Ling Bian

University at Buffalo, State University of New York

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Shiran Zhong

Western University

Liang Mao

University of Florida

Stephen J. Walsh

University of North Carolina at Chapel Hill

Guiyun Yan

University of California, Irvine

Daniel G. Brown

University of Waterloo

Yuxia Huang

Texas A&M University – Corpus Christi

Peter A. Rogerson

Arizona State University

Shixiong Hu

East Stroudsburg University

Yujia Pan

Nanjing Forestry University

Guofa Zhou

University of California, Irvine

Cooperative Institutions

University at Buffalo, State University of New York

Shanghai Jiao Tong University

Shanghai Ninth People's Hospital

Chinese Academy of Sciences

North China Electric Power University

Lanzhou University

University of North Carolina at Chapel Hill

Tsinghua University

Kenya Medical Research Institute

University of California, Irvine

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Agent-based simulation for a dual-diffusion process of influenza and human preventive behavior

International Journal of Geographical Information Science (2011)

Liang Mao Ling Bian

Influenza disease diffuses from one individual to another through their close contacts, while preventive behavior propagates through inter-personal influences. These two diffusion processes take place simultaneously and interact with one another in opposite directions. Many current influenza studies consider the diffusion of influenza, but few of them have incorporated the diffusion of human preventive behavior. This article proposes an original dual-diffusion model to couple these two diffusion processes. The conceptual framework of the model incorporates these two opposite processes into a human contact network. An agent-based approach, network theory, disease model, and behavioral model are integrated to formulate the conceptual framework. Model implementation simulates an influenza epidemic in an urbanized area of one million individuals. The simulation results suggest that the proposed model offers a close representation of the observed data. The research findings will provide a more rational basis for influenza control.

Representation

Conceptual model

Human influenza

Diffusion theory

Agent-Based Model

10.1080/13658816.2011.556121

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Citations (17)

Analysis of Pollution Hazard Intensity: A Spatial Epidemiology Case Study of Soil Pb Contamination

International Journal of Environmental Research and Public Health (2016)

Hoehun Ha Peter A. Rogerson James R. Olson Daikwon Han Ling Bian

Heavy industrialization has resulted in the contamination of soil by metals from anthropogenic sources in Anniston, Alabama. This situation calls for increased public awareness of the soil contamination issue and better knowledge of the main factors contributing to the potential sources contaminating residential soil. The purpose of this spatial epidemiology research is to describe the effects of physical factors on the concentration of lead (Pb) in soil in Anniston AL, and to determine the socioeconomic and demographic characteristics of those residing in areas with higher soil contamination. Spatial regression models are used to account for spatial dependencies using these explanatory variables. After accounting for covariates and multicollinearity, results of the analysis indicate that lead concentration in soils varies markedly in the vicinity of a specific foundry (Foundry A), and that proximity to railroads explained a significant amount of spatial variation in soil lead concentration. Moreover, elevated soil lead levels were identified as a concern in industrial sites, neighborhoods with a high density of old housing, a high percentage of African American population, and a low percent of occupied housing units. The use of spatial modelling allows for better identification of significant factors that are correlated with soil lead concentrations.

10.3390/ijerph13090915

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Citations (8)

Modelling urban expansion using a multi agent-based model in the city of Changsha

Journal of Geographical Sciences (2010)

Honghui Zhang Yongnian Zeng Ling Bian Xijun Yu

Urban expansion

Agent-Based Model

10.1007/s11442-010-0540-z

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Citations (47)

A conceptual framework for an individual-based spatially explicit epidemiological model

Environment and Planning B Planning and Design (2004)

Ling Bian

This paper presents a conceptual framework to formalize an individual-based and spatially explicit model of the epidemiology of infectious diseases. The framework differs from that of the traditional population-based epidemiological models in terms of assumptions, conceptual models, and model structures. In particular, the author discusses four aspects of the model: (1) population segments or unique individuals as the modeling unit, (2) continuous process or discrete events to represent the disease development through time, (3) traveling wave or network dispersion to represent the disease transmission in space, and (4) within-group and between-group interactions to represent local and long-distance transmissions. Based on these conceptual discussions, a simple influenza epidemic is simulated in order to illustrate the application of the proposed framework.

Conceptual model

Conceptual framework

Epidemic model

Population model

10.1068/b2833

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Citations (104)

Response of a distributed watershed erosion model to variations in input data aggregation levels

Computers & Geosciences (1993)

Daniel G. Brown Ling Bian Stephen J. Walsh

Distributed element model

Deposition

10.1016/0098-3004(93)90078-j

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Citations (36)

Exploring the Structure of Contact Networks at Multiple Scales

International Conference on GIScience Short Paper Proceedings (2016)

Peng Gao Ling Bian

This study examines the scale effects on the network structure and spatial structure of contact networks.Regular grids with different cell sizes are used to divide one observed and two reference random networks into multiple scales.Four metrics are used to represent the two structures.Results show that the network structure of the observed network is sensitive to scale changes at fine scales.In comparison, the clustered spatial structure is scale independent.

Network Structure

Spatial dispersion

10.21433/b31126g8f7cd

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Citations (0)

Scale effects on spatially embedded contact networks

Computers Environment and Urban Systems (2016)

Peng P. Gao Ling Bian

Network Structure

Spatial network

10.1016/j.compenvurbsys.2016.06.002

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Citations (12)

Identifying geospatial services across heterogeneous taxonomies

Geocarto International (2015)

Hao Li Yandong Wang Ling Bian Teng Wang

Geospatial services with different functions are assembled together to solve complex problems. Different taxonomies are developed to categorize these services into classes. As differences in granularity and semantics exist among these taxonomies, the identification of services across different taxonomies has become a challenge. In this paper, an approach to identify geospatial services across heterogeneous taxonomies is proposed. Using formal concept analysis, existing heterogeneous taxonomies are decomposed into semantic factors and their various combinations. With these semantic factors, a super taxonomy is established to integrate the original heterogeneous taxonomies. Finally, with the super taxonomy as a cross-referencing system, geospatial services with classes in original taxonomies are identifiable across taxonomies. Experiments in service registries and a social media-based spatial-temporal analysis project are presented to illustrate the effectiveness of this approach.

Identification

10.1080/10106049.2015.1110208

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Citations (0)

Scale Dependencies of Vegetation and Topography in a Mountainous Environment of Montana

The Professional Geographer (1993)

Ling Bian Stephen J. Walsh

This research examines the effects of spatial scale on estimating the relationship between vegetation biomass and topography within a portion of Glacier National Park, Montana. The Reflectance/Absorptance vegetation index, developed from processed Landsat Thematic Mapper digital data, is related to three topographic variables obtained through processed Digital Elevation Models: elevation, slope angle, and slope aspect. R2 values between the vegetation index and the topographic variables are obtained from regression analyses at a series of aggregated spatial scales. The effective range of spatial scales within which the two sets of variables are spatially dependent and the degree of the spatial dependencies are characterized through semivariance and fractal analyses. Key Words: scale dependenciestopographic controlsremote sensingGIS

Semivariance

Elevation (ballistics)

Thematic Mapper

10.1111/j.0033-0124.1993.00001.x

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Citations (171)

Geographical image classification and retrieval

Gholamhosein Sheikholeslami Aidong Zhang Ling Bian

Article Geographical image classification and retrieval Share on Authors: Gholamhosein Sheikholeslami Department of Computer Science, State University of New York at Buffalo, Buffalo, NY Department of Computer Science, State University of New York at Buffalo, Buffalo, NYView Profile , Aidong Zhang Department of Computer Science, State University of New York at Buffalo, Buffalo, NY Department of Computer Science, State University of New York at Buffalo, Buffalo, NYView Profile , Ling Bian Department of Geography, State University of New York at Buffalo, Buffalo, NY Department of Geography, State University of New York at Buffalo, Buffalo, NYView Profile Authors Info & Claims GIS '97: Proceedings of the 5th ACM international workshop on Advances in geographic information systemsNovember 1997 Pages 58–61https://doi.org/10.1145/267825.267841Online:01 November 1997Publication History 8citation543DownloadsMetricsTotal Citations8Total Downloads543Last 12 Months4Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

10.1145/267825.267841

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Citations (15)