Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

This archived public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties.

The COVID-19 community levels were developed using a combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days. The COVID-19 community level was determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge.

Using these data, the COVID-19 community level was classified as low, medium, or high.

COVID-19 Community Levels were used to help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals.

For the most accurate and up-to-date data for any county or state, visit the relevant health department website. COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

Archived Data Notes:

This dataset was renamed from "United States COVID-19 Community Levels by County as Originally Posted" to "United States COVID-19 Community Levels by County" on March 31, 2022.

March 31, 2022: Column name for county population was changed to “county_population”. No change was made to the data points previous released.

March 31, 2022: New column, “health_service_area_population”, was added to the dataset to denote the total population in the designated Health Service Area based on 2019 Census estimate.

March 31, 2022: FIPS codes for territories American Samoa, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands were re-formatted to 5-digit numeric for records released on 3/3/2022 to be consistent with other records in the dataset.

March 31, 2022: Changes were made to the text fields in variables “county”, “state”, and “health_service_area” so the formats are consistent across releases.

March 31, 2022: The “%” sign was removed from the text field in column “covid_inpatient_bed_utilization”. No change was made to the data. As indicated in the column description, values in this column represent the percentage of staffed inpatient beds occupied by COVID-19 patients (7-day average).

March 31, 2022: Data values for columns, “county_population”, “health_service_area_number”, and “health_service_area” were backfilled for records released on 2/24/2022. These columns were added since the week of 3/3/2022, thus the values were previously missing for records released the week prior.

April 7, 2022: Updates made to data released on 3/24/2022 for Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands to correct a data mapping error.

April 21, 2022: COVID-19 Community Level (CCL) data released for counties in Nebraska for the week of April 21, 2022 have 3 counties identified in the high category and 37 in the medium category. CDC has been working with state officials to verify the data submitted, as other data systems are not providing alerts for substantial increases in disease transmission or severity in the state.

May 26, 2022: COVID-19 Community Level (CCL) data released for McCracken County, KY for the week of May 5, 2022 have been updated to correct a data processing error. McCracken County, KY should have appeared in the low community level category during the week of May 5, 2022. This correction is reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for several Florida counties for the week of May 19th, 2022, have been corrected for a data processing error. Of note, Broward, Miami-Dade, Palm Beach Counties should have appeared in the high CCL category, and Osceola County should have appeared in the medium CCL category. These corrections are reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for Orange County, New York for the week of May 26, 2022 displayed an erroneous case rate of zero and a CCL category of low due to a data source error. This county should have appeared in the medium CCL category.

June 2, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a data processing error. Tolland County, CT should have appeared in the medium community level category during the week of May 26, 2022. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a misspelling. The medium community level category for Tolland County, CT on the week of May 26, 2022 was misspelled as “meduim” in the data set. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Mississippi counties for the week of June 9, 2022 should be interpreted with caution due to a reporting cadence change over the Memorial Day holiday that resulted in artificially inflated case rates in the state.

July 7, 2022: COVID-19 Community Level (CCL) data released for Rock County, Minnesota for the week of July 7, 2022 displayed an artificially low case rate and CCL category due to a data source error. This county should have appeared in the high CCL category.

July 14, 2022: COVID-19 Community Level (CCL) data released for Massachusetts counties for the week of July 14, 2022 should be interpreted with caution due to a reporting cadence change that resulted in lower than expected case rates and CCL categories in the state.

July 28, 2022: COVID-19 Community Level (CCL) data released for all Montana counties for the week of July 21, 2022 had case rates of 0 due to a reporting issue. The case rates have been corrected in this update.

July 28, 2022: COVID-19 Community Level (CCL) data released for Alaska for all weeks prior to July 21, 2022 included non-resident cases. The case rates for the time series have been corrected in this update.

July 28, 2022: A laboratory in Nevada reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate will be inflated in Clark County, NV for the week of July 28, 2022.

August 4, 2022: COVID-19 Community Level (CCL) data was updated on August 2, 2022 in error during performance testing. Data for the week of July 28, 2022 was changed during this update due to additional case and hospital data as a result of late reporting between July 28, 2022 and August 2, 2022. Since the purpose of this data set is to provide point-in-time views of COVID-19 Community Levels on Thursdays, any changes made to the data set during the August 2, 2022 update have been reverted in this update.

August 4, 2022: COVID-19 Community Level (CCL) data for the week of July 28, 2022 for 8 counties in Utah (Beaver County, Daggett County, Duchesne County, Garfield County, Iron County, Kane County, Uintah County, and Washington County) case data was missing due to data collection issues. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 4, 2022: Due to a reporting cadence change, case rates for all Alabama counties will be lower than expected. As a result, the CCL levels published on August 4, 2022 should be interpreted with caution.

August 11, 2022: COVID-19 Community Level (CCL) data for the week of August 4, 2022 for South Carolina have been updated to correct a data collection error that resulted in incorrect case data. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 18, 2022: COVID-19 Community Level (CCL) data for the week of August 11, 2022 for Connecticut have been updated to correct a data ingestion error that inflated the CT case rates. CDC, in collaboration with CT, has resolved the issue and the correction is reflected in this update.

August 25, 2022: A laboratory in Tennessee reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate may be inflated in many counties and the CCLs published on August 25, 2022 should be interpreted with caution.

August 25, 2022: Due to a data source error, the 7-day case rate for St. Louis County, Missouri, is reported as zero in the COVID-19 Community Level data released on August 25, 2022. Therefore, the COVID-19 Community Level for this county should be interpreted with caution.

September 1, 2022: Due to a reporting issue, case rates for all Nebraska counties will include 6 days of data instead of 7 days in the COVID-19 Community Level (CCL) data released on September 1, 2022. Therefore, the CCLs for all Nebraska counties should be interpreted with caution.

September 8, 2022: Due to a data processing error, the case rate for Philadelphia County, Pennsylvania,

These quarterly reports show the number of receipts, dispositions and pending New Court Cases (NCCs) during the defined period. The data shown is by month with quarterly and fiscal year (FY) summaries through the most recently completed quarter. Report for FY 2012.

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance public use dataset has 19 elements for all COVID-19 cases shared with CDC and includes demographics, geography (county and state of residence), any exposure history, disease severity indicators and outcomes, and presence of any underlying medical conditions and risk behaviors.

Currently, CDC provides the public with three versions of COVID-19 case surveillance line-listed data: this 19 data element dataset with geography, a 12 data element public use dataset, and a 33 data element restricted access dataset.

The following apply to the public use datasets and the restricted access dataset:

• Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.
• Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.
• Some data are suppressed to protect individual privacy.
• Datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the current datasets. This 14-day lag allows case reporting to be stabilized and ensure that time-dependent outcome data are accurately captured.
• Datasets are updated monthly.
• Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy.
• For more information about data collection and reporting, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.
• For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html
  

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (<a href="https://cdn.ymaws.com/www.cste.org/resource/resmgr/ps/positionstatement2020/Interim-20-ID-01_COVID

Reporting of Aggregate Case and Death Count data was discontinued on May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

The surveillance case definition for COVID-19, a nationally notifiable disease, was first described in a position statement from the Council for State and Territorial Epidemiologists, which was later revised. However, there is some variation in how jurisdictions implemented these case definitions. More information on how CDC collects COVID-19 case surveillance data can be found at FAQ: COVID-19 Data and Surveillance.

Aggregate Data Collection Process Since the beginning of the COVID-19 pandemic, data were reported from state and local health departments through a robust process with the following steps:

• Aggregate county-level counts were obtained indirectly, via automated overnight web collection, or directly, via a data submission process.
• If more than one official county data source existed, CDC used a comprehensive data selection process comparing each official county data source to retrieve the highest case and death counts, unless otherwise specified by the state.
• A CDC data team reviewed counts for congruency prior to integration and set up alerts to monitor for discrepancies in the data.
• CDC routinely compiled these data and post the finalized information on COVID Data Tracker.
• County level data were aggregated to obtain state- and territory- specific totals.
• Counting of cases and deaths is based on date of report and not on the date of symptom onset. CDC calculates rates in these data by using population estimates provided by the US Census Bureau Population Estimates Program (2019 Vintage).
• COVID-19 aggregate case and death data are organized in a time series that includes cumulative number of cases and deaths as reported by a jurisdiction on a given date. New case and death counts are calculated as the week-to-week change in cumulative counts of cases and deaths reported (i.e., newly reported cases and deaths = cumulative number of cases/deaths reported this week minus the cumulative total reported the prior week.

This process was collaborative, with CDC and jurisdictions working together to ensure the accuracy of COVID-19 case and death numbers. County counts provided the most up-to-date numbers on cases and deaths by report date. Throughout data collection, CDC retrospectively updated counts to correct known data quality issues.

Description This archived public use dataset focuses on the cumulative and weekly case and death rates per 100,000 persons within various sociodemographic factors across all states and their counties. All resulting data are expressed as rates calculated as the number of cases or deaths per 100,000 persons in counties meeting various classification criteria using the US Census Bureau Population Estimates Program (2019 Vintage).

Each county within jurisdictions is classified into multiple categories for each factor. All rates in this dataset are based on classification of counties by the characteristics of their population, not individual-level factors. This applies to each of the available factors observed in this dataset. Specific factors and their corresponding categories are detailed below.

Population-level factors Each unique population factor is detailed below. Please note that the “Classification” column describes each of the 12 factors in the dataset, including a data dictionary describing what each numeric digit means within each classification. The “Category” column uses numeric digits (2-6, depending on the factor) defined in the “Classification” column.

Metro vs. Non-Metro – “Metro_Rural” Metro vs. Non-Metro classification type is an aggregation of the 6 National Center for Health Statistics (NCHS) Urban-Rural classifications, where “Metro” counties include Large Central Metro, Large Fringe Metro, Medium Metro, and Small Metro areas and “Non-Metro” counties include Micropolitan and Non-Core (Rural) areas. 1 – Metro, including “Large Central Metro, Large Fringe Metro, Medium Metro, and Small Metro” areas 2 – Non-Metro, including “Micropolitan, and Non-Core” areas

Urban/rural - “NCHS_Class” Urban/rural classification type is based on the 2013 National Center for Health Statistics Urban-Rural Classification Scheme for Counties. Levels consist of:

1 Large Central Metro
2 Large Fringe Metro 3 Medium Metro 4 Small Metro 5 Micropolitan 6 Non-Core (Rural)

American Community Survey (ACS) data were used to classify counties based on their age, race/ethnicity, household size, poverty level, and health insurance status distributions. Cut points were generated by using tertiles and categorized as High, Moderate, and Low percentages. The classification “Percent non-Hispanic, Native Hawaiian/Pacific Islander” is only available for “Hawaii” due to low numbers in this category for other available locations. This limitation also applies to other race/ethnicity categories within certain jurisdictions, where 0 counties fall into the certain category. The cut points for each ACS category are further detailed below:

Age 65 - “Age65”

1 Low (0-24.4%) 2 Moderate (>24.4%-28.6%) 3 High (>28.6%)

Non-Hispanic, Asian - “NHAA”

1 Low (<=5.7%) 2 Moderate (>5.7%-17.4%) 3 High (>17.4%)

Non-Hispanic, American Indian/Alaskan Native - “NHIA”

1 Low (<=0.7%) 2 Moderate (>0.7%-30.1%) 3 High (>30.1%)

Non-Hispanic, Black - “NHBA”

1 Low (<=2.5%) 2 Moderate (>2.5%-37%) 3 High (>37%)

Hispanic - “HISP”

1 Low (<=18.3%) 2 Moderate (>18.3%-45.5%) 3 High (>45.5%)

Population in Poverty - “Pov”

1 Low (0-12.3%) 2 Moderate (>12.3%-17.3%) 3 High (>17.3%)

Population Uninsured- “Unins”

1 Low (0-7.1%) 2 Moderate (>7.1%-11.4%) 3 High (>11.4%)

Average Household Size - “HH”

1 Low (1-2.4) 2 Moderate (>2.4-2.6) 3 High (>2.6)

Community Vulnerability Index Value - “CCVI” COVID-19 Community Vulnerability Index (CCVI) scores are from Surgo Ventures, which range from 0 to 1, were generated based on tertiles and categorized as:

1 Low Vulnerability (0.0-0.4) 2 Moderate Vulnerability (0.4-0.6) 3 High Vulnerability (0.6-1.0)

Social Vulnerability Index Value – “SVI" Social Vulnerability Index (SVI) scores (vintage 2020), which also range from 0 to 1, are from CDC/ASTDR’s Geospatial Research, Analysis & Service Program. Cut points for CCVI and SVI scores were generated based on tertiles and categorized as:

1 Low Vulnerability (0-0.333) 2 Moderate Vulnerability (0.334-0.666) 3 High Vulnerability (0.667-1)

Daily count of NYC residents who tested positive for SARS-CoV-2, who were hospitalized with COVID-19, and deaths among COVID-19 patients. Note that this dataset currently pulls from https://raw.githubusercontent.com/nychealth/coronavirus-data/master/trends/data-by-day.csv on a daily basis.

On October 20, 2022, CDC began retrieving aggregate case and death data from jurisdictional and state partners weekly instead of daily. This dataset contains archived historical community transmission and related data elements by county. Although these data will continue to be publicly available, this dataset has not been updated since October 20, 2022. An archived dataset containing weekly historical community transmission data by county can also be found here: Weekly COVID-19 County Level of Community Transmission Historical Changes | Data | Centers for Disease Control and Prevention (cdc.gov).

Related data CDC has been providing the public with two versions of COVID-19 county-level community transmission level data: this historical dataset with the daily county-level transmission data from January 22, 2020, and a dataset with the daily values as originally posted on the COVID Data Tracker. Similar to this dataset, the original dataset with daily data as posted is archived on 10/20/2022. It will continue to be publicly available but will no longer be updated. A new dataset containing community transmission data by county as originally posted is now published weekly and can be found at: Weekly COVID-19 County Level of Community Transmission as Originally Posted | Data | Centers for Disease Control and Prevention (cdc.gov).

This public use dataset has 7 data elements reflecting historical data for community transmission levels for all available counties and jurisdictions. It contains historical data for the county level of community transmission and includes updated data submitted by states and jurisdictions. Each day, the dataset was updated to include the most recent days’ data and incorporate any historical changes made by jurisdictions. This dataset includes data since January 22, 2020. Transmission level is set to low, moderate, substantial, or high using the calculation rules below.

Methods for calculating county level of community transmission indicator The County Level of Community Transmission indicator uses two metrics: (1) total new COVID-19 cases per 100,000 persons in the last 7 days and (2) percentage of positive SARS-CoV-2 diagnostic nucleic acid amplification tests (NAAT) in the last 7 days. For each of these metrics, CDC classifies transmission values as low, moderate, substantial, or high (below and here). If the values for each of these two metrics differ (e.g., one indicates moderate and the other low), then the higher of the two should be used for decision-making.

CDC core metrics of and thresholds for community transmission levels of SARS-CoV-2

Total New Case Rate Metric: "New cases per 100,000 persons in the past 7 days" is calculated by adding the number of new cases in the county (or other administrative level) in the last 7 days divided by the population in the county (or other administrative level) and multiplying by 100,000. "New cases per 100,000 persons in the past 7 days" is considered to have transmission level of Low (0-9.99); Moderate (10.00-49.99); Substantial (50.00-99.99); and High (greater than or equal to 100.00).

Test Percent Positivity Metric: "Percentage of positive NAAT in the past 7 days" is calculated by dividing the number of positive tests in the county (or other administrative level) during the last 7 days by the total number of tests resulted over the last 7 days. "Percentage of positive NAAT in the past 7 days" is considered to have transmission level of Low (less than 5.00); Moderate (5.00-7.99); Substantial (8.00-9.99); and High (greater than or equal to 10.00).

If the two metrics suggest different transmission levels, the higher level is selected. If one metric is missing, the other metric is used for the indicator.

The reported transmission categories include:

Low Transmission Threshold: Counties with fewer than 10 total cases per 100,000 population in the past 7 days, and a NAAT percent test positivity in the past 7 days below 5%;

Moderate Transmission Threshold: Counties with 10-49 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 5.0-7.99%;

Substantial Transmission Threshold: Counties with 50-99 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 8.0-9.99%;

High Transmission Threshold: Counties with 100 or more total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 10.0% or greater.

Blank: total new cases in the past 7 days are not reported (county data known to be unavailable) and the percentage of positive NAATs tests during the past 7 days (blank) are not reported.

Data Suppression To prevent the release of data that could be used to identify people, data cells are suppressed for low frequency. When the case counts used to calculate the total new case rate metric ("cases_per_100K_7_day_count_change") is greater than zero and less than 10, this metric is set to "suppressed" to protect individual privacy. If the case count is 0, the total new case rate metric is still displayed.

The data in this dataset are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers. This datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access.

Duplicate Records Issue A bug was found on 12/28/2021 that caused many records in the dataset to be duplicated. This issue was resolved on 01/06/2022.

Note: This COVID-19 data set is no longer being updated as of December 1, 2023. Access current COVID-19 data on the CDPH respiratory virus dashboard (https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/Respiratory-Viruses/RespiratoryDashboard.aspx) or in open data format (https://data.chhs.ca.gov/dataset/respiratory-virus-dashboard-metrics).

As of August 17, 2023, data is being updated each Friday.

For death data after December 31, 2022, California uses Provisional Deaths from the Center for Disease Control and Prevention’s National Center for Health Statistics (NCHS) National Vital Statistics System (NVSS). Prior to January 1, 2023, death data was sourced from the COVID-19 registry. The change in data source occurred in July 2023 and was applied retroactively to all 2023 data to provide a consistent source of death data for the year of 2023.

As of May 11, 2023, data on cases, deaths, and testing is being updated each Thursday. Metrics by report date have been removed, but previous versions of files with report date metrics are archived below.

All metrics include people in state and federal prisons, US Immigration and Customs Enforcement facilities, US Marshal detention facilities, and Department of State Hospitals facilities. Members of California's tribal communities are also included.

The "Total Tests" and "Positive Tests" columns show totals based on the collection date. There is a lag between when a specimen is collected and when it is reported in this dataset. As a result, the most recent dates on the table will temporarily show NONE in the "Total Tests" and "Positive Tests" columns. This should not be interpreted as no tests being conducted on these dates. Instead, these values will be updated with the number of tests conducted as data is received.

Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. This dataset will receive a final update on June 1, 2023, to reconcile historical data through May 10, 2023, and will remain publicly available.

This archived public use dataset contains historical case and percent positivity data updated weekly for all available counties and jurisdictions. Each week, the dataset was refreshed to capture any historical updates. Please note, percent positivity data may be incomplete for the most recent time period.

Related data CDC provides the public with two active versions of COVID-19 county-level community transmission level data: this dataset with historical case and percent positivity data for each county from January 22, 2020 (Weekly Historical Changes dataset) and a dataset with the levels as originally posted (Weekly Originally Posted dataset) since October 20, 2022. Please navigate to the Weekly Originally Posted dataset for the Community Transmission Levels published weekly on Thursdays.

Methods for calculating county level of community transmission indicator The County Level of Community Transmission indicator uses two metrics: (1) total new COVID-19 cases per 100,000 persons in the last 7 days and (2) percentage of positive SARS-CoV-2 diagnostic nucleic acid amplification tests (NAAT) in the last 7 days. For each of these metrics, CDC classifies transmission values as low, moderate, substantial, or high (below and here). If the values for each of these two metrics differ (e.g., one indicates moderate and the other low), then the higher of the two should be used for decision-making.

CDC core metrics of and thresholds for community transmission levels of SARS-CoV-2 Total New Case Rate Metric: "New cases per 100,000 persons in the past 7 days" is calculated by adding the number of new cases in the county (or other administrative level) in the last 7 days divided by the population in the county (or other administrative level) and multiplying by 100,000. "New cases per 100,000 persons in the past 7 days" is considered to have transmission level of Low (0-9.99); Moderate (10.00-49.99); Substantial (50.00-99.99); and High (greater than or equal to 100.00).

Test Percent Positivity Metric: "Percentage of positive NAAT in the past 7 days" is calculated by dividing the number of positive tests in the county (or other administrative level) during the last 7 days by the total number of tests resulted over the last 7 days. "Percentage of positive NAAT in the past 7 days" is considered to have transmission level of Low (less than 5.00); Moderate (5.00-7.99); Substantial (8.00-9.99); and High (greater than or equal to 10.00).

The data in this dataset are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.

This dataset is created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access.

Archived data CDC has archived two prior versions of these datasets. Both versions contain the same 7 data elements reflecting community transmission levels for all available counties and jurisdictions; however, the datasets updated daily. The archived datasets can be found here:

<a href="https://data.cdc.gov/Public-Health-Surveillance/United-States-COVID-19-County-Level-of-Community-T

This dataset contains oil and gas agreements cases derived from Legal Land Descriptions (LLD) contained in the US Bureau of Land Management's, BLM, Mineral and Land Record System(MLRS) and geocoded (mapped) using the Public Land Survey System (PLSS) derived from the most accurate survey data available through BLM Cadastral Survey workforce. Geospatial representations might be missing for some cases that can not be geocoded using the MLRS algorithm. Each case is given a data quality score based on how well it mapped. These can be lumped into seven groups to provide a simplified way to understand the scores.Group 1: Direct PLSS Match. Scores “0”, “1”, “2”, “3” should all have a match to the PLSS data. There are slight differences, but the primary expectation is that these match the PLSS.Group 2: Calculated PLSS Match. Scores “4”, “4.1”, “5”, “6”, “7” and “8” were generated through a process of creating the geometry that is not a direct capture from the PLSS. They represent a best guess based on the underlining PLSS Group 3 – Mapped to Section. Score of “8.1”, “8.2”, “8.3”, “9” and “10” are mapped to the Section.Group 4- Combination of mapped and unmapped areas. Score of 15 represents a case that has some portions that would map and other that do not.Group 5 – No NLSDB Geometry, Only Attributes. Scores “11”, “12”, “20”, “21” and “22” do not have a match to the PLSS and no geometry is in the NLSDB, and only attributes exist in the data. Group 6 – Mapped to County. Scores of “25” map to the County.Group 7 – Improved Geometry. Scores of “100” are cases that have had their geometry edited by BLM staff using ArcGIS Pro or MLRS bulk upload tool.

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. The Precipice Aquifer & …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. The Precipice Aquifer & Equivalents - Thickness and Extent data sets, are part of a set that represents the hydrostratigraphic units of the Great Artesian Basin, which include five major aquifers, four intervening aquitards, and the Cenozoic cover to the GAB. There are five layers in the Precipice Aquifer and Equivalents map data A: Formation Extent B: Outcrop extent C: Isopach Raster D: Isopach Contours E: Data Point Locations The datasets have been derived from the lithostratigraphic intercepts in drillhole data from petroleum exploration wells, water bores, and stratigraphic wells. Seismic correlation and assessment of hydrogeological character based on electrofacies have not been used. The working datasest for this study has been derived primarily from the following databases: PEPS-SA (Petroleum Exploration and Production System - South Australia) (Department of Primary Industries and Regions SA, 2011) WaterConnect Groundwater database (Govt. of SA, 2011) QPED (Queensland Petroleum exploration database) (Geological Survey of Queensland, 2010). GABLOG (Great Artesian Basin Well Log Dataset) (Habermehl, 2001) Additional supplementary information was derived from published reports listed in the following section. Interpretations by O'Brien & Wells, (1994); and O'Brien 2011 were used in generating the isopach data (thickness surface and contours) along the boundary of the Surat and Clarence-Moreton Basins. Isolated polygons in the North West have represent the extents of a basal Lower Jurassic sandstone underlying the Hutton Sandstone which is equivalent to the Precipice Sandstone,. This dataset and associated metadata can be obtained from www.ga.gov.au, using catalogue number 81684. Associated report reference: Ransley, T., Radke, B., Feitz, A., Kellett, J., Owens, R., Bell, J. and Stewart, G., 2014. Hydrogeological Atlas the Great Artesian Basin. Geoscience Australia. Canberra. [available from www.ga.gov.au using catalogue number 79790] REFERENCES: References - main data sources * Department of Primary Industries and Regions SA (2011). Petroleum Exploration and Production System - South Australia (PEPS-SA). Version 2011-06-15. Retrieved from http://www.pir.sa.gov.au/petroleum/access_to_data/peps-sa_database * Geological Survey of Queensland (2010). Queensland Petroleum Exploration Data (QPED) database. Retrieved 25 September 2011, from http://mines.industry.qld.gov.au/geoscience/geoscience-wireline-log-data.htm. * Geoscience Australia, 2013. Mesozoic Geology of the Carpentaria and Laura Basins (dataset). Scale 1:6000000. Geoscience Australia, Canberra. [available from www.ga.gov.au using catalogue number 75840] * Gibson, D. L., B. S. Powell & Smart, J. (1974). Shallow stratigraphic drilling, northern Cape York Peninsula, 1973. Record 1974/76. Australia, Bureau of Mineral Resources. * Govt. of South Australia (2011). WaterConnect Groundwater database [available at https://www.waterconnect.sa.gov.au]. * Habermehl, M. A. and J. E. Lau (1997). Hydrogeology of the Great Artesian Basin Australia (Map at scale 1:2,500,000). Canberra, Australian Geological Survey Organisation. * O'Brien, P. E. (2011). The eastern edge of the Great Artesian Basin: relationships between the Surat and Clarence-Moreton basins. Internal report. Canberra, Geoscience Australia. * Wells, A.T. , O'Brien, P.E. 1994 Lithostratigraphic framework of the Clarence-Moreton Basin IN Wells, A.T. and O'Brien, P.E. (eds.) "Geology and Petroleum Potential of the Clarence-Moreton Basin, New South Wales and Queensland" Australian Geological Survey Organisation. Bulletin 241 p4-47 References - Seismic Surveys * none References - Well Completion Reports and drilling logs * None Dataset History SOURCE DATA: Data was obtained from a variety of sources, as listed below: WaterConnect Groundwater database (Govt. of SA, 2011) Great Artesian Basin Well Log Dataset (GABLOG) (Habermehl, M. A., 2001). Petroleum Exploration and Production System - South Australia (PEPS-SA) (Department of Primary Industries and Regions SA, 2011). Queensland Petroleum Exploration Database (QPED) (Geological Survey of Queensland, 2010). Well completion and drill log reports (see references in abstract) Other reports (see references in abstract) Additional lithostratigraphic information from the Clarence-Moreton Basin was reinterpreted by P. O'Brien (Pers. Comm., 2011) from the earlier study of Wells & O'Brien (1994). METHOD: Formation Extent Extents were based on drillhole data (see References for main data sources). Extent lines were adjusted to envelop all intercepts of the Hydrostratigraphic unit. This produced some varied and irregular shapes, some patchy regions, and required some interpretation to establish the most likely extent boundary. This is a regional interpretation for mapping at approximately 1:1 000 000 to produce a broad scale overview, and examination of small areas by collecting extra data is most likely to produce results that differ from this regional interpretation. Outcrop Extent Outcrop extents were sourced and extracted from Hydrogeology of the Great Artesian Basin Australia (Habermehl & Lau, 1997) for the Eromanga and Surat sub-basins. For the Carpentaria Basin, Mesozoic Geology of the Carpentaria and Laura Basins (Geoscience Australia, 2013) was used. Isopach Raster Source point thickness values calculated from drillhole intercepts by using the depth to top and bottom values of formations within the drillhole database attributes, and adding them together to form the isopach values for each data point across the whole aquifer/aquitard. These thickness values were extrapolated using the ESRI ANUDEM Topo-To-Raster surface modeller. Zero thickness constraints were applied at the known extent of the aquifer/aquitard, except in cases where the formation extends beyond the GAB boundary (for example the Precipice formation on the eastern side of the GAB, where the formation is quite thick and is exposed as a cliff). In these cases, constraints were not applied and the software was allowed to model a thickness right up to the GAB boundary. Resulting grids were modified using the ESRI Grid Calculator to set the minimum thickness to 0, and clipped to the aquifer/aquitard extent. Isopach Contours Isopach conours were calculated from the thickness grid using the ESRI Contour Tool. These were calculated at 50m intervals. In most cases the zero contour lines generated by the tool were replaced by the extent of the aquifer due to the erratic nature of the generated lines. In cases where the aquifer/aquitard is thick at the extent, the zero isoline is outside the extent and is not mapped in that area. Isopachs were clipped to the aquifer/aquitard extent. Data Point Locations Data Point Locations have been derived from the bore hole data collected for this project. Only the location has been included. SOFTWARE: All modifications/edits and geoprocessing were performed using ESRI ArcGIS 10 software. QAQC: Data sets were searched for errors such as negative thickness, missing data, incorrectly calculated thickness, aquifers/aquitards with missing formations, and false XY data. The data was given a second Q&A after the thickness grids had been calculated. This involve plotting the points and the thickness grid and looking carefully for bad values. Sometimes a false outlier value would cause a 'bullseye' effect on the grid. To check the veracity, nearby data would be compared, and if necessary the original data would be searched check the value. Some petroleum fields would have wildcat picks at certain bore holes and these were compared with nearby boreholes and adjusted or deleted. Additionally, if whole subregions had suspect values the data was check to ensure the relevant data had all been included. Finally, data sets were also checked to ensure the bore whole data recorded the full thickness of the Aquifer. In many cases water bores only go down until a suitable water source is found and often will not penetrate the whole aquifer. This data was considered on a case by case basis, in areas where plenty of suitable data was available they were removed, and in areas of sparse borehole data they were included to establish the occurrence of the formation albeit as a minimum thickness value. Data has undergone a QAQC verification process in order to capture and repair attribute and geometric errors. Dataset Citation Geoscience Australia (2015) GABATLAS - Precipice Aquifer & Equivalents - Thickness and Extent. Bioregional Assessment Source Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/aeeead0e-9637-4f6f-b870-df4bc66dc81c.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 cases, hospitalizations, and associated deaths that have been reported among Connecticut residents. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Hospitalization data were collected by the Connecticut Hospital Association and reflect the number of patients currently hospitalized with laboratory-confirmed COVID-19. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the daily COVID-19 update. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics Data are reported d

This dataset contains numbers of COVID-19 outbreaks and associated cases, categorized by setting, reported to CDPH since January 1, 2021.

AB 685 (Chapter 84, Statutes of 2020) and the Cal/OSHA COVID-19 Emergency Temporary Standards (Title 8, Subchapter 7, Sections 3205-3205.4) required non-healthcare employers in California to report workplace COVID-19 outbreaks to their local health department (LHD) between January 1, 2021 – December 31, 2022. Beginning January 1, 2023, non-healthcare employer reporting of COVID-19 outbreaks to local health departments is voluntary, unless a local order is in place. More recent data collected without mandated reporting may therefore be less representative of all outbreaks that have occurred, compared to earlier data collected during mandated reporting. Licensed health facilities continue to be mandated to report outbreaks to LHDs.

LHDs report confirmed outbreaks to the California Department of Public Health (CDPH) via the California Reportable Disease Information Exchange (CalREDIE), the California Connected (CalCONNECT) system, or other established processes. Data are compiled and categorized by setting by CDPH. Settings are categorized by U.S. Census industry codes. Total outbreaks and cases are included for individual industries as well as for broader industrial sectors.

The first dataset includes numbers of outbreaks in each setting by month of onset, for outbreaks reported to CDPH since January 1, 2021. This dataset includes some outbreaks with onset prior to January 1 that were reported to CDPH after January 1; these outbreaks are denoted with month of onset “Before Jan 2021.” The second dataset includes cumulative numbers of COVID-19 outbreaks with onset after January 1, 2021, categorized by setting. Due to reporting delays, the reported numbers may not reflect all outbreaks that have occurred as of the reporting date; additional outbreaks may have occurred that have not yet been reported to CDPH.

While many of these settings are workplaces, cases may have occurred among workers, other community members who visited the setting, or both. Accordingly, these data do not distinguish between outbreaks involving only workers, outbreaks involving only residents or patrons, or outbreaks involving both.

Several additional data limitations should be kept in mind:

• Outbreaks are classified as “Insufficient information” for outbreaks where not enough information was available for CDPH to assign an industry code.

• Some sectors, particularly congregate residential settings, may have increased testing and therefore increased likelihood of outbreak recognition and reporting. As a result, in congregate residential settings, the number of outbreak-associated cases may be more accurate.

• However, in most settings, outbreak and case counts are likely underestimates. For most cases, it is not possible to identify the source of exposure, as many cases have multiple possible exposures.

• Because some settings have been at times been closed or open with capacity restrictions, numbers of outbreak reports in those settings do not reflect COVID-19 transmission risk.

• The number of outbreaks in different settings will depend on the number of different workplaces in each setting. More outbreaks would be expected in settings with many workplaces compared to settings with few workplaces.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

The Adori-Springbok Aquifer - Thickness and Extent data sets, are part of a set that represents the hydrostratigraphic units of the Great Artesian Basin, which include five major aquifers, four intervening aquitards, and the Cenozoic cover to the GAB.

There are five layers in the Adori-Springbok Aquifer map data.

A: Formation Extent

B: Outcrop extent

C: Isopach Raster

D: Isopach Contours

E: Data Point Locations

The datasets have been derived from the lithostratigraphic intercepts in drillhole data from petroleum exploration wells, water bores, and stratigraphic wells. Seismic correlation and assessment of hydrogeological character based on electrofacies have not been used. The working datasest for this study has been derived primarily from the following databases:

1. PEPS-SA (Petroleum Exploration and Production System - South Australia) (Department of Primary Industries and Regions SA, 2011)

2. WaterConnect Groundwater database (Govt. of SA, 2011)

3. QPED (Queensland Petroleum exploration database) (Geological Survey of Queensland, 2010).

4. GABLOG (Great Artesian Basin Well Log Dataset) (Habermehl, 2001)

5. Additional supplementary information was derived from published reports listed in the following section.

This is a regional interpretation for mapping at approximately 1:1 000 000 to produce a broad scale overview, and examination of small areas by collecting extra data is most likely to produce results that differ from this regional interpretation.

This dataset and associated metadata can be obtained from www.ga.gov.au, using catalogue number 81680.

Associated report reference:

Ransley, T., Radke, B., Feitz, A., Kellett, J., Owens, R., Bell, J. and Stewart, G., 2014. Hydrogeological Atlas the Great Artesian Basin. Geoscience Australia. Canberra. [available from www.ga.gov.au using catalogue number 79790]

References - main data sources

· Department of Primary Industries and Regions SA (2011). Petroleum Exploration and Production System - South Australia (PEPS-SA). Version 2011-06-15. Retrieved from http://www.pir.sa.gov.au/petroleum/access_to_data/peps-sa_database

· Geological Survey of Queensland (2010). Queensland Petroleum Exploration Data (QPED) database. Retrieved 25 September 2011, from

http://mines.industry.qld.gov.au/geoscience/geoscience-wireline-log-data.htm.

· Govt. of South Australia (2011). WaterConnect Groundwater database [available at https://www.waterconnect.sa.gov.au].

· Geoscience Australia, 2013. Mesozoic Geology of the Carpentaria and Laura Basins. Scale 1:6000000. Geoscience Australia, Canberra. [available from www.ga.gov.au using catalogue number 75840]

· Habermehl, M. A. (2001). Wire-line logged water bores in the Great Artesian Basin, Australia - digital data of logs and water bore data acquired by AGSO. Australian Geological Survey Organisation Bulletin 245. Canberra, Bureau of Rural Sciences: ix, 98 p.

Dataset History

SOURCE DATA:

Data was obtained from a variety of sources, as listed below:

1. WaterConnect Groundwater database (Govt. of SA, 2011)

2. Great Artesian Basin Well Log Dataset (GABLOG) (Habermehl, M. A., 2001).

3. Petroleum Exploration and Production System - South Australia (PEPS-SA) (Department of Primary Industries and Regions SA, 2011).

4. Queensland Petroleum Exploration Database (QPED) (Geological Survey of Queensland, 2010).

5. Well completion and drill log reports (see references in abstract)

6. Other reports (see references in abstract)

7. Seismic surveys and associated reports (see seismic references section in abstract)

METHOD:

Formation Extent

Extents were based on drillhole data from GABLOG (Habermehl, M. A., 2001), PEPS-SA (Department of Primary Industries and Regions SA, 2011), QPED (Geological Survey of Queensland, 2010) and WaterConnect Groundwater database (Govt. of SA, 2011). Extent lines were adjusted to envelop all intercepts of the Hydrostratigraphic unit. This produced some varied and irregular shapes, some patchy regions, and required some interpretation to establish the most likely extent boundary.

Outcrop Extent

Outcrop extents came from 'Hydrogeology of the Great Artesian Basin Australia' (Habermehl & Lau, 1997) for the Eromanga and Surat sub-basins. For the Carpentaria Basin, Mesozoic Geology of the Carpentaria and Laura Basins (Geoscience Australia, 2013) was used.

Isopach Raster

Source point thickness values calculated from drillhole intercepts were extrapolated using the ESRI ANUDEM Topo-To-Raster surface modeller. Zero thickness constraints were applied at the known extent of the aquifer/aquitard, except in cases where the formation extends beyond the GAB boundary (for example the Precipice formation on the eastern side of the GAB, where the formation is quite thick and is exposed as a cliff). In these cases, constraints were not applied and the software was allowed to model a thickness right up to the GAB boundary. Resulting grids were modified using the ESRI Grid Calculator to set the minimum thickness to 0, and clipped to the aquifer/aquitard extent.

Isopach Contour

Isopach contours were calculated from the Adori-Springbok aquifer thickness grid using the ESRI Contour Tool. These were calculated at 50m intervals. In most cases the zero contour lines generated by the tool were replaced by the extent of the aquifer due to the erratic nature of the generated lines. In cases where the aquifer/aquitard is thick at the extent, the zero isoline is outside the extent and is not mapped in that area. Isopachs were clipped to the aquifer/aquitard extent.

Data Point Locations

Data Point Locations have been derived from the bore hole data collected for this project. Only the location has been included.

SOFTWARE:

All modifications/edits and geoprocessing were performed using ESRI ArcGIS 10 software.

QAQC:

Data sets were searched for errors such as negative thickness, missing data, incorrectly calculated thickness, aquifers/aquitards with missing formations, and false XY data. The data was given a second Q&A after the thickness grids had been calculated. This involve plotting the points and the thickness grid and looking carefully for bad values. Sometimes a false outlier value would cause a 'bullseye' effect on the grid. To check the veracity, nearby data would be compared, and if necessary the original data would be searched check the value. Some petroleum fields would have wildcat picks at certain bore holes and these were compared with nearby boreholes and adjusted or deleted. Additionally, if whole subregions had suspect values the data was check to ensure the relevant data had all been included. Finally, data sets were also checked to ensure the bore whole data recorded the full thickness of the Aquifer. In many cases water bores only go down until a suitable water source is found and often will not penetrate the whole aquifer. This data was considered on a case by case basis, in areas where plenty of suitable data was available they were removed, and in areas of sparse borehole data they were included to establish the occurrence of the formation albeit as a minimum thickness value.

Data has undergone a QAQC verification process in order to capture and repair attribute and geometric errors.

Dataset Citation

Geoscience Australia (2014) GABATLAS 15 Winton Mackunda aquifer and equivalents. Bioregional Assessment Source Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/2e1e0572-a43c-448e-be15-18abf848ab5c.

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. The Cadna-owie-Hooray Aquifer …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. The Cadna-owie-Hooray Aquifer and Equivalents - Thickness and Extent data sets, are part of a set that represents the hydrostratigraphic units of the Great Artesian Basin, which include five major aquifers, four intervening aquitards, and the Cenozoic cover to the GAB. There are five layers in the Cadna-owie-Hooray Aquifer and Equivalents map data A: Formation Extent B: Outcrop extent C: Isopach Raster D: Isopach Contours E: Data Point Locations The datasets have been derived from the lithostratigraphic intercepts in drillhole data from petroleum exploration wells, water bores, and stratigraphic wells. Seismic correlation and assessment of hydrogeological character based on electrofacies have not been used. The working dataset for this study has been derived primarily from the following databases: PEPS-SA (Petroleum Exploration and Production System - South Australia) (Department of Primary Industries and Regions SA, 2011) WaterConnect Groundwater database (Govt. of SA, 2011) QPED (Queensland Petroleum exploration database) (Geological Survey of Queensland, 2010). GABLOG (Great Artesian Basin Well Log Dataset) (Habermehl, 2001) Additional supplementary information was derived from published reports listed in the following section. This is a regional interpretation for mapping at approximately 1:1 000 000 to produce a broad scale overview, and examination of small areas by collecting extra data is most likely to produce results that differ from this regional interpretation. This dataset and associated metadata can be obtained from www.ga.gov.au, using catalogue number 81678. Associated report reference: Ransley, T., Radke, B., Feitz, A., Kellett, J., Owens, R., Bell, J. and Stewart, G., 2014. Hydrogeological Atlas the Great Artesian Basin. Geoscience Australia. Canberra. [available from www.ga.gov.au using catalogue number 79790] Dataset History SOURCE DATA: Data was obtained from a variety of sources, as listed below: WaterConnect Groundwater database (Govt. of SA, 2011) Great Artesian Basin Well Log Dataset (GABLOG) (Habermehl, M. A., 2001). Petroleum Exploration and Production System - South Australia (PEPS-SA) (Department of Primary Industries and Regions SA, 2011). Queensland Petroleum Exploration Database (QPED) (Geological Survey of Queensland, 2010). Well completion and drill log reports (see references in abstract) Other reports (see references in abstract) Seismic surveys and associated reports (see seismic references section in abstract) METHOD: Formation Extent Extents were based on drillhole data (see References). For the offshore Carpentaria Basin, extent is taken from the isopach boundary of 'Thickness of Jurassic-Cretaceous sequence in the Carpentaria and Laura basins' (Geoscience Australia, 2013). Extent lines were adjusted to envelop all borehole intercepts of the Hydrostratigraphic unit. This produced some varied and irregular shapes, some patchy regions, and required some interpretation to establish the most likely extent boundary. Outcrop Extents Outcrop extents came from 'Hydrogeology of the Great Artesian Basin Australia' (Habermehl & Lau, 1997) for the Eromanga and Surat sub-basins. This was modified in the northernmost region for the Algebuckina Sandstone extent. For the Carpentaria Basin, the boundary of 'Thickness of Jurassic-Cretaceous sequence in the Carpentaria and Laura basins' (Geoscience Australia, 2013) was used. Isopach Raster Source point thickness values calculated from drillhole intercepts were extrapolated using the ESRI ANUDEM Topo-To-Raster surface modeller. Zero thickness constraints were applied at the known extent of the aquifer/aquitard, except in cases where the formation extends beyond the GAB boundary (for example the Precipice formation on the eastern side of the GAB, where the formation is quite thick and is exposed as a cliff). In these cases, constraints were not applied and the software was allowed to model a thickness right up to the GAB boundary. Resulting grids were modified using the ESRI Grid Calculator to set the minimum thickness to 0, and clipped to the aquifer/aquitard extent. Isopach Contours For the onshore Carpentaria Basin, well completion reports of individual wells (see References), as well as BMR drill Records (Gibson et al., 1974) have been used as a source of thicknesses. In other areas, GABLOG (Habermehl, M. A., 2001), PEPS-SA (Department of Primary Industries and Regions SA, 2011), QPED (Geological Survey of Queensland, 2010) and the SA Govt. Groundwater database (Govt. of SA, 2011) were used. Isopach contours were calculated from the Cadna-Owie-Hooray aquifer thickness grid (generated from drillhole intercepts in Clarence-Moreton from O'Brien (2011)) using the ESRI Contour Tool. These were calculated at 50m intervals. In most cases the zero contour lines generated by the tool were replaced by the extent of the aquifer due to the erratic nature of the generated lines. In cases where the aquifer/aquitard is thick at the extent, the zero isoline is outside the extent and is not mapped in that area. Isopachs were clipped to the aquifer/aquitard extent. Data Point Locations Data Point Locations have been derived from the bore hole data collected for this project. Only the location has been included. SOFTWARE: All modifications/edits and geoprocessing were performed using ESRI ArcGIS 10 software. QAQC: Data sets were searched for errors such as negative thickness, missing data, incorrectly calculated thickness, aquifers/aquitards with missing formations, and false XY data. The data was given a second Q&A after the thickness grids had been calculated. This involve plotting the points and the thickness grid and looking carefully for bad values. Sometimes a false outlier value would cause a 'bullseye' effect on the grid. To check the veracity, nearby data would be compared, and if necessary the original data would be searched check the value. Some petroleum fields would have wildcat picks at certain bore holes and these were compared with nearby boreholes and adjusted or deleted. Additionally, if whole subregions had suspect values the data was check to ensure the relevant data had all been included. Finally, data sets were also checked to ensure the bore whole data recorded the full thickness of the Aquifer. In many cases water bores only go down until a suitable water source is found and often will not penetrate the whole aquifer. This data was considered on a case by case basis, in areas where plenty of suitable data was available they were removed, and in areas of sparse borehole data they were included to establish the occurrence of the formation albeit as a minimum thickness value. Data has undergone a QAQC verification process in order to capture and repair attribute and geometric errors. These datasets have been compiled or interpreted from existing and new data sets that vary in scale. They are intended to be used for broad, regional understanding of the basin and are not designed to be used at a local scale. Where existing data sets have been used we have attempted to correct any errors, however errors may remain. It has to be stressed that this generalised basin-wide concept is scale dependant, and may exaggerate the distinction between the superposed aquitards and aquifers. Although this hydrostratigraphy offers more accessible comprehension of the regional hydroarchitecture, the generalisation comes with the inherent dangers of simplification and apparent enhanced contrast of a complex system. For local hydrogeological study, such generalisations may not necessarily survive closer scrutiny. See "Metadata.pdf" for complete metadata Dataset Citation Geoscience Australia (2015) GABATLAS - Cadna-owie-Hooray Aquifer and Equivalents - Thickness and Extent. Bioregional Assessment Source Dataset. Viewed 10 December 2018, http://data.bioregionalassessments.gov.au/dataset/bc55589c-1c6f-47ba-a1ac-f81b0151c630.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

The Hutton Aquifer and Equivalents - Thickness and Extent data sets, are part of a set that represents the hydrostratigraphic units of the Great Artesian Basin, which include five major aquifers, four intervening aquitards, and the Cenozoic cover to the GAB. This data set includes the Algebuckina which is continuous with both the 'Hutton Aquifer and Equivalents' and the 'Cadna-owie-Hooray Aquifer and Equivalents' and thus forms a continuous aquifer from east to west across the GAB. Here it has been grouped with the Hutton.

There are five layers in the Hutton Aquifer and Equivalents map data.

A: Formation Extent

B: Outcrop extent

C: Isopach Raster

D: Isopach Contours

E: Data Point Locations

The datasets have been derived from the lithostratigraphic intercepts in drillhole data from petroleum exploration wells, water bores, and stratigraphic wells. Seismic correlation and assessment of hydrogeological character based on electrofacies have not been used. The working dataset for this study has been derived primarily from the following databases:

1.\tPEPS-SA (Petroleum Exploration and Production System - South Australia) (Department of Primary Industries and Regions SA, 2011)

2.\tWaterConnect Groundwater database (Govt. of SA, 2011)

3.\tQPED (Queensland Petroleum exploration database) (Geological Survey of Queensland, 2010).

4.\tGABLOG (Great Artesian Basin Well Log Dataset) (Habermehl, 2001)

5.\tAdditional supplementary information was derived from published reports listed in the following section.

This is a regional interpretation for mapping at approximately 1:1 000 000 to produce a broad scale overview, and examination of small areas by collecting extra data is most likely to produce results that differ from this regional interpretation.

This dataset and associated metadata can be obtained from www.ga.gov.au, using catalogue number 81682.

\t

Associated report reference:

Ransley, T., Radke, B., Feitz, A., Kellett, J., Owens, R., Bell, J. and Stewart, G., 2014. Hydrogeological Atlas the Great Artesian Basin. Geoscience Australia. Canberra. \[available from www.ga.gov.au using catalogue number 79790\]

REFERENCES:

References - main data sources

\*\tDepartment of Primary Industries and Regions SA (2011). Petroleum Exploration and Production System - South Australia (PEPS-SA). Version 2011-06-15. Retrieved from http://www.pir.sa.gov.au/petroleum/access_to_data/peps-sa_database

\*\tGeological Survey of Queensland (2010). Queensland Petroleum Exploration Data (QPED) database. Retrieved 25 September 2011, from http://mines.industry.qld.gov.au/geoscience/geoscience-wireline-log-data.htm.

\*\tGeoscience Australia, 2013. Mesozoic Geology of the Carpentaria and Laura Basins (dataset). Scale 1:6000000. Geoscience Australia, Canberra. \[available from www.ga.gov.au using catalogue number 75840\]

\*\tGibson, D. L., B. S. Powell & Smart, J. (1974). Shallow stratigraphic drilling, northern Cape York Peninsula, 1973. Record 1974/76. Australia, Bureau of Mineral Resources.

\*\tGovt. of South Australia (2011). WaterConnect Groundwater database \[available at https://www.waterconnect.sa.gov.au\].

\*\tHabermehl, M. A. and J. E. Lau (1997). Hydrogeology of the Great Artesian Basin Australia (Map at scale 1:2,500,000). Canberra, Australian Geological Survey Organisation.

Dataset History

SOURCE DATA:

Data was obtained from a variety of sources, as listed below:

1.\tWaterConnect Groundwater database (Govt. of SA, 2011)

2.\tGreat Artesian Basin Well Log Dataset (GABLOG) (Habermehl, M. A., 2001).

3.\tPetroleum Exploration and Production System - South Australia (PEPS-SA) (Department of Primary Industries and Regions SA, 2011).

4.\tQueensland Petroleum Exploration Database (QPED) (Geological Survey of Queensland, 2010).

5.\tWell completion and drill log reports (see references in abstract)

6.\tOther reports (see references in abstract)

7.\tSeismic surveys and associated reports (see seismic references section in abstract)

METHOD:

Formation Extent

Extents were based on drillhole data (see References for main data sources). Extent boundaries were adjusted to envelop all intercepts of the Hydrostratigraphic unit. This produced some varied and irregular shapes, some patchy regions, and required some interpretation to establish the most likely extent boundary.

Outcrop Extent

Outcrop extents were sourced and extracted from Hydrogeology of the Great Artesian Basin Australia (Habermehl & Lau, 1997) for the Eromanga and Surat sub-basins. For the Carpentaria Basin, Mesozoic Geology of the Carpentaria and Laura Basins (Geoscience Australia, 2013) was used.

Isopach Raster

Drillhole intercepts in Clarence-Moreton from O'Brien (2011) were used to calculate isopach values by using the depth to top and bottom values of formations within the drillhole database attributes, and adding them together to form the isopach values for each data point across the whole aquifer/aquitard. These values were extrapolated using the ESRI ANUDEM Topo-To-Raster surface modeller tool. Zero thickness constraints were applied at the known extent of the aquifer/aquitard, except in cases where the formation extends beyond the GAB boundary (for example the Precipice formation on the eastern side of the GAB, where the formation is quite thick and is exposed as a cliff). In these cases, constraints were not applied and the software was allowed to model a thickness right up to the GAB boundary. Resulting grids were modified using the ESRI Grid Calculator to set the minimum thickness to 0, and clipped to the aquifer/aquitard extent.

Isopach Contours

Isopach contours were calculated from the Hutton Aquifer and equivalents thickness grid (generated from drillhole intercepts in Clarence-Moreton from O'Brien (2011)) using the ESRI Contour Tool. These were calculated at 50m intervals. In most cases the zero contour lines generated by the tool were replaced by the extent of the aquifer due to the erratic nature of the generated lines. In cases where the aquifer/aquitard is thick at the extent, the zero isoline is outside the extent and is not mapped in that area. Isopachs were clipped to the aquifer/aquitard extent.

Data Point Locations

Data Point Locations have been derived from the bore hole data collected for this project. Only the location has been included.

SOFTWARE:

All modifications/edits and geoprocessing were performed using ESRI ArcGIS 10 software.

QAQC:

Data sets were searched for errors such as negative thickness, missing data, incorrectly calculated thickness, aquifers/aquitards with missing formations, and false XY data.

The data was given a second Q&A after the thickness grids had been calculated. This involve plotting the points and the thickness grid and looking carefully for bad values. Sometimes a false outlier value would cause a 'bullseye' effect on the grid. To check the veracity, nearby data would be compared, and if necessary the original data would be searched check the value. Some petroleum fields would have wildcat picks at certain bore holes and these were compared with nearby boreholes and adjusted or deleted.

Additionally, if whole subregions had suspect values the data was check to ensure the relevant data had all been included. Finally, data sets were also checked to ensure the bore whole data recorded the full thickness of the Aquifer. In many cases water bores only go down until a suitable water source is found and often will not penetrate the whole aquifer. This data was considered on a case by case basis, in areas where plenty of suitable data was available they were removed, and in areas of sparse borehole data they were included to establish the occurrence of the formation albeit as a minimum thickness value.

Data has undergone a QAQC verification process in order to capture and repair attribute and geometric errors.

Dataset Citation

Geoscience Australia (2015) GABATLAS - Hutton Aquifer and Equivalents - Thickness and Extent. Bioregional Assessment Source Dataset. Viewed 27 November 2017, http://data.bioregionalassessments.gov.au/dataset/97def8b6-2c88-41cf-b77a-3433dfdc4470.

The dataset includes annual data for average processing time and counts of initial disability claims in which there was a medical determination made. The data is broken out by those cases handled by each Processing Center (PC), the total for all PCs, and total claims processed by the agency for all offices. The cases processed by PC8 are international claims. This dataset provides data for federal fiscal years 2012 on.

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.The Adori-Springbok Aquifer - …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.The Adori-Springbok Aquifer - Thickness and Extent data sets, are part of a set that represents the hydrostratigraphic units of the Great Artesian Basin, which include five major aquifers, four intervening aquitards, and the Cenozoic cover to the GAB. There are five layers in the Adori-Springbok Aquifer map data. A: Formation Extent B: Outcrop extent C: Isopach Raster D: Isopach Contours E: Data Point Locations The datasets have been derived from the lithostratigraphic intercepts in drillhole data from petroleum exploration wells, water bores, and stratigraphic wells. Seismic correlation and assessment of hydrogeological character based on electrofacies have not been used. The working datasest for this study has been derived primarily from the following databases: PEPS-SA (Petroleum Exploration and Production System - South Australia) (Department of Primary Industries and Regions SA, 2011) WaterConnect Groundwater database (Govt. of SA, 2011) QPED (Queensland Petroleum exploration database) (Geological Survey of Queensland, 2010). GABLOG (Great Artesian Basin Well Log Dataset) (Habermehl, 2001) Additional supplementary information was derived from published reports listed in the following section. This is a regional interpretation for mapping at approximately 1:1 000 000 to produce a broad scale overview, and examination of small areas by collecting extra data is most likely to produce results that differ from this regional interpretation. This dataset and associated metadata can be obtained from www.ga.gov.au, using catalogue number 81680. Associated report reference: Ransley, T., Radke, B., Feitz, A., Kellett, J., Owens, R., Bell, J. and Stewart, G., 2014. Hydrogeological Atlas the Great Artesian Basin. Geoscience Australia. Canberra. [available from www.ga.gov.au using catalogue number 79790] References - main data sources · Department of Primary Industries and Regions SA (2011). Petroleum Exploration and Production System - South Australia (PEPS-SA). Version 2011-06-15. Retrieved from http://www.pir.sa.gov.au/petroleum/access_to_data/peps-sa_database · Geological Survey of Queensland (2010). Queensland Petroleum Exploration Data (QPED) database. Retrieved 25 September 2011, from http://mines.industry.qld.gov.au/geoscience/geoscience-wireline-log-data.htm. · Govt. of South Australia (2011). WaterConnect Groundwater database [available at https://www.waterconnect.sa.gov.au]. · Geoscience Australia, 2013. Mesozoic Geology of the Carpentaria and Laura Basins. Scale 1:6000000. Geoscience Australia, Canberra. [available from www.ga.gov.au using catalogue number 75840] · Habermehl, M. A. (2001). Wire-line logged water bores in the Great Artesian Basin, Australia - digital data of logs and water bore data acquired by AGSO. Australian Geological Survey Organisation Bulletin 245. Canberra, Bureau of Rural Sciences: ix, 98 p. Dataset History SOURCE DATA: Data was obtained from a variety of sources, as listed below: WaterConnect Groundwater database (Govt. of SA, 2011) Great Artesian Basin Well Log Dataset (GABLOG) (Habermehl, M. A., 2001). Petroleum Exploration and Production System - South Australia (PEPS-SA) (Department of Primary Industries and Regions SA, 2011). Queensland Petroleum Exploration Database (QPED) (Geological Survey of Queensland, 2010). Well completion and drill log reports (see references in abstract) Other reports (see references in abstract) Seismic surveys and associated reports (see seismic references section in abstract) METHOD: Formation Extent Extents were based on drillhole data from GABLOG (Habermehl, M. A., 2001), PEPS-SA (Department of Primary Industries and Regions SA, 2011), QPED (Geological Survey of Queensland, 2010) and WaterConnect Groundwater database (Govt. of SA, 2011). Extent lines were adjusted to envelop all intercepts of the Hydrostratigraphic unit. This produced some varied and irregular shapes, some patchy regions, and required some interpretation to establish the most likely extent boundary. Outcrop Extent Outcrop extents came from 'Hydrogeology of the Great Artesian Basin Australia' (Habermehl & Lau, 1997) for the Eromanga and Surat sub-basins. For the Carpentaria Basin, Mesozoic Geology of the Carpentaria and Laura Basins (Geoscience Australia, 2013) was used. Isopach Raster Source point thickness values calculated from drillhole intercepts were extrapolated using the ESRI ANUDEM Topo-To-Raster surface modeller. Zero thickness constraints were applied at the known extent of the aquifer/aquitard, except in cases where the formation extends beyond the GAB boundary (for example the Precipice formation on the eastern side of the GAB, where the formation is quite thick and is exposed as a cliff). In these cases, constraints were not applied and the software was allowed to model a thickness right up to the GAB boundary. Resulting grids were modified using the ESRI Grid Calculator to set the minimum thickness to 0, and clipped to the aquifer/aquitard extent. Isopach Contour Isopach contours were calculated from the Adori-Springbok aquifer thickness grid using the ESRI Contour Tool. These were calculated at 50m intervals. In most cases the zero contour lines generated by the tool were replaced by the extent of the aquifer due to the erratic nature of the generated lines. In cases where the aquifer/aquitard is thick at the extent, the zero isoline is outside the extent and is not mapped in that area. Isopachs were clipped to the aquifer/aquitard extent. Data Point Locations Data Point Locations have been derived from the bore hole data collected for this project. Only the location has been included. SOFTWARE: All modifications/edits and geoprocessing were performed using ESRI ArcGIS 10 software. QAQC: Data sets were searched for errors such as negative thickness, missing data, incorrectly calculated thickness, aquifers/aquitards with missing formations, and false XY data. The data was given a second Q&A after the thickness grids had been calculated. This involve plotting the points and the thickness grid and looking carefully for bad values. Sometimes a false outlier value would cause a 'bullseye' effect on the grid. To check the veracity, nearby data would be compared, and if necessary the original data would be searched check the value. Some petroleum fields would have wildcat picks at certain bore holes and these were compared with nearby boreholes and adjusted or deleted. Additionally, if whole subregions had suspect values the data was check to ensure the relevant data had all been included. Finally, data sets were also checked to ensure the bore whole data recorded the full thickness of the Aquifer. In many cases water bores only go down until a suitable water source is found and often will not penetrate the whole aquifer. This data was considered on a case by case basis, in areas where plenty of suitable data was available they were removed, and in areas of sparse borehole data they were included to establish the occurrence of the formation albeit as a minimum thickness value. Data has undergone a QAQC verification process in order to capture and repair attribute and geometric errors. See "Metadata.pdf" for complete metadata Dataset Citation Geoscience Australia (2015) GABATLAS - Adori-Springbok Aquifer - Thickness and Extent. Bioregional Assessment Source Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/6df0da09-5e9f-4656-b2f8-b87e5dbfde92.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

The Birkhead-Walloon Aquitard - Thickness and Extent data sets, are part of a set that represents the hydrostratigraphic units of the Great Artesian Basin, which include five major aquifers, four intervening aquitards, and the Cenozoic cover to the GAB.

There are five layers in the Birkhead-Walloon Aquitard map data.

A: Formation Extent

B: Outcrop extent

C: Isopach Raster

D: Isopach Contours

E: Data Point Locations

The datasets have been derived from the lithostratigraphic intercepts in drillhole data from petroleum exploration wells, water bores, and stratigraphic wells. Seismic correlation and assessment of hydrogeological character based on electrofacies have not been used. The working datasets for this study has been derived primarily from the following databases:

1.\tPEPS-SA (Petroleum Exploration and Production System - South Australia) (Department of Primary Industries and Regions SA, 2011)

2.\tWaterConnect Groundwater database (Govt. of SA, 2011)

3.\tQPED (Queensland Petroleum exploration database) (Geological Survey of Queensland, 2010).

4.\tGABLOG (Great Artesian Basin Well Log Dataset) (Habermehl, 2001)

5.\tAdditional supplementary information was derived from published reports listed in the following section.

This is a regional interpretation for mapping at approximately 1:1 000 000 to produce a broad scale overview, and examination of small areas by collecting extra data is most likely to produce results that differ from this regional interpretation.

This dataset and associated metadata can be obtained from www.ga.gov.au, using catalogue number 81681.

\t

Associated report reference:

Ransley, T., Radke, B., Feitz, A., Kellett, J., Owens, R., Bell, J. and Stewart, G., 2014. Hydrogeological Atlas the Great Artesian Basin. Geoscience Australia. Canberra. \[available from www.ga.gov.au using catalogue number 79790\]

REFERENCES:

References - main data sources

\*\tDepartment of Primary Industries and Regions SA (2011). Petroleum Exploration and Production System - South Australia (PEPS-SA). Version 2011-06-15. Retrieved from http://www.pir.sa.gov.au/petroleum/access_to_data/peps-sa_database

\*\tGeological Survey of Queensland (2010). Queensland Petroleum Exploration Data (QPED) database. Retrieved 25 September 2011, from http://mines.industry.qld.gov.au/geoscience/geoscience-wireline-log-data.htm.

\*\tGeoscience Australia, 2013. Mesozoic Geology of the Carpentaria and Laura Basins (dataset). Scale 1:6000000. Geoscience Australia, Canberra. \[available from www.ga.gov.au using catalogue number 75840\]

\*\tGovt. of South Australia (2011). WaterConnect Groundwater database \[available at https://www.waterconnect.sa.gov.au\].

\*\tHabermehl, M. A. and J. E. Lau (1997). Hydrogeology of the Great Artesian Basin Australia (Map at scale 1:2,500,000). Canberra, Australian Geological Survey Organisation.

\*\tO'Brien, P. E. (2011). The eastern edge of the Great Artesian Basin: relationships between the Surat and Clarence-Moreton basins. Internal report. Canberra, Geoscience Australia.

\*\tWells, A.T. , O'Brien, P.E. 1994 Lithostratigraphic framework of the Clarence-Moreton Basin IN Wells, A.T. and O'Brien, P.E. (eds.) "Geology and Petroleum Potential of the Clarence-Moreton Basin, New South Wales and Queensland" Australian Geological Survey Organisation. Bulletin 241 p4-47

Dataset History

SOURCE DATA:

Data was obtained from a variety of sources, as listed below:

\*\tWaterConnect Groundwater database (Govt. of SA, 2011)

\*\tGreat Artesian Basin Well Log Dataset (GABLOG) (Habermehl, M. A., 2001).

\*\tPetroleum Exploration and Production System - South Australia (PEPS-SA) (Department of Primary Industries and Regions SA, 2011).

\*\tQueensland Petroleum Exploration Database (QPED) (Geological Survey of Queensland, 2010).

\*\tWell completion and drill log reports (see references in abstract)

\*\tOther reports (see references in abstract)

\*\tSeismic surveys and associated reports (see seismic references section in abstract)

METHOD:

Formation Extent

The extent was based on 1:500k mapping of Wells & O'Brien (1994) and drillhole data (see References for main data sources).

Extent lines were adjusted to envelop all borehole intercepts of the Hydrostratigraphic unit. This produced some varied and irregular shapes, some patchy regions, and required some interpretation to establish the most likely extent boundary.

Outcrop Extent

Outcrop extents were taken from Hydrogeology of the Great Artesian Basin Australia (Habermehl & Lau, 1997) for the Eromanga and Surat sub-basins. For the Carpentaria Basin, Mesozoic Geology of the Carpentaria and Laura Basins (Geoscience Australia, 2013) was used.

Isopach Raster

Drillhole intercepts in Clarence-Moreton from O'Brien (2011) were used to calculate isopach values by using the depth to top and bottom values of formations within the drillhole database attributes, and adding them together to form the isopach values for each data point across the whole aquifer/aquitard. These values were extrapolated using the ESRI ANUDEM Topo-To-Raster surface modeller tool to generate the isopach raster grid (thickness). Zero thickness constraints were applied at the known extent of the aquifer/aquitard, except in cases where the formation extends beyond the GAB boundary (for example the Precipice formation on the eastern side of the GAB, where the formation is quite thick and is exposed as a cliff). In these cases, constraints were not applied and the software was allowed to model a thickness right up to the GAB boundary. Resulting grids were modified using the ESRI Grid Calculator to set the minimum thickness to 0, and clipped to the aquifer/aquitard extent.

Isopach Contours

Isopach contours were calculated from the Birkhead Walloon Aquitard thickness grid (generated from drillhole intercepts in Clarence-Moreton from O'Brien (2011)) using the ESRI Contour Tool. These were calculated at 50m intervals. In most cases the zero contour lines generated by the tool were replaced by the extent of the aquifer due to the erratic nature of the generated lines. In cases where the aquifer/aquitard is thick at the extent, the zero isoline is outside the extent and is not mapped in that area. Isopachs were clipped to the aquifer/aquitard extent.

Data Point Locations

Data Point Locations have been derived from the bore hole data collected for this project. Only the location has been included.

SOFTWARE:

All modifications/edits and geoprocessing were performed using ESRI ArcGIS 10 software.

QAQC:

Data sets were searched for errors such as negative thickness, missing data, incorrectly calculated thickness, aquifers/aquitards with missing formations, and false XY data.

The data was given a second Q&A after the thickness grids had been calculated. This involve plotting the points and the thickness grid and looking carefully for bad values. Sometimes a false outlier value would cause a 'bullseye' effect on the grid. To check the veracity, nearby data would be compared, and if necessary the original data would be searched check the value. Some petroleum fields would have wildcat picks at certain bore holes and these were compared with nearby boreholes and adjusted or deleted.

Additionally, if whole subregions had suspect values the data was check to ensure the relevant data had all been included. Finally, data sets were also checked to ensure the bore whole data recorded the full thickness of the Aquifer. In many cases water bores only go down until a suitable water source is found and often will not penetrate the whole aquifer. This data was considered on a case by case basis, in areas where plenty of suitable data was available they were removed, and in areas of sparse borehole data they were included to establish the occurrence of the formation albeit as a minimum thickness value.

Data has undergone a QAQC verification process in order to capture and repair attribute and geometric errors.

Dataset Citation

Geoscience Australia (2015) GABATLAS - Birkhead-Walloon Aquitard - Thickness and Extent. Bioregional Assessment Source Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/a5912292-10cd-42e2-aefe-49aae2eead4b.

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset contains two EXCEL spreadsheets: a spreadsheet showing the location (easting and northing) of groundwater bores in the Logan-Albert river basin, the elevation of the bore …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset contains two EXCEL spreadsheets: a spreadsheet showing the location (easting and northing) of groundwater bores in the Logan-Albert river basin, the elevation of the bore location and the depth of the bore a shreadsheet that contains the cleaned stratigraphic data of groundwater bores (in many cases converted from lithological logs in the QLD DNRM groundwater database). Dataset History This dataset contains two EXCEL spreadsheets: a spreadsheet showing the location (easting and northing) of groundwater bores in the Logan-Albert river basins, the elevation of the bore location and the depth of the bore a shreadsheet that contains the cleaned stratigraphic data of groundwater bores (in many cases converted from lithological logs in the QLD DNRM groundwater database). The following steps were involved in deriving the stratigraphic logs of the groundwater bores: Ensure consistent use of terminology and spelling. Identification of geological inconsistencies or geological errors Verification of bore elevation data. Simplification of lithological logs. Conversion of lithological logs into stratigraphic logs. The procedure is described in detail in Clarence-Moreton bioregional assessment product 2.1 (Raiber et al., 2016). Dataset Citation Bioregional Assessment Programme (XXXX) Logan-Albert GW bores stratigraphy. Bioregional Assessment Derived Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/5ba6ab69-8442-4697-863e-e02ac4b049d0. Dataset Ancestors Derived From QLD Department of Natural Resources and Mines Groundwater Database Extract 20142808