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Articles 1 - 30 of 486

Full-Text Articles in Physical Sciences and Mathematics

Development Of A Land Cover Characteristics Data Base For The Conterminous U.S., Thomas R. Loveland, James W. Merchant, Donald O. Ohlen, Jesslyn F. Brown Jan 9999

Development Of A Land Cover Characteristics Data Base For The Conterminous U.S., Thomas R. Loveland, James W. Merchant, Donald O. Ohlen, Jesslyn F. Brown

Conservation and Survey Division

No abstract provided.


Catch The King Tide 2018: All King Tide Data, Jon Derek Loftis Dec 2018

Catch The King Tide 2018: All King Tide Data, Jon Derek Loftis

Data

"Catch the King" is a citizen-science GPS data collection effort centered in Hampton Roads, VA, that seeks to interactively map the King Tide's maximum inundation extents. The goal is to validate and improving predictive model accuracy for future forecasting of increasingly pervasive "nuisance" flooding.


Polynomial Fitting, R. Steven Turley Sep 2018

Polynomial Fitting, R. Steven Turley

All Faculty Publications

This article reviews the theory and some good practice for fitting polynomials to data. I show by theory and example why fitting using a basis of orthogonal polynomials rather than monomials is desirable. I also show how to scale the independent variable for a more stable fit. I also demonstrate how to compute the uncertainty in the fit parameters. Finally, I discuss regression analysis: how to determine whether adding an additional term to the fit is justified.


Implementing The Use Of Personal Activity Data In An Introductory Statistics Course, Lacy Christensen Aug 2018

Implementing The Use Of Personal Activity Data In An Introductory Statistics Course, Lacy Christensen

All Graduate Theses and Dissertations

Integrating real data into a classroom is one of the recommendations in the Guidelines for Assessment and Instruction in Statistics Education (GAISE) college report which lays out guidelines for an introductory statistics course (Committee, GAISE College Report ASA Revision, 2016). In order to assess the effect of using real data in a classroom, the students received physical activity trackers to wear during an undergraduate introductory statistics course taught in the summer. This tracker, a Fitbit, enabled students to monitor and record their steps, calories, and active time throughout the class. Collecting personal activity data (PAD) creates a large database which ...


From Data To Decisions: Navigating The “So What?” And “What’S Next?” Conversation Around Nearshore Data, Dawn Spilsbury Pucci Apr 2018

From Data To Decisions: Navigating The “So What?” And “What’S Next?” Conversation Around Nearshore Data, Dawn Spilsbury Pucci

Salish Sea Ecosystem Conference

Ever wonder what happens to all those data being collected? In Island County, we are fortunate to be the focus of a plethora of data collection efforts. We have comprehensive nearshore assessments that describe where our habitats are and how those habitats are built. We have habitat assessments that are a collaborative effort between citizen science groups and state agencies. We have long-term status and trends data sets on intertidal habitats and presence for a few specific species. We have a high resolution shoreline armor dataset and we have a predictive probability model for coastal flood risk. But what do ...


Development Of An Interactive Web Map To Visualize A Complex Dataset, Lisa Ferrier Apr 2018

Development Of An Interactive Web Map To Visualize A Complex Dataset, Lisa Ferrier

Salish Sea Ecosystem Conference

The Washington State Department of Natural Resources (DNR) is steward to 2.6 million acres of state-owned aquatic lands. As part of its management responsibilities, DNR’s Submerged Vegetation Monitoring Program (SVMP) documents occurrences of seagrass throughout greater Puget Sound. Seagrasses are recognized globally as a sensitive indicator of water quality while providing productive, critical habitat utilized by many fish, bird and invertebrate species. The SVMP has been conducting seagrass surveys in Puget Sound, using underwater video, since 2000. Video is reviewed, classified for presence/absence, and ultimately used to characterize abundance, distribution and change in seagrass at local, regional ...


Bot Or Not: Detecting Bots In Online Multiplayer Video Games Through User Input, Alexander Boutelle Apr 2018

Bot Or Not: Detecting Bots In Online Multiplayer Video Games Through User Input, Alexander Boutelle

Judith Ramaley Undergraduate Research Celebration 2018

No abstract provided.


2018 Wachapreague Station Tide Prediction Calendars, Virginia Institute Of Marine Science, David A. Evans Jan 2018

2018 Wachapreague Station Tide Prediction Calendars, Virginia Institute Of Marine Science, David A. Evans

Miscellaneous

These calendars are produced monthly using David Evans' Tidecal.


2018 Gloucester Point Station Tide Prediction Calendars, Virginia Institute Of Marine Science, David A. Evans Jan 2018

2018 Gloucester Point Station Tide Prediction Calendars, Virginia Institute Of Marine Science, David A. Evans

Miscellaneous

These calendars are produced monthly using David Evans' Tidecal.


2018 Cheaspeake Bay Bridge Tunnel Station Tide Prediction Calendars, Virginia Institute Of Marine Science, David A. Evans Jan 2018

2018 Cheaspeake Bay Bridge Tunnel Station Tide Prediction Calendars, Virginia Institute Of Marine Science, David A. Evans

Miscellaneous

These calendars are produced monthly using David Evans' Tidecal.


2018 Hampton Roads Station Tide Prediction Calendars, Virginia Institute Of Marine Science, David A. Evans Jan 2018

2018 Hampton Roads Station Tide Prediction Calendars, Virginia Institute Of Marine Science, David A. Evans

Miscellaneous

These calendars are produced monthly using David Evans' Tidecal.


Design And Empirical Evaluation Of Interactive And Interpretable Machine Learning, Forough Poursabzi-Sangdeh Jan 2018

Design And Empirical Evaluation Of Interactive And Interpretable Machine Learning, Forough Poursabzi-Sangdeh

Computer Science Graduate Theses & Dissertations

Machine learning is ubiquitous in making predictions that affect people's decisions. While most of the research in machine learning focuses on improving the performance of the models on held-out data sets, this is not enough to convince end-users that these models are trustworthy or reliable in the wild. To address this problem, a new line of research has emerged that focuses on developing interpretable machine learning methods and helping end-users make informed decisions.

Despite the growing body of research in developing interpretable models, there is still no consensus on the definition and quantification of interpretability. We argue that to ...


Fy 2018 Umass Amherst Waste Management Report, Ezra Small Jan 2018

Fy 2018 Umass Amherst Waste Management Report, Ezra Small

Campus Data

Each year the Office of Waste Management publishes this report which totals recycling and refuse data for the campus.


Summary Tables: 2018 Caroline County, Virginia Shoreline Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner Jan 2018

Summary Tables: 2018 Caroline County, Virginia Shoreline Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner

Reports

The Shoreline Inventory Summary Tables quantify observed conditions based on river systems, such as the combined length of linear features (e.g. shoreline miles surveyed, miles of bulkhead and revetment), the total number of point features (e.g. docks, boathouses, boat ramps) & total acres of polygon features (tidal marshes).


Summary Tables: 2018 Richmond County, Virginia Shoreline Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner Jan 2018

Summary Tables: 2018 Richmond County, Virginia Shoreline Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner

Reports

The Shoreline Inventory Summary Tables quantify observed conditions based on river systems, such as the combined length of linear features (e.g. shoreline miles surveyed, miles of bulkhead and revetment), the total number of point features (e.g. docks, boathouses, boat ramps) & total acres of polygon features (tidal marshes).


Summary Tables: 2018 Essex County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, David Stanhope, Carl Hershner Jan 2018

Summary Tables: 2018 Essex County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, David Stanhope, Carl Hershner

Reports

The Shoreline Inventory Summary Tables quantify observed conditions based on river systems, such as the combined length of linear features (e.g. shoreline miles surveyed, miles of bulkhead and revetment), the total number of point features (e.g. docks, boathouses, boat ramps) & total acres of polygon features (tidal marshes).


Summary Tables: 2018 New Kent County, Virginia Shoreline Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner Jan 2018

Summary Tables: 2018 New Kent County, Virginia Shoreline Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner

Reports

The Shoreline Inventory Summary Tables quantify observed conditions based on river systems, such as the combined length of linear features (e.g. shoreline miles surveyed, miles of bulkhead and revetment), the total number of point features (e.g. docks, boathouses, boat ramps) & total acres of polygon features (tidal marshes).


Gis Data: New Kent County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner Jan 2018

Gis Data: New Kent County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner

Data

The Shoreline Management Model is a GIS spatial model that determines appropriate shoreline best management practices using available spatial data and decision tree logic. Available shoreline conditions used in the model include the presence or absence of tidal marshes, beaches, and forested riparian buffers, bank vegetation cover, bank height, wave exposure (fetch), nearshore water depth, and proximity of coastal development to the shoreline. The model output for shoreline best management practices is displayed in the locality Comprehensive Map Viewer. One GIS shapefile is developed that describes two arcs or lines representing practices in the upland area and practices at the ...


Gis Data: Caroline County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon A. Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl H. Hershner Jan 2018

Gis Data: Caroline County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon A. Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl H. Hershner

Data

No abstract provided.


Gis Data: Caroline County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner Jan 2018

Gis Data: Caroline County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner

Data

The Shoreline Management Model is a GIS spatial model that determines appropriate shoreline best management practices using available spatial data and decision tree logic. Available shoreline conditions used in the model include the presence or absence of tidal marshes, beaches, and forested riparian buffers, bank vegetation cover, bank height, wave exposure (fetch), nearshore water depth, and proximity of coastal development to the shoreline. The model output for shoreline best management practices is displayed in the locality Comprehensive Map Viewer. One GIS shapefile is developed that describes two arcs or lines representing practices in the upland area and practices at the ...


Gis Data: Caroline County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner Jan 2018

Gis Data: Caroline County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner

Data

No abstract provided.


Gis Data: Richmond County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner Jan 2018

Gis Data: Richmond County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner

Data

No abstract provided.


Gis Data: Essex County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, David Stanhope, Carl Hershner Jan 2018

Gis Data: Essex County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, David Stanhope, Carl Hershner

Data

No abstract provided.


Gis Data: Essex County, Virginia Shoreline Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, David Stanhope, Carl Hershner Jan 2018

Gis Data: Essex County, Virginia Shoreline Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, David Stanhope, Carl Hershner

Data

No abstract provided.


Gis Data: Essex County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, David Stanhope, Carl Hershner Jan 2018

Gis Data: Essex County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, David Stanhope, Carl Hershner

Data

The Shoreline Management Model is a GIS spatial model that determines appropriate shoreline best management practices using available spatial data and decision tree logic. Available shoreline conditions used in the model include the presence or absence of tidal marshes, beaches, and forested riparian buffers, bank vegetation cover, bank height, wave exposure (fetch), nearshore water depth, and proximity of coastal development to the shoreline. The model output for shoreline best management practices is displayed in the locality Comprehensive Map Viewer. One GIS shapefile is developed that describes two arcs or lines representing practices in the upland area and practices at the ...


Gis Data: New Kent County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner Jan 2018

Gis Data: New Kent County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner

Data

No abstract provided.


Gis Data: New Kent County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner Jan 2018

Gis Data: New Kent County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner

Data

The 2018 Inventory for New Kent County was generated using on-screen, digitizing techniques in ArcGIS® -ArcMap v10.4.1while viewing conditions observed in Bing high resolution oblique imagery, Google Earth, and2017imagery from the Virginia Base Mapping Program (VBMP).Four GIS shapefiles are developed. The first describes land use and bank conditions (New_Kent_lubc_2018). The second portrays the presence of beaches (New_Kent_beaches_2018). The third reports shoreline structures that are described as arcs or lines(e.g. riprap)(New_Kent_sstru_2018). The final shapefile includes all structures that are represented as points(e.g. piers)(New_Kent_astru_2018).The metadata file accompanies the shapefiles and defines attribute ...


Understanding Database Reconstruction Attacks On Public Data, Simson L. Garfinkel, John M. Abowd, Christian Martindale Jan 2018

Understanding Database Reconstruction Attacks On Public Data, Simson L. Garfinkel, John M. Abowd, Christian Martindale

Labor Dynamics Institute

In 2020 the U.S. Census Bureau will conduct the Constitutionally mandated decennial Census of Population and Housing. Because a census involves collecting large amounts of private data under the promise of confidentiality, traditionally statistics are published only at high levels of aggregation. Published statistical tables are vulnerable to DRAs (database reconstruction attacks), in which the underlying microdata is recovered merely by finding a set of microdata that is consistent with the published statistical tabulations. A DRA can be performed by using the tables to create a set of mathematical constraints and then solving the resulting set of simultaneous equations ...


Gis Data: Richmond County, Virginia Shoreline Inventory Report, Marcia Berman, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner Jan 2018

Gis Data: Richmond County, Virginia Shoreline Inventory Report, Marcia Berman, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner

Data

No abstract provided.


Gis Data: Richmond County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner Jan 2018

Gis Data: Richmond County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner

Data

The Shoreline Management Model is a GIS spatial model that determines appropriate shoreline best management practices using available spatial data and decision tree logic. Available shoreline conditions used in the model include the presence or absence of tidal marshes, beaches, and forested riparian buffers, bank vegetation cover, bank height, wave exposure (fetch), nearshore water depth, and proximity of coastal development to the shoreline. The model output for shoreline best management practices is displayed in the locality Comprehensive Map Viewer. One GIS shapefile is developed that describes two arcs or lines representing practices in the upland area and practices at the ...