VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME Population estimates

LAST UPDATED October 2019

DESCRIPTION Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

DATA SOURCES U.S Census Bureau: Decennial Census No link available (1960-1990) http://factfinder.census.gov (2000-2010)

California Department of Finance: Population and Housing Estimates Table E-6: County Population Estimates (1961-1969) Table E-4: Population Estimates for Counties and State (1971-1989) Table E-8: Historical Population and Housing Estimates (2001-2018) Table E-5: Population and Housing Estimates (2011-2019) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census - via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University Population Estimates (1970 - 2010) http://www.s4.brown.edu/us2010/index.htm

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2011-2017) http://factfinder.census.gov

U.S. Census Bureau: Intercensal Estimates Estimates of the Intercensal Population of Counties (1970-1979) Intercensal Estimates of the Resident Population (1980-1989) Population Estimates (1990-1999) Annual Estimates of the Population (2000-2009) Annual Estimates of the Population (2010-2017) No link available (1970-1989) http://www.census.gov/popest/data/metro/totals/1990s/tables/MA-99-03b.txt http://www.census.gov/popest/data/historical/2000s/vintage_2009/metro.html https://www.census.gov/data/datasets/time-series/demo/popest/2010s-total-metro-and-micro-statistical-areas.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) All legal boundaries and names for Census geography (metropolitan statistical area, county, city, and tract) are as of January 1, 2010, released beginning November 30, 2010, by the U.S. Census Bureau. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of August 2019. For more information on PDA designation see http://gis.abag.ca.gov/website/PDAShowcase/.

Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970 -2010) and the American Community Survey (2008-2012 5-year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

Population estimates for Bay Area PDAs are from the decennial Census (1970 - 2010) and the American Community Survey (2006-2010 5 year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Population estimates for PDAs are derived from Census population counts at the tract level for 1970-1990 and at the block group level for 2000-2017. Population from either tracts or block groups are allocated to a PDA using an area ratio. For example, if a quarter of a Census block group lies with in a PDA, a quarter of its population will be allocated to that PDA. Tract-to-PDA and block group-to-PDA area ratios are calculated using gross acres. Estimates of population density for PDAs use gross acres as the denominator.

Annual population estimates for metropolitan areas outside the Bay Area are from the Census and are benchmarked to each decennial Census. The annual estimates in the 1990s were not updated to match the 2000 benchmark.

The following is a list of cities and towns by geographical area: Big Three: San Jose, San Francisco, Oakland Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville Unincorporated: all unincorporated towns

Forecasts for Year 2010 through 2040 containing values for Households by Inc. Quartile; Households; Jobs; Population by Gender, Age; Units; Employed Residents; Population by Age; Population for Priority Development Areas (PDAs) in the nine county San Francisco Bay Area region.

Forecasts for Year 2010 through 2040 containing values for Households by Inc. Quartile; Households; Jobs; Population by Gender, Age; Units; Employed Residents; Population by Age; Population for Subregional Study Areas (Sphere's of Influence of Jurisdictions) in the nine county San Francisco Bay Area region.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This workbook provides data and data dictionaries for the SFMTA 2017 Travel Decision Survey. On behalf of San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis (CC&G) undertook a Mode Share Survey within the City and County of San Francisco as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma and Solano. The primary goals of this study were to: • Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.3: Mode Share target of 50% non-private auto travel by FY2018 with a 95% confidence level and MOE +/- 5% or less. • Evaluate the above statement based on the following parameters: number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. • Provide additional trip details, including trip purpose for each trip in the mode share question series. • Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. • Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs. The survey was conducted as a telephone study among 804 Bay Area residents aged 18 and older. Interviewing was conducted in English, Spanish, Mandarin, Cantonese, and Tagalog. Surveying was conducted via random digit dial (RDD) and cell phone sample. All survey datasets incorporate respondent weighting based on age and home location; utilize the “weight” field when appropriate in your analysis. The survey period for this survey is as follows: 2017: February - April 2017 The margin of error is related to sample size (n). For the total sample, the margin of error is 3.4% for a confidence level of 95%. When looking at subsets of the data, such as just the SF population, just the female population, or just the population of people who bicycle, the sample size decreases and the margin of error increases. Below is a guide of the margin of error for different samples sizes. Be cautious in making conclusions based off of small sample sizes. At the 95% confidence level is: • n = 804(Total Sample). Margin of error = +/- 3.4% • n = 400. Margin of error = +/- 4.85% • n = 100. Margin of error = +/- 9.80%

Forecasts for Year 2010 through 2040 containing values for Households by Inc. Quartile; Households; Jobs; Population by Gender, Age; Units; Employed Residents; Population by Age; Population for jurisdictions in the nine county San Francisco Bay Area region.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Population density in 1990 within the boundaries of the Narragansett Bay watershed, the Southwest Coastal Ponds watershed, and the Little Narragansett Bay watershed. The methods for analyzing population were developed by the US Environmental Protection Agency ORD Atlantic Coastal Environmental Sciences Division in collaboration with the Narragansett Bay Estuary Program and other partners. Population rasters were generated using the USGS dasymetric mapping tool (see http://geography.wr.usgs.gov/science/dasymetric/index.htm) which uses land use data to distribute population data more accurately than simply within a census mapping unit. The 1990 10m cell population density raster was produced using Rhode Island 1988 state land use data, Massachusetts 1985 state land use, Connecticut 1992 NLCD land use data, and U.S. Census data (1990). To generate a population estimate (number of persons) for any given area within the boundaries of this raster, use the Zonal Statistics as Table tool to sum the 10m cell density values within your zone dataset (e.g., watershed polygon layer). For more information, please reference the 2017 State of Narragansett Bay & Its Watershed Technical Report (nbep.org).

Along the East Asian-Australasian flyway (EAAF), waterbirds are threatened by a wide range of human activities. Studies have shown that wintering populations of many species have declined in Australia and Japan; however, long term data along China’s coast are limited. In this study, we analyzed data collected from monthly bird surveys to quantify population trends of wintering waterbirds from 1998 to 2017 in the Deep Bay area, South China. Of the 42 species studied, 12 declined, while nine increased significantly. Phylogenetic comparative analysis revealed that population trends were negatively correlated to reliance on the Yellow Sea and body size. Further, waterbird species breeding in Southern Siberia declined more than those breeding in East Asia. These findings, coupled with a relatively high number of increasing species, support the continual preservation of wetlands in the Deep Bay area. This study provides another case study showing that data collected from wintering sites provide insights on the threats along migratory pathway and inform conservation actions. As such, we encourage population surveys in the EAAF to continue, particularly along the coast of China.

VITAL SIGNS INDICATOR Income (EC4)

FULL MEASURE NAME Household income by place of residence

LAST UPDATED May 2019

DESCRIPTION Income reflects the median earnings of individuals and households from employment, as well as the income distribution by quintile. Income data highlight how employees are being compensated for their work on an inflation-adjusted basis.

DATA SOURCE U.S. Census Bureau: Decennial Census Count 4Pb (1970) Form STF3 (1980-1990) Form SF3a (2000) https://nhgis.org

U.S. Census Bureau: American Community Survey Form B19013 (2006-2017; place of residence) http://api.census.gov

Bureau of Labor Statistics: Consumer Price Index All Urban Consumers Data Table (1970-2017; specific to each metro area) http://data.bls.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Income data reported in a given year reflects the income earned in the prior year (decennial Census) or in the prior 12 months (American Community Survey); note that this inconsistency has a minor effect on historical comparisons (for more information, go to: http://www.census.gov/acs/www/Downloads/methodology/ASA_nelson.pdf). American Community Survey 1-year data is used for larger geographies – metropolitan areas and counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Quintile income for 1970-2000 is imputed from Decennial Census data using methodology from the California Department of Finance (for more information, go to: http://www.dof.ca.gov/Forecasting/Demographics/Census_Data_Center_Network/documents/How_to_Recalculate_a_Median.pdf). Bay Area income is the population weighted average of county-level income.

Income has been inflated using the Consumer Price Index specific to each metro area; however, some metro areas lack metro-specific CPI data back to 1970 and therefore adjusted data is unavailable for some historical data points. Note that current MSA boundaries were used for historical comparison by identifying counties included in today’s metro areas.

VITAL SIGNS INDICATOR Income (EC4)

FULL MEASURE NAME Household income by place of residence

LAST UPDATED May 2019

DESCRIPTION Income reflects the median earnings of individuals and households from employment, as well as the income distribution by quintile. Income data highlight how employees are being compensated for their work on an inflation-adjusted basis.

DATA SOURCE U.S. Census Bureau: Decennial Census Count 4Pb (1970) Form STF3 (1980-1990) Form SF3a (2000) https://nhgis.org

U.S. Census Bureau: American Community Survey Form B19013 (2006-2017; place of residence) http://api.census.gov

Bureau of Labor Statistics: Consumer Price Index All Urban Consumers Data Table (1970-2017; specific to each metro area) http://data.bls.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Income data reported in a given year reflects the income earned in the prior year (decennial Census) or in the prior 12 months (American Community Survey); note that this inconsistency has a minor effect on historical comparisons (for more information, go to: http://www.census.gov/acs/www/Downloads/methodology/ASA_nelson.pdf). American Community Survey 1-year data is used for larger geographies – metropolitan areas and counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Quintile income for 1970-2000 is imputed from Decennial Census data using methodology from the California Department of Finance (for more information, go to: http://www.dof.ca.gov/Forecasting/Demographics/Census_Data_Center_Network/documents/How_to_Recalculate_a_Median.pdf). Bay Area income is the population weighted average of county-level income.

Income has been inflated using the Consumer Price Index specific to each metro area; however, some metro areas lack metro-specific CPI data back to 1970 and therefore adjusted data is unavailable for some historical data points. Note that current MSA boundaries were used for historical comparison by identifying counties included in today’s metro areas.

This excel contains results from the 2017 State of Narragansett Bay and Its Watershed Technical Report (nbep.org), Chapter 4: "Population." The methods for analyzing population were developed by the US Environmental Protection Agency ORD Atlantic Coastal Environmental Sciences Division in collaboration with the Narragansett Bay Estuary Program and other partners. Population rasters were generated using the USGS dasymetric mapping tool (see http://geography.wr.usgs.gov/science/dasymetric/index.htm) which uses land use data to distribute population data more accurately than simply within a census mapping unit. The 1990, 2000, and 2010 10m cell population density rasters were produced using Rhode Island state land use data, Massachusetts state land use, Connecticut NLCD land use data, and U.S. Census data. To generate a population estimate (number of persons) for any given area within the boundaries of this raster, NBEP used the the Zonal Statistics as Table tool to sum the 10m cell density values within a given zone dataset (e.g., watershed polygon layer). Results presented include population estimates (1990, 2000, 2010) as well as calculation of acres of developed lands per 100 persons and percent change in estimated population (1990-2000; 2000-2010; 1990-2010).

On behalf of the San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis undertook a Travel Decision Survey within the City and County of San Francisco, as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma, and Solano. The primary goals of this study were to: 1. Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.3: Mode Share target of 50% non-private auto travel by FY 2018 with a 95% confidence level and margin of error ±5% or less. 2. Evaluate the above statement based on number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. 3. Provide additional trip details, including trip purpose for each trip in the mode-share question series. 4. Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. 5. Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs.

Database uses data on shorebird counts from around Australia. The majority of the records are from Birdlife’s Birdata database. We supplemented this data with bird surveys within the Coorong, from David Paton, from data available from the South Australian Government (Paton, Paton, and Bailey 2016). Data for some shorebird areas, namely eighty mile beach, Roebuck Bay, Werribee/Avalon, did not have count area level data for a number of recent years (2019-2022), but had aggregated summary data available. Within the database, observations of the number of individuals per species are organized in “count areas”, which are generally one high tide roost, or segments of beach. Count areas are situated within “Shorebird areas”, which are the maximum areas in which individual birds are likely to move during the non-breeding season (Clemens, Herrod, and Weston 2014). The database contains >380,000 records from 448 shorebird areas around the country. Data on individual species generation times was sourced from Birdlife’s Data Zone (‘BirdLife Data Zone’ 2022). For our analysis, we aggregated the data into independent count occasions for each shorebird area within each Australian summer, here termed “season”. To achieve this, we summarized the total number of each species observed at a shorebird area for each month during the summer months (October, November, December, January, and February). First, the database was subset to records that had complete fields for “shorebird area”, “point count ID”, “count”, and “date”. Data were then aggregated to find the max observations per species per count area per month. The max observations per count area per month were then summed per shorebird area. For shorebird areas with counts across multiple months, we used the top two counts per season as input for our data analysis. Finally, we only included shorebird areas that had at least 500 birds observed over the entire time series and had at least one count for at least half of the years in the entire time series (14 years of the 29 years in the time series). Structured, regular monitoring began in 1993, so we used data from 1993-2022. Modelling abundance and population trends - The objective was to estimate abundance and population trends of the targeted species at the national level, using the time-series data described above. Following the successful example of modelling population trends of shorebirds in Australia by Studds et al (2017), we also used N-mixture models, which estimate the abundance of each species at each shorebird area each year, while accounting for imperfect detection of individuals as well as among-area difference, temporal trends, and over-dispersion in abundance. The model allowed us to estimate: (i) the abundance of each species at each shorebird area each year, (ii) the total abundance of each species across all areas each year, and (iii) the nationwide population index of each species, which shows “average” changes in the species abundance across all shorebird areas. As N-mixture models tend to be highly complex with many parameters and thus require much information (i.e., data) for those parameters to be successfully estimated, we developed two types of N-mixture models with varying levels of complexity/assumption: (i) the model assuming that detection probabilities at a given area vary among months within each year, and (ii) the model assuming that detection probabilities at a given area are constant across months within each year. We first fitted model (i) above to all targeted species using the program JAGS (Hornik et al. 2003) through the R2jags package (Su, Yajima, and Edu 2022) in R version 4.2.1 (R Core Team 2015). Model convergence was checked with R-hat values and trace plots. If the model still did not converge, we next fitted model (ii) above and increased the number of iterations until the model converged. If both models did not converge, we fitted a simpler model, which had the same structure but without accounting for the imperfect detection of individuals (i.e., assuming that all individuals are detectable). Using model outputs, we then estimated the rate of change in abundance. For a given time frame (29 years, three generations, 1993-2013, 2013-2022) we calculated growth rates using generalized least squares regression to account for temporal autocorrelation. We then sampled 1000 growth rates from each regression result and calculated the mean and standard deviation based on these samples. We then took the difference in the samples and calculated the probability of the difference being larger than zero using the code: 100*length(difference_in_samples[difference_in_samples <0])/length(difference_in_samples) We used IUCN criteria A2, to assess how the species should be listed based on the estimated declines from our analysis. These thresholds are: 80% - critically endangered 50% - endangered, and 30% - vunerable From the IUCN IUCN Red List of Threatened Species “

Population Parameters of Leiognathus brevirostris from the trawl catches of the Portugal Bay area, Puttalam Estuary, Sri Lanka were investigated based on length frequency data, analysed with LFSA and compleat ELEFAN computer programs. The asymptotic length (L∞) and growth constant (K) were estimated to be 13.8 cm (total length) and 0.9 year-1, respectively. Based on these growth parameters, the total mortality coefficient (Z) during the study period was estimated to be 4.62. The estimated ...

Metropolitan area from resources and environment science and social economic data, including data center, the Beijing municipal emergency administration, China's seismic data set download: 2015 beijing-tianjin-hebei, Yangtze river delta urban agglomeration (flow) of the floating population and large bay area characteristic research data sets, the density of population data (2000-2005-2010-2015-2020), human settlements, 1978-2017 (30 m by 30 m), the seventh in 2020 census data with vector (form), GDP raster data (2019), the data of the construction land expansion in 1978, 1985-2017 (30 m by 30 m), the population birth rate (1 km x 1 km) in 2015, the population spatial distribution of the 2000-2005-2010-2015-2020 (100 m by 100 m), and other social and economic data, statistical yearbook, three large scale urban agglomeration districts and counties, villages and towns social economic statistics and metropolis POI data (20 cities).

VITAL SIGNS INDICATOR Greenfield Development (LU5)

FULL MEASURE NAME The acres of construction on previously undeveloped land

LAST UPDATED November 2019

DESCRIPTION Greenfield development refers to construction on previously undeveloped land and the corresponding expansion of our region’s developed footprint, which includes the extent of urban and built-up lands. The footprint is defined as land occupied by structures, with a building density of at least 1 unit to 1.5 acres.

DATA SOURCE Department of Conservation: Farmland Mapping and Monitoring Program GIS Data Tables/Layers (1990-2016) https://www.conservation.ca.gov/dlrp/fmmp

U.S. Census Bureau: Decennial Census Population by Census Block Group (2000-2010) http://factfinder.census.gov

U.S. Census Bureau: American Community Survey (5-year) Population by Census Block Group (2000-2017) http://factfinder.census.gov

METHODOLOGY NOTES (across all datasets for this indicator) For regional and local data, FMMP maps the extent of “urban and built-up” lands, which generally reflect the developed urban footprint of the region. The footprint is defined as land occupied by structures with building density of at least 1 unit to 1.5 acres. Uses include residential, industrial, commercial, construction, institutional, public administration, railroad and other transportation yards, cemeteries, airports, golf courses, sanitary landfills, sewage treatment, water control structures, and other developed purposes.

To determine the amount of greenfield development (in acres) occurring in a given two-year period, the differences in urban footprint are computed on a county-level. FMMP makes slight refinements to urban boundaries over time, so changes in urban footprint +/- 100 acres are not regionally significant. The GIS shapefile represents the 2016 urban footprint and thus does not show previously urbanized land outside of the footprint (i.e. Hamilton Air Force Base).

For metro comparisons, a different methodology had to be used to avoid the geospatial limitations associated with FMMP. U.S. Census population by census block group was gathered for each metro area for 2000, 2010, and 2017. Population data for years 2000 and 2010 come from the Decennial Census while data for 2018 comes from the 2017 5-year American Community Survey. The block group was considered urbanized if its average/gross density was greater than 1 housing unit per acre (a slightly higher threshold than FMMP uses for its definition). Because a block group cannot be flagged as partially urbanized, and non-residential uses are not fully captured, the urban footprint of the region calculated with this methodology is smaller than in FMMP. The metro data should be primarily used for looking at comparative growth rate in greenfield development rather than the acreage totals themselves.

VITAL SIGNS INDICATOR Vulnerability to Sea Level Rise (EN11)

FULL MEASURE NAME Share of population living in zones at risk from various sea level rise forecast scenarios

LAST UPDATED July 2017

DESCRIPTION Vulnerability to sea level rise refers to the share of the historical and current Bay Area population located in areas at risk from forecasted sea level rise over the coming decades. Given that there are varying forecasts for the heightened high tides (i.e., mean highest high water mark), projected sea level impacts are presented for six scenarios ranging from a one foot rise to six feet. A neighborhood is considered vulnerable to sea level rise when at least 10 percent of its land area is forecasted to be inundated by peak high tides in the coming years. The dataset includes at-risk population and population share data for the region, counties, and neighborhoods.

DATA SOURCE San Francisco Bay Conservation and Development Commission/Metropolitan Transportation Commission ART (Adaption to Rising Tides) Bay Area Sea Level Rise Analysis and Mapping Project (2017) 2017 Sea Level Rise Maps http://www.adaptingtorisingtides.org/project/regional-sea-level-rise-mapping-and-shoreline-analysis/

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Projected areas of inundation were developed by BCDC and NOAA at one-foot intervals ranging from one foot to four feet of sea level rise. Regional and local sea level rise analysis is based on data from BCDC’s ART (Adapting to Rising Tides) Bay Area Sea Level Rise and Mapping Project. This data reflects the most up-to-date and detailed sea level rise mapping for the Bay Area. Sea level rise analysis for metro areas is based on national sea level rise mapping from NOAA, which is best for metro-to-metro comparison. To determine the impacts on historical and current populations, inundation areas were overlaid on a U.S. Census shapefile of 2010 Census tracts using Census Bureau population data.

Because census tracts can extend beyond the coastline, the baseline scenario of zero feet was used to determine existing sea level coverage of census tracts. Sea level rise refers to the change from this level. The area of the tract was determined by measuring the component of the tract area not currently under water. This area, rather than the total tract area, was used as the denominator to determine the percentage of the census tract that is inundated under future sea level rise projection scenarios. When at least 10 percent of tract land area is inundated with a given sea level, its residents are considered to be affected by sea level rise.

For the purpose of this analysis, SLR scenarios were assumed not to reflect periodic inundation due to extreme weather events, which may lead to an even greater share of residents affected on a less frequent basis. Prior to the impacts from sea level rise, neighborhoods will experience temporary flooding from extreme weather events which can create significant damage to homes and neighborhoods. It should be noted that by directly reviewing maps and tools through the ART (Adapting to Rising Tides) program, regular inundation sea level rise and temporary flooding from extreme weather events are both available. More information on this approach is available here: http://www.adaptingtorisingtides.org/project/regional-sea-level-rise-mapping-and-shoreline-analysis/

Sea level rise analysis for metro areas reflects local, as opposed to global, sea level rise. Recent data has shown sea level is rising faster in the southeast region of the United States. Regional differences in the rate of sea level rise. More information and data related to the rate of sea level rise for different coastal regions is available here: https://oceanservice.noaa.gov/facts/sealevel-global-local.html