Next Article in Journal
Evaluating Spatial Scenarios for Sustainable Development in Quito, Ecuador
Next Article in Special Issue
Visit Probability in Space–Time Prisms Based on Binomial Random Walk
Previous Article in Journal
An English-Chinese Machine Translation and Evaluation Method for Geographical Names
Previous Article in Special Issue
A Multi-Mode PDR Perception and Positioning System Assisted by Map Matching and Particle Filtering
Article

Uncovering the Relationship between Human Connectivity Dynamics and Land Use

1
BioSense Institute, University of Novi Sad, 21000 Novi Sad, Serbia
2
Department of Geography, Tourism and Hotel Management, Faculty of Sciences, University of Novi Sad, 21000 Novi Sad, Serbia
3
School of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(3), 140; https://doi.org/10.3390/ijgi9030140
Received: 31 December 2019 / Revised: 6 February 2020 / Accepted: 17 February 2020 / Published: 26 February 2020
(This article belongs to the Special Issue Human Dynamics Research in the Age of Smart and Intelligent Systems)
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. View Full-Text
Keywords: network analysis; mobile phone networks; human dynamics; big data; knowledge discovery network analysis; mobile phone networks; human dynamics; big data; knowledge discovery
Show Figures

Figure 1

MDPI and ACS Style

Novović, O.; Brdar, S.; Mesaroš, M.; Crnojević, V.; N. Papadopoulos, A. Uncovering the Relationship between Human Connectivity Dynamics and Land Use. ISPRS Int. J. Geo-Inf. 2020, 9, 140. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9030140

AMA Style

Novović O, Brdar S, Mesaroš M, Crnojević V, N. Papadopoulos A. Uncovering the Relationship between Human Connectivity Dynamics and Land Use. ISPRS International Journal of Geo-Information. 2020; 9(3):140. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9030140

Chicago/Turabian Style

Novović, Olivera, Sanja Brdar, Minučer Mesaroš, Vladimir Crnojević, and Apostolos N. Papadopoulos. 2020. "Uncovering the Relationship between Human Connectivity Dynamics and Land Use" ISPRS International Journal of Geo-Information 9, no. 3: 140. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9030140

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop