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.

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.

SFI Talks on YouTube

Via Twitter (@SFI_News), I have just come come across some excellent talks that took place at the Santa Fe Institute and thought they were worth sharing. At this time their YouTube channel has 95 videos ranging across complexity science such as the Emergence of Complex Societies and Cities, Scaling and Sustainability.

In another video which is relevant to some of the work we are doing at the Department of Computational Social Science,  Leysia Palen talks about "How Social Media Might Help You Survive the Next Big Disaster."

The SFI YouTube channel is really worth checking out.

Tweets from President Obama's inauguration 2013-01-21

Following on from a previous post on agent-based modeling and elections. Here we show geo-located tweets during the day of President Obama's inauguration 2013-01-21.

If you want to explore what people are currently saying about President Obama check out our Geosocial Gauge Website.

Screen shot of Geo social Gauge. Clockwise from top left: Location of tweets, basic sentiment of tweets (green positive, red: negative and gray: neutral), most active countries tweeting and a word cloud of the most popular words in the tweets.

Employment Growth through Labor Flow Network

Omar Guerrero and Robert Axtell from the Department of Computational Social Science at GMU have recently published a paper in PLoS ONE entitled "Employment Growth through Labor Flow Networks." The work uses "newly available micro-data and the ability to work with large-scale, complex networks computationally, to study labor dynamics." Below is the abstract from the paper:

It is conventional in labor economics to treat all workers who are seeking new jobs as belonging to a labor pool, and all firms that have job vacancies as an employer pool, and then match workers to jobs. Here we develop a new approach to study labor and firm dynamics. By combining the emerging science of networks with newly available employment micro-data, comprehensive at the level of whole countries, we are able to broadly characterize the process through which workers move between firms. Specifically, for each firm in an economy as a node in a graph, we draw edges between firms if a worker has migrated between them, possibly with a spell of unemployment in between. An economy's overall graph of firm-worker interactions is an object we call the labor flow network (LFN). This is the first study that characterizes a LFN for an entire economy. We explore the properties of this network, including its topology, its community structure, and its relationship to economic variables. It is shown that LFNs can be useful in identifying firms with high growth potential. We relate LFNs to other notions of high performance firms. Specifically, it is shown that fewer than 10% of firms account for nearly 90% of all employment growth. We conclude with a model in which empirically-salient LFNs emerge from the interaction of heterogeneous adaptive agents in a decentralized labor market.

Communities of firms.

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