Map Wikipedia is a web mapping app that allows you to map an entire Wikipedia category. It uses the ArcGIS JS API, Node.js, Mongodb, and the Wikipedia API. It is still a work in progress, but I hope you will find it useful or fun.
Please post your comments and ideas for future releases here! I’ll add GeoJSON exporting and a few other things in the coming weeks. I hope to release the source code on Github after I clean it up a bit.
I just released a tool that utilizes Terraformer and Restify to make it easy to convert (and map using GitHub’s GeoJSON maps) the JSON you get from ArcGIS Server Endpoints (like this) into GeoJSON. There’s even a bookmarklet that gives you a 1-click option.
Tip: Make sure you set the Out Spatial Reference when doing your query on the ArcGIS Server to 4326 to get the best chance of success.
Here are the pertinent links:
GitHub recently released a landing page to help governmental organizations get their data into the GitHub cloud. I am very excited to see more government organizations use GitHub to share, collaborate, and publish our data.
Here’s an example of the “open” data that is available in my neck of the woods. Having crime data published in a PDF, grouped by area of city (so you have to collate multiple PDFs to see the entire city) is simply pathetic. You cannot use this data other than seeing it with your eyes. So much room for improvement!
Posting public data in machine-readable formats has been proven beneficial. Brett Goldstein, former Chief Data and Information Officer for the City of Chicago, writes on his website about sweeparound.us, a web app born because Chicago street sweeping data is available:
The sweeparound.us story exemplifies a couple key lessons that continue to hold true. First, we, as a city, needed to learn to produce data in machine-readable formats as part of our standard business practices. Second, a variety of communities demonstrated an enormous appetite for government data, including civic developers, researchers, and journalists. We saw the emergence of the civic developer community both in the philanthropic and for-profit models. Places like Chapin Hall at the University of Chicago had been struggling for years to extract administrative data for the purpose of research. Open data programs make it substantially easier, removing the need to negotiate non-disclosure or other types of agreements. Open data also has also stimulated new research. A Ph.D. candidate tweeted her gratitude at finally being able to finish her dissertation, and more traditional organizations have now embarked in multi-year studies, based on what has been released on the City of Chicago’s data portal.
Chapter 2: Open Data in Chicago: Game On
University City, let’s start posting this data on GitHub! Post it in JSON, XML, or even CSV. It will allow us – the citizens that you represent – to actually use this data and maybe make our cities, states, and nations a better place for us all.
Did you know there are about fourteen thousand McDonald’s stores in the USA? 14,170 in fact, according to a script I cooked up this past weekend.
Click here to see the map!
I utilized MongoDB, Node.js, and a knowledge of REST APIs to get what I believe to be all the McDonald’s locations in the US, based on the public McDonald’s Restaurant Locator.
This gave me a CSV file with the McDonald’s locations and a lot of metadata, like this:
I then used my other project, CSV to GeoJSON, to create a GeoJSON file from the CSV. This GeoJSON file is available on my GitHub account, if you’d like to use or see a map the data.
I was excited last week when GitHub announced support for in-repository rendering of GeoJSON. I had a few CSV files with points in them, and I wanted to try out this new feature.
I couldn’t find an easy way to do it! There was this python script. There was this NPM Package. And more python.
So I wrote my own solution.
- I’m just using two JS libraries (see the Github Readme for links and licenses)
- Your CSV does not get uploaded to my server – it’s all in-browser.
- Only supports points for now.
It was a simple tool that I threw together. I hope you will find it useful.
A few random GeoJSON resources I found: