Social media and the city: Rethinking urban socio-spatial inequality using user-generated geographic information

Shelton, Taylor; Poorthuis, Ate; Zook, Matthew

doi:10.1016/j.landurbplan.2015.02.020

Cited by 292 publications

(201 citation statements)

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“…Twitter's daily use profiles serve to classify the space according to the type of dominant activity, whether business, leisure/weekend, nightlife and residential (Frias-Martinez et al, 2012). Geolocated tweets have also been used to analyse the degree of social mixing in the use of space by tracking the movement of social groups in highly segregated cities such as Río de Janeiro (Netto et al, 2005) and Louisville (Shelton et al, 2015). Unlike the information supplied by official sources, which offer data relating to place of residence, these studies apply indicators of multiculturalness and mixing to examine the use of space throughout the day.…”

Section: Being Connected: Twittermentioning

confidence: 99%

Tourists' digital footprint in cities: Comparing Big Data sources

et al. 2018

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Abstract.-There is little knowledge available on the spatial behaviour of urban tourists, and yet tourists generate an enormous quantity of data (Big Data) when they visit cities. These data sources can be used to track their presence through their activities. The aim of this paper is to analyse the digital footprint of urban tourists through Big Data. Unlike other papers that use a single data source, this article examines three sources of data to reflect different tourism activities in cities: Panoramio (sightseeing), Foursquare (consumption), and Twitter (being connected). Tourist density in the three data sources is compared via maps, correlation analysis (OLS) and spatial self-correlation analysis (Global Moran's I statistic and LISA). Finally the data are integrated using cluster analysis and combining the spatial clusters identified in the LISA analysis in the different data sources. The results show that the data from the three activities are partly spatially redundant and partly complementary, and allow the characterisation of multifunction tourist spaces (with several activities) and spaces specialising in one or various activities (for example, sightseeing and consumption). The case study analysed (Madrid) reveals a significant presence of tourists in the city centre, and increasing specialisation from the centre outwards towards the periphery. The main conclusion of the paper is that it is not sufficient to use one data source to analyse the presence of tourists in cities; several must be used in a complementary manner.

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Section: Being Connected: Twittermentioning

confidence: 99%

Tourists' digital footprint in cities: Comparing Big Data sources

et al. 2018

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“…Podemos relacionar, por ejemplo, los niveles de contaminación de la ciudad (obtenidos por medio de sensores) con la presencia de población (estimada a partir de datos de telefonía móvil) para conocer la cantidad de población expuesta a la contaminación en cada momento del día y cada área de la ciudad (Dewulf et al, 2016;Castell et al, 2017). O relacionar los datos de movilidad de la población obtenidos a partir de Twitter con las características del lugar de residencia según los datos censales, para conocer el uso del espacio de la ciudad según grupos sociales o raciales (Netto et al, 2015;Shelton et al, 2015). Así mismo, se puede indagar sobre el ritmo diario de la ciudad analizando el núme-ro de usuarios activos en Twitter según usos del suelo y formulando modelos explicativos (García-Palomares et al, 2018) (ver figura 1).…”

Section: La Revolución Del Big Data: Características De Los Datos Masunclassified

Big Data y nuevas geografías: la huella digital de las actividades humanas

Puebla

2018

dag

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ResumenEl término Big Data se ha popularizado en los últimos años y hace referencia a la producción de cantidades ingentes de datos. La actividad humana es captada a través de múltiples redes de sensores y dispositivos, dejando por tanto una huella digital. El análisis de esta huella digital tiene un gran potencial para la investigación geográfica del comportamiento humano. En este artículo se describen las principales características del Big Data y se destaca la importancia de los datos masivos para la ciencia y particularmente para la Geografía, centrando la atención en el estudio de los patrones espacio-temporales de la actividad humana. Palabras clave: Big Data; datos geolocalizados; comportamiento humano Resum. Big Data i noves geografies: l'empremta digital de les activitats humanesEl terme Big Data s'ha popularitzat en els últims anys i fa referència a la producció de quantitats ingents de dades. L'activitat humana és captada a través de múltiples xarxes de sensors i dispositius i, per tant, deixa una empremta digital. L'anàlisi d'aquesta empremta digital té un gran potencial per a la investigació geogràfica del comportament humà. En aquest article es descriuen les principals característiques del Big Data i es destaca la importància de les dades massives per a la ciència i, particularment, per a la Geografia, centrant l'atenció en l'estudi dels patrons espaciotemporals de l'activitat humana. Paraules clau: Big Data; dades geolocalitzades; comportament humà * Este artículo se basa parcialmente en la conferencia «Big Data y nuevas geografías», pronunciada en la Universidad Autónoma de Barcelona en 2017, y en la ponencia «El uso del Big Data en la investigación de la ciudad, la movilidad y el turismo», presentada en el XXV Congreso de la Asociación de Geógrafos Españoles. Résumé. Big Data et nouvelles géographies : l'empreinte digitale des activités humainesLe terme Big Data est devenu populaire ces dernières années et il se réfère à la production d'énormes quantités de données. L'activité humaine est captée par une multitude de réseaux de capteurs et de dispositifs, laissant ainsi une empreinte digitale. L'analyse de cette empreinte présente un grand potentiel pour l'étude géographique du comportement humain. Cet article décrit les principales caractéristiques du Big Data et il souligne l'importance des données massives pour la science et en particulier pour la géographie, en se concentrant sur l'étude des modèles spatio-temporels de l'activité humaine. Mots-clés: Big Data; données géolocalisées; comportement humain Abstract. Big data and new geographies: The digital footprint of human activityThe term 'big data' has become popular in recent years and refers to the production of huge amounts of data. Human activity is captured through multiple networks of sensors and devices, thus leaving a digital footprint. The analysis of this digital footprint has a great potential for geographical research on human behavior. This article describes the main characteristics of big data and highlights the importance of m...

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“…In particular, they review the current technologies that enable an easy creation and discovery of mobile services and list the identified requirements for UGSs. The interest in this topic is further motivated by the result of diverse studies that evaluate, for example, the impact of UGSs and especially UGC on society [21,22], tourism [8], and advertising [23]. With regard to aspects explicitly focused on service composition, some works were proposed to prevent conflicting behaviors when policies are composed.…”

Section: Related Workmentioning

confidence: 99%

User-Generated Services Composition in Smart Multi-User Environments

Catania

Torre

Monteleone

et al. 2017

JSAN

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Abstract:The increasing complexity shown in Smart Environments, together with the spread of social networks, is increasingly moving the role of users from simple information and services consumers to actual producers. In this work, we focus on security issues raised by a particular kind of services: those generated by users. User-Generated Services (UGSs) are characterized by a set of features that distinguish them from conventional services. To cope with UGS security problems, we introduce three different policy management models, analyzing benefits and drawbacks of each approach. Finally, we propose a cloud-based solution that enables the composition of multiple UGSs and policy models, allowing users' devices to share features and services in Internet of Things (IoT) based scenarios.

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Social media and the city: Rethinking urban socio-spatial inequality using user-generated geographic information

Cited by 292 publications

References 60 publications

Tourists' digital footprint in cities: Comparing Big Data sources

Tourists' digital footprint in cities: Comparing Big Data sources

Big Data y nuevas geografías: la huella digital de las actividades humanas

User-Generated Services Composition in Smart Multi-User Environments

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