The land area of Tillmans Corner, AL was 13 in 2013.
Land Area
Water Area
Land area is a measurement providing the size, in square miles, of the land portions of geographic entities for which the Census Bureau tabulates and disseminates data. Area is calculated from the specific boundary recorded for each entity in the Census Bureau's geographic database. Land area is based on current information in the TIGER® data base, calculated for use with Census 2010.
Water Area figures include inland, coastal, Great Lakes, and territorial sea water. Inland water consists of any lake, reservoir, pond, or similar body of water that is recorded in the Census Bureau's geographic database. It also includes any river, creek, canal, stream, or similar feature that is recorded in that database as a two- dimensional feature (rather than as a single line). The portions of the oceans and related large embayments (such as Chesapeake Bay and Puget Sound), the Gulf of Mexico, and the Caribbean Sea that belong to the United States and its territories are classified as coastal and territorial waters; the Great Lakes are treated as a separate water entity. Rivers and bays that empty into these bodies of water are treated as inland water from the point beyond which they are narrower than 1 nautical mile across. Identification of land and inland, coastal, territorial, and Great Lakes waters is for data presentation purposes only and does not necessarily reflect their legal definitions.
Above charts are based on data from the U.S. Census American Community Survey | ODN Dataset | API -
Geographic and Area Datasets Involving Tillmans Corner, AL
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Artificial Reefs Managed by New York State Department of Environmental Conservation
data.ny.gov | Last Updated 2019-10-28T22:13:16.000ZThe dataset is composed of information from Marine Artificial Reef Map and includes GPS location coordinates as well as other information regarding the reef. New York State Department of Environmental Conservation's (NYSDEC) Bureau of Marine Resources created and manages these reef sites as well as other marine resources in the area and in New York State in general.
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Pedestrian Ramp Locations
data.cityofnewyork.us | Last Updated 2022-05-09T22:26:49.000ZPedestrian ramps provide access on and off streets and sidewalks and are an essential tool for all pedestrians. This data is a comprehensive list of all pedestrian ramps throughout New York City. Please note that measurements shown are not indicative of whether a particular ramp is compliant with design and construction standards pursuant to the Americans with Disabilities Act (ADA). DOT applies additional parameters in its compliance assessment of the data collected by Cyclomedia, including specific site constraints located at or near a pedestrian ramp, otherwise referred to as a technical infeasibility in the ADA. The constraints that constitute a technical infeasibility can include but are not limited to elements such as underground vaults, transit facilities, steep terrain conditions, and limited public right-of-way, which are not readily apparent through the data and imagery collected. As such, compliance determinations at some locations require further analysis and site inspection. These locations are noted as “Pending Technical Review” in the published assessment available at: https://www.nycpedramps.info/survey.
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Suitability of City-Owned and Leased Property for Urban Agriculture (LL 48 of 2011)
data.cityofnewyork.us | Last Updated 2024-10-01T14:27:08.000ZList of City-Owned and Operated Real Property. List fulfils requirements of Local Law 48 of 2011. Among other property information, list includes an assessment provided by the Department of Parks and Recreation regarding the potential suitability of parcels for urban agriculture.
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National Community Based Survey of Supports for Healthy Eating and Active Living (CBS HEAL)
data.cdc.gov | Last Updated 2023-08-25T15:08:48.000ZCommunity-Based Survey of Supports for Healthy Eating and Active Living (CBS HEAL) is a CDC survey of a nationally representative sample of U.S. municipalities to better understand existing community-level policies and practices that support healthy eating and active living. The survey collects information about policies such as nutrition standards, incentives for healthy food retail, bike/pedestrian-friendly design, and Complete Streets. About 2,000 municipalities respond to the survey. Participating municipalities receive a report that allows them to compare their policies and practices with other municipalities of similar geography, population size, and urban status. The CBS HEAL survey was first administered in 2014 and was administered again in 2021. Data is provided in multiple formats for download including as a SAS file. A methods report and a SAS program for formatting the data are also provided.
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OLAS/SCL WASH Household Survey Interpolated Dataset
mydata.iadb.org | Last Updated 2024-09-26T15:04:31.000ZThis dataset is an interpolated version of the OLAS/SCL Household Survey Data Set, and includes data from Latin America and Caribbean countries from 2003-2023. The interpolation can be used for understanding trends in water and sanitation access in the region.
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Land Use_data
opendata.utah.gov | Last Updated 2024-04-10T19:40:16.000ZThis dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the Northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the Southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe’s Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe’s Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS.
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Primary Land Use Tax Lot Output (PLUTO)
data.cityofnewyork.us | Last Updated 2024-09-30T20:56:40.000ZExtensive land use and geographic data at the tax lot level in comma-separated values (CSV) file format. The PLUTO files contain more than seventy fields derived from data maintained by city agencies. All previously released versions of this data are available at <a href="https://www.nyc.gov/site/planning/data-maps/open-data/bytes-archive.page?sorts%5Byear%5D=0">BYTES of the BIG APPLE- Archive</a>
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Final Disadvantaged Communities (DAC) 2023
data.ny.gov | Last Updated 2024-07-01T16:29:21.000ZThe Climate Leadership and Community Protection Act (CLCPA) directs the Climate Justice Working Group (CJWG) to establish criteria for defining disadvantaged communities. This dataset identifies areas throughout the State that meet the final disadvantaged community definition as voted on by the Climate Justice Working Group. The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, accelerate economic growth, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.
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Development of an Empirically Derived Measure of Food Safety Culture in Restaurants
data.cdc.gov | Last Updated 2023-11-06T16:17:03.000ZA poor food safety culture has been described as an emerging risk factor for foodborne illness outbreaks, yet there has been little research on this topic in the retail food industry. The purpose of this study was to identify and validate conceptual domains around food safety culture and develop an assessment tool that can be used to assess food workers’ perceptions of their restaurant’s food safety culture. The study, conducted from March 2018 through March 2019, surveyed restaurant food workers for their level of agreement with 28 statements. We received 579 responses from 331 restaurants spread across eight different health department jurisdictions. Factor analysis and structural equation modeling supported a model composed of four primary constructs. The highest rated construct was Resource Availability ( =4.69, sd=0.57), which assessed the availability of resources to maintain good hand hygiene. The second highest rated construct was Employee Commitment (=4.49, sd=0.62), which assessed workers’ perceptions of their coworkers’ commitment to food safety. The last two constructs were related to management. Leadership (=4.28, sd=0.69) assessed the existence of food safety policies, training, and information sharing. Management Commitment (=3.94, sd=1.05) assessed whether food safety was a priority in practice. Finally, the model revealed one higher-order construct, Worker Beliefs about Food Safety Culture (=4.35, sd=0.53). The findings from this study can support efforts by the restaurant industry, food safety researchers, and health departments to examine the influence and effects of food safety culture within restaurants.
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SLR Passive Flooding - 3.2 Ft. Scenario
highways.hidot.hawaii.gov | Last Updated 2023-03-24T02:38:08.000ZPassive flooding was modeled by the University of Hawaii Coastal Geology Group using a modified "bathtub" approach following methods described in Cooper et al. 2013. The passive flooding model provides an initial assessment of low-lying areas susceptible to flooding by sea level rise. Passive flooding includes areas that are hydrologically connected to the ocean (marine flooding) and low-lying areas that are not hydrologically connected to the ocean (groundwater). Data used in modeling passive flooding include global sea level rise projections, digital elevation models (DEM), and the mean higher high water (MHHW) datum from local tide gauges. DEMs used in this study are freely available from NOAA and the U.S. Army Corps of Engineers (USACE). DEMs are derived from aerial light detection and ranging (LiDAR) data. The horizontal and vertical positional accuracies of the DEMs conform to flood hazard mapping standards of the Federal Emergency Management Agency (FEMA 2012). The IPCC AR5 RCP8.5 sea level rise scenario was used in modeling exposure to passive flooding from sea level rise at 0.5, 1.1, 2.0, and 3.2 feet. This particular layer depicts passive flooding using the 3.2-ft (0.9767-m) sea level rise scenario. While the RCP8.5 predicts that this scenario would be reached by the year 2100, questions remain around the exact timing of sea level rise and recent observations and projections suggest a sooner arrival. Passive flooding was modeled using the DEMs in geographic information systems software to identify areas below a certain sea level height (flooded by sea level rise) when raising water levels above current Mean Higher High Water (MHHW) tidal datum. In other words, water levels are shown as they would appear during MHHW, or the average higher high water height of each tidal day. The area flooded was derived by subtracting a tidal surface model from the DEM. Assumptions and Limitations: In many areas around the State, representing sea level rise from passive marine flooding will likely produce an underestimate of the area inundated or permanently submerged because the model does not account for waves and coastal erosion, important processes along Hawaii's highly dynamic coasts. For this reason, coastal erosion and annual high wave flooding (provided separately) are also modeled to provide a more comprehensive picture of the extent of hazard exposure. The passive flooding model does not explicitly include flooding through storm drain systems and other underground infrastructure, which would contribute to flooding in many low-lying areas identified in the model. The DEMs used in the modeling depict a smoothed topographic surface and do not identify basements, parking garages, and other development below ground that would be affected by marine and groundwater flooding with sea level rise. The passive flooding model is intended to provide an initial screening tool for sea level rise vulnerability. More detailed hydrologic and engineering modeling may be necessary to fully assess passive marine flooding hazards at the scale of individual properties. Data compiled by the Pacific Islands Ocean Observing System (PacIOOS) for the Hawaii Sea Level Rise Viewer hosted at https://pacioos.org/shoreline/slr-hawaii/. For further information, please see the Hawaii Sea Level Rise Vulnerability and Adaptation Report: https://climateadaptation.hawaii.gov/wp-content/uploads/2017/12/SLR-Report_Dec2017.pdf