China
Africa
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“Mapping” Smart Cities
Published in Tommi Inkinen, Tan Yigitcanlar, Mark Wilson, Smart Cities and Innovative Urban Technologies, 2020
Becky P. Y. Loo, Winnie S. M. Tang
In other words, decision-making becomes a data-driven process. A smart city fed by location intelligence, spatial analytics, and real-time data will have a wide range of applications, from optimizing supply chain management to using real-time updates for public utilities to predict demand in advance based on analytics. In a smart city with good connection and collaboration of data infrastructure, stakeholders can have quick access to the same up-to-date data on a common platform. Technologies like GIS could help enable collaboration with real-time location-based tools that allow everyone on the same platform to stay connected. For example, field workers may take their mobile devices and record the maintenance needs of the infrastructure. Data including spatio-temporal information can be instantly fed back to dashboards at the office (Barry, 2018). Location intelligence is crucial for deploying maintenance engineers during bad weather or emergency response that is both time and location dependent.
Launch of SD-WAN Service
Published in David W. Wang, Software Defined-WAN for the Digital Age, 2018
For instance, sales team can use the real-time location intelligence to more effectively design and deliver competitively priced and diversely routed network services. Such sale process automating tool enables sales reps, channels, and agent partners to quickly deliver solution design, multi-carrier circuit quoting, pricing, provisioning, and ultimately increase sales of SA-WAN.
Incentivizing catastrophe risk sharing
Published in IISE Transactions, 2020
The second direction is to develop a data-driven contract and pricing model. There have been enduring efforts to share data between government, financial institutions, real estate agents, and communities, to reduce the information bias (Kunreuther and Pauly, 2018) and promote risk communication (Kousky and Kunreuther, 2018). Montgomery and Kunreuther (2018) conduct a cost-effectiveness analysis under different disaster scenarios to illustrate the importance of using flood risk data to price flood insurance. Risk Management Solutions (Ireland, 2019) develops the Location Intelligence API for assessing risks and underwriting insurance products with large-volume data (such as geocoding data, building characteristics, hazard information, and loss data) to assist pricing. We can build up a data-driven decision model that integrates prediction and optimization to design and price insurance policies.