Analysis of Twitter messages using big data tools to evaluate and locate the activity in the city of Valencia (Spain)

Martín, Angel; Julián, Ana Belén Anquela; Cos-Gayón, Fernando

doi:10.1016/j.cities.2018.12.014

Cited by 29 publications

(16 citation statements)

References 40 publications

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“…After using the QGIS to construct the grid map for the island of Taiwan, Python's Shapefile pack (17) is used to import data from regional observatories and the iAIR system into the map data, and inverse distance weighting (IDW) is used to compute air pollution over the island of Taiwan. Python's Folium pack (18) is used to overlap the map data onto a Leaflet web map and the result is exported as an HTML file and displayed on the iAIR system.…”

Section: Air Map Simulationmentioning

confidence: 99%

Smart iAIR System with Integration of the Internet of Things and Mobile Applications

Chang¹,

Tsai²,

Kuo³

et al. 2019

Sensors and Materials

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Commercially available crowdsourced air boxes have nonuniform distributions and cannot achieve suitable observation densities. As a result, current air quality data are limited to the vicinity of an observation station, rather than providing accurate actual data around a user. In this research, we propose the implementation of iAIR air pollution detection air boxes, which can be installed on air purifiers or air conditioners in large numbers and distributed over a wide area; this provides a real-time air pollution information map according to various air qualities and shows a higher resolution. The provided real-time information enables a user to immediately take appropriate response measures once poor air quality has been detected. When air quality degrades or the user's heart rate exceeds the normal range, the smart iAIR system installed on a cloud service platform will transmit warning messages to a mobile app and a wearable personal smart wristband, and at the same time, activate the air purifier (or air conditioner) to improve the current air quality and advise the user to move to an environment with better air quality with guidance by an air navigation map.

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Section: Air Map Simulationmentioning

confidence: 99%

Smart iAIR System with Integration of the Internet of Things and Mobile Applications

Chang¹,

Tsai²,

Kuo³

et al. 2019

Sensors and Materials

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“…Por tanto, estos datos permiten identificar, tanto los patrones espaciales de concentración de usuarios en una franja temporal -a lo largo del día o de la semana- (Fujita, 2013), como la afluencia de personas a eventos de todo tipo -culturales, festivos, de protesta, etc. - (Villatoro et al, 2013;Croitoru et al, 2015;Hua et al, 2016;Bolognesi y Galli, 2017;Panteras et al, 2015;Martín, Julián y Cos-Gayón, 2019) o cómo se distribuyen en la ciudad los usuarios que participan en un determinado tipo de evento.…”

Section: Patrones Espaciotemporales De Uso De Los Espacios Urbanosunclassified

Los datos geolocalizados de las redes sociales en el estudio de cuestiones urbanas complejas: cinco temas, cinco redes

Ciriquián¹,

Nolasco‐Cirugeda²,

Serrano-Estrada³

2019

ACE: Architecture, City and Environment

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Geolocated data generated by social networks allow various kinds of urban-related issues to be addressed, broadening knowledge on the different dynamics that occur in the city. This study has a twofold objective: (1) to identify which data variables from five well known social networks-Foursquare, Instagram, Twitter, Google Places and Airbnb-offer potential insights on the study of specific urban phenomena; and, (2) to delve into five exemplary urban topics that can be tackled by analysing the data from these social networks. Methodology Social media are analysed and specific urban issues that can be assessed through their data variables are identified. Specifically, five themes that encompass several research issues are identified through the combination of different data variables. Conclusions Data generated in social networks used for urban study purposes provide a valuable opportunity to address issues such as: [1] the use and preference for certain activities and places; [2] the image and perception of urban spaces; [3] spatiotemporal patterns of people presence and urban space-use; [4] supply and demand of economic activity; and, [5] identification of areas of concentration and specialization in the city. Originality This up to date and easily accessible information provides a thorough picture of complex issues in relation to urban dynamics that, until recently, were not easily measurable and quantifiable. Thus, the interpretation of these data broadens knowledge on city dynamics that can be useful for decision-making processes in the planning and design of the city.

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“…Günümüzde kullanılan akıllı telefonlar, tabletler ve sensörler gibi teknolojik cihazlar ile Twitter, Instagram, Facebook gibi sosyal ağlar bilgisayar ortamına aktarılan veri miktarında çok önemli bir artışa neden olmaktadır [1][2][3][4][5]. Veri miktarının bu kadar arttığı bir ortamda artık klasik veri işleme yöntemleri yetersiz kalmaktadır.…”

Section: Gi̇ri̇ş (Introduction)unclassified

Kd-tree and adaptive radius (KD-AR Stream) based real-time data stream clustering

Şenol

Karacan

2019

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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Highlights:Graphical/Tabular Abstract  Fully online data stream clustering  Evolutionary based clustering  Adaptive radius  Time based summarization  Memory for past status of clusters Figure A. Comparision of clustering quality and run-time complexity of algorithms on KDD datasetPurpose: The aim of this article to propose a new data stream clustering algorithm, which has an adaptive radius, can adapt itself to the evolutionary structure of streaming data and works in a fully online manner. Theory and Methods:In this study, kd-tree is used to forming and splitting clusters, adaptive radius approach is used to support increasing and decreasing the size of clusters, active/inactive status of clusters is used to adapt to the evolutionary structure of streaming data and all the operations are done online. In order to create a new cluster, the data that does not belong to any cluster are placed in a kd-tree, and the rangesearch operation is performed on those data according to predefined variables r (the radius of candidate cluster) and N (the number of data must be in the area). After forming the clusters, the radius of each cluster could be increased or decreased over time if necessary. Some clusters may be split and some may be merged over time because of dynamically changing structure of streaming data. Inactivation and reactivation of the status of clusters is used to allow for the identification of clusters formed in the same region at a different time interval with same cluster labels in accordance with the nature of the streaming data contrary to literature. This feature increases clustering quality of the proposed method. A summarization method that consist of time window and sliding window is used to support time based summarization without reduce performance. Results:To verify the effectiveness of KD-AR Stream algorithm, it is compared with SE-Stream, DPStream, and CEDAS on a variety of well-known datasets in terms of clustering quality and run-time complexity. The results show that KD-AR Stream outperforms other algorithms with a higher clustering success in a reasonable time as shown in Fig. A. Conclusion:The aim of this study is to propose a novel data stream clustering algorithm that adapts to the dynamic structure of the streaming data. The aim achieved by using the five evolutionary process which are appearance, activation/inactivation, self-evolution, merge, and split. According to the results, the proposed method is very successful in terms of clustering quality and run-time complexity.

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Analysis of Twitter messages using big data tools to evaluate and locate the activity in the city of Valencia (Spain)

Cited by 29 publications

References 40 publications

Smart iAIR System with Integration of the Internet of Things and Mobile Applications

Smart iAIR System with Integration of the Internet of Things and Mobile Applications

Los datos geolocalizados de las redes sociales en el estudio de cuestiones urbanas complejas: cinco temas, cinco redes

Kd-tree and adaptive radius (KD-AR Stream) based real-time data stream clustering

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