Social-media data for urban sustainability

Ilieva, Rositsa T.; McPhearson, Timon

doi:10.1038/s41893-018-0153-6

Cited by 219 publications

(118 citation statements)

References 110 publications

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“…While the use of demographic statistics (traditional data sources) was helpful to describe and situate the case areas within the intermediate city debate, the analysis of geolocated SMD as a form of innovative data sources enabled a direct exploration of urban activity. This finding supports previous research claiming the value of SMD for identifying attractive public and semi-public urban spaces (Ilieva and McPhearson 2018). Both traditional and innovative data sources produced interesting results.…”

Section: Discussionsupporting

confidence: 88%

“…From a methodological perspective, although SMD are easily accessible, they typically lack important background information that may affect sentiments and movement patterns, such as socioeconomic status -e.g. age, ethnicity, gender, education, income, occupation- (Ilieva and McPhearson 2018) and in particular where the informants live and work (Huang and Wong 2016). The present study went to some length to link the number of LBSN datapoints to the mean income levels and social diversity of neighborhoods, but evidently much more consideration needs to be taken to socioeconomic aspects.…”

Section: Discussionmentioning

confidence: 99%

“…The present study went to some length to link the number of LBSN datapoints to the mean income levels and social diversity of neighborhoods, but evidently much more consideration needs to be taken to socioeconomic aspects. On the flipside, such retrieval of personal information from combined data sources leads to privacy concerns (Ilieva and McPhearson 2018). Still, it was found that in Hammarkullen, the case area with the lowest car ownership and the lowest income level but the highest ethnic diversity among the four selected case areas, local inhabitants have highlighted the importance of public open space through their SMD.…”

Section: Discussionmentioning

confidence: 99%

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Social Activity in Gothenburg’s Intermediate City: Mapping Third Places through Social Media Data

Adelfio

Serrano-Estrada

Ciriquián

et al. 2020

Appl. Spatial Analysis

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This research focuses on the intermediate city, composed of urban areas located right outside the city center typically maintaining an in-between urban/suburban character. It aims to explore the degree to which this segment of the city exhibits urban activity and social life through the identification of activity areas in the so-called Third Places. Four intermediate city neighborhoods in Gothenburg, Sweden are adopted as case areas and are analyzed using a twofold approach. First, socioeconomic statistics provide a quantitative understanding of the case areas and, second, geolocated Social Media Data (SMD) from Foursquare, Google Places and Twitter makes it possible to identify the intermediate city's urban activity areas and socially preferred urban spaces. The findings suggest that a) the four analyzed intermediate city areas of Gothenburg all have a degree of social activity, especially where economic activities are clustered together; b) Third Places in more affluent areas tend to be linked to commodified consumption of urban space while neighborhoods with lower income levels and higher ethnic diversity seem to emphasize open public space as Third Places; and c) nowadays the typology of Third Places has evolved from the types identified in previous decades to include additional types of places, such as those you pass on the way to something else (e.g. gas and bus stations). The study has verified the value of SMD for studies of urban social life but also identified a number of topics for further research. Additional sources of SMD should be identified to secure a just representation of Third Places across diverse social groups. Furthermore, new methods for effective cross validation of SMD with other types of data are crucial, including e.g. statistics, on-site observations and surveys/interviews, not least to identify Third Places that are not frequently present (or are misrepresented) in SMD.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Social Activity in Gothenburg’s Intermediate City: Mapping Third Places through Social Media Data

Adelfio

Serrano-Estrada

Ciriquián

et al. 2020

Appl. Spatial Analysis

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“…(A) UHI is reduced by less compact, and more linear cities (Zhou et al 2017); (B) Green areas have a cooling effect radiating into surrounding built-up areas (Cheng et al 2015, Lin et al 2015; (C) urban economic models can replicate the distribution of cities with respect to population density and modal share as a function of transport costs Kenworthy 1989, Creutzig 2014); (D) the effect of fuel prices on urban GHG emissions is empirically verified across a sample of 274 cities world-wide . However, there is scope for empirical studies that use emerging big data on cities (Nangini et al 2019) to investigate the geometric features of cities and their relevance for climate mitigation and adaptation in more detail (Ilieva andMcPhearson 2018, Creutzig et al 2019). In particular, combining remote sensing data with data on transport activities and temperatures at high spatial resolution for a number of cities that are comparable in terms of climate and socio-economic characteristics, will make it possible to disentangle the relevance of urban planning for climate change mitigation and adaptation.…”

Section: Resultsmentioning

confidence: 99%

Star-shaped cities alleviate trade-off between climate change mitigation and adaptation

Pierer

Creutzig

2019

Environ. Res. Lett.

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To deal with climate change, cities must reduce their greenhouse gas (GHG) emissions and at the same time mitigate climate impacts associated with the physical infrastructure of the built environment. One strand of literature demonstrates that compact cities of sufficient density result in lower GHG emissions in the transport and the buildings sectors compared to sprawled cities. Another strand of literature, however, reveals that compactness hinders climate adaptation by amplifying the urban heat island (UHI) effect. As a result, mitigation and adaptation objectives of cities appear to contradict each other. Here, we develop a geometrical optimization framework and model of a three-dimensional city that minimizes this conflict. It makes use of the observation that low-carbon efficient transport can be realized via linear public transport axes, and that GHG emissions and UHI effects scale differently with varying geometric properties, thus enabling design that reflects both the economics and the climate of cities. We find that star-shaped cities, in contrast to radially symmetric cities, are well suited to alleviate the problematic trade-off. We also demonstrate that urban design considerations depend on transport fuel prices. The results are of particular importance for city planners of rapidly urbanizing cities in Asia and Africa who still have the potential to shape urban layout.

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“…Linking human behaviour in cities to downscaled climate projections and remotely sensed observations of urban form, land-use patterns, land cover and social-demographic information from national and international databases has the potential to drive a much more nuanced and highly spatially resolved platform for improved decision-making. Over the past decade, with the advance of big data and the digital social sciences, as well as the growing use of social media data (SMD) in geography studies, a host of new opportunities to augment and expand urban systems and climate impacts research have emerged (Ilieva & McPhearson, 2018).…”

Section: Big Data Approaches At City-scalementioning

confidence: 99%

Upscaling urban data science for global climate solutions

Lohrey²,

et al. 2019

Self Cite

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Non-technical summaryManhattan, Berlin and New Delhi all need to take action to adapt to climate change and to reduce greenhouse gas emissions. While case studies on these cities provide valuable insights, comparability and scalability remain sidelined. It is therefore timely to review the state-of-the-art in data infrastructures, including earth observations, social media data, and how they could be better integrated to advance climate change science in cities and urban areas. We present three routes for expanding knowledge on global urban areas: mainstreaming data collections, amplifying the use of big data and taking further advantage of computational methods to analyse qualitative data to gain new insights. These data-based approaches have the potential to upscale urban climate solutions and effect change at the global scale.

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Social-media data for urban sustainability

Cited by 219 publications

References 110 publications

Social Activity in Gothenburg’s Intermediate City: Mapping Third Places through Social Media Data

Social Activity in Gothenburg’s Intermediate City: Mapping Third Places through Social Media Data

Star-shaped cities alleviate trade-off between climate change mitigation and adaptation

Upscaling urban data science for global climate solutions

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