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Exploiting smart technologies to build smart resilient cities
Published in Paolo Gardoni, Routledge Handbook of Sustainable and Resilient Infrastructure, 2018
In general, all big data solutions must cope with data volume, variety, and veracity. Most of the big data problems connected to the (smart) city platform for resilience monitoring are related to real time data (e.g. vehicle and human mobility, energy consumption, IOT, etc.). The city level architecture should be capable of taking advantage of huge amount of data coming from several domains, at different generation rates for exploiting and analyzing them for computing integrated and multi domain information, making predictions, detecting anomalies for early warning, and for producing suggestions and recommendations to city users and operators. The RESOLUTE platform is a three-layer architecture model capable of managing such a complexity. It is organized as follows: The big data management layer, or data services layer, deals with data acquisition, aggregation, and fusion from various sources acting in the smart city. It addresses the computational and heterogeneity challenges of multi-source data collection and integration. It is decoupled respect to the upper level of the system making the data available in a standardized way to the applications.The mission critical layer consists of core system intelligence. This is the layer where developers can solve mission-critical business problems, enabling data sharing among sub-systems and achieving major productivity advantages. These components can be used to enforce business rules, such as business algorithms and legal or governmental regulations, and data rules, which are designed to keep the data structures consistent. This layer consumes the data aggregated and standardized by the lower level.The presentation layer, or user services layer, gives a user access to the application. This layer presents the results of the data analysis to multiple users through the resilience dashboard and optionally permits data manipulation and data entry. The two main types of user interface for this layer are the traditional application and the web-based application.
Digital world meets urban planet – new prospects for evidence-based urban studies arising from joint exploitation of big earth data, information technology and shared knowledge
Published in International Journal of Digital Earth, 2020
Thomas Esch, Hubert Asamer, Felix Bachofer, Jakub Balhar, Martin Boettcher, Enguerran Boissier, Pablo d' Angelo, Caroline M. Gevaert, Andreas Hirner, Katerina Jupova, Franz Kurz, Andy Yaw Kwarteng, Emmanuel Mathot, Mattia Marconcini, Alessandro Marin, Annekatrin Metz-Marconcini, Fabrizio Pacini, Marc Paganini, Hans Permana, Tomas Soukup, Soner Uereyen, Christopher Small, Vaclav Svaton, Julian Nils Zeidler
Global urban settlement patterns and the associated socio-economic, ecological, as well as technical developments, are rapidly changing the human environment. In this context, the integration of multi-source data brings powerful analysis applications to the user. It enables the analyst and decision maker to combine existing datasets on the U-TEP with external datasets and thereby generate added value. The following four exemplary VISAT applications (I–IV) are snapshots of global multi-source and multi-temporal analyses – and their related results – that were defined by planners and decision makers. Nightlights, electric power consumption and social media: The VISAT analysis presented in Figure 6 focuses on the intersections of data related to population, settlements and energy. The globe image (A) illustrates the accumulated nightlights intensity 2015 product of the cloud-free average radiance values of the Visible Infrared Imaging Radiometer Suite (VIIRS) (Elvidge et al. 2017) in relation to the total/country-based population in 2015 derived from the World Bank Open Data Catalogue (The World Bank 2018b). A higher intensity of blue in Figure 6 indicates higher nightlights intensity per capita.