Thursday, June 20, 2013

Forest Fires

Recently I had a opportunity to catch up with Stephen Guerin who  was showing me their recent work with simtable.

With the recent spate of forest fires in the US,  Stephen and his group have also been capturing forest fire progressions such as the Thompson Ridge fire in NM, and have released a tool to embed the fires spread in your own browser as shown below.

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MAIA Framework for ABM

Looking at frameworks and toolkits to easily create agent-based models, I recntly came across Modelling Agent systems based on Institutional Analysis (MAIA) framework via JASSS. The idea for MAIA is to lower the cost of implementing agent-based models. In the sense, you don't need to code the model from scratch but use a simple web interface. From first glance it appears to have the same ease of use as the Modelling4All project.

More information about MAIA can be found here.

Sunday, June 16, 2013

New Publication: GIS and Agent-Based models for Humanitarian Assistance

Inputs to the model
As the readers of the blog know, we have an interest in GIS, agent-based modeling and crowdsourcing. Now we have a paper that combines all these three elements. Its entitled "GIS and Agent-Based models for Humanitarian Assistance" and is published in Computers, Environment and Urban Systems. 

The model itself was written in MASON and uses extensively GeoMASON. Data comes from several different sources (both raster and vector) including OpenStreetMap and LandScan. Below you can read an abstract of the paper and see a movie of one of the scenarios.

"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 damaged infrastructure and devastation caused by natural disasters, such as those in Haiti and Pakistan. 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. This paper demonstrates 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 modelled 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. We explore how people react to the distribution of aid, as well as how rumours relating to aid availability propagate through the population. Such a model could potentially provide a link between socio-cultural information about the people affected and the relevant humanitarian relief organizations."

Full Reference: 
Crooks, A.T. and Wise, S. (2013), GIS and Agent-Based models for Humanitarian Assistance, Computers, Environment and Urban Systems, 41: 100-111. (pdf)

Tuesday, June 04, 2013

Completeness and Spatial Error of Features in VGI

I have had an interest in volunteered geographic information (VGI) for quite some time (see my publications or blog posts) but only recently have I had an opportunity to look at the spatial error of features within VGI. To this end, our paper entitled "Assessing Completeness and Spatial Error of Features in Volunteered Geographic Information" has just been published in ISPRS International Journal of Geo-Information. Below is the abstract of the paper along with some figures. Further details about the paper can be seen at the bottom of the page.
The assessment of the quality and accuracy of Volunteered Geographic Information (VGI) contributions, and by extension the ultimate utility of VGI data has fostered much debate within the geographic community. The limited research to date has been focused on VGI data of linear features and has shown that the error in the data is heterogeneously distributed. Some have argued that data produced by numerous contributors will produce a more accurate product than an individual and some research on crowd-sourced initiatives has shown that to be true, although research on VGI is more infrequent. This paper proposes a method for quantifying the completeness and accuracy of a select subset of infrastructure-associated point datasets of volunteered geographic data within a major metropolitan area using a national geospatial dataset as the reference benchmark with two datasets from volunteers used as test datasets. The results of this study illustrate the benefits of including quality control in the collection process for volunteered data. 

Keywords: volunteered geographic information (VGI); OpenStreetMap; quality; error; point.
Comparison of OSM, OSMCP, and ORNL data.
Various identified locations of Southwest Early College
Full reference:
Jackson, S. P., Mullen W., Agouris, P., Crooks, A., Croitoru, A. and Stefanidis, A. (2013), Assessing Completeness and Spatial Error of Features in Volunteered Geographic Information, ISPRS International Journal of Geo-Information, 2 (2): 507-530. Download from here.