- What is the Population Count?
- What is the Land Area?
- What is the Percent who did not finish the 9th grade?
- What is the Median Earnings?
- What is the Number of Employees?
- What is the Crime incident count?
- What is the Population Rate of Change?
- What is the Water Area?
- What is the High School Graduation Rate?
- What is the Median Female Earnings?
The population density of St. Petersburg, FL was 3,981 in 2009.
Geographic and Population Datasets Involving St. Petersburg, FL
- API stat.stpete.org | Last Updated 2022-07-02T08:15:32.000Z
The following data is from the St. Petersburg Police Department’s Computer-Aided Dispatch (CAD) system. Under Florida State Statute 119.071, victim information (i.e. addresses) associated with Sexual Battery, Sexual Offenses, Child Abuse, and Adult Abuse are considered confidential and exempt from public release. The data includes all officer responses to Priority 1, 2, 3, 4, 6, 7 and 9, calls for service. These calls for service don’t necessarily result in official police reports under UCR (Uniform Crime Reporting) standards. The calls do not include the Forensic Technicians, Off Duty, Administrative or similarly classified calls.
- API stat.stpete.org | Last Updated 2020-03-27T14:42:02.000Z
Data of positive COVID-19 data provided by the Florida Department of Health. This is not updated on the same schedule as the Florida Department of Health dashboard so the numbers will not match.
- API opendata.maryland.gov | Last Updated 2022-04-08T18:59:46.000Z
Resident population density for Maryland and Jurisdictions per square mile from 2010 to 2020. Source: U.S. Bureau of Census
- API data.bayareametro.gov | Last Updated 2018-07-06T18:06:55.000Z
VITAL SIGNS INDICATOR Population (LU1) FULL MEASURE NAME Population estimates LAST UPDATED September 2016 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 1960-1990 Decennial Census http://factfinder2.census.gov California Department of Finance 1961-2016 Population and Housing Estimates http://www.dof.ca.gov/research/demographic/ CONTACT INFORMATION firstname.lastname@example.org METHODOLOGY NOTES (across all datasets for this indicator) All legal boundaries and names for Census geography (metropolitan statistical area, county, city, 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 infill 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 as current as July 2016. Population estimates for PDAs were derived from Census population counts at the block group level for 2000-2014 and at the tract level for 1970-1990. 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). 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. Estimates of density for tracts and 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 InlandCoastalDelta: American Canyon, Benicia, Clayton, Concord, Cotati, Danville, Dublin, Lafayette, Martinez, Moraga, Napa, Novato, Orinda, Petaluma, Pleasant Hill, Pleasanton, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Walnut Creek, Antioch, Brentwood, Calistoga, Cloverdale, Dixon, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Livermore, Morgan Hill, Oakley, Pittsburg, Rio Vista, Sonoma, St. Helena, Suisun City, Vacaville, Windsor, Yountville Unincorporated: all unincorporated towns
RSBS: Single Family On-Site Inspections, Measure Level, New York State Residential Statewide Baseline Studydata.ny.gov | Last Updated 2019-11-15T21:48:02.000Z
How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. The New York State Energy Research and Development Authority (NYSERDA), in collaboration with the New York State Department of Public Service (DPS), conducted a statewide residential baseline study (study) from 2011 to 2014 of the single-family and multifamily residential housing segments, including new construction, and a broad range of energy uses and efficiency measures. This dataset includes data collected from a total of 700 on-site inspections of single family buildings. The types of data collected during the inspections covers property characteristics, heating and cooling equipment, water heating equipment, appliances, lighting, clothes washing and drying, miscellaneous energy using equipment, and observable operating behavior. The objective of the inspections was to enhance the residential baseline study with detailed on-site information and, to the degree possible, verify self-reported data from the phone and web surveys.