Sunday, April 24, 2016

NetLogo Reproduction of Walk This Way

Several months ago, we posted that we had just had a paper accepted entitled "Walk this Way: Improving Pedestrian Agent-Based Models through Scene Activity Analysis. The original model was created in MASON but know it has been reproduced in NetLogo. The purpose of this exercise was to see if the model could be reproduced from the description in said paper along with the availability of the source code and data. Specifically whether there is sufficient information in the paper to reproduce the model and the results. It was an interesting exercise translating methods from MASON into NetLogo procedures (also a lot less lines of code). 

A: NetLogo Graphical User Interface (GUI), B: Original MASON GUI.

In order to really ensure this was a good reproduction it was also necessary to provide the data we compared the results to from the original model (something which is not very common in ABM publications). This way we could could see if the re-implemented model really did match the results of the original model. 

To the left, you can see the graphical user interfaces for the NetLogo model and the original model implemented in MASON.

More information about the re-implementation and the code can be found over at Yang's Blog:

Full Reference to the Original Paper:
Crooks, A.T., Croitoru, A., Lu, X., Wise, S., Irvine, J. and Stefanidis, A. (2015),  Walk this Way: Improving Pedestrian Agent-Based Models through Scene Activity AnalysisISPRS International Journal of Geo-Information, 4(3): 1627-1656. (pdf

Wednesday, April 06, 2016

Crowdsourcing A Collective Sense of Place

Following on with our GeoSocial Analysis work, we recently had a paper published in  PLOS ONE entitled "Crowdsourcing A Collective Sense of Place." In the paper we discuss and showcase how one can take a quantitative approach to derive a collective sense of place from Twitter contributions and also from corresponding Wikipedia entries.

To illustrate this we present a brief study three cities, that of New York, Los Angeles and Singapore. Below you read the abstract of the paper, see some images from the paper especially the flowchart describing overall process used to discover platial alignment, along with the full reference to the paper.
Place can be generally defined as a location that has been assigned meaning through human experience, and as such it is of multidisciplinary scientific interest. Up to this point place has been studied primarily within the context of social sciences as a theoretical construct. The availability of large amounts of user-generated content, e.g. in the form of social media feeds or Wikipedia contributions, allows us for the first time to computationally analyze and quantify the shared meaning of place. By aggregating references to human activities within urban spaces we can observe the emergence of unique themes that characterize different locations, thus identifying places through their discernible sociocultural signatures. In this paper we present results from a novel quantitative approach to derive such sociocultural signatures from Twitter contributions and also from corresponding Wikipedia entries. By contrasting the two we show how particular thematic characteristics of places (referred to herein as platial themes) are emerging from such crowd-contributed content, allowing us to observe the meaning that the general public, either individually or collectively, is assigning to specific locations. Our approach leverages probabilistic topic modelling, semantic association, and spatial clustering to find locations are conveying a collective sense of place. Deriving and quantifying such meaning allows us to observe how people transform a location to a place and shape its characteristics.  

Keywords: Online Encyclopedias, Twitter, Data Visualization, Social Media, Semantics.

Flowchart describing overall process used to discover platial alignment.

Statistically significant clusters of recreation and entertainment categories concentrated over Manhattan, NYC

Maps depict significant hotspots for each of the high-level categories for (A) Singapore, (B) London, (C) Los Angeles, and (D) New York City.

Full Reference: 
Jenkins A., Croitoru, A. Crooks, A.T. and Stefanidis, A. (2016), Crowdsourcing A Collective Sense of Place, PLoS ONE 11(4): e0152932. doi:10.1371/journal.pone.0152932