Wednesday, April 25, 2012

SimTable and fires

To quote from the site "The SimTable takes sandtable exercise to the next level by making sandtables real. The SimTable is a 3D interactive fire simulator, bringing sandtable exercises to life." Below is a Los Alamos National Lab video demonstrating their use of the SimTable in their Emergency Operations Center.

The movie below demonstrates more of the functionality of SimTable , specifically how one can simulate rainfall and how it flows over the terrain. Or how it can be used to simulate a wildfire spreading and how residents might evacuate from the area. 

Monday, April 16, 2012

Natural Disasters and Crowdsourcing: Haiti

Natural disasters such as earthquakes and tsunamis occur all over the world, altering the physical landscape and often severely disrupting people’s daily lives. Recently researchers’ attention has focused on using crowds of volunteers to help map the infrastructure and devastation caused by natural disasters, such as those in Haiti and Pakistan. For example, in the movie below shows the response to the earthquake by the OpenStreetMap community within 12 hours of the earthquake. The white flashes indicate edits to the map (often by tracing satellite/aerial photography).

While this data is extremely useful, as it is allows us to assess damage and thus aid the distribution of relief, but it tells us little about how the people in such areas will react to the devastation, the supply of food, or the reconstruction. To address this, we are exploring how agent-based modeling can be used to explore peoples reactions. To do this we have created a prototype spatially explicit agent-based model, created using crowdsourced geographic information and other sources of publicly available data, which can be used to study the aftermath of a catastrophic event. The specific case modeled here is the Haiti earthquake of January 2010. Crowdsourced data is used to build the initial populations of people affected by the event, to construct their environment, and to set their needs based on the damage to buildings. 

The idea behind the model is to explore how people react to the distribution of aid, as well as how rumors propagating through the population and crowding around aid distribution points might lead to food riots and similar social phenomena. Such a model could potentially provide a link between socio-cultural information of the people affected and relevant humanitarian relief organizations.

The animation above shows one simulation run where there is the spread of  information and agent movement (red dots) around one center (blue dot). While the chart below shows how over time the density of agents around the food station increases over time.

The idea behind such a model is one can take crowdsourced information and fuse it into an agent-based model and see how people will react to the distribution of food centers. For example, the movie below shows how agents find out about four (hypothetical) different food centers and decide whether or not to go to them in a 6 by 8km area of Port-au-Prince.

Spread of information and agent movement (red dots) in a 6 by 8km area of Port-au-Prince.

More details about this model to come......

Friday, April 13, 2012

#Earthquake: Twitter as a Distributed Sensor System

Our work on using social media continues to develop and we have recently had a paper accepted in Transactions in GIS, entitled "#Earthquake: Twitter as a Distributed Sensor System". Below we present our abstract and some of the results.
Social media feeds are rapidly emerging as a novel avenue for the contribution and dissemination of information that is often geographic. Their content often includes references to events occurring at, or affecting specific locations. Within this paper we analyze the spatial and temporal characteristics of the twitter feed activity responding to a 5.8 magnitude earthquake which occurred on the East Coast of the United States (US) on August 23, 2011. We argue that these feeds represent a hybrid form of a sensor system that allows for the identification and localization of the impact area of the event. By contrasting this to comparable content collected through the dedicated crowdsourcing ‘Did You Feel It?’ (DYFI) website of the US Geological Survey we assess the potential of the use of harvested social media content for event monitoring. The experiments support the notion that people act as sensors to give us comparable results in a timely manner, and can complement other sources of data to enhance our situational awareness and improve our understanding and response to such events.
The movie below show geolocated tweets with references to the earthquake through keyword (earthquake or earth and quake) and hashtag search (#earthquake or #quake) for the first hour after the earthquake.

The following images give a glimpse at some of our analysis.
Response pattern as function of distance from epicenter for the first 400 seconds after the earthquake. At the top we see a plot of (reaction time, distance) of all tweets during that period. At the bottom we show the histogram of the number of tweets as a function of distance.
Locations of the 40 tweets in the shaded area of the figure above overlaid over the USGS CDI scale map. Tweet locations are marked as green circles. Color-coding in the graph is ranging from red (high perceived intensity) to yellow (lower perceived intensity). The dashed line shows a distance of approximately 950 km (8.5 degrees of angular distance) from the epicenter.
The movie below gives you an idea of some of the tweet content:

Full reference to this paper is:
Crooks, A.T., Croitoru, A., Stefanidis, A. and Radzikowski, J. (2013), #Earthquake: Twitter as a Distributed Sensor System, Transactions in GIS, 17(1): 124-147. (pdf)