- What is the Population Count?
- What is the Percent who did not finish the 9th grade?
- What is the Student Teacher Ratio?
- What is the Median Earnings?
- What is the Number of Employees?
- What is the High School Graduation Rate?
- What is the Median Female Earnings?
- What is the Percent Employed?
- What is the Percent with an associate's degree?
- What is the Median Male Earnings?
The population rate of change of Bay City Micro Area (TX) was 0.00% in 2018.
Demographics and Population Datasets Involving Bay City Micro Area (TX)
- API data.cambridgema.gov | Last Updated 2019-09-17T17:16:51.000Z
This data set provides demographic and journey to work characteristics of the Cambridge Labor Force by primary mode of their journey to work. Attributes include age, presence of children, racial and ethnic minority status, vehicles available, time leaving home, time spent traveling, and annual household income. The data set originates from a special tabulation of the American Community Survey - the 2012 - 2016 version of the Census Transportation Planning Products (CTPP). The Cambridge Labor Force consist of all persons who live in Cambridge who work or are actively seeking employment. For more information on Journey to Work data in Cambridge, please see the full 2015 report (https://www.cambridgema.gov/~/media/Files/CDD/FactsandMaps/profiles/moving_forward_20150930.ashx?la=en).
- API data.cambridgema.gov | Last Updated 2019-09-17T17:17:39.000Z
This data set provides demographic and journey to work characteristics of the Cambridge Workforce by primary mode of their journey to work. Attributes include age, presence of children, racial and ethnic minority status, vehicles available, time arriving at work, time spent traveling, and annual household income. The data set originates from a special tabulation of the American Community Survey - the 2012 - 2016 version of the Census Transportation Planning Products (CTPP). The Cambridge Workforce consist of all persons who work in Cambridge, regardless of home location. For more information on Journey to Work data in Cambridge, please see the full 2015 report: https://www.cambridgema.gov/~/media/Files/CDD/FactsandMaps/profiles/moving_forward_20150930.ashx?la=en).
- API data.bayareametro.gov | Last Updated 2019-08-13T16:16:34.000Z
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 firstname.lastname@example.org 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.
- API data.bayareametro.gov | Last Updated 2020-12-09T01:58:17.000Z
This data set represents all tracts within the San Francisco Bay Region, and contains attributes for the eight Metropolitan Transportation Commission (MTC) Communities of Concern (CoC) tract-level variables for exploratory purposes. MTC 2018 Communities of Concern (tract geography) is based on eight ACS 2014-2018 (ACS 2018) tract-level variables: ● Minority (70% threshold) ● Low-Income (less than 200% of Fed. poverty level, 28% threshold) ● Level of English Proficiency (12% threshold) ● Seniors 75 Years and Over (8% threshold) ● Zero-Vehicle Households (15% threshold) ● Single Parent Households (18% threshold) ● People with a Disability (12% threshold) ● Rent-Burdened Households (14% threshold) If a tract exceeds both threshold values for Low-Income and Minority shares OR exceeds the threshold value for Low-Income AND also exceeds the threshold values for three or more variables, it is a CoC. Detailed documentation on the production of this feature set can be found at https://github.com/BayAreaMetro/Spatial-Analysis-Mapping-Projects/blob/master/Project-Documentation/Communities-of-Concern/README.md
- API data.sfgov.org | Last Updated 2020-01-31T22:45:39.000Z
**Please refer to the downloadable XLSX attachment (http://bit.ly/SFMTATravelSurvey2019) for the complete dataset, metadata, and instructions for use.** This workbook provides data and data dictionaries for the SFMTA 2019 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 2.2: Mode Share target of 80% sustainable travel by 2030. • 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 801 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: 2019: May - August 2019 The margin of error is related to sample size (n). For the total sample, the margin of error is 3.3% 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 = 801(Total Sample). Margin of error = +/- 3.3% • n = 400. Margin of error = +/- 4.85% • n = 100. Margin of error = +/- 9.80%
- API data.sonomacounty.ca.gov | Last Updated 2019-07-12T18:26:35.000Z
The County of Sonoma conducts an annual homeless count for the entire county. The survey data is derived from a sample of about 600 homeless persons countywide per year. The resulting information is statistically reliable only for the county as a whole, not for individual locations. The exception is the City of Santa Rosa, where the sample taken within the city is large enough to be predictive of the overall homeless population in that city.