Data from mobile phone operators: A tool for smarter cities?

Abstract The use of mobile phone data provides new spatio-temporal tools for improving urban planning, and for reducing inefficiencies in present-day urban systems. Data from mobile phones, originally intended as a communication tool, are increasingly used as innovative tools in geography and social sciences research. Empirical studies on complex city systems from human-centred and urban dynamics perspectives provide new insights to develop promising applications for supporting smart city initiatives. This paper provides a comprehensive review and a typology of spatial studies on mobile phone data, and highlights the applicability of such digital data to develop innovative applications for enhanced urban management

Geographical Counterpoint to Choreographic Information based on Approaches in GIScience and Visualization

This study provides geographical counterpoint to existing knowledge of a dance piece through approaches from GIScience and visualization by focusing on spatio-temporal movement of dancers in a large dataset of the dance. The goal of this study is to introduce a new application to bridging art and science in the domain of dance and geography disciplines. The study utilizes existing methodologies in GIScience, including exploratory spatial data analysis (ESDA), spatial analysis, Relative Motion (REMO) analysis, and Qualitative Trajectory Calculus (QTC) analysis for the reasoning of the dance data. The results of the study demonstrate the following. First, spatio-temporal information in the dance can be better understood by using approaches in geography, including ESDA, spatial analysis, REMO analysis, QTC analysis, and visualization. Second, the REMO analysis measured relative azimuth, speed, and δ-speed of the dancers per space and time and intuitively visualized their interactions. Third, the QTC analysis showed an example of measuring similarity and difference between repetitive movements of the dancers. The study exhibits how approaches of GIScience in geography could contribute to finding new knowledge of choreographic information that has been, in general, hard to recognize through other disciplines such as dance and statistics

Artificial intelligence and visual analytics in geographical space and cyberspace: Research opportunities and challenges

In recent decades, we have witnessed great advances on the Internet of Things, mobile devices, sensor-based systems, and resulting big data infrastructures, which have gradually, yet fundamentally influenced the way people interact with and in the digital and physical world. Many human activities now not only operate in geographical (physical) space but also in cyberspace. Such changes have triggered a paradigm shift in geographic information science (GIScience), as cyberspace brings new perspectives for the roles played by spatial and temporal dimensions, e.g., the dilemma of placelessness and possible timelessness. As a discipline at the brink of even bigger changes made possible by machine learning and artificial intelligence, this paper highlights the challenges and opportunities associated with geographical space in relation to cyberspace, with a particular focus on data analytics and visualization, including extended AI capabilities and virtual reality representations. Consequently, we encourage the creation of synergies between the processing and analysis of geographical and cyber data to improve sustainability and solve complex problems with geospatial applications and other digital advancements in urban and environmental sciences

Mobile networks and internet of things infrastructures to characterize smart human mobility

The evolution of Mobile Networks and Internet of Things (IoT) architectures allows one to rethink the way smart cities infrastructures are designed and managed, and solve a number of problems in terms of human mobility. The territories that adopt the sensoring era can take advantage of this disruptive technology to improve the quality of mobility of their citizens and the rationalization of their resources. However, with this rapid development of smart terminals and infrastructures, as well as the proliferation of diversified applications, even current networks may not be able to completely meet quickly rising human mobility demands. Thus, they are facing many challenges and to cope with these challenges, different standards and projects have been proposed so far. Accordingly, Artificial Intelligence (AI) has been utilized as a new paradigm for the design and optimization of mobile networks with a high level of intelligence. The objective of this work is to identify and discuss the challenges of mobile networks, alongside IoT and AI, to characterize smart human mobility and to discuss some workable solutions to these challenges. Finally, based on this discussion, we propose paths for future smart human mobility researches.This work has been supported by FCT–Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. This work has also been supported by national funds through FCT–Fundação para a Ciência e Tecnologia through project UIDB/04728/202

Capturing time in space : Dynamic analysis of accessibility and mobility to support spatial planning with open data and tools

Understanding the spatial patterns of accessibility and mobility are a key (factor) to comprehend the functioning of our societies. Hence, their analysis has become increasingly important for both scientific research and spatial planning. Spatial accessibility and mobility are closely related concepts, as accessibility describes the potential to move by modeling, whereas spatial mobility describes the realized movements of individuals. While both spatial accessibility and mobility have been widely studied, the understanding of how time and temporal change affects accessibility and mobility has been rather limited this far. In the era of ‘big data’, the wealth of temporally sensitive spatial data has made it possible, better than ever, to capture and understand the temporal realities of spatial accessibility and mobility, and hence start to understand better the dynamics of our societies and complex living environment. In this thesis, I aim to develop novel approaches and methods to study the spatio-temporal realities of our living environments via concepts of accessibility and mobility: How people can access places, how they actually move, and how they use space. I inspect these dynamics on several temporal granularities, covering hourly, daily, monthly, and yearly observations and analyses. With novel big data sources, the methodological development and careful assessment of the information extracted from them is extremely important as they are increasingly used to guide decision-making. Hence, I investigate the opportunities and pitfalls of different data sources and methodological approaches in this work. Contextually, I aim to reveal the role of time and the mode of transportation in relation to spatial accessibility and mobility, in both urban and rural environments, and discuss their role in spatial planning. I base my findings on five scientific articles on studies carried out in: Peruvian Amazonia; national parks of South Africa and Finland; Tallinn, Estonia; and Helsinki metropolitan area, Finland. I use and combine data from various sources to extract knowledge from them, including GPS devices; transportation schedules; mobile phones; social media; statistics; land-use data; and surveys. My results demonstrate that spatial accessibility and mobility are highly dependent on time, having clear diurnal and seasonal changes. Hence, it is important to consider temporality when analyzing accessibility, as people, transport and activities all fluctuate as a function of time that affects e.g. the spatial equality of reaching services. In addition, different transport modes should be considered as there are clear differences between them. Furthermore, I show that, in addition to the observed spatial population dynamics, also nature’s own dynamism affects accessibility and mobility on a regional level due to the seasonal variation in river-levels. Also, the visitation patterns in national parks vary significantly over time, as can be observed from social media. Methodologically, this work demonstrates that with a sophisticated fusion of methods and data, it is possible to assess; enrich; harmonize; and increase the spatial and temporal accuracy of data that can be used to better inform spatial planning and decision-making. Finally, I wish to emphasize the importance of bringing scientific knowledge and tools into practice. Hence, all the tools, analytical workflows, and data are openly available for everyone whenever possible. This approach has helped to bring the knowledge and tools into practice with relevant stakeholders in relation to spatial planning