GeoComputation and Disease Exploration

In public health, GeoComputation application faces additional challenges: legal issues related to personal data collection, social and cultural areas and human mobility. The use of space data opens up the prospect of increased productivity in the private and public sectors as well. The introduction of specific and accurate paraphernalia in the health and GeoComputational analysis can enhance these approaches. The ability to transform and visualize space is defined here, but has been enhanced by using GeoComputational analyses that are still dependent on the validity and lawfulness of the requested sources. This chapter demonstrates how biological applications affect the core organizational concepts and techniques of GeoComputations and how computationally complex biological process simulation is at the cutting edge of biological science.

[1]  Bernd Resch,et al.  Towards the Live City – Paving the Way to Real-time Urbanism , 2012 .

[2]  Robert Haining,et al.  Spatial Data Analysis in the Social and Environmental Sciences , 1990 .

[3]  Florian Verhein.  Mining Complex Spatio-Temporal Sequence Patterns , 2009, SDM.

[4]  A. Fauci,et al.  The challenge of emerging and re-emerging infectious diseases , 2004, Nature.

[5]  Andrew W. Moore,et al.  Detection of emerging space-time clusters , 2005, KDD '05.

[6]  W. Gesler.  The uses of spatial analysis in medical geography: a review. , 1986, Social science & medicine.

[7]  Derya Birant,et al.  Spatio-temporal outlier detection in large databases , 2006, 28th International Conference on Information Technology Interfaces, 2006..

[8]  James F. Allen.  Maintaining knowledge about temporal intervals , 1983, CACM.

[9]  Dimitrios Gunopulos,et al.  Automatic subspace clustering of high dimensional data for data mining applications , 1998, SIGMOD '98.

[10]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[11]  Edzer Pebesma,et al.  A Topology Based Spatio-Temporal Map Algebra for Big Data Analysis , 2019, Data.

[12]  S. Levin.  The problem of pattern and scale in ecology , 1992 .

[13]  H. Pitkin.  Wittgenstein and justice : on the significance of Ludwig Wittgenstein for social and political thought , 1974 .

[14]  L. Anselin.  Spatial Econometrics: Methods and Models , 1988 .

[15]  Chang-Tien Lu,et al.  Spatial Weighted Outlier Detection , 2006, SDM.

[16]  Alexander Zipf,et al.  OSHDB: a framework for spatio-temporal analysis of OpenStreetMap history data , 2019, Open Geospatial Data, Software and Standards.

[17]  C. Krebs.  Ecology: The Experimental Analysis of Distribution and Abundance , 1973 .

[18]  Derya Birant,et al.  ST-DBSCAN: An algorithm for clustering spatial-temporal data , 2007, Data Knowl. Eng..

[19]  Shashi Shekhar,et al.  Summarizing trajectories into k-primary corridors: a summary of results , 2012, SIGSPATIAL/GIS.

[20]  Bing Chen,et al.  High Granularity Remote Sensing and Crop Production over Space and Time: NDVI over the Growing Season and Prediction of Cotton Yields at the Farm Field Level in Texas , 2008, 2008 IEEE International Conference on Data Mining Workshops.

[21]  Sanjay Ranka,et al.  A LRT framework for fast spatial anomaly detection , 2009, KDD.

[22]  Yan Huang,et al.  Correlation Analysis of Spatial Time Series Datasets: A Filter-and-Refine Approach , 2003, PAKDD.

[23]  Marijke F. Augusteijn,et al.  Fusion of image classifications using Bayesian techniques with Markov random fields , 1999 .

[24]  Graham Dunn,et al.  Geographical epidemiology, spatial analysis and geographical information systems: a multidisciplinary glossary , 2007, Journal of Epidemiology and Community Health.

[25]  Chang-Tien Lu,et al.  On detecting spatial categorical outliers , 2014, GeoInformatica.

[26]  J. Wakefield,et al.  Spatial epidemiology: methods and applications. , 2000 .

[27]  Chang-Tien Lu,et al.  Algorithms for spatial outlier detection , 2003, Third IEEE International Conference on Data Mining.

[28]  K. McLeod,et al.  Our sense of Snow: the myth of John Snow in medical geography. , 2000, Social science & medicine.

[29]  Chang-Tien Lu,et al.  On Detecting Spatial Outliers , 2008, GeoInformatica.

[30]  Kai N. Lee,et al.  Greed, Scale Mismatch, and Learning. , 1993, Ecological applications : a publication of the Ecological Society of America.

[31]  M. Monmonier.  How to Lie with Maps , 1991 .

[32]  Sw. Banerjee,et al.  Hierarchical Modeling and Analysis for Spatial Data , 2003 .

[33]  T. Richards,et al.  Geographic information systems and public health: mapping the future. , 1999, Public health reports.

[34]  Mahesh Kumar Jat,et al.  Geographic information system based spatio-temporal dengue fever cluster analysis and mapping , 2019 .

[35]  Jianguo Wu,et al.  Dealing with Scale in Landscape Analysis: An Overview , 2000, Ann. GIS.

[36]  Yan Huang,et al.  Exploiting Spatial Autocorrelation to Efficiently Process Correlation-Based Similarity Queries , 2003, SSTD.

[37]  G. Rushton,et al.  Geographically masking health data to preserve confidentiality. , 1999, Statistics in medicine.

[38]  Sangkyum Kim,et al.  Motion-Alert: Automatic Anomaly Detection in Massive Moving Objects , 2006, ISI.

[39]  Vipin Kumar,et al.  Summarization – compressing data into an informative representation , 2006, Knowledge and Information Systems.

[40]  Hui Xiong,et al.  A Stochastic Model for Context-Aware Anomaly Detection in Indoor Location Traces , 2012, 2012 IEEE 12th International Conference on Data Mining.

[41]  K. Rook,et al.  Economic stress and suicide: multilevel analyses. Part 1: Aggregate time-series analyses of economic stress and suicide. , 1989, Suicide & life-threatening behavior.

[42]  W. Tobler.  A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .

[43]  Gerard Rushton,et al.  Public health, GIS, and spatial analytic tools. , 2003, Annual review of public health.

[44]  Ralf Hartmut Güting,et al.  Moving Objects Databases (The Morgan Kaufmann Series in Data Management Systems) (The Morgan Kaufmann Series in Data Management Systems) , 2005 .

[45]  Maged N Kamel Boulos,et al.  International Journal of Health Geographics Open Access towards Evidence-based, Gis-driven National Spatial Health Information Infrastructure and Surveillance Services in the United Kingdom , 2022 .

[46]  Raul Queiroz Feitosa,et al.  Multilevel object-oriented classification of quickbird images for urban population estimates , 2007, GIS.

[47]  Noel A Cressie,et al.  Statistics for Spatio-Temporal Data , 2011 .

[48]  Phaedon C. Kyriakidis,et al.  Geostatistical Space–Time Models: A Review , 1999 .

[49]  Heather J. Lynch,et al.  A spatiotemporal Ripley's K-function to analyze interactions between spruce budworm and fire in British Columbia, Canada , 2008 .

[50]  T. Tango,et al.  A Space–Time Scan Statistic for Detecting Emerging Outbreaks , 2011, Biometrics.

[51]  Jiawei Han,et al.  CLARANS: A Method for Clustering Objects for Spatial Data Mining , 2002, IEEE Trans. Knowl. Data Eng..

[52]  Robert B McMaster,et al.  Scale and Geographic Inquiry , 2004 .

[53]  Nikos Mamoulis,et al.  Discovery of Collocation Episodes in Spatiotemporal Data , 2006, Sixth International Conference on Data Mining (ICDM'06).

[54]  P. J. Clark,et al.  Distance to Nearest Neighbor as a Measure of Spatial Relationships in Populations , 1954 .

[55]  Julian Besag,et al.  The Detection of Clusters in Rare Diseases , 1991 .

[56]  J. May.  THE ECOLOGY OF HUMAN DISEASE , 1960, Annals of the New York Academy of Sciences.

[57]  P. Elliott,et al.  Spatial Epidemiology: Current Approaches and Future Challenges , 2004, Environmental health perspectives.

[58]  Hans-Peter Kriegel,et al.  Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection , 2012, Data Mining and Knowledge Discovery.

[59]  Min Wang,et al.  Mining Spatial-temporal Clusters from Geo-databases , 2006, ADMA.

[60]  A. Govardhan,et al.  SPATIOTEMPORAL DATA MINING : ISSUES , TASKS AND APPLICATIONS , 2012 .

[61]  Anthony J. T. Lee,et al.  Mining frequent trajectory patterns in spatial-temporal databases , 2009, Inf. Sci..

[62]  Qiang Cai,et al.  Validation tests of an improved kernel density estimation method for identifying disease clusters , 2012, J. Geogr. Syst..

[63]  S. Rayner,et al.  Analysis on factors related to rabies epidemic in China from 2007–2011 , 2012, Virologica Sinica.

[64]  J. Berry,et al.  Analyzing Longitudinal Data with Multilevel Models: An Example with Individuals Living with Lower Extremity Intra-articular Fractures. , 2008, Rehabilitation psychology.

[65]  R. Devillers,et al.  Multidimensional Management of Geospatial Data Quality Information for its Dynamic Use Within GIS , 2005 .

[66]  Lars Jarup,et al.  Health and Environment Information Systems for Exposure and Disease Mapping, and Risk Assessment , 2004, Environmental health perspectives.

[67]  D. Briske,et al.  Vegetation dynamics on rangelands: a critique of the current paradigms , 2003 .

[68]  Aryya Gangopadhyay,et al.  Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets , 2013, Data Mining and Knowledge Discovery.