpass Turbo screenshot 4

Screenshot 4: The GeoJSON file exported from Overpass Turbo has now been loaded into the QGIS table of contents.

Overpass Turbo screenshot 5

Screenshot 5: In QGIS, right-click the layer, select “Save As…” and set the dialog box to have these settings before clicking OK.

Query for finding subways in your current Overpass Turbo map view

This has been generated by the overpass-turbo wizard.
The original search was:
// gather results
// query part for: “railway=subway”
/*relation is for "routes", which are not always
well-defined, so I would ignore it*/
// print results
out body;
out skel qt;

How to make a map of places of worship in Cook County using OpenStreetMap data

The screenshot shows the configuration you need to find and download places of worship in Cook County, Illinois, using the Overpass Turbo website.

If you’re looking to make a map of churches, mosques, synagogues and other places of worship, you’ll need data. The Yellow Pages won’t help because you can’t download that. And Google Maps doesn’t let you have a slice of their database, either. That’s where OpenStreetMap comes in. It’s a virtual planet that anyone can edit and anyone can have for free.

First we need to figure out what tag people use to identify these places. Sometimes on OSM there are multiple tags that identify the same kind of place. You should prefer the one that’s either more accurate (and mentioned as such in the wiki) or widespread.

The OSM tag info website says that editors have added over 1.2 million places of worship to the planet using “amenity=place_of_worship”.

Now that we know which tag to look for, we need an app that will help us get those places, but only within our desired boundary. Open up Overpass Turbo, which is a website that helps construct calls to the Overpass API, which is one way to find and download data from OSM.

In the default Overpass Turbo query, there’s probably a tag in brackets that says “[amenity=drinking_fountain]”. Change that to say “[amenity=place_of_worship]” (without the quotes). Now change the viewport of the map to show only the area in which you want Overpass Turbo to look for these places of worship. In the query this argument is listed as “({{bbox}})”.

The map has a search bar to find boundaries (cities, counties, principalities, neighborhoods, etc.) so type in “Cook County” and press Enter. The Cook County in Illinois, United States of America, will probably appear first. Select that one and the map will zoom to show the whole county in the viewport.

Now that we’ve set the tag to [amenity=place_of_worship] and moved the map to show Cook County we can click “Run”. In a few seconds you’ll see a circle over each place of worship.

It’s now simple to download: Click on the “Export” button and click “KML” to be able to load the data into Google Earth, “GeoJSON” to load it into a GIS app like QGIS, or “save GeoJSON to gist” to create an instant map within GitHub.

The best way to store directions on your smartphone is low tech

map of southern illinois as seen in the OsmAnd app

OsmAnd has great offline mapping features but it was tedious to ensure I had all of the maps at the desired zoom levels for the three-city bike ride in southern Illinois (pictured).

My friend is going to pick up a unique bicycle in Ohio and ride it back to Chicago. He designed a good route on Google Maps but now he needs to save it to his smartphone so he doesn’t have to constantly load directions and use data and waste battery life.

I gave him these instructions:

The best way to get a mobile view of the route is to use the Google Maps print feature and save it as a PDF. Then transfer that PDF to your phone through the Dropbox app. Then, in the Dropbox app, mark the PDF file as a favorite so that it’s stored offline, onto the phone.

There’s probably an app that can do what he wants, but I don’t know about it. There are hundreds of “maps” apps to sort through in each the App Store for iOS and the Play Store for Android.

In fact, I’ve downloaded OsmAnd, an offline maps app, for my Android tablet. I installed it and tried to learn how to use it in order to follow a downstate, intercity bike camping route. The app, though, required that you zoom in to each part of the map you wanted to store and then press “download”.


I spent 30 minutes downloading parts of the map, manually panning to the next section, before I decided to instead obtain one of the Illinois Department of Transportation’s regional bike maps and just draw it on there and write out a “cue sheet” (turn by turn directions).

Meet Chicago’s newest street view fleet: bikes

Bicycle holds an iPhone to take street view-style images

This position gives the smartphone an unimpeded view of the street but prevents the user from manipulating it.

I first used the Mapillary app on iPhone last fall, in August, and I uploaded one photo, of my arm, which I can’t delete from the website. I uploaded a couple more photos from a street in Roscoe Village in November.

This week, though, I uploaded 500 photos from a three mile journey on California and Milwaukee Avenues in Chicago – streets that no one else had photographed for the Mapillary street view service.

Mapillary is an open source (sort of) street view service, originally developed in Sweden, which allows the public to contribute photos taken with their smartphone app.

What’s “sort of” about Mapillary being open source is that it appears that the company owns the photos once you upload them. People are free to use the photos to edit OpenStreetMap, or publish elsewhere – for personal use only – with attribution that adheres to Creative Commons 4.0. People who want to use the photos in a commercial application must subscribe to a pay service.

Mounting an iPhone to a bicycle

I took the jump from contributing nothing to uploading a whole lot because I bought an iPhone mount for my bicycle. After months of research – okay, chalk it up to my being lazy and it being really cold outside – I settled on the DgRock Universal Bicycle Mount from Amazon for $9. I was perplexed that there was a gap in choices between this decent $9 product and the next group, hovering around $30-40.

After three days of use, I’m satisfied, despite limitations that are present in all mounts I surveyed. The DgRock mount is solid, barely moves even as the bike bounces along Chicago’s pothole-ridden streets, and securely holds the iPhone with a strong, spring-loaded grip. It’s universal in two ways: it holds nearly any smartphone (it probably can’t hold one with a screen 5″ or larger) and it attaches to most bicycle handlebars.

The first day I used the DgRock mount Mapillary complained with a red icon that it couldn’t get a proper fix on its location and therefore it wouldn’t start photographing. Fine, I was in downtown Chicago where connecting to GPS satellites can be hard. I figured the wifi positioning system that all smartphones and tablets use would suffice.

There are problems with the mount, but I think these apply to all bicycle smartphone mounts: When the phone is in position to take photos, meaning its horizontal and level to the ground, you can’t see the screen. That’s because the screen, mounted on the handlebars, is much lower than your eyes and faces vertically, instead of angled towards your face. The only way around this, I believe, is to either get an upright bicycle (like my WorkCycles Fr8) or an adjustable lens (I can’t find any).

Smartphone mount holding an iPhone on a bicycle

This position allows the user to manipulate the smartphone but you cannot take street view-style images.

The possible position angles of the smartphone when held by the mount was my main concern as I was shopping on Amazon: The mount need to have the flexibility to position the smartphone so its rear camera could be level with the ground. Smartphone mounts, though, are made to put the device in a position to be used and viewed frequently by the bicycle rider – it was unclear if many of the other smartphone mounts could accommodate the street view angles requirement.

The DgRock has no issue moving the iPhone into the right position, as you can see in the photos from my journey (or scroll to the end). Its issue, though, is that you have to put the smartphone in “backwards” so that the claw covers up part of the screen. I call it an issue but it doesn’t disturb the mount’s primary purpose when using Mapillary – the phone still has a clear view of the street.

Even with an upright bike like mine, though, it’s difficult to see the screen. I believe that Mapillary can actually design its app to help overcome this physical limitation.

Using Mapillary

The Mapillary app has improved greatly since the first version. It allows you to delete bad or undesired photos before uploading, and it has a simpler interface to go from opening the app to making your own street view. There are a couple changes I think would improve the user experience and lead to more contributions.

I would like to be able to turn off the screen while using Mapillary to save battery life. I think that the screen could fade to black and a small white dot or halo appears frequently to remind you that it’s working. I’d also like it to chime when iOS throws the “low storage” warning. Otherwise I may be riding along, thinking Mapillary is capturing the street, when iOS had actually run out of storage 10 minutes ago.

This is why we need more people editing OpenStreetMap

Unmapped homes in the Irving Park community area

These homes were built after the City of Chicago’s building footprints dataset was created (2010?). Ian Dees imported the dataset in 2012. Many of the buildings that you can now see on Bing Maps have not been present on Bing’s satellite imagery since at least 2012.

1. OpenStreetMap is the world’s most complete free map, to which anyone can contribute their “ground truth” data (the location of wells and convenience stores, road names, and whether Lula Café at 2537 N Kedzie Boulevard in Logan Square has outdoor seating).

2. OpenStreetMap is used by thousands of non-profit and non-governmental organizations, corporations, apps, and people daily to locate themselves, locate others, get directions, and find places.

3. Nearly every map is out of date the moment it is published, including online, “current” maps like Google Maps, Bing Maps, their competitors, and OpenStreetMap.

4. Bing Maps provides its satellite imagery to OpenStreetMap editors – you and me – so that we can trace (copy) things on the planet to be things on the map. Google Maps doesn’t allow tracing (copying).

5. Bing updated its satellite imagery for Chicago (and probably a lot of other places) within the last six weeks…and there are hundreds of objects that aren’t yet mapped in OpenStreetMap. In Chicago most of these buildings are newly constructed houses.

Those hundreds of houses now need to be added to OpenStreetMap, with addresses, to complete the buildings collection in Chicago, and to expand the gazetteer (an address book) of places in Chicago.

I’m glad you want to help me do it! Here are two helpful things you can do:

  1. Start tracing the buildings yourself (here’s how new mappers can get started), or
  2. Leave notes at buildings which aren’t yet mapped so that map editors like myself know where to look to trace buildings.

Update: There’s a bonus third thing you can do, and that’s come to the next MaptimeCHI event on Thursday, February 26th, at the Chicago Community Trust (225 N Michigan, 22nd floor). RSVP for Anatomy of a Web Map. The Trust will also provide food and beverages. I’ll be there to teach new mappers and assist generally.

Adding notes is extremely helpful

You can contribute without editing by adding notes describing new things, or identifying problems with existing things. Click the “Add a note” button on OpenStreetMap.org.

Why architects should learn OpenStreetMap

I’m teaching OpenStreetMap 101 at the first MaptimeCHI.

Architects will learn that OpenStreetMap can be used as a data source when developing projects and as a basis for designing custom maps in project publications (website, anthology, monograph, client presentations).

This meeting is about getting an introduction to OpenStreetMap and learning to make your first edit in the “Wikipedia of maps”.

Thursday, July 17th, from 6-8 PM
Thoughtworks office
200 E Randolph St

RSVP on EventBrite.

Here are two examples of how architects could use OpenStreetMap data.

Example 1 of how to use OpenStreetMap. Instead of publishing a screenshot of Google Maps in your documents or website, create a custom design map like this without having to spend so much time tweaking it in Illustrator. This map was created by Stamen Design using TileMill.

Example 1 of how to use OpenStreetMap. Instead of publishing a screenshot of Google Maps in your documents or website, create a custom design map like this without having to spend so much time tweaking it in Illustrator. This map was created by Stamen Design using TileMill.

And the second.

Willow Creek Church on OpenStreetMap: After

Here’s one example where OpenStreetMap could be useful. Let’s say you’re working on a site plan for Willow Creek Church in South Barrington and you need a general layout of the parking lot. 1. You can get it from OpenStreetMap because it’s already there. 2. You can draw it in OpenStreetMap yourself (to benefit all other OSM users) and then extract it as a shapefile.

Maptime is time for mapmaking and it’s taking the country by storm.

How to ascertain the area of Chicago beach parking lots to find the largest one

This tutorial is a direct response to a question about which Chicago beach has the largest parking lot. Matt Nardella of Moss Design, in a response to a Twitter-based conversation about Alderman Cappleman’s suggestion that perhaps Montrose beach has too much parking, researched on Wikipedia to find the answer. This is where it said that Montrose beach has the largest parking lot of any of Chicago’s 27 beaches.

Now we’re going to try and prove which beach has the largest associated parking lot.

This tutorial will teach how you to (1) display Chicago beaches, (2) download data held in OpenStreetMap, (3) find the parking lots within the OpenStreetMap data, (4) find the parking lots near the beaches, and (5) calculate each parking lot’s area (in square feet). You can use this tutorial to accomplish any one of these three tasks, or the same tasks but on a different part of OpenStreetMap data (like the area of indoor shopping malls).

You’ll need the QGIS software before starting. You’ll also need at least 500 MB of free space. Start a project folder called “Biggest Parking Lots in Chicago” and make two more folders, within this folder, called “origdata” and “data”.

First, let’s get some data about beaches

Since we only want to know about the parking lots near Chicago beaches we need to get a dataset that locates them. This data is presumably within the same OpenStreetMap extract we’re waiting for, but it’s best to go to the most reliable source.

  1. Download the Parks – Facilities & Features shapefile from the City of Chicago open data portal. I’ve already verified that it has all the beaches (as points).
  2. Open the parks shapefile in a new document in QGIS (call it “map01a.qgs”). You might not see the data so right-click the parks layer and select “Zoom to layer extent”.
  3. Filter out all the points that aren’t beaches by using the query builder. Right-click the layer and select “Filter…” and input this filter expression: “FACILITY_N” = ‘BEACH’
  4. Your map will now show 26 points along an invisible lakefront and then the beach at Humboldt Park.
  5. For the rest of this tutorial we’ll reference the beaches layer as ParkFacilities.

Second, let’s get some data from OpenStreetMap

The easiest way to grab data from OpenStreetMap is by using QGIS, a free, open source desktop GIS application that has myriad plugins that match the capabilities of the heavyweight ESRI ArcGIS line of software. We can download OpenStreetMap data straight into QGIS.

  1. Click on the Vector menu and select OpenStreetMap>Download data.
  2. We want as much data as will cover the beaches information so in the Extent section of the dialog box choose “From layer” and select the beaches layer (called ParkFacilities).
  3. Browse to the “origdata” folder you created in the first task and choose the filename “chicago.osm”.
  4. Click OK and watch the progress meter tell you how much data you’ve downloaded from OpenStreetMap.
  5. Once it’s completed downloading, click “Close”. Now we want to add this data to our map.
  6. Drag the chicago.osm file from your file system into the QGIS Layers list. A dialog box will appear asking which layers you want to add.
  7. Select the layer that has the type “MultiPolygon”. This represents areas like buildings and parking lots.

Third, display the OpenStreetMap data and eliminate everything but the parking lots

We only want to compare parking lots in this dataset with beaches in the previous dataset so we need to eliminate everything from the OpenStreetMap data that’s not a parking lot. Since OSM data depends on tags we can easily select and show all the objects where “amenity” = “parking”.

  1. Filter out all the polygons that aren’t parking lots by using the query builder. Right-click the layer and select “Filter…” and input this filter expression: “amenity” = ‘parking’. Hopefully all the parking lots have been drawn so we can analyze a complete dataset!
  2. Your map will now show little squares, rectangles, and myriad odd shapes that represent parking lots around Chicagoland. (Most of these have been drawn by hand.) It should look like Image XXX.
  3. Since this data is stored in a projection with the codename of EPSG:3435 and the OpenStreetMap data is stored with codename of EPSG:4326 we need to convert the beaches to match the beaches (because we’re going to be using feet as a  measuring distance instead of degrees).
  4. Right-click the layer and select “Save As…” and choose the format “ESRI Shapefile”. Then click the top Browse button and select a location on your hard drive for the converted file.
  5. For “CRS” choose “Selected CRS”. Then click the bottom Browse button and search for the EPSG with the codename 3435. Select the checkbox named “Add saved file to map” so the new layer will be immediately added to our map.

Fourth, select all the parking lots near a beach

This task will select all the parking lots near the beaches. I chose 2,000 feet but you could easily choose a different distance. You might want to measure on Google Earth some minimum and maximum distances between beaches and their respective, associated parking lots.

(This task is easier using PostGIS which has a ST_DWithin function to find objects within a certain distance because we can avoid having to create the buffer in QGIS.)

  1. Create a 2,000 feet buffer. Select Vector>Geoprocessing tools>Buffer.
  2. In the Buffer(s) dialog box, select ParkFacilities (which has your beaches) as the “Input vector layer”. Choose a distance of 2000 (the units are pre-chosen by the projection and since we’re using a projection that’s in feet, the distance unit will be feet).
  3. Browse to your project folder’s “data” folder and save the “Output shapefile” as “beaches buffer 2000ft.shp”.
  4. Click “Add results to canvas” and then click OK.
  5. Double check that 2,000 feet was enough to select the parking lots. In my case, I see that the point representing Montrose beach was further than 2,000 feet away from a parking lot.
  6. Let’s do it again but with 3,000 feet this time, and saving the “Output shapefile” as “beaches buffer 3000ft.shp”.
  7. This time it worked and the nearest parking lots are now in the 3,000 feet radius buffer. You can see in Image XXX how the two concentric circles stretch out from the beach point towards the parking lots.

We’re not done. We’re next going to use our newly created 3,000 feet buffers to tell us which parking lots are in them. These will be presumed to be our beach parking lots.

  1. Use the “Select by location” tool to find the beaches that intersect our 3,000 feet buffers. Select Vector>Research Tools>Select by location.
  2. Follow me: we want to select features in parking 3435 [our parking lots] that intersect features in beaches buffer 3000ft [our beaches]. We’ll modify the current selection by creating a new selection so that we don’t accidentally include any features previously selected.
  3. You’ll now see a bunch of parking lots turn yellow meaning they are actively selected.
  4. Let’s save our selected parking lots as a new file so it will be easier to analyze just them. Right-click “parking 3435” and select “Save Selection As…” (it’s important to choose “Save Selection As” instead of “Save As” because the former will save just the parking lots we’ve selected).
  5. Save it as “selected parking 3435.shp” in your “data” folder. The CRS should be EPSG:3435 (NAD83 Illinois StatePlane East Feet). Check off “Add saved file to map” and click OK.
  6. Turn off all other layers except ParkFacilities to see what we’re left with and you’ll see what I show in Image XXX.

Fifth, let’s calculate

Calculating the area is probably the easiest part of this tutorial.

  1. Close all attribute tables you may have opened.
  2. Select Vector>Geometry Tools>Export/Add geometry columns and choose “selected parking 3435” as your input vector layer.
  3. Leave all other options as-is and press OK. When told about how QGIS can’t access something simultaneously, choose “Yes”.
  4. QGIS should have told you that “selected parking 3435” has been updated. Right-click the layer and choose “Open Attribute Table”.
  5. Scroll to the far right and you’ll see a new column called AREA. This represents the parking lot’s area in square feet.
  6. Click on the AREA column heading to sort it from smallest to largest. Scroll to the bottom of the list and you’ll find the parking lot with the largest area. Double check – is it near a beach?


With my analysis, and with the data available from OpenStreetMap when I created this tutorial, there are three abnormally large parking lots:

  1. A linear lot near the Lincoln Park Zoo and North Avenue beach (6.8 acres)
  2. A curving lot near Montrose Beach (4.75 acres)
  3. An irregularly shaped lot near Montrose Beach (4.5 acres)

There’s one major caveat in this analysis and that’s the missing parking lots on beaches south of Navy Pier. This means that no one has drawn them into OpenStreetMap so it’s time to start editing!

Mapping a campground that doesn’t exist: a before and after view of OpenStreetMap

Pretty soon there will be a campground shown in OpenStreetMap, and added to its geocoding database, when I’m done adding it.

I temporarily become addicted to mapping places on OpenStreetMap. In my quest to find and map all campgrounds in Chicagoland – in order to publish them in the Chicago Bike Guide – I came across a campground that was constructed this year and opened in August 2013. This is the story of figuring out how to map the Big Rock Forest Preserve campground in Big Rock, Illinois.

I found on the Kane County Forest Preserve District website that the organization operated a campground at Big Rock Forest Preserve. I couldn’t locate the campground in Google Maps by the address the website gave. I couldn’t find it in OpenStreetMap, either, because no one had mapped it, but it’s there now.

When I searched for the park by name, Google Maps zoomed me to the main entrance of the park, but I still couldn’t see a campground. I downloaded the forest preserve district’s park map (always as a PDF) and followed the roads in Google Maps until I came across the campgrounds approximate position. There was a new road here so I followed that to find a campground under construction.

Google Maps shows the campground and artificial lake under construction.

Google’s imagery of the under-construction campground was taken on May 23, 2013 (get the date from Google Earth). This was great because now I could open JOSM, a powerful desktop OpenStreetMap editor, and locate the site, load Bing’s imagery and start tracing the campground to upload to OSM.

Bing’s imagery in JOSM, the OpenStreetMap-editing app, doesn’t show the campground.

The problem was that Bing’s imagery – and this is typical – was outdated. I could easily compare the imagery side-by-side and based on other landscape features (like the forest edge) guess where to trace the campground, but OSM needs better quality data. Enter MapWarper.

Read the rest of this post on Web Map Academy.

Compiling and mapping Chicago-area campgrounds

I’m adding Chicago-area campgrounds to the Chicago Bike Guide to entice new users and to espouse the enjoyment of medium-distance bike camping (which I’ve now done officially once, earlier this year).

<The Chicago Bike Guide is available for Android and iOS.>

I’m taking a systematic approach to finding all the publicly-owned campgrounds in the area by looking at primary sources.

First, though, I’ve used Overpass Turbo to create a list of all existing campgrounds in OpenStreetMap. You can see a gist of these places.

Camp sites at Greene Valley forest preserve I mapped.

Camp sites at Greene Valley forest preserve I mapped.

The next method is to find out which campgrounds are operated by the county forest preserves, which are usually well-documented on their respective websites. Then I will look at state parks in Illinois, Indiana, and Wisconsin, operated by states’ respective Departments of Natural Resources (DNR). Next I will look at national parks and finally commercial campgrounds.

The app will display campground information such as alcohol rules, if cabins or lodging is available, and how you can get there (which trails or train lines).

I’ve so far mapped the campgrounds in two ways, as nodes and as areas. At the Greene Valley forest preserve in DuPage County, for example, I’ve mapped the 11 individual camp sites (see map), but at Blackwell forest preserve in the same county, I’ve mapped the area as the camp site (see map).

Blackwell has over 50 sites in a discrete area and it’s more efficient to map them as a single node, while Greene Valley had far fewer sites but scattered over a couple areas.

Cross-posted to Web Map Academy.

Google Maps’s lies are why I made my own bike map


Bright green lines indicate trails while darker green lines indicate bike lanes.

Because Google Maps lies about where bike facilities are. Check out the bicycling layer at 16th Street and Wood Street in Pilsen, Chicago, Illinois.

Google Maps bicycling layer legend

Google Maps bicycling layer legend

The bright green lines represent trails, but the ones in this part of Pilsen are not trails. All of the north-south “trails” you see are actually sidewalks underneath railroad viaducts. The east-west trails…well, I’m not sure what they are but they are on elevated railroad property. Don’t go up there.

I created my own bike map to deal with the inaccuracies across Google Maps. I used my local knowledge (“ground truth”) and high-quality bike facility data from the Chicago Department of Transportation.

I also used contributions to OpenStreetMap, mainly for trails. I’ve been correcting and adding new bike lane data to OSM as CDOT installs them. View this area with OpenCycleMap’s tiles, which shows only bike lanes on 18th Street.

If you’re an editor on Google’s Map Maker data editing platform, please correct these errors. I would do it except that Google doesn’t allow others to benefit from my data contributions like OpenStreetMap does, where anyone can give and take. (I also had a negative experience on Map Maker, getting myself into a tiff with an unnamed user who disagreed with my changes, based on a personal visit to the location, that were not visible on the satellite imagery because that location had outdated imagery.)


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