Abstract

Source: https://data2.nhgis.org/

The National Historical Geographic Information System (NHGIS) provides easy access to summary tables and time series of population, housing, agriculture, and economic data, along with GIS-compatible boundary files, for years from 1790 through the present and for all levels of U.S. census geography, including states, counties, tracts, and blocks.

Direct Link to Download Page: https://data2.nhgis.org/mainDOWNLOAD U.S. CENSUS DATA TABLES & MAPPING FILESThe National Historical Geographic Information System (NHGIS) provides easy access to summary tables and time series of population, housing, agriculture, and economic data, along with GIS-compatible boundary files, for years from 1790 through the present and for all levels of U.S. census geography, including states, counties, tracts, and blocks. Read more.WHAT IS IPUMS?IPUMS provides census and survey data from around the world integrated across time and space. IPUMS integration and documentation makes it easy to study change, conduct comparative research, merge information across data types, and analyze individuals within family and community context. Data and services are available free of charge. Learn more about IPUMS.

VITAL SIGNS INDICATOR

Displacement Risk (EQ3)

FULL MEASURE NAME

Share of lower-income households living in tracts at risk of displacement

LAST UPDATED

January 2023

DESCRIPTION

Displacement risk refers to the share of lower-income households living in neighborhoods that have been losing lower-income residents over time, thus earning the designation "at risk". While "at risk" households may not necessarily be displaced in the short-term or long-term, neighborhoods identified as being "at risk" signify pressure as reflected by the decline in lower-income households (who are presumed to relocate to other more affordable communities). The dataset includes metropolitan area, regional, county and census tract tables.

DATA SOURCE

U.S. Census Bureau: Decennial Census - https://nhgis.org

Form STF3 (1990-2000)

U.S. Census Bureau: American Community Survey (5-year rolling average) - https://data.census.gov/

2009-2021

Form B19001, B19013

CONTACT INFORMATION

vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)

Aligning with the approach used for Plan Bay Area 2040, displacement risk is calculated by comparing the analysis year with the most recent year prior to identify census tracts that are losing lower-income households. Tract data, as well as regional income data, are calculated using 5-year rolling averages for consistency – given that tract data is only available on a 5-year basis. Using household tables by income level, the number of households in each tract falling below the median are summed, which involves summing all brackets below the regional median and then summing a fractional share of the bracket that includes the regional median (assuming a uniform distribution within that bracket).

Once all tracts in a given county or metro area are synced to today’s boundaries, the analysis identifies census tracts of greater than 500 lower-income people (in the prior year) to filter out low-population areas. For those tracts, any net loss between the prior year and the analysis year results in that tract being flagged as being at risk of displacement, and all lower-income households in that tract are flagged. To calculate the share of households at risk, the number of lower-income households living in flagged tracts are summed and divided by the total number of lower-income households living in the larger geography (county or metro). Minor deviations on a year-to-year basis should be taken in context, given that data on the tract level often fluctuates and has a significant margin of error; changes on the county and regional level are more appropriate to consider on an annual basis instead.

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.

description: This polygon shapefile provides county or county-equivalent boundaries for the conterminous United States and was created specifically for use with the data tables published as Selected Items from the Census of Agriculture for the Conterminous United States, 1950-2012 (LaMotte, 2015). This data layer is a modified version of Historic Counties for the 2000 Census of Population and Housing produced by the National Historical Geographic Information System (NHGIS) project, which is identical to the U.S. Census Bureau TIGER/Line Census 2000 file, with the exception of added shorelines. Excluded from the CAO_STCOFIPS boundary layer are Broomfield County, Colorado, Menominee County, Wisconsin, and the independent cities of Virginia with the exception of the 3 county-equivalent cities of Chesapeake City, Suffolk, and Virginia Beach. The census of agriculture was not taken in the District of Columbia for 1959, but available data indicate few if any farms in that area, the polygon was left in place to preserve the areas of the surrounding counties. Baltimore City, Maryland was combined with Baltimore County and the St. Louis City, Missouri, was combined with St. Louis County. La Paz County, Arizona was combined with Yuma County, Arizona and Cibola County, New Mexico was combined with Valencia County, New Mexico. Minor county border changes were at a level of precision beyond the scope of the data collection. A major objective of the census data tabulation is to maintain a reasonable degree of comparability of agricultural data from census to census. The tabular data collection is from 14 different censuses where definitions and data collection techniques may change over time and while the data are mostly comparable, a degree of caution should be exercised when using the data in analysis procedures. While the data are at a county-level resolution, a regional approach is more appropriate than a county-by-county analysis. The main purpose of this layer is to provide a base to generate a county raster for the allocation of agricultural census values to specific (agricultural) pixels. Vector format is provided so the raster pixel size can be user designated. References cited: LaMotte, A.E., 2015, Selected items from the Census of Agriculture at the county level for the conterminous United States, 1950-2012: U.S. Geological Survey data release, http://dx.doi.org/10.5066/F7H13016. National Historical Geographic Information System, Minnesota Population Center, 2004, Historic counties for the 2000 census of population and housing: Minneapolis, MN, University of Minnesota, accessed 03/18/2013 at http://nhgis.org; abstract: This polygon shapefile provides county or county-equivalent boundaries for the conterminous United States and was created specifically for use with the data tables published as Selected Items from the Census of Agriculture for the Conterminous United States, 1950-2012 (LaMotte, 2015). This data layer is a modified version of Historic Counties for the 2000 Census of Population and Housing produced by the National Historical Geographic Information System (NHGIS) project, which is identical to the U.S. Census Bureau TIGER/Line Census 2000 file, with the exception of added shorelines. Excluded from the CAO_STCOFIPS boundary layer are Broomfield County, Colorado, Menominee County, Wisconsin, and the independent cities of Virginia with the exception of the 3 county-equivalent cities of Chesapeake City, Suffolk, and Virginia Beach. The census of agriculture was not taken in the District of Columbia for 1959, but available data indicate few if any farms in that area, the polygon was left in place to preserve the areas of the surrounding counties. Baltimore City, Maryland was combined with Baltimore County and the St. Louis City, Missouri, was combined with St. Louis County. La Paz County, Arizona was combined with Yuma County, Arizona and Cibola County, New Mexico was combined with Valencia County, New Mexico. Minor county border changes were at a level of precision beyond the scope of the data collection. A major objective of the census data tabulation is to maintain a reasonable degree of comparability of agricultural data from census to census. The tabular data collection is from 14 different censuses where definitions and data collection techniques may change over time and while the data are mostly comparable, a degree of caution should be exercised when using the data in analysis procedures. While the data are at a county-level resolution, a regional approach is more appropriate than a county-by-county analysis. The main purpose of this layer is to provide a base to generate a county raster for the allocation of agricultural census values to specific (agricultural) pixels. Vector format is provided so the raster pixel size can be user designated. References cited: LaMotte, A.E., 2015, Selected items from the Census of Agriculture at the county level for the conterminous United States, 1950-2012: U.S. Geological Survey data release, http://dx.doi.org/10.5066/F7H13016. National Historical Geographic Information System, Minnesota Population Center, 2004, Historic counties for the 2000 census of population and housing: Minneapolis, MN, University of Minnesota, accessed 03/18/2013 at http://nhgis.org

VITAL SIGNS INDICATOR Displacement Risk (EQ3)

FULL MEASURE NAME Share of lower-income households living in tracts at risk of displacement

LAST UPDATED December 2018

DESCRIPTION Displacement risk refers to the share of lower-income households living in neighborhoods that have been losing lower-income residents over time, thus earning the designation “at risk”. While “at risk” households may not necessarily be displaced in the short-term or long-term, neighborhoods identified as being “at risk” signify pressure as reflected by the decline in lower-income households (who are presumed to relocate to other more affordable communities). The dataset includes metropolitan area, regional, county and census tract tables.

DATA SOURCE U.S. Census Bureau: Decennial Census 1980-1990 Form STF3 https://nhgis.org

U.S. Census Bureau: Decennial Census 2000 Form SF3a https://nhgis.org

U.S. Census Bureau: Decennial Census 1980-2010 Longitudinal Tract Database http://www.s4.brown.edu/us2010/index.htm

U.S. Census Bureau: American Community Survey 2010-2015 Form S1901 5-year rolling average http://factfinder2.census.gov

U.S. Census Bureau: American Community Survey 2010-2017 Form B19013 5-year rolling average http://factfinder2.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Aligning with the approach used for Plan Bay Area 2040, displacement risk is calculated by comparing the analysis year with the most recent year prior to identify census tracts that are losing lower-income households. Historical data is pulled from U.S. Census datasets and aligned with today’s census tract boundaries using crosswalk tables provided by LTDB. Tract data, as well as regional income data, are calculated using 5-year rolling averages for consistency – given that tract data is only available on a 5-year basis. Using household tables by income level, the number of households in each tract falling below the median are summed, which involves summing all brackets below the regional median and then summing a fractional share of the bracket that includes the regional median (assuming a simple linear distribution within that bracket).

Once all tracts in a given county or metro area are synced to today’s boundaries, the analysis identifies census tracts of greater than 500 lower-income people (in the prior year) to filter out low-population areas. For those tracts, any net loss between the prior year and the analysis year results in that tract being flagged as being at risk of displacement, and all lower-income households in that tract are flagged. To calculate the share of households at risk, the number of lower-income households living in flagged tracts are summed and divided by the total number of lower-income households living in the larger geography (county or metro). Minor deviations on a year-to-year basis should be taken in context, given that data on the tract level often fluctuates and has a significant margin of error; changes on the county and regional level are more appropriate to consider on an annual basis instead.

The Alabama Counties - 1790-1990 dataset contains 20 layers that cover 200 years of historical county boundary changes. The layers were created using GIS datasets created by IMPUS NHGIS. In addition to these datasets, summary tables with various data covering several hundred years are available to download at https://data2.nhgis.org/main.Research using this or any other NHGIS data should cite it as:Steven Manson, Jonathan Schroeder, David Van Riper, Katherine Knowles, Tracy Kugler, Finn Roberts, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 18.0 [dataset]. Minneapolis, MN: IPUMS. 2023. https://urldefense.com/v3/_https://doi.org/10.18128/D050.V18.0_;!!I47Zg8fJQnY!fZ3XF6YoR0tRSPWg1WfAq5Wqo6gAd5Gx4Ind1k9HUjsDQPgwJ9BoQvB0h6SIrBuREDwbGFmgUXUJaEALI-2sgjSK$

These data show languages spoken in the household for people over the age of 5 in Alaska, in addition to the total population, by community. These data come from census surveys, both from the American Community Survey and the decennial census Population and language use data were originally extracted from IPUMS National Historic Geographic Information Systems website: https://data2.nhgis.org/main (Citation: Steven Manson, Jonathan Schroeder, David Van Riper, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 12.0 [Database]. Minneapolis: University of Minnesota. 2017. http://doi.org/10.18128/D050.V12.0 ). The file "household_language.csv" is a consolidation of a number of tables downloaded from this system (see methods for more information). The "language.Rmd" file is a script which combines the files by year into a single file. It also cleans up place names (including typographical errors) and uses the USGS place names dataset and the SASAP regions dataset to assign latitude and longitude values and region values to each place in the dataset. Additionally, the "language_vis.Rmd" file is a script that uses this data to visualize Native language use by community, displayed in the "language_vis.html" file.

These data comprise Census records relating to the Alaskan people's population demographics for the State of Alaskan Salmon and People (SASAP) Project. Decennial census data were originally extracted from IPUMS National Historic Geographic Information Systems website: https://data2.nhgis.org/main (Citation: Steven Manson, Jonathan Schroeder, David Van Riper, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 12.0 [Database]. Minneapolis: University of Minnesota. 2017. http://doi.org/10.18128/D050.V12.0). A number of relevant tables of basic demographics on age and race, household income and poverty levels, and labor force participation were extracted. These particular variables were selected as part of an effort to understand and potentially quantify various dimensions of well-being in Alaskan communities. The file "censusdata_master.csv" is a consolidation of all 21 other data files in the package. For detailed information on how the datasets vary over different years, view the file "readme.docx" available in this data package. The included .Rmd file is a script which combines the 21 files by year into a single file (censusdata_master.csv). It also cleans up place names (including typographical errors) and uses the USGS place names dataset and the SASAP regions dataset to assign latitude and longitude values and region values to each place in the dataset. Note that some places were not assigned a region or location because they do not fit well into the regional framework. Considerable heterogeneity exists between census surveys each year. While we have attempted to combine these datasets in a way that makes sense, there may be some discrepancies or unexpected values. The RMarkdown document SASAPWebsiteGraphicsCensus.Rmd is used to generate a variety of figures using these data, including the additional file Chignik_population.png. An additional set of 25 figures showing regional trends in population and income metrics are also included.

Housing 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.

These data comprise Census records relating to the Alaskan people's population demographics for the State of Alaskan Salmon and People (SASAP) Project. Decennial census data were originally extracted from IPUMS National Historic Geographic Information Systems website: https://data2.nhgis.org/main (Citation: Steven Manson, Jonathan Schroeder, David Van Riper, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 12.0 [Database]. Minneapolis: University of Minnesota. 2017. http://doi.org/10.18128/D050.V12.0). A number of relevant tables of basic demographics on age and race, household income and poverty levels, and labor force participation were extracted. These particular variables were selected as part of an effort to understand and potentially quantify various dimensions of well-being in Alaskan communities. The file "censusdata_master.csv" is a consolidation of all 21 other data files in the package. For detailed information on how the datasets vary over different years, view the file "readme.docx" available in this data package. The included .Rmd file is a script which combines the 21 files by year into a single file (censusdata_master.csv). It also cleans up place names (including typographical errors) and uses the USGS place names dataset and the SASAP regions dataset to assign latitude and longitude values and region values to each place in the dataset. Note that some places were not assigned a region or location because they do not fit well into the regional framework. Considerable heterogeneity exists between census surveys each year. While we have attempted to combine these datasets in a way that makes sense, there may be some discrepancies or unexpected values. The RMarkdown document SASAPWebsiteGraphicsCensus.Rmd is used to generate a variety of figures using these data, including the additional file Chignik_population.png

VITAL SIGNS INDICATOR Poverty (EQ5)

FULL MEASURE NAME The share of the population living in households that earn less than 200 percent of the federal poverty limit

LAST UPDATED December 2018

DESCRIPTION Poverty refers to the share of the population living in households that earn less than 200 percent of the federal poverty limit, which varies based on the number of individuals in a given household. It reflects the number of individuals who are economically struggling due to low household income levels.

DATA SOURCE U.S Census Bureau: Decennial Census http://www.nhgis.org (1980-1990) http://factfinder2.census.gov (2000)

U.S. Census Bureau: American Community Survey Form C17002 (2006-2017) http://api.census.gov

METHODOLOGY NOTES (across all datasets for this indicator) The U.S. Census Bureau defines a national poverty level (or household income) that varies by household size, number of children in a household, and age of householder. The national poverty level does not vary geographically even though cost of living is different across the United States. For the Bay Area, where cost of living is high and incomes are correspondingly high, an appropriate poverty level is 200% of poverty or twice the national poverty level, consistent with what was used for past equity work at MTC and ABAG. For comparison, however, both the national and 200% poverty levels are presented.

For Vital Signs, the poverty rate is defined as the number of people (including children) living below twice the poverty level divided by the number of people for whom poverty status is determined. Poverty rates do not include unrelated individuals below 15 years old or people who live in the following: institutionalized group quarters, college dormitories, military barracks, and situations without conventional housing. The household income definitions for poverty change each year to reflect inflation. The official poverty definition uses money income before taxes and does not include capital gains or noncash benefits (such as public housing, Medicaid, and food stamps). For the national poverty level definitions by year, see: https://www.census.gov/hhes/www/poverty/data/threshld/index.html For an explanation on how the Census Bureau measures poverty, see: https://www.census.gov/hhes/www/poverty/about/overview/measure.html

For the American Community Survey datasets, 1-year data was used for region, county, and metro areas whereas 5-year rolling average data was used for city and census tract.

To be consistent across metropolitan areas, the poverty definition for non-Bay Area metros is twice the national poverty level. Data were not adjusted for varying income and cost of living levels across the metropolitan areas.

This dataset provides census-block level estimates of outdoor air pollutant concentrations averaged over a 32-month period spanning from May 2015 through December 2017. Measurements were taken using mobile monitoring along every street of 13 cities, towns, and urban districts (93 km2) distributed through four counties of the San Francisco Bay Area, comprising over 2,100 hours of sampling. Two Google Street View cars equipped with the Aclima mobile platform repeatedly measured city block air quality, providing estimates of outdoor air pollution for a year-2010 population of ~450,000 individuals. This dataset includes measurements of four pollutants:NO (units: ppb)NO2 (units:ppb)BC (black carbon, units: µg/m3)UFP (ultrafine particle count, units: #x103/cm3)For the purposes of quality control, the set includes:a count of the unique days each block was visited while each of the monitoring instruments were operational (uniqueDays_xx, with xx representing the relevant pollutant), the total visits to the census block (visits_xx), and the cumulative sampling time in seconds (samplingTime_xx).The dataset includes the population data from the US census (TotalPop.x) and populations divided by census-based self-identified race and ethnicity. The categories include:-Hispanic or Latino (HispLat)-Not Hispanic or Latino: White alone or in combination with one or more other races (WhiteNH)-Not Hispanic or Latino: Black or African American alone or in combination with one or more other races (BlackNH)-Not Hispanic or Latino: Asian alone or in combination with one or more other races (AsianNH)-Not Hispanic or Latino: American Indian and Alaska Native alone or in combination with one or more other races (NativeNH)-Not Hispanic or Latino: Native Hawaiian and Other Pacific Islander alone or in combination with one or more other races (PacIsl)-Not Hispanic or Latino: Some Other Race alone or in combination with one or more other races (OtherNH)Consistent with Chambliss et al. 2021, there is a grouping of "OtherRace" which is the sum of the last three categories.Additional methodological details can be found in Chambliss et al. 2021 (https://chemrxiv.org/engage/chemrxiv/article-details/60f731b1880443777ae27104).Data are provided as a table that may be joined to GIS data from the US census using the unique "GISJOIN" identifier matching 2010 census block geographic units. Such GIS data is available online from several sources, including https://www.nhgis.org/

VITAL SIGNS INDICATOR Poverty (EQ5)

FULL MEASURE NAME The share of the population living in households that earn less than 200 percent of the federal poverty limit

LAST UPDATED December 2018

DESCRIPTION Poverty refers to the share of the population living in households that earn less than 200 percent of the federal poverty limit, which varies based on the number of individuals in a given household. It reflects the number of individuals who are economically struggling due to low household income levels.

DATA SOURCE U.S Census Bureau: Decennial Census http://www.nhgis.org (1980-1990) http://factfinder2.census.gov (2000)

U.S. Census Bureau: American Community Survey Form C17002 (2006-2017) http://api.census.gov

METHODOLOGY NOTES (across all datasets for this indicator) The U.S. Census Bureau defines a national poverty level (or household income) that varies by household size, number of children in a household, and age of householder. The national poverty level does not vary geographically even though cost of living is different across the United States. For the Bay Area, where cost of living is high and incomes are correspondingly high, an appropriate poverty level is 200% of poverty or twice the national poverty level, consistent with what was used for past equity work at MTC and ABAG. For comparison, however, both the national and 200% poverty levels are presented.

For Vital Signs, the poverty rate is defined as the number of people (including children) living below twice the poverty level divided by the number of people for whom poverty status is determined. Poverty rates do not include unrelated individuals below 15 years old or people who live in the following: institutionalized group quarters, college dormitories, military barracks, and situations without conventional housing. The household income definitions for poverty change each year to reflect inflation. The official poverty definition uses money income before taxes and does not include capital gains or noncash benefits (such as public housing, Medicaid, and food stamps). For the national poverty level definitions by year, see: https://www.census.gov/hhes/www/poverty/data/threshld/index.html For an explanation on how the Census Bureau measures poverty, see: https://www.census.gov/hhes/www/poverty/about/overview/measure.html

For the American Community Survey datasets, 1-year data was used for region, county, and metro areas whereas 5-year rolling average data was used for city and census tract.

To be consistent across metropolitan areas, the poverty definition for non-Bay Area metros is twice the national poverty level. Data were not adjusted for varying income and cost of living levels across the metropolitan 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.

These data represent a meta-dataset of observations on per household economic value - represented by per household willingness-to-pay (WTP) - for improvements in coastal marsh habitat, drawn from stated-preference studies in the research literature. The metadata allow estimation of benefit transfer functions via meta-regression modeling. Within these econometric functions, the dependent variable is a comparable estimate of economic value (e.g., WTP) drawn from extant primary valuation studies. Independent variables represent observable factors hypothesized to explain variation in this value measure across observations. These functions can be used to produce out-of-sample predictions of WTP for coastal marsh habitat improvements at sites for which no primary valuation studies have been conducted. They can also be used to understand the factors associated with systematic variations in marsh habitat values across different sites and studies. These data are described in Vedogbeton, H. and R.J. Johnston. 2020. Commodity Consistent Meta-Analysis of Wetland Values: An Illustration for Coastal Marsh Habitat. Environmental and Resource Economics 75(4), 835-865, and allow replication of the results presented therein. The metadata are extracted from primary studies that estimate total (use and nonuse) per household WTP for changes in the quantity or quality of coastal marsh wildlife habitats or their services, in US and Canada. These studies were identified via a systematic review of the literature. The metadata combine information provided by these primary non-market valuation studies with publicly available external data extracted from sources such as the US Census, US National Historical GIS (https://www.nhgis.org/), and US Fish and Wildlife Service National Wetlands Inventory (https://www.fws.gov/wetlands/Data/Mapper.html). Studies included in the metadata are restricted to those that estimate total per household WTP for coastal wetland habitat changes using generally accepted stated preference methods, report theoretically comparable and quantifiable measures of economic value, and provide sufficient information to enable inclusion in the metadata. The data are further restricted to observations from studies conducted in the US or Canada, and published between 1990 and 2016, inclusive. The resulting metadata include 141 total observations of WTP per household from 23 studies published from 1990 to 2016, with all values adjusted to 2016 USD. These 141 habitat-value observations are identified by the variable changsize = 0 within the data. Because the meta-analysis is restricted to WTP in the positive domain, two negative-WTP observations were subsequently dropped, leading to the 139 habitat-value observations reported in Vedogbeton and Johnston (2020). An additional 18 metadata observations are drawn from similar primary stated preference studies that estimate total WTP for changes in coastal marsh area (or size), where these area increases provide habitat combined with other wetland services such as flood control, water filtration, aesthetics, recreation, and habitat. These additional observations are used for the habitat-and-area value models in the paper, and are identified by the variable changsize = 1 within the data. The combined data include 159 total observations (141 habitat-value and 18 habitat-and-area value observations). The metadata compile variables characterizing (1) the scope [magnitude] of the valued habitat change and the spatial scale of the wetland area affected by the change, (2) the type of habitat, marsh and uses affected, (3) regions sampled by the primary study, and (4) original study methodology used to measure the value(s), sample size from these studies, and year. The categorical variable "code" identifies how each of these observations are used within the data analysis of Vedogbeton and Johnston (2020). The attached pdf file, "Stata Code_Marsh Meta", provides illustrative Stata (v16) code used to generate some of the primary model results in this article, using the data. Note that some variable labels in the Stata code differ slightly from those used in the published paper. This project was supported by National Science Foundation grant 1427105.

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.

The SPATIAL LOCATION of railroads/ is based upon locations as given in the National Transportation Atlas Database (United States Department of Transportation, Bureau of Transportation Statistics) and contemporary and historical U.S. topographical maps (United States Department of the Interior, U.S. Geological Survey)./The EXISTENCE of a railroad serving locations at a specific date (see variable "InOpBy") was determined using the following resources: 1911: state maps from William D. Whitney and Benjamin E. Smith (eds) The Century dictionary and cyclopedia, with a new atlas of the world, New York: Century Co., 1911 (using scanned images from http://www.goldbug.com); 1903: regional maps from Rand McNally, Rand McNally & Co.'s Enlarged Business Atlas And Shippers' Guide ... Showing In Detail The Entire Railroad System ... Accompanied By A New And Original Compilation And Ready Reference Index…, Chicago: Rand McNally & Company, 1903 (using images 2844006, 2844007 and 2844008 from http://www.davidrumey.com); 1898: regional maps from Rand McNally, United States. Rand, McNally & Co., Map Publishers and Engravers, Chicago, 1898. Rand, McNally & Co.'s New Business Atlas Map of the United States…, Chicago: Rand McNally & Company, 1898 (using images 0772003, 0772004 and 0772005 from http://www.davidrumey.com); 1893: state maps from Rand McNally and Company, Rand, McNally & Co.'s enlarged business atlas and shippers guide ; containing large-scale maps of all the states and territories in the United States, of the Dominion of Canada, the Republic of Mexico, Central America, the West Indies and Cuba. Chicago: Rand McNally, 1893 (images courtesy of Murray Hudson, www.antiquemapsandglobes.com) except for Louisiana, Maryland/Delaware, Michigan, and Mississippi which were taken from Rand McNally, Universal Atlas of the World, Chicago: Rand McNally, 1893 (images courtesy of the University of Alabama Cartographic Lab) and Texas which was digitized by Amanda Gregg from Rand McNally & Co. Indexed county and railroad pocket map and shippers' guide of Texas : accompanied by a new and original compilation and ready reference index, showing in detail the entire railroad system ...Chicago: Rand McNally & Co., c1893 (Yale University Beinecke Library, Call Number: Zc52 893ra); 1889: state maps from Rand McNally, Rand, McNally & Co.'s enlarged business atlas and shippers guide…, Chicago: Rand McNally & Co., 1889 (using images 2094016 through 2094062 from http://www.davidrumey.com); 1881: state maps from Rand McNally, New Indexed Business Atlas and Shippers Guide, Chicago: Rand McNally & Co., 1881 (photographed by Amanda Gregg from a copy in the Yale University Beinecke Library, 2009 Folio 63); 1877: state maps from Rand McNally and Company, Rand McNally & Co’s Business Atlas, Chicago: Rand McNally & Co., 1877 (digitized by Matthew Van den Berg from a copy in the Library of Congress, Call no. G1200 .R3358 1877); 1872: regional maps from Warner & Beers, Atlas of the United States, Chicago: Warner & Beers, 1872 (using images 2585069 through 2585078 from http://www.davidrumey.com);1868: national map by J. T. Lloyd, Lloyd's New Map of the United States The Canadas and New Brunswick From The Latest Surveys Showing Every Railroad & Station Finished … 1868, New York: J. T. Lloyd, 1868 (using image 2859002 from http://www.davidrumey.com)1863: national map by J. T. Lloyd, Lloyd's New Map of the United States The Canadas And New Brunswick From the latest Surveys Showing Every Railroad & Station Finished to June 1863, New York: J. T. Lloyd, 1863 (using image 2591002 from http://www.davidrumey.com)1861: regional maps by G. R. Taylor and Irene D. Neu, The American Railroad Network 1861-1890, Cambridge, Mass: Harvard University Press, 1956;1858: national map by Hugo Stammann, J. Sage & Son's new & reliable rail road map comprising all the railroads of the United States and Canadas with their stations and distances, Buffalo, NY: J Sage & Sons, 1858 using image rr000360 from the Library of Congress at http://hdl.loc.gov/loc.gmd/g3701p.rr000360;1856: national map by Richard S. Fisher, Dinsmore's complete map of the railroads & canals in the United States & Canada carefully compiled from authentic sources by Richard S. Fisher, editor of the American Rail Road & Steam Navigation Guide, New York, 1856 using image rr000300 from the Library of Congress at http://hdl.loc.gov/loc.gmd/g3701p.rr000300;1854: national map by E. D. Sanford, H. V. Poor's rail road map showing particularly the location and connections of the North East & South West Alabama Rail Road, by E. D. Sanford, Civil Engineer, n.p.: 1854 using image rr004950 from the Library of Congress at http://hdl.loc.gov/loc.gmd/g3701p.rr004950;1852: national map by J. H. Colton, Colton's Map Of The United States, The Canadas &c. Showing The Rail Roads, Canals & Stage Roads: With Distances from Place to Place, New York: J. H. Colton, 1852 (using image 0172002 from http://www.davidrumey.com)1850 and earlier dates: Curran Dinsmore, Dinsmore & Company's new and complete map of the railway system of the United States and Canada; compiled from official sources, under the direction of the editor of the "American Railway Guide.", New York: 1850, the early railroad database assembled by Professor Milton C. Hallberg (deceased, Pensylvania State University) and appearing on http://oldrailhistory.com/, various railroad histories, on-line google search results and Wikipedia entries for specific railroads appearing in Hallberg’s database. Digitized maps were geo-referenced using ArcGIS 10’s spline algorithm against the National Historical Geographic Information System’s 2009 TIGER-based historical state and county boundary files (see www.nhgis.org) and the U.S. National Atlas’s database of cities and town.No effort was made to identify or preserve double tracking. Sidings, yards, and turnouts, etc., were deleted whenever possible absent any knowledge as to when these features were constructed.See Jeremy Atack "Procedures and Issues Relating to the Creration of Historical Transportation Shapfiles of Navigabale Rivers, Canals, and Railroads in the United States" available at https://my.vanderbilt.edu/jeremyatack/files/2015/09/HistoricalTransportationSHPfilesDocumenation.pdf. Also Jeremy Atack, "On the Use of Geographic Informations Systems in Economic History" Journal of Economic History, 73:2 (June 2013): 313-338. Also available at https://my.vanderbilt.edu/jeremyatack/files/2011/08/EHAPresidentialAddress.pdfRevision History: Edited = 1 ==> minor modifications by Jeremy Atack, September 20, 2015 amending dates for "InOpBy" and/or endpoints to fix microfractures and inconsistencies,1861 or earlier.= 2 ==> JA; 9/21/2015 switched dates and names (1861-1903) on Charleston & Savannah RR just west of Ashley River to accurately reflect LOC map for this RR= 3 ==> JA: 12/22/2015 modification to RR dates and locations around Baltimore, New York city, Philadelphia and Washington DC reflecting (some but not all) of the 1860 mapping by C. Baer et al., Canals and Railroads of the Mid-Atlantic States, 1800-1860 (Hagley Foundation 1981)SHP file edited 5/9/2016 to fix error message in ArcCatalog caused by 4 "phantom" features (InOpBy=blank/zero) that had no geometry associated with them.

VITAL SIGNS INDICATOR Rent Payments (EC8)

FULL MEASURE NAME Median rent payment

LAST UPDATED August 2019

DESCRIPTION Rent payments refer to the cost of leasing an apartment or home and serves as a measure of housing costs for individuals who do not own a home. The data reflect the median monthly rent paid by Bay Area households across apartments and homes of various sizes and various levels of quality. This differs from advertised rents for available apartments, which usually are higher. Note that rent can be presented using nominal or real (inflation-adjusted) dollar values; data are presented inflation-adjusted to reflect changes in household purchasing power over time.

DATA SOURCE U.S. Census Bureau: Decennial Census 1970-2000 https://nhgis.org Note: Count 1 and Count 2; Form STF1; Form SF3a

U.S. Census Bureau: American Community Survey 2005-2017 http://api.census.gov Note: Form B25058; 1-YR

Bureau of Labor Statistics: Consumer Price Index 1970-2017 http://www.bls.gov/data/ Note: All Urban Consumers Data Table (by metro)

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Rent data reflects median rent payments rather than list rents (refer to measure definition above). Larger geographies (metro and county) rely upon ACS 1-year data, while smaller geographies rely upon ACS 5-year rolling average data. 1970 Census data for median rent payments has been imputed by ABAG staff as the source data only provided the mean, rather than the median, monthly rent. Metro area boundaries reflects today’s metro area definitions by county for consistency, rather than historical metro area boundaries.

Inflation-adjusted data are presented to illustrate how rent payments have grown relative to overall price increases; that said, the use of the Consumer Price Index does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself.

Output generated from running the Join Features analysis tool.

PLURAL (Place-level urban-rural indices) is a framework to create continuous classifications of "rurality" or "urbanness" based on the spatial configuration of populated places. PLURAL makes use of the concept of "remoteness" to characterize the level of spatial isolation of a populated place with respect to its neighbors. There are two implementations of PLURAL, including (a) PLURAL-1, based on distances to the nearest places of user-specified population classes, and (b) PLURAL-2, based on neighborhood characterization derived from spatial networks. PLURAL requires simplistic input data, i.e., the coordinates (x,y) and population p of populated places (villages, towns, cities) in a given point in time. Due to its simplistic input, the PLURAL rural-urban classification scheme can be applied to historical data, as well as to data from data-scarce settings. Using the PLURAL framework, we created place-level rural-urban indices for the conterminous United States from 1930 to 2018. Rural-urban classifications are essential for analyzing geographic, demographic, environmental, and social processes across the rural-urban continuum. Most existing classifications are, however, only available at relatively aggregated spatial scales, such as at the county scale in the United States. The absence of rurality or urbanness measures at high spatial resolution poses significant problems when the process of interest is highly localized, as with the incorporation of rural towns and villages into encroaching metropolitan areas. Moreover, existing rural-urban classifications are often inconsistent over time, or require complex, multi-source input data (e.g., remote sensing observations or road network data), thus, prohibiting the longitudinal analysis of rural-urban dynamics. We developed a set of distance- and spatial-network-based methods for consistently estimating the remoteness and rurality of places at fine spatial resolution, over long periods of time. Based on these methods, we constructed indices of urbanness for 30,000 places in the United States from 1930 to 2018. We call these indices the place-level urban-rural index (PLURAL), enabling long-term, fine-grained analyses of urban and rural change in the United States. The method paper has been peer-reviewed and is published in "Landscape and Urban Planning". The PLURAL indices from 1930 to 2018 are available as CSV files, and as point-based geospatial vector data (.SHP). Moreover, we provide animated GIF files illustrating the spatio-temporal variation of the different variants of the PLURAL indices, illustrating the dynamics of the rural-urban continuum in the United States from 1930 to 2018. Apply the PLURAL rural-urban classification to your own data: Python code is fully open source and available at https://github.com/johannesuhl/plural. Data sources: Place-level population counts (1980-2010) and place locations 1930 - 2018 were obtained from IPUMS NHGIS, (University of Minnesota, www.nhgis.org; Manson et al. 2022). Place-level population counts 1930-1970 were digitized from historical census records (U.S. Census Bureau 1942, 1964). References: Uhl, J.H., Hunter, L.M., Leyk, S., Connor, D.S., Nieves, J.J., Hester, C., Talbot, C. and Gutmann, M., 2023. Place-level urban–rural indices for the United States from 1930 to 2018. Landscape and Urban Planning, 236, p.104762. DOI: https://doi.org/10.1016/j.landurbplan.2023.104762 Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 16.0 [dataset]. Minneapolis, MN: IPUMS. 2021. http://doi.org/10.18128/D050.V16.0 U.S. Census Bureau (1942). U.S. Census of Population: 1940. Vol. I, Number of Inhabitants. U.S. Government Printing Office, Washington, D.C. U.S. Census Bureau (1964). U.S. Census of Population: 1960. Vol. I, Characteristics of the Population. Part I, United States Summary. U.S. Government Printing Office, Washington, D.C.