Special Issue "Human Dynamics Research in the Age of Smart and Intelligent Systems"

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

Deadline for manuscript submissions: closed (31 December 2019).

Special Issue Editors

Prof. Dr. Shih-Lung Shaw
E-Mail Website
Guest Editor
Department of Geography, University of Tennessee, Knoxville, TN 37996, USA
Interests: transportation geography; geographic information science; time geography; human dynamics
Special Issues and Collections in MDPI journals
Dr. Yang Xu
E-Mail Website
Guest Editor
Department of Land-Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Interests: space-sime GIS; human mobility mining and modelling; urban data analytics and visualization; transport geography; computational social science

Special Issue Information

Dear Colleagues,

Aims and Scope

The proliferation of location-aware and information communication technologies, along with advancements in the Internet of Things (IOT) and Artificial Intelligence (AI), are transforming the ways human activities and interactions are sensed, analyzed, and understood. With the ever-increasing adoption of these technologies, detailed data on human behaviors, movements and communications are being collected by vendors, service providers, social media services, and government agencies at finer spatial and temporal granularities. Such data bring new perspectives and opportunities for human dynamics research. On the one hand, it is critical to examine to what extent human dynamics have changed and become smarter and more intelligent due to the new technologies, which has profound implications on the current and future economic, social, cultural, political, and transportation systems. On the other hand, it is important to identify potential applications of smart and intelligent systems to enhance and improve quality of life. Building on a series of paper and panel sessions organized at the 2019 AAG meetings in Washington, DC and this open call for submissions, this Special issue aims at bringing together researchers in the relevant disciplines to share their research on the challenges and opportunities of human dynamics in the age of smart and intelligent systems.

Possible Topics (not exhaustive)

This Special Issue encourages contributions from researchers on the theories, methods, and/or applications related to human dynamics research in the age of smart and intelligent systems. Example topics include:

  • Conceptual/theoretical framework for human dynamics research
  • Development of smart and intelligent systems for collecting and analyzing human dynamic data (e.g., smartphone-based travel and activity survey)
  • Development of new ways of tackling the five Vs of big human behavior data (volume, velocity, variety, veracity, and value)
  • Data and algorithm biases and implications for reproducible human dynamics research
  • GeoAI and deep learning for understanding human dynamics and human AI
  • Human mobility mining and modeling based on large-scale human dynamic data
  • Development of new ways of representing and visualizing human dynamics
  • Multi-source heterogenous data fusion and user profiling in human dynamics research
  • Multi-scale spatiotemporal analysis and modeling of human dynamics
  • Modeling and analysis of human dynamics in physical and virtual spaces
  • Agent-based models of human–environment interactions
  • Social media analytics for human dynamics research
  • Spatial and social network analysis of human dynamics
  • Human dynamics research for health and well-being
  • Accuracy and uncertainty of human dynamics data
  • Data sharing and dissemination of human dynamics research

If you are not sure whether your potential contribution might fit the scope of this Special Issue, please get in touch with the Guest Editors listed at the end of this Call for Submissions.

Submission procedure:

Interested authors should notify the guest editors of their intention to submit a paper by sending the title and a 250 word abstract to Shih-Lung Shaw ([email protected]) and Yang Xu ([email protected]) by August 1, 2019. The deadline for the submission of the final papers is December 31, 2019.

A condition of submission and acceptance is that papers must pass the normal ISPRS International Journal of Geo-Information review process. For author instructions, please refer to "Information for Authors" at the ISPRS International Journal of Geo-Information homepage. All manuscripts, including support materials, must be submitted using the journal's online “Submission System” link. Please select the correct Special Issue in the submission process and indicate this Special Issue as the target issue. First-time users of the online submission site must register themselves as an author. For questions, please contact the Guest Editors.

Important dates:

  • Send the title and an abstract of no more than 250 words to the Guest Editors: August 1, 2019
  • Full paper submission to the ISPRS International Journal of Geo-Information website: December 31, 2019

(Note: Manuscripts accepted for publication in this Special Issue will be published online as soon as possible after the final proof copy has been received by the publisher.)

Prof. Dr. Shih-Lung Shaw
Dr. Yang Xu
Guest Editors

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 papers will be 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 1400 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.

Published Papers (4 papers)

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Research

Article
Visit Probability in Space–Time Prisms Based on Binomial Random Walk
ISPRS Int. J. Geo-Inf. 2020, 9(9), 555; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090555 - 18 Sep 2020
Viewed by 688
Abstract
Space–time prisms are used to model the uncertainty of space–time locations of moving objects between (for instance, GPS-measured) sample points. However, not all space–time points in a prism are equally likely and we propose a simple, formal model for the so-called “visit probability” [...] Read more.
Space–time prisms are used to model the uncertainty of space–time locations of moving objects between (for instance, GPS-measured) sample points. However, not all space–time points in a prism are equally likely and we propose a simple, formal model for the so-called “visit probability” of space–time points within prisms. The proposed mathematical framework is based on a binomial random walk within one- and two-dimensional space–time prisms. Without making any assumptions on the random walks (we do not impose any distribution nor introduce any bias towards the second anchor point), we arrive at the conclusion that binomial random walk-based visit probability in space–time prisms corresponds to a hypergeometric distribution. Full article
(This article belongs to the Special Issue Human Dynamics Research in the Age of Smart and Intelligent Systems)
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Article
Uncovering the Relationship between Human Connectivity Dynamics and Land Use
ISPRS Int. J. Geo-Inf. 2020, 9(3), 140; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9030140 - 26 Feb 2020
Cited by 5 | Viewed by 1292
Abstract
CDR (Call Detail Record) data are one type of mobile phone data collected by operators each time a user initiates/receives a phone call or sends/receives an sms. CDR data are a rich geo-referenced source of user behaviour information. In this work, we perform [...] Read more.
CDR (Call Detail Record) data are one type of mobile phone data collected by operators each time a user initiates/receives a phone call or sends/receives an sms. CDR data are a rich geo-referenced source of user behaviour information. In this work, we perform an analysis of CDR data for the city of Milan that originate from Telecom Italia Big Data Challenge. A set of graphs is generated from aggregated CDR data, where each node represents a centroid of an RBS (Radio Base Station) polygon, and each edge represents aggregated telecom traffic between two RBSs. To explore the community structure, we apply a modularity-based algorithm. Community structure between days is highly dynamic, with variations in number, size and spatial distribution. One general rule observed is that communities formed over the urban core of the city are small in size and prone to dynamic change in spatial distribution, while communities formed in the suburban areas are larger in size and more consistent with respect to their spatial distribution. To evaluate the dynamics of change in community structure between days, we introduced different graph based and spatial community properties which contain latent footprint of human dynamics. We created land use profiles for each RBS polygon based on the Copernicus Land Monitoring Service Urban Atlas data set to quantify the correlation and predictivennes of human dynamics properties based on land use. The results reveal a strong correlation between some properties and land use which motivated us to further explore this topic. The proposed methodology has been implemented in the programming language Scala inside the Apache Spark engine to support the most computationally intensive tasks and in Python using the rich portfolio of data analytics and machine learning libraries for the less demanding tasks. Full article
(This article belongs to the Special Issue Human Dynamics Research in the Age of Smart and Intelligent Systems)
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Article
A Multi-Mode PDR Perception and Positioning System Assisted by Map Matching and Particle Filtering
ISPRS Int. J. Geo-Inf. 2020, 9(2), 93; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9020093 - 02 Feb 2020
Cited by 2 | Viewed by 945
Abstract
Currently, pedestrian dead reckoning (PDR) is widely used in indoor positioning. Since there are restrictions on a device’s pose in the procedure of using a smartphone to perform the PDR algorithm, this study proposes a novel heading estimation solution by calculating the integral [...] Read more.
Currently, pedestrian dead reckoning (PDR) is widely used in indoor positioning. Since there are restrictions on a device’s pose in the procedure of using a smartphone to perform the PDR algorithm, this study proposes a novel heading estimation solution by calculating the integral of acceleration along the direction of the user’s movement. First, a lightweight algorithm, that is, a finite state machine (FSM)-decision tree (DT), is used to monitor and recognize the device mode, and the characteristics of the gyroscope at the corners are used to improve the heading estimate performance during the linear phase. Moreover, to solve the problem of heading angle deviation accumulation on positioning, a map-aided particle filter (PF) and behavior perception techniques are introduced to constrain the heading and correct the trajectory through the wall after filtering. The results indicate that the recognition of phone pose can be 93.25%. The improved heading estimation method can achieve higher stability and accuracy than the traditional step-wise method. The localization error can reduce to approximately 2.2 m when the smartphone is held at certain orientations. Full article
(This article belongs to the Special Issue Human Dynamics Research in the Age of Smart and Intelligent Systems)
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Article
Decision Model for Predicting Social Vulnerability Using Artificial Intelligence
ISPRS Int. J. Geo-Inf. 2019, 8(12), 575; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8120575 - 11 Dec 2019
Cited by 2 | Viewed by 1018
Abstract
Social vulnerability, from a socio-environmental point of view, focuses on the identification of disadvantaged or vulnerable groups and the conditions and dynamics of the environments in which they live. To understand this issue, it is important to identify the factors that explain the [...] Read more.
Social vulnerability, from a socio-environmental point of view, focuses on the identification of disadvantaged or vulnerable groups and the conditions and dynamics of the environments in which they live. To understand this issue, it is important to identify the factors that explain the difficulty of facing situations with a social disadvantage. Due to its complexity and multidimensionality, it is not always easy to point out the social groups and urban areas affected. This research aimed to assess the connection between certain dimensions of social vulnerability and its urban and dwelling context as a fundamental framework in which it occurs using a decision model useful for the planning of social and urban actions. For this purpose, a holistic approximation was carried out on the census and demographic data commonly used in this type of study, proposing the construction of (i) a knowledge model based on Artificial Neural Networks (Self-Organizing Map), with which a demographic profile is identified and characterized whose indicators point to a presence of social vulnerability, and (ii) a predictive model of such a profile based on rules from dwelling variables constructed by conditional inference trees. These models, in combination with Geographic Information Systems, make a decision model feasible for the prediction of social vulnerability based on housing information. Full article
(This article belongs to the Special Issue Human Dynamics Research in the Age of Smart and Intelligent Systems)
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