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RHNA Draft Performance Measures - Categorized v2
data.bayareametro.gov | Last Updated 2020-05-13T23:48:02.000ZDataset describes jurisdictions according to 8 measures which will be used to gauge RHNA performance. Each measure has been categorized into discrete buckets, for use in summarizing the jurisdiction-specific RHNA allocation. The core metrics mapping directly to CA HCD objective metrics include: Measure 1a: Lower Income RHNA in High Cost Areas Measure 1b: Lower Income RHNA in Single-Family Home Areas Measure 2a: Household Growth in Job Centers Measure 3a: Lower Income RHNA in Jobs-Housing Fit Imbalanced Areas Measure 4a: Lower Income RHNA in Areas with High Share of Low-Income Households Measure 4b: Lower Income RHNA in Areas with High Share of High-Income Households Measure 5a: Lower Income RHNA in High Opportunity Areas Measure 5b: Household Growth in High Divergence Score Areas with High-Income Households Measure 6b: Household Growth in High Hazard Risk Areas
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Vital Signs: Greenhouse Gas Emissions - by county
data.bayareametro.gov | Last Updated 2018-07-10T01:36:51.000ZVITAL SIGNS INDICATOR Greenhouse Gas Emissions (EN3) FULL MEASURE NAME Greenhouse gas emissions from primary sources LAST UPDATED August 2017 DESCRIPTION Greenhouse gas emissions refer to carbon dioxide and other chemical compounds that contribute to global climate change. Vital Signs tracks greenhouse gas emissions linked to consumption from the three largest sources in the region: surface transportation, electricity consumption, and natural gas consumption. This measure helps track progress towards achieving regional greenhouse gas reduction targets, including the region's per-capita greenhouse gas target for surface transportation under Senate Bill 375. This dataset includes emissions estimates on the regional and county levels. DATA SOURCE California Energy Commission: Retail Fuel Outlet Annual Reporting 2010-2012, 2015 Form CEC-A15 http://www.energy.ca.gov/almanac/transportation_data/gasoline/piira_retail_survey.html Energy Information Administration: CO2 Conversion Data 2015 conversion purposes only; consistent over time http://www.eia.gov/tools/faqs/faq.cfm?id=307&t=11 California Energy Commission: Electricity Consumption by County 2003-2015 http://www.ecdms.energy.ca.gov/elecbycounty.aspx Pacific Gas & Electric Company: Greenhouse Gas Emission Factors 2003-2013 audited by the Climate Registry; conversion purposes only https://www.pge.com/includes/docs/pdfs/shared/environment/calculator/pge_ghg_emission_factor_info_sheet.pdf Pacific Gas & Electric Company: Greenhouse Gas Emission Factors 2014-2015 audited by the Climate Registry; conversion purposes only http://www.pgecurrents.com/2017/02/09/pge-cuts-carbon-emissions-with-clean-energy-2/ California Energy Commission: Natural Gas Consumption by County 1990-2015 http://www.ecdms.energy.ca.gov/gasbycounty.aspx Pacific Gas & Electric Company: Climate Footprint Calculator 2015 conversion purposes only; consistent over time https://www.pge.com/includes/docs/pdfs/about/environment/calculator/assumptions.pdf California Department of Finance: Population and Housing Estimates 1990-2015 http://www.dof.ca.gov/research/demographic/ CONTACT INFORMATION vitalsigns.info@mtc.ca.gov METHODOLOGY NOTES (across all datasets for this indicator) For surface transportation, the dataset is based on a survey of fueling stations, the vast majority of which respond to the survey; the Energy Commission corrects for non-response bias by imputing the remaining share of fuel sales. Note that 2014 data was excluded to data abnormalities for several counties in the region; methodology improvements in 2012 affected estimated by +/- 5% according to CEC estimates. For years 2013 and 2014, a linear trendline assumption was used instead between 2012 and 2015 data points. Greenhouse gas emissions are calculated based on the gallons of gasoline and diesel sales, relying upon standardized Energy Information Administration conversion rates for E10 fuel (gasoline with 10% ethanol) and standard diesel. Per-capita greenhouse gas emissions are calculated simply by dividing emissions attributable to fuel sold in that county by the total number of county residents; there may be a slight bias in the data given that a fraction of fuel sold in a given county may be purchased by non-residents. For electricity consumption, the dataset is based on electricity consumption data for the nine Bay Area counties; note that this is different than electricity production as the region imports electricity. Because such data is not disaggregated by utility provider, a simple assumption is made that electricity consumed has the greenhouse gas emissions intensity (on a kilowatt-hour basis) of Pacific Gas & Electric, the primary electricity provider in the Bay Area. For this reason, with the small but growing market share of low- and zero-GHG community choice aggregation (CCA) providers, the greenhouse gas emissions estimate in more recent years may be slightly overe
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Vital Signs: Greenhouse Gas Emissions - Bay Area
data.bayareametro.gov | Last Updated 2018-07-10T01:38:19.000ZVITAL SIGNS INDICATOR Greenhouse Gas Emissions (EN3) FULL MEASURE NAME Greenhouse gas emissions from primary sources LAST UPDATED August 2017 DESCRIPTION Greenhouse gas emissions refer to carbon dioxide and other chemical compounds that contribute to global climate change. Vital Signs tracks greenhouse gas emissions linked to consumption from the three largest sources in the region: surface transportation, electricity consumption, and natural gas consumption. This measure helps track progress towards achieving regional greenhouse gas reduction targets, including the region's per-capita greenhouse gas target for surface transportation under Senate Bill 375. This dataset includes emissions estimates on the regional and county levels. DATA SOURCE California Energy Commission: Retail Fuel Outlet Annual Reporting 2010-2012, 2015 Form CEC-A15 http://www.energy.ca.gov/almanac/transportation_data/gasoline/piira_retail_survey.html Energy Information Administration: CO2 Conversion Data 2015 conversion purposes only; consistent over time http://www.eia.gov/tools/faqs/faq.cfm?id=307&t=11 California Energy Commission: Electricity Consumption by County 2003-2015 http://www.ecdms.energy.ca.gov/elecbycounty.aspx Pacific Gas & Electric Company: Greenhouse Gas Emission Factors 2003-2013 audited by the Climate Registry; conversion purposes only https://www.pge.com/includes/docs/pdfs/shared/environment/calculator/pge_ghg_emission_factor_info_sheet.pdf Pacific Gas & Electric Company: Greenhouse Gas Emission Factors 2014-2015 audited by the Climate Registry; conversion purposes only http://www.pgecurrents.com/2017/02/09/pge-cuts-carbon-emissions-with-clean-energy-2/ California Energy Commission: Natural Gas Consumption by County 1990-2015 http://www.ecdms.energy.ca.gov/gasbycounty.aspx Pacific Gas & Electric Company: Climate Footprint Calculator 2015 conversion purposes only; consistent over time https://www.pge.com/includes/docs/pdfs/about/environment/calculator/assumptions.pdf California Department of Finance: Population and Housing Estimates 1990-2015 http://www.dof.ca.gov/research/demographic/ CONTACT INFORMATION vitalsigns.info@mtc.ca.gov METHODOLOGY NOTES (across all datasets for this indicator) For surface transportation, the dataset is based on a survey of fueling stations, the vast majority of which respond to the survey; the Energy Commission corrects for non-response bias by imputing the remaining share of fuel sales. Note that 2014 data was excluded to data abnormalities for several counties in the region; methodology improvements in 2012 affected estimated by +/- 5% according to CEC estimates. For years 2013 and 2014, a linear trendline assumption was used instead between 2012 and 2015 data points. Greenhouse gas emissions are calculated based on the gallons of gasoline and diesel sales, relying upon standardized Energy Information Administration conversion rates for E10 fuel (gasoline with 10% ethanol) and standard diesel. Per-capita greenhouse gas emissions are calculated simply by dividing emissions attributable to fuel sold in that county by the total number of county residents; there may be a slight bias in the data given that a fraction of fuel sold in a given county may be purchased by non-residents. For electricity consumption, the dataset is based on electricity consumption data for the nine Bay Area counties; note that this is different than electricity production as the region imports electricity. Because such data is not disaggregated by utility provider, a simple assumption is made that electricity consumed has the greenhouse gas emissions intensity (on a kilowatt-hour basis) of Pacific Gas & Electric, the primary electricity provider in the Bay Area. For this reason, with the small but growing market share of low- and zero-GHG community choice aggregation (CCA) providers, the greenhouse gas emissions estimate in more recent years may be slightly ov
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Vital Signs: Housing Affordability - Metro
data.bayareametro.gov | Last Updated 2019-10-25T20:42:43.000ZHousing Affordability (EQ2) FULL MEASURE NAME Housing Affordability LAST UPDATED October 2018 DATA SOURCE U.S Census Bureau: Decennial Census Form STF3 – https://nhgis.org (1980-1990) Form SF3a – https://nhgis.org (2000) U.S. Census Bureau: American Community Survey Form B25074 (2009-2017) Form B25095 (2009-2017) http://api.census.gov Image: Flickr (Creative Commons license), Photographer: Frank Kehren, https://www.flickr.com/photos/fkehren/8481894011 CONTACT INFORMATION vitalsigns.info@bayareametro.gov METHODOLOGY NOTES (across all datasets for this indicator) The share of income brackets used for different Census and ACS forms varied over time. To allow for historical comparisons, the Census Bureau merges housing expenditure brackets into three consistent bins (less than 20 percent, 20 percent to 34 percent, and more than 35 percent) that work for all years. The highest income bracket for renters in the ACS data was $100,000 or more, while the homeowner dataset included brackets for $100,000 to $149,999 and $150,000 and above. These brackets were merged together to allow for uniform comparison across tenure. While some studies use 30 percent as the affordability threshold, Vital Signs uses 35 percent as this is the closest break point using the standardized affordability brackets above. Historical data for Napa County is unavailable due to an insufficient sample size for renters in a number of years, making it impossible to calculate affordability for all households. All ACS data is for a single year, rather than a rolling average. Income breakdown data is only provided for one year as it is not possible to compare consistent inflation-adjusted income brackets over time given Census data limitations.
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RHNA Draft Performance Measures - Categorized v3
data.bayareametro.gov | Last Updated 2020-09-08T05:03:52.000ZDataset describes jurisdictions according to 8 measures which will be used to gauge RHNA performance. Each measure has been categorized into two groups, for most the top 25 cities in a category versus the remainder. The core metrics mapping directly to CA HCD objective metrics include: Percent of RHNA as lower income units for jurisdictions with the highest housing costs. Measure: Housing costs Share of homeowners living in units valued above $750,000. Threshold grouping: upper third vs rest Percent of RHNA as lower income units for jurisdictions with highest percent of single-family homes. Measure: Percent single family Threshold grouping: upper third vs rest Total unit allocations for jurisdictions with the most jobs. Measure: Total Jobs Threshold grouping: upper third vs rest Allocations of lower income units for jurisdictions with the most low-wage jobs. Measure: Low wage jobs Threshold grouping: upper third vs rest Percent of RHNA as lower income units for jurisdictions with the highest ratio of low-wage jobs to housing units affordable to low-wage workers. Measure: jobs-housing fit Threshold grouping: upper third vs rest Percent of RHNA as lower income units for low-income jurisdictions. Measure: median household income. Low income threshold: bottom third Percent of RHNA as lower income units for high-income jurisdiction. Measure: median household income. High income threshold: upper third Percent of RHNA as lower income units for jurisdictions with the most households in High Resource/Highest Resource tracts. Measure: Share households in HRAs Threshold grouping: upper third vs rest
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Vital Signs: Housing Affordability - County Overall
data.bayareametro.gov | Last Updated 2019-10-25T20:43:28.000ZHousing Affordability (EQ2) FULL MEASURE NAME Housing Affordability LAST UPDATED October 2018 DATA SOURCE U.S Census Bureau: Decennial Census Form STF3 – https://nhgis.org (1980-1990) Form SF3a – https://nhgis.org (2000) U.S. Census Bureau: American Community Survey Form B25074 (2009-2017) Form B25095 (2009-2017) http://api.census.gov Image: Flickr (Creative Commons license), Photographer: Frank Kehren, https://www.flickr.com/photos/fkehren/8481894011 CONTACT INFORMATION vitalsigns.info@bayareametro.gov METHODOLOGY NOTES (across all datasets for this indicator) The share of income brackets used for different Census and ACS forms varied over time. To allow for historical comparisons, the Census Bureau merges housing expenditure brackets into three consistent bins (less than 20 percent, 20 percent to 34 percent, and more than 35 percent) that work for all years. The highest income bracket for renters in the ACS data was $100,000 or more, while the homeowner dataset included brackets for $100,000 to $149,999 and $150,000 and above. These brackets were merged together to allow for uniform comparison across tenure. While some studies use 30 percent as the affordability threshold, Vital Signs uses 35 percent as this is the closest break point using the standardized affordability brackets above. Historical data for Napa County is unavailable due to an insufficient sample size for renters in a number of years, making it impossible to calculate affordability for all households. All ACS data is for a single year, rather than a rolling average. Income breakdown data is only provided for one year as it is not possible to compare consistent inflation-adjusted income brackets over time given Census data limitations.
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Vital Signs: Housing Affordability - County by Income
data.bayareametro.gov | Last Updated 2019-10-25T20:43:10.000ZHousing Affordability (EQ2) FULL MEASURE NAME Housing Affordability LAST UPDATED October 2018 DATA SOURCE U.S Census Bureau: Decennial Census Form STF3 – https://nhgis.org (1980-1990) Form SF3a – https://nhgis.org (2000) U.S. Census Bureau: American Community Survey Form B25074 (2009-2017) Form B25095 (2009-2017) http://api.census.gov Image: Flickr (Creative Commons license), Photographer: Frank Kehren, https://www.flickr.com/photos/fkehren/8481894011 CONTACT INFORMATION vitalsigns.info@bayareametro.gov METHODOLOGY NOTES (across all datasets for this indicator) The share of income brackets used for different Census and ACS forms varied over time. To allow for historical comparisons, the Census Bureau merges housing expenditure brackets into three consistent bins (less than 20 percent, 20 percent to 34 percent, and more than 35 percent) that work for all years. The highest income bracket for renters in the ACS data was $100,000 or more, while the homeowner dataset included brackets for $100,000 to $149,999 and $150,000 and above. These brackets were merged together to allow for uniform comparison across tenure. While some studies use 30 percent as the affordability threshold, Vital Signs uses 35 percent as this is the closest break point using the standardized affordability brackets above. Historical data for Napa County is unavailable due to an insufficient sample size for renters in a number of years, making it impossible to calculate affordability for all households. All ACS data is for a single year, rather than a rolling average. Income breakdown data is only provided for one year as it is not possible to compare consistent inflation-adjusted income brackets over time given Census data limitations.
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Vital Signs: Housing Affordability - Bay Area Overall
data.bayareametro.gov | Last Updated 2019-10-25T20:42:56.000ZHousing Affordability (EQ2) FULL MEASURE NAME Housing Affordability LAST UPDATED October 2018 DATA SOURCE U.S Census Bureau: Decennial Census Form STF3 – https://nhgis.org (1980-1990) Form SF3a – https://nhgis.org (2000) U.S. Census Bureau: American Community Survey Form B25074 (2009-2017) Form B25095 (2009-2017) http://api.census.gov Image: Flickr (Creative Commons license), Photographer: Frank Kehren, https://www.flickr.com/photos/fkehren/8481894011 CONTACT INFORMATION vitalsigns.info@bayareametro.gov METHODOLOGY NOTES (across all datasets for this indicator) The share of income brackets used for different Census and ACS forms varied over time. To allow for historical comparisons, the Census Bureau merges housing expenditure brackets into three consistent bins (less than 20 percent, 20 percent to 34 percent, and more than 35 percent) that work for all years. The highest income bracket for renters in the ACS data was $100,000 or more, while the homeowner dataset included brackets for $100,000 to $149,999 and $150,000 and above. These brackets were merged together to allow for uniform comparison across tenure. While some studies use 30 percent as the affordability threshold, Vital Signs uses 35 percent as this is the closest break point using the standardized affordability brackets above. Historical data for Napa County is unavailable due to an insufficient sample size for renters in a number of years, making it impossible to calculate affordability for all households. All ACS data is for a single year, rather than a rolling average. Income breakdown data is only provided for one year as it is not possible to compare consistent inflation-adjusted income brackets over time given Census data limitations.
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Vital Signs: Housing Affordability - Bay Area by Income
data.bayareametro.gov | Last Updated 2019-10-25T20:42:30.000ZHousing Affordability (EQ2) FULL MEASURE NAME Housing Affordability LAST UPDATED October 2018 DATA SOURCE U.S Census Bureau: Decennial Census Form STF3 – https://nhgis.org (1980-1990) Form SF3a – https://nhgis.org (2000) U.S. Census Bureau: American Community Survey Form B25074 (2009-2017) Form B25095 (2009-2017) http://api.census.gov Image: Flickr (Creative Commons license), Photographer: Frank Kehren, https://www.flickr.com/photos/fkehren/8481894011 CONTACT INFORMATION vitalsigns.info@bayareametro.gov METHODOLOGY NOTES (across all datasets for this indicator) The share of income brackets used for different Census and ACS forms varied over time. To allow for historical comparisons, the Census Bureau merges housing expenditure brackets into three consistent bins (less than 20 percent, 20 percent to 34 percent, and more than 35 percent) that work for all years. The highest income bracket for renters in the ACS data was $100,000 or more, while the homeowner dataset included brackets for $100,000 to $149,999 and $150,000 and above. These brackets were merged together to allow for uniform comparison across tenure. While some studies use 30 percent as the affordability threshold, Vital Signs uses 35 percent as this is the closest break point using the standardized affordability brackets above. Historical data for Napa County is unavailable due to an insufficient sample size for renters in a number of years, making it impossible to calculate affordability for all households. All ACS data is for a single year, rather than a rolling average. Income breakdown data is only provided for one year as it is not possible to compare consistent inflation-adjusted income brackets over time given Census data limitations.
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ART Bay Area Inundation Scenario - 36" Sea Level Rise
data.bayareametro.gov | Last Updated 2023-06-09T00:07:18.000ZInundation feature set representing areas vulnerable to a 36 inch rise in sea level for the San Francisco Bay Region. This is a derivative feature set, assembled by the Metropolitan Transportation Commission (MTC), created by merging county-specific, land-only inundation feature sets. The source, county-level feature sets were produced for Adapting to Rising Tides (ART), a program led by the San Francisco Bay Conservation and Development Commission (BCDC), in September 2017. The sea level rise (SLR) scenario used to produce this data represents 36 inches (three feet) of water level above the current mean higher high water (MHHW) tidal datum. This is considered the most likely level of sea level rise expected by 2100; or an existing 50-year extreme tide. The polygons contain the extent and depth of land-only inundation (in feet) flooding of the bayside shoreline. Depth of flooding were created by subtracting a land surface Digital Elevation Model (DEM) from the water surface DEM representing the SLR scenario (MHHW + SLR). Extent of flooding were created by employing a two rule assessment to determine if an area is inundated. It must be below the assigned water surface DEM elevation value, and it must be connected to an adjacent area that was either flooded or open water. This method applies an "eight-side rule" for connectedness, where the area is considered "connected" if any of its cardinal or diagonal directions is connected to a flooded area or open water. Hydraulic connectivity assessment removes areas from the inundation zone if they are protected by levees or other topographic features that prevent inland inundation. This assessment also removed areas that are low lying but inland and not directly connected to an adjacent inundated area. The 36 inch SLR scenario can be used to approximate all extreme tide/sea level rise combinations that produce a water level in the range of MHHW + 33 inches to MHHW + 39 inches, including: - 36 inches of SLR; - 1-year extreme tide event coupled with 24 inches of SLR; - 2-year extreme tide event coupled with 18 inches of SLR; - 5-year extreme tide event coupled with 12 inches of SLR; - 25-year extreme tide event coupled with 6 inches of SLR, and - 50-year extreme tide event under existing conditions (no SLR). Publication Date: June 2019 Creation Date: March 2019 Status: Progress: Complete Maintenance and Update Frequency: None planned Contact Information: Contact Organization: Metropolitan Transportation Commission Contact Person: Data & Visualization Contact Address: Address Type: mailing and physical Address: 375 Beale Street, Suite 800 City: San Francisco State or Province: California Postal Code: 94105 Country: United States of America Contact Voice Telephone: (415) 778-6700 Contact Electronic Mail Address: dataviz@bayareametro.gov Hours: 9:00 AM - 5:00 PM (PST) Monday through Friday