Identifying borders of activity spaces and quantifying border effects on intra-urban travel through spatial interaction network

2021 ◽  
Vol 87 ◽  
pp. 101625
Author(s):  
Meihan Jin ◽  
Lunsheng Gong ◽  
Yanqin Cao ◽  
Pengcheng Zhang ◽  
Yongxi Gong ◽  
...  
Keyword(s):  
Spatial Interaction ◽  
Interaction Network ◽  
Border Effects ◽  
Activity Spaces ◽  
Urban Travel

scholarly journals Evolution Model of Spatial Interaction Network in Online Social Networking Services

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 434
Author(s):  
Jian Dong ◽  
Bin Chen ◽  
Pengfei Zhang ◽  
Chuan Ai ◽  
Fang Zhang ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Information Dissemination ◽  
Preferential Attachment ◽  
Spatial Interaction ◽  
Interaction Network ◽  
Evolution Model ◽  
Social Networking Services ◽  
The City ◽  
Real City

The development of online social networking services provides a rich source of data of social networks including geospatial information. More and more research has shown that geographical space is an important factor in the interactions of users in social networks. In this paper, we construct the spatial interaction network from the city level, which is called the city interaction network, and study the evolution mechanism of the city interaction network formed in the process of information dissemination in social networks. A network evolution model for interactions among cities is established. The evolution model consists of two core processes: the edge arrival and the preferential attachment of the edge. The edge arrival model arranges the arrival time of each edge; the model of preferential attachment of the edge determines the source node and the target node of each arriving edge. Six preferential attachment models (Random-Random, Random-Degree, Degree-Random, Geographical distance, Degree-Degree, Degree-Degree-Geographical distance) are built, and the maximum likelihood approach is used to do the comparison. We find that the degree of the node and the geographic distance of the edge are the key factors affecting the evolution of the city interaction network. Finally, the evolution experiments using the optimal model DDG are conducted, and the experiment results are compared with the real city interaction network extracted from the information dissemination data of the WeChat web page. The results indicate that the model can not only capture the attributes of the real city interaction network, but also reflect the actual characteristics of the interactions among cities.

Understanding neighborhood isolation through spatial interaction network analysis using location big data

2019 ◽  
Vol 52 (6) ◽  
pp. 1027-1031 ◽  
Author(s):  
Timothy Prestby ◽  
Joseph App ◽  
Yuhao Kang ◽  
Song Gao
Keyword(s):  
Network Analysis ◽  
Large Scale ◽  
Spatial Interaction ◽  
Interaction Network ◽  
Location Based Services ◽  
Interaction Patterns ◽  
Individual Level ◽  
Mobility Data ◽  
The Usa ◽  
Hidden Biases

Hidden biases of racial and socioeconomic preferences shape residential neighborhoods throughout the USA. Thereby, these preferences shape neighborhoods composed predominantly of a particular race or income class. However, the assessment of spatial extent and the degree of isolation outside the residential neighborhoods at large scale is challenging, which requires further investigation to understand and identify the magnitude and underlying geospatial processes. With the ubiquitous availability of location-based services, large-scale individual-level location data have been widely collected using numerous mobile phone applications and enable the study of neighborhood isolation at large scale. In this research, we analyze large-scale anonymized smartphone users’ mobility data in Milwaukee, Wisconsin, to understand neighborhood-to-neighborhood spatial interaction patterns of different racial classes. Several isolated neighborhoods are successfully identified through the mobility-based spatial interaction network analysis.

Spatial-interaction network analysis of built environmental influence on daily public transport demand

2021 ◽  
Vol 92 ◽  
pp. 102991
Author(s):  
Xintao Liu ◽  
Jiawei Wu ◽  
Jianwei Huang ◽  
Junwei Zhang ◽  
Bi Yu Chen ◽  
...  
Keyword(s):  
Network Analysis ◽  
Public Transport ◽  
Environmental Influence ◽  
Spatial Interaction ◽  
Interaction Network

scholarly journals Short-range interactions govern cellular dynamics in microbial multi-genotype systems

2019 ◽  
Author(s):  
A. Dal Co ◽  
S. van Vliet ◽  
D. J. Kiviet ◽  
S. Schlegel ◽  
M. Ackermann
Keyword(s):  
Microbial Communities ◽  
Single Cell ◽  
Spatial Organization ◽  
Single Cell Analysis ◽  
Spatial Interaction ◽  
Interaction Network ◽  
Spatial Arrangement ◽  
Emergent Properties ◽  
Interaction Range ◽  
Microbial Systems

AbstractEcosystem processes result from interaction between organisms. When interactions are local, the spatial organization of organisms defines their network of interactions, and thus influences the system’s functioning. This can be especially relevant for microbial systems, which often consist of spatially structured communities of cells connected by a dense interaction network. Here we measured the spatial interaction network between cells in microbial systems and identify the factors that determine it. Combining quantitative single-cell analysis of synthetic bacterial communities with mathematical modeling, we find that cells only interact with other cells in their immediate neighbourhood. This short interaction range impacts the functioning of the whole system by reducing its ability to perform metabolic processes collectively. Our experiments and models demonstrate that the spatial scale of cell-to-cell interaction plays a fundamental role in understanding and controlling natural communities, and in engineering microbial systems for specific purposes.Significance StatementCommunities of interacting microbes perform fundamental processes on earth. We do not understand well how these processes emerge from the interactions between individual microbial cells. Our work investigates how strongly individual cells interact and how the strength of the interaction depends on the distance between cells. The discovery that individual cells ‘live in a small world’, i.e. they only interact with a small number of cells around them, changes our understanding of how cells in natural microbial communities are metabolically coupled and how their spatial arrangement determines emergent properties at the community level. Our quantitative single-cell approach allows to address central questions on systems composed of interacting genotypes and to increase our understanding and ability to control microbial communities.

scholarly journals Dissecting the chromatin interactome of microRNA genes

2013 ◽  
Vol 42 (5) ◽  
pp. 3028-3043 ◽  
Author(s):  
Dijun Chen ◽  
Liang-Yu Fu ◽  
Zhao Zhang ◽  
Guoliang Li ◽  
Hang Zhang ◽  
...  
Keyword(s):  
Rna Polymerase Ii ◽  
Spatial Organization ◽  
Spatial Interaction ◽  
Interaction Network ◽  
Cell Types ◽  
Disease Category ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Interacting Networks ◽  
Microrna Genes

Abstract Our knowledge of the role of higher-order chromatin structures in transcription of microRNA genes (MIRs) is evolving rapidly. Here we investigate the effect of 3D architecture of chromatin on the transcriptional regulation of MIRs. We demonstrate that MIRs have transcriptional features that are similar to protein-coding genes. RNA polymerase II–associated ChIA-PET data reveal that many groups of MIRs and protein-coding genes are organized into functionally compartmentalized chromatin communities and undergo coordinated expression when their genomic loci are spatially colocated. We observe that MIRs display widespread communication in those transcriptionally active communities. Moreover, miRNA–target interactions are significantly enriched among communities with functional homogeneity while depleted from the same community from which they originated, suggesting MIRs coordinating function-related pathways at posttranscriptional level. Further investigation demonstrates the existence of spatial MIR–MIR chromatin interacting networks. We show that groups of spatially coordinated MIRs are frequently from the same family and involved in the same disease category. The spatial interaction network possesses both common and cell-specific subnetwork modules that result from the spatial organization of chromatin within different cell types. Together, our study unveils an entirely unexplored layer of MIR regulation throughout the human genome that links the spatial coordination of MIRs to their co-expression and function.

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