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Greenhouse Gas Emissions over Time (Residential Energy and Transportation)
www.transparentrichmond.org | Last Updated 2021-02-16T09:56:33.000ZThis data includes residential energy and on road, off road, and BART transportation emissions. Complete commercial/industrial data is not currently available to the City so it is not included. Solid waste data is pending additional data for 2018 and 2019.
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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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RICAPS On-road Transportation Emissions roll-up
datahub.smcgov.org | Last Updated 2018-06-13T15:39:17.000ZData 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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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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Election Results, Special Election Runoff, February 3, 2015
data.fultoncountyga.gov | Last Updated 2024-01-30T22:52:06.000ZThis data set consists of all Fulton County Election results from the Special Election Runoff, February 3, 2015, to present. Included with each record is the race, candidate, precinct, number of election day votes, number of absentee by mail votes, number of advance in person votes, number of provisional votes, total number of votes, name of election, and date of election. This data set is updated after each election.
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Election Results, Special Election Runoff, December 5, 2017
data.fultoncountyga.gov | Last Updated 2024-01-30T22:52:27.000ZThis data set consists of all Fulton County Election results from the Special Election Runoff, December 5, 2017 to present. Included with each record is the race, candidate, precinct, number of election day votes, number of absentee by mail votes, number of advance in person votes, number of provisional votes, total number of votes, name of election, and date of election. This data set is updated after each election.
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Election Results, Special Election Runoff, August 11, 2015
data.fultoncountyga.gov | Last Updated 2024-01-23T18:41:10.000ZThis data set consists of all Fulton County Election results from the Special Election Runoff, August 11, 2015 to present. Included with each record is the race, candidate, precinct, number of election day votes, number of absentee by mail votes, number of advance in person votes, number of provisional votes, total number of votes, name of election, and date of election. This data set is updated after each election.
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Low Income Home Energy Assistance Program FY 2008 Household Data
healthdata.gov | Last Updated 2023-07-25T18:42:49.000Z<p>State-reported annual data collected on the presence of elderly, disabled, and young children in eligible households receiving Low Income Home Energy Assistance Program (LIHEAP) heating assistance, cooling assistance, crisis assistance or weatherization assistance.</p>
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Election Results, General Election, November 6, 2018
data.fultoncountyga.gov | Last Updated 2024-01-30T22:52:39.000ZThis data set consists of all Fulton County Election results from the General Election, November 6, 2018 to present. Included with each record is the race, candidate, precinct, number of election day votes, number of absentee by mail votes, number of advance in person votes, number of provisional votes, total number of votes, name of election, and date of election. This data set is updated after each election.
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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.