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The inconceivable ability and common practice to collect personal data as well as the power of data‐driven approaches to businesses, services and security nowadays also introduce significant privacy issues. There have been extensive studies on addressing privacy preserving problems in the data mining community but relatively few have provided supervised control over the anonymization process. Preserving both the value and privacy of the data is largely a non‐trivial task. We present the design and evaluation of a visual interface that assists users in employing commonly used data anonymization techniques for making privacy preserving visualizations. Specifically, we focus on event sequence data due to its vulnerability to privacy concerns. Our interface is designed for data owners to examine potential privacy issues, obfuscate information as suggested by the algorithm and fine‐tune the results per their discretion. Multiple use case scenarios demonstrate the utility of our design. A user study similarly investigates the effectiveness of the privacy preserving strategies. Our results show that using a visual‐based interface is effective for identifying potential privacy issues, for revealing underlying anonymization processes, and for allowing users to balance between data utility and privacy. The inconceivable ability and common practice to collect personal data as well as the power of data‐driven approaches to businesses, services and security nowadays also introduce significant privacy issues. There have been extensive studies on addressing privacy preserving problems in the data mining community but relatively few have provided supervised control over the anonymization process. Preserving both the value and privacy of the data is largely a non‐trivial task. We present the design and evaluation of a visual interface that assists users in employing commonly used data anonymization techniques for making privacy preserving visualizations. Specifically, we focus on event sequence data due to its vulnerability to privacy concerns. Our interface is designed for data owners to examine potential privacy issues, obfuscate information as suggested by the algorithm and fine‐tune the results per their discretion. [ABSTRACT FROM AUTHOR]

Evaluation on urban commercial centers' attractiveness not only benefits business strategy making and location choice, but also helps traffic management and urban planning. Traditionally, it is studied using questionnaires and field research, which are labor-intensive and time-consuming. To overcome these problems when evaluating the urban commercial centers' attractiveness, massive data analytics with datasets from taxi traffic, population, area, and road networks are adopted in this paper. Taking fifteen commercial centers at Shenzhen as a case study, a Cyber-Physical-Social System is built up to deal with these massive data for statistical analysis. An "Attractiveness Degree Model" is proposed to describe the degree to which customers desire to visit a commercial center. Then attractiveness thematic maps are drawn. Results show that YiTianJiaRiGuangChang has the highest attractiveness degree even though it has a small size and low commercial value. The attractiveness degree rankings are corroborated by annual customer satisfaction survey from Shenzhen Retail Business Association. Attractiveness thematic maps show that about 50–65% visits by taxis are within 5 km range. These results can be applied to support market analysis, urban planning, traffic management, and related areas. • Commercial center is precisely defined geographically and sociologically. • Attractiveness degree is the proposed metric to evaluate the attractiveness. • A Cyber-Physical-Social System is implemented for massive data processing. • Statistical analysis and thematic maps shed new insights into attractiveness. • Commercial centers are classified into four representative types in China. [ABSTRACT FROM AUTHOR]