Overview The Office of the Geographer and Global Issues at the U.S. Department of State produces the Large Scale International Boundaries (LSIB) dataset. The current edition is version 11.4 (published 24 February 2025). The 11.4 release contains updated boundary lines and data refinements designed to extend the functionality of the dataset. These data and generalized derivatives are the only international boundary lines approved for U.S. Government use. The contents of this dataset reflect U.S. Government policy on international boundary alignment, political recognition, and dispute status. They do not necessarily reflect de facto limits of control. National Geospatial Data Asset This dataset is a National Geospatial Data Asset (NGDAID 194) managed by the Department of State. It is a part of the International Boundaries Theme created by the Federal Geographic Data Committee. Dataset Source Details Sources for these data include treaties, relevant maps, and data from boundary commissions, as well as national mapping agencies. Where available and applicable, the dataset incorporates information from courts, tribunals, and international arbitrations. The research and recovery process includes analysis of satellite imagery and elevation data. Due to the limitations of source materials and processing techniques, most lines are within 100 meters of their true position on the ground. Cartographic Visualization The LSIB is a geospatial dataset that, when used for cartographic purposes, requires additional styling. The LSIB download package contains example style files for commonly used software applications. The attribute table also contains embedded information to guide the cartographic representation. Additional discussion of these considerations can be found in the Use of Core Attributes in Cartographic Visualization section below. Additional cartographic information pertaining to the depiction and description of international boundaries or areas of special sovereignty can be found in Guidance Bulletins published by the Office of the Geographer and Global Issues: https://data.geodata.state.gov/guidance/index.html Contact Direct inquiries to internationalboundaries@state.gov. Direct download: https://data.geodata.state.gov/LSIB.zip Attribute Structure The dataset uses the following attributes divided into two categories: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | Core CC1_GENC3 | Extension CC1_WPID | Extension COUNTRY1 | Core CC2 | Core CC2_GENC3 | Extension CC2_WPID | Extension COUNTRY2 | Core RANK | Core LABEL | Core STATUS | Core NOTES | Core LSIB_ID | Extension ANTECIDS | Extension PREVIDS | Extension PARENTID | Extension PARENTSEG | Extension These attributes have external data sources that update separately from the LSIB: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | GENC CC1_GENC3 | GENC CC1_WPID | World Polygons COUNTRY1 | DoS Lists CC2 | GENC CC2_GENC3 | GENC CC2_WPID | World Polygons COUNTRY2 | DoS Lists LSIB_ID | BASE ANTECIDS | BASE PREVIDS | BASE PARENTID | BASE PARENTSEG | BASE The core attributes listed above describe the boundary lines contained within the LSIB dataset. Removal of core attributes from the dataset will change the meaning of the lines. An attribute status of “Extension” represents a field containing data interoperability information. Other attributes not listed above include “FID”, “Shape_length” and “Shape.” These are components of the shapefile format and do not form an intrinsic part of the LSIB. Core Attributes The eight core attributes listed above contain unique information which, when combined with the line geometry, comprise the LSIB dataset. These Core Attributes are further divided into Country Code and Name Fields and Descriptive Fields. County Code and Country Name Fields “CC1” and “CC2” fields are machine readable fields that contain political entity codes. These are two-character codes derived from the Geopolitical Entities, Names, and Codes Standard (GENC), Edition 3 Update 18. “CC1_GENC3” and “CC2_GENC3” fields contain the corresponding three-character GENC codes and are extension attributes discussed below. The codes “Q2” or “QX2” denote a line in the LSIB representing a boundary associated with areas not contained within the GENC standard. The “COUNTRY1” and “COUNTRY2” fields contain the names of corresponding political entities. These fields contain names approved by the U.S. Board on Geographic Names (BGN) as incorporated in the ‘"Independent States in the World" and "Dependencies and Areas of Special Sovereignty" lists maintained by the Department of State. To ensure maximum compatibility, names are presented without diacritics and certain names are rendered using common cartographic abbreviations. Names for lines associated with the code "Q2" are descriptive and not necessarily BGN-approved. Names rendered in all CAPITAL LETTERS denote independent states. Names rendered in normal text represent dependencies, areas of special sovereignty, or are otherwise presented for the convenience of the user. Descriptive Fields The following text fields are a part of the core attributes of the LSIB dataset and do not update from external sources. They provide additional information about each of the lines and are as follows: ATTRIBUTE NAME | CONTAINS NULLS RANK | No STATUS | No LABEL | Yes NOTES | Yes Neither the "RANK" nor "STATUS" fields contain null values; the "LABEL" and "NOTES" fields do. The "RANK" field is a numeric expression of the "STATUS" field. Combined with the line geometry, these fields encode the views of the United States Government on the political status of the boundary line. ATTRIBUTE NAME | | VALUE | RANK | 1 | 2 | 3 STATUS | International Boundary | Other Line of International Separation | Special Line A value of “1” in the “RANK” field corresponds to an "International Boundary" value in the “STATUS” field. Values of ”2” and “3” correspond to “Other Line of International Separation” and “Special Line,” respectively. The “LABEL” field contains required text to describe the line segment on all finished cartographic products, including but not limited to print and interactive maps. The “NOTES” field contains an explanation of special circumstances modifying the lines. This information can pertain to the origins of the boundary lines, limitations regarding the purpose of the lines, or the original source of the line. Use of Core Attributes in Cartographic Visualization Several of the Core Attributes provide information required for the proper cartographic representation of the LSIB dataset. The cartographic usage of the LSIB requires a visual differentiation between the three categories of boundary lines. Specifically, this differentiation must be between: International Boundaries (Rank 1); Other Lines of International Separation (Rank 2); and Special Lines (Rank 3). Rank 1 lines must be the most visually prominent. Rank 2 lines must be less visually prominent than Rank 1 lines. Rank 3 lines must be shown in a manner visually subordinate to Ranks 1 and 2. Where scale permits, Rank 2 and 3 lines must be labeled in accordance with the “Label” field. Data marked with a Rank 2 or 3 designation does not necessarily correspond to a disputed boundary. Please consult the style files in the download package for examples of this depiction. The requirement to incorporate the contents of the "LABEL" field on cartographic products is scale dependent. If a label is legible at the scale of a given static product, a proper use of this dataset would encourage the application of that label. Using the contents of the "COUNTRY1" and "COUNTRY2" fields in the generation of a line segment label is not required. The "STATUS" field contains the preferred description for the three LSIB line types when they are incorporated into a map legend but is otherwise not to be used for labeling. Use of the “CC1,” “CC1_GENC3,” “CC2,” “CC2_GENC3,” “RANK,” or “NOTES” fields for cartographic labeling purposes is prohibited. Extension Attributes Certain elements of the attributes within the LSIB dataset extend data functionality to make the data more interoperable or to provide clearer linkages to other datasets. The fields “CC1_GENC3” and “CC2_GENC” contain the corresponding three-character GENC code to the “CC1” and “CC2” attributes. The code “QX2” is the three-character counterpart of the code “Q2,” which denotes a line in the LSIB representing a boundary associated with a geographic area not contained within the GENC standard. To allow for linkage between individual lines in the LSIB and World Polygons dataset, the “CC1_WPID” and “CC2_WPID” fields contain a Universally Unique Identifier (UUID), version 4, which provides a stable description of each geographic entity in a boundary pair relationship. Each UUID corresponds to a geographic entity listed in the World Polygons dataset. These fields allow for linkage between individual lines in the LSIB and the overall World Polygons dataset. Five additional fields in the LSIB expand on the UUID concept and either describe features that have changed across space and time or indicate relationships between previous versions of the feature. The “LSIB_ID” attribute is a UUID value that defines a specific instance of a feature. Any change to the feature in a lineset requires a new “LSIB_ID.” The “ANTECIDS,” or antecedent ID, is a UUID that references line geometries from which a given line is descended in time. It is used when there is a feature that is entirely new, not when there is a new version of a previous feature. This is generally used to reference countries that have dissolved. The “PREVIDS,” or Previous ID, is a UUID field that contains old versions of a line. This is an additive field, that houses all Previous IDs. A new version of a feature is defined by any change to the

This map layer includes Global Map data showing the counties and equivalent entities of the United States, Puerto Rico, and the U.S. Virgin Islands. States and the national extent may be derived from the information included in the map layer. The data are a modified version of the National Atlas of the United States 1:1,000,000-Scale County Boundaries of the United States; that data set was created by extracting county polygon features from the CENSUS 2006 TIGER/Line files produced by the U.S. Census Bureau.

The National Flood Hazard Layer (NFHL) data incorporates all Digital Flood Insurance Rate Map(DFIRM) databases published by FEMA, and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. The DFIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper Flood Insurance Rate Maps(FIRMs). The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The NFHL data are derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The specifications for the horizontal control of DFIRM data are consistent with those required for mapping at a scale of 1:12,000. The NFHL data contain layers in the Standard DFIRM datasets except for S_Label_Pt and S_Label_Ld. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all DFIRMs and corresponding LOMRs available on the publication date of the data set.

This dataset consists of cartographic data in digital line graph (DLG) form for the northeastern states (Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island and Vermont). Information is presented on two planimetric base categories, political boundaries and administrative boundaries, each available in two formats: the topologically structured format and a simpler format optimized for graphic display. These DGL data can be used to plot base maps and for various kinds of spatial analysis. They may also be combined with other geographically referenced data to facilitate analysis, for example the Geographic Names Information System.

Overview The Office of the Geographer and Global Issues at the U.S. Department of State produces the Large Scale International Boundaries (LSIB) dataset. The current edition is version 11.4 (published 24 February 2025). The 11.4 release contains updated boundary lines and data refinements designed to extend the functionality of the dataset. These data and generalized derivatives are the only international boundary lines approved for U.S. Government use. The contents of this dataset reflect U.S. Government policy on international boundary alignment, political recognition, and dispute status. They do not necessarily reflect de facto limits of control. National Geospatial Data Asset This dataset is a National Geospatial Data Asset (NGDAID 194) managed by the Department of State. It is a part of the International Boundaries Theme created by the Federal Geographic Data Committee. Dataset Source Details Sources for these data include treaties, relevant maps, and data from boundary commissions, as well as national mapping agencies. Where available and applicable, the dataset incorporates information from courts, tribunals, and international arbitrations. The research and recovery process includes analysis of satellite imagery and elevation data. Due to the limitations of source materials and processing techniques, most lines are within 100 meters of their true position on the ground. Cartographic Visualization The LSIB is a geospatial dataset that, when used for cartographic purposes, requires additional styling. The LSIB download package contains example style files for commonly used software applications. The attribute table also contains embedded information to guide the cartographic representation. Additional discussion of these considerations can be found in the Use of Core Attributes in Cartographic Visualization section below. Additional cartographic information pertaining to the depiction and description of international boundaries or areas of special sovereignty can be found in Guidance Bulletins published by the Office of the Geographer and Global Issues: https://res1datad-o-tgeodatad-o-tstated-o-tgov.vcapture.xyz/guidance/index.html Contact Direct inquiries to internationalboundaries@state.gov. Direct download: https://res1datad-o-tgeodatad-o-tstated-o-tgov.vcapture.xyz/LSIB.zip Attribute Structure The dataset uses the following attributes divided into two categories: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | Core CC1_GENC3 | Extension CC1_WPID | Extension COUNTRY1 | Core CC2 | Core CC2_GENC3 | Extension CC2_WPID | Extension COUNTRY2 | Core RANK | Core LABEL | Core STATUS | Core NOTES | Core LSIB_ID | Extension ANTECIDS | Extension PREVIDS | Extension PARENTID | Extension PARENTSEG | Extension These attributes have external data sources that update separately from the LSIB: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | GENC CC1_GENC3 | GENC CC1_WPID | World Polygons COUNTRY1 | DoS Lists CC2 | GENC CC2_GENC3 | GENC CC2_WPID | World Polygons COUNTRY2 | DoS Lists LSIB_ID | BASE ANTECIDS | BASE PREVIDS | BASE PARENTID | BASE PARENTSEG | BASE The core attributes listed above describe the boundary lines contained within the LSIB dataset. Removal of core attributes from the dataset will change the meaning of the lines. An attribute status of “Extension” represents a field containing data interoperability information. Other attributes not listed above include “FID”, “Shape_length” and “Shape.” These are components of the shapefile format and do not form an intrinsic part of the LSIB. Core Attributes The eight core attributes listed above contain unique information which, when combined with the line geometry, comprise the LSIB dataset. These Core Attributes are further divided into Country Code and Name Fields and Descriptive Fields. County Code and Country Name Fields “CC1” and “CC2” fields are machine readable fields that contain political entity codes. These are two-character codes derived from the Geopolitical Entities, Names, and Codes Standard (GENC), Edition 3 Update 18. “CC1_GENC3” and “CC2_GENC3” fields contain the corresponding three-character GENC codes and are extension attributes discussed below. The codes “Q2” or “QX2” denote a line in the LSIB representing a boundary associated with areas not contained within the GENC standard. The “COUNTRY1” and “COUNTRY2” fields contain the names of corresponding political entities. These fields contain names approved by the U.S. Board on Geographic Names (BGN) as incorporated in the ‘"Independent States in the World" and "Dependencies and Areas of Special Sovereignty" lists maintained by the Department of State. To ensure maximum compatibility, names are presented without diacritics and certain names are rendered using common cartographic abbreviations. Names for lines associated with the code "Q2" are descriptive and not necessarily BGN-approved. Names rendered in all CAPITAL LETTERS denote independent states. Names rendered in normal text represent dependencies, areas of special sovereignty, or are otherwise presented for the convenience of the user. Descriptive Fields The following text fields are a part of the core attributes of the LSIB dataset and do not update from external sources. They provide additional information about each of the lines and are as follows: ATTRIBUTE NAME | CONTAINS NULLS RANK | No STATUS | No LABEL | Yes NOTES | Yes Neither the "RANK" nor "STATUS" fields contain null values; the "LABEL" and "NOTES" fields do. The "RANK" field is a numeric expression of the "STATUS" field. Combined with the line geometry, these fields encode the views of the United States Government on the political status of the boundary line. ATTRIBUTE NAME | | VALUE | RANK | 1 | 2 | 3 STATUS | International Boundary | Other Line of International Separation | Special Line A value of “1” in the “RANK” field corresponds to an "International Boundary" value in the “STATUS” field. Values of ”2” and “3” correspond to “Other Line of International Separation” and “Special Line,” respectively. The “LABEL” field contains required text to describe the line segment on all finished cartographic products, including but not limited to print and interactive maps. The “NOTES” field contains an explanation of special circumstances modifying the lines. This information can pertain to the origins of the boundary lines, limitations regarding the purpose of the lines, or the original source of the line. Use of Core Attributes in Cartographic Visualization Several of the Core Attributes provide information required for the proper cartographic representation of the LSIB dataset. The cartographic usage of the LSIB requires a visual differentiation between the three categories of boundary lines. Specifically, this differentiation must be between: International Boundaries (Rank 1); Other Lines of International Separation (Rank 2); and Special Lines (Rank 3). Rank 1 lines must be the most visually prominent. Rank 2 lines must be less visually prominent than Rank 1 lines. Rank 3 lines must be shown in a manner visually subordinate to Ranks 1 and 2. Where scale permits, Rank 2 and 3 lines must be labeled in accordance with the “Label” field. Data marked with a Rank 2 or 3 designation does not necessarily correspond to a disputed boundary. Please consult the style files in the download package for examples of this depiction. The requirement to incorporate the contents of the "LABEL" field on cartographic products is scale dependent. If a label is legible at the scale of a given static product, a proper use of this dataset would encourage the application of that label. Using the contents of the "COUNTRY1" and "COUNTRY2" fields in the generation of a line segment label is not required. The "STATUS" field contains the preferred description for the three LSIB line types when they are incorporated into a map legend but is otherwise not to be used for labeling. Use of the “CC1,” “CC1_GENC3,” “CC2,” “CC2_GENC3,” “RANK,” or “NOTES” fields for cartographic labeling purposes is prohibited. Extension Attributes Certain elements of the attributes within the LSIB dataset extend data functionality to make the data more interoperable or to provide clearer linkages to other datasets. The fields “CC1_GENC3” and “CC2_GENC” contain the corresponding three-character GENC code to the “CC1” and “CC2” attributes. The code “QX2” is the three-character counterpart of the code “Q2,” which denotes a line in the LSIB representing a boundary associated with a geographic area not contained within the GENC standard. To allow for linkage between individual lines in the LSIB and World Polygons dataset, the “CC1_WPID” and “CC2_WPID” fields contain a Universally Unique Identifier (UUID), version 4, which provides a stable description of each geographic entity in a boundary pair relationship. Each UUID corresponds to a geographic entity listed in the World Polygons dataset. These fields allow for linkage between individual lines in the LSIB and the overall World Polygons dataset. Five additional fields in the LSIB expand on the UUID concept and either describe features that have changed across space and time or indicate relationships between previous versions of the feature. The “LSIB_ID” attribute is a UUID value that defines a specific instance of a feature. Any change to the feature in a lineset requires a new “LSIB_ID.” The “ANTECIDS,” or antecedent ID, is a UUID that references line geometries from which a given line is descended in time. It is used when there is a feature that is entirely new, not when there is a new version of a previous feature. This is generally used to reference countries that have dissolved. The “PREVIDS,” or Previous ID, is a UUID field that contains old versions of a line. This is an additive field, that houses all Previous IDs. A new

Global Map data are part of a cooperative effort among national mapping organizations around the world to produce and maintain a standard set of basic cartographic data that supports global investigations of natural and human-built environments. Participating nations prepare data using a common data structure and description to ensure compatibility and to tangibly promote the tenets of the Global Spatial Data Infrastructure. These data are intended for geographic display and analysis at the national level, and for large regional areas. The data should be displayed and analyzed at scales appropriate for 1:1,000,000-scale data. No responsibility is assumed by the National Atlas of the United States in the use of these data.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12064410%2F466e70c347974ab1f64280395bb45974%2Fpolitical%20polarization%20flag.png?generation=1677875491440013&alt=media" alt="">

52,540 DAYS (March 4th, 1879 to January 7th, 2023)

This is a dataset that tracks political polarization in US Congress (46th to 117th) through proportions from 1879 to 2023.

All data are official figures from Voteview that have been compiled and structured by myself. Ideological positions are calculated using the DW-NOMINATE (Dynamic Weighted NOMINAl Three-step Estimation). This procedure was developed by Keith T. Poole and Howard Rosenthal in the 1980s and is a "scaling procedure", representing legislators on a spatial map. In this sense, a spatial map is much like a road map--the closeness of two legislators on the map shows how similar their voting records are. Using this measure of distance, DW-NOMINATE is able to recover the "dimensions" that inform congressional voting behavior.

Why did I create this dataset? In my personal opinion, political polarization is the greatest threat to democracy today, particularly in America. Polarization not only creates an "US VS THEM" situation, but also renders legislative bodies less effective at passing meaningful legislation. By uploading time-series data regarding American polarization over the past two centuries, I hope that the community will use my dataset to determine insightful statistical trends. Achieving a quantitative yet objective viewpoint of political polarization is crucial to understanding both its underlying causes and its everlasting effects.

IMPORTANT:

The first dimension picks up differences in ideolology, which is represented through the "liberal" vs. "conservative" (also referred to as "left" vs. "right") proportions throughout American history. The second dimension picks up differences within the major political parties over slavery, currency, nativism, civil rights, and lifestyle issues during periods of American history.

Data Sources

The primary data source used was Voteview's official website, which publishes ideological data pertaining to America. Considering the meticulous documentation of congressional activities by such an accredited organization, no other organization is more equipped to provide insight on US polarization.

1. Voteview's Realtime NOMINATE Ideology and Related Data - Voteview's download links for NOMINATE scores, tutorials for generating ancillary data, and other publicly available databases are stored here.
2. Voteview's "Polarization in Congress" Data Analysis - Jeff Lewis, the project lead of Voteview, published a data analysis of the available polarization data. The detailed work gave insight into the true analytical potential of the dataset, and inspired many elements of my accompanying notebook.
3. Voteview's "The Polarization of the Congressional Parties" - Voteview released a detailed overview of their polarization data, the methodology behind their data, and the proper definitions and terminologies for the variables tracked. The guide mainly provided essential contextual knowledge needed to create a meaningful dataset.

Statistics Being Tracked

• Chamber (House or Senate)
• Congress (Number)
• Year (First Year of Congress)
• Party.mean.diff.d1 (Difference in Party Means - First Dimension)
• Prop.moderate.d1 (Proportion Moderates)
• Prop.moderate.dem.d1 (Proportion of Moderate Democrats (-0.25 to +0.25))
• Prop.moderate.rep.d1 (Proportion of Moderate Republicans (-0.25 to +0.25))
• Overlap
• Chamber.mean.d1 (Chamber Mean - First Dimension)
• Chamber.mean.d2 (Chamber Mean - Second Dimension)
• Dem.mean.d1 (Democratic Party Mean - First Dimension)
• Dem.mean.d2 (Democratic Party Mean - Second Dimension)
• Rep.mean.d1 (Republican Party Mean - First Dimension)
• Rep.mean.d2 (Republican Party Mean - Second Dimension)
• North.rep.mean.d1 (Northern Republican Mean - First Dimension)
• North.rep.mean.d2 (Northern Republican Mean - Second Dimension)
• South.rep.mean.d1 (Southern Republican Mean - First Dimension)
• South.rep.mean.d2 (Southern Republican Mean - Second Dimension)
• North.dem.mean.d1 (Northern Democrat Mean - First Dimension)
• North.dem.mean.d2 (Northern Democrat Mean - Second Dimension)
• South.dem.mean.d1 (Southern Democrat Mean - First Dimension)
• South.dem.mean.d2 (Southern Democrat Mean - Second Dimension)

Dataset History

2023-03-03 - Dataset is created (52,595 days after temporal coverage start date).

GitHub Repository - The same data but on GitHub.

Polarization Plots Notebook

[Link to Notebook](h...

Northeastern United States State Boundary data are intended for geographic display of state boundaries at statewide and regional levels. Use it to map and label states on a map. These data are derived from Northeastern United States Political Boundary Master layer. This information should be displayed and analyzed at scales appropriate for 1:24,000-scale data. The State of Connecticut, Department of Environmental Protection (CTDEP) assembled this regional data layer using data from other states in order to create a single, seamless representation of political boundaries within the vicinity of Connecticut that could be easily incorporated into mapping applications as background information. More accurate and up-to-date information may be available from individual State government Geographic Information System (GIS) offices. Not intended for maps printed at map scales greater or more detailed than 1:24,000 scale (1 inch = 2,000 feet.)

Authoritative CBRS boundary locations are shown on maps administered by the U.S. Fish and Wildlife Service. Normally, these areas are already shown on existing FIRMs for the area. CBRS areas have restrictions on Federal funding (including flood insurance coverage) after specified dates for new or substantially improved structures. This information is needed for the Coastal Barrier Resources System Information table in the FIS report and for the FIRM panels.

This layer is a component of Region Preliminary Data.

Connecticut and Vicinity State Boundary data are intended for geographic display of state boundaries at statewide and regional levels. Use it to map and label states on a map. These data are derived from Northeastern United States Political Boundary Master layer. This information should be displayed and analyzed at scales appropriate for 1:24,000-scale data. The State of Connecticut, Department of Environmental Protection (CTDEP) assembled this regional data layer using data from other states in order to create a single, seamless representation of political boundaries within the vicinity of Connecticut that could be easily incorporated into mapping applications as background information. More accurate and up-to-date information may be available from individual State government Geographic Information System (GIS) offices. Not intended for maps printed at map scales greater or more detailed than 1:24,000 scale (1 inch = 2,000 feet.)

Northeastern United States Town Boundary data are intended for geographic display of state, county and town (municipal) boundaries at statewide and regional levels. Use it to map and label towns on a map. These data are derived from Northeastern United States Political Boundary Master layer. This information should be displayed and analyzed at scales appropriate for 1:24,000-scale data. The State of Connecticut, Department of Environmental Protection (CTDEP) assembled this regional data layer using data from other states in order to create a single, seamless representation of political boundaries within the vicinity of Connecticut that could be easily incorporated into mapping applications as background information. More accurate and up-to-date information may be available from individual State government Geographic Information System (GIS) offices. Not intended for maps printed at map scales greater or more detailed than 1:24,000 scale (1 inch = 2,000 feet.)

Connecticut and Vicinity Town Boundary data are intended for geographic display of state, county and town (municipal) boundaries at statewide and regional levels. Use it to map and label towns on a map. These data are derived from Northeastern United States Political Boundary Master layer. This information should be displayed and analyzed at scales appropriate for 1:24,000-scale data. The State of Connecticut, Department of Environmental Protection (CTDEP) assembled this regional data layer using data from other states in order to create a single, seamless representation of political boundaries within the vicinity of Connecticut that could be easily incorporated into mapping applications as background information. More accurate and up-to-date information may be available from individual State government Geographic Information System (GIS) offices. Not intended for maps printed at map scales greater or more detailed than 1:24,000 scale (1 inch = 2,000 feet.)

This digitally compiled map includes geology, geologic provinces, and oil and gas fields of South America. The map is part of a worldwide series on CD-ROM by World Energy Project released of the U.S. Geological Survey . The goal of the project is to assess the undiscovered, technically recoverable oil and gas resources of the world and report these results by the year 2000. For data management purposes the world is divided into eight energy regions corresponding approximately to the economic regions of the world as defined by the U.S. Department of State. South America (Region 6) includes Argentina, Bolivia, Brazil, Chile, Columbia, Ecuador, Falkland Islands, French Guiana, Guyuna, Netherlands, Netherlands Antilles, Paraguay, Peru, Suriname, Trinidad and Tobago, Uruguay, and Venezuela. Each region is then further divided into geologic provinces on the basis of natural geologic entities and may include a dominant structural element or a number of contiguous elements. Some provinces contain multiple genetically related basins. Geologic province boundaries for the South America are delineated using data from a number of geologic maps, and other tectonic and geographic data (see References). Offshore province boundaries are defined by the 4000 meter bathymetric contour. Each province is assigned a unique number; the first digit is the region number. It is attempted to number the provinces in geographical groups; onshore, offshore, and combined on and offshore. The list of the provinces sorted by Code is shown in Adobe Acrobat samgeo.pdf file (see section V below). Oil and gas field data from Petroconsultants International Data Corporation worldwide oil and gas field database are allocated to these provinces. The geologic provinces are being further subdivided into petroleum systems and assessment units in order to appraise the undiscovered petroleum potential of selected provinces of the world. Specific details of the data sources and map compilation are given in the metadata file on this CD-ROM. Smaller stratigraphic subdivisions of Phanerozoic rock are combined to simplify the map and to maintain consistency with other maps of the series. Precambrian rocks are undivided. Oil and gas field markers represent field centerpoints published with permission from Petroconsultants International Data Corp.,1996 database. This map is compiled using Environmental Systems Research Institute, Inc. (ESRI) ARC/INFO software. Political boundaries and cartographic representations on this map were taken, with permission, from ESRI's ArcWorld 1:3M digital coverages, have no political significance, and are displayed as general reference only. Portions of this database covering the coastline and country boundaries contain intellectual property of Environmental Systems Research Institute, Inc. (ESRI), and are used herein with permission. Copyright 1992 and 1996, Environmental Systems Research Institute, Inc. All rights reserved.

The map title is Yarmouth. Tactile map scale. 2 centimetres = 3 kilometres North arrow pointing to the north. Yarmouth and surrounding area. Atlantic Ocean is shown with a wavy symbol to indicate water. Dashed lines indicate ferry crossings to Maine, USA. Main roads, Route 101 and Route 103. A circle with a dot in the middle indicates a bus terminal west of the city. Tactile maps are designed with Braille, large text, and raised features for visually impaired and low vision users. The Tactile Maps of Canada collection includes: (a) Maps for Education: tactile maps showing the general geography of Canada, including the Tactile Atlas of Canada (maps of the provinces and territories showing political boundaries, lakes, rivers and major cities), and the Thematic Tactile Atlas of Canada (maps showing climatic regions, relief, forest types, physiographic regions, rock types, soil types, and vegetation). (b) Maps for Mobility: to help visually impaired persons navigate spaces and routes in major cities by providing information about streets, buildings and other features of a travel route in the downtown area of a city. (c) Maps for Transportation and Tourism: to assist visually impaired persons in planning travel to new destinations in Canada, showing how to get to a city, and streets in the downtown area.

This digitally compiled map includes geology, geologic provinces, and oil and gas fields of Europe including Turkey. The maps are part of a worldwide series of maps on CD-ROM released by the U.S. Geological Survey's World Energy Project. The goal of the project is to assess the undiscovered, technically recoverable oil and gas resources of the world. For data management purposes the world was divided into eight energy regions corresponding approximately to the economic regions of of the world as defined by the U.S. Department of State. Europe (Region 4) includes Albania, Andorra, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Liechtenstein, Luxembourg, The Former Yugoslav Republic of Macedonia, Malta, Monaco, Netherlands, Norway, Poland, Portugal, Romania, San Marino, Serbia and Montenegro, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom and Vatican. The depicted portion of Region 2 includes Turkey. Each region is divided into geologic provinces. Each province has a set of geologic characteristics that distinguish it from surrounding provinces. These characteristics may include the predominant lithologies, the age of the strata, and the structural style. Some provinces include multiple genetically-related basins. Geologic province boundaries are delineated using data from a number of geologic maps and other tectonic and geographic data (see References). Offshore province boundaries are defined by the 2000 meter bathymetric contour. Each province is assigned a unique number. Because geologic trends are independent of political boundaries, some provinces overlap two regions. The code of those provinces that lie entirely within Europe begin with the number 4 and those provinces that lie entirely within Turkey begin with the number 2. The code of those provinces that lie partly within another region may start with a 1, for the Former Soviet Union (Persits and others 1998) or a 2, for Middle East and North Africa (Pollastro , 1998; Persits and others, 1997). The centerpoint locations of oil and gas fields are plotted based on the locations in the Petroconsultants International Data Corp. (1996) database with permission. Selected provinces are currently being investigated, by Total Petroleum System analysis, and assessments are being made of the undiscovered oil and gas resource potential of these provinces. Klett and others (1997) discuss the worldwide geologic provinces and their relative ranking in terms of total known petroleum volume. Specific details of the data sources and map compilation are given in the metadata files on this CD-ROM. Some stratigraphic units are combined to simplify the map and to ensure consistency across the region. All rocks are colored by age. Igneous and metamorphic rocks are identified with fill patterns and colors. These maps are compiled using Environmental Systems Research Institute Inc. (ESRI) ARC/INFO software. Political boundaries and cartographic representations on this map are taken, with permission from ESRI's ArcWorld 1:3M digital coverage; they have no political significance and are displayed as general reference only. Portions of this database covering the coastline and country boundaries contain intellectual property of ESRI. (© 1992 and 1996, Environmental Systems Research Institute Inc. All rights reserved.)

Aim

To understand the representativeness and accuracy of expert range maps, and explore alternate methods for accurately mapping species distributions.

Location

Global

Time period

Contemporary

Major taxa studied

Terrestrial vertebrates, and Odonata

Methods

We analyzed the biases in 50,768 animal IUCN, GARD and BirdLife species maps, assessed the links between these maps and existing political and various non-ecological boundaries to assess their accuracy for certain types of analysis. We cross-referenced each species map with data from GBIF to assess if maps captured the whole range of a species, and what percentage of occurrence points fall within the species’ assessed ranges. In addition, we use a number of alternate methods to map diversity patterns and compare these to high resolution models of distribution patterns.

Results

On average 20-30% of species’ non-coastal range boundaries overlapped with administrative national boundaries. In total, 60% of areas with the highest spatial turnover in species (high densities of species range boundaries marking high levels of shift in the community of species present) occurred at political boundaries, especially commonly in Southeast Asia. Different biases existed for different taxa, with gridded analysis in reptiles, river-basins in Odonata (except the Americas) and county-boundaries for Amphibians in the US. On average, up to half (25-46%) species recorded range points fall outside their mapped distributions. Filtered Minimum-convex polygons performed better than expert range maps in reproducing modeled diversity patterns.

Main conclusions

Expert range maps showed high bias at administrative borders in all taxa, but this was highest at the transition from tropical to subtropical regions. Methods used were inconsistent across space, time and taxa, and ranges mapped did not match species distribution data. Alternate approaches can better reconstruct patterns of distribution than expert maps, and data driven approaches are needed to provide reliable alternatives to better understand species distributions.

Methods Materials and methods

We use a combination of approaches to explore the relationship between species range maps and geopolitical boundaries and a subset of geographic features. In some cases we used the density of species range boundaries to explore the relationship between these and various features (i.e. administrative boundaries, river basin boundaries etc.). Additionally, species richness and spatial turnover are used to explore changes in richness over short geographic distances. Analyses were conducted in R statistical software unless noted otherwise. All code scripts are available at https://github.com/qiaohj/iucn_fix. Workflows are shown in Figure S1a-c with associated scripts listed.

Species ranges and boundary density maps

ERMs (Expert range maps) were downloaded from the IUCN RedList website for mammals (5,709 species), odonates (2,239 species) and amphibians (6,684 species; https://www.iucnredlist.org/resources/grid/spatial-data). Shapefile maps for birds were downloaded from BirdLife (10,423 species, http://datazone.birdlife.org/species/requestdis), and for reptiles from the Global Assessment of Reptile Distributions (GARD) (10,064 species; Roll et al., 2017). Each species’ polygon boundaries were converted to a polylines to show the boundary of each species range (Figure S1a-II; codes are lines 7 – 18 in line2raster_xxxx.r ; xxxx varies based on the taxa). The associated shapefile was then split to produce independent polyline files for each species within each taxon (see Figure S1a-I, codes are lines 29 to 83 in the same file above.).

To generate species boundary density maps, species range boundaries were rasterized at 1km spatial resolution with an equal area projection (Eckert-IV), and stacked to form a single raster for each taxon (at the level of amphibians, odonates, etc.). This represented the number of species in each group and their overlapping range boundaries (Figure S1b-II, codes are in line2raster_all.r). Each cell value indicated the number of species whose distribution boundaries overlapped with each cell, enabling us to overlay this rasterized information with other features (i.e. administrative boundaries) so that the overlaps between them can be calculated in R. These species boundary density maps underlie most subsequent analyses. R code and caveats are given in the supplements, links are provided in text and Figure S1.

Geographic boundaries

Spatial exploration of species range boundaries in ArcGIS suggested that numerous geographic datasets (i.e. political and in few cases geographic features such as river basins) were used to delineate the species ranges for different regions and taxa (this is sometimes part of the methodology in developing ERMs as detailed by Ficetola et al., 2014). Thus in addition to analyzing the administrative bias and the percentage of occurrence records within each species’ ERM for all taxa, additional analyses were conducted when other biases were evident in any given taxa or region (detailed later in methods on a case-by-case basis).

For all taxa, we assessed the percentage of overlap between species range boundaries and national and provincial boundaries by digitizing each to 1km (equivalent to buffering thie polyline by 500m), both with and without coastal boundaries. An international map was used because international (Western) assessors use them, and does not necessarily denote agreed country boundaries (https://gadm.org/). The different buffers (500m, 1000m, 2500m, 5000m) were added to these administrative boundaries in ArcMap to account for potential, insignificant deviations from political boundaries (Figure S1b). An R script for the same function is provided in “country_line_buffer.r”.

To establish where multiple species shared range boundaries we reclassified the species range boundary density rasters for each taxa into richness classes using the ArcMap quartile function (Figure S1). From these ten classes the percentage of the top-two, and top-three quartiles of range densities within different buffers (500m, 1000m, 2500m, 5000m) was calculated per country to determine what percentage of highest range boundary density approximately followed administrative borders. This was done because people drawing ERMs may use detailed administrative maps or generalize near political borders, or may use political shapefiles that deviate slightly. It is consequently useful to include varying distances from administrative features to assess how range boundary densities vary in relation to administrative boundaries. Analyses of relationships between individual species range boundaries and administrative boundaries (coastal, non-coastal) were made in R and scripts provided (quantile_country_buffer_overlap.r).

Spatial turnover and administrative boundaries

Heatmaps of species richness were generated by summing entire sets of compiled species ranges for each taxon in polygonal form (Figure 1; Figure S1b-I). To assess abrupt diversity changes, standard deviations for 10km blocks were calculated using the block statistics function in ArcMap. Abrupt changes in diversity were signified by high standard deviations based on the cell statistics function in ArcGIS, which represented rapid changes in the number of species present. Maps were then classified into ten categories using the quartile function. Given the high variation in maximum diversity and taxonomic representation, only the top two –three richness categories were retained per taxon. This was then extracted using 1km buffers of national administrative boundaries to assess percentages of administrative boundaries overlapping turnover hotspots by assessing what proportion of political boundaries were covered by these turnover hotspots.

Taxon-specific analyses

Data exploration and mapping exposed taxon and regional-specific biases requiring additional analysis. Where other biases and irregularities were clear from visual inspection of the range boundary density maps for each taxa, the possible causes of biases were assessed by comparing range boundary density maps to high-resolution imagery and administrative maps via the ArcGIS server (AGOL). Standardized overlay of the taxon boundary sets with administrative or geophysical features from the image-server revealed three types of bias which were either spatially or taxonomically limited between: 1) amphibians with county borders in the United States, 2) dragonflies and river basins globally and 3) gridding of distributions of reptiles. In these cases, species boundary density maps were used as a basis to identify potential biases which were then explored empirically using appropriate methods.

For amphibians, counties in the United States (US) were digitized using a county map from the US (https://gadm.org/), then buffered by with 2.5km either side. Amphibian species range boundary density maps were reclassified showing where species range boundaries existed (with other non-range boundary areas reclassified as “no data,”) and all species boundaries numerically indicated (i.e. values of 1 indicates one species range boundary, values of 10 indicates ten species range boundaries). Percentages of species boundary areas falling on county and in the buffers, in addition to species range boundaries which did not overlap with county boundaries were calculated to give measures of what percentage of the species boundaries fell within 2.5km of county boundaries.

For Odonata, many species were mapped to river basin borders. We used river basins of levels 6-8 (sub-basin to basin) in the river hierarchy (https://hydrosheds.org) to assess the relationship between Odonata boundaries and river boundaries. Two IUCN datasets exist for Odonata; the IUCN Odonata specialist group spatial dataset

The map title is Quebec. Tactile map scale. 1.8 centimetres = 200 kilometres North arrow pointing to the top of the page. Borders of the province of Quebec, shown as dashed and solid lines. Part of Hudson Bay and James Bay, shown with a wavy symbol to indicate water. A circle and the city name to show the location of Montreal. A filled star and the city name to show the location of Quebec City. Text labels for Hudson Bay, James Bay, St Lawrence River and the Labrador Sea. The word River is abbreviated as R. The abbreviation "ON" to indicate the province of Ontario. The abbreviation "NB" to indicate the province of New Brunswick. The abbreviation "NS" to indicate the province of Nova Scotia. The abbreviation "PE" to indicate the province of Prince Edward Island. The abbreviation "NF" to indicate the province of Newfoundland and Labrador. The abbreviation "USA" to indicate the neighbouring country, the United States of America. Tactile maps are designed with Braille, large text, and raised features for visually impaired and low vision users. The Tactile Maps of Canada collection includes: (a) Maps for Education: tactile maps showing the general geography of Canada, including the Tactile Atlas of Canada (maps of the provinces and territories showing political boundaries, lakes, rivers and major cities), and the Thematic Tactile Atlas of Canada (maps showing climatic regions, relief, forest types, physiographic regions, rock types, soil types, and vegetation). (b) Maps for Mobility: to help visually impaired persons navigate spaces and routes in major cities by providing information about streets, buildings and other features of a travel route in the downtown area of a city. (c) Maps for Transportation and Tourism: to assist visually impaired persons in planning travel to new destinations in Canada, showing how to get to a city, and streets in the downtown area.

The map title is Ontario. Tactile map scale. 2.1 centimetres = 200 kilometres North arrow pointing to the top of the page. Borders of the province of Ontario, shown as dashed and solid lines. The Great Lakes and part of Hudson Bay, shown with a wavy symbol to indicate water. A circle and the city name to show the location of Thunder Bay and Windsor. A filled star and the abbreviation "TO" to show the location of Toronto. An unfilled star and the city name to show the location of Ottawa. Text labels for Hudson Bay, James Bay, Lake Superior, Lake Michigan, Lake Huron, Lake Erie, and the abbreviation LO to indicate Lake Ontario. The word lake is abbreviated as L. The abbreviation "MB" to indicate the province of Manitoba. The abbreviation "QC" to indicate the province of Quebec. The abbreviation "USA" to indicate the neighbouring country, the United States of America. Tactile maps are designed with Braille, large text, and raised features for visually impaired and low vision users. The Tactile Maps of Canada collection includes: (a) Maps for Education: tactile maps showing the general geography of Canada, including the Tactile Atlas of Canada (maps of the provinces and territories showing political boundaries, lakes, rivers and major cities), and the Thematic Tactile Atlas of Canada (maps showing climatic regions, relief, forest types, physiographic regions, rock types, soil types, and vegetation). (b) Maps for Mobility: to help visually impaired persons navigate spaces and routes in major cities by providing information about streets, buildings and other features of a travel route in the downtown area of a city. (c) Maps for Transportation and Tourism: to assist visually impaired persons in planning travel to new destinations in Canada, showing how to get to a city, and streets in the downtown area.

The geology data set for this map includes arcs, polygons, and labels that outline and describe the general geologic age and type of bedrock of Iran. The geologic provinces data set includes arcs, polygons, and labels of geologic and petroleum provinces interpreted and designated by R.M. Pollastro from a number of literature and map resources to assist in the assessment of oil and gas resources for the USGS World Energy Project. The oil and gas field centerpoints data set is a point coverage that marks the approximate centerpoints of oil and gas fields in Iran. Political boundaries are provided to show the general location of country and/or other reference 'political' boundaries.

The map title is Maritimes. Tactile map scale. 3.3 centimetres = 100 kilometres North arrow pointing to the top of the page. Borders of the provinces of New Brunswick, Nova Scotia and Prince Edward Island, shown as dashed and solid lines. The Atlantic Ocean, shown with a wavy symbol to indicate water. Circles and the city name to show the location of Saint John and Yarmouth. A filled star and the city name Charlottetown to show its location. A filled star and the city name Fredericton to show its location. A filled star and the city name Halifax to show its location. Text labels for Cape Breton Island and the Atlantic Ocean. The abbreviation "QC" to indicate the province of Quebec. The abbreviation "NF" to indicate the province of Newfoundland. The abbreviation "NB" to indicate the province of New Brunswick. The abbreviation "PE" to indicate the province of Prince Edward Island. The abbreviation "NS" to indicate the province of Nova Scotia. The abbreviation "USA" to indicate the neighbouring country, the United States of America. Tactile maps are designed with Braille, large text, and raised features for visually impaired and low vision users. The Tactile Maps of Canada collection includes: (a) Maps for Education: tactile maps showing the general geography of Canada, including the Tactile Atlas of Canada (maps of the provinces and territories showing political boundaries, lakes, rivers and major cities), and the Thematic Tactile Atlas of Canada (maps showing climatic regions, relief, forest types, physiographic regions, rock types, soil types, and vegetation). (b) Maps for Mobility: to help visually impaired persons navigate spaces and routes in major cities by providing information about streets, buildings and other features of a travel route in the downtown area of a city. (c) Maps for Transportation and Tourism: to assist visually impaired persons in planning travel to new destinations in Canada, showing how to get to a city, and streets in the downtown area.