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

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.

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A Framework for Spatial Interaction Analysis Based on Large-Scale Mobile Phone Data

Computational Intelligence and Neuroscience ◽

10.1155/2014/363502 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Weifeng Li ◽

Xiaoyun Cheng ◽

Zhengyu Duan ◽

Dongyuan Yang ◽

Gaohua Guo

Keyword(s):

Mobile Phone ◽

Large Scale ◽

Pattern Mining ◽

Interaction Analysis ◽

Spatial Interaction ◽

Frequent Pattern Mining ◽

Frequent Pattern ◽

Mobile Phone Data ◽

Interaction Patterns ◽

Critical Activity

The overall understanding of spatial interaction and the exact knowledge of its dynamic evolution are required in the urban planning and transportation planning. This study aimed to analyze the spatial interaction based on the large-scale mobile phone data. The newly arisen mass dataset required a new methodology which was compatible with its peculiar characteristics. A three-stage framework was proposed in this paper, including data preprocessing, critical activity identification, and spatial interaction measurement. The proposed framework introduced the frequent pattern mining and measured the spatial interaction by the obtained association. A case study of three communities in Shanghai was carried out as verification of proposed method and demonstration of its practical application. The spatial interaction patterns and the representative features proved the rationality of the proposed framework.

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How cultural differences in uncertainty avoidance affect product perceptions

International Marketing Review ◽

10.1108/02651330710755320 ◽

2007 ◽

Vol 24 (3) ◽

pp. 330-349 ◽

Cited By ~ 40

Author(s):

Julie Anne Lee ◽

Ellen Garbarino ◽

Dawn Lerman

Keyword(s):

Large Scale ◽

Uncertainty Avoidance ◽

Diverse Range ◽

Content Type ◽

Behavioural Intentions ◽

Individual Level ◽

Quality Perceptions ◽

The Usa ◽

Large Scale Survey ◽

Highly Correlated

PurposeTo examine how people from countries that vary in uncertainty avoidance (UA) use information about product uncertainty when evaluating products.Design/methodology/approachTwo studies were conducted that vary in methodology, sampling and analysis. First, an experiment was designed to manipulate product uncertainty through the use of country of origin (COO) quality‐stereotypes. It was administered to university students from a diverse range of countries, all studying in the USA. Next, data from a large‐scale survey of consumers from ten countries was submitted to hierarchical binary regression analyses to include variables at the country and individual level.FindingsThe studies support an interaction between product uncertainty (PU) and cultural UA on quality perceptions and behavioural intentions. Consumers from high UA countries evaluated high PU offerings less positively and held weaker behavioural intentions than those from low UA countries, but for low PU offerings, no difference was found. The effect of UA was reduced for people with more experience and those who were younger.Research limitations/implicationsAlthough we isolated the effects of UA from other cultural and individual level variables, it would be useful to directly cross individualism with UA in an experimental design, as these two variables are highly correlated.Practical implicationsThis study suggests products with higher levels of PU will have more opportunity to prove themselves in low uncertainty cultures.Originality/valueThis study should be valuable for marketing managers devising rollout strategies for products with higher levels of PU or weaker quality stereotypes.

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

Journal of Transport Geography ◽

10.1016/j.jtrangeo.2021.102991 ◽

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

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Topology-driven protein-protein interaction network analysis detects genetic sub-networks regulating reproductive capacity

10.1101/852897 ◽

2019 ◽

Cited By ~ 1

Author(s):

Tarun Kumar ◽

Leo Blondel ◽

Cassandra G. Extavour

Keyword(s):

Network Analysis ◽

Large Scale ◽

Fundamental Problem ◽

Genetic Regulation ◽

Interaction Network ◽

Egg Laying ◽

Reproductive Capacity ◽

Ovariole Number ◽

Gene Regulatory ◽

Hippo Signalling

AbstractUnderstanding the genetic regulation of organ structure is a fundamental problem in developmental biology. Here, we use egg-producing structures of insect ovaries, called ovarioles, to deduce systems-level gene regulatory relationships from quantitative functional genetic analysis. We previously showed that Hippo signalling, a conserved regulator of animal organ size, regulates ovariole number in Drosophila melanogaster. To comprehensively determine how Hippo signalling interacts with other pathways in this regulation, we screened all known signalling pathway genes, and identified Hpo-dependent and Hpo-independent signalling requirements. Network analysis of known protein-protein interactions among screen results identified independent gene regulatory sub-networks regulating one or both of ovariole number and egg laying. These sub-networks predict involvement of previously uncharacterised genes with higher accuracy than the original candidate screen. This shows that network analysis combining functional genetic and large-scale interaction data can predict function of novel genes regulating development.

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Using Mobility Data to Understand and Forecast COVID19 Dynamics

10.1101/2020.12.13.20248129 ◽

2020 ◽

Author(s):

Lijing Wang ◽

Xue Ben ◽

Aniruddha Adiga ◽

Adam Sadilek ◽

Ashish Tendulkar ◽

...

Keyword(s):

Neural Network ◽

Message Passing ◽

Spatial Interaction ◽

Human Mobility ◽

State Level ◽

Interaction Patterns ◽

Disease Dynamics ◽

Mobility Data ◽

Spatio Temporal ◽

The Impact

Disease dynamics, human mobility, and public policies co-evolve during a pandemic such as COVID-19. Understanding dynamic human mobility changes and spatial interaction patterns are crucial for understanding and forecasting COVID-19 dynamics. We introduce a novel graph-based neural network(GNN) to incorporate global aggregated mobility flows for a better understanding of the impact of human mobility on COVID-19 dynamics as well as better forecasting of disease dynamics. We propose a recurrent message passing graph neural network that embeds spatio-temporal disease dynamics and human mobility dynamics for daily state-level new confirmed cases forecasting. This work represents one of the early papers on the use of GNNs to forecast COVID-19 incidence dynamics and our methods are competitive to existing methods. We show that the spatial and temporal dynamic mobility graph leveraged by the graph neural network enables better long-term forecasting performance compared to baselines.

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Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies

Malaria Journal ◽

10.1186/s12936-015-0562-1 ◽

2015 ◽

Vol 14 (1) ◽

Cited By ~ 8

Author(s):

Madhumita Bhattacharyya ◽

Saikat Chakrabarti

Keyword(s):

Plasmodium Falciparum ◽

Network Analysis ◽

In Silico ◽

Large Scale ◽

Interaction Network ◽

Interacting Proteins ◽

Knock Out ◽

Scale Interaction

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Topology-driven protein-protein interaction network analysis detects genetic sub-networks regulating reproductive capacity

eLife ◽

10.7554/elife.54082 ◽

2020 ◽

Vol 9 ◽

Author(s):

Tarun Kumar ◽

Leo Blondel ◽

Cassandra G Extavour

Keyword(s):

Network Analysis ◽

Large Scale ◽

Fundamental Problem ◽

Genetic Regulation ◽

Interaction Network ◽

Egg Laying ◽

Reproductive Capacity ◽

Ovariole Number ◽

Gene Regulatory ◽

Hippo Signalling

Understanding the genetic regulation of organ structure is a fundamental problem in developmental biology. Here, we use egg-producing structures of insect ovaries, called ovarioles, to deduce systems-level gene regulatory relationships from quantitative functional genetic analysis. We previously showed that Hippo signalling, a conserved regulator of animal organ size, regulates ovariole number in Drosophila melanogaster. To comprehensively determine how Hippo signalling interacts with other pathways in this regulation, we screened all known signalling pathway genes, and identified Hpo-dependent and Hpo-independent signalling requirements. Network analysis of known protein-protein interactions among screen results identified independent gene regulatory sub-networks regulating one or both of ovariole number and egg laying. These sub-networks predict involvement of previously uncharacterised genes with higher accuracy than the original candidate screen. This shows that network analysis combining functional genetic and large-scale interaction data can predict function of novel genes regulating development.

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JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data

10.1101/099234 ◽

2017 ◽

Author(s):

Jiadong Ji ◽

Di He ◽

Yang Feng ◽

Yong He ◽

Fuzhong Xue ◽

...

Keyword(s):

Network Analysis ◽

Probability Distribution ◽

Interaction Network ◽

Joint Density ◽

High Dimensional ◽

Omics Data ◽

Interaction Patterns ◽

Differential Interaction ◽

Differential Network ◽

Non Parametric

AbstractMotivationA complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application.ResultsWe propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation between two groups. At the same time, JDINAC uses the network biomarkers to build a classification model. The novelty of JDINAC lies in its potential to capture non-linear relations between molecular interactions using high-dimensional sparse data as well as to adjust confounding factors, without the need of the assumption of a parametric probability distribution of gene measurements. Simulation studies demonstrate that JDINAC provides more accurate differential network estimation and lower classification error than that achieved by other state-of-the-art methods. We apply JDINAC to a Breast Invasive Carcinoma dataset, which includes 114 patients who have both tumor and matched normal samples. The hub genes and differential interaction patterns identified were consistent with existing experimental studies. Furthermore, JDINAC discriminated the tumor and normal sample with high accuracy by virtue of the identified biomarkers. JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data.Availability:R scripts available at https://github.com/jijiadong/JDINACContact:[email protected] information:Supplementary data are available at bioRxiv online.

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