CITY PROFILE: USING SMART DATA TO CREATE DIGITAL URBAN SPACES

In the process of modern urban development, cities face various challenges such as climate change, air pollution and poverty, which have negative effects on urban sustainable development and self-regulation. The construction of smart cities can effectively improve the capability of urban management and operation. In this paper, we aim to explore how to use the big data in urban physical, social and cyber spaces to construct smart cities. The concept of digital urban space is proposed to help achieve the construction of smart cities, and city profiling is accordingly presented as a construction method of digital urban spaces and city profile as a product. According to the goals of constructing digital urban spaces, we illustrate the conception and core implementation steps of city profiling, including urban facets modelling and urban facets profiling with smart data. With three application scenarios, we discuss how city profile can be used to meet the factual needs of management, operation and decision-making. City profile can model the cities with urban data and make them become organisms managed and operated by data, so that various information services related to the city can be provided to different users

An Integrated Information Lifecycle Management Framework For Exploiting Social Network Data to Identify Dynamic Large Crowd Concentration Events in Smart Cities Applications

With the current availability of an extreme diversity of data sources and services, emerging from the Internet of Things and Cloud domains, the challenge is shifted towards identifying intelligent, abstracted and adaptive ways of correlating and combining the various levels of information. The purpose of this work is to demonstrate such a combination, on one hand at the service level, through integrating smart cities platforms for user level data, and on the other hand at Complex Event Processing, Storage and Analytics capabilities together with Twitter data. The final goal is to identify events of interest to the user such as Large Crowd Concentration (LCC) in a given area, in order to enrich application level information with related event identification that can enable more sophisticated actions on behalf of that user. The identification is based on observation of Twitter activity peaks compared to historical data on a dynamic time and location of interest. The approach is validated through a two-month experiment in the city of Madrid, identifying LCCs in sporting events around two sports venues and analyzing various approaches with relation to the needed thresholds definition