The 119th Congressional Districts dataset reflects boundaries from January 3rd, 2025 from the United States Census Bureau (USCB), and the attributes are updated every Sunday from the United States House of Representatives and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Information for each member of Congress is appended to the Census Congressional District shapefile using information from the Office of the Clerk, U.S. House of Representatives' website https://clerk.house.gov/xml/lists/MemberData.xml and its corresponding XML file. Congressional districts are the 435 areas from which people are elected to the U.S. House of Representatives. This dataset also includes 9 geographies for non-voting at large delegate districts, resident commissioner districts, and congressional districts that are not defined. After the apportionment of congressional seats among the states based on census population counts, each state is responsible for establishing congressional districts for the purpose of electing representatives. Each congressional district is to be as equal in population to all other congressional districts in a state as practicable. The 119th Congress is seated from January 3, 2025 through January 3, 2027. In Connecticut, Illinois, and New Hampshire, the Redistricting Data Program (RDP) participant did not define the CDs to cover all of the state or state equivalent area. In these areas with no CDs defined, the code "ZZ" has been assigned, which is treated as a single CD for purposes of data presentation. The TIGER/Line shapefiles for the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands) each contain a single record for the non-voting delegate district in these areas. The boundaries of all other congressional districts reflect information provided to the Census Bureau by the states by May 31, 2024. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529006

Boundaries (polygons) of US Congressional (House of Representatives) districts in New York State with name and contact info for Congressperson. Districts based on Legislative Task Force redistricting 2024. Information on representative based on congressional website as of 9-26-2025. Please contact Geospatial Services at nysgis@its.ny.gov if you have any questions. All district boundaries have been clipped to the NYS shoreline. This affects the following counties: Bronx, Cayuga, Chautauqua, Clinton, Erie, Essex, Franklin, Jefferson, Kings, Monroe, Nassau, New York, Niagara, Orleans, Oswego, Queens, Richmond, St. Lawrence, Suffolk, Washington, Wayne, Westchester.

This map shows Congressional District boundaries for the United States. The map is set to middle Georgia.

Congressional districts are the 435 areas from which members are elected to the U.S. House of Representatives. After the apportionment of congressional seats among the states, which is based on decennial census population counts, each state with multiple seats is responsible for establishing congressional districts for the purpose of electing representatives. Each congressional district is to be as equal in population to all other congressional districts in a state as practicable. The boundaries and numbers shown for the congressional districts are those specified in the state laws or court orders establishing the districts within each state.

Congressional districts for the 108th through 112th sessions were established by the states based on the result of the 2000 Census. Congressional districts for the 113th through 115th sessions were established by the states based on the result of the 2010 Census. Boundaries are effective until January of odd number years (for example, January 2015, January 2017, etc.), unless a state initiative or court ordered redistricting requires a change. All states established new congressional districts in 2011-2012, with the exception of the seven single member states (Alaska, Delaware, Montana, North Dakota, South Dakota, Vermont, and Wyoming).

For the states that have more than one representative, the Census Bureau requested a copy of the state laws or applicable court order(s) for each state from each secretary of state and each 2010 Redistricting Data Program state liaison requesting a copy of the state laws and/or applicable court order(s) for each state. Additionally, the states were asked to furnish their newly established congressional district boundaries and numbers by means of geographic equivalency files. States submitted equivalency files since most redistricting was based on whole census blocks. Kentucky was the only state where congressional district boundaries split some of the 2010 Census tabulation blocks. For further information on these blocks, please see the user-note at the bottom of the tables for this state.

The Census Bureau entered this information into its geographic database and produced tabulation block equivalency files that depicted the newly defined congressional district boundaries. Each state liaison was furnished with their file and requested to review, submit corrections, and certify the accuracy of the boundaries.

This data represents the 2021 Adopted Congressional districts from SB 881A

January 2023

This study contains files of election votes for the U.S. House of Representatives by State and County for each election year from 2006-2022. From Dave Leip, Atlas of U.S. Presidential Elections. These files were obtained from Dave Leip’s Atlas of U.S. Presidential Elections site for use by current faculty, staff, and students at Cornell University. Note: Similar publicly available data beginning with 1976 was posted by MIT at https://doi.org/10.7910/DVN/IG0UN2

Dave Leip's website

The Dave Leip website here: https://uselectionatlas.org/BOTTOM/store_data.php has additional years of data available going back to 1992 but at a fee. Sometimes the files are updated by Dave Leip, and new versions are made available, but CCSS is not notified. If you suspect the file you want may be updated, please get in touch with CCSS Data Services. These files were last checked for updates on 19 February 2024.

Note that file version numbers are those assigned to them by Dave Leip's Election Atlas. Please refer to the CISER Data and Reproduction Archive Version number in your citations for the full dataset.

For additional information on file layout, etc. see: https://uselectionatlas.org/BOTTOM/DOWNLOAD/spread_usrep.html

This study contains files of Presidential election votes by Congressional District for each U.S. Presidential election year from 2016-2020. From Dave Leip, Atlas of U.S. Presidential Elections.

Dave Leip's website

At the Dave Leip website here: https://uselectionatlas.org/BOTTOM/store_data.php sometimes the files are updated by Dave Leip, and new versions are made available, but CCSS is not notified. If you suspect the file you want may be updated, please get in touch with CCSS Data Services. These files were last checked for updates on 19 February 2024.

Note that file version numbers are those assigned to them by Dave Leip's Election Atlas. Please refer to the CCSS Data and Reproduction Archive Version number in your citations for the full dataset.

For additional information on file layout, etc. see: https://uselectionatlas.org/BOTTOM/DOWNLOAD/spread_national.html

Voting DistrictsThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau (USCB), depicts Voting Districts (VTDs) in the United States and Puerto Rico. Per the USCB, "VTDs refer to the generic name for geographic entities, such as precincts, wards, and election districts, established by state governments for the purpose of conducting elections.”Voting District 027 Danbury 1 (Ottawa County, OH)Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Voting Districts) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 63 (Series Information for 2020 Census Voting District (VTD) State-based TIGER/Line Shapefiles, Current)OGC API Features Link: (Voting Districts - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: Voting Districts; My Congressional DistrictFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

I share similar concerns as those raised by Suzanna Garcia's public comment about racial gerrymandering and competitiveness. Your proposed map essentially "cracks" the Denver metro Hispanic community stretching from Aurora to Berkley and Sherrelwood across 4 congressional districts. I believe this is a racial gerrymander meant to dilute the voting power of Hispanics in the Denver area. I am particularly concerned that Commerce City, which has one of the largest Hispanic populations in our state, is split in two with basically an "arm" shooting off from district 4 and into the eastern half of the city. District 4 is predominantly rural and yet this "arm" essentially pulls in half of Commerce City, which is a highly urban and industrial setting in the Denver metro. Commerce City has nothing in common with rural communities in Southern CO and I cannot see any reasonable justification for this. It is a racial gerrymander that I believe flagrantly violates the Voting Rights Act. Our community is facing serious issues around pollution and environmental racism because of the Suncor refinery. I believe it will basically be impossible for us to get our representative to address this if we are grouped mainly with rural counties up to 250 miles away from Commerce City. I agree with Ms. Garcia that a congressional district should be drawn that consolidates as much of the urban Hispanic community as possible by combining Aurora, northeast Denver, and the inner nothern suburbs including all of Commerce City. In my opinion, this is far more important than keeping the city of Denver whole- especially given that the airport neighborhoods are far more economically and culturally linked to Aurora and Commerce City than they are to downtown Denver.

I am also extremely concerned that the map doesn't seem to produce

competitive congressional districts. I thought a major purpose of the commission was to produce a competitive map. That was a huge part of the campaign for putting it in place and it was even in the language of the ballot measure we all voted on. It seems to me that there should be at least 3 districts that are competitive, if not more. I have drawn a map using Dave's Redistricting tool. This map is somewhat similar to the commission's map but I have made some adjustments to address the problems with racial gerrymandering and non-competitiveness. I have also restored Western Boulder county to district 2 and Castle Rock to district 4 because I saw in the public comments that these were frequent complaints. Now, district 6 is 37% Hispanic and 59% non-White. Additionally, district 3, 7, and 8 would all be competitive. In the 2018 AG's race, Brauchler would have won district 3 by 4% and district 7 by 2% while Weiser would have won district 8 by 3%. This is much more competitive than your current draft map. Thank you for reading my comment. MAP IS HERE: https://davesredistricting.org/join/f7835dfb-84af-41ec-aaa2-0081298fd57e

The legislative districts contain the geographically defined territories used for representation in the California State Assembly, California State Senate and the U.S. House of Representatives from California. These three boundary layers were approved by the California Citizens Redistricting Commission in 2021 following the completion of the 2020 United States Census.

My Elected Representatives provides contact information for Elected Officials in Loudoun County, VA. The information includes: US Senate, US House of Representatives (10th District), Virginia Senate and House, Loudoun Board of Supervisors, School Board and Constitutional Officers. Links to additional State and Federal legislators information is provided.Loudoun Election Districts (2022): Algonkian, Ashburn, Broad Run, Catoctin, Dulles, Leesburg, Little River, Sterling. For questions regarding the election please contact the Office of Voter Registration & Elections. For questions regarding this application please contact the Office of Mapping and Geographic Information.

The United States Congress is the bicameral legislature of the federal government of the United States consisting of two houses: the Senate and the House of Representatives. Post-redistricting boundaries were downloaded directly from the Capitol Data Portal, maintained by the Texas Legislative Council. The data is provided here for your convenience only. Please see disclaimer below.2021 Redistricting Report:https://redistricting.capitol.texas.gov/docs/pubs/data_for_2021_redistricting.pdf2021 Redistricting District Viewer Tool:https://dvr.capitol.texas.gov/

I examine whether indirect and direct elections lead to the selection of different types of legislators. My research design, which compares senators to representatives who were elected from statewide districts, takes advantage of two unique features of the nineteenth century congressional districting process. First, some states elected their entire congressional delegation in at-large districts. Second, many states that gained a seat during reapportionment would elect the new representative in a statewide contest rather than redrawing district lines. As a result, there are not only more representatives elected statewide, but they also come from a more diverse set of states than in contemporary elections. Overall, I find that indirectly elected legislators were more comparable to directly elected legislators on some dimensions than prior studies suggest.

The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

This study contains files of Presidential election votes in the state of New York by Congressional District, Legislative District, County, Town, and Precinct for each U.S. Presidential election year from 2016-2020. From Dave Leip, Atlas of U.S. Presidential Elections.

Dave Leip's website

The Dave Leip website here: https://uselectionatlas.org/BOTTOM/store_data.php has additional states and years of data available going back to 1992 but at a fee.

Sometimes the files are updated by Dave Leip, and new versions are made available, but CCSS is not notified. If you suspect the file you want may be updated, please get in touch with CCSS Data Services.

Note that file version numbers are those assigned to them by Dave Leip's Election Atlas. Please refer to the CCSS Data and Reproduction Archive Version number in your citations for the full dataset.

The 2023 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Block Groups (BGs) are clusters of blocks within the same census tract. Each census tract contains at least one BG, and BGs are uniquely numbered within census tracts. BGs have a valid code range of 0 through 9. BGs have the same first digit of their 4-digit census block number from the same decennial census. For example, tabulation blocks numbered 3001, 3002, 3003,.., 3999 within census tract 1210.02 are also within BG 3 within that census tract. BGs coded 0 are intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. Block groups generally contain between 600 and 3,000 people. A BG usually covers a contiguous area but never crosses county or census tract boundaries. They may, however, cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. The generalized BG boundaries in this release are based on those that were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

This dissertation addresses the questions of what kind of political information is provided by media outlets and how media environments affect electoral politics. In my first essay, I investigate the effect of the entry of television on U.S. presidential elections from 1944 to 1964. I first show that television increases the importance of the national economy. Second, I show that television weakens the relationship between the circulation of partisan newspapers and the party vote share. In addition, I show that the crowding out of political information by television does not drive these results. I find that television is not associated with a drop in newspaper circulation and people are just as likely to read about campaigns in newspapers when television becomes available. These findings suggest that television can be a valuable source of political information. In the second essay, coauthored with Angela Fonseca Galvis and James Snyder, we study the effect of competition on media bias in the context of U.S. newspapers in the period 1870-1910. Our results indicate that partisan newspapers cover scandals involving the opposition party's politicians more intensely and cover scandals involving their own party's politicians more lightly. More importantly, we find evidence that competition decreases the degree of media bias. The point estimates suggest that compared to a newspaper in a monopoly position, a newspaper facing two competitors will on average exhibits less than 50% as much overall bias in coverage intensity. In the third essay, I study whether newspaper coverage of scandals can help voters punish the party of politicians involved in a scandal. I focus on the US House of Representatives from 1982 to 2004. I use the congruence between newspaper markets and congressional districts as a measure of newspaper coverage of scandals. I show that newspapers write more stories about representatives involved in a scandal in districts that are more congruent. I find that the parties in scandals suffer moderately in elections. More importantly, my results suggest that the parties in scandal do worse in districts/counties with higher congruence: they get fewer votes and are less likely to win.

Key Features

If you find this dataset useful, give it an upvote – it's a small gesture that goes a long way! Thanks for your support. 😄

Welcome to Real Estate Across the United States (REXUS) Inventory (Building) dataset—a comprehensive repository meticulously maintained by the Public Building Service (PBS). REXUS serves as PBS's cornerstone tool, orchestrating the tracking and management of the U.S. government's real property assets. This encompasses a wealth of crucial data, including inventory specifics, building details, customer information, and lease particulars.

Dataset Overview: The REXUS Inventory (Building) dataset encapsulates the intricate landscape of the nation's real estate. Managed by the System for Tracking and Administration of Real Property (STAR), it undertakes the monumental task of space management, identifying every facet of building space and overseeing 22,000 assignments daily for various Federal agencies. This dataset spans PBS's building inventory, spanning both owned and leased structures, each classified with active or excess status.

Key Variables: This dynamic dataset comprises 16 columns, each a crucial dimension illuminating the intricacies of real estate management:

Location Code Region Code Bldg Address1 Bldg Address2 Bldg City Bldg County Bldg State Bldg Zip Congressional District Bldg Status Property Type Bldg ANSI Usable Total Parking Spaces Owned/Leased Construction Date Historical Type Historical Status ABA Accessibility Flag

Dataset Dimensions: Comprising 16 columns and 8,770 rows, the REXUS Inventory (Building) dataset offers an expansive canvas for exploration and analysis, unlocking insights into the diverse facets of real property assets.

Unsupervised Nature: A noteworthy characteristic of this dataset lies in its unsupervised essence. Devoid of a specific target variable, it challenges data enthusiasts to delve into the intricacies of the real estate landscape without predefined outcomes, fostering exploration and discovery.

Embark on a journey through the REXUS Inventory (Building) dataset on Kaggle—a treasure trove of information awaiting your analytical prowess. Explore, discover, and unravel the narratives embedded in the dynamic realm of United States real estate.

important points : 1. The dataset is prepared for a scientific work. If the data set owners are not satisfied, it will be deleted. 2. The dataset is taken from the following main source : https://catalog.data.gov/dataset/real-estate-across-the-united-states-rexus-inventory-building

A voting district is a geographical area which is represented by a seat or numerous seats in a legislative body. These boundaries are created by State and local governments for the purpose of administering elections. These boundaries are updated by the Indiana General Assembly by aggregating local data annually, and are often used to build Congress and General Assembly districts during the redistricting process.This dataset contains attributes for the district's county FIPS code, the district name, a full FIPS ID, and what US Congress, General Assembly House, and General Assembly Senate district each polygon resides in. This data is made available by the Indiana General Assembly on an annual basis.