Mobile big-data-driven rating framework: measuring the relationship between human mobility and app usage behavior

IEEE Network ◽  
2016 ◽  
Vol 30 (3) ◽  
pp. 14-21 ◽  
Author(s):  
Yuanyuan Qiao ◽  
Xiaoxing Zhao ◽  
Jie Yang ◽  
Jiajia Liu
Keyword(s):  
Big Data ◽  
Human Mobility ◽  
Data Driven ◽  
Mobile Big Data ◽  
Usage Behavior ◽  
The Relationship

Mobile Big Data: The Fuel for Data-Driven Wireless

2017 ◽  
Vol 4 (5) ◽  
pp. 1489-1516 ◽  
Author(s):  
Xiang Cheng ◽  
Luoyang Fang ◽  
Liuqing Yang ◽  
Shuguang Cui
Keyword(s):  
Big Data ◽  
Data Driven ◽  
Mobile Big Data

scholarly journals Sustainable targeted interventions to mitigate the COVID-19 pandemic: A big data-driven modeling study in Hong Kong

2021 ◽  
Author(s):  
Hanchu Zhou ◽  
Qingpeng Zhang ◽  
Zhidong Cao ◽  
Helai Huang ◽  
Daniel Dajun Zeng
Keyword(s):  
Big Data ◽  
Hong Kong ◽  
Control Measure ◽  
Human Mobility ◽  
Local Economy ◽  
Data Driven ◽  
Agent Based Model ◽  
Agent Based ◽  
Targeted Interventions ◽  
The City

AbstractBackgroundThe nonpharmaceutical interventions (NPIs) for contact suppression have been widely used worldwide, which impose harmful burdens on the population and the local economy. The evaluation of alternative NPIs is needed to confront the pandemic with less disruption. By harnessing human mobility data, we develop an agent-based model that can evaluate the efficacies of NPIs with individualized mobility simulations. Based on the model, we propose the data-driven targeted interventions to mitigate the COVID-19 pandemic in Hong Kong without city-wide NPIs.MethodsWe develop a data-driven agent-based model for 7.55 million Hong Kong residents to evaluate the efficacies of various NPIs in the first 80 days of the initial outbreak. The entire territory of Hong Kong is split into 4,905 500m×500m grids. The model can simulate detailed agent interactions based on the demographics data, public facilities and functional buildings, transportation systems, and travel patterns. The general daily human mobility patterns are adopted from Google’s Community Mobility Report. The scenario without any NPIs is set as the baseline. By simulating the epidemic progression and human movement at the individual level, we proposed model-driven targeted interventions, which focus on the surgical testing and quarantine of only a small portion of regions instead of enforcing NPIs in the whole city. The efficacious of common NPIs and the proposed targeted interventions are evaluated by extensive Monte Carlo simulations.FindingsWithout NPIs, we estimate that there are 128,711 total infections (IQR 23,511-70,310) by the end of the 80-day simulation. The proposed targeted intervention averts 95.85% and 94.13% of baseline infections with only 100 (2.04%) and 50 (1.02%) grids being quarantined, respectively. Mild social distancing without testing results in 16,503 total cases (87.18% infections averted), rapid implementation of full lockdown and testing measures (such as the control measure in Mainland China) performs the best, with only 805 infections (99.37% infections averted). Testing-and-quarantining 10%, 20%, 50% of all symptomatic cases with 24-hour/48-hour avert 89.92%/ 87.78%, 95.47%/ 92.42%, and 97.93%/ 95.61% infections, respectively.InterpretationBig data-driven mobility modeling can inform targeted interventions, which are able to effectively contain the COVID-19 outbreak with much lower disruption of the city. It represents a promising approach to sustainable NPIs to help us revive the economy of the city and the world.

scholarly journals Using mobile big data to support emergency preparedness and address economically vulnerable communities during the COVID-19 pandemic in Nigeria

Data & Policy ◽  
2021 ◽  
Vol 3 ◽  
Author(s):  
Joanne Gilbert ◽  
Olubayo Adekanmbi ◽  
Charlie Harrison
Keyword(s):  
Big Data ◽  
Resource Planning ◽  
Data Sources ◽  
Data Driven ◽  
Second Phase ◽  
Case Scenario ◽  
Worst Case ◽  
Mobile Big Data ◽  
The Government ◽  
The Impact

Abstract With the declaration of the coronavirus disease 2019 (COVID-19) pandemic in Nigeria in 2020, the Nigeria Governors’ Forum (NGF) instigated a collaboration with MTN Nigeria to develop data-driven insights, using mobile big data (MBD) and other data sources, to shape the planning and response to the pandemic. First, a model was developed to predict the worst-case scenario for infections in each state. This was used to support state-level health committees to make local resource planning decisions. Next, as containment interventions resulted in subsistence/daily paid workers losing their income and ability to buy essential food supplies, NGF and MTN agreed a second phase of activity, to develop insights to understand the population clusters at greatest socioeconomic risk from the impact of the pandemic. This insight was used to promote available financial relief to the economically vulnerable population clusters in Lagos state via the HelpNow crowdfunding initiative. This article discusses how anonymized and aggregated mobile network data (MBD), combined with other data sources, were used to create valuable insights and inform the government, and private business, response to the pandemic in Nigeria. Finally, we discuss lessons learnt. Firstly, how a collaboration with, and support from, the regulator enabled MTN to deliver critical insights at a national scale. Secondly, how the Nigeria Data Protection Regulation and the GSMA COVID-19 Privacy Guidelines provided an initial framework to open the discussion and define the approach. Thirdly, why stakeholder management is critical to the understanding, and application, of insights. Fourthly, how existing relationships ease new project collaborations. Finally, how MTN is developing future preparedness by creating a team that is focused on developing data-driven insights for social good.

Competitive advantage of data-driven analytical capabilities: the role of big data visualization and of organizational agility

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mauricius Munhoz de Medeiros ◽  
Antônio Carlos Gastaud Maçada
Keyword(s):  
Big Data ◽  
Competitive Advantage ◽  
Data Visualization ◽  
Data Driven ◽  
Mediating Effects ◽  
Organizational Agility ◽  
Content Type ◽  
Big Data Visualization ◽  
The Relationship

PurposeIn the digital age, the use of data and analytical capabilities to guide business decisions and operations plays a strategic role for organizations to gain competitive advantage (CA). However, the paths by which analytical capabilities convey their effect to CA are not yet fully known and few studies address the role of behavioral and cultural aspects of related of analytical capabilities. The purpose of this paper is to analyze how data-driven culture (DDC) and business analytics (BA) affect CA, considering the mediating effects of big data visualization (BDV) and organizational agility (OA).Design/methodology/approachA survey was conducted with 173 managers who are BDV and BA users in Brazilian organizations of various economic segments. The data were analyzed through structural equation modeling and mediation tests.FindingsThe evidence indicates that DDC and BDV are antecedents of BA. The following complementary mediations were discovered: BDV in the relationship between DDC and BA; BA in the relationship between DDC and CA; and OA in the relationship between BA and CA. It was also discovered that OA explains the transmission of most of the effect of BA to CA.Practical implicationsThis study can help organizations to understand the importance of cultural and behavioral aspects related to the use of the analytical capabilities. Thereby, managers can establish policies and strategies to extract value from data and leverage business agility and competitiveness through use BDV and BA.Originality/valueThis study fills an important research gap by developing an original research model and discussing empirical evidence on how DDC and BA affect CA, considering the mediating effects of BDV and OA.

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