A voluminous and complex amount of information — ‘big data’ — from social media such as Twitter and Flickr is now ubiquitous and of increasing interest to researchers studying human behaviour in cities. Yet the value of social-media data (SMD) for urban-sustainability research is still poorly understood. Here, we discuss key opportunities and challenges for the use of SMD by sustainability scholars in the natural and social sciences as well as by practitioners making daily decisions about urban systems. Evidence suggests that the vast scale and near-real-time observation are unique advantages of SMD and that solutions to most SMD challenges already exist.
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Elmqvist, T. et al. (eds) The Urban Planet: Knowledge Toward Sustainable Cities (Cambridge Univ. Press, 2018).
Parnell, S., Elmqvist, T., McPhearson, T., Nagendra, H. & Sörlin, S. in The Urban Planet: Knowledge Towards Sustainable Cities (eds T. Elmqvist et al.) (Cambridge Univ. Press, 2018).
Berger, M. The unsustainable city. Sustainability 6, 365–374 (2014).
Acuto, M., Parnell, S. & Seto, K. C. Building a global urban science. Nat. Sustain. 1, 2–4 (2018). Provides an overview and synthesis of the state of global urban science and underscores the need to develop better tools for data-driven urban policy at the global level.
McPhearson, T. Scientists must have a say in the future of cities. Nature 538, 165–166 (2016).
McPhearson, T. et al. Advancing urban ecology toward a science of cities. Bioscience 66, 198–212 (2016). Calls for a science of cities and advances in the use of ‘Big Data’ to address urban sustainability challenges. It highlights the need for an integrated social–ecological–technological systems approach.
Ürge-Vorsatz, D. et al. Locking in positive climate responses in cities. Nat. Clim. Change 8, 174–177 (2018).
Acuto, M. Global science for city policy. Science 359, 165–166 (2018).
Langemeyer, J., Baró, F., Roebeling, P. & Gómez-Baggethun, E. Contrasting values of cultural ecosystem services in urban areas: The case of park Montjuïc in Barcelona. Ecosyst. Serv. 12, 178–186 (2015).
Transforming Our World: The 2030 Agenda for Sustainable Development A/RES/70/1 (United Nations, 2015).
Hamstead, Z. A. et al. Geolocated social media as a rapid indicator of park visitation and equitable park access. Comput. Environ. Urban Syst. 72, 38–50 (2018).
Brouwer, T. et al. Probabilistic flood extent estimates from social media flood observations. Nat. Hazards Earth Syst. Sci. 17, 735–747 (2017).
Yang, W., Mu, L. & Shen, Y. Effect of climate and seasonality on depressed mood among twitter users. Appl. Geogr. 63, 184–191 (2015). This paper provides a good example of how ‘big data’ from social media can help researchers to study the relationship between depression and specific aspects of climate and seasonality in different urban areas.
Hoogendoorn, G. & Gregory, J. Instagrammers, urban renewal and the Johannesburg inner city. Urban Forum 27, 399–414 (2016).
Yang, F., Jin, P. J., Cheng, Y., Zhang, J. & Ran, B. Origin-destination estimation for non-commuting trips using location-based social networking data. Int. J. Sustain. Transp. 9, 551–564 (2015).
Wu, C. et al. Spatial and social media data analytics of housing prices in Shenzhen, China. PLoS One 11, 1–19 (2016).
Gómez-Baggethun, E. & Barton, D. N. Classifying and valuing ecosystem services for urban planning. Ecol. Econ. 86, 235–245 (2013).
Urbanization, Biodiversity and Ecosystem Services: Challenges and Opportunities: a Global Assessment (Springer, 2013).
de Groot, R. et al. Global estimates of the value of ecosystems and their services in monetary units. Ecosyst. Serv. 1, 50–61 (2012).
Small, N. & Munday, M. & Durance, I. The challenge of valuing ecosystem services that have no material benefits. Glob. Environ. Change 44, 57–67 (2017).
Wood, S. A., Guerry, A. D., Silver, J. M. & Lacayo, M. Using social media to quantify nature-based tourism and recreation. Sci. Rep. 3, 2976 (2013). This is a pioneering study to ground-truth the use of social-media data (Flickr) to test its potential to predict visitation rates in recreational sites worldwide.
Sessions, C., Wood, S. A., Rabotyagov, S. & Fisher, D. M. Measuring recreational visitation at US National Parks with crowd-sourced photographs. J. Environ. Manage. 183, 703–711 (2016).
Sonter, L. J., Watson, K. B., Wood, S. A. & Ricketts, T. H. Spatial and temporal dynamics and value of nature-based recreation, estimated via social media. PLoS One 11, 1–16 (2016).
Keeler, B. L. et al. Recreational demand for clean water: evidence from geotagged photographs by visitors to lakes. Front. Ecol. Environ. 13, 76–81 (2015).
Donahue, M. L. et al. Using social media to understand drivers of urban park visitation in the Twin Cities, MN. Landsc. Urban Plan. 175, 1–10 (2018).
Roberts, H. V. Using Twitter data in urban green space research: A case study and critical evaluation. Appl. Geogr. 81, 13–20 (2017).
Guerrero, P., Møller, M. S., Olafsson, A. S. & Snizek, B. Revealing cultural ecosystem services through Instagram images: the potential of social media volunteered geographic information for urban green infrastructure planning and governance. Urban Plan. 1, 1–17 (2016). This article shows how volunteered geographic information from social media (Instagram) can be used to gain knowledge on how people interact with and perceive urban green spaces and inform future green infrastructure planning.
Schwartz, R. & Hochman, N. in Locative Media (eds. R. Wilken & G. Goggin) 52–65 (Routledge, 2015).
Murakami, D., Peters, G. W., Yamagata, Y. & Matsui, T. Participatory sensing data tweets for micro-urban real-time resiliency monitoring and risk management. IEEE Access 4, 347–372 (2016).
Stefanidis, A., Crooks, A. & Radzikowski, J. Harvesting ambient geospatial information from social media feeds. GeoJournal 78, 319–338 (2011).
Kent, J. D. & Capello, H. T. Spatial patterns and demographic indicators of effective social media content during the Horsethief Canyon fire of 2012. Cartogr. Geogr. Inf. Sci. 40, 78–89 (2013).
Wang, Y., Wang, T., Ye, X., Zhu, J. & Lee, J. Using social media for emergency response and urban sustainability: a case study of the 2012 Beijing rainstorm. Sustainability 8, 25 (2015).
Kusumo, A. N. L., Reckien, D. & Verplanke, J. Utilising volunteered geographic information to assess resident’s flood evacuation shelters. Case study: Jakarta. Appl. Geogr. 88, 174–185 (2017).
Tkachenko, N., Jarvis, S. & Procter, R. Predicting floods with Flickr tags. PLoS One 12, e0172870 (2017).
Cervone, G., Schnebele, E., Waters, N., Moccaldi, M. & Sicignano, R. in Seeing Cities Through Big Data (eds. P. Thakuriah, N. Tilahun, & M. Zellner) 443–457 (Springer, 2017).
Shelton, T., Poorthuis, A., Graham, M. & Zook, M. Mapping the data shadows of Hurricane Sandy: Uncovering the sociospatial dimensions of ‘big data’. Geoforum 52, 167–179 (2014).
Liu, J. C.-E. & Zhao, B. Who speaks for climate change in China? Evidence from Weibo. Clim. Change 140, 413–422 (2017).
Cody, E. M., Reagan, A. J., Mitchell, L., Dodds, P. S. & Danforth, C. M. Climate change sentiment on Twitter: An unsolicited public opinion poll. PLoS One 10, 1–18 (2015).
Morris, G. P., Beck, S. A., Hanlon, P. & Robertson, R. Getting strategic about the environment and health. Public Health 120, 889–903 (2006).
Widener, M. J. & Li, W. Using geolocated Twitter data to monitor the prevalence of healthy and unhealthy food references across the US. Appl. Geogr. 54, 189–197 (2014).
Chen, X. & Yang, X. Does food environment influence food choices? A geographical analysis through ‘tweets’. Appl. Geogr. 51, 82–89 (2014). This is one of the first studies using social-media data (Twitter) to assess how people’s food-related activities are influenced by their local food environment.
Gore, R. J., Diallo, S. & Padilla, J. You are what you tweet: Connecting the geographic variation in America’s obesity rate to twitter content. PLoS One 10, 1–16 (2015).
Ranney, M. L. et al. Tweet now, see you in the ED later? Examining the association between alcohol-related tweets and emergency care visits. Acad. Emerg. Med. 23, 831–834 (2016).
Mitchell, L., Frank, M. R., Harris, K. D., Dodds, P. S. & Danforth, C. M. The geography of happiness: connecting Twitter sentiment and expression, demographics, and objective characteristics of place. PLoS One. https://doi.org/10.1371/journal.pone.0064417 (2013).
Yang, W. & Mu, L. GIS analysis of depression among Twitter users. Appl. Geogr. 60, 217–223 (2015).
Nguyen, Q. C. et al. Leveraging geotagged Twitter data to examine neighborhood happiness, diet, and physical activity. Appl. Geogr. 73, 77–88 (2016).
Ben-Harush, O., Carroll, J.-A. & Marsh, B. Using mobile social media and GIS in health and place research. Continuum 26, 715–730 (2012).
Hurst, C. E., Gibbon, H. M. F. & Nurse, A. M. Social Inequality: Forms, Causes, and Consequences (Routledge, 2017).
Shelton, T., Poorthuis, A. & Zook, M. Social media and the city: Rethinking urban socio-spatial inequality using user-generated geographic information. Landsc. Urban Plan. 142, 198–211 (2015). This paper offers important insights into how geotagged social-media data (Twitter) can help study questions of socio-spatial inequality and contribute to the advancement of critical GIScience in cities.
Adnan, M., Lansley, G. & Longley, P. A geodemographic analysis of the ethnicity and identity of Twitter users in Greater London. Proc. 21st Conf. GIS 44, 1–6 (2013).
Quercia, D. & Saez, D. Mining urban deprivation from Foursquare: Implicit crowdsourcing of city land use. IEEE Pervasive Comput. 13, 30–36 (2014).
Li, J., Qin, Q., Han, J., Tang, L. A. & Lei, K. H. Mining trajectory data and geotagged data in social media for road map inference. Trans. GIS 19, 1–18 (2015).
Zhou, X., Wang, M. & Li, D. From stay to play — A travel planning tool based on crowdsourcing user-generated contents. Appl. Geogr. 78, 1–11 (2017).
Hasan, S. & Ukkusuri, S. V. Urban activity pattern classification using topic models from online geo-location data. Transp. Res. Part C Emerg. Technol. 44, 363–381 (2014). This paper uses social-media data (Twitter) to extract typologies and patterns of daily urban activities with the potential to inform activity-based modelling and future urban transportation planning.
Luo, F., Cao, G., Mulligan, K. & Li, X. Explore spatiotemporal and demographic characteristics of human mobility via Twitter: A case study of Chicago. Appl. Geogr. 70, 11–25 (2016).
Schweitzer, L. Planning and social media: A case study of public transit and stigma on twitter. J. Am. Plan. Assoc. 80, 218–238 (2014).
Lucchese, C., Perego, R. & Silvestri, F. in Advances in Information Retrieval (eds. Baeza-Yates, R. et al.) 195–206 (Springer-Verlag, 2012).
Assem, H., Buda, T. S. & O’Sullivan, D. RCMC : Recognizing crowd-mobility patterns in cities based on: location based social networks data. ACM Trans. Intell. Syst. Technol. 8, 70–100 (2017).
Hawelka, B. et al. Geo-located Twitter as proxy for global mobility patterns. Cartogr. Geogr. Inf. Sci. 41, 260–271 (2014).
Paldino, S., Bojic, I., Sobolevsky, S., Ratti, C. & González, M. C. Urban magnetism through the lens of geo-tagged photography. EPJ Data Sci. 4, 1–17 (2015).
Martí, P., Serrano-Estrada, L. & Nolasco-Cirugeda, A. Using locative social media and urban cartographies to identify and locate successful urban plazas. Cities 64, 66–78 (2017).
Brandt, T., Bendler, J. & Neumann, D. Social media analytics and value creation in urban smart tourism ecosystems. Information Manag. 54, 703–713 (2017).
Zhai, S. et al. Mapping the popularity of urban restaurants using social media data. Appl. Geogr. 63, 113–120 (2015).
Lovelace, R., Birkin, M., Cross, P. & Clarke, M. From big noise to big data: Toward the verification of large data sets for understanding regional retail flows. Geogr. Anal. 48, 59–81 (2016). This paper offers an approach to test the reliability of geotagged ‘big data’ from social media (Twitter) for mapping retail flows in an urban region, by using mobile phone and consumer-survey data.
Boyd, D. & Crawford, K. Critical questions for big data. Information Commun. Soc. 15, 662–679 (2012).
Twitter now has 330 million monthly active users. PC Tech Magazine (2017).
Crampton, J. et al. Beyond the geotag: situating ‘big data’ and leveraging the potential of the geoweb. Cartogr. Geogr. Inf. Sci. 40, 130–139 (2013).
Ratkiewicz, J. et al. Detecting and tracking political abuse in social media. Int. Conf. Web Soc. Media 11, 297–304 (2011).
Housley, W. et al. Big and broad social data and the sociological imagination: A collaborative response. Big Data Soc. 1, 1–15 (2014).
Schoen, H. et al. The power of prediction with social media. Internet Res. 23, 528–543 (2013).
Sloan, L., Morgan, J., Burnap, P. & Williams, M. Who tweets? Deriving the demographic characteristics of age, occupation and social class from Twitter user meta-data. PLoS One 10, e0115545 (2015).
Acquisti, A. & Gross, R. Predicting social security numbers from public data. Proc. Natl Acad. Sci. USA 106, 10975–10980 (2009).
Ruths, D. & Pfeffer, J. Social media for large studies of behavior. Science 346, 1–2 (2014). This article offers an in-depth overview of the potential biases and flows in social-media data and how these can be reduced.
Maeve, D. & Brenner, J. The Demographics of Social Media Users — 2012 (Pew Research Center, 2013).
Dunkel, A. Visualizing the perceived environment using crowdsourced photo geodata. Landsc. Urban Plan. 142, 173–186 (2015).
Abel, F., Araujo, S., Gao, Q. & Houben, G.-J. Analyzing cross-system user modeling on the social web. in Proc. 11th Int. Conf. Web Eng. 28–43 (2011).
Jendryke, M., Balz, T., McClure, S. C. & Liao, M. Putting people in the picture: Combining big location-based social media data and remote sensing imagery for enhanced contextual urban information in Shanghai. Comput. Environ. Urban Syst. 62, 99–112 (2017).
Zickuhr, K. Who’s Not Online and Why (Pew Research Center, 2013).
Internet/Broadband Fact Sheet (Pew Research Center, 2017).
Social Media Fact Sheet: Demographics of Social Media Users and Adoption in the United States (Pew Research Center, 2016).
Leetaru, K. H., Shaowen, W., Guofeng, C., Padmanabhan, A. & Shook, E. Mapping the global Twitter hearbeat: The geography of Twitter. First Monday 18, 5–6 (2013).
Catt, R. D. 100,000,000 Geotagged Photos (Plus) (2009); code.flickr.net/2009/02/04/100000000-geotagged-photos-plus
Cranshaw, J., Schwartz, R., Hong, J. & Sadeh, N. The livehoods project: utilizing social media to understand the dynamics of a city. in Int. Conf. Web Soc. Media 58–65 (2012).
Rattenbury, T. & Naaman, M. Methods for extracting place semantics from Flickr tags. ACM Trans. Web 3, 1–30 (2009).
Diaz, F., Gamon, M., Hofman, J. M., Kiciman, E. & Rothschild, D. Online and social media data as an imperfect continuous panel survey. PLoS One 11, 1–21 (2016).
Liu, B., Yuan, Q., Cong, G. & Xu, D. Where your photo is taken: Geolocation prediction for social images. J. Assoc. Inf. Sci. Technol. 65, 1232–1243 (2014).
Ribeiro, S. & Pappa, G. L. Strategies for combining Twitter users geo-location methods. Geoinformatica 22, 563–587 (2017). This paper provides a comprehensive evaluation of sixteen different approaches for social-media user (Twitter) location inference and highlights those with the greatest accuracy.
Mahmud, J., Nichols, J. & Drews, C. Where is this tweet from? Inferring home locations of Twitter users. in Proc. Sixth Int. AAAI Conf. Weblogs Soc. Media 511–514 (2012).
Schulz, A., Hadjakos, A., Paulheim, H., Nachtwey, J. & Mühlhäuser, M. A multi-indicator approach for geolocalization of tweets. in Int. Conf. Web Soc. Media 573–582 (2013).
Cheng, Z., Caverlee, J. & Lee, K. You are where you tweet: a content-based approach to geo-locating Twitter users. in Proc. 19th ACM Int. Conf. Inform Knowledge Manag. 759–768 (ACM, 2010).
Paraskevopoulos, P. & Palpanas, T. Where has this tweet come from? Fast and fine-grained geolocalization of non-geotagged tweets. Soc. Netw. Anal. Min. 6, 89 (2016).
Oku, K., Hattori, F. & Kawagoe, K. Tweet-mapping method for tourist spots based on now-tweets and spot-photos. Procedia Comput. Sci. 60, 1318–1327 (2015).
Hauff, C. & Houben, G. in Advances in Information Retrieval (eds. R. Baeza-Yates et al.) 7224, 85–96 (Springer-Verlag, 2012).
Schwartz, R. & Halegoua, G. R. The spatial self: Location-based identity performance on social media. New Media Soc. 17, 1643–1660 (2014).
Graham, M., Stephens, M. & Hale, S. Featured graphic. Mapping the geoweb: A geography of Twitter. Environ. Plan. A 45, 100–102 (2013).
Naaman, M. Geographic information from georeferenced social media data. SIGSPATIAL Spec. 3, 54–61 (2011).
Goodspeed, R. The limited usefulness of social media and digital trace data for urban social research. in Proc. Sixth Int. AAAI Conf. Weblogs Soc. Media Underst. 2–4 (2013).
Bacallao-Pino, L. M. Social media mobilisations: Articulating participatory processes or visibilizing dissent? Cyberpsychology J. Psychosoc. Res. Cybersp. https://doi.org/10.5817/CP2014-3-3 (2014).
Lovelace, R., Malleson, N., Harland, K. & Birkin, M. Geotagged tweets to inform a spatial interaction model: a case study of museums. Preprint at https://arxiv.org/ftp/arxiv/papers/1403/1403.5118.pdf (2014).
Cardullo, P. ‘Hacking multitude’ and big data: Some insights from the Turkish ‘digital coup’. Big Data Soc. 2, 1–14 (2015).
Grieve, R., Witteveen, K. & Tolan, G. A. Social media as a tool for data collection: examining equivalence of socially value-laden constructs. Curr. Psychol. 33, 532–544 (2014).
Hollenstein, L. & Purves, R. Exploring place through user-generated content: Using Flickr to describe city cores. J. Spat. Inf. Sci. https://doi.org/10.5311/JOSIS.2010.1.3 (2010).
Schockaert, S. Vague regions in geographic information retrieval. SigSpatial Spec. 3, 24–28 (2011).
Hong, L., Ahmed, A., Gurumurthy, S., Smola, A. J. & Tsioutsiouliklis, K. Discovering geographical topics in the twitter stream. Proc. 21st Int. Conf. World Wide Web https://doi.org/10.1145/2187836.2187940 (2012).
Pettit, A. The promises and pitfalls of SMR: Prevailing discussions and the naked truth. Mark. Res. 14–22 (2011).
Schwartz, H. A. & Ungar, L. H. Data-driven content analysis of social media: A systematic overview of automated methods. Ann. Am. Acad. Pol. Soc. Sci. 659, 78–94 (2015).
Gonçalves, P., Araújo, M., Benevenuto, F. & Cha, M. Comparing and combining sentiment analysis methods. in Proc. First ACM Conf. Online Soc. Networks https://doi.org/10.1145/2512938.2512951 27–38 (ACM, 2013). This paper assesses the merits of different existing approaches to sentiment analysisbased on social-media data and points to possible way to combine them to achieve better coverageand agreement results.
Jaewoo, K., Cha, M., Lee, W. & Sandholm, T. Identifying crime-prone areas based on tweet sentiments. Telecommun. Rev. 24, 339–347 (2014).
Bradley, M. M. & Lang, P. J. Affective Norms for English Words (ANEW): Instruction Manual and Affective Ratings (Citeseer, 1999).
Yuan, J., Mcdonough, S., You, Q. & Luo, J. Sentribute: Image sentiment analysis from a mid-level perspective. Proc. Second Int. Work. Issues Sentim. Discov. Opin. Min. https://doi.org/10.1145/2502069.250207 (2013).
Borth, D., Ji, R., Chen, T., Breuel, T. & Chang, S.-F. Large-scale visual sentiment ontology and detectors using adjective noun pairs. Proc. 21st ACM Int. Conf. Multimed. https://doi.org/10.1145/2502081.2502282 (2013).
Wang, Y., Wang, S., Tang, J., Liu, H. & Li, B. Unsupervised sentiment analysis for social media images. Proc. 24th Int. Conf. Artif. Intell. 2378–2379 (2015).
Godbey, G. C., Caldwell, L. L., Floyd, M. & Payne, L. L. Contributions of leisure studies and recreation and park management research to the active living agenda. Am. J. Prev. Med. 28, 150–158 (2005).
Young, O. R. et al. Earth System Challenges and a Multi-Layered Approach for the Sustainable Development Goals (United Nations University Institute for the Advanced Study of Sustainability, 2014).
Perry, P. Certain problems in election survey methodology. Public Opin. Q. 43, 312–325 (1979).
Barocas, S. & Nissenbaum, H. Big data’s end run around procedural privacy protections. Commun. ACM 57, 31–33 (2014).
Sui, D. & Goodchild, M. The convergence of GIS and social media: challenges for GIScience. Int. J. Geogr. Inf. Sci. 25, 1737–1748 (2011).
A GIS Code of Ethics (GIS Certification Institute, 2003).
Kennedy, H., Moss, G., Birchall, C. & Moshonas, S. Balancing the potential and problems of digital methods through action research: methodological reflections. Inform. Commun. Soc. 18, 172–186 (2014).
Young, S. D. Behavioral insights on big data: using social media for predicting biomedical outcomes. Trends Microbiol. 22, 601–602 (2014).
Yang, C., Huang, Q., Li, Z., Liu, K. & Hu, F. Big data and cloud computing: innovation opportunities and challenges. Int. J. Digit. Earth 10, 13–53 (2017).
Ediger, D., Jiang, K., Riedy, J., Bader, D. A. & Corley, C. Massive social network analysis: Mining Twitter for social good. in ICPP 2010 39th Int. Conf. Parallel Proc. 583–593 (IEEE, 2010).
Cao, G. et al. A scalable framework for spatiotemporal analysis of location-based social media data. Comput. Environ. Urban Syst. 51, 70–82 (2015).
McCall, M. K. Seeking good governance in participatory-GIS: A review of processes and governance dimensions in applying GIS to participatory spatial planning. Habitat Int. 27, 549–573 (2003).
Dunn, C. E. Participatory GIS — a people’s GIS? Prog. Hum. Geogr. 31, 616–637 (2007).
Campbell, S. Green Cities, Growing Cities, Just Cities?: Urban Planning and the Contradictions of Sustainable Development. J. Am. Plan. Assoc. 62, 296–312 (1996).
Hawkes, J. The Fourth Pillar of Sustainability: Culture’s Essential Role in Public Planning (Common Ground, 2001).
Conway, T. M. Tending their urban forest: Residents’ motivations for tree planting and removal. Urban For. Urban Green. 17, 23–32 (2016).
Pincetl, S., Gillespie, T., Pataki, D. E., Saatchi, S. & Saphores, J. D. Urban tree planting programs, function or fashion? Los Angeles and urban tree planting campaigns. GeoJournal 78, 475–493 (2013).
Jayasooriya, V. M., Ng, A. W. M., Muthukumaran, S. & Perera, B. J. C. Green infrastructure practices for improvement of urban air quality. Urban For. Urban Green. 21, 34–47 (2017).
Newell, J. P. et al. Green Alley Programs: Planning for a sustainable urban infrastructure? Cities 31, 144–155 (2013).
Li, L., Goodchild, M. F. & Xu, B. Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr. Cartogr. Geogr. Inf. Sci. 40, 61–77 (2013).
T.M. was supported by the Urban Resilience to Extreme Weather-Related Events Sustainability Research Network (URExSRN; NSF grant no. SES 1444755). T.M.’s research was also carried out as part of the project ENABLE, funded through the 2015–2016 BiodivERsA COFUND call for research proposals, with the national funders The Swedish Research Council for Environment, Agricultural Sciences, and Spatial Planning, Swedish Environmental Protection Agency, German Aeronautics and Space Research Centre, National Science Centre (Poland), The Research Council of Norway and the Spanish Ministry of Economy and Competitiveness.
The authors declare no competing interests.
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Ilieva, R.T., McPhearson, T. Social-media data for urban sustainability. Nat Sustain 1, 553–565 (2018). https://doi.org/10.1038/s41893-018-0153-6
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