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Abstract
The global number of individuals experiencing forced displacement has reached its highest level in the past decade. In this context, the provision of services for those in need requires timely and evidence-based approaches. How can mobile phone data (MPD) based analyses address the knowledge gap on mobility patterns and needs assessments in forced displacement settings? To answer this question, in this paper, we examine the capacity of MPD to function as a tool for anticipatory analysis, particularly in response to natural disasters and conflicts that lead to internal or cross-border displacement. The paper begins with a detailed review of the processes involved in acquiring, processing, and analyzing MPD in forced displacement settings. Following this, we critically assess the challenges associated with employing MPD in policy-making, with a specific focus on issues of user privacy and data ethics. The paper concludes by evaluating the potential benefits of MPD analysis for targeted and effective policy interventions and discusses future research avenues, drawing on recent studies and ongoing collaborations with mobile network operators.

Accurate, real-time and fine-spatial population distribution is crucial for urban planning, government management, and advertisement promotion. Limited by technics and tools, we rely on the census to obtain this information in the past, which is coarse and costly. The popularity of mobile phones gives us a new opportunity to investigate population estimation. However, real-time and accurate population estimation is still a challenging problem because of the coarse localization and complicated user behaviors. With the help of the passively collected human mobility and locations from the mobile networks including call detail records and mobility management signals, we develop a bimodal model beyond the prior work to better estimate real-time population distribution at metropolitan scales. We discuss how the estimation interval, space granularity, and data type will influence the estimation accuracy, and find the data collected from the mobility management signals with the 30 min estimation interval performs better which reduces the population estimation error by 30% in terms of Root Mean Square Error (RMSE). These results show us the great potential of using bimodal model and mobile phone data to estimate real-time population distribution.

Despite growing studies on the distinction between morphological and functional polycentricity, the present methods for identifying polycentricity often focus on the morphological dimension due to a lack of information about intra-urban functional flows, and are limited by the multifarious nature of people's spatiotemporal interactions. This study proposes a new approach, examining the degree of polycentricity in Shanghai at the intra-urban level using passive mobile phone data. A series of polycentricity indicators are used and are benchmarked against previous studies. Notably, we found that people's daily movements within a subcenter indicate that morphological polycentricity is also at play in Shanghai. We conclude that morphological and functional polycentricity may coexist at the intra-urban level, and that a mobile phone data approach can offer an alternative method to elucidate both the morphological and functional features of subcenters.

Abstract. Floods are acknowledged as one of the most serious
threats to people's lives and properties worldwide. To mitigate the flood
risk, it is possible to act separately on its components: hazard,
vulnerability, exposure. Emergency management plans can actually provide
effective non-structural practices to decrease both human exposure and
vulnerability. Crowding maps depending on characteristic time patterns,
herein referred to as dynamic exposure maps, represent a valuable tool to
enhance the flood risk management plans. In this paper, the suitability of
mobile phone data to derive crowding maps is discussed. A test case is
provided by a strongly urbanized area subject to frequent flooding located
on the western outskirts of Brescia (northern Italy). Characteristic
exposure spatiotemporal patterns and their uncertainties were detected
with regard to land cover and calendar period. This novel methodology still
deserves verification during real-world flood episodes, even though it
appears to be more reliable than crowdsourcing strategies, and seems to have
potential to better address real-time rescues and relief supplies.

Obtaining the time and space features of the travel of urban residents can facilitate urban traffic optimization and urban planning. As traditional methods often have limited sample coverage and lack timeliness, the application of big data such as mobile phone data in urban studies makes it possible to rapidly acquire the features of residents' travel. However, few studies have attempted to use them to recognize the travel modes of residents. Based on mobile phone call detail records and the Web MapAPI, the present study proposes a method to recognize the travel mode of urban residents. The main processes include: (a) using DBSCAN clustering to analyze each user's important location points and identify their main travel trajectories; (b) using an online map API to analyze user's means of travel; (c) comparing the two to recognize the travel mode of residents. Applying this method in a GIS platform can further help obtain the traffic flow of various means, such as walking, driving, and public transit, on different roads during peak hours on weekdays. Results are cross-checked with other data sources and are proven effective. Besides recognizing travel modes of residents, the proposed method can also be applied for studies such as travel costs, housing–job balance, and road traffic pressure. The study acquires about 6 million residents' travel modes, working place and residence information, and analyzes the means of travel and traffic flow in the commuting of 3 million residents using the proposed method. The findings not only provide new ideas for the collection and application of urban traffic information, but also provide data support for urban planning and traffic management.

Il paper propone una riflessione sulle possibilità offerte dai dati di traffico telefonico nel fornire conoscenze utili a costruire politiche per la mobilità più efficaci ed eque. A partire dai risultati di una esperienza di ricerca avviata presso il Dastu Politecnico di Milano sulla significatività dei dati di traffico telefonico di Telecom Italia nel restituire le densità d'uso del territorio (Manfredini, Pucci & Tagliolato, 2012 e 2013) e le origini e destinazioni dei movimenti giornalieri di mobilità (Tagliolato, Manfredini & Pucci, 2013), si evidenziano le potenzialità analitiche e interpretative offerte da questi dati nel descrivere le modalità con cui differenti popolazioni urbane usano il territorio e le possibili ricadute sulle politiche per la mobilità.Nella ricerca condotta, il trattamento dei dati di traffico telefonico ha consentito di restituire la variabilità spazio-temporale delle pratiche d'uso in Lombardia, a partire dalle quali si sono individuati "comunità di pratiche" e "territori contingenti", generati cioè dalle pratiche di diverse popolazioni temporanee, che si sono assunti come perimetri utili per una diversa articolazione delle competenze e distribuzione delle risorse disponibili. Nel paper l'individuazione di popolazioni urbane temporanee attraverso i dati di traffico telefonico non ha unicamente una finalità interpretativa, ma rappresenta la condizione attraverso cui riconoscere le nuove domande disaggregate per "comunità di pratiche", su cui costruire politiche di offerta più efficaci e meno onerose finanziariamente, poiché non generaliste. ; The paper focusses on the potentialities offered by mobile phone data to provide useful knowledge of site practices and rhythms of usage of contemporary city, for more effective and equitable mobility policies.Starting from the results of a research carried out at the Politecnico di Milano, using mobile phone data provided by Telecom Italia and finalized to verify the meaning of mobile phone data in returning the density of land use (Manfredini, Pucci & Tagliolato, 2012 and 2013) and the origins and destinations of daily movements (Tagliolato, Manfredini & Pucci, 2013), we will highlight how new maps, based on the processing of mobile phone data can represent spatialized urban practices and how they can give new insights for analyze space-time patterns of mobility practices. In our research, mobile phone data, returning new maps of site practices in Lombardy Region with information on temporary populations and city usages patterns (daily/nightly practices, non-systematic mobility), allowed to trace "fuzzy boundaries" as perimeters of practices, proposed like a tool for supporting and increasing the efficiency of urban policies and mobility services.In the paper, the identification of temporary urban populations through two types of mobile phone data (density of the calls and origin - destination traces of the calls) has not only a knowing purpose, but it is the condition for recognize new claims referred to "communities of practice", by which to build mobility policies incisive, also because not generalist.