PPT-Spatial and temporal patterns of PM

25 in Santiago Chile Carolina Magri Penn State MGIS What is PM 25 Particulate matter or PM is a common air pollutant that consists of a mix of solid and liquid particles that varies by . to Spatial Data Mining. Spatial data mining. is the process of discovering interesting, useful, non-trivial patterns from large . spatial. datasets. Reading Material: . http://en.wikipedia.org/wiki/Spatial_analysis. and Ice Conditions In and Near the Marginal Ice Zone: . The "Marginal Ice Zone Observations and Processes EXperiment" (. MIZOPEX. ). . Goals. : .  . Assess ocean and sea ice variability during the melt season within a key Marginal Ice Zone (MIZ) region. . Tempora. l. . and Spatial Constraints on Text Similarity. James Pustejovsky. Brandeis . University. March . 13, . 2012. Measuring Similarity. Objects. Events. Object similarity is a function of:. Sortal. Jamie M. Kneitel. Department of Biological Sciences. CSU Sacramento. Peters (2011). Spatial and temporal heterogeneity. Important in all ecosystems. Climate variation. Increasing focus on effects in natural ecosystems. Prahlad Jat. (1). and Marc Serre. (1). (1) University of North Carolina at Chapel Hill. Agenda. Introduction. Mean Trend Analysis. Space/Time Covariance Analysis. Introduction. Temporal GIS analysis process. using a floor sensor system. By: Omar Costilla- Reyes (Ph.D. student). Email: . omar.costillareyes@manchester.ac.uk. Sensing, Imaging and Signal Processing Group. School of Electrical and Electronics Engineering. trend of mother to child HIV transmission in . western . Kenya, . 2007-2013. Anthony Waruru. , Thomas Achia, . Hellen . Muttai, . Lucy . Ng’ang’a, . Abraham . Katana, . Peter . Young, . Jim . Tobias, Peter Juma, . “…the problem of relating phenomena across scales is the central problem in biology and in all of science”. . Simon . Levin . 1992.. Why be concerned about scale?. Scale greatly influences our understanding of ecological causality. Oral Presentation at The 143. rd. APHA Annual Meeting and Exposition(October . 31 – November 4, 2015. ), Chicago. . George Siaway, PhD, Christine A. Clarke, MS, Fern Johnson-Clarke, PhD and Rowena Samala, MS. Steven E. Lohrenz. University of Southern Mississippi. Gary Kirkpatrick. Mote Marine Laboratory. Oscar Schofield. Rutgers University. Overview. Introduction. Application of satellite ocean color to HAB detection. Kim A. cheek and . caroline. George. College of education and human services. University of north . florida. Defining Scale*. Spatial, temporal, or numeric . magnitude . of an object or event; measurable in standard or nonstandard units:. Introduction. Region Discovery—Finding Interesting Places in Spatial Datasets . Project3. CLEVER: a Spatial Clustering Algorithm Supporting Plug-in Fitness Functions. [Spatial Regression]. Brief Introduction . Yaxing . Wei. . &. Suresh K.S. . Vannan. Environmental Sciences Division. Oak Ridge National Laboratory. Spatial Data. Any data with location information. Feature data: . “. object. ”. with location and other properties. Raghu Machiraju. Firdaus. . Janoos. , Fellow, Harvard Medical. Istavan. (. Pisti. ) . Morocz. , . Instuctor. , Harvard . Medical. Premise. Understanding the mind not only requires a comprehension of the workings of low–level neural networks but also demands a detailed map of the brain’s functional architecture and a description of the large–scale connections between populations of neurons and insights into how relations between these simpler networks give rise to higher–level thought.