GIS, Twitter Trackers and Mash-ups

Crisis Mapping by ESRI

My initial interest in this subject stems from my Masters Project and a map I found of a social media tracker of Tahrir Square during the uprisings beginning January 25th, 2011. The map is no longer accessible. Official word from ESRI is that it was taken down because the protests are over. I did, however, find these active maps of Libya and the Occupy Movement (look for the OM to pick back up when the weather improves).

Libya Social Media Tracker

Occupy Movement Social Media Tracker

The possibilities of this technology is provocative. Imagine how these maps could be leveraged in the event of a natural disaster when information on the ground is limited and highly disorganized. It relies heavily on Volunteer Geographic Information. I’m not sure how useful this is if communications go down, anyone remember the Detroit/Toronto/New York blackout? This technology can only work if the information being tracked is geo-tagged. Tweets are and you can geo-tag photos and video. Some cameras automatically geo-tag photos.

Esri sent me this link which very roughly lays out how to recreate these maps.

Alternative Mash-ups

While the above information could be considered a mash-up, I was interested if any other similar mash-ups existed and especially if they were on platforms or used technology that was easier to work with or which may provide for a more forward starting point.

Twitter Mash-up

This application allows you to select which map supplier you want to use for the mash-up

Geo Chirp Google Maps Mash-up

This Mash-up is probably my favorite and actually does everything I wanted to do for the class so I’m not sure why I would recreate it. I am looking for alternatives. You can serch by tweet content, and distance from a location. The only change I would make is to move the tweet list to the sidebar.


This application is still in beta but I thought the flood of tweets onto the screen was awesome. Here is the Google apps page where you can find different api’s to mash-up with Google’s products.


Case study: Events, Social Media, and urban information modeling

My case study  mainly focused on three main events that happened during 2011, which were the Egyptian revolution, Japan’s Tsunami, and Occupy movement in New York. Those events could be like any events happened in the world, except that regular people started to publish and make news within the big headlines.Users of social media websites, mainly twitter, where the news publishers,reading the whole picture of the event and through them,where able to understand the social context of the events.

Besides, I was focusing on the news feeds, which are instant tweets coming from the users at that certain place of event. The collection of tweeps (people who tweet) in the certain geographical location starts a hotspot or a hub of information production. Without understanding where this information is coming from ,geographically, the information will not be complete and could be misleading.

This fluid of information coming from people through social media could be a valuable source in understanding human landscape, the social complex structures and relationships between people and how they interact with each other. It is far more vibrant and interactive than other information sources such as the census since it shows how people with similar opinions interact together and “geographically” presents the human terrain of ideas. Specialists in campaigns, marketing, advertising, and socio cultural analysts could benefit from such information.

Harvesting social media feeds is through three operations:

-Extracting Data from social media servers using API (Parsing, integrating) and storing this data in a resident data base.

-Analyze the data

Twitter content revealed emergence of socio-cultural hot sports ,and provides advanced warning of forth coming events, as was the case with Tahrir square reference in Arab spring events of spring 2011.It also offers a mechanism to obtain a rapid assessment of the impact area of natural disasters as demonstrates by data collected during Japan’s Tsunami .It provides unparalled situational awareness by supporting the monitoring of evolving events, as was the case with the Occupy Brooklyn bridge experiment.