Category: Web Applications

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

August 7, 2015 / / 1 Comment

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.
  • There’s no open data* for this because churches don’t pay taxes, don’t have business licenses, and aren’t required to register in any way

This where OpenStreetMap (OSM) 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 taginfo 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.

Tutorial

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.

*You could probably get creative and ask a municipality for a list of certificates of occupancy or building permits that had marked “religious assembly” as the zoning use for the property.

Use Turf to perform GIS functions in a web browser

March 11, 2015 / / 0 Comments
Turf's merge function joins invisible buffers around each Divvy station into a single, super buffer.

Turf’s merge function joins invisible buffers around each Divvy station into a single, super buffer –all client-side, in your web browser.

I’m leading the development of a website for Slow Roll Chicago that shows the distribution of bike lane infrastructure in Chicago relative to key and specific demographics to demonstrate if the investment has been equitable.

We’re using GitHub to store code, publish meeting notes, and host discussions with the issues tracker. Communication is done almost entirely in GitHub issues. I chose GitHub over Slack and Google Groups because:

  1. All of our research and code should be public and open source so it’s clear how we made our assumptions and came to our conclusions (“show your work”).
  2. Using git, GitHub, and version control is a desirable skill and more people should learn it; this project will help people apply that skill.
  3. There are no emails involved. I deplore using email for group communication.*

The website focuses on using empirical research, maps, geographic analysis to tell the story of bike lane distribution and requires processing this data using GIS functions. Normally the data would be transformed in a desktop GIS software like QGIS and then converted to a format that can be used in Leaflet, an open source web mapping library.

Relying on desktop software, though, slows down development of new ways to slice and dice geographic data, which, in our map, includes bike lanes, wards, Census tracts, Divvy stations, and grocery stores (so far). One would have to generate a new dataset if our goals or needs changed .

I’ve built maps for images and the web that way enough in the past and I wanted to move away from that method for this project and we’re using Turf.js to replicate many GIS functions – but in the browser.

Yep, Turf makes it possible to merge, buffer, contain, calculate distance, transform, dissolve, and perform dozens of other functions all within the browser, “on the fly”, without any software.

After dilly-dallying in Turf for several weeks, our group started making progress this month. We have now pushed to our in-progress website a map with three features made possible by Turf:

  1. Buffer and dissolving buffers to show the Divvy station walk shed, the distance a reasonable person would walk from their home or office to check out a Divvy station. A buffer of 0.25 miles (two Chicago blocks) is created around each of the 300 Divvy stations, hidden from display, and then merged (dissolved in traditional GIS parlance) into a single buffer. The single buffer –called a “super buffer” in our source code – is used for another feature. Currently the projection is messed up and you see ellipsoid shapes instead of circles.
  2. Counting grocery stores in the Divvy station walk shed. We use the “feature collection” function to convert the super buffer into an object that the “within” function can use to compare to a GeoJSON object of grocery stores. This process is similar to the “select by location” function in GIS software. Right now this number is printed only to the console as we look for the best way to display stats like this to the user. A future version of the map could allow the user to change the 0.25 miles distance to an arbitrary distance they prefer.
  3. Find the nearest Divvy station from any place on the map. Using Turf’s “nearest” function and the Context Menu plugin for Leaflet, the user can right-click anywhere on the map and choose “Find nearby Divvy stations”. The “nearest” function compares the place where the user clicked against the GeoJSON object of Divvy stations to select the nearest one. The problem of locating 2+ nearby Divvy stations remains. The original issue asked to find the number of Divvy stations near the point; we’ll likely accomplish this by drawing an invisible, temporary buffer around the point and then using “within” to count the number of stations inside that buffer and then destroy the buffer.
Right-click the map and select "Find nearby Divvy stations" and Turf will locate the nearest Divvy station.

Right-click the map and select “Find nearby Divvy stations” and Turf will locate the nearest Divvy station.

* I send one email to new people who join us at Open Gov Hack Night on Tuesdays at the Mart to send them a link to our GitHub repository, and to invite them to a Dropbox folder to share large files for those who don’t learn to use git for file management.

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.

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

December 27, 2013 / / 8 Comments

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.