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Spatial Data Science and Artificial Intelligence for Human Mobility Research
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Spatial Data Science and Artificial Intelligence for Human Mobility Research

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 16939

Special Issue Editor

Dr. Luca Pappalardo

E-Mail Website
Guest Editor
Institute of Information Science and Technologies (ISTI), National Research Council of Italy (CNR), 00185 Roma, Italy
Interests: human mobility; data science; explainable AI; mathematical modelling; machine learning; network science; sports analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit your contribution to the Special Issue “Spatial Data Science and Artificial Intelligence for Human Mobility Research” on the ISPRS International Journal of Geo-Information. The deadline for submitting your contribution is March 30th, 2021 (anywhere on earth).

The availability of massive digital traces of human activities, such as phone detail records, GPS traces, and social media posts, offers nowadays the opportunity to investigate the quantitative patterns characterizing human behaviour at different spatiotemporal resolutions. This broad social microscope has attracted scientists from diverse disciplines, from physics and network science to artificial intelligence, fueling advances from public health to transportation engineering, urban planning and computational epidemiology.

This Special Issue aims to collect contributions on the recent advances in (i) human mobility description, i.e., the discovery of mobility patterns and relationships between human mobility and socio-economic interactions; (ii) mobility modelling, i.e., develop mechanistic or AI-based models to generate trajectories or flows; (iii) mobility prediction, i.e., forecast trajectories of flows between geographic locations; (iv) applications, such as computational epidemiology, what-if analysis, and estimation of pollution.

Potential topics include, but are not limited to:

  • Next-location and trajectory prediction;
  • Crowd flow prediction;
  • Human mobility and spreading processes;
  • Human mobility and social networks;
  • Generative models of individual (trajectories) and collective (flows) mobility;
  • Trip demand estimation, commute flows, migration flows;
  • Pedestrian dynamics: crowd dynamics, indoor and short distance mobility;
  • Human mobility and socio-economic indicators;
  • Application of Machine Learning and Deep Learning to human mobility;
  • Prediction of traffic congestion and road usage;
  • Activity Recognition and modeling.

Dr. Luca Pappalardo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Human Mobility
  • AI
  • Explainable AI
  • Mathematical Modelling
  • Human Migration
  • Generative Models
  • Human Behaviour
  • Human Dynamics

Published Papers (5 papers)

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Research

18 pages, 1465 KiB  
Article
Identification and Classification of Routine Locations Using Anonymized Mobile Communication Data
by Gonçalo Ferreira, Ana Alves, Marco Veloso and Carlos Bento
ISPRS Int. J. Geo-Inf. 2022, 11(4), 228; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi11040228 - 29 Mar 2022
Cited by 4 | Viewed by 2929
Abstract
Digital location traces are a relevant source of insights into how citizens experience their cities. Previous works using call detail records (CDRs) tend to focus on modeling the spatial and temporal patterns of human mobility, not paying much attention to the semantics of [...] Read more.
Digital location traces are a relevant source of insights into how citizens experience their cities. Previous works using call detail records (CDRs) tend to focus on modeling the spatial and temporal patterns of human mobility, not paying much attention to the semantics of places, thus failing to model and enhance the understanding of the motivations behind people’s mobility. In this paper, we applied a methodology for identifying individual users’ routine locations and propose an approach for attaching semantic meaning to these locations. Specifically, we used circular sectors that correspond to cellular antennas’ signal areas. In those areas, we found that all contained points of interest (POIs), extracted their most important attributes (opening hours, check-ins, category) and incorporated them into the classification. We conducted experiments with real-world data from Coimbra, Portugal, and the initial experimental results demonstrate the effectiveness of the proposed methodology to infer activities in the user’s routine areas. Full article
(This article belongs to the Special Issue Spatial Data Science and Artificial Intelligence for Human Mobility Research)
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15 pages, 9593 KiB  
Article
Assessing Reliability of Chinese Geotagged Social Media Data for Spatiotemporal Representation of Human Mobility
by Lingbo Liu, Ru Wang, Weihe Wendy Guan, Shuming Bao, Hanchen Yu, Xiaokang Fu and Hongqiang Liu
ISPRS Int. J. Geo-Inf. 2022, 11(2), 145; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi11020145 - 18 Feb 2022
Cited by 5 | Viewed by 3879
Abstract
Understanding the space-time dynamics of human activities is essential in studying human security issues such as climate change impacts, pandemic spreading, or urban sustainability. Geotagged social media posts provide an open and space-time continuous data source with user locations which is convenient for [...] Read more.
Understanding the space-time dynamics of human activities is essential in studying human security issues such as climate change impacts, pandemic spreading, or urban sustainability. Geotagged social media posts provide an open and space-time continuous data source with user locations which is convenient for studying human movement. However, the reliability of Chinese geotagged social media data for representing human mobility remains unclear. This study compares human movement data derived from the posts of Sina Weibo, one of the largest social media software in China, and that of Baidu Qianxi, a high-resolution human movement dataset from ‘Baidu Map’, a popular location-based service in China with 1.3 billion users. Correlation analysis was conducted from multiple dimensions of time periods (weekly and monthly), geographic scales (cities and provinces), and flow directions (inflow and outflow), and a case study on COVID-19 transmission was further explored with such data. The result shows that Sina Weibo data can reveal similar patterns as that of Baidu Qianxi, and that the correlation is higher at the provincial level than at the city level and higher at the monthly scale than at the weekly scale. The study also revealed spatial variations in the degree of similarity between the two sources. Findings from this study reveal the values and properties and spatiotemporal heterogeneity of human mobility data extracted from Weibo tweets, providing a reference for the proper use of social media posts as the data sources for human mobility studies. Full article
(This article belongs to the Special Issue Spatial Data Science and Artificial Intelligence for Human Mobility Research)
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19 pages, 7568 KiB  
Article
To Drive or to Be Driven? The Impact of Autopilot, Navigation System, and Printed Maps on Driver’s Cognitive Workload and Spatial Knowledge
by Iuliia Brishtel, Thomas Schmidt, Igor Vozniak, Jason Raphael Rambach, Bruno Mirbach and Didier Stricker
ISPRS Int. J. Geo-Inf. 2021, 10(10), 668; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10100668 - 02 Oct 2021
Cited by 4 | Viewed by 2890
Abstract
The technical advances in navigation systems should enhance the driving experience, supporting drivers’ spatial decision making and learning in less familiar or unfamiliar environments. Furthermore, autonomous driving systems are expected to take over navigation and driving in the near future. Yet, previous studies [...] Read more.
The technical advances in navigation systems should enhance the driving experience, supporting drivers’ spatial decision making and learning in less familiar or unfamiliar environments. Furthermore, autonomous driving systems are expected to take over navigation and driving in the near future. Yet, previous studies pointed at a still unresolved gap between environmental exploration using topographical maps and technical navigation means. Less is known about the impact of the autonomous system on the driver’s spatial learning. The present study investigates the development of spatial knowledge and cognitive workload by comparing printed maps, navigation systems, and autopilot in an unfamiliar virtual environment. Learning of a new route with printed maps was associated with a higher cognitive demand compared to the navigation system and autopilot. In contrast, driving a route by memory resulted in an increased level of cognitive workload if the route had been previously learned with the navigation system or autopilot. Way-finding performance was found to be less prone to errors when learning a route from a printed map. The exploration of the environment with the autopilot was not found to provide any compelling advantages for landmark knowledge. Our findings suggest long-term disadvantages of self-driving vehicles for spatial memory representations. Full article
(This article belongs to the Special Issue Spatial Data Science and Artificial Intelligence for Human Mobility Research)
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21 pages, 1773 KiB  
Article
A Mechanistic Data-Driven Approach to Synthesize Human Mobility Considering the Spatial, Temporal, and Social Dimensions Together
by Giuliano Cornacchia and Luca Pappalardo
ISPRS Int. J. Geo-Inf. 2021, 10(9), 599; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10090599 - 11 Sep 2021
Cited by 4 | Viewed by 2265
Abstract
Modelling human mobility is crucial in several areas, from urban planning to epidemic modelling, traffic forecasting, and what-if analysis. Existing generative models focus mainly on reproducing the spatial and temporal dimensions of human mobility, while the social aspect, though it influences human movements [...] Read more.
Modelling human mobility is crucial in several areas, from urban planning to epidemic modelling, traffic forecasting, and what-if analysis. Existing generative models focus mainly on reproducing the spatial and temporal dimensions of human mobility, while the social aspect, though it influences human movements significantly, is often neglected. Those models that capture some social perspectives of human mobility utilize trivial and unrealistic spatial and temporal mechanisms. In this paper, we propose the Spatial, Temporal and Social Exploration and Preferential Return model (STS-EPR), which embeds mechanisms to capture the spatial, temporal, and social aspects together. We compare the trajectories produced by STS-EPR with respect to real-world trajectories and synthetic trajectories generated by two state-of-the-art generative models on a set of standard mobility measures. Our experiments conducted on an open dataset show that STS-EPR, overall, outperforms existing spatial-temporal or social models demonstrating the importance of modelling adequately the sociality to capture precisely all the other dimensions of human mobility. We further investigate the impact of the tile shape of the spatial tessellation on the performance of our model. STS-EPR, which is open-source and tested on open data, represents a step towards the design of a mechanistic data-driven model that captures all the aspects of human mobility comprehensively. Full article
(This article belongs to the Special Issue Spatial Data Science and Artificial Intelligence for Human Mobility Research)
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21 pages, 6162 KiB  
Article
Pedestrian Flow Prediction in Open Public Places Using Graph Convolutional Network
by Menghang Liu, Luning Li, Qiang Li, Yu Bai and Cheng Hu
ISPRS Int. J. Geo-Inf. 2021, 10(7), 455; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10070455 - 02 Jul 2021
Cited by 11 | Viewed by 3358
Abstract
Open public places, such as pedestrian streets, parks, and squares, are vulnerable when the pedestrians thronged into the sidewalks. The crowd count changes dynamically over time with various external factors, such as surroundings, weekends, and peak hours, so it is essential to predict [...] Read more.
Open public places, such as pedestrian streets, parks, and squares, are vulnerable when the pedestrians thronged into the sidewalks. The crowd count changes dynamically over time with various external factors, such as surroundings, weekends, and peak hours, so it is essential to predict the accurate and timely crowd count. To address this issue, this study introduces graph convolutional network (GCN), a network-based model, to predict the crowd flow in a walking street. Compared with other grid-based methods, the model is capable of directly processing road network graphs. Experiments show the GCN model and its extension STGCN consistently and significantly outperform other five baseline models, namely HA, ARIMA, SVM, CNN and LSTM, in terms of RMSE, MAE and R2. Considering the computation efficiency, the standard GCN model was selected to predict the crowd. The results showed that the model obtains superior performances with higher prediction precision on weekends and peak hours, of which R2 are above 0.9, indicating the GCN model can capture the pedestrian features in the road network effectively, especially during the periods with massive crowds. The results will provide practical references for city managers to alleviate road congestion and help pedestrians make smarter planning and save travel time. Full article
(This article belongs to the Special Issue Spatial Data Science and Artificial Intelligence for Human Mobility Research)
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