The Participatory Sensing Platform Driven by UGC for the Evaluation of Living Quality in the City

In this paper, we present a mobile-based participatory sensing method to engage citizens’ participation in the living quality evaluation. The system called City Probe consists of the APP and the platform to provide the location-based services. City Probe allows citizens to identify and assess the spatial issues. By using the rating function of City Probe APP, citizens can turn the qualitative spatial issues into measurable UGC data. In addition, the UGC data are visualized as (1) rating value map, (2) rating amount map, and (3) rating heat map to present the different quantitative patterns of city. The experiment of arcade survey verifies the value of City Probe by locating and assessing the OCCUPANCY issue.

[1]  Deborah Estrin,et al.  Image browsing, processing, and clustering for participatory sensing: lessons from a DietSense prototype , 2007, EmNets '07.

[2]  Pablo Rodriguez,et al.  I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system , 2007, IMC '07.

[3]  S. Livingstone.  The Challenge of Changing Audiences , 2004 .

[4]  Patrick Weber,et al.  OpenStreetMap: User-Generated Street Maps , 2008, IEEE Pervasive Computing.

[5]  Wen Hu,et al.  Ear-phone: an end-to-end participatory urban noise mapping system , 2010, IPSN '10.

[6]  M. Goodchild.  Citizens as sensors: the world of volunteered geography , 2007 .

[7]  T. Daugherty,et al.  Exploring Consumer Motivations for Creating User-Generated Content , 2008 .

[8]  Amit P. Sheth,et al.  Citizen Sensing, Social Signals, and Enriching Human Experience , 2009, IEEE Internet Computing.

[9]  S. Cox,et al.  Citizen-based sensing of crisis events: sensor web enablement for volunteered geographic information , 2013 .

[10]  Kirsi Virrantaus,et al.  Developing GIS-supported location-based services , 2001, Proceedings of the Second International Conference on Web Information Systems Engineering.

[11]  Salil S. Kanhere,et al.  Automatic Collection of Fuel Prices from a Network of Mobile Cameras , 2008, DCOSS.

[12]  B. Silverman.  Density estimation for statistics and data analysis , 1986 .

[13]  Emiliano Miluzzo,et al.  BikeNet: A mobile sensing system for cyclist experience mapping , 2009, TOSN.

[14]  Ramesh Raskar,et al.  Streetscore -- Predicting the Perceived Safety of One Million Streetscapes , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[15]  Daren C. Brabham.  Crowdsourcing as a Model for Problem Solving , 2008 .

[16]  Sarah Williams,et al.  Two Cities, Five Industries: Similarities and Differences within and between Cultural Industries in New York and Los Angeles , 2010 .

[17]  Mani Srivastava,et al.  PARTISAN: an architecture for participatory sensing overview , 2007 .

[18]  Michael F. Goodchild,et al.  Please Scroll down for Article International Journal of Digital Earth Crowdsourcing Geographic Information for Disaster Response: a Research Frontier Crowdsourcing Geographic Information for Disaster Response: a Research Frontier , 2022 .

[19]  Heidrun Schumann,et al.  Space, time and visual analytics , 2010, Int. J. Geogr. Inf. Sci..

[20]  Yang Zhang,et al.  CarTel: a distributed mobile sensor computing system , 2006, SenSys '06.

[21]  Alan M. MacEachren,et al.  Constructing knowledge from multivariate spatiotemporal data: integrating geographical visualization with knowledge discovery in database methods , 1999, Int. J. Geogr. Inf. Sci..

[22]  Yi-Shiang Shiu,et al.  City Probe: The Crowdsourcing Platform Driven by Citizen-Based Sensing for Spatial Identification and Assessment , 2016, CDVE.

[23]  José van Dijck,et al.  Users like you? Theorizing agency in user-generated content , 2009 .

[24]  Mark Gahegan,et al.  The Integration of Geographic Visualization with Knowledge Discovery in Databases and Geocomputation , 2001 .

[25]  Joel Brumm,et al.  Towards Structured Public Involvement in Highway Design: A Comparative Study of Visualization Methods and Preference Modeling using CAVE (Casewise Visual Evaluation) , 2001 .

[26]  Jeremy Mennis,et al.  Spatial data mining and geographic knowledge discovery - An introduction , 2009, Comput. Environ. Urban Syst..