Towards Characterizing Cities with Social Media Images Daniela - PowerPoint PPT Presentation

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Towards Characterizing Cities with Social Media Images Daniela Opitz Universidad del Desarrollo Eduardo Graells-Garrido Barcelona Supercomputing Center Universidad del Desarrollo Ignacio Prez-Messina Universidad del Desarrollo I(mages)

Towards Characterizing Cities with Social Media Images Daniela Opitz Universidad del Desarrollo Eduardo Graells-Garrido Barcelona Supercomputing Center Universidad del Desarrollo Ignacio Pérez-Messina Universidad del Desarrollo
I(mages) Cities Images are at the heart of the digital life, registering what , where , and when people do activities.
Can we use images to study cities? Their rich visual information may allow to characterize cities at different spatial granularities.
How can we extract activities from images?
Situations (Imsitu) Situation : concise summary of an activity (Yatskar et al. 2016) resnet-101 base network
How can we characterize places according to their activities ?
Data Set YFCC100m Yahoo Flickr Creative Commons 100 Million Contains 99.2 million ● photos and 0.8 million videos Images taken around the ● world and uploaded to Flickr between 2004 and 2014 We select images with ● geographical coordinates in the city of interest
Santiago, Chile 338 census districts in Total Santiago population of 7 million 28.748 images .
Santiago, Chile Fig. 1 Santiago Activities Word Cloud Fig.2 Santiago Activities from Imsitu
Santiago, Chile n=districts 494 activities
NMF
How many topics (k)?
Visualization Visualization by Ignacio Perez-Messina
Santiago, Chile http://flickering.baltazarperez.com/Hoods/
Santiago, Chile
Discussion The topics obtained can characterize the urban ● space, as they exhibit spatial patterns that can be interpreted using visualization. Santiago, a highly segregated city, ● center-periphery and land-value spatial tensions were reproduced in the topic level. For the case of Santiago, k = 4 seems to offer a ● balanced number of topics in terms of diversity and possible interpretability. However, that the patterns we found need to be ● studied further, to include spatial autocorrelation into the analysis
Discussion k0: indoor activities (dining, shopping, etc) k1: garden or park activities k2: social activities at public places k3: outdoor
Methodology
Future Work Include more granular units, such as ● neighborhoods. Include other variables and data sets to ● enable new directions of analysis Study the evolution of topics in time. This ● would allow to study the dynamics of activities in areas. Evaluation of the visual design of the glyph ● and its effectiveness in portraying what characterizes the city . Consider other cv methods to extract ● activities.
Thank you!

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