Identifying spatiotemporal urban activities through linguistic signatures

Fu, Cheng; McKenzie, Grant; Frías-Martínez, Vanessa; Stewart, Kathleen

doi:10.1016/j.compenvurbsys.2018.07.003

Cited by 35 publications

(19 citation statements)

References 21 publications

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“…Given this context, recent research on urban life has increasingly involved the interpretation of data retrieved from social networks (Fu et al 2017). Previous work using such data sources has addressed a diversity of urban-related issues.…”

Section: The Potential Of Geolocated Social Media Data For Urban Analmentioning

confidence: 99%

“…The analytical value of such information is twofold. On the one hand, researchers have broadly used SMD to identify the quantity and types of urban activities as well as their characteristics (Agryzkov et al 2016a(Agryzkov et al , 2016bFu et al 2017;García-Palomares et al 2018;Salas-Olmedo et al 2018;Steiger et al 2015). On the other hand, such data has been applied to infer and interpret intangible phenomena occurring in the city, such as emotions, social preferences or the detection of sociability spaces (Agryzkov et al 2016a(Agryzkov et al , 2016bCerrone 2015;Frank et al 2013).…”

Section: The Potential Of Geolocated Social Media Data For Urban Analmentioning

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: The Potential Of Geolocated Social Media Data For Urban Analmentioning

confidence: 99%

Section: The Potential Of Geolocated Social Media Data For Urban Analmentioning

confidence: 99%

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

Adelfio

Serrano-Estrada

Ciriquián

et al. 2020

Appl. Spatial Analysis

View full text Add to dashboard Cite

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“…Spatiotemporal patterns can be mined from Big Data (social media, phone locations, bus lines and traffic zones) collected within cities and used to better understand our urban settings and broader patterns such as human mobility and accessibility (Pappalardo and Simini 2018). This is true also for population dynamics (Liu et al 2018a), crash indices (Bao et al 2019), poverty distribution (Njuguna and McSharry 2017), segregation based on environment, gender, racial/ethnic and socioeconomic aspects (Park and Kwan 2017), patterns of social activities for policy development (Fu et al 2018), and the stability of urban human convergence and divergence patterns (Fang et al 2017). For crime reduction and public safety, Big Data (such as surveillance videos) have been used to improve data service sustainability and to analyze crime patterns based on city to city distance and connectivity (Kotevska et al 2017).…”

Section: Big Spatiotemporal Data Analytics In Actionmentioning

confidence: 99%

Big Spatiotemporal Data Analytics: a research and innovation frontier

Yang

Clarke

Shekhar

et al. 2019

International Journal of Geographical Information Science

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“…The vector of topic counts for an ISC object formed the topic pattern for the object. We employed the same natural language processing (NLP) workflow integrating the ST-LDA model in a previous study of the Washington D.C. area [48].…”

Section: Building Activity Signatures For Impervious Surface Cover Obmentioning

confidence: 99%

Integrating Activity-Based Geographic Information and Long-Term Remote Sensing to Characterize Urban Land Use Change

Song

Stewart

2019

Remote Sensing

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The land use structure is a key component to understand the complexity of urban systems because it provides a snapshot of urban dynamics and how people use space. This paper integrates socially sensed activity data with a remotely sensed land cover product in order to infer urban land use and its changes over time. We conducted a case study in the Washington D.C.-Baltimore metropolitan area to identify the pattern of land use change from undeveloped to developed land, including residential and non-residential uses for a period covering 1986-2008. The proposed approach modeled physical and behavioral features of land parcels from a satellite-based impervious surface cover change product and georeferenced Tweets, respectively. A model assessment with random forests classifiers showed that the proposed classification workflow could classify residential and non-residential land uses at an accuracy of 81%, 4% better than modeling the same land uses from physical features alone. Using the timestamps of the impervious surface cover change product, the study also reconstructed the timeline of the identified land uses. The results indicated that the proposed approach was capable of mapping detailed land use and change in an urban region, and represents a new and viable way forward for urban land use surveying that could be especially useful for surveying and tracking changes in cities where traditional approaches and mapping products (i.e., from remote sensing products) may have a limited capacity to capture change.characterize the biophysical properties of the land surface (i.e., land cover, such as impervious surface cover), but not how humans use land (i.e., land use or the social function of land, such as residential or commercial lands) [10,11]. Other forms of data that can contribute information about human land use activities are needed to reconstruct detailed land use (instead of land cover) history of urban areas.Recently, socially-sensed geographical data have been studied to model the spatial-temporal patterns of detailed human activities [12]. Previous studies in the GIScience field have explored the applications of solely using different socially sensed data sources to model land use or the function of places in cities, including call detailed records (CDRs) [13], georeferenced Tweets [14], taxi trajectories [15], wireless data requests [16], Foursquare check-in data [17], and photos from Google Street View [18].However, socially-sensed data often lack a historical archive as these data rely on the recent prevalence of GPS-embedded devices, e.g., smartphones. Thus, they are unable, on their own, to reveal the evolution of long-term land change. Methodologically, the pre-defined geographic units from static GIS layers to aggregate socially-sensed data (e.g., Reference [19]) could change over time, making it difficult to obtain an up-to-date representation. In addition, only a few sources, such as georeferenced Tweets, are freely accessible by researchers. Location accuracy of these data is subject to the GPS-e...

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Identifying spatiotemporal urban activities through linguistic signatures

Cited by 35 publications

References 21 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

Big Spatiotemporal Data Analytics: a research and innovation frontier

Integrating Activity-Based Geographic Information and Long-Term Remote Sensing to Characterize Urban Land Use Change

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