Every 10 years, the number of seats a state has in the U.S. House of Representatives, and therefore the Electoral College, changes based on population. While many states experienced no change in representation due to the 2020 Census, a few states gained or lost seats. Texas notably gained *** seats due to an increase in population, while New York, Michigan, California, West Virginia, Pennsylvania, Ohio, and Illinois all lost *** seat.

This change will stay in place until 2030, when the next Census is conducted in the United States.

Though the issue of adding the citizenship question to the census largely has been thought of as a partisan one, a deeper investigation reveals there may be consequences for both parties. The map uses data from the Census Bureau’s new Response Outreach Area Mapper and shows predicted mail non-response rates.The darker blue areas depict low mail-in response areas. While these areas tend to be most concentrated in immigrant-dense areas along the West Coast, battleground states like Colorado and Florida as well as states like Mississippi and the Carolinas with difficult-to-reach populations could also be adversely affected.  Undercounts in those areas may lead to loss of congressional seats in states that might otherwise expect to gain seats after 2020 Census. Undercounts also would lead to a loss of funding for states, since many federal programs base funding on population counts.Source: CityLab - Mapping the Threat of a Census Disaster in 2020 - https://www.citylab.com/equity/2018/03/mapping-the-threat-of-a-census-disaster/556814/

Every four years in the United States, the electoral college system is used to determine the winner of the presidential election. In this system, each state has a fixed number of electors based on their population size, and (generally speaking) these electors then vote for their candidate with the most popular votes within their state or district. Since 1964, there have been 538 electoral votes available for presidential candidates, who need a minimum of 270 votes to win the election. Because of this system, candidates do not have to win over fifty percent of the popular votes across the country, but just win in enough states to receive a total of 270 electoral college votes. Popular results From 1789 until 1820, there was no popular vote, and the President was then chosen only by the electors from each state. George Washington was unanimously voted for by the electorate, receiving one hundred percent of the votes in both elections. From 1824, a popular vote has been conducted among American citizens (with varying levels of access for women, Blacks, and poor voters), to help electors in each state decide who to vote for (although the 1824 winner was chosen by the House of Representatives, as no candidate received over fifty percent of electoral votes). Since 1924, the difference in the share of both votes has varied, with several candidates receiving over 90 percent of the electoral votes while only receiving between fifty and sixty percent of the popular vote. The highest difference was for Ronald Reagan in 1980, where he received just 50.4 percent of the popular vote, but 90.9 percent of the electoral votes. Unpopular winners Since 1824, there have been 51 elections, and in 19 of these the winner did not receive over fifty percent of the popular vote. In the majority of these cases, the winner did receive a plurality of the votes, however there have been five instances where the winner of the electoral college vote lost the popular vote to another candidate. The most recent examples of this were in 2000, when George W. Bush received roughly half a million fewer votes than Al Gore, and in 2016, where Hillary Clinton won approximately three million more votes than Donald Trump.

The USDA Forest Service Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program produces geospatial and related data representing post-fire vegetation condition by means of standardized change detection methods based on Landsat or similar multispectral satellite imagery. RAVG data products characterize the impact of disturbance (fire) on vegetation within a fire perimeter, and include estimates of percent change in live basal area (BA), percent change in canopy cover (CC), and the standardized composite burn index (CBI). Standard thematic products include 7-class percent change in basal area (BA-7), 5-class percent change in canopy cover (CC-5), and 4-class CBI (CBI-4). Contingent upon the availability of suitable imagery, RAVG products are prepared for all wildland fires reported within the conterminous United States (CONUS) that include at least 1000 acres of forested National Forest System (NFS) land (500 acres for Regions 8 and 9 as of 2016). Data for individual fires are typically made available within 45 days after fire containment ("initial assessments"). Late-season fires, however, may be deferred until the following spring or summer ("extended assessments"). Annual national mosaics of each thematic product are prepared at the end of the fire season and updated, as needed, when additional fires from the given year are processed. The annual mosaics are available via the Raster Data Warehouse (RDW, see https://apps.fs.usda.gov/arcx/rest/services/RDW_Wildfire). A combined perimeter dataset, including the burn boundaries for all published Forest Service RAVG fires from 2012 to the present, is likewise updated as needed (at least annually). This current dataset is derived from the combined perimeter dataset and adds spatial information about land ownership (National Forest) and wilderness status, as well as the areal extent of forested land (pre-fire) that experience a modeled BA loss above 50 and 75 percent.

There are 435 seats in the U.S. House of Representatives, of which 52 are allocated to the state of California. Seats in the House are allocated based on the population of each state. To ensure proportional and dynamic representation, congressional apportionment is reevaluated every 10 years based on census population data. After the 2020 census, six states gained a seat - Colorado, Florida, Montana, North Carolina, and Oregon. The states of California, Illinois, Michigan, New York, Ohio, Pennsylvania, and West Virginia lost a seat.

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.

This resource is a member of a series. 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) System (MTS). The MTS 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. Block groups are clusters of blocks within the same census tract. Each census tract contains at least one block group, and are uniquely numbered within census tracts. Block groups have a valid code range of 0 through 9. They also 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 block group 3 within that census tract. Block groups 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 block group 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 block group boundaries in this release are those that were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

The property level flood risk statistics generated by the First Street Foundation Flood Model Version 2.0 come in CSV format.

The data that is included in the CSV includes:

An FSID; a First Street ID (FSID) is a unique identifier assigned to each location.

The latitude and longitude of a parcel as well as the zip code, census block group, census tract, county, congressional district, and state of a given parcel.

The property’s Flood Factor as well as data on economic loss.

The flood depth in centimeters at the low, medium, and high CMIP 4.5 climate scenarios for the 2, 5, 20, 100, and 500 year storms this year and in 30 years.

Data on the cumulative probability of a flood event exceeding the 0cm, 15cm, and 30cm threshold depth is provided at the low, medium, and high climate scenarios for this year and in 30 years.

Information on historical events and flood adaptation, such as ID and name.

This dataset includes First Street's aggregated flood risk summary statistics. The data is available in CSV format and is aggregated at the congressional district, county, and zip code level. The data allows you to compare FSF data with FEMA data. You can also view aggregated flood risk statistics for various modeled return periods (5-, 100-, and 500-year) and see how risk changes due to climate change (compare FSF 2020 and 2050 data). There are various Flood Factor risk score aggregations available including the average risk score for all properties (flood factor risk scores 1-10) and the average risk score for properties with risk (i.e. flood factor risk scores of 2 or greater). This is version 2.0 of the data and it covers the 50 United States and Puerto Rico. There will be updated versions to follow.

If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

The data dictionary for the parcel-level data is below.

Field Name

Type

Description

fsid

int

First Street ID (FSID) is a unique identifier assigned to each location

long

float

Longitude

lat

float

Latitude

zcta

int

ZIP code tabulation area as provided by the US Census Bureau

blkgrp_fips

int

US Census Block Group FIPS Code

tract_fips

int

US Census Tract FIPS Code

county_fips

int

County FIPS Code

cd_fips

int

Congressional District FIPS Code for the 116th Congress

state_fips

int

State FIPS Code

floodfactor

int

The property's Flood Factor, a numeric integer from 1-10 (where 1 = minimal and 10 = extreme) based on flooding risk to the building footprint. Flood risk is defined as a combination of cumulative risk over 30 years and flood depth. Flood depth is calculated at the lowest elevation of the building footprint (largest if more than 1 exists, or property centroid where footprint does not exist)

CS_depth_RP_YY

int

Climate Scenario (low, medium or high) by Flood depth (in cm) for the Return Period (2, 5, 20, 100 or 500) and Year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_depth_002_year00

CS_chance_flood_YY

float

Climate Scenario (low, medium or high) by Cumulative probability (percent) of at least one flooding event that exceeds the threshold at a threshold flooding depth in cm (0, 15, 30) for the year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_chance_00_year00

aal_YY_CS

int

The annualized economic damage estimate to the building structure from flooding by Year (today or 30 years in the future) by Climate Scenario (low, medium, high). Today as year00 and 30 years as year30. ex: aal_year00_low

hist1_id

int

A unique First Street identifier assigned to a historic storm event modeled by First Street

hist1_event

string

Short name of the modeled historic event

hist1_year

int

Year the modeled historic event occurred

hist1_depth

int

Depth (in cm) of flooding to the building from this historic event

hist2_id

int

A unique First Street identifier assigned to a historic storm event modeled by First Street

hist2_event

string

Short name of the modeled historic event

hist2_year

int

Year the modeled historic event occurred

hist2_depth

int

Depth (in cm) of flooding to the building from this historic event

adapt_id

int

A unique First Street identifier assigned to each adaptation project

adapt_name

string

Name of adaptation project

adapt_rp

int

Return period of flood event structure provides protection for when applicable

adapt_type

string

Specific flood adaptation structure type (can be one of many structures associated with a project)

fema_zone

string

Specific FEMA zone categorization of the property ex: A, AE, V. Zones beginning with "A" or "V" are inside the Special Flood Hazard Area which indicates high risk and flood insurance is required for structures with mortgages from federally regulated or insured lenders

footprint_flag

int

Statistics for the property are calculated at the centroid of the building footprint (1) or at the centroid of the parcel (0)

South Carolina has taken part in all U.S. presidential elections ever held, with the exception of the 1864 election when the Palmetto State was a part of the Confederate States of America. In these 58 elections, South Carolina has allocated all of its electoral votes to the nationwide winner on 33 occasions, giving a success rate of 57 percent (one of the lowest in the country). South Carolina, as with other southern states, was a Democratic stronghold throughout most of the nineteenth century, before turning Republican in the 1960s; South Carolina has voted for the Republican Party's nominee in all elections since 1980, and in 14 of the 15 most recent elections. In the 2020 election, South Carolina was a comfortable victory for Donald Trump, although his margin of victory was lower than his 14 point victory there in the 2016 election. South Carolinians in the White House Only one U.S. president, Andrew Jackson, was born in South Carolina, however, he was born there during the colonial era and the exact location remains unknown. It is known that Jackson was born in the Waxhaws region along the border of North and South Carolina; some historians have suggested that Jackson was born on the northern side of the border, and that he only claimed to be from the south to garner political support, however most historians have accepted Jackson's claim that he was born south of the border. Charles C. Pinckney is the only other South Carolinian to have headed a major party ticket, although he lost in both the 1804 and 1808 elections, while Strom Thurmond was the only third-party candidate from South Carolina to win electoral votes. Electoral votes As with most of the original thirteen colonies, South Carolina's influence on presidential elections has generally decreased throughout U.S. history. In early elections, South Carolina's allocation of electoral votes increased from seven in 1789, to eleven votes between 1812 and 1840. This number then fell going into the Civil War and Reconstruction era, before plateauing at eight or nine votes since 1884. South Carolina holds the distinction of being the final state to introduce a popular voting system to choose the statewide winner, making the switch after it was readmitted to the union in 1868; the winners in all presidential elections held in South Carolina between 1789 and 1860 were decided by the state legislature.