Blog: Mapping

Using Maps to Convey Information and Get People More Involved

Yesterday's announcement that the UNHCR and Google are mapping humanitarian work in refugee camps is a really encouraging sign of what's to come in online advocacy. Maps are incredible for the amount of digestible information they can quickly convey to a reader. 

Here's the part I loved most from what the UNHCR is saying about it:

Highlighted are not only the physical area of the camp and surrounding country, but key parts of daily life such as education and health in photo, text and video format. Within seconds, Google Earth brings the daily life of a refugee camp into your home thousands of kilometres away.

UNHCR is doing great work on the ground in places like Darfur, and they're generating content that really wouldn't have as strong an impact displayed in another way. In one glance, you can quickly get a sense of the water resource situation in this camp and the gaping lack of a health care facility.

Using fresh, quality ingredients to geocode, geotag, and geoviews your news

We are currently working on a project for a disaster relief organization. Our client was interested in mapping aggregated disaster relief news over a map. This is something of a mini-project in itself, but a combination of awesome modules and some custom pieces made this job a relatively straightforward process.

The goal:

fig.1: A map with new stories + prominent country tags

fig.2: Click a map point to see the headline

The recipe:

• 40 disaster relief organization news feeds: These will serve as the raw sources for our news page — they provide the titles, stories and urls

• Yahoo Pipes: Mixes up and mashes together these 40 feeds into a few feeds for us to aggregate. In addition, it will geocode stories when it can, adding geo:lat and geo:lon tags to rss items.

• FeedAPI: The same core feed aggregation engine we are using on Managing News and other projects.

• FeedAPI Geo: A simple FeedAPI plugin I hacked together to tag a geocoded item with its respective country (in development, to be released soon)

• Geonames: A great API module that lets you hook into the free geonames service — FeedAPI Geo is using this to reverse-lookup a country name from a lat/lon point.

• Graphite: A custom mapping stack that allows nodes with a CCK lat/lon field to be mapped onto a simple CSS/XHTML based map with JavaScript popups. It provides a Views plugin, so all you do is choose the nodes you want, add the appropriate CCK lat/lon field, choose the graphite view style, and voila your nodes are on the map. (in development, to be released soon, expect delays)

• Tagadelic + Tagadelic Views: Two bread + butter tag visualization modules that let us take our country tags and show story distribution.

Bake in the oven for 45 minutes.

Beautiful!

A Glimpse at how Geocoding Helps Identify Stories in Managing News

I first noticed the trend at 4:00 pm yesterday afternoon when I was testing Managing News - it looked like something was going on in the Caribbean. I jumped over to CNN to see what was happening and saw that a major earthquake hit the Caribbean yesterday two hours before.

This morning I went back to Managing News to check out the earthquake coverage and checked out the geocoded map that Jeff talked about yesterday. The Caribbean earthquake news really jumps out.

Putting News Stories on the Map

We’ve long wanted our team aggregator and media analyzer Managing News to automatically geotag the news that it tracks. But getting this to happen presented some interesting questions and challenges. What does it mean to put a news story on map? Should it show where the news is coming from or what part of the world is being talked about?

We decided that in the case of Managing News and the people using it to monitor the news, it’s more important to map what the news is about. We want to be able to show a map that people can look at and immediately know what is being talked about – like in this map, showing news tracked about several key financial institutions activities.

But this raised more questions. How can you geocode the content of a news item, and could we do this meaningfully? And most importantly, how do we identify locations in a stream of text?