Friday, May 13, 2016

A Semester with Urban Analytics

This past semester I gave a new class at GMU entitled "Urban Analytics". In a nutshell the class was about introducing students to a broad interdisciplinary field that focuses on the use of data to study cities. More specifcally the emphasis of the class was to provide students with a understanding of what methods, tools and theory can be used to monitor, analyze and model cities. 

From my past research and also when preparing the class material,  I have come to the realization that to study cities (like many others, you know who you are) that there is no one general model, tool or dataset. Therefore, one needs to maintain a toolbox of specialized tools than can be applied to different aspects of urban problems and questions. 

The toolbox that we used in class included a variety of software such as ArcGIS, QGIS, GeoDa, SANET along with programing and scripting in Python and R to modeling  cities via UrbanSim, NetLogo and MASON. Data we used ranged from crowdsourced (e.g. volunteered geographical information) data such as from OpenStreetMap or Wikipedia, to crowd harvested (ambient geographical information) data such as Twitter and Flickr, as-well as more traditional sources of data such as the US Census.

The Urban Analytics Toolbox

As an introduction to urban analytics, the course had the following objectives:
  1. to understand the motivation for the use of data to study cities, including some historical aspects; 
  2. to learn about the variety of Urban Analytics research programs across the several disciplines (urban planning, regional science, public policy, geography, computational social science etc.), through a survey of the literature and case studies. 
  3. to understand the distinct contribution that Urban Analytics can make by providing specific insights about cities at multiple scales. 
  4. to provide the foundations for more advanced work in the area of Urban Analytics. 
As with many of my courses, students were expected to complete a end of semester project. Below is a selection of these projects which explored some aspect of urban life.

I would like to thank the students for participating in this new class. It was a fun trip.

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

Tuesday, March 29, 2016

Accuracy Of Image Tagging In Flickr: A Natural Disaster Case Study

We recently received word that we had a paper accepted for the upcoming  2016 International Conference on Social Media and Society entitled "Accuracy Of User-Contributed Image Tagging In Flickr:  A Natural Disaster Case Study." In the paper we assess the reliability of user-generated tags during a natural disaster event. Below you read the abstract of the paper.
Social media platforms have become extremely popular during the past few years, presenting an alternate, and often preferred, avenue for information dissemination within massive global communities. Such user-generated multimedia content is emerging as a critical source of information for a variety of applications, and particularly during times of crisis. In order to fully explore this potential, there is a need to better assess, and improve when possible, the accuracy of such information. This paper addresses this issue by focusing in particular on user-contributed image tagging in Flickr. We use as case study a natural disaster event (wildfire), and assess the reliability of user-generated tags. Furthermore, we compare these data to the results of a content-based annotation approach in order to assess the potential performance of an alternative, user-independent, automated approach to annotate such imagery. Our results show that Flickr user annotations can be considered quite reliable (at the level of ~50%), and that using a spatially distributed training dataset for our content-based image retrieval (CBIR) annotation process improves the performance of the content-based image labeling (to the level of ~75%).
Study methodology for comparing user-annotated to CBIR-annotated Flickr imagery
Sample of training set images (yellow) and the retrieved fire images (green).
Full reference:
Panteras, G., Lu, X., Croitoru, A., Crooks, A.T. and Stefanidis, A. (2016), Accuracy Of User-Contributed Image Tagging In Flickr:  A Natural Disaster Case Study. The 2016 International Conference on Social Media & Society, London, UK.

Drop us a message if you would like to read the paper.

Wednesday, March 23, 2016

Call For Papers: Smart Buildings and Cities

Special Issue on Smart Buildings and Cities for IEEE Pervasive Computing

Submission deadline: 1 July 2016
Publication date: April–June 2017

One of Mark Weiser’s first envisionments of ubiquitous and pervasive computing had the smart home as its central core. Since then, researchers focused on realizing this vision have built out from the smart home to the smart city. Such environments aim to improve the transparency of information and the quality of life through access to smarter and more appropriate services.

Despite efforts to build these environments, there are still many unanswered questions: What does it mean to make a building or a city “smart”? What infrastructure is necessary to support smart environments? What is the return on investment of a smart environment?

The key to building smart environments is the fusion of multiple technologies including sensing, advanced networks, the Internet of Things, cloud computing, big data analytics, and mobile devices. This special issue aims to explore new technologies, methodologies, case studies, and applications related to smart buildings and cities. Contributions may come from diverse fields such as distributed systems, HCI, ambient intelligence, architecture, transportation and urban planning, policy development, and cyber-physical systems. Relevant topics for issue include
  • Applications, evaluations, or case studies of smart buildings/cities
  • Architectures and systems software to support smart environments
  • Big data analytics for monitoring and managing smart environments
  • Economic models for smart buildings/cities
  • Models for user interaction in smart environments
  • Formative studies regarding the design, use, and acceptance of smart services
  • Configuration and management of smart environments
  • Embedded, mobile ,and crowd sensing approaches
  • Cloud computing for smart environments
  • Domain-specific investigations (such as transportation or healthcare)
The guest editors invite original and high-quality submissions addressing all aspects of this field, as long as the connection to the focus topic is clear and emphasized.

Guest Editors
Submission Information