City dynamics through Twitter: Relationships between land use and spatiotemporal demographics

Palomares, Juan Carlos García; Salas-Olmedo, María Henar; Moya-Gómez, Borja; Condeço-Melhorado, Ana; Puebla, Javier Gutiérrez

doi:10.1016/j.cities.2017.09.007

Cited by 99 publications

(62 citation statements)

References 25 publications

(29 reference statements)

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“…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%

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%

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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“…Thus, social media data and mainly location-based social networks (LBSN) have become an information source for studying and identifying human activity patterns. The analysis of LBSN data has been an active area of research in urban studies over the last decade which has allowed for developing applications in urban planning [19,30,[35][36][37], human activity [1,2,17,20,38,39], population dynamics [24,28,40,41], and event detection and disaster management [4,29,31,32,42,43], among others, as well as, implementing several analytical techniques.…”

Section: Background and Related Workmentioning

confidence: 99%

“…From data mining, it stands out: self-organizing maps (SOM) [19,35], hierarchical SOM [44], independent component analysis (ICA) [17], density-based spatial clustering of Applications with Noise (DBSCAN) and ST-DBSCAN [29,43], and random forests [40]. Meanwhile, from the branch of statistical analysis, it emerges ordinary least-squares (OLS) [30], generalized additive models (GAM) [32], local indicators of spatial association (LISA) [24], space-time scan statistics (STSS) [42], Gaussian mixture models (GMM) [45], and kernel density estimation (KDE) [1,38]. Generally, both alternatives combine the discovery of patterns for the spatio-temporal locations, as well as, for the content data.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Then, considering those patterns as a reflection of population dynamics in the cities, this line of investigation can provide instruments to define public policies regarding the provision of services and infrastructures and the planning, management, and mitigation of risks. For example, identifying of places commonly visited by people and hours of the day when that happens can suggest changes in the frequency of service of public transport systems and define strategies for disaster management, among others [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

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A Statistical Approach for Studying the Spatio-Temporal Distribution of Geolocated Tweets in Urban Environments

et al. 2019

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An in-depth descriptive approach to the dynamics of the urban population is fundamental as a first step towards promoting effective planning and designing processes in cities. Understanding the behavioral aspects of human activities can contribute to their effective management and control. We present a framework, based on statistical methods, for studying the spatio-temporal distribution of geolocated tweets as a proxy for where and when people carry out their activities. We have evaluated our proposal by analyzing the distribution of collected geolocated tweets over a two-week period in the summer of 2017 in Lisbon, London, and Manhattan. Our proposal considers a negative binomial regression analysis for the time series of counts of tweets as a first step. We further estimate a functional principal component analysis of second-order summary statistics of the hourly spatial point patterns formed by the locations of the tweets. Finally, we find groups of hours with a similar spatial arrangement of places where humans develop their activities through hierarchical clustering over the principal scores. Social media events are found to show strong temporal trends such as seasonal variation due to the hour of the day and the day of the week in addition to autoregressive schemas. We have also identified spatio-temporal patterns of clustering, i.e., groups of hours of the day that present a similar spatial distribution of human activities.

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“…between Twitter and Facebook), which allows to communicate behaviours and activities in real-time across various platforms [6]. Since online social networks are applications based on the Internet protocol and enable the creation and exchange of user-generated content [8], information extracted from these networks can include temporal and spatial data related to different events [9]. The information extracted from these networks can then be represented as geo-referenced patterns that establish relationships between the publication and the geographical and temporal characteristics of the publishing entity.…”

Section: Introductionmentioning

confidence: 99%

Sensing Earthquake Disaster Information: A Named Entity Recognition Approach Using Twitter Collaborative Data

Hernandez-Suarez¹,

Sánchez-Pérez²,

Toscano-Medina³

et al. 2018

Preprint

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Abstract:In recent years, online social networks have received important consideration in spatial modelling fields given the critical information that can be extracted from them for events in real time; one of the most latent issues is that regarding various natural disasters such as earthquakes. Although it is possible to retrieve data from these social networks with embedded geographic information provided by GPS, in many cases this is not possible. An alternative solution is to reconstruct specific locations using probabilistic language models, more specifically those based on Name Entity Recognition (NER), which extracts names from a user's description about an event occurring in a specific place (e.g., a collapsed building on a specific avenue). In this work, we present a methodology to use twitter as a social sensor system for disasters. The methodology scores NER locations with a kernel density estimation function for different subtopics originating from a natural disaster and that maps them into a geographic space is proposed. The proposed methodology is evaluated with tweets related to the 2017 earthquake in Mexico.

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City dynamics through Twitter: Relationships between land use and spatiotemporal demographics

Cited by 99 publications

References 25 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

A Statistical Approach for Studying the Spatio-Temporal Distribution of Geolocated Tweets in Urban Environments

Sensing Earthquake Disaster Information: A Named Entity Recognition Approach Using Twitter Collaborative Data

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