Enhanced Fujita scale
The Enhanced Fujita scale rates the intensity of tornadoes in some countries, including the United States and Canada, based on the damage they cause. Implemented in place of the Fujita scale introduced in 1971 by Tetsuya Theodore Fujita, it began operational use in the United States on February 1, 2007, followed by Canada on April 1, 2013, it has been proposed for use in France. The scale has the same basic design as the original Fujita scale—six categories from zero to five, representing increasing degrees of damage, it was revised to reflect better examinations of tornado damage surveys, so as to align wind speeds more with associated storm damage. Better standardizing and elucidating what was subjective and ambiguous, it adds more types of structures and vegetation, expands degrees of damage, better accounts for variables such as differences in construction quality; the newer scale was publicly unveiled by the National Weather Service at a conference of the American Meteorological Society in Atlanta on February 2, 2006.
It was developed from 2000 to 2004 by the Fujita Scale Enhancement Project of the Wind Science and Engineering Research Center at Texas Tech University, which brought together dozens of expert meteorologists and civil engineers in addition to its own resources. As with the Fujita scale, the Enhanced Fujita scale remains a damage scale and only a proxy for actual wind speeds. While the wind speeds associated with the damage listed have not undergone empirical analysis owing to excessive cost, the wind speeds were obtained through a process of expert elicitation based on various engineering studies since the 1970s as well as from field experience of meteorologists and engineers. In addition to damage to structures and vegetation, radar data and cycloidal marks may be utilized when available; the scale was used for the first time in the United States a year after its public announcement when parts of central Florida were struck by multiple tornadoes, the strongest of which were rated at EF3 on the new scale.
It was used for the first time in Canada shortly after its implementation there when a tornado developed near the town on Shelburne, Ontario on April 18, 2013, causing up to EF1 damage. The six categories for the EF scale are listed below, in order of increasing intensity. Although the wind speeds and photographic damage examples are updated, the damage descriptions given are those from the Fujita scale, which are more or less still accurate. However, for the actual EF scale in practice, damage indicators are predominantly used in determining the tornado intensity; the EF scale has 28 damage indicators, or types of structures and vegetation, each with a varying number of degrees of damage. Larger degrees of damage done to the damage indicators correspond to higher wind speeds; the links in the right column of the following table describe the degrees of damage for the damage indicators listed in each row. The new scale takes into account the quality of construction and standardizes different kinds of structures.
The wind speeds on the original scale were deemed by meteorologists and engineers as being too high, engineering studies indicated that slower winds than estimated cause the respective degrees of damage. The old scale lists an F5 tornado as wind speeds of 261–318 mph, while the new scale lists an EF5 as a tornado with winds above 200 mph, found to be sufficient to cause the damage ascribed to the F5 range of wind speeds. None of the tornadoes recorded on or before January 31, 2007, will be re-categorized. There is no functional difference in how tornadoes are rated; the old ratings and new ratings are smoothly connected with a linear formula. The only differences are adjusted wind speeds, measurements of which were not used in previous ratings, refined damage descriptions. Twenty-eight Damage Indicators, with descriptions such as "double-wide mobile home" or "strip mall", are used along with Degrees of Damage to determine wind estimates. Different structures, depending on their building materials and ability to survive high winds, have their own DIs and DODs.
Damage descriptors and wind speeds will be updated as new information is learned. Since the new system still uses actual tornado damage and similar degrees of damage for each category to estimate the storm's wind speed, the National Weather Service states that the new scale will not lead to an increase in a number of tornadoes classified as EF5. Additionally, the upper bound of the wind speed range for EF5 is open—in other words, there is no maximum wind speed designated. For purposes such as tornado climatology studies, Enhanced Fujita scale ratings may be grouped into classes; the table shown to the right shows other variations of the tornado rating classifications based on certain areas. Edwards, Roger. "Tornado Intensity Estimation: Past and Future". Bull. Amer. Meteor. Soc. 94: 641–53. Bibcode:2013BAMS...94..641E. Doi:10.1175/BAMS-D-11-00006.1. National Oceanic and Atmospheric Administration NOAA National Weather Service Improves Tornado Rating System at NOAA News The Enhanced Fujita Scale at Storm Prediction Center EF-Scale Training at The Warning Decision Training Branch of National Weather Service The Enhanced Fujita Tornado Scale at National Climatic Data Center The Tornado: An Engineering-Oriented Perspective A Guide for Conducting Convective Windstorm Surveys Fuji
Texas State Highway 64
State Highway 64 is a Texas state highway that runs from Wills Point via Tyler to Henderson. SH 64 was designated on August 21, 1923 to replace SH 15A from Wills Point to Carthage. On November 19, 1923, it extended east to the Louisiana state line. On September 26, 1939, the portion east of Henderson was part of U. S. Highway 79, which it was cosigned with since 1935; the remaining portion has not changed since. SH 64 has one business route in Henderson, inventoried by TxDOT as Business SH 64-E; the route was designated on June 21, 1990, along with Bus. US 79, replaced segments of Loop 153 and Loop 154; the two business routes are concurrent through downtown Henderson. Loop 153 was designated on May 18, 1944 from SH 64 and SH 323 southeast to downtown Henderson and east to US 79. On December 19, 1955, the section from US 79 & FM 840 to US 79 was removed from the state highway system. On June 21, 1990, Loop 153 was cancelled, as it was transferred to Bus. SH 64-E and Bus. US 79-F. Loop 154 was designated on May 18, 1944 from SH 64 southward through Henderson to US 79.
On June 21, 1990, Loop 154 was cancelled. SH 64-E and Bus. US 79-F
Per capita income
Per capita income or average income measures the average income earned per person in a given area in a specified year. It is calculated by dividing the area's total income by its total population. Per capita income is national income divided by population size. Per capita income is used to measure an area's average income and compare the wealth of different populations. Per capita income is used to measure a country's standard of living, it is expressed in terms of a used international currency such as the euro or United States dollar, is useful because it is known, is calculable from available gross domestic product and population estimates, produces a useful statistic for comparison of wealth between sovereign territories. This helps to ascertain a country's development status, it is one of the three measures for calculating the Human Development Index of a country. In the United States, it is defined by the U. S. Census Bureau as the following: "Per capita income is the mean money income received in the past 12 months computed for every man and child in a geographic area."
Critics claim that per capita income has several weaknesses in measuring prosperity: Comparisons of per capita income over time need to consider inflation. Without adjusting for inflation, figures tend to overstate the effects of economic growth. International comparisons can be distorted by cost of living differences not reflected in exchange rates. Where the objective is to compare living standards between countries, adjusting for differences in purchasing power parity will more reflect what people are able to buy with their money, it does not reflect income distribution. If a country's income distribution is skewed, a small wealthy class can increase per capita income while the majority of the population has no change in income. In this respect, median income is more useful when measuring of prosperity than per capita income, as it is less influenced by outliers. Non-monetary activity, such as barter or services provided within the family, is not counted; the importance of these services varies among economies.
Per capita income does not consider whether income is invested in factors to improve the area's development, such as health, education, or infrastructure. List of countries by average wage List of countries by GDP per capita—GDP at market or government official exchange rates per inhabitant List of countries by GDP per capita—GDP calculated at purchasing power parity exchange per inhabitant List of countries by GNI per capita List of countries by GNI per capita List of countries by income equality Total personal income
Van Zandt County, Texas
Van Zandt County is a county located in the U. S. state of Texas, in the northeastern part of the state. As of the 2010 census, its population was 52,579, its county seat is Canton. The county is named for a member of the Congress of the Republic of Texas. According to the U. S. Census Bureau, the county has a total area of 860 square miles, of which 843 square miles is land and 17 square miles is covered by water. Van Zandt County is unique in topography; the western and northwestern parts of the county are in the eastern edge of the Texas Blackland Prairies, the central part of the county is located in the post oak belt of Northeast Texas, the eastern part of the county stretches into the East Texas Piney Woods. Two major rivers, the Neches and the Sabine, flow through Van Zandt County. Van Zandt County is referred to as the "Gateway to East Texas" due to its diverse topography. Van Zandt County is known as the Free State of Van Zandt; the title was prevalent through the Reconstruction Era, but is still in use today.
Many versions of the county's history may account for this moniker, historians within the county and throughout its existence, do not agree how it became known as the Free State. One story of how the Free State of Van Zandt came to be originates with the county's formation. In 1848, Henderson County was split into three counties: Kaufman, Van Zandt, what remained as Henderson County. Henderson County had been in debt, yet the new Van Zandt County was founded without any obligations. Many believed that this was a mistake on the state's part, bitter citizens and politicians from Henderson County referred to the new county as the Free State. Van Zandt County tried on two distinct occasions to separate itself from Texas; the first was in 1861. About 350 citizens of Van Zandt County met to protest the secession; the practice of slavery was infrequent in the county. Slave-owners, worried about losing their slaves in the Civil War, refused to bring their slaves to Van Zandt, because slavery was so uncommon there.
The majority of Van Zandt wanted to stay with the Union, reasoned that if Texas could secede from the United States, they could secede from Texas, began organizing a government until they were threatened with military intervention. Although the secession was unsuccessful, the title of "Free State" stuck. After Texas reentered the Union after the Civil War, Van Zandt County again tried to secede from Texas, the Confederate States of America, the United States. A convention was held in 1867 in which the citizens elected delegates, the delegates voted for secession, penned a Declaration of Independence modeled after the United States Declaration of Independence; the event was seen as a rebellion by the nation, when word reached General Sheridan, he dispatched a cavalry unit to quell it. The citizens of Van Zandt called an emergency meeting which ended with the delegates declaring war on the United States; the wooded landscape at the time made it difficult for horses to move through, so the citizens of Van Zandt, familiar with the area, were able to ambush the unit, until they retreated.
The citizens, elated with their victory, celebrated with an excess of alcohol. During their celebration, they were surrounded by Sheridan's troops, were put in anklets and in a rough prison of wooden posts. Two ex-Confederate soldiers, W. A. Allen and Hardy Allen, were in the group, W. A. Allen used a hidden knife to wear down the anklets. A combination of the beginning of the rainy season and a decreasing of the guard to one man allowed the prisoners to escape. After that, not much action on the part of Van Zandt or the United States was taken in the issue. Arrest warrants were sent, but none was carried out, none of the prisoners went to trial; as of the census of 2000, there were 48,140 people, 18,195 households, 13,664 families residing in the county. The population density was 57 people per square mile. There were 20,896 housing units at an average density of 25 per square mile; the racial makeup of the county was 91.96% White, 2.94% Black or African American, 0.62% Native American, 0.18% Asian, 0.03% Pacific Islander, 2.71% from other races, 1.56% from two or more races.
6.65% of the population were Hispanic or Latino of any race. There were 18,195 households out of which 31.80% had children under the age of 18 living with them, 62.60% were married couples living together, 8.70% had a female householder with no husband present, 24.90% were non-families. 22.00% of all households were made up of individuals and 11.60% had someone living alone, 65 years of age or older. The average household size was 2.59 and the average family size was 3.01. In the county, the population was spread out with 25.50% under the age of 18, 7.30% from 18 to 24, 25.20% from 25 to 44, 24.90% from 45 to 64, 17.00% who were 65 years of age or older. The median age was 40 years. For every 100 females there were 97.00 males. For every 100 females age 18 and over, there were 93.70 males. The median income for a household in the county was $35,029, the median income for a family was $41,175. Males had a median income of $31,887 versus $21,344 for females; the per capita income for the county was $16,930.
About 10.30% of families and 13.30% of the population were below the poverty line, including 15.90% of those under age 18 and 12.10% of those age 65 or over. Van Zandt County Regional Airport Canton-Hackney Airport Interstate 20 U. S. Highway 80 State Highway 19 State Highway 64 State Highway 110 State Highway 198 Rains County Wood County Smith County Henderson County Kaufman County Hunt C
Population density is a measurement of population per unit area or unit volume. It is applied to living organisms, most of the time to humans, it is a key geographical term. In simple terms population density refers to the number of people living in an area per kilometer square. Population density is population divided by total land water volume, as appropriate. Low densities may lead to further reduced fertility; this is called the Allee effect after the scientist. Examples of the causes in low population densities include: Increased problems with locating sexual mates Increased inbreeding For humans, population density is the number of people per unit of area quoted per square kilometer or square mile; this may be calculated for a county, country, another territory or the entire world. The world's population is around 7,500,000,000 and Earth's total area is 510,000,000 square kilometers. Therefore, the worldwide human population density is around 7,500,000,000 ÷ 510,000,000 = 14.7 per km2. If only the Earth's land area of 150,000,000 km2 is taken into account human population density is 50 per km2.
This includes all continental and island land area, including Antarctica. If Antarctica is excluded population density rises to over 55 people per km2. However, over half of the Earth's land mass consists of areas inhospitable to human habitation, such as deserts and high mountains, population tends to cluster around seaports and fresh-water sources. Thus, this number by itself does not give any helpful measurement of human population density. Several of the most densely populated territories in the world are city-states and dependencies; these territories have a small area and a high urbanization level, with an economically specialized city population drawing on rural resources outside the area, illustrating the difference between high population density and overpopulation The potential to maintain the agricultural aspects of deserts is limited as there is not enough precipitation to support a sustainable land. The population in these areas are low. Therefore, cities in the Middle East, such as Dubai, have been increasing in population and infrastructure growth at a fast pace.
Cities with high population densities are, by some, considered to be overpopulated, though this will depend on factors like quality of housing and infrastructure and access to resources. Most of the most densely populated cities are in Southeast Asia, though Cairo and Lagos in Africa fall into this category. City population and area are, however dependent on the definition of "urban area" used: densities are invariably higher for the central city area than when suburban settlements and the intervening rural areas are included, as in the areas of agglomeration or metropolitan area, the latter sometimes including neighboring cities. For instance, Milwaukee has a greater population density when just the inner city is measured, the surrounding suburbs excluded. In comparison, based on a world population of seven billion, the world's inhabitants, as a loose crowd taking up ten square feet per person, would occupy a space a little larger than Delaware's land area; the Gaza Strip has a population density of 5,046 pop/km.
Although arithmetic density is the most common way of measuring population density, several other methods have been developed to provide a more accurate measure of population density over a specific area. Arithmetic density: The total number of people / area of land Physiological density: The total population / area of arable land Agricultural density: The total rural population / area of arable land Residential density: The number of people living in an urban area / area of residential land Urban density: The number of people inhabiting an urban area / total area of urban land Ecological optimum: The density of population that can be supported by the natural resources Demography Human geography Idealized population Optimum population Population genetics Population health Population momentum Population pyramid Rural transport problem Small population size Distance sampling List of population concern organizations List of countries by population density List of cities by population density List of city districts by population density List of English districts by population density List of European cities proper by population density List of United States cities by population density List of islands by population density List of U.
S. states by population density List of Australian suburbs by population density Selected Current and Historic City, Ward & Neighborhood Density Duncan Smith / UCL Centre for Advanced Spatial Analysis. "World Population Density". Exploratory map shows data from the Global Human Settlement Layer produced by the European Commission JRC and the CIESIN Columbia University
A thicket is a dense stand of trees or tall shrubs dominated by only one or a few species, to the exclusion of all others. They may be formed by species that shed large numbers of viable seeds that are able to germinate in the shelter of the maternal plants. In some conditions the formation or spread of thickets may be assisted by human disturbance of an area. Where a thicket is formed of briar, a common name for any of a number of unrelated thorny plants, it may be called a briar patch. Plants termed briar include species in the genera Rosa and Smilax. Patent thicket
Race and ethnicity in the United States Census
Race and ethnicity in the United States Census, defined by the federal Office of Management and Budget and the United States Census Bureau, are self-identification data items in which residents choose the race or races with which they most identify, indicate whether or not they are of Hispanic or Latino origin. The racial categories represent a social-political construct for the race or races that respondents consider themselves to be and, "generally reflect a social definition of race recognized in this country." OMB defines the concept of race as outlined for the US Census as not "scientific or anthropological" and takes into account "social and cultural characteristics as well as ancestry", using "appropriate scientific methodologies" that are not "primarily biological or genetic in reference." The race categories include both national-origin groups. Race and ethnicity are considered separate and distinct identities, with Hispanic or Latino origin asked as a separate question. Thus, in addition to their race or races, all respondents are categorized by membership in one of two ethnic categories, which are "Hispanic or Latino" and "Not Hispanic or Latino".
However, the practice of separating "race" and "ethnicity" as different categories has been criticized both by the American Anthropological Association and members of US Commission on Civil Rights. In 1997, OMB issued a Federal Register notice regarding revisions to the standards for the classification of federal data on race and ethnicity. OMB developed race and ethnic standards in order to provide "consistent data on race and ethnicity throughout the Federal Government; the development of the data standards stem in large measure from new responsibilities to enforce civil rights laws." Among the changes, OMB issued the instruction to "mark one or more races" after noting evidence of increasing numbers of interracial children and wanting to capture the diversity in a measurable way and having received requests by people who wanted to be able to acknowledge their or their children's full ancestry rather than identifying with only one group. Prior to this decision, the Census and other government data collections asked people to report only one race.
The OMB states, "many federal programs are put into effect based on the race data obtained from the decennial census. Race data are critical for the basic research behind many policy decisions. States require these data to meet legislative redistricting requirements; the data are needed to monitor compliance with the Voting Rights Act by local jurisdictions". "Data on ethnic groups are important for putting into effect a number of federal statutes. Data on Ethnic Groups are needed by local governments to run programs and meet legislative requirements." The 1790 United States Census was the first census in the history of the United States. The population of the United States was recorded as 3,929,214 as of Census Day, August 2, 1790, as mandated by Article I, Section 2 of the United States Constitution and applicable laws."The law required that every household be visited, that completed census schedules be posted in'two of the most public places within, there to remain for the inspection of all concerned...' and that'the aggregate amount of each description of persons' for every district be transmitted to the president."
This law along with U. S. marshals were responsible for governing the census. One third of the original census data has been lost or destroyed since documentation; the data was lost in 1790–1830 time period and included data from: Connecticut, Maryland, New Hampshire, New York, North Carolina, Rhode Island, South Carolina, Delaware, New Jersey, Virginia. Census data included the name of the head of the family and categorized inhabitants as follows: free white males at least 16 years of age, free white males under 16 years of age, free white females, all other free persons, slaves. Thomas Jefferson the Secretary of State, directed marshals to collect data from all thirteen states, from the Southwest Territory; the census was not conducted in Vermont until 1791, after that state's admission to the Union as the 14th state on March 4 of that year. There was some doubt surrounding the numbers, President George Washington and Thomas Jefferson maintained the population was undercounted; the potential reasons Washington and Jefferson may have thought this could be refusal to participate, poor public transportation and roads, spread out population, restraints of current technology.
No microdata from the 1790 population census is available, but aggregate data for small areas and their compatible cartographic boundary files, can be downloaded from the National Historical Geographic Information System. In 1800 and 1810, the age question regarding free white males was more detailed; the 1820