Big City, Big Data:

2018 ◽  
pp. 169-177
Author(s):  
Jessa Lingel
Keyword(s):  
Big Data ◽  
interactions ◽  
2015 ◽  
Vol 22 (4) ◽  
pp. 70-73 ◽  
Author(s):  
Kathi R. Kitner ◽  
Thea de Wet
Keyword(s):  
Big Data ◽  

2019 ◽  
Vol 2 (3) ◽  
pp. 203-217
Author(s):  
Setiyono

Abstract—Smart solutions are needed by the city government to overcome various city problems. One solution is smart city. To realize smart city, one of the main challenges is the solution to overcome the city's security problems. Currently cities in Indonesia do not yet know the level of security of their cities. The level of city security can be obtained by surveying various cities. But surveys require personnel, time and cost that is not small. In this study the authors propose a method by designing a model to determine the level of security of cities in Indonesia by utilizing big data through the prediction of sentiment analysis of people's perceptions of city security on Twitter. This research was conducted in 25 cities in Indonesia which are divided into 8 big cities, 9 medium cities and 8 small cities. The results of the prediction models designed in this study are generally not much different from the results of the 2019 RKCI (Indonesia Smart Cities Rating) survey in the field of security and disaster. The results of this study found that 4 cities with a maturity level of security are at the Integrative level (score 60 to 79 in GSCM Maturity Level), namely Tangerang, Kediri, Parepare and Probolinggo, while the other 21 cities are at the Scattered level (score 40 to 59). The average score for the big city category is 55.41, while the middle city score is 55.48 and the small city is 53.70. The results of performance measurement of this prediction model are for an accuracy value of 80.10% while a precision value of 81.10% and a recall value of 82.62%.


ASHA Leader ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 59-59
Keyword(s):  

Find Out About 'Big Data' to Track Outcomes


2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.


2017 ◽  
Vol 225 (3) ◽  
pp. 287-288
Keyword(s):  

An associated conference will take place at ZPID – Leibniz Institute for Psychology Information in Trier, Germany, on June 7–9, 2018. For further details, see: http://bigdata2018.leibniz-psychology.org


PsycCRITIQUES ◽  
2014 ◽  
Vol 59 (2) ◽  
Author(s):  
David J. Pittenger
Keyword(s):  

2014 ◽  
Author(s):  
Daniel Maurath
Keyword(s):  
Big Data ◽  

2015 ◽  
Author(s):  
Kirsten Weir
Keyword(s):  

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