Tag: OpenStreetMap

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

May 18, 2015 / / 3 Comments
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

March 2, 2015 / / 0 Comments
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

February 16, 2015 / / 1 Comment
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

July 15, 2014 / / 0 Comments

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

July 15, 2014 / / 8 Comments

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?

Conclusion

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!