Our perceptions of public spaces are central for our experience in the city. Understanding which factors shape this perception informs both urban planners, that aim at improving city life, as well as computational models that help us navigate in urban spaces. To understand cities at scale, crowdsourcing games have been employed successfully to evaluate citizens' opinions about cities and urban scenes. By analyzing human perceptions from residents of a mid-sized Brazilian city, this work brings three novel contributions. First, we consider theories from urban design to explore through crowdsourcing which high and low level features in an urban space are linked to perceptions of safety and pleasantness. Secondly, this paper leverages theory from urban sociology and anthropology to show how the sociodemographic profile of the citizens significantly mediate their perception of safeness and pleasantness of places. Finally, we show that features of the urban form proposed by urbanists can be combined with sociodemographics to improve the accuracy of machine learning models that predict which scene a person will find more safe or pleasant. This last result paves the road for more personalized recommendations in cold-start scenarios.
Several systems so far to collect people perception of places. They made own system - Como e Campina to assess "pleasantry" and :Safefy" in 3 neighborhoods in the city of Campinas.
Looked on factors - what affect. Trees? Order? People on street?
Basically, good maintenance was supporting both; pleasantry and safe while disorder negatively affects both
Now, could we predict what a persona will say given demography and features - but prediction quality is low. - just above 0.6
%0 Conference Paper
%1 citeulike:14389345
%A Candeia, David
%A Figueiredo, Flavio
%A Andrade, Nazareno
%A Quercia, Daniele
%B Proceedings of the 28th ACM Conference on Hypertext and Social Media
%C New York, NY, USA
%D 2017
%I ACM
%K crowdsourcing game hypertext2017 perception
%P 135--144
%R 10.1145/3078714.3078728
%T Multiple Images of the City: Unveiling Group-Specific Urban Perceptions Through a Crowdsourcing Game
%U http://dx.doi.org/10.1145/3078714.3078728
%X Our perceptions of public spaces are central for our experience in the city. Understanding which factors shape this perception informs both urban planners, that aim at improving city life, as well as computational models that help us navigate in urban spaces. To understand cities at scale, crowdsourcing games have been employed successfully to evaluate citizens' opinions about cities and urban scenes. By analyzing human perceptions from residents of a mid-sized Brazilian city, this work brings three novel contributions. First, we consider theories from urban design to explore through crowdsourcing which high and low level features in an urban space are linked to perceptions of safety and pleasantness. Secondly, this paper leverages theory from urban sociology and anthropology to show how the sociodemographic profile of the citizens significantly mediate their perception of safeness and pleasantness of places. Finally, we show that features of the urban form proposed by urbanists can be combined with sociodemographics to improve the accuracy of machine learning models that predict which scene a person will find more safe or pleasant. This last result paves the road for more personalized recommendations in cold-start scenarios.
%@ 978-1-4503-4708-2
@inproceedings{citeulike:14389345,
abstract = {{Our perceptions of public spaces are central for our experience in the city. Understanding which factors shape this perception informs both urban planners, that aim at improving city life, as well as computational models that help us navigate in urban spaces. To understand cities at scale, crowdsourcing games have been employed successfully to evaluate citizens' opinions about cities and urban scenes. By analyzing human perceptions from residents of a mid-sized Brazilian city, this work brings three novel contributions. First, we consider theories from urban design to explore through crowdsourcing which high and low level features in an urban space are linked to perceptions of safety and pleasantness. Secondly, this paper leverages theory from urban sociology and anthropology to show how the sociodemographic profile of the citizens significantly mediate their perception of safeness and pleasantness of places. Finally, we show that features of the urban form proposed by urbanists can be combined with sociodemographics to improve the accuracy of machine learning models that predict which scene a person will find more safe or pleasant. This last result paves the road for more personalized recommendations in cold-start scenarios.}},
added-at = {2017-11-15T17:02:25.000+0100},
address = {New York, NY, USA},
author = {Candeia, David and Figueiredo, Flavio and Andrade, Nazareno and Quercia, Daniele},
biburl = {https://www.bibsonomy.org/bibtex/2e30c61446f87f6bc850d3da88dad7bbc/brusilovsky},
booktitle = {Proceedings of the 28th ACM Conference on Hypertext and Social Media},
citeulike-article-id = {14389345},
citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=3078714.3078728},
citeulike-linkout-1 = {http://dx.doi.org/10.1145/3078714.3078728},
comment = {Several systems so far to collect people perception of places. They made own system - Como e Campina to assess "pleasantry" and :Safefy" in 3 neighborhoods in the city of Campinas.
Looked on factors - what affect. Trees? Order? People on street?
Basically, good maintenance was supporting both; pleasantry and safe while disorder negatively affects both
Now, could we predict what a persona will say given demography and features - but prediction quality is low. - just above 0.6},
doi = {10.1145/3078714.3078728},
interhash = {5d306234c583a6a69b195817e78dc06f},
intrahash = {e30c61446f87f6bc850d3da88dad7bbc},
isbn = {978-1-4503-4708-2},
keywords = {crowdsourcing game hypertext2017 perception},
location = {Prague, Czech Republic},
pages = {135--144},
posted-at = {2017-07-07 10:07:01},
priority = {2},
publisher = {ACM},
series = {HT '17},
timestamp = {2023-12-06T00:00:54.000+0100},
title = {{Multiple Images of the City: Unveiling Group-Specific Urban Perceptions Through a Crowdsourcing Game}},
url = {http://dx.doi.org/10.1145/3078714.3078728},
year = 2017
}