The land area of Holiday Shores, IL was 2 in 2018.
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 Holiday Shores, IL
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Waterfront Public Access Areas (WPAAs)
data.cityofnewyork.us | Last Updated 2024-08-08T18:14:57.000ZWaterfront Public Access Areas (WPAAs) are privately owned waterfront zoning lots where publicly accessible open space is provided to and along the shoreline for public enjoyment, as shown on the <a href="https://waterfrontaccess.planning.nyc.gov/">Waterfront Access Map (WAM)</a>.
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CPS Schools 2013-2014 Academic Year
data.cityofchicago.org | Last Updated 2013-11-26T20:27:57.000ZList of CPS schools for the 2013-2014 academic year. This dataset includes various identifiers used to identify school districts, including names; local, state, and federal IDs; and geographic descriptions on the location of each school.
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Beach Lab Data
data.cityofchicago.org | Last Updated 2024-09-04T19:00:17.000ZThe Chicago Park District collects and analyzes water samples from beaches along Chicago’s Lake Michigan lakefront. The Chicago Park District partners with the University of Illinois at Chicago Department of Public Health Laboratory to analyze water samples using a new DNA testing method called Rapid Testing Method (qPCR analysis) which tests for Enterococci in order to monitor swimming safety. The rapid testing method (qPCR analysis) is a new method that measures levels of pathogenic DNA in beach water. Unlike the culture based test that requires up to 24 hours of processing, the new rapid testing method requires a 4-5 hours for results. The Chicago Park District can use results of the rapid test to notify the public when levels exceed UPEPA recommended levels, which is 1000* CCE. When DNA bacteria levels exceed 1000 CCE, a yellow swim advisory flag is implemented. For more information please refer to the USEPA Recreational Water Quality Criteria (http://water.epa.gov/scitech/swguidance/standards/criteria/health/recreation). Historically, the Chicago Park District used the culture based analysis method and statistical prediction models to monitor beach water quality. The culture based method tests for Escherichia coli (E. coli) bacteria which is an indicator species for the presence of disease-causing bacteria, viruses, and protozoans that may pose health risks to the public. This method requires 18-24 hours of processing to receive results. The Chicago Park District would use results of the culture based method to notify the public when levels exceed UPEPA recommended levels, which is 235* CFU. When bacteria levels exceed 235 CFU, a yellow swim advisory flag was implemented. This standard is still used at most beaches throughout the Great Lakes region. For more information please refer to the USEPA Recreational Water Quality Criteria. The statistical prediction model forecasted real-time Escherichia coli (E. coli) bacteria levels present in the water. The Chicago Park District (CPD) in partnership with the US Geological Survey, developed statistical prediction models by using weather data pulled from CPD buoys (https://data.cityofchicago.org/d/qmqz-2xku) and weather stations (https://data.cityofchicago.org/d/k7hf-8y75). The Chicago Park District would use results of the predictive model to notify the public when bacteria levels would exceed 235 CFU. When bacteria levels exceed 235 CFU, a yellow swim advisory flag was implemented. * The unit of measurement for Escherichia coli is Colony Forming Units (CFU) per 100 milliliters of water. (Culture Based Method / Statistical Prediction Model) *The unit of measuring DNA is Enterococci Calibrator Cell Equivalents (CCE) per 100 milliliters of water. (Rapid Testing Analysis)
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Horseshoe Crab Spawning Survey
data.delaware.gov | Last Updated 2022-10-06T19:41:25.000ZDelaware Bay shore survey data starting with 1999 which denotes peak spawning occurrences by day and lunar period, proportion of spawning in May (coinciding with shorebird stopovers), average water temperature, index values for female and male crabs per square meter by beach and bay-wide, the annual sex ratio, and index of abundance per beach.
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Environmental Sensitivity Project (2015)
data.edmonton.ca | Last Updated 2022-12-13T23:03:09.000ZHistorically, the City of Edmonton has managed ‘natural areas’ within the North Saskatchewan River Valley and the Tablelands separately, guided by inventories such as the Ribbon of Green and Geowest (1993). Over the past decade, City policy has shifted to manage natural areas with consideration of their role within an ecological network. Today, a goal of the City is to protect, preserve and enhance a functioning ecological network throughout the city limits. This network should include lands in both the river valley and the Tablelands. To further this goal, a model was developed in 2015 for determining environmental sensitivity scores across the entirety of the city. This model guided the collection of several digital data layers with coverage across the entire study area (including several ecological assets, threats to assets, and development and cultural constraints). Data layers were then used to develop spatial outputs that summarized the distribution of these assets, threats and constraints. These base layers have been compiled into this dataset to help inform planning, development and conservation throughout Edmonton. Environmental sensitivity analysis incorporated recent mapping of the ecological network of native and non-native vegetation, streams, wetlands and other waterbodies as much as possible, with practical limitations. The City’s urban Primary Land and Vegetation Inventory (uPLVI) and remote sensing data used for this assessment were completed in 2015 and 2013 respectively, which is relatively recent, but not current. Similarly, infrastructure data (roads, subdivision development and stormwater facilities) provided varied in month of acquisition from 2015. Some discrepancy between mapped and actual features may result, due to loss and changes from ongoing development activities.
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Assessor [Archived 05-31-2023] - Parcel Universe
datacatalog.cookcountyil.gov | Last Updated 2023-05-31T21:51:45.000ZA complete, historic universe of Cook County parcels with attached geographic, governmental, and spatial data. When working with Parcel Index Numbers (PINs) make sure to zero-pad them to 14 digits. Some datasets may lose leading zeros for PINs when downloaded. Additional notes:<ul><li>Data is attached via spatial join (st_contains) to each parcel's centroid.</li> <li>Centroids are based on <a href="https://datacatalog.cookcountyil.gov/Property-Taxation/ccgisdata-Parcel-2021/77tz-riq7">Cook County parcel shapefiles</a>.</li> <li>Older properties may be missing coordinates and thus also missing attached spatial data (usually they are missing a parcel boundary in the shapefile).</li> <li>Newer properties may be missing a mailing or property address, as they need to be assigned one by the postal service.</li> <li>Attached spatial data does NOT go all the way back to 1999. It is only available for more recent years, primarily those after 2012.</li> <li>The universe contains data for the current tax year, which may not be complete or final. PINs can still be added and removed to the universe up until the Board of Review closes appeals.</li> <li>Data will be updated monthly.</li> <li>Rowcount and characteristics for a given year are final once the Assessor <a href="https://www.cookcountyassessor.com/assessment-calendar-and-deadlines">has certified the assessment roll</a> for all townships.</li> <li>Depending on the time of year, some third-party and internal data will be missing for the most recent year. Assessments mailed this year represent values from last year, so this isn't an issue. By the time the Data Department models values for this year, those data will have populated.</li> <li>Current property class codes, their levels of assessment, and descriptions can be found <a href="https://prodassets.cookcountyassessor.com/s3fs-public/form_documents/classcode.pdf">on the Assessor's website</a>. Note that class codes details can change across time.</li> <li>Due to decrepencies between the systems used by the Assessor and Clerk's offices, <i>tax_district_code</i> is not currently up-to-date in this table.</li></ul> For more information on the sourcing of attached data and the preparation of this dataset, see the <a href="https://gitlab.com/ccao-data-science---modeling/data-architecture">Assessor's data architecture repo</a> on GitLab. <a href="https://datacatalog.cookcountyil.gov/stories/s/i22y-9sd2">Read about the Assessor's 2022 Open Data Refresh.</a>
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Feed the Future Tajikistan Zone of Influence Population Based Survey, Household Data
datahub.usaid.gov | Last Updated 2024-06-25T02:12:18.000ZThe baseline survey in Tajikistan captures data in the Feed the Future Zones of Influence (ZOI), comprised of 12 of the 24 districts in Khatlon province. A total of 2,000 households in the ZOI were surveyed for the PBS data collection activity. These households are spread across 100 standard enumeration areas in the targeted districts. The survey is comprised of ten CSV files: a children's file, a household-level file, a household member level file, a women's file, several files describing consumption, and two files used to construct the Women's Empowerment in Agriculture Index. This file reports survey results related to households.
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Dig Ticket Notifications
data.cityofchicago.org | Last Updated 2024-10-15T09:27:14.000ZIn order to help contractors and private residents avoid existing utility lines (including gas, electrical, and water lines) when digging, the Chicago Department of Transportation maintains 811 Chicago, a free, 24-hour service to private contractors and homeowners in Chicago. Anyone planning to dig within Chicago must obtain a “dig ticket” from 811 Chicago. 811 Chicago notifies all utilities of the impending excavations. The utility owners then send out staff to mark the location of the underground facilities within 48 hours (excluding emergencies), not counting Saturdays, Sundays, and holidays. This dataset shows these utility notifications. Since it is common for the same dig ticket to produce multiple notifications, the same dig ticket will appear multiple times and this dataset cannot be used without further refinement to count, map, or analyze unique excavations in Chicago. See https://ipi.cityofchicago.org/Digger for more information on the dig ticket system.
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SLR Annual High Wave Flooding - 2.0 Ft. Scenario
highways.hidot.hawaii.gov | Last Updated 2023-03-24T01:40:19.000ZHawaii is exposed to large waves annually on all open coasts due to its location in the Central North Pacific Ocean. The distance over which waves run-up and wash across the shoreline will increase with sea level rise. As water levels increase, less wave energy will be dissipated through breaking on nearshore reefs and waves will arrive at a higher elevation at the shoreline. Computer model simulations of future annual high wave flooding were conducted by the University of Hawaii Coastal Geology Group using the XBeach (for eXtreme Beach behavior) numerical model developed by a consortium of research institutions. The model propagates the maximum annually recurring wave, calculated from offshore wave buoy data, over the reef and to the shore along one-dimensional (1D) cross-shore profiles extracted from a 1-meter DEM. Profiles are spaced 20 meters apart along the coast. This approach was used to model the transformation of the wave as it breaks across the reef and includes shallow water wave processes such as wave set-up and overtopping. The IPCC AR5 RCP8.5 sea level rise scenario was used in modeling exposure to annual high wave flooding from sea level rise at 0.5, 1.1, 2.0, and 3.2 feet. This particular layer depicts annual high wave flooding using the 2.0-ft (0.5991-m) sea level rise scenario. While the RCP8.5 predicts that this scenario would be reached by the year 2075, questions remain around the exact timing of sea level rise and recent observations and projections suggest a sooner arrival. Historical data used to model annual high wave flooding include hourly measurements of significant wave height, peak wave period, and peak wave direction, and was acquired from offshore wave buoy data from PacIOOS. Maximum surface elevation and depth of the annual high wave flooding is calculated from the mean of the five highest modeled water elevations at each model location along each profile. Output from the simulations is interpolated between transects and compiled in a 5-meter map grid. Depth grid cells with values less than 10 centimeters are not included in the impact assessment. This was done to remove very thin layers of water excursions that (1) are beyond the accuracy of the model and (2) might not constitute a significant impact to land and resources when only occurring once annually. Any low-lying flooded areas that are not connected to the ocean are also removed. Annual high wave flood modeling covered wave-exposed coasts with low-lying development on Maui, Oahu, and Kauai. Annual high wave flooding was not available for the islands of Hawaii, Molokai, and Lanai, nor for harbors or other back-reef areas throughout all the islands. Additional studies would be needed to add the annual high wave flooding for those areas. The maximum annually recurring wave parameters (significant wave height, period, direction) were statistically determined using historical wave climate records and do not include potential changes in future wave climate, the effects of storm surge, or less-frequent high wave events (e.g., a 1-in-10 year wave event). In some locations, the extent of flooding modeled was limited by the extent of the 1-meter DEM. Changes in shoreline location due to coastal erosion are not included in this modeling. As shorelines retreat, annual high wave flooding will reach farther inland along retreating shorelines. Waves are propagated along a "bare earth" DEM which is void of shoreline structures, buildings, and vegetation, and waves are assumed to flow over an impermeable surface. The DEM represents a land surface at one particular time, and may not be representative of the beach shape during the season of most severe wave impact, particularly for highly variable north and west-exposed beaches. Undesirable artifacts of 1D modeling include over-predicted flooding along some transects with deep, shore-perpendicular indentations in the sea bottom such as nearshore reef channels. The 1D modeling does not account for the pres
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SLR Coastal Erosion - 1.1 Ft. Scenario
highways.hidot.hawaii.gov | Last Updated 2023-03-24T01:38:18.000ZUPDATED - Nov. 2020. Studies of historical shoreline change using aerial photographs and survey maps show that 70% of beaches on Kauai, Oahu, and Maui are eroding (receding landward) (Fletcher et al. 2012). Beaches exist in a delicate balance between existing water levels, wave energy, and sand supply. Coastal erosion modeling was conducted for sandy shorelines of Kauai, Oahu, and Maui by the University of Hawaii Coastal Geology Group. The methods are described in Anderson et al. (2015) and combine historical shoreline change data with a model of beach profile response to sea level rise from Davidson-Arnott (2005) in order to estimate probabilities of future exposure to erosion at transects (shore-perpendicular measurement locations) spaced approximately 20 meters apart along the shoreline. The model accounts for localized alongshore variability in shoreline change by incorporating trends from the historical erosion mapping studies. Historical data used to model coastal erosion consisted of: (1) historical shoreline positions and erosion rates measured from high-resolution (0.5 meters) ortho-rectified aerial photographs and NOAA topographic charts dating back to the early 1900s (Fletcher et al. 2012, Romine et al. 2013), and (2) beach profile field survey data (Gibbs et al. 2001, Fletcher et al. 2012). The vegetation line was identified in the most recent aerial photography dating from 2006 to 2008. The output of the modeling is the estimated exposure zone to future erosion hazards. Based on the model and IPCC AR5 RCP8.5 sea level rise scenario, there is an 80% probability that land impacted by erosion would be confined within the exposure zone at that particular time. The exposure zones extend landward from the current-day shoreline (vegetation line) up to the 80% cumulative probability contour from sea level rise at 0.5, 1.1, 2.0, and 3.2 feet, which incorporates the uncertainty (upper and lower bounds) of the IPCC RCP8.5 sea level rise projection. This particular layer depicts coastal erosion using the 1.1-ft (0.3224-m) sea level rise scenario. While the RCP8.5 predicts that this scenario would be reached by the year 2050, questions remain around the exact timing of sea level rise and recent observations and projections suggest a sooner arrival. Assumptions and Limitations: Historical shoreline change data and beach profiles needed to model coastal erosion are available only for sandy shores of Kauai, Oahu, and Maui. Exposure was not modeled for less-erodible rocky coasts and bluffs, though the latter can be prone to sudden failure in some areas. In addition, modeling did not account for: (1) existing seawalls or other coastal armoring in the backshore; (2) increasing wave energy across the fringing reef with sea level rise; (3) possible changes in reef accretion and nearshore sediment processes with sea level rise; and (4) possible changes to sediment supply from future shoreline development and engineering, such as construction or removal of coastal armoring or other coastal engineering. 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/. Users of these data should cite the following publication: Anderson, T.R., Fletcher, C.H., Barbee, M.M., Frazer, L.N., and B.M. Romine (2015). Doubling of Coastal Erosion Under Rising Sea Level by Mid-Century in Hawaii, Natural Hazards, doi:10.1007/s11069-015-1698-6. 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