Dynamic, Interactive and Visual Analysis of Population Distribution and Mobility Dynamics in an Urban Environment Using the Mobility Explorer Framework

Abstract

This paper investigates the extent to which a mobile data source can be utilised to generate new information intelligence for decision-making in smart city planning processes. In this regard, the Mobility Explorer framework is introduced and applied to the City of Vienna (Austria) by using anonymised mobile phone data from a mobile phone service provider. This framework identifies five necessary elements that are needed to develop complex planning applications. As part of the investigation and experiments a new dynamic software tool, called Mobility Explorer, has been designed and developed based on the requirements of the planning department of the City of Vienna. As a result, the Mobility Explorer enables city stakeholders to interactively visualise the dynamic diurnal population distribution, mobility patterns and various other complex outputs for planning needs. Based on the experiences during the development phase, this paper discusses mobile data issues, presents the visual interface, performs various user-defined analyses, demonstrates the application's usefulness and critically reflects on the evaluation results of the citizens' motion exploration that reveal the great potential of mobile phone data in smart city planning but also depict its limitations. These experiences and lessons learned from the Mobility Explorer application development provide useful insights for other cities and planners who want to make informed decisions using mobile phone data in their city planning processes through dynamic visualisation of Call Data Record (CDR) data.

Smart city big data analytics: An advanced review

“…According to literature review, ICT is an essential component in smart city governance to gain necessary intelligence that provides basis for evidence‐based decision‐making. At the core of ICT based solutions are three main components: (i) data collection through sensors, Internet‐of‐Things, smart phones, remote sensing (e.g., satellite or in‐situ) and city databases; (ii) data processing and/or pre‐processing (e.g., filtering, data quality and format translations); (iii) data analysis using machine learning, data mining and other statistical algorithms to generate new knowledge in various cross‐thematic applications such as mobility (Docherty, Marsden, & Anable, ; Peters‐Anders et al, ; Rathore et al,), energy (Antonić, Marjanović, Pripužić, & Žarko, ; Carli, Albino, Dotoli, Mummolo, & Savino, ; Silva, Khan, & Han, ), health (Anisetti et al, ; Farahani et al, ), environment (Antonić et al, ), public services (Pérez‐González & Díaz‐Díaz, ; Zhang et al, ), economy (Chatfield & Reddick, ; Saggi & Jain, ; Zaman et al, ), waste management (Digiesi, Facchini, Mossa, Mummolo, & Verriello, ), social analysis (Kousiouris et al, ; Terroso‐Saenz, Gonzalez‐Vidal, Cuenca‐Jara, & Skarmeta, ), waste water management (Edmondson et al, ), urban planning (Eirinaki et al, ; Pettit et al, ; Rathore, Ahmad, Paul, & Rho, ), tourism and cultural heritage (Sun, Song, Jara, & Bie, ), buildings (Linder, Vionnet, Bacher, & Hennebert, ), agriculture (Kamilaris, Gao, Prenafeta‐Boldu, & Ali, ), emergency response (Abu‐Elkheir, Hassanein, & Oteafy, ), etc. The above three components—with some variations—are common among the most of smart city data analytics literature for example, Zhang et al (); Khan et al (); Rathore et al ().…”

“…Relational databases are still the most commonly used storage mechanism for smart city applications Eirinaki et al (); Motta, You, Sacco, and Ma (); Peters‐Anders, Khan, Loibl, Augustin, and Breinbauer (). A number of tools and techniques are used to store, transfer and manage big data in smart cities.…”