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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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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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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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Vital Signs: Transit Cost-Effectiveness – by operator
data.bayareametro.gov | Last Updated 2018-07-06T18:04:51.000ZVITAL SIGNS INDICATOR Transit Cost-Effectiveness (T13) FULL MEASURE NAME Net cost per transit boarding (cost per boarding minus fare per boarding) LAST UPDATED May 2017 DESCRIPTION Transit cost-effectiveness refers to both the total and net costs per transit boarding, both of which are adjusted to reflect inflation over time. Net costs reflect total operating costs minus farebox revenue (i.e. operating costs that are not directly funded by system users). The dataset includes metropolitan area, regional, mode, and system tables for net cost per boarding, total cost per boarding, and farebox recovery ratio. DATA SOURCE Federal Transit Administration: National Transit Database http://www.ntdprogram.gov/ntdprogram/data.htm Bureau of Labor Statistics: Consumer Price Index http://www.bls.gov/data/ CONTACT INFORMATION vitalsigns.info@mtc.ca.gov METHODOLOGY NOTES (across all datasets for this indicator) 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. 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. Financial data was inflation-adjusted to match 2015 dollar values using metro-specific Consumer Price Indices.
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Vital Signs: Commute Mode Choice (by Place of Residence) – Bay Area
data.bayareametro.gov | Last Updated 2020-05-20T21:50:47.000ZVITAL SIGNS INDICATOR Commute Mode Choice (T1) FULL MEASURE NAME Commute mode share by residential location LAST UPDATED April 2020 DESCRIPTION Commute mode choice, also known as commute mode share, refers to the mode of transportation that a commuter uses to travel to work, such as driving alone, biking, carpooling or taking transit. The dataset includes metropolitan area, regional, county, city and census tract tables by place of residence. DATA SOURCE U.S. Census Bureau: Decennial Census (1960-2000) - via MTC/ABAG Bay Area Census http://www.bayareacensus.ca.gov/transportation/Means19802000.htm U.S. Census Bureau: American Community Survey Form B08301 (2006-2018; place of residence) www.api.census.gov CONTACT INFORMATION vitalsigns.info@bayareametro.gov METHODOLOGY NOTES (across all datasets for this indicator) For the decennial Census datasets, the breakdown of auto commuters between drive alone and carpool is not available before 1980. "Other" includes bicycle, motorcycle, taxi, and other modes of transportation. For the American Community Survey datasets, 1-year rolling average data was used for metros, region, and county geographic levels, while 5-year rolling average data was used for cities and tracts. This is due to the fact that more localized data is not included in the 1-year dataset across all Bay Area cities. Regional mode shares are population-weighted averages of the nine counties’ modal shares. "Auto" includes drive alone and carpool for the simple data tables and is broken out in the detailed data tables accordingly, as it was not available before 1980. “Transit” includes public operators (Muni, BART, etc.) and employer-provided shuttles (e.g., Google shuttle buses). "Other" includes motorcycle, taxi, and other modes of transportation; bicycle mode share was broken out separately for the first time in the 2006 data and is shown in the detailed data tables. Census tract data is not available for tracts with insufficient numbers of residents or workers. The metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary MSAs for the nine other major metropolitan areas.
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The Omnibus Surveys - Omnibus Monthly Survey 2002 Dec SAS Data
datahub.transportation.gov | Last Updated 2018-12-19T00:13:38.000ZThe 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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The Omnibus Surveys - Omnibus Monthly Survey 2002 Jan SAS Data
datahub.transportation.gov | Last Updated 2018-12-19T00:13:37.000ZThe 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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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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BHO MH Engagement in Care: 2010-2014
data.ny.gov | Last Updated 2019-06-10T18:02:12.000ZThe Behavioral Health Organization (BHO) initiative oversees the transition to managed care for Medicaid recipients who receive mental health (MH) and substance use disorder (SUD) services in New York State. The metrics emphasize improving rates of timely follow-up treatment post discharge, timely filling of appropriate medication prescriptions post discharge, and reducing rates of readmission.The BHO MH Engagement in Care dataset is designed to assess the degree to which individuals discharged from mental health inpatient treatment engage in outpatient treatment post discharge where "engagement" is defined as receiving two or more outpatient mental health visits within thirty days of discharge and the degree to which individuals discharged from mental health inpatient treatment engage in outpatient treatment post discharge where "engagement" is defined as receiving four or more outpatient mental health visits within 60 days of discharge. The year 2015 saw the conclusion of the first phase of the Behavioral Health Organization initiative (BHO). A new Behavioral Health Managed Care Transition phase II is underway. The data contained in the BHO metrics span 2010 to 2014, using the 2010 calendar year for a baseline. Earlier in the program (2011‐2012) the metrics were calculated quarterly and on a year‐to‐date basis, later in (2013‐2014), New York State Office of Mental Health opted for semi‐annual and year‐to‐date aggregations.
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Contribution to High Asia Runoff from Ice and Snow (CHARIS) Melt Model Output, 2001 - 2014, Version 1
data.nasa.gov | Last Updated 2022-01-17T05:15:59.000ZThis data set contains input and output data for temperature index (TI) model runs completed for the Contributions to High Asia Runoff from Ice and Snow (CHARIS) project at NSIDC in 2018 and 2019. The input data are the area of snow on land, snow on ice, and exposed glacier ice as well as surface air temperature. These inputs are used to model the volumes of melt runoff from the snow on land, snow on ice, and exposed glacier ice in certain areas of High Mountain Asia.