- Population
The population density of Experiment, GA was 960 in 2018.
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 -
Geographic and Population Datasets Involving Experiment, GA
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COVID-19 Deaths by Demographic Grouping
sharefulton.fultoncountyga.gov | Last Updated 2023-01-30T16:59:04.000ZThis dataset consists of death rates from COVID-19 among Fulton County residents segmented by race, age and sex. The dataset is derived from data provided by the Georgia Department of Public Health at https://ga-covid19.ondemand.sas.com.
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DPH Covid Vaccinations: Persons vaccinated by race and county
sharefulton.fultoncountyga.gov | Last Updated 2024-10-23T11:37:35.000ZPersons vaccinated by race and county For a detailed description of the data, see "About Data" on the DPH Vaccine Distribution Dashboard: https://experience.arcgis.com/experience/3d8eea39f5c1443db1743a4cb8948a9c
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Mobility Trends County Modeling Dataset
datahub.transportation.gov | Last Updated 2024-08-08T20:59:05.000ZThe Mobility Trends County Modeling dataset consists of the accumulation of the three performance metrics: VMT, GHG, and TMS, alongside each of the trend indicators: GDP, Population, Lane Miles, Unemployment Rate, Charging Stations, Telework, Unlinked Passenger Trips, E-commerce, Population Density, and on-demand service revenue. The goal of Mobility Trends and Future Demand research project is to enhance FHWA’s empirical understanding of the impact of trends on travel behavior and transportation demand, and ultimately system performance and the user experience. At the core of this research project is the identification and analysis of trends to support a variety of modeling, forecasting, and ‘what if’ projections to support policy and decision making.
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DPH Covid Vaccinations: Persons fully vaccinated by county by day
sharefulton.fultoncountyga.gov | Last Updated 2024-10-23T11:37:31.000ZPersons fully vaccinated by county by day of administrations For a detailed description of the data, see "About Data" on the DPH Vaccine Distribution Dashboard: https://experience.arcgis.com/experience/3d8eea39f5c1443db1743a4cb8948a9c
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HE.C.2 Peer Cities Table V3
datahub.austintexas.gov | Last Updated 2024-10-18T18:24:13.000ZPARD’s Long Range Plan for Land, Facilities and Programs, Our Parks, Our Future (adopted November 2019) compared Austin’s park system to five peer cities: Atlanta, GA, Dallas, TX, Portland, OR, San Antonio, TX, and San Diego, CA. The peer cities were selected based on characteristics such as population, size, density, and governance type. Portland and San Diego were selected as aspirational cities known for their park systems. Note that the table below presents each scoring area’s 1 to 100 index, where 100 is the highest possible score.
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Internet Master Plan: Adoption and Infrastructure Data by Neighborhood
data.cityofnewyork.us | Last Updated 2022-09-23T19:23:10.000ZKey indicators of broadband adoption, service and infrastructure in New York City.</p> <b>Data Limitations:</b> Data accuracy is limited as of the date of publication and by the methodology and accuracy of the original sources. The City shall not be liable for any costs related to, or in reliance of, the data contained in these datasets.
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Surface Drinking Water Importance - Forests on the Edge_data
opendata.utah.gov | Last Updated 2024-04-10T19:40:35.000ZAmerica’s private forests provide a vast array of public goods and services, including abundant, clean surface water. Forest loss and development can affect water quality and quantity when forests are removed and impervious surfaces, such as paved roads, spread across the landscape. We rank watersheds across the conterminous United States according to the contributions of private forest land to surface drinking water and by threats to surface water from increased housing density. Private forest land contributions to drinking water are greatest in the East but are also important in Western watersheds. Development pressures on these contributions are concentrated in the Eastern United States but are also found in the North-Central region, parts of the West and Southwest, and the Pacific Northwest; nationwide, more than 55 million acres of rural private forest land are projected to experience a substantial increase in housing density from 2000 to 2030. Planners, communities, and private landowners can use a range of strategies to maintain freshwater ecosystems, including designing housing and roads to minimize impacts on water quality, managing home sites to protect water resources, and using payment schemes and management partnerships to invest in forest stewardship on public and private lands.This data is based on the digital hydrologic unit boundary layer to the Subwatershed (12-digit) 6th level for the continental United States. To focus this analysis on watersheds with private forests, only watersheds with at least 10% forested land and more than 50 acres of private forest were analyzed. All other watersheds were labeled “Insufficient private forest for this analysis"and coded -99999 in the data table. This dataset updates forest and development statistics reported in the the 2011 Forests to Faucet analysis using 2006 National Land Cover Database for the Conterminous United States, Grid Values=41,42,43,95. and Theobald, Dr. David M. 10 March 2008. bhc2000 and bhc2030 (Housing density for the coterminous US in 2000 and 2030, respectively.) Field Descriptions:HUC_12: Twelve Digit Hydrologic Unit Code: This field provides a unique 12-digit code for each subwatershed.HU_12_DS: Sixth Level Downstream Hydrologic Unit Code: This field was populated with the 12-digit code of the 6th level hydrologic unit that is receiving the majority of the flow from the subwatershed.IMP1: Index of surface drinking water importance (Appendix Map). This field is from the 2011 Forests to Faucet analysis and has not been updated for this analysis.HDCHG_AC: Acres of housing density change on private forest in the subwatershed. HDCHG_PER: Percent of the watershed to experience housing density change on private forest. IMP_HD_PFOR: Index Private Forest importance to Surface Drinking Water with Development Pressure - identifies private forested areas important for surface drinking water that are likely to be affected by future increases in housing density, Ptle_IMP_HD: Private Forest importance to Surface Drinking Water with Development Pressure (Figure 7), percentile. Ptle_HDCHG: Percentage of each subwatershed to Experience an increase in House Density in Private Forest (Figure 6), percentile. FOR_AC: Acres forest (2006) in the subwatershed. PFOR_AC: Acres private forest (2006) in the subwatershed. PFOR_PER: Percent of the subwatershed that is private forest. HU12_AC: Acreage of the subwatershedFOR_PER: Percent of the subwatershed that is forest. PFOR_IMP: Index of Private Forest Importance to Surface Drinking Water. .Ptle_PFIMP: Private forest importance to surface drinking water(Figure 4), percentile. TOP100: Top 100 subwatersheds. 50 from the East, 50 from the west (using the Mississippi River as the divide.) (Figure 8)TOP50EW: 1 = EAST; 2=WESTPoint of Contact: Rebecca Lilja GIS SpecialistForest ServiceNortheastern Area State and Private Forestryp: 603-868-7627 c: 603-953-4307 rlilja@fs.fed.us271 Mast Rd Durham, NH 03824