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The land area of Assumption, IL was 1 in 2011.

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 - Notes:

1. ODN datasets and APIs are subject to change and may differ in format from the original source data in order to provide a user-friendly experience on this site.

2. To build your own apps using this data, see the ODN Dataset and API links.

3. If you use this derived data in an app, we ask that you provide a link somewhere in your applications to the Open Data Network with a citation that states: "Data for this application was provided by the Open Data Network" where "Open Data Network" links to http://opendatanetwork.com. Where an application has a region specific module, we ask that you add an additional line that states: "Data about REGIONX was provided by the Open Data Network." where REGIONX is an HREF with a name for a geographical region like "Seattle, WA" and the link points to this page URL, e.g. http://opendatanetwork.com/region/1600000US5363000/Seattle_WA

Geographic and Area Datasets Involving Assumption, IL

  • API

    CPS Schools 2013-2014 Academic Year

    data.cityofchicago.org | Last Updated 2013-11-26T20:27:57.000Z

    List 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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    Historical Pierce County Fees by Type

    internal.open.piercecountywa.gov | Last Updated 2024-02-14T20:36:25.000Z

    Average Pierce County fees by type. The fees and assumptions used include Sewer Service, Sewer Connection, Building Permit (2,500 square foot home, with a 400 square foot garage, and a 100 square foot deck), Traffic Impact, Surface Water, and Solid Waste Disposal (per ton) fees.

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    Beach Lab Data

    data.cityofchicago.org | Last Updated 2024-09-04T19:00:17.000Z

    The 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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    Assessor [Archived 05-31-2023] - Parcel Universe

    datacatalog.cookcountyil.gov | Last Updated 2023-05-31T21:51:45.000Z

    A 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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    Kenya RAPID Performance Evaluation, 2020 Dataset

    datahub.usaid.gov | Last Updated 2024-06-25T02:49:58.000Z

    The Evaluation Team conducted interviews, by phone or other remote media, with approximately 70 key informants from USAID/KEA, SDC, Government of Kenya (national level), Kenya RAPID staff, Kenya RAPID implementing partner staff, county government officials (for WASH, pastoralist support, rangeland management, health, livestock, and nutrition, among other areas), private sector partners, and utility staff. To analyze the qualitative information, the Evaluation Team developed a simple coding tree and set of three to five key codes for each of the evaluation questions, as well as a special code to highlight key quotations from the qualitative data. Excerpts from interviews were grouped by codes based on the evaluation questions and topics (e.g., water, sanitation, nutrition, etc.). Coded excerpts were then allocated to team members by area of expertise for analysis and to identify patterns of responses by partner group, county and level of seniority. The dataset consists of the results from coding the interviews.

  • API

    SLR Passive Flooding - 2.0 Ft. Scenario

    highways.hidot.hawaii.gov | Last Updated 2023-03-24T19:24:49.000Z

    Passive 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 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. 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

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    SLR Passive Flooding - 3.2 Ft. Scenario

    highways.hidot.hawaii.gov | Last Updated 2023-03-24T02:38:08.000Z

    Passive 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

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    SLR Potential Economic Loss - 3.2 Ft. Scenario

    highways.hidot.hawaii.gov | Last Updated 2023-03-24T01:38:45.000Z

    Vulnerability was assessed for the main Hawaiian Islands using the outputs of coastal hazard exposure modeling (provided separately). Potential economic loss was based on the value of the land and structures from the county tax parcel database permanently lost in the sea level rise exposure area (SLR-XA) for four future sea level rise scenarios: 0.5 foot, 1.1 foot, 2.0 feet and 3.2 feet based on the upper end of the IPCC AR5 RCP8.5 projections. This particular layer depicts potential economic loss 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. Potential economic loss was analyzed individually for each hazard (passive flooding, annual high wave flooding, or coastal erosion) at the parcel level and subsequently aggregated in 1-hectare (100 square meter or 1,076 square foot) grids. For the islands of Hawaii, Lanai, and Molokai, the potential economic loss was based solely on passive flooding. Potential economic loss in the SLR-XA area was determined from the highest loss value of any one hazard within the 1-hectare grid, thus avoiding double counting a loss of a particular asset from multiple hazards. Those maximum values for each sector are then summed to determine the total economic loss to property in each grid. Assumptions and Limitations: The vulnerability assessment addressed exposure to chronic flooding with sea level rise. Key assumptions of the economic analysis for the SLR-XA included: (a) loss is permanent; (b) economic loss is based on the value in U.S. dollars in 2016 as property values in the future are unknown; (c) economic loss is based on the value of the land and structures exposed to flooding in the SLR-XA excluding the contents of the property and does not include the economic loss or cost to replace roads, water conveyance systems and other critical infrastructure; and (d) no adaptation measures are put in place that could reduce impacts in the SLR-XA. Economic value data were not available for length of roads, water and wastewater lines, and other public infrastructure due to the variable cost of such infrastructure depending on location, and the complexity and uncertainty involved in design, siting, and construction. Additionally, environmental assets such as beaches and wetlands were not assessed economically due to the complexity in valuing ecosystem services. The loss of both public infrastructure and environmental assets from flooding would result in significant economic loss. Therefore, the total potential economic loss figures estimated in these data are likely an underestimate. 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

  • API

    SLR Passive Flooding - 0.5 Ft. Scenario

    highways.hidot.hawaii.gov | Last Updated 2023-03-24T02:12:39.000Z

    Passive 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 0.5-ft (0.1660-m) sea level rise scenario. While the RCP8.5 predicts that this scenario would be reached by the year 2030, 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

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    SLR Passive Flooding - 1.1 Ft. Scenario

    highways.hidot.hawaii.gov | Last Updated 2023-03-24T02:14:20.000Z

    Passive 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 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. 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