Could Data from Location-Based Social Networks Be Used to Support Urban Planning?

A great quantity of information is required to support urban planning. Usually there are many (not integrated) data sources, originating from different government bodies, in distinct formats and variable properties (e.g. reliability, completeness). The effort to handle these data, integrate and analyze them is high, taking to much time for the information to be available to help decision making. We argue that data from location-based social networks (LBSN) could be used to provide useful information in reasonable time, despite several limitations they have. To asses this, as a case study, we used data from different LBSN to calculate the Local Availability Index (IOL) for a Brazilian city. This index is part of a methodology to estimate quality of urban life inside cities and is used to support urban planning. The results suggest that data from LBSN are useful and could be used to provide insights for local governments.

[1]  Matthew Zook,et al.  Social Media and the City: Rethinking Urban Socio-Spatial Inequality Using User-Generated Geographic Information , 2015 .

[2]  B. Giles-Corti,et al.  Urban liveability: emerging lessons from Australia for exploring the potential for indicators to measure the social determinants of health. , 2014, Social science & medicine.

[3]  J. Helliwell,et al.  How&Apos;S Life? Combining Individual and National Variables to Explain Subjective Well-Being , 2002 .

[4]  Mirella M. Moro,et al.  Taxonomia e Desafios de Recomendação para Coleta de Dados Geográficos por Cidadãos , 2015, SBBD.

[5]  Giovanni Quattrone,et al.  Measuring Urban Deprivation from User Generated Content , 2014, CSCW.

[6]  Hossain Mansourian,et al.  Developing Small Cities by Promoting Village to Town and its Effects on Quality of Life for the Local Residents , 2013 .

[7]  Yu Zheng,et al.  Location-Based Social Networks: Users , 2011, Computing with Spatial Trajectories.

[8]  Nicu Sebe,et al.  The Death and Life of Great Italian Cities: A Mobile Phone Data Perspective , 2016, WWW.

[9]  Daniele Quercia,et al.  Mining Urban Deprivation from Foursquare: Implicit Crowdsourcing of City Land Use , 2014, IEEE Pervasive Computing.

[10]  Amit Prasad,et al.  Urban health indicators and indices—current status , 2015, BMC Public Health.

[11]  Dorota Węziak-Białowolska,et al.  Quality of life in cities – Empirical evidence in comparative European perspective , 2016 .

[12]  Susanne Boll,et al.  Visual Overlay on OpenStreetMap Data to Support Spatial Exploration of Urban Environments , 2015, ISPRS Int. J. Geo Inf..

[13]  Xing Xie,et al.  Discovering regions of different functions in a city using human mobility and POIs , 2012, KDD.

[14]  Ioannis A. Pissourios,et al.  An interdisciplinary study on indicators: A comparative review of quality-of-life, macroeconomic, environmental, welfare and sustainability indicators , 2013 .

[15]  Diana Andreea Onose,et al.  Urban Landscape Quality Index – Planning Tool for Evaluating Urban Landscapes and Improving the Quality of Life , 2016 .

[16]  J. Jacobs.  The Death and Life of Great American Cities , 1962 .

[17]  J. Church.  Human Development Report , 2001 .

[18]  D. Giannone,et al.  Now-Casting and the Real-time Data Flow , 2012, SSRN Electronic Journal.

[19]  Virgílio A. F. Almeida,et al.  We know where you live: privacy characterization of foursquare behavior , 2012, UbiComp.

[20]  R. B.,et al.  The United Nations , 1947, Nature.

[21]  Henriette Cramer,et al.  Representation and communication: challenges in interpreting large social media datasets , 2013, CSCW.

[22]  Robert W. Marans,et al.  Quality of urban life & environmental sustainability studies: Future linkage opportunities , 2015 .

[23]  Flávio Codeço Coelho,et al.  InfoDengue: a nowcasting system for the surveillance of dengue fever transmission , 2016, bioRxiv.

[24]  Guy Lapalme,et al.  A systematic analysis of performance measures for classification tasks , 2009, Inf. Process. Manag..