The population density of St. Petersburg, FL was 3,981 in 2009.

Population Density

Population Density is computed by dividing the total population by Land Area Per Square Mile.

Above charts are based on data from the U.S. Census American Community Survey | ODN Dataset | API - Notes:

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Geographic and Population Datasets Involving St. Petersburg, FL

  • API

    Police Calls

    stat.stpete.org | Last Updated 2020-07-12T12:15:51.000Z

    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.

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    COVID-19 Positive by Florida City

    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.

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    Maryland Resident Population Per Square Mile: 2010-2017

    opendata.maryland.gov | Last Updated 2018-10-17T16:23:18.000Z

    Resident population density for Maryland and Jurisdictions per square mile from 2010 to 2017. Source: U.S. Bureau of Census

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    Vital Signs: Population – by region shares

    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 vitalsigns.info@mtc.ca.gov 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

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    AFSC/RACE/SAP: Detailed Crab Data From NOAA Fisheries Service Annual Eastern Bering Sea Summer Bottom Trawl Surveys 1975 - 2018

    noaa-fisheries-afsc.data.socrata.com | Last Updated 2018-11-26T22:13:35.000Z

    This dataset contains detailed crab data collected from the annual NOAA/NMFS/AFSC/RACE crab-groundfish bottom trawl survey of the eastern Bering Sea continental shelf. The standard survey area, surveyed each year since 1975, encompasses a major portion of the eastern Bering Sea shelf between the 20 meter and 200 meter isobaths and from the Alaska Peninsula to the north of St. Matthew Island. The study area is divided into a grid with cell sizes of 20 x 20 nautical miles (37 x 37 kilometers). Sampling takes place within each 20 x 20 nautical mile grid cell. In areas surrounding St. Matthew (1983-present) and the Pribilof Islands (1981-present), grid corners were also sampled to better assess king crab concentrations. In 1975, tows were 1 hour in duration; from 1976 to present, each tow is one-half hour in duration, averaging 1.54 nautical miles (2.86 kilometers) - exact tow duration and distance fished for each haul can be found in RACEBASE.HAUL. 100% of the catch is sorted for red, blue, and golden king crab, bairdi Tanner, snow crab, hybrid Tanner, and hair crab. Crabs are sorted by species and sex, and a sample is measured to the nearest millimeter to provide a size-frequency distribution (see note under use constraints for analyzing catches where crab were subsampled for measurement). Carapace width is measured for Tanner crabs, and carapace length is measured for king and hair crabs.

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    Noaansf Ecohab Heterosigma Ctd

    noaa-fisheries-nwfsc.data.socrata.com | Last Updated 2017-06-05T22:59:14.000Z

    InPort Dataset ID: 17794 InPort Entity ID: 36788 Over one half of the worlds fish production for human consumption currently comes from aquaculture, while wild fisheries yields are either stable or declining. Recurring threats from the raphidophyte, Heterosigma akashiwo Hada (Sournia) have caused extensive damage ($2-6 million per episode) to wild and net-penned fish of Puget Sound, Washington, and are believed to be increasing in scope and magnitude in this region, and elsewhere in the world over the past two decades. The mechanism of H. akashiwo toxicity is not well understood. The toxic activity of H. akashiwo has been attributed to the production of reactive oxygen species, brevetoxin-like compound(s), excessive mucus, or hemolytic activity; however these mechanisms are not confirmed consistently in all fish-killing events or cultured strains. The difficulty of conducting research with active, toxin-producing field populations of H. akashiwo have resulted in conflicting findings from those obtained in lab culture studies, thereby limiting the ability of fish farmers to respond to these episodic blooms. Collaborators in this project are: Vera Trainer (NWFSC), William Cochlan (San Francisco State University), Charles Trick (University of Western Ontario), and Mark Wells (University of Maine). The overall goal of this project is to identify the primary toxic element and the specific environmental factors that stimulate fish-killing H. akashiwo blooms, and thereby provide managers with the fundamental tools needed to help reduce the frequency and toxic magnitude of these harmful algal events. Studies to date have provided incomplete and conflicting observations on the mode of toxicity and the environmental stimulation of toxification. We propose a three-pronged approach to study the environmental controls of H. akashiwo growth and toxin production; laboratory culture experiments, field observations, and bottle and mesocosm manipulation experiments.The project objectives are to: 1. identify the element(s) of toxic activity (inorganic, organic, or synergistic) associated with blooms of H. akashiwo and the various cellular morphologies of this alga, 2. determine the environmental parameters that stimulate the growth success and expression of cell toxicity in the H. akashiwo populations of Puget Sound. Because previous studies have used H. akashiwo cultures with little or no toxic activity, our approach is to use a living laboratory to study H. akashiwo bloom ecology and toxicity using natural assemblages. Using a mobile lab at field sites where H. akashiwo cells are regularly found will enable us to fully characterize the toxic element(s) responsible for fish mortality, and the environmental factors influencing toxicity. Findings from annual field studies in June and two rapid response deployments during major bloom events will be confirmed using laboratory studies with fresh ( 6 mo. old) isolates. The expected results are: 1. determination of the key elements of toxicity of H. akashiwo, 2. characterization of the environmental variables that influence either the induction or depression of elements of toxic activity in H. akashiwo, 3. characterization of environmentally-induced metabolites corresponding to condition of toxin production (metabolomics) and 4. design of a strategy for realistic mitigation of H. akashiwo activities in Puget Sound, Washington. This is a stand-alone project funded for 3 years through the NOAA/NSF ECOHAB program.

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    RSBS: Single Family On-Site Inspections, Measure Level, New York State Residential Statewide Baseline Study

    data.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.

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    AFSC/RACE/SAP/Long: Data from: Habitat, predation, growth, and coexistence: Could interactions between juvenile red and blue king crabs limit blue king crab productivity?

    noaa-fisheries-afsc.data.socrata.com | Last Updated 2017-09-19T04:42:26.000Z

    This data set is from a series of laboratory experiments examining the interactions between red and blue king crabs and habitat. We examined how density and predator presence affect habitat choice by red and blue king crabs. Further experiments determined how temperature and habitat affect predation by year-1 red king crab on year-0 blue king crab. Finally, long-term interaction experiments examined how habitat and density affected growth, survival, and intra-guild interactions between red and blue king crab.

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    AFSC/RACE/SAP/Long: Data from: Habitat, predation, growth, and coexistence: Could interactions between juvenile red and blue king crabs limit blue king crab productivity?

    noaa-fisheries-afsc.data.socrata.com | Last Updated 2017-09-19T04:41:31.000Z

    This data set is from a series of laboratory experiments examining the interactions between red and blue king crabs and habitat. We examined how density and predator presence affect habitat choice by red and blue king crabs. Further experiments determined how temperature and habitat affect predation by year-1 red king crab on year-0 blue king crab. Finally, long-term interaction experiments examined how habitat and density affected growth, survival, and intra-guild interactions between red and blue king crab.

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    AFSC/RACE/SAP/Long: Data from: Habitat, predation, growth, and coexistence: Could interactions between juvenile red and blue king crabs limit blue king crab productivity?

    noaa-fisheries-afsc.data.socrata.com | Last Updated 2017-09-19T04:41:59.000Z

    This data set is from a series of laboratory experiments examining the interactions between red and blue king crabs and habitat. We examined how density and predator presence affect habitat choice by red and blue king crabs. Further experiments determined how temperature and habitat affect predation by year-1 red king crab on year-0 blue king crab. Finally, long-term interaction experiments examined how habitat and density affected growth, survival, and intra-guild interactions between red and blue king crab.