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An Effective And Efficient Transportation Network Indicator Summary
stat.montgomerycountymd.gov | Last Updated 2018-07-02T19:09:26.000ZAn Effective And Efficient Transportation Network Indicator Summary. To see details for each benchmark county, go to https://reports.data.montgomerycountymd.gov/dataset/An-Effective-And-Efficient-Transportation-Network-/qxyx-qs79
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Northern Hemisphere Snow Cover Monthly Statistics at 1 Degree Resolution V001 (NHSNOWM) at GES DISC
data.nasa.gov | Last Updated 2022-01-17T05:45:22.000ZThis product is Snow Cover Statistics. The dataset was prepared by Dr. Peter Romanov at Cooperative Institute for Climate Studies(CICS) of the University of Maryland for Northern Eurasia Earth Science Partnership Initiative (NEESPI) program. The product includes the monthly snow statistics (frequency of occurrence) for Northern Hemisphere at 1x1 degree spatial resolution. The dataset covers the time period from January 2000 to November 2014. Monthly data were derived from daily snow cover charts produced at NOAA/NESDIS within Interactive Multisensor Ice Mapping System (IMS).
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Northern Hemisphere EASE-Grid 2.0 Weekly Snow Cover and Sea Ice Extent
nasa-test-0.demo.socrata.com | Last Updated 2015-07-19T09:14:22.000ZThe Northern Hemisphere EASE-Grid 2.0 Weekly Snow Cover and Sea Ice Extent Version 4 product combine snow cover and sea ice extent at weekly intervals from 23 October 1978 through 29 June 2014, and snow cover alone from 03 October 1966 through 29 June 2014. Snow cover extent for this data set is based on the NOAA/NCDC Climate Data Record (CDR) of Northern Hemisphere (NH) Snow Cover Extent (SCE) by D. Robinson (2012) and regridded to the EASE-Grid. The NOAA/NCDC CDR of Northern Hemisphere Snow Cover Extent data were derived from the manual interpretation of AVHRR, GOES, and other visible-band satellite data (Helfrich et al. 2007). Sea ice extent is regridded to EASE-Grid from Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data. Designed to facilitate study of Northern Hemisphere seasonal fluctuations of snow cover and sea ice extent, the data set also includes monthly climatologies describing average extent, probability of occurrence, and variance. Data are provided in flat, unsigned binary files and are available via FTP.
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Nimbus-7 SMMR Derived Monthly Global Snow Cover and Snow Depth
nasa-test-0.demo.socrata.com | Last Updated 2015-07-19T09:14:15.000ZThe data set consists of monthly global snow cover and snow depth derived from Nimbus-7 SMMR data for 1978 through 1987. The SMMR data are interpolated for spatial and temporal gaps, and averaged for display in polar stereographic projection. Maps are based on six-day average brightness temperature data from the middle week of each month. Data are placed into 1/2 degree latitude by 1/2 degree longitude grid cells uniformly subdividing a polar stereographic map according to the geographic coordinates of the center of the radiometers' fields of view. Overlapping data from separate orbits in the same six-day period are averaged to give a single brightness temperature assumed to be at the cell's center. Oceans and bays are masked so that only microwave data for land areas are displayed. Comparisons of SMMR snow cover maps with previous maps produced by NOAA/NESDIS and US Air Force Global Weather Center indicate that the total snow covered area derived from SMMR is usually about ten percent less than that measured by the earlier products, because passive microwave sensors often can't detect shallow dry snow less than about 5 cm deep. Snow depths are comparable, showing SMMR results to be especially good for uniform snow covered areas such as the Canadian high plains and Russian steppes. Heavily forested and mountainous areas tend to mask the microwave snow signatures, and SMMR snow depth derivations are less reliable in those areas. Formerly distributed by NASA/GSFC/NSSDC and NASA Pilot Land Data System (PLDS), these data are now available via ftp from NSIDC.
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The Omnibus Surveys - Omnibus Monthly Survey 2002 May EXCEL Data
datahub.transportation.gov | Last Updated 2018-12-19T00:13:36.000ZThe Omnibus Surveys are a convenient way to get very quick input on transportation issues; to see who uses what, how they use it, and how users view it, and what they think about it; and to gauge public satisfaction with the transportation system and government programs.The series of surveys include: A monthly household survey of 1,000 households each month, which collects data on core questions about general travel experiences, satisfaction with the system, and some demographic data. Targeted surveys to address special transportation issues, as the U.S. Department of Transportation (DOT) operating administrations need them
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RICAPS On-road Transportation Emissions roll-up 2
datahub.smcgov.org | Last Updated 2019-05-22T23:00:49.000ZData by city showing transportation contribution to greenhouse gas emissions in the County. This data is part of the Regionally Integrated Climate Action Planning Suite (RICAPS) program. The majority of cities used the “in-boundary” methodology that relies on data from the Highway Performance Monitoring System. The inventories for South San Francisco and Unincorporated County use the “origin-destination” methodology from that relies on data from Metropolitan Transportation Commission (MTC). So, directly comparing vehicle miles traveled (VMT) across all cities is not statistically possible. Each city in San Mateo County has the opportunity to develop its own Climate Action Plan (CAP) using tools developed by C/CAG in conjunction with DNV KEMA https://www.dnvgl.com/ and Hara. http://www.verisae.com/default.aspx. This project was funded by grants from the Bay Area Air Quality Management District (BAAQMD) and Pacific Gas and Electric Company (PG&E). Climate Action Plans developed from these tools will meet BAAQMD's California Environmental Quality Act (CEQA) guidelines for a Qualified Greenhouse Gas Reduction Strategy. For more information, please see the RICAPS site: http://www.smcenergywatch.com/progress_report.html
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TRMM Microwave Imager (TMI) Gridded Oceanic Rainfall Product (TRMM Product 3A11) V7
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T04:52:56.000ZThe Tropical Rainfall Measuring Mission (TRMM) is a joint U.S.-Japan satellite mission to monitor tropical and subtropical precipitation and to estimate its associated latent heating. TRMM was successfully launched on November 27, at 4:27 PM (EST) from the Tanegashima Space Center in Japan. The TRMM Microwave Imager (TMI) is a nine-channel passive microwave radiometer, which builds on the heritage of the Special Sensor Microwave/Imager (SSM/I) instrument flown aboard the Defense Meteorological Satellite Program (DMSP) platforms. Microwave radiation is emitted by the Earth's surface and by water droplets within clouds. However, when layers of large ice particles are present in upper cloud regions - a condition highly correlated with heavy rainfall - microwave radiation tends to scatter at frequencies above 19 GHz. The TMI detects radiation at five frequencies chosen to discriminate among these processes, thus revealing the likelihood of rainfall. The key to accurate retrieval of rainfall rates by this method is the deduction of cloud precipitation consistent with the radiation measurement at each frequency. The TMI frequencies are 10.65, 19.35, 37 and 85.5 GHz (dual polarization), and 21 GHz (vertical polarization only). The TMI Gridded Oceanic Rainfall Product, also known as TMI Emission, consists of 5 degree by 5 degree monthly oceanic rainfall maps using TMI Level 1 data as input. Statistics of the monthly rainfall, including number of samples, standard deviation, goodness-of-fit (of the brightness temperature histogram to the lognormal rainfall distribution function) and rainfall probability are also included in the output for each grid box. Spatial coverage is between 40 degrees North and 40 degrees South owing to the 35 degree inclination of the TRMM satellite. TMI brightness temperature histograms at 1 degree intervals are generated based on the 19, 21 and 19-21 GHz combination channels obtained from the Level 1B (calibrated brightness temperature) TMI product. Monthly rainfall indices over the ocean are derived by statistically matching monthly histograms of brightness temperatures with model calculated rainfall Probability Distribution Functions (PDF) using the 19-21 GHz combination data. Retrieved monthly rainfall data must pass a quality test based on the quality of the PDF fit. The data are stored in the Hierarchical Data Format (HDF), which includes both core and product specific metadata applicable to the TMI measurements. A file contains 12 arrays of rainfall data and supporting information each of dimension 72 x 16, with a file size of about 40 KB (uncompressed). The HDF-EOS "grid" structure is used to accommodate the actual geophysical data arrays. There is 1 file of TMI 3A11 data produced per month.
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New Brunswick Road Network (NBRN) / Réseau Routier du Nouveau-Brunswick (RRNB)
gnb.socrata.com | Last Updated 2022-06-03T11:09:51.000ZThe NBRN is the official source for road data in New Brunswick. The NBRN includes road centerlines, road names, road class, surface type, address ranges and other road attributes. / Le RRNB est la source de données sur les routes officielle du Nouveau-Brunswick. Il contient des renseignements tels que les lignes médianes, les noms, la classification et le type de surface des routes, ainsi que les tranches d'adresses et d’autres attributs des voies de circulation.
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Vital Signs: Transit Ridership by Mode – by Bay Area
data.bayareametro.gov | Last Updated 2022-06-29T23:30:44.000ZVITAL SIGNS INDICATOR Transit Ridership (T11) FULL MEASURE NAME Daily transit boardings LAST UPDATED May 2017 DESCRIPTION Transit ridership refers to the number of passenger boardings on public transportation, which includes buses, rail systems and ferries. The dataset includes metropolitan area, regional, mode and operator tables for total typical weekday boardings. DATA SOURCE Federal Transit Administration: National Transit Database http://www.ntdprogram.gov/ntdprogram/data.htm CONTACT INFORMATION vitalsigns.info@mtc.ca.gov METHODOLOGY NOTES (across all datasets for this indicator) The NTD dataset was lightly cleaned to correct for erroneous zero values - in which null values (unsubmitted data) were incorrectly marked as zeroes. Paratransit data is sparse in early years of the NTD dataset, meaning that transit ridership estimates in the early 1990s are likely underestimated. Simple modes were aggregated to combine the various bus modes (e.g. rapid bus, express bus, local bus) into a single mode to avoid incorrect conclusions resulting from mode recoding over the lifespan of NTD. 2016 data should be considered preliminary, as it comes from the monthly data tables rather than the longer-term time-series dataset. Weekday ridership is calculated by taking the total annual ridership and dividing by 300, an assumption which is consistent with MTC travel modeling procedures; it was also compared to observed weekday boarding data (which is more limited in availability) to ensure consistency on the regional level. Per-capita transit ridership is calculated for the operator's general service area or taxation district; for example, BART includes the three core counties (San Francisco, Alameda, and Contra Costa) as well as northern San Mateo County post-SFO extension and AC Transit includes the cities located within its service area. For other metro areas, operators were identified by developing a list of all urbanized areas within a current MSA boundary and then using that UZA list to flag relevant operators; this means that all operators (both large and small) were included in the metro comparison data.
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Curb Ramps and Sidewalks on NYS-Owned Highways Map
data.ny.gov | Last Updated 2023-04-12T20:17:38.000ZThis data set includes all sidewalks and curb ramps located on the road system under the jurisdiction of New York State Department of Transportation (DOT). It contains the Regional Office responsible for the management of the feature, the state county where the feature is located and the route name and number for sidewalks on state-owned routes within New York State. The data set does not include sidewalks or curb ramps located on non-state owned routes such as those owned by cities, towns or villages within the state.