Physical Sciences and Mathematics Commons^™

Subset Data From The Nauru 1999 And African Monsoon Multidisciplinary Analysis (Amma) 2006 Cruise And Matlab Code For Generating Plots For The Paper: Wong And Minnett (2017): The Response Of The Ocean Thermal Skin Layer To Variations In Incident Infrared Radiation., Elizabeth Wong

Supplementary Data and Tools

Data in this collection is largely comprised of subsetted data from two research cruises, the Nauru cruise held from June to July 1999 and the African Monsoon Multidisciplinary Analysis (AMMA) cruise held from May to July 2006. The data subset is limited to night conditions under low wind speeds of < 10 m/s and consists of the surface fluxes, radiance measurements from the Marine Atmospheric Emitted Radiance Interferometer (M-AERI), the retrievals of the skin sea surface temperatures and skin sea surface temperature profiles from the M-AERI's radiance spectral measurements. Also included are line-by-line-radiative transfer simulations provided by Dr. Goshka Szczodrak, transmission coefficient spectra obtained from the HITRAN database, and wind speed data from the Special Sensing Microwave Imager (SSM/I) version 6. provided by Dr. Chelle Gentemann. Matlab code is provided which reads the datafile (DATA.mat) and outputs the figures illustrated in the paper Wong and Minnett (2017) (https://doi.org/10.1002/2017JC013351).

Data From: Observing System Simulation Experiments For An Array Of Autonomous Biogeochemical Profiling Floats In The Southern Ocean, Igor Kamenkovich, Angelique Haza, Alison R. Gray, Carolina O. Dufour, Zulema Garraffo

Supplementary Data and Tools

Data in this collection is from Observation System Simulation Experiments (OSSEs) that were carried in support of the SOCCOM program. Synthetic profiles were extracted from model-simulated dissolved oxygen and inorganic carbon. Full maps were then reconstructed from these sparse datasets, using objective mapping. For description of the model and reconstruction method please see Kamenkovich, I., A. Haza, A. Gray, C. Dufour, and Z. Garraffo: "Observing System Simulation Experiments for an array of autonomous biogeochemical profiling floats in the Southern Ocean", Journal of Geophysical Research, DOI: 10.1002/2017JC012819

Catch The King Tide 2017 Data: Gloucester & Mathews, Virginia, Jon Derek Loftis

Data

"Catch the King" was a citizen science GPS data collection effort centered in Hampton Roads, VA, that sought to map the King Tide's maximum inundation extents with the goal of validating and improving predictive models for future forecasting of increasingly pervasive "nuisance" flooding. GPS data points were collected by volunteers to effectively breadcrumb/trace the high water line by pressing the 'Save Data' button in the Sea Level Rise App every few steps along the water's edge during the high tide on the morning of Nov. 5th, 2017.

Response from the event's dedicated volunteers, fueled by the local media …

Gis Data: Henrico County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Karen Duhring, Kallie Brown, Jessica Hendricks, David Weiss, Carl Hershner

Data

The 2017 Tidal Marsh Inventory update for Henrico County, Virginia was generated using on-screen digitizing techniques in the most recent version of ArcGIS® - ArcMap while viewing conditions observed in the most recent imagery from the Virginia Base Mapping Program (VBMP). Dominant plant community types were primarily determined during field surveys from shallow-draft boats moving along the shoreline. Land-based surveys were performed in some locations. One shapefile is developed that portrays tidal marsh areas represented as polygons. A metadata file accompanies the shapefile to define attribute accuracy, data development, and any use restrictions that pertain to the data.

Evaluation Of Property Management Agent Performance : A Novel Empirical Model, Yung Yau, Daniel Chi Wing Ho

Faculty of Design & Environment (THEi)

For many different reasons, property management agents (PMAs) are appointed for managing housing developments in both public and private housing sectors in many different cities. While third-party housing management eases the burdens of property owners and tenants in taking care of their properties, it may lead to agency problems. In fact, cases of mismanagement of multi-owned properties are common in Hong Kong and other Asian cities, leading to accelerated urban decay and augmented confrontations between property owners, users and PMAs. To promote better property management services, the performance of PMAs should be evaluated so market players can benchmark the performance …

Catch The King Tide 2017 Data: Outside Hampton Roads, Virginia, Jon Derek Loftis

Data

"Catch the King" was a citizen science GPS data collection effort centered in Hampton Roads, VA, that sought to map the King Tide's maximum inundation extents with the goal of validating and improving predictive models for future forecasting of increasingly pervasive "nuisance" flooding. GPS data points were collected by volunteers to effectively breadcrumb/trace the high water line by pressing the 'Save Data' button in the Sea Level Rise App every few steps along the water's edge during the high tide on the morning of Nov. 5th, 2017.

Response from the event's dedicated volunteers, fueled by the local media …

Gis Data: Henrico County, Virginia, Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Karen Duhring, Kallie Brown, Jessica Hendricks, David Weiss, Carl Hershner

Data

The 2017 Inventory for Henrico County was generated using on-screen, digitizing techniques in ArcGIS® -ArcMap v10.4.1 while viewing conditions observed in Bing high resolution oblique imagery, Google Earth, and 2013 imagery from the Virginia Base Mapping Program (VBMP). Four GIS shapefiles are developed.The first describes land use and bank conditions (Henrico _lubc_2017). The second portrays the presence of beaches (Henrico _beaches_2017). The third reports shoreline structures that are described as arcs or lines(e.g. riprap)(Henrico _sstru_2017). The final shapefile includes all structures that are represented as points(e.g. piers)(Henrico _astru_2017).The metadata file accompanies the shapefiles and defines attribute accuracy, data development, and …

Gis Data: City Of Fredericksburg, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Karen A. Duhring, Kallie Brown, Jessica Hendricks, David Weiss, 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: Surry County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Karen Duhring, Kallie Brown, Jessica Hendricks, David Stanhope, David Weiss, 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 …

Biometeorological Modelling And Forecasting Of Monthly Ambulancedemand For Hong Kong, H. T. Wong, P. C. Lai, Sissi Si Chen

Faculty of Design & Environment (THEi)

Given the aging population in Hong Kong and the ever rising demand for emergency ambulance services, this study aimed to examine the effects of seasonality and weather on the demand for emergency ambulance services in Hong Kong. The feasibility of using time series models and selected weather factors to forecast average daily ambulance demand over a month was also assessed.

Gis Data: Henrico County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Karen Duhring, Kallie Brown, Jessica Hendricks, David Weiss, 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: King George County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Kory Angstadt, Karen Duhring, Kallie Brown, Jessica Hendricks, David Weiss, Carl Hershner

Data

The 2017 Tidal Marsh Inventory update for King George County, Virginia was generated using on-screen digitizing techniques in the most recent version of ArcGIS® - ArcMap while viewing conditions observed in the most recent imagery from the Virginia Base Mapping Program (VBMP). Dominant plant community types were primarily determined during field surveys from shallow-draft boats moving along the shoreline. Land-based surveys were performed in some locations. One shapefile is developed that portrays tidal marsh areas represented as polygons. A metadata file accompanies the shapefile to define attribute accuracy, data development, and any use restrictions that pertain to the data.

Gis Data: The County Of Isle Of Wight, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Tamia Rudnicky, Julie Bradshaw, Karen Duhring, Kallie Brown, Jessica Hendricks, David Stanhope, Kory Angstadt, Christine Tombleson, David Weiss, 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 …

Code For "Noise-Enhanced Coding In Phasic Neuron Spike Trains", Cheng Ly, Brent D. Doiron

Statistical Sciences and Operations Research Data

This zip file contains Matlab scripts and ode (XPP) files to calculate the statistics of the models in "Noise-Enhanced Coding in Phasic Neuron Spike Trains". This article is published in PLoS ONE.

Perihelion Precession In The General Theory Of Relativity, Charles G. Torre

Tutorials on... in 1 hour or less

This is a relatively quick and informal sketch of a demonstration that general relativistic corrections to the bound Kepler orbits introduce a perihelion precession. Any decent textbook on the general theory of relativity will derive this result. My analysis aligns with that found in the good old text "Introduction to General Relativity", by Adler, Bazin and Schiffer.

Gis Data: King George County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Kory Angstadt, Karen Duhring, Kallie Brown, Jessica Hendricks, David Weiss, 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: Surry County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Karen Duhring, Kallie Brown, Jessica Hendricks, David Stanhope, David Weiss, Carl Hershner

Data

The 2017 Tidal Marsh Inventory update for Surry County, Virginia was generated using on-screen digitizing techniques in the most recent version of ArcGIS® - ArcMap while viewing conditions observed in the most recent imagery from the Virginia Base Mapping Program (VBMP). Dominant plant community types were primarily determined during field surveys from shallow-draft boats moving along the shoreline. Land-based surveys were performed in some locations. One shapefile is developed that portrays tidal marsh areas represented as polygons. A metadata file accompanies the shapefile to define attribute accuracy, data development, and any use restrictions that pertain to the data.

Gis Data: The County Of Chesterfield And The Cities Of Colonial Heights, Petersburg, And Richmond, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Karen Duhring, Kallie Brown, Jessica Hendricks, David Weiss, Carl Hershner

Data

The 2017 Tidal Marsh Inventory update for the County of Chesterfield and the Cities of Colonial Heights, Petersburg, and Richmond, Virginia was generated using on-screen digitizing techniques in the most recent version of ArcGIS® - ArcMap while viewing conditions observed in the most recent imagery from the Virginia Base Mapping Program (VBMP). Dominant plant community types were primarily determined during field surveys from shallow-draft boats moving along the shoreline. Land-based surveys were performed in some locations. One shapefile is developed that portrays tidal marsh areas represented as polygons. A metadata file accompanies the shapefile to define attribute accuracy, data development, …

Gis Data: The County Of Spotsylvania Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Tamia Rudnicky, Julie Bradshaw, Karen Duhring, Kallie Brown, Jessica Hendricks, David Weiss, Carl Hershner

Data

The 2017 Inventory for Spotsylvania 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, and2013imagery from the Virginia Base Mapping Program (VBMP).Four GIS shapefiles are developed. The first describes land use and bank conditions (Spotsylvania_lubc_2017). The second portrays the presence of beaches (Spotsylvania_beaches_2017). The third reports shoreline structures that are described as arcs or lines (e.g. riprap)(Spotsylvania_sstru_2017). The final shapefile includes all structures that are represented as points (e.g. piers)(Spotsylvania_astru_2017).The metadata file accompanies the shapefiles and defines attribute accuracy, data development, and any use restrictions that pertain …

Gis Data: The County Of Spotsylvania, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Tamia Rudnicky, Julie G. Bradshaw, Karen Duhring, Kallie Brown, Jessica Hendricks, David Weiss, Carl Hershner

Data

The 2017 Tidal Marsh Inventory update for the County of Spotsylvania, Virginia was generated using on-screen digitizing techniques in the most recent version of ArcGIS® - ArcMap while viewing conditions observed in the most recent imagery from the Virginia Base Mapping Program (VBMP). Dominant plant community types were primarily determined during field surveys from shallow-draft boats moving along the shoreline. Land-based surveys were performed in some locations. One shapefile is developed that portrays tidal marsh areas represented as polygons. A metadata file accompanies the shapefile to define attribute accuracy, data development, and any use restrictions that pertain to the data.

Gis Data: The County Of Spotsylvania, Virginia Shoreline Manangement Model, Marcia Berman, Karinna Nunez, Tamia Rudnicky, Julie Bradshaw, Karen Duhring, Kallie Brown, Jessica Hendricks, David Weiss, 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: The County Of Isle Of Wight, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Tamia Rudnicky, Julie Bradshaw, Karen Duhring, Kallie Brown, Jessica Hendricks, David Stanhope, Kory Angstadt, Christine Tombleson, David Weiss, Carl Hershner

Data

The 2017 Inventory for the Isle of Wight 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, and2013imagery from the Virginia Base Mapping Program (VBMP). Four GIS shapefiles are developed. The first describes land use and bank conditions (IsleofWight_lubc_2017). The second portrays the presence of beaches (IsleofWight_beaches_2017). The third reports shoreline structures that are described as arcs or lines(e.g. riprap)(IsleofWight_sstru_2017). The final shapefile includes all structures that are represented as points(e.g. piers)(IsleofWight_astru_2017).The metadata file accompanies the shapefiles and defines attribute accuracy, data development, and any use restrictions …

Gis Data: The County Of Isle Of Wight, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Tamia Rudnicky, Julie Bradshaw, Karen Duhring, Kallie Brown, Jessica Hendricks, David Stanhope, Kory Angstadt, Christine Tombleson, David Weiss, Carl Hershner

Data

The 2017 Tidal Marsh Inventory update for the County of Isle of Wight, Virginia was generated using on-screen digitizing techniques in the most recent version of ArcGIS® - ArcMap while viewing conditions observed in the most recent imagery from the Virginia Base Mapping Program (VBMP). Dominant plant community types were primarily determined during field surveys from shallow-draft boats moving along the shoreline. Land-based surveys were performed in some locations. One shapefile is developed that portrays tidal marsh areas represented as polygons. A metadata file accompanies the shapefile to define attribute accuracy, data development, and any use restrictions that pertain to …

Associated Dataset: Assimilating Bio-Optical Glider Data During A Phytoplankton Bloom In The Southern Ross Sea, Daniel E. Kaufman, Marjorie A.M. Friedrichs, John C.P. Hemmings, Walker O. Smith

Data

No abstract provided.

Gis Data: City Of Fredericksburg,Virginia, Shoreline Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Karen Duhring, Kallie Brown, Jessica Hendricks, David Weiss, Carl Hershner

Data

The 2017 Inventory for the City of Fredericksburg was generated using on-screen, digitizing techniques in ArcGIS® -ArcMap v10.4.1 while viewing conditions observed in Bing high resolution oblique imagery, Google Earth, and 2013 imagery from the Virginia Base Mapping Program (VBMP).Four GIS shapefiles are developed. The first describes land use and bank conditions (Fredericksburg_lubc_2017). The second portrays the presence of beaches (Fredericksburg_beaches_2017). The third reports shoreline structures that are described as arcs or lines(e.g. riprap)(Fredericksburg _sstru_2017). The final shapefile includes all structures that are represented as points(e.g. piers)(Fredericksburg _astru_2017).The metadata file accompanies the shapefiles and defines attribute accuracy, data development, and …

Gis Data: Surry County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Karen Duhring, Kallie Brown, Jessica Hendricks, David Stanhope, David Weiss, Carl Hershner

Data

The 2017 Inventory for Surry County was generated using on-screen, digitizing techniques in ArcGIS® -ArcMap v10.4.1 while viewing conditions observed in Bing high resolution oblique imagery, Google Earth, and 2013 imagery from the Virginia Base Mapping Program (VBMP).Four GIS shapefiles are developed.The first describes land use and bank conditions (Surry_lubc_2017). The second portrays the presence of beaches (Surry_beaches_2017). The third reports shoreline structures that are described as arcs or lines(e.g. riprap)(Surry_sstru_2017). The final shapefile includes all structures that are represented as points(e.g. piers)(Surry_astru_2017).The metadata file accompanies the shapefiles and defines attribute accuracy, data development, and any use restrictions that pertain …

Gis Data: King George County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon A. Killeen, Tamia Rudnicky, Julie G. Bradshaw, Kory Angstadt, Karen A. Duhring, Kallie Brown, Jessica Hendricks, David Weiss, Carl Hershner

Data

The 2017 Inventory for King George County was generated using on-screen, digitizing techniques in ArcGIS® -ArcMap v10.4.1 while viewing conditions observed in Bing high resolution oblique imagery, Google Earth, and 2013 imagery from the Virginia Base Mapping Program (VBMP).Four GIS shapefiles are developed. The first describes land use and bank conditions (King_George_lubc_2017). The second portrays the presence of beaches (King_George _beaches_2017). The third reports shoreline structures that are described as arcs or lines(e.g. riprap)(King_George _sstru_2017). The final shapefile includes all structures that are represented as points(e.g. piers)(King_George_astru_2017).The metadata file accompanies the shapefiles and defines attribute accuracy, data development, and any …

Catch The King Tide 2017 Data: Virginia Beach, Virginia, Jon Derek Loftis

Data

No abstract provided.

Catch The King Tide 2017 Data: Chesapeake, Virginia, Jon Derek Loftis

Data

"Catch the King" was a citizen science GPS data collection effort centered in Hampton Roads, VA, that sought to map the King Tide's maximum inundation extents with the goal of validating and improving predictive models for future forecasting of increasingly pervasive "nuisance" flooding. GPS data points were collected by volunteers to effectively breadcrumb/trace the high water line by pressing the 'Save Data' button in the Sea Level Rise App every few steps along the water's edge during the high tide on the morning of Nov. 5th, 2017.

Response from the event's dedicated volunteers, fueled by the local media …

Catch The King Tide 2017 Data: Newport News, Virginia, Jon Derek Loftis

Data

"Catch the King" was a citizen science GPS data collection effort centered in Hampton Roads, VA, that sought to map the King Tide's maximum inundation extents with the goal of validating and improving predictive models for future forecasting of increasingly pervasive "nuisance" flooding. GPS data points were collected by volunteers to effectively breadcrumb/trace the high water line by pressing the 'Save Data' button in the Sea Level Rise App every few steps along the water's edge during the high tide on the morning of Nov. 5th, 2017.

Response from the event's dedicated volunteers, fueled by the local media …