This layer shows computer ownership and type of internet subscription. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of households with no internet connection. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B28001, B28002 (Not all lines of ACS table B28002 are available in this feature layer)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

ABSTRACT This is a preliminary discussion that seeks to highlight the meanings of flavors and foods to create relationships between mankind and nature, understanding that cultural reasons attribute meanings to the flavors of foods. Anthropological references are the theoretical basis for a discussion of the possibilities of the existence of an alimentary identity. Furthermore, food is already one aspect of identity. In addition we discuss what conditions the use of nature and the rejection of the consumption of certain products from nature as food. Our concern is to present the characteristics of a geography of food and flavors. The research included the work of various authors from the fields of Geography, History, Anthropology and Social Sciences.

This feature class describes properties listed on the National Register of Historic Places, classified as historic buildings, object, sites, structures, and districts, and depicted as points. The National Register of Historic Places requires the submission of a single UTM coordinate pair for properties under 10 acres. Attribute data in this dataset are intentionally limited to those necessary for spatial data maintenance and feature level metadata necessary to document the lineage of the geography itself. Data from external database systems, such as the National Register Information System, are intended to link with these data to provide basic feature attributes. The means to maintain unique identifiers for each historic site (CR_ID), Survey_ID, as well as unique geometries associated with that feature (Geometry_ID) are through the use of Globally Unique Identifiers (GUIDs) assigned by the database. Information about the genesis of individual points is documented by feature level metadata fields in the spatial attribute table.

Abstract This dataset maps the coastal landform types (geomorphology) of continental Australia and most adjacent islands (excluding the Great Barrier Reef). The dataset was compiled primarily using existing spatial data sets provided by a number of Commonwealth, State, Territory and other agencies from across Australia. The Smartline is a line map that represents the coastline (typically the High Water Mark), and is split into segments wherever any of the coastal landform attributes change. Each individual coastline segment has a series of attributes which describe the landform types of that segment of the coast. The coastal characteristics recorded refer not only to those at the precise location of the coastline itself, but to a coastal area nominally extending up 500m inland and offshore of the coastline itself. Where such information is available, these attributes describe the landforms in the subtidal, intertidal and backshore zones; the backshore profile and intertidal zone slope; the shoreline segment exposure; and the geological substrate. The dataset also contains attribute-level metadata: for each geomorphic attribute in each coastline segment, there are two additional attributes that identify the source dataset from which the attributes were derived, and defining the scale of the source dataset. Finally, there are a series of attributes which classify each coastline segment according to a shoreline landform stability classification scheme.

Currency Date Modified: 06 May 2019 Modification Frequency: As needed

Data Extent Coordinate reference: GDA94 / Australian Albers Spatial Extent North: -9°South: -44°East: 154°West: 112°

Source informationeCAT record: https://pid.geoscience.gov.au/service/ga/104560Geopackage download: https://pid.geoscience.gov.au/dataset/ga/104160Geodatabase download: https://pid.geoscience.gov.au/dataset/ga/104100Sharples, C., & Mount, R., 2009: The Australian Coastal Smartline Geomorphic and Stability Map Version 1: Manual and Data Dictionary; School of Geography & Environmental Studies, University of Tasmania. https://d28rz98at9flks.cloudfront.net/104100/104100_02_0.pdf Sharples, C., Mount, R., Pedersen, T., Lacey, M., Newton, J., Jaskierniak, D., & Wallace, L., 2009: The Australian Coastal Smartline Geomorphic and Stability Map Version 1: Project Report; School of Geography & Environmental Studies, University of Tasmania. https://d28rz98at9flks.cloudfront.net/83568/SmartlineProjectReport_2009_v1.pdfLineage statementThis dataset was derived from a total of 240 individual datasets. A complete list of these source datasets with additional metadata is available as a table in the Manual and Data Dictionary report and the Project report. Because of the wide variety of source datasets, a variety of different methods were used to extract and reclassify the source data. A generalised workflow is described below. Most of these steps were carried out using existing or newly-developed tools in ArcGIS 9.3:

For each coastal state, the best available coastline dataset was identified and obtained for use as a Smartline base map. Attributes from each source dataset were transferred to a copy of the Smartline base map for the relevant state. For aspatial and non-georeferenced source datasets, the transfer process was manual; for georeferenced datasets, the transfer process was automated as much as possible. The transferred attributes were reclassified and inserted into the appropriate Smartline field or fields using transfer tables and the Smartline Classification Scheme. The individual Smartline base maps containing attribute data from different source datasets were merged into a single state Smartline. Where conflicting or concordant attribute data were present for a single Smartline field, a resolution process was followed to choose a single attribute from those available. As a general rule, priority was given to the source datasets that contained the most relevant and detailed attribute information; if the available source datasets contained similar attribute information, preference was given to the source dataset with the best spatial resolution. A series of logical checks were carried out to ensure consistency between attributes in related Smartline attribute fields. Coastal features less than 10m long were merged with the most similar adjacent feature (based on attributes) where possible. Some features which were less than 10m long (generally very small islands) could not be merged because there were no adjacent features to merge with. A topology was developed and used to ensure that the Smartline geometry was identical to the original Smartline base map, and that the Smartline did not have any self-overlapping segments. All topological errors were repaired. Incorrectly spelled attribute data were detected and repaired using tables exported from an Access database containing the complete Smartline Classification Scheme. The Smartline Stability fields were populated using queries based on particular combinations of geomorphic attributes.For further details of the data compilation, processing, editing and verification processes, please refer to the Smartline Manual and Data Dictionary Report, and the Smartline Project Report. Data dictionary

Attribute name Description

OBJECTIDUnique IDBASELINEReference ID for source of base line mapBASEMAPSCALEScale of base mapBASEFEATURECoastal feature upon which base line map is basedAUSCOASTFIDUnique Australian coastal segment identifier number (v.1.0)UPDATEDDate of data currency or last updateABSAMP_IDBeach numberINTSLOPE_NClass code of Intertidal zone slope (Slope of the intertidal zone, defined as the average broadly-categorised slope between the high and low water tide lines)INTSLOPE_VClass name of Intertidal zone slope (Slope of the intertidal zone, defined as the average broadly-categorised slope between the high and low water tide lines)INTSLOPE_RData source (reference) ID of Intertidal zone slope (Slope of the intertidal zone, defined as the average broadly-categorised slope between the high and low water tide lines)INTSLOPE_SSource data scale of Intertidal zone slope (Slope of the intertidal zone, defined as the average broadly-categorised slope between the high and low water tide lines)COMMENTSGeneral notes and commentsContactOceans, Reefs, Coasts and the Antarctic (ORCA), Geoscience Australia. clientservices@ga.gov.au

This layer shows housing occupancy, tenure, and median rent/housing value. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Homeownership rate on Census Bureau's website is owner-occupied housing unit rate (called B25003_calc_pctOwnE in this layer). This layer is symbolized by the overall homeownership rate. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25002, B25003, B25058, B25077, B25057, B25059, B25076, B25078Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission to represent the United States Census Bureau's 2010 Decennial Census data at the block group geography.Attributes:STATEFP10 = The Federal Information Processing Series (FIPS) state code. FIPS codes were formerly known as Federal Information Processing Standards codes, until the National Institute of Standards and Technology (NIST) announced its decision in 2005 to remove geographic entity codes from its oversight. The Census Bureau continues to maintain and issue codes for geographic entities covered under FIPS oversight, albeit with a revised meaning for the FIPS acronym. Geographic entities covered under FIPS include states, counties, congressional districts, core based statistical areas, places, county subdivisions, subminor civil divisions, consolidated cities, and all types of American Indian, Alaska Native, and Native Hawaiian areas. FIPS codes are assigned alphabetically according to the name of the geographic entity and may change to maintain alphabetic sort when new entities are created or names change. FIPS codes for specific geographic entity types are usually unique within the next highest level of geographic entity with which a nesting relationship exists. For example, FIPS state, congressional district, and core based statistical area codes are unique within nation; FIPS county, place, county subdivision, and subminor civil division codes are unique within state. The codes for American Indian, Alaska Native, and Native Hawaiian areas also are unique within state; those areas in multiple states will have different codes for each state.COUNTYFP10 = The Federal Information Processing Series (FIPS) county code. TRACTCE10 = Census Tract Codes and Numbers—Census tracts are identified by an up to four-digit integer number and may have an optional two-digit suffix; for example 1457.02 or 23. The census tract codes consist of six digits with an implied decimal between the fourth and fifth digit corresponding to the basic census tract number but with leading zeroes and trailing zeroes for census tracts without a suffix. The tract number examples above would have codes of 145702 and 002300, respectively.BLKGRPCE10 = Block Group Codes—Block Groups have a valid code range of 0 through 9. Block Groups beginning with a zero only contain water area and are generally in coastal and Great Lakes water and territorial seas, but also in larger inland water bodies.GEOID10 = A concatenation of STATEFP10, COUNTYFP10, TRACTCE10, and BLKGRPCE10, which produces the entire FIPS code for this geography.NAMELSAD10 = Block Group nameMTFCC10 = MAF/TIGER feature class codeFUNCSTAT10 = Functional statusALAND10 = Land area in square metersAWATER10 = Water area in square metersINTPTLAT10 = Latitude of the centroid (center of this geography)INTPLON10 = Longitude of the centroid (center of this geography)STFID = The entire FIPS code of this geographyCOUNTY_NM = County namePLNG_REGIO = Planning regionSUMLEV = Summary level of census geography (code)STATE = The state FIPS codeCOUNTY = The county FIPS codeTRACT = The tract FIPS codeBLKGRP = The block group FIPS codetotpop10 = Total populationoner_10 = One race populationwhite_or10 = White, one race populationbl_or10 = Black, one race populationaian_or10 = American Indian and Alaska Native, one race populationasia_or10 = Asian, one race populationnhpi_or10 = Native Hawaiian and Other Pacific Islander, one race populationsomoth_or1 = Some other, one race populationtwoplusr10 = Two-plus races populationtotpop101 = Total populationhisp_lat10 = Total Hispanic/Latino populationnonhisp10 = Total non-Hispanic/Latino populationnh_or10 = Non-Hispanic/Latino, one race populationnhw_or10 = Non-Hispanic/Latino White, one race populationnhbl_or10 = Non-Hispanic/Latino Black, one race populationnhai_or10 = Non-Hispanic/Latino American Indian and Alaskan Native, one race populationnhas_or10 = Non-Hispanic/Latino Asian, one race populationnhhp_or10 = Non-Hispanic/Latino Native Hawaiian and Other Pacific Islander, one race populationnhot_or10 = Non-Hispanic/Latino Other, one race populationnh_twor10 = Non-Hispanic/Latino, two or more races populationtothu10 = Total housing unitstotoccu_10 = Total occupied housing unitstotvach_10 = Total vacant housing unitsAcresLand = Land area in acresAcresWater = Water area in acresAcresTotal = Total area in acresSq_Miles = Total area in square milesShape.STArea() = Total area in square feetSource: U.S. Census Bureau, Atlanta Regional CommissionDate: 2010For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com

Abstract This dataset maps the coastal landform types (geomorphology) of continental Australia and most adjacent islands (excluding the Great Barrier Reef). The dataset was compiled primarily using existing spatial data sets provided by a number of Commonwealth, State, Territory and other agencies from across Australia. The Smartline is a line map that represents the coastline (typically the High Water Mark), and is split into segments wherever any of the coastal landform attributes change. Each individual coastline segment has a series of attributes which describe the landform types of that segment of the coast. The coastal characteristics recorded refer not only to those at the precise location of the coastline itself, but to a coastal area nominally extending up 500m inland and offshore of the coastline itself. Where such information is available, these attributes describe the landforms in the subtidal, intertidal and backshore zones; the backshore profile and intertidal zone slope; the shoreline segment exposure; and the geological substrate. The dataset also contains attribute-level metadata: for each geomorphic attribute in each coastline segment, there are two additional attributes that identify the source dataset from which the attributes were derived, and defining the scale of the source dataset. Finally, there are a series of attributes which classify each coastline segment according to a shoreline landform stability classification scheme.

Currency Date Modified: 06 May 2019 Modification Frequency: As needed

Data Extent Coordinate reference: GDA94 / Australian Albers Spatial Extent North: -9°South: -44°East: 154°West: 112°

Source informationeCAT record: https://pid.geoscience.gov.au/service/ga/104560Geopackage download: https://pid.geoscience.gov.au/dataset/ga/104160Geodatabase download: https://pid.geoscience.gov.au/dataset/ga/104100Sharples, C., & Mount, R., 2009: The Australian Coastal Smartline Geomorphic and Stability Map Version 1: Manual and Data Dictionary; School of Geography & Environmental Studies, University of Tasmania. https://d28rz98at9flks.cloudfront.net/104100/104100_02_0.pdf Sharples, C., Mount, R., Pedersen, T., Lacey, M., Newton, J., Jaskierniak, D., & Wallace, L., 2009: The Australian Coastal Smartline Geomorphic and Stability Map Version 1: Project Report; School of Geography & Environmental Studies, University of Tasmania. https://d28rz98at9flks.cloudfront.net/83568/SmartlineProjectReport_2009_v1.pdfLineage statementThis dataset was derived from a total of 240 individual datasets. A complete list of these source datasets with additional metadata is available as a table in the Manual and Data Dictionary report and the Project report. Because of the wide variety of source datasets, a variety of different methods were used to extract and reclassify the source data. A generalised workflow is described below. Most of these steps were carried out using existing or newly-developed tools in ArcGIS 9.3:

For each coastal state, the best available coastline dataset was identified and obtained for use as a Smartline base map. Attributes from each source dataset were transferred to a copy of the Smartline base map for the relevant state. For aspatial and non-georeferenced source datasets, the transfer process was manual; for georeferenced datasets, the transfer process was automated as much as possible. The transferred attributes were reclassified and inserted into the appropriate Smartline field or fields using transfer tables and the Smartline Classification Scheme. The individual Smartline base maps containing attribute data from different source datasets were merged into a single state Smartline. Where conflicting or concordant attribute data were present for a single Smartline field, a resolution process was followed to choose a single attribute from those available. As a general rule, priority was given to the source datasets that contained the most relevant and detailed attribute information; if the available source datasets contained similar attribute information, preference was given to the source dataset with the best spatial resolution. A series of logical checks were carried out to ensure consistency between attributes in related Smartline attribute fields. Coastal features less than 10m long were merged with the most similar adjacent feature (based on attributes) where possible. Some features which were less than 10m long (generally very small islands) could not be merged because there were no adjacent features to merge with. A topology was developed and used to ensure that the Smartline geometry was identical to the original Smartline base map, and that the Smartline did not have any self-overlapping segments. All topological errors were repaired. Incorrectly spelled attribute data were detected and repaired using tables exported from an Access database containing the complete Smartline Classification Scheme. The Smartline Stability fields were populated using queries based on particular combinations of geomorphic attributes.For further details of the data compilation, processing, editing and verification processes, please refer to the Smartline Manual and Data Dictionary Report, and the Smartline Project Report. Data dictionary

Attribute name Description

OBJECTIDUnique IDBASELINEReference ID for source of base line mapBASEMAPSCALEScale of base mapBASEFEATURECoastal feature upon which base line map is basedAUSCOASTFIDUnique Australian coastal segment identifier number (v.1.0)UPDATEDDate of data currency or last updateABSAMP_IDBeach numberUNDFROCK_NClass code of Undifferentiated rock shores (dominated by bedrock landforms in the intertidal and backshore zones, where the bedrock type (hardness) is unknown)UNDFROCK_VClass name of Undifferentiated rock shores (dominated by bedrock landforms in the intertidal and backshore zones, where the bedrock type (hardness) is unknown)UNDFROCK_LClass short name of Undifferentiated rock shores (dominated by bedrock landforms in the intertidal and backshore zones, where the bedrock type (hardness) is unknown)COMMENTSGeneral notes and commentsContactOceans, Reefs, Coasts and the Antarctic (ORCA), Geoscience Australia. clientservices@ga.gov.au

Abstract This dataset maps the coastal landform types (geomorphology) of continental Australia and most adjacent islands (excluding the Great Barrier Reef). The dataset was compiled primarily using existing spatial data sets provided by a number of Commonwealth, State, Territory and other agencies from across Australia. The Smartline is a line map that represents the coastline (typically the High Water Mark), and is split into segments wherever any of the coastal landform attributes change. Each individual coastline segment has a series of attributes which describe the landform types of that segment of the coast. The coastal characteristics recorded refer not only to those at the precise location of the coastline itself, but to a coastal area nominally extending up 500m inland and offshore of the coastline itself. Where such information is available, these attributes describe the landforms in the subtidal, intertidal and backshore zones; the backshore profile and intertidal zone slope; the shoreline segment exposure; and the geological substrate. The dataset also contains attribute-level metadata: for each geomorphic attribute in each coastline segment, there are two additional attributes that identify the source dataset from which the attributes were derived, and defining the scale of the source dataset. Finally, there are a series of attributes which classify each coastline segment according to a shoreline landform stability classification scheme.

Currency Date Modified: 06 May 2019 Modification Frequency: As needed

Data Extent Coordinate reference: GDA94 / Australian Albers Spatial Extent North: -9°South: -44°East: 154°West: 112°

Source informationeCAT record: https://pid.geoscience.gov.au/service/ga/104560Geopackage download: https://pid.geoscience.gov.au/dataset/ga/104160Geodatabase download: https://pid.geoscience.gov.au/dataset/ga/104100Sharples, C., & Mount, R., 2009: The Australian Coastal Smartline Geomorphic and Stability Map Version 1: Manual and Data Dictionary; School of Geography & Environmental Studies, University of Tasmania. https://d28rz98at9flks.cloudfront.net/104100/104100_02_0.pdf Sharples, C., Mount, R., Pedersen, T., Lacey, M., Newton, J., Jaskierniak, D., & Wallace, L., 2009: The Australian Coastal Smartline Geomorphic and Stability Map Version 1: Project Report; School of Geography & Environmental Studies, University of Tasmania. https://d28rz98at9flks.cloudfront.net/83568/SmartlineProjectReport_2009_v1.pdfLineage statementThis dataset was derived from a total of 240 individual datasets. A complete list of these source datasets with additional metadata is available as a table in the Manual and Data Dictionary report and the Project report. Because of the wide variety of source datasets, a variety of different methods were used to extract and reclassify the source data. A generalised workflow is described below. Most of these steps were carried out using existing or newly-developed tools in ArcGIS 9.3:

For each coastal state, the best available coastline dataset was identified and obtained for use as a Smartline base map. Attributes from each source dataset were transferred to a copy of the Smartline base map for the relevant state. For aspatial and non-georeferenced source datasets, the transfer process was manual; for georeferenced datasets, the transfer process was automated as much as possible. The transferred attributes were reclassified and inserted into the appropriate Smartline field or fields using transfer tables and the Smartline Classification Scheme. The individual Smartline base maps containing attribute data from different source datasets were merged into a single state Smartline. Where conflicting or concordant attribute data were present for a single Smartline field, a resolution process was followed to choose a single attribute from those available. As a general rule, priority was given to the source datasets that contained the most relevant and detailed attribute information; if the available source datasets contained similar attribute information, preference was given to the source dataset with the best spatial resolution. A series of logical checks were carried out to ensure consistency between attributes in related Smartline attribute fields. Coastal features less than 10m long were merged with the most similar adjacent feature (based on attributes) where possible. Some features which were less than 10m long (generally very small islands) could not be merged because there were no adjacent features to merge with. A topology was developed and used to ensure that the Smartline geometry was identical to the original Smartline base map, and that the Smartline did not have any self-overlapping segments. All topological errors were repaired. Incorrectly spelled attribute data were detected and repaired using tables exported from an Access database containing the complete Smartline Classification Scheme. The Smartline Stability fields were populated using queries based on particular combinations of geomorphic attributes.For further details of the data compilation, processing, editing and verification processes, please refer to the Smartline Manual and Data Dictionary Report, and the Smartline Project Report. Data dictionary

Attribute name Description

OBJECTIDUnique IDBASELINEReference ID for source of base line mapBASEMAPSCALEScale of base mapBASEFEATURECoastal feature upon which base line map is basedAUSCOASTFIDUnique Australian coastal segment identifier number (v.1.0)UPDATEDDate of data currency or last updateABSAMP_IDBeach numberCORAL_NClass code of Coral Coasts (characterised by hard actively growing biogenic carbonate structures (coralgal reefs) and associated derived sediments)CORAL_VClass name of Coral Coasts (characterised by hard actively growing biogenic carbonate structures (coralgal reefs) and associated derived sediments)CORAL_LClass short name of Coral Coasts (characterised by hard actively growing biogenic carbonate structures (coralgal reefs) and associated derived sediments)COMMENTSGeneral notes and commentsContactOceans, Reefs, Coasts and the Antarctic (ORCA), Geoscience Australia. clientservices@ga.gov.au

Abstract This dataset maps the coastal landform types (geomorphology) of continental Australia and most adjacent islands (excluding the Great Barrier Reef). The dataset was compiled primarily using existing spatial data sets provided by a number of Commonwealth, State, Territory and other agencies from across Australia. The Smartline is a line map that represents the coastline (typically the High Water Mark), and is split into segments wherever any of the coastal landform attributes change. Each individual coastline segment has a series of attributes which describe the landform types of that segment of the coast. The coastal characteristics recorded refer not only to those at the precise location of the coastline itself, but to a coastal area nominally extending up 500m inland and offshore of the coastline itself. Where such information is available, these attributes describe the landforms in the subtidal, intertidal and backshore zones; the backshore profile and intertidal zone slope; the shoreline segment exposure; and the geological substrate. The dataset also contains attribute-level metadata: for each geomorphic attribute in each coastline segment, there are two additional attributes that identify the source dataset from which the attributes were derived, and defining the scale of the source dataset. Finally, there are a series of attributes which classify each coastline segment according to a shoreline landform stability classification scheme.

Currency Date Modified: 06 May 2019 Modification Frequency: As needed

Data Extent Coordinate reference: GDA94 / Australian Albers Spatial Extent North: -9°South: -44°East: 154°West: 112°

Source informationeCAT record: https://pid.geoscience.gov.au/service/ga/104560Geopackage download: https://pid.geoscience.gov.au/dataset/ga/104160Geodatabase download: https://pid.geoscience.gov.au/dataset/ga/104100Sharples, C., & Mount, R., 2009: The Australian Coastal Smartline Geomorphic and Stability Map Version 1: Manual and Data Dictionary; School of Geography & Environmental Studies, University of Tasmania. https://d28rz98at9flks.cloudfront.net/104100/104100_02_0.pdf Sharples, C., Mount, R., Pedersen, T., Lacey, M., Newton, J., Jaskierniak, D., & Wallace, L., 2009: The Australian Coastal Smartline Geomorphic and Stability Map Version 1: Project Report; School of Geography & Environmental Studies, University of Tasmania. https://d28rz98at9flks.cloudfront.net/83568/SmartlineProjectReport_2009_v1.pdfLineage statementThis dataset was derived from a total of 240 individual datasets. A complete list of these source datasets with additional metadata is available as a table in the Manual and Data Dictionary report and the Project report. Because of the wide variety of source datasets, a variety of different methods were used to extract and reclassify the source data. A generalised workflow is described below. Most of these steps were carried out using existing or newly-developed tools in ArcGIS 9.3:

For each coastal state, the best available coastline dataset was identified and obtained for use as a Smartline base map. Attributes from each source dataset were transferred to a copy of the Smartline base map for the relevant state. For aspatial and non-georeferenced source datasets, the transfer process was manual; for georeferenced datasets, the transfer process was automated as much as possible. The transferred attributes were reclassified and inserted into the appropriate Smartline field or fields using transfer tables and the Smartline Classification Scheme. The individual Smartline base maps containing attribute data from different source datasets were merged into a single state Smartline. Where conflicting or concordant attribute data were present for a single Smartline field, a resolution process was followed to choose a single attribute from those available. As a general rule, priority was given to the source datasets that contained the most relevant and detailed attribute information; if the available source datasets contained similar attribute information, preference was given to the source dataset with the best spatial resolution. A series of logical checks were carried out to ensure consistency between attributes in related Smartline attribute fields. Coastal features less than 10m long were merged with the most similar adjacent feature (based on attributes) where possible. Some features which were less than 10m long (generally very small islands) could not be merged because there were no adjacent features to merge with. A topology was developed and used to ensure that the Smartline geometry was identical to the original Smartline base map, and that the Smartline did not have any self-overlapping segments. All topological errors were repaired. Incorrectly spelled attribute data were detected and repaired using tables exported from an Access database containing the complete Smartline Classification Scheme. The Smartline Stability fields were populated using queries based on particular combinations of geomorphic attributes.For further details of the data compilation, processing, editing and verification processes, please refer to the Smartline Manual and Data Dictionary Report, and the Smartline Project Report. Data dictionary

Attribute name Description

OBJECTIDUnique IDBASELINEReference ID for source of base line mapBASEMAPSCALEScale of base mapBASEFEATURECoastal feature upon which base line map is basedAUSCOASTFIDUnique Australian coastal segment identifier number (v.1.0)UPDATEDDate of data currency or last updateABSAMP_IDBeach numberBACKPROX_NClass code of Backshore proximal landforms (The first notable landform type or assemblage present immediately to landwards of or above the intertidal zone (may include supratidal landforms))BACKPROX_VClass name of Backshore proximal landforms (The first notable landform type or assemblage present immediately to landwards of or above the intertidal zone (may include supratidal landforms))BACKPROX_RData source (reference) ID of Backshore proximal landforms (The first notable landform type or assemblage present immediately to landwards of or above the intertidal zone (may include supratidal landforms))BACKPROX_SSource data scale of Backshore proximal landforms (The first notable landform type or assemblage present immediately to landwards of or above the intertidal zone (may include supratidal landforms))COMMENTSGeneral notes and commentsContactOceans, Reefs, Coasts and the Antarctic (ORCA), Geoscience Australia. clientservices@ga.gov.au

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission to represent the United States Census Bureau's 2000 Decennial Census data at the block geography.Attributes:FIPSSTCO = The Federal Information Processing Series (FIPS) state and county codes. FIPS codes were formerly known as Federal Information Processing Standards codes, until the National Institute of Standards and Technology (NIST) announced its decision in 2005 to remove geographic entity codes from its oversight. The Census Bureau continues to maintain and issue codes for geographic entities covered under FIPS oversight, albeit with a revised meaning for the FIPS acronym. Geographic entities covered under FIPS include states, counties, congressional districts, core based statistical areas, places, county subdivisions, subminor civil divisions, consolidated cities, and all types of American Indian, Alaska Native, and Native Hawaiian areas. FIPS codes are assigned alphabetically according to the name of the geographic entity and may change to maintain alphabetic sort when new entities are created or names change. FIPS codes for specific geographic entity types are usually unique within the next highest level of geographic entity with which a nesting relationship exists. For example, FIPS state, congressional district, and core based statistical area codes are unique within nation; FIPS county, place, county subdivision, and subminor civil division codes are unique within state. The codes for American Indian, Alaska Native, and Native Hawaiian areas also are unique within state; those areas in multiple states will have different codes for each state.TRACT2000 = Census Tract Codes and Numbers. Census tracts are identified by an up to four-digit integer number and may have an optional two-digit suffix; for example 1457.02 or 23. The census tract codes consist of six digits with an implied decimal between the fourth and fifth digit corresponding to the basic census tract number but with leading zeroes and trailing zeroes for census tracts without a suffix. The tract number examples above would have codes of 145702 and 002300, respectively.BLOCK2000= Census Block Numbers are numbered uniquely with a four-digit census block number from 0000 to 9999 within census tract, which nest within state and county. The first digit of the census block number identifies the block group. Block numbers beginning with a zero (in Block Group 0) are only associated with water-only areas.STFID = A concatenation of FIPSSTCO, TRACT2000, and BLOCK2000, which creates the entire FIPS code for this geography.WFD = Workforce Development Area (WFD) is a seven-county area created by agreement of county chief-elected officials, administered by the Atlanta Regional Commission and funded for training and employment activities under the federal Workforce Investment Act (WIA). For more information on ARC’s Workforce Development programs and services please consult www.atlantaregional.com/workforce/workforce.html.RDC_AAA = ARC Area Agency on Aging is a 10-county area funded by the Department of Human Resources and designated by the Older Americans Act to plan for the needs of the rapidly expanding group of older citizens in the Atlanta region. It is part of a statewide network of 12 AAAs and a national network of more than 670 AAAs. For more information on aging services please consult www.agewiseconnection.com.MNGWPD = The Metro North Georgia Water Planning District provides water resource plans, policies and coordination for metropolitan Atlanta. The District has developed regional plans for stormwater management, wastewater treatment and water supply and water conservation. The 15-county Water Planning District includes the ten counties in the ARC plus five additional counties (Bartow, Coweta, Forsyth, Hall, & Paulding). For more information please consult www.northgeorgiawater.org. MPO = The Metropolitan Planning Organization (MPO) is a 19-county area federally-designated for regional transportation planning to meet air quality standards and for programming projects to implement the adopted Regional Transportation Plan (RTP). The MPO planning area boundary includes the 10-county state-designated Regional Commission and nine additional counties (all of Coweta, Forsyth, & Paulding and parts of Barrow, Dawson, Newton, Pike, Spalding and Walton). This boundary takes into consideration both the current urbanized area as well as areas forecast to become urbanized in the next 20 years.MSA = the 29-County “Atlanta-Sandy Springs-Roswell, GA” Metropolitan Statistical Area (MSA) and the 39-county “Atlanta--Athens-Clarke County--Sandy Springs, GA” Combined Statistical Area (CSA), which includes the 29 counties of the Atlanta MSA along with the Athens-Clarke County and Gainesville MSAs and the micropolitan statistical areas of Calhoun, Cedartown, Jefferson, LaGrange and Thomaston, GA. The U.S. Office of Management and Budget (OMB) defines CSAs, MSAs and the smaller micropolitan statistical areas nationwide according to published standards applied to U.S. Census Bureau data. These various statistical areas describe substantial core areas of population together with adjacent communities having a high degree of economic and social integration, often illustrated in high rates of commuting from the adjacent areas to job locations in the core. For more information, please consult http://www.census.gov/population/metro/data/metrodef.htmlF1HR_NA = The Federal 1-Hour Air Quality Non-Attainment Area is a fine particulate matter standard (PM2.5). The non-attainment area under this standard includes the 15-county eight-hour ozone nonattainment area plus Barrow, Carroll, Hall, Spalding, Walton, and small parts of Heard and Putnam counties.F8HR_NA: The Federal 8-Hour Air Quality Non-Attainment Area for the 2008 eight-hour ozone standard is 15 counties.ACRES = The number of acres contained within the Block.SQ_MILES = The number of square miles contained within the Block.Source: United States Census Bureau, Atlanta Regional CommissionDate: 2000For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com

A list of the various 'regional' (zone/subzone/variant/phase) ecological units of the current biogeoclimatic ecosystem classification. At this 'regional' level, vegetation, soils and topography are used to infer the climate and to identify geographic areas that have relatively uniform climate. These geographic areas are termed biogeoclimatic units. The basic biogeoclimatic unit is the Subzone. These units are grouped into Zones and may be further subdivided into variants based on further refinements of climate (e.g., wetter, drier, snowier). The map units of the Biogeoclimatic map are mapped to the highest possible thematic resolution - subzone or variant. In some cases, where further sampling is required to define the unit climatically, polygons are labelled as an undifferentiated unit (e.g. CWH un)

Abstract This dataset maps the coastal landform types (geomorphology) of continental Australia and most adjacent islands (excluding the Great Barrier Reef). The dataset was compiled primarily using existing spatial data sets provided by a number of Commonwealth, State, Territory and other agencies from across Australia. The Smartline is a line map that represents the coastline (typically the High Water Mark), and is split into segments wherever any of the coastal landform attributes change. Each individual coastline segment has a series of attributes which describe the landform types of that segment of the coast. The coastal characteristics recorded refer not only to those at the precise location of the coastline itself, but to a coastal area nominally extending up 500m inland and offshore of the coastline itself. Where such information is available, these attributes describe the landforms in the subtidal, intertidal and backshore zones; the backshore profile and intertidal zone slope; the shoreline segment exposure; and the geological substrate. The dataset also contains attribute-level metadata: for each geomorphic attribute in each coastline segment, there are two additional attributes that identify the source dataset from which the attributes were derived, and defining the scale of the source dataset. Finally, there are a series of attributes which classify each coastline segment according to a shoreline landform stability classification scheme.

Currency Date Modified: 06 May 2019 Modification Frequency: As needed

Data Extent Coordinate reference: GDA94 / Australian Albers Spatial Extent North: -9°South: -44°East: 154°West: 112°

Source informationeCAT record: https://pid.geoscience.gov.au/service/ga/104560Geopackage download: https://pid.geoscience.gov.au/dataset/ga/104160Geodatabase download: https://pid.geoscience.gov.au/dataset/ga/104100Sharples, C., & Mount, R., 2009: The Australian Coastal Smartline Geomorphic and Stability Map Version 1: Manual and Data Dictionary; School of Geography & Environmental Studies, University of Tasmania. https://d28rz98at9flks.cloudfront.net/104100/104100_02_0.pdf Sharples, C., Mount, R., Pedersen, T., Lacey, M., Newton, J., Jaskierniak, D., & Wallace, L., 2009: The Australian Coastal Smartline Geomorphic and Stability Map Version 1: Project Report; School of Geography & Environmental Studies, University of Tasmania. https://d28rz98at9flks.cloudfront.net/83568/SmartlineProjectReport_2009_v1.pdfLineage statementThis dataset was derived from a total of 240 individual datasets. A complete list of these source datasets with additional metadata is available as a table in the Manual and Data Dictionary report and the Project report. Because of the wide variety of source datasets, a variety of different methods were used to extract and reclassify the source data. A generalised workflow is described below. Most of these steps were carried out using existing or newly-developed tools in ArcGIS 9.3:

For each coastal state, the best available coastline dataset was identified and obtained for use as a Smartline base map. Attributes from each source dataset were transferred to a copy of the Smartline base map for the relevant state. For aspatial and non-georeferenced source datasets, the transfer process was manual; for georeferenced datasets, the transfer process was automated as much as possible. The transferred attributes were reclassified and inserted into the appropriate Smartline field or fields using transfer tables and the Smartline Classification Scheme. The individual Smartline base maps containing attribute data from different source datasets were merged into a single state Smartline. Where conflicting or concordant attribute data were present for a single Smartline field, a resolution process was followed to choose a single attribute from those available. As a general rule, priority was given to the source datasets that contained the most relevant and detailed attribute information; if the available source datasets contained similar attribute information, preference was given to the source dataset with the best spatial resolution. A series of logical checks were carried out to ensure consistency between attributes in related Smartline attribute fields. Coastal features less than 10m long were merged with the most similar adjacent feature (based on attributes) where possible. Some features which were less than 10m long (generally very small islands) could not be merged because there were no adjacent features to merge with. A topology was developed and used to ensure that the Smartline geometry was identical to the original Smartline base map, and that the Smartline did not have any self-overlapping segments. All topological errors were repaired. Incorrectly spelled attribute data were detected and repaired using tables exported from an Access database containing the complete Smartline Classification Scheme. The Smartline Stability fields were populated using queries based on particular combinations of geomorphic attributes.For further details of the data compilation, processing, editing and verification processes, please refer to the Smartline Manual and Data Dictionary Report, and the Smartline Project Report. Data dictionary

Attribute name Description

OBJECTIDUnique IDBASELINEReference ID for source of base line mapBASEMAPSCALEScale of base mapBASEFEATURECoastal feature upon which base line map is basedAUSCOASTFIDUnique Australian coastal segment identifier number (v.1.0)UPDATEDDate of data currency or last updateABSAMP_IDBeach numberUNCLASS_NClass code of No stability classification (shores not classified into any stability classes)UNCLASS_VClass name of No stability classification (shores not classified into any stability classes)UNCLASS_LClass short name of No stability classification (shores not classified into any stability classes)COMMENTSGeneral notes and commentsContactOceans, Reefs, Coasts and the Antarctic (ORCA), Geoscience Australia. clientservices@ga.gov.au

Abstract This dataset maps the coastal landform types (geomorphology) of continental Australia and most adjacent islands (excluding the Great Barrier Reef). The dataset was compiled primarily using existing spatial data sets provided by a number of Commonwealth, State, Territory and other agencies from across Australia. The Smartline is a line map that represents the coastline (typically the High Water Mark), and is split into segments wherever any of the coastal landform attributes change. Each individual coastline segment has a series of attributes which describe the landform types of that segment of the coast. The coastal characteristics recorded refer not only to those at the precise location of the coastline itself, but to a coastal area nominally extending up 500m inland and offshore of the coastline itself. Where such information is available, these attributes describe the landforms in the subtidal, intertidal and backshore zones; the backshore profile and intertidal zone slope; the shoreline segment exposure; and the geological substrate. The dataset also contains attribute-level metadata: for each geomorphic attribute in each coastline segment, there are two additional attributes that identify the source dataset from which the attributes were derived, and defining the scale of the source dataset. Finally, there are a series of attributes which classify each coastline segment according to a shoreline landform stability classification scheme.

Currency Date Modified: 06 May 2019 Modification Frequency: As needed

Data Extent Coordinate reference: GDA94 / Australian Albers Spatial Extent North: -9°South: -44°East: 154°West: 112°

Source informationeCAT record: https://pid.geoscience.gov.au/service/ga/104560Geopackage download: https://pid.geoscience.gov.au/dataset/ga/104160Geodatabase download: https://pid.geoscience.gov.au/dataset/ga/104100Sharples, C., & Mount, R., 2009: The Australian Coastal Smartline Geomorphic and Stability Map Version 1: Manual and Data Dictionary; School of Geography & Environmental Studies, University of Tasmania. https://d28rz98at9flks.cloudfront.net/104100/104100_02_0.pdf Sharples, C., Mount, R., Pedersen, T., Lacey, M., Newton, J., Jaskierniak, D., & Wallace, L., 2009: The Australian Coastal Smartline Geomorphic and Stability Map Version 1: Project Report; School of Geography & Environmental Studies, University of Tasmania. https://d28rz98at9flks.cloudfront.net/83568/SmartlineProjectReport_2009_v1.pdfLineage statementThis dataset was derived from a total of 240 individual datasets. A complete list of these source datasets with additional metadata is available as a table in the Manual and Data Dictionary report and the Project report. Because of the wide variety of source datasets, a variety of different methods were used to extract and reclassify the source data. A generalised workflow is described below. Most of these steps were carried out using existing or newly-developed tools in ArcGIS 9.3:

For each coastal state, the best available coastline dataset was identified and obtained for use as a Smartline base map. Attributes from each source dataset were transferred to a copy of the Smartline base map for the relevant state. For aspatial and non-georeferenced source datasets, the transfer process was manual; for georeferenced datasets, the transfer process was automated as much as possible. The transferred attributes were reclassified and inserted into the appropriate Smartline field or fields using transfer tables and the Smartline Classification Scheme. The individual Smartline base maps containing attribute data from different source datasets were merged into a single state Smartline. Where conflicting or concordant attribute data were present for a single Smartline field, a resolution process was followed to choose a single attribute from those available. As a general rule, priority was given to the source datasets that contained the most relevant and detailed attribute information; if the available source datasets contained similar attribute information, preference was given to the source dataset with the best spatial resolution. A series of logical checks were carried out to ensure consistency between attributes in related Smartline attribute fields. Coastal features less than 10m long were merged with the most similar adjacent feature (based on attributes) where possible. Some features which were less than 10m long (generally very small islands) could not be merged because there were no adjacent features to merge with. A topology was developed and used to ensure that the Smartline geometry was identical to the original Smartline base map, and that the Smartline did not have any self-overlapping segments. All topological errors were repaired. Incorrectly spelled attribute data were detected and repaired using tables exported from an Access database containing the complete Smartline Classification Scheme. The Smartline Stability fields were populated using queries based on particular combinations of geomorphic attributes.For further details of the data compilation, processing, editing and verification processes, please refer to the Smartline Manual and Data Dictionary Report, and the Smartline Project Report. Data dictionary

Attribute name Description

OBJECTIDUnique IDBASELINEReference ID for source of base line mapBASEMAPSCALEScale of base mapBASEFEATURECoastal feature upon which base line map is basedAUSCOASTFIDUnique Australian coastal segment identifier number (v.1.0)UPDATEDDate of data currency or last updateABSAMP_IDBeach numberMUDDY_NClass code of Muddy shores (dominated by mud-grade (clay, silt) soft sediment in the intertidal zone)MUDDY_VClass name of Muddy shores (dominated by mud-grade (clay, silt) soft sediment in the intertidal zone)MUDDY_LClass short name of Muddy shores (dominated by mud-grade (clay, silt) soft sediment in the intertidal zone)COMMENTSGeneral notes and commentsContactOceans, Reefs, Coasts and the Antarctic (ORCA), Geoscience Australia. clientservices@ga.gov.au

Global prevalence of non-perennial rivers and streamsJune 2021prepared by Mathis L. Messager (mathis.messager@mail.mcgill.ca)Bernhard Lehner (bernhard.lehner@mcgill.ca)1. Overview and background 2. Repository content3. Data format and projection4. License and citations4.1 License agreement4.2 Citations and acknowledgements1. Overview and backgroundThis documentation describes the data produced for the research article: Messager, M. L., Lehner, B., Cockburn, C., Lamouroux, N., Pella, H., Snelder, T., Tockner, K., Trautmann, T., Watt, C. & Datry, T. (2021). Global prevalence of non-perennial rivers and streams. Nature. https://doi.org/10.1038/s41586-021-03565-5In this study, we developed a statistical Random Forest model to produce the first reach-scale estimate of the global distribution of non-perennial rivers and streams. For this purpose, we linked quality-checked observed streamflow data from 5,615 gauging stations (on 4,428 perennial and 1,187 non-perennial reaches) with 113 candidate environmental predictors available globally. Predictors included variables describing climate, physiography, land cover, soil, geology, and groundwater as well as estimates of long-term naturalised (i.e., without anthropogenic water use in the form of abstractions or impoundments) mean monthly and mean annual flow (MAF), derived from a global hydrological model (WaterGAP 2.2; Müller Schmied et al. 2014). Following model training and validation, we predicted the probability of flow intermittence for all river reaches in the RiverATLAS database (Linke et al. 2019), a digital representation of the global river network at high spatial resolution.The data repository includes two datasets resulting from this study:1. a geometric network of the global river system where each river segment is associated with:i. 113 hydro-environmental predictors used in model development and predictions, andii. the probability and class of flow intermittence predicted by the model.2. point locations of the 5,516 gauging stations used in model training/testing, where each station is associated with a line segment representing a reach in the river network, and a set of metadata.These datasets have been generated with source code located at messamat.github.io/globalirmap/.Note that, although several attributes initially included in RiverATLAS version 1.0 have been updated for this study, the dataset provided here is not an established new version of RiverATLAS. 2. Repository contentThe data repository has the following structure (for usage, see section 3. Data Format and Projection; GIRES stands for Global Intermittent Rivers and Ephemeral Streams):— GIRES_v10_gdb.zip/ : file geodatabase in ESRI® geodatabase format containing two feature classes (zipped) |——— GIRES_v10_rivers : river network lines |——— GIRES_v10_stations : points with streamflow summary statistics and metadata— GIRES_v10_shp.zip/ : directory containing ten shapefiles (zipped) Same content as GIRES_v10_gdb.zip for users that cannot read ESRI geodatabases (tiled by region due to size limitations). |——— GIRES_v10_rivers_af.shp : Africa |——— GIRES_v10_rivers_ar.shp : North American Arctic |——— GIRES_v10_rivers_as.shp : Asia |——— GIRES_v10_rivers_au.shp : Australasia|——— GIRES_v10_rivers_eu.shp : Europe|——— GIRES_v10_rivers_gr.shp : Greenland|——— GIRES_v10_rivers_na.shp : North America|——— GIRES_v10_rivers_sa.shp : South America|——— GIRES_v10_rivers_si.shp : Siberia|——— GIRES_v10_stations.shp : points with streamflow summary statistics and metadata— Other_technical_documentations.zip/ : directory containing three documentation files (zipped)|——— HydroATLAS_TechDoc_v10.pdf : documentation for river network framework|——— RiverATLAS_Catalog_v10.pdf : documentation for river network hydro-environmental attributes|——— Readme_GSIM_part1.txt : documentation for gauging stations from the Global Streamflow Indices and Metadata (GSIM) archive— README_Technical_documentation_GIRES_v10.pdf : full documentation for this repository3. Data format and projectionThe geometric network (lines) and gauging stations (points) datasets are distributed both in ESRI® file geodatabase and shapefile formats. The file geodatabase contains all data and is the prime, recommended format. Shapefiles are provided as a copy for users that cannot read the geodatabase. Each shapefile consists of five main files (.dbf, .sbn, .sbx, .shp, .shx), and projection information is provided in an ASCII text file (.prj). The attribute table can be accessed as a stand-alone file in dBASE format (.dbf) which is included in the Shapefile format. These datasets are available electronically in compressed zip file format. To use the data files, the zip files must first be decompressed.All data layers are provided in geographic (latitude/longitude) projection, referenced to datum WGS84. In ESRI® software this projection is defined by the geographic coordinate system GCS_WGS_1984 and datum D_WGS_1984 (EPSG: 4326).4. License and citations4.1 License agreement This documentation and datasets are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC-BY-4.0 License). For all regulations regarding license grants, copyright, redistribution restrictions, required attributions, disclaimer of warranty, indemnification, liability, waiver of damages, and a precise definition of licensed materials, please refer to the License Agreement (https://creativecommons.org/licenses/by/4.0/legalcode). For a human-readable summary of the license, please see https://creativecommons.org/licenses/by/4.0/.4.2 Citations and acknowledgements.Citations and acknowledgements of this dataset should be made as follows:Messager, M. L., Lehner, B., Cockburn, C., Lamouroux, N., Pella, H., Snelder, T., Tockner, K., Trautmann, T., Watt, C. & Datry, T. (2021). Global prevalence of non-perennial rivers and streams. Nature. https://doi.org/10.1038/s41586-021-03565-5 We kindly ask users to cite this study in any published material produced using it. If possible, online links to this repository (https://doi.org/10.6084/m9.figshare.14633022) should also be provided.

Abstract This dataset maps the coastal landform types (geomorphology) of continental Australia and most adjacent islands (excluding the Great Barrier Reef). The dataset was compiled primarily using existing spatial data sets provided by a number of Commonwealth, State, Territory and other agencies from across Australia. The Smartline is a line map that represents the coastline (typically the High Water Mark), and is split into segments wherever any of the coastal landform attributes change. Each individual coastline segment has a series of attributes which describe the landform types of that segment of the coast. The coastal characteristics recorded refer not only to those at the precise location of the coastline itself, but to a coastal area nominally extending up 500m inland and offshore of the coastline itself. Where such information is available, these attributes describe the landforms in the subtidal, intertidal and backshore zones; the backshore profile and intertidal zone slope; the shoreline segment exposure; and the geological substrate. The dataset also contains attribute-level metadata: for each geomorphic attribute in each coastline segment, there are two additional attributes that identify the source dataset from which the attributes were derived, and defining the scale of the source dataset. Finally, there are a series of attributes which classify each coastline segment according to a shoreline landform stability classification scheme.

Currency Date Modified: 06 May 2019 Modification Frequency: As needed

Data Extent Coordinate reference: GDA94 / Australian Albers Spatial Extent North: -9°South: -44°East: 154°West: 112°

Source informationeCAT record: https://pid.geoscience.gov.au/service/ga/104560Geopackage download: https://pid.geoscience.gov.au/dataset/ga/104160Geodatabase download: https://pid.geoscience.gov.au/dataset/ga/104100Sharples, C., & Mount, R., 2009: The Australian Coastal Smartline Geomorphic and Stability Map Version 1: Manual and Data Dictionary; School of Geography & Environmental Studies, University of Tasmania. https://d28rz98at9flks.cloudfront.net/104100/104100_02_0.pdf Sharples, C., Mount, R., Pedersen, T., Lacey, M., Newton, J., Jaskierniak, D., & Wallace, L., 2009: The Australian Coastal Smartline Geomorphic and Stability Map Version 1: Project Report; School of Geography & Environmental Studies, University of Tasmania. https://d28rz98at9flks.cloudfront.net/83568/SmartlineProjectReport_2009_v1.pdfLineage statementThis dataset was derived from a total of 240 individual datasets. A complete list of these source datasets with additional metadata is available as a table in the Manual and Data Dictionary report and the Project report. Because of the wide variety of source datasets, a variety of different methods were used to extract and reclassify the source data. A generalised workflow is described below. Most of these steps were carried out using existing or newly-developed tools in ArcGIS 9.3:

For each coastal state, the best available coastline dataset was identified and obtained for use as a Smartline base map. Attributes from each source dataset were transferred to a copy of the Smartline base map for the relevant state. For aspatial and non-georeferenced source datasets, the transfer process was manual; for georeferenced datasets, the transfer process was automated as much as possible. The transferred attributes were reclassified and inserted into the appropriate Smartline field or fields using transfer tables and the Smartline Classification Scheme. The individual Smartline base maps containing attribute data from different source datasets were merged into a single state Smartline. Where conflicting or concordant attribute data were present for a single Smartline field, a resolution process was followed to choose a single attribute from those available. As a general rule, priority was given to the source datasets that contained the most relevant and detailed attribute information; if the available source datasets contained similar attribute information, preference was given to the source dataset with the best spatial resolution. A series of logical checks were carried out to ensure consistency between attributes in related Smartline attribute fields. Coastal features less than 10m long were merged with the most similar adjacent feature (based on attributes) where possible. Some features which were less than 10m long (generally very small islands) could not be merged because there were no adjacent features to merge with. A topology was developed and used to ensure that the Smartline geometry was identical to the original Smartline base map, and that the Smartline did not have any self-overlapping segments. All topological errors were repaired. Incorrectly spelled attribute data were detected and repaired using tables exported from an Access database containing the complete Smartline Classification Scheme. The Smartline Stability fields were populated using queries based on particular combinations of geomorphic attributes.For further details of the data compilation, processing, editing and verification processes, please refer to the Smartline Manual and Data Dictionary Report, and the Smartline Project Report. Data dictionary

Attribute name Description

OBJECTIDUnique IDBASELINEReference ID for source of base line mapBASEMAPSCALEScale of base mapBASEFEATURECoastal feature upon which base line map is basedAUSCOASTFIDUnique Australian coastal segment identifier number (v.1.0)UPDATEDDate of data currency or last updateABSAMP_IDBeach numberSUBTID1_NClass code of Subtidal zone landform element – dominant (The dominant, co-equal or only subtidal landform element)SUBTID1_VClass name of Subtidal zone landform element – dominant (The dominant, co-equal or only subtidal landform element)SUBTID1_RData source (reference) ID of Subtidal zone landform element – dominant (The dominant, co-equal or only subtidal landform element)SUBTID1_SSource data scale of Subtidal zone landform element – dominant (The dominant, co-equal or only subtidal landform element)COMMENTSGeneral notes and commentsContactOceans, Reefs, Coasts and the Antarctic (ORCA), Geoscience Australia. clientservices@ga.gov.au

Abstract This dataset maps the coastal landform types (geomorphology) of continental Australia and most adjacent islands (excluding the Great Barrier Reef). The dataset was compiled primarily using existing spatial data sets provided by a number of Commonwealth, State, Territory and other agencies from across Australia. The Smartline is a line map that represents the coastline (typically the High Water Mark), and is split into segments wherever any of the coastal landform attributes change. Each individual coastline segment has a series of attributes which describe the landform types of that segment of the coast. The coastal characteristics recorded refer not only to those at the precise location of the coastline itself, but to a coastal area nominally extending up 500m inland and offshore of the coastline itself. Where such information is available, these attributes describe the landforms in the subtidal, intertidal and backshore zones; the backshore profile and intertidal zone slope; the shoreline segment exposure; and the geological substrate. The dataset also contains attribute-level metadata: for each geomorphic attribute in each coastline segment, there are two additional attributes that identify the source dataset from which the attributes were derived, and defining the scale of the source dataset. Finally, there are a series of attributes which classify each coastline segment according to a shoreline landform stability classification scheme.

Currency Date Modified: 06 May 2019 Modification Frequency: As needed

Data Extent Coordinate reference: GDA94 / Australian Albers Spatial Extent North: -9°South: -44°East: 154°West: 112°

Source informationeCAT record: https://pid.geoscience.gov.au/service/ga/104560Geopackage download: https://pid.geoscience.gov.au/dataset/ga/104160Geodatabase download: https://pid.geoscience.gov.au/dataset/ga/104100Sharples, C., & Mount, R., 2009: The Australian Coastal Smartline Geomorphic and Stability Map Version 1: Manual and Data Dictionary; School of Geography & Environmental Studies, University of Tasmania. https://d28rz98at9flks.cloudfront.net/104100/104100_02_0.pdf Sharples, C., Mount, R., Pedersen, T., Lacey, M., Newton, J., Jaskierniak, D., & Wallace, L., 2009: The Australian Coastal Smartline Geomorphic and Stability Map Version 1: Project Report; School of Geography & Environmental Studies, University of Tasmania. https://d28rz98at9flks.cloudfront.net/83568/SmartlineProjectReport_2009_v1.pdfLineage statementThis dataset was derived from a total of 240 individual datasets. A complete list of these source datasets with additional metadata is available as a table in the Manual and Data Dictionary report and the Project report. Because of the wide variety of source datasets, a variety of different methods were used to extract and reclassify the source data. A generalised workflow is described below. Most of these steps were carried out using existing or newly-developed tools in ArcGIS 9.3:

For each coastal state, the best available coastline dataset was identified and obtained for use as a Smartline base map. Attributes from each source dataset were transferred to a copy of the Smartline base map for the relevant state. For aspatial and non-georeferenced source datasets, the transfer process was manual; for georeferenced datasets, the transfer process was automated as much as possible. The transferred attributes were reclassified and inserted into the appropriate Smartline field or fields using transfer tables and the Smartline Classification Scheme. The individual Smartline base maps containing attribute data from different source datasets were merged into a single state Smartline. Where conflicting or concordant attribute data were present for a single Smartline field, a resolution process was followed to choose a single attribute from those available. As a general rule, priority was given to the source datasets that contained the most relevant and detailed attribute information; if the available source datasets contained similar attribute information, preference was given to the source dataset with the best spatial resolution. A series of logical checks were carried out to ensure consistency between attributes in related Smartline attribute fields. Coastal features less than 10m long were merged with the most similar adjacent feature (based on attributes) where possible. Some features which were less than 10m long (generally very small islands) could not be merged because there were no adjacent features to merge with. A topology was developed and used to ensure that the Smartline geometry was identical to the original Smartline base map, and that the Smartline did not have any self-overlapping segments. All topological errors were repaired. Incorrectly spelled attribute data were detected and repaired using tables exported from an Access database containing the complete Smartline Classification Scheme. The Smartline Stability fields were populated using queries based on particular combinations of geomorphic attributes.For further details of the data compilation, processing, editing and verification processes, please refer to the Smartline Manual and Data Dictionary Report, and the Smartline Project Report. Data dictionary

Attribute name Description

OBJECTIDUnique IDBASELINEReference ID for source of base line mapBASEMAPSCALEScale of base mapBASEFEATURECoastal feature upon which base line map is basedAUSCOASTFIDUnique Australian coastal segment identifier number (v.1.0)UPDATEDDate of data currency or last updateABSAMP_IDBeach numberSUBTID2_NClass code of Subtidal zone landform element – secondary (Secondary, co-equal or additional subtidal landform elements)SUBTID2_VClass name of Subtidal zone landform element – secondary (Secondary, co-equal or additional subtidal landform elements)SUBTID2_RData source (reference) ID of Subtidal zone landform element – secondary (Secondary, co-equal or additional subtidal landform elements)SUBTID2_SSource data scale of Subtidal zone landform element – secondary (Secondary, co-equal or additional subtidal landform elements)COMMENTSGeneral notes and commentsContactOceans, Reefs, Coasts and the Antarctic (ORCA), Geoscience Australia. clientservices@ga.gov.au

Abstract This dataset maps the coastal landform types (geomorphology) of continental Australia and most adjacent islands (excluding the Great Barrier Reef). The dataset was compiled primarily using existing spatial data sets provided by a number of Commonwealth, State, Territory and other agencies from across Australia. The Smartline is a line map that represents the coastline (typically the High Water Mark), and is split into segments wherever any of the coastal landform attributes change. Each individual coastline segment has a series of attributes which describe the landform types of that segment of the coast. The coastal characteristics recorded refer not only to those at the precise location of the coastline itself, but to a coastal area nominally extending up 500m inland and offshore of the coastline itself. Where such information is available, these attributes describe the landforms in the subtidal, intertidal and backshore zones; the backshore profile and intertidal zone slope; the shoreline segment exposure; and the geological substrate. The dataset also contains attribute-level metadata: for each geomorphic attribute in each coastline segment, there are two additional attributes that identify the source dataset from which the attributes were derived, and defining the scale of the source dataset. Finally, there are a series of attributes which classify each coastline segment according to a shoreline landform stability classification scheme.

Currency Date Modified: 06 May 2019 Modification Frequency: As needed

Data Extent Coordinate reference: GDA94 / Australian Albers Spatial Extent North: -9°South: -44°East: 154°West: 112°

Source informationeCAT record: https://pid.geoscience.gov.au/service/ga/104560Geopackage download: https://pid.geoscience.gov.au/dataset/ga/104160Geodatabase download: https://pid.geoscience.gov.au/dataset/ga/104100Sharples, C., & Mount, R., 2009: The Australian Coastal Smartline Geomorphic and Stability Map Version 1: Manual and Data Dictionary; School of Geography & Environmental Studies, University of Tasmania. https://d28rz98at9flks.cloudfront.net/104100/104100_02_0.pdf Sharples, C., Mount, R., Pedersen, T., Lacey, M., Newton, J., Jaskierniak, D., & Wallace, L., 2009: The Australian Coastal Smartline Geomorphic and Stability Map Version 1: Project Report; School of Geography & Environmental Studies, University of Tasmania. https://d28rz98at9flks.cloudfront.net/83568/SmartlineProjectReport_2009_v1.pdfLineage statementThis dataset was derived from a total of 240 individual datasets. A complete list of these source datasets with additional metadata is available as a table in the Manual and Data Dictionary report and the Project report. Because of the wide variety of source datasets, a variety of different methods were used to extract and reclassify the source data. A generalised workflow is described below. Most of these steps were carried out using existing or newly-developed tools in ArcGIS 9.3:

For each coastal state, the best available coastline dataset was identified and obtained for use as a Smartline base map. Attributes from each source dataset were transferred to a copy of the Smartline base map for the relevant state. For aspatial and non-georeferenced source datasets, the transfer process was manual; for georeferenced datasets, the transfer process was automated as much as possible. The transferred attributes were reclassified and inserted into the appropriate Smartline field or fields using transfer tables and the Smartline Classification Scheme. The individual Smartline base maps containing attribute data from different source datasets were merged into a single state Smartline. Where conflicting or concordant attribute data were present for a single Smartline field, a resolution process was followed to choose a single attribute from those available. As a general rule, priority was given to the source datasets that contained the most relevant and detailed attribute information; if the available source datasets contained similar attribute information, preference was given to the source dataset with the best spatial resolution. A series of logical checks were carried out to ensure consistency between attributes in related Smartline attribute fields. Coastal features less than 10m long were merged with the most similar adjacent feature (based on attributes) where possible. Some features which were less than 10m long (generally very small islands) could not be merged because there were no adjacent features to merge with. A topology was developed and used to ensure that the Smartline geometry was identical to the original Smartline base map, and that the Smartline did not have any self-overlapping segments. All topological errors were repaired. Incorrectly spelled attribute data were detected and repaired using tables exported from an Access database containing the complete Smartline Classification Scheme. The Smartline Stability fields were populated using queries based on particular combinations of geomorphic attributes.For further details of the data compilation, processing, editing and verification processes, please refer to the Smartline Manual and Data Dictionary Report, and the Smartline Project Report. Data dictionary

Attribute name Description

OBJECTIDUnique IDBASELINEReference ID for source of base line mapBASEMAPSCALEScale of base mapBASEFEATURECoastal feature upon which base line map is basedAUSCOASTFIDUnique Australian coastal segment identifier number (v.1.0)UPDATEDDate of data currency or last updateABSAMP_IDBeach numberBACKDIST_NClass code of Backshore distal landforms (The dominant distinctive backshore landform type or assemblage inland of the proximal backshore landform (may include supratidal landforms))BACKDIST_VClass name of Backshore distal landforms (The dominant distinctive backshore landform type or assemblage inland of the proximal backshore landform (may include supratidal landforms))BACKDIST_RData source (reference) ID of Backshore distal landforms (The dominant distinctive backshore landform type or assemblage inland of the proximal backshore landform (may include supratidal landforms))BACKDIST_SSource data scale of Backshore distal landforms (The dominant distinctive backshore landform type or assemblage inland of the proximal backshore landform (may include supratidal landforms))COMMENTSGeneral notes and commentsContactOceans, Reefs, Coasts and the Antarctic (ORCA), Geoscience Australia. clientservices@ga.gov.au

The Index of Household Advantage and Disadvantage (IHAD) provides a summary measure of relative socio-economic advantage and disadvantage for households, based on the characteristics of dwellings and the people living within them, using 2021 Census data.

All in-scope households are ordered from lowest to highest score. A low score indicates relatively greater disadvantage and a lack of advantage in general. A high score indicates a relative lack of disadvantage and greater advantage in general.

This dataset presents IHAD data in quartiles. The lowest 25% of households are given a quartile number of 1, the next lowest 25% of households are given a quartile number of 2 and so on, up to the highest 25% of households which are given a quartile number of 4. This means that households are divided into four equal sized groups, depending on their score. In practice these groups won’t each be exactly 25% of households as it depends on the distribution of the IHAD scores. The data is grouped by Statistical Area Level 2 (SA2 2021). SA2s are defined by the Australian Statistical Geography Standard (ASGS) Edition 3.

Key Attributes:

      Field alias
      Field name
      Description


      Statistical Areas Level 2 2021 code
      SA2_CODE_2021
      2021 Statistical Areas Level 2 (SA2) codes from the Australian Statistical Geography Standard (ASGS), Edition 3. SA2s are medium-sized general purpose areas built to represent communities that interact together socially and economically. 


        Statistical Areas Level 2 2021 name
      SA2_NAME_2021
      2021 Statistical Areas Level 2 (SA2) names from the Australian Statistical Geography Standard (ASGS), Edition 3. SA2s are medium-sized general purpose areas built to represent communities that interact together socially and economically. 


      Area in square kilometres
      AREA_ALBERS_SQKM
      The area of a region in square kilometres, based on the Albers equal area conic projection.


      Uniform Resource Identifier
      ASGS_LOCI_URI_2021
      A uniform resource identifier can be used in web linked applications for data integration. 


      IHAD quartile 1
      IHAD_QUARTILE1
      Proportion of in-scope dwellings in the SA2 that fall into IHAD quartile 1, indicating relatively greater disadvantage and a lack of advantage in general.


      IHAD quartile 2
      IHAD_QUARTILE2
      Proportion of in-scope dwellings in the SA2 that fall into IHAD quartile 2.


      IHAD quartile 3
      IHAD_QUARTILE3
      Proportion of in-scope dwellings in the SA2 that fall into IHAD quartile 3.


      IHAD quartile 4
      IHAD_QUARTILE4
      Proportion of in-scope dwellings in the SA2 that fall into IHAD quartile 4, indicating a relative lack of disadvantage and greater advantage in general.


      Occupied private dwellings
      OPD_2021
      Dwellings in-scope of the IHAD i.e. classifiable occupied private dwellings.


      SEIFA IRSAD quartile
      IRSAD_QUARTILE
      Index of Relative Socio-economic Advantage and Disadvantage quartile. All SA2s are ordered from lowest to highest score, the lowest 25% of SA2s are given a quartile number of 1, the next lowest 25% of SA2s are given a quartile number of 2 and so on, up to the highest 25% of SA2s which are given a quartile number of 4. This means that SA2s are divided into four equal sized groups, depending on their score. In practice these groups won’t each be exactly 25% of SA2s as it depends on the distribution of SEIFA scores.


      Usual resident population
      URP_2021
      Population counts in this column are based on place of usual residence as reported on Census Night. These include persons out of scope of the IHAD.


      Dwellings
      DWELLING
      Total dwellings at Census time, including dwellings out of scope of the IHAD e.g. unoccupied private dwellings.

Please note: Proportional totals may equal more than 100% due to rounding and random adjustments made to the data. When calculating proportions, percentages, or ratios from cross-classified or small area tables, the random error introduced can be ignored except when very small cells are involved, in which case the impact on percentages and ratios can be significant. Refer to the Introduced random error / perturbation Census page on the ABS website for more information.

Data and geography references

Source data publication: Index of Household Advantage and Disadvantage Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3 Further information: Index of Household Advantage and Disadvantage methodology, 2021 Source: Australian Bureau of Statistics (ABS)

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This feature class describes properties listed on the National Register of Historic Places, classified as historic structures, and depicted as points. The National Register of Historic Places requires the submission of a single UTM coordinate pair for properties under 10 acres. A structure is a building whose functional construction is made usually for purposes other than creating human shelter. Structures include: bridges, tunnels, gold dredges, firetowers, canals, turbines, dams, power plants, corncribs, silos, roadways, shot towers, windmills, grain elevators, kilns, mounds, cairns, palisade fortifications, earthworks, railroad grades, systems of roadways and paths, boats and ships, railroad locomotives and cars, telescopes, carousels, bandstands, gazebos and aircraft. Attribute data in this dataset are intentionally limited to those necessary for spatial data maintenance and feature level metadata necessary to document the lineage of the geography itself. Data from external database systems, such as the National Register Information System, are intended to link with these data to provide basic feature attributes. The means to maintain unique identifiers for each historic site (CR_ID), Survey_ID, as well as unique geometries associated with that feature (Geometry_ID) are through the use of Globally Unique Identifiers (GUIDs) assigned by the database. Information about the genesis of individual points is documented by feature level metadata fields in the spatial attribute table.

Coastal Area & Boundary Polygon:

The Coastal Area layer is a 1:24,000-scale, polygon feature-based layer that includes the land and waters that lie within the Coastal Area as defined by Connecticut General Statute (C.G.S.) 22a-94(a). Activities and actions conducted within the coastal area by Federal and State Agencies (i.e., U.S. Army Corps of Engineers (USACOE), DEP regulatory programs, and state plans and actions) must be consistent with all of the applicable standards and criteria contained in the Connecticut Coastal Management Act (C.G.S. 22a-90 to 22a-113). A subset of the Coastal Area, the Coastal Boundary, represents an area within which activities regulated or conducted by coastal municipalities must be consistent with the Coastal Management Act. As defined in this section of the statutes, the Coastal Area includes the land and water within the area delineated by the following: the westerly, southerly and easterly limits of the state's jurisdiction in Long Island Sound; the towns of Greenwich, Stamford, Darien, Norwalk, Westport, Fairfield, Bridgeport, Stratford, Shelton, Milford, Orange, West Haven, New Haven, Hamden, North Haven, East Haven, Branford, Guilford, Madison, Clinton, Westbrook, Deep River, Chester, Essex, Old Saybrook, Lyme, Old Lyme, East Lyme, Waterford, New London, Montville, Norwich, Preston, Ledyard, Groton and Stonington. This layer includes a single polygon feature defined by the boundaries described above. Attribute information is comprised of an Av_Legend to denote the coastal area. Data is compiled at 1:24,000 scale. This data is not updated.

The Coastal Boundary layer is a 1:24,000-scale, polygon feature-based layer of the legal mylar-based maps adopted by the Commissioner of the Department of Environmental Protection (DEP) (i.e., maps were adopted on a town by town basis) showing the extent of lands and coastal waters as defined by Connecticut General Statute (C.G.S.) 22a-93(5)) within Connecticut's coastal area (defined by C.G.S. 22a-94(c)). The coastal boundary is a hybrid of the original 1:24,000 version maps prepared by DEP consistent with C.G.S. 22a-94(d) (Coastal Area) and the revised boundary mapping undertaken by twenty-two coastal towns prepared pursuant to C.G.S. 22a-94(f). This layer therefore does not replace the legal maps and may not be used for legal determinations. The Coastal Boundary layer includes a single polygon feature that represents the coastal boundary. No other features are included in this layer. Data is compiled at 1:24,000 scale. Attribute information is comprised of an Av_Legend attribute and a CoastB_Flg attribute to denote the coastal boundary. Other attributes include automatically calculated Shape_Length and Shape_Area fields. This data is not updated. Any regulated activity conducted within the coastal boundary by a municipal agency (i.e., plans of development, zoning regulations, municipal coastal programs and coastal site plan review (i.e., site plans submitted to zoning commission, subdivision or resubdivision plans submitted to planning commission, application for special permit or exception to the zoning or planning commissions or zoning board of appeals, variance submitted to zoning board of appeals and a referral of a municipal project)) must be conducted in a manner consistent with the requirements of the Connecticut Coastal Management Act (CMA; C.G.S. 22a-90 to 22a-113). As the Coastal Boundary is a hybrid of the Coastal Area, all state and federal agency activities must be consistent with the requirements of the CMA. As defined in C.G.S. 22a-94(b) the coastal boundary is a "continuous line delineated on the landward side by the interior contour elevation of the one hundred year frequency coastal flood zone, as defined and determined by the National Flood Insurance Act, as amended (USC 42 Section 4101, P.L. 93-234), or a one thousand foot linear setback measured from the mean high water mark in coastal waters, or a one thousand foot linear setback measured from the inland boundary of tidal wetlands mapped under section 22a-20, whichever is farthest inland; and shall be delineated on the seaward side by the seaward extent of the jurisdiction of the state." The original boundary maps were created in 1979 on stable mylar overlay using the 1:24,000-scale US Geological Survey topographic quadrangle maps (mylar film format). The source for tidal wetland maps were the legal 1:24,000 maps (mylar format) adopted by the Commissioner of DEP and transformed to 1:24,000 mylar-scale maps by the Office of Policy and Management (OPM) using an accurate pantograph. OPM similarly converted FEMA's flood insurance maps (various scales) to a 1:24,000 mylar overlay. The inland extent of coastal waters was plotted on 1:24,000 USGS topographic maps following the procedures and sources described in The Boundary Between Saltwater and Freshwater in Connecticut, December 1978 prepared by the State of Connecticut, Department of Environmental Protection, Coastal Area Management Program. The following twenty-two towns have adopted municipal coastal boundaries: Chester, Clinton, Darien, Deep River, East Haven, Essex, Fairfield, Greenwich, Groton, Guilford, Hamden, Ledyard, Madison, Milford, New Haven, New London, North Haven, Norwalk, Old Lyme, Old Saybrook, Stamford and Waterford. The coastal boundary maps for these towns may be at different scales than the original DEP draft maps and may contain minor adjustments to the boundary as permitted in C.G.S. 22a-94(f).