Summary File 2 contains 100-percent United States decennial Census data, which is the information compiled from the questions asked of all people and about every housing unit. Population items include sex, age, race, Hispanic or Latino origin, household relationship, and group quarters occupancy. Housing items include occupancy status, vacancy status, and tenure (owner occupied or renter occupied). The 100-percent data are presented in 36 population tables ("PCT") and 11 housing tables ("HCT") down to the census tract level. Each table is iterated for 250 population groups: the total population, 132 race groups, 78 American Indian and Alaska Native tribe categories (reflecting 39 individual tribes), and 39 Hispanic or Latino groups. The presentation of tables for any of the 250 population groups is subject to a population threshold of 100 or more people. That is, if there were fewer than 100 people in a specific population group in a specific geographic area, their population and housing characteristics data are not available for that geographic area.

The U.S. Population Grids (Summary File 1), 2000: Alabama, Louisiana, Mississippi and Texas, Alpha Version data set contains an ARC/INFO Workspace with grids of demographic data from the year 2000 census. The grids have a resolution of 30 arc-seconds (0.0083 decimal degrees), or approximately 1 square km. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables (population, households, and housing variables). This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN). To provide gridded demographic data, including characteristics of age, race, ethnicity, and housing, for Alabama, Louisiana, Mississippi and Texas.

Summary File 3 contains sample data, which is the information compiled from the questions asked of a sample of all people and housing units in the United States. Population items include basic population totals as well as counts for the following characteristics: urban and rural, households and families, marital status, grandparents as caregivers, language and ability to speak English, ancestry, place of birth, citizenship status, year of entry, migration, place of work, journey to work (commuting), school enrollment and educational attainment, veteran status, disability, employment status, industry, occupation, class of worker, income, and poverty status. Housing items include basic housing totals and counts for urban and rural, number of rooms, number of bedrooms, year moved into unit, household size and occupants per room, units in structure, year structure built, heating fuel, telephone service, plumbing and kitchen facilities, vehicles available, value of home, and monthly rent and shelter costs. The Summary File 3 population tables are identified with a "P" prefix and the housing tables are identified with an "H," followed by a sequential number. The "P" and "H" tables are shown for the block group and higher level geography, while the "PCT" and "HCT" tables are shown for the census tract and higher level geography. There are 16 "P" tables, 15 "PCT" tables, and 20 "HCT" tables that bear an alphabetic suffix on the table number, indicating that they are repeated for nine major race and Hispanic or Latino groups. There are 484 population tables and 329 housing tables for a total of 813 unique tables.

1. The spread of many diseases depends on the demography and dispersal of arthropod vectors. Classic epidemiological theory typically ignores vector dynamics and instead makes the simplifying assumption of frequency-dependent transmission. Yet vector ecology may be critical for understanding the spread of disease over space and time and how disease dynamics respond to environmental change.
2. Here, we ask how environmental change shapes vector demography and dispersal, and how these traits of vectors govern the spatiotemporal spread of disease.
3. We developed disease models parameterized by traits of vectors and fit them to experimental epidemics. The experiment featured a viral pathogen (CYDV-RPV) vectored by aphids (Rhopalosiphum padi) among populations of grass hosts (Avena sativa) under two rates of environmental resource supply (i.e., fertilization of the host). We compared a non-spatial model that ignores vector movement, a lagged dispersal model that emphasizes the delay between vector reproduction and dispersal, and a travelling wave model that generates waves of infections across space and time.
4. Resource supply altered both vector demography and dispersal. The lagged dispersal model fit best, indicating that vectors first reproduced and then dispersed among hosts in the experiment. Elevated resources decreased vector population growth rates, nearly doubled their carrying capacity per host, increased dispersal rates when vectors carried the virus, and homogenized disease risk across space.
5. Together, the models and experiment show how environmental eutrophication can shape spatial disease dynamics – for example, homogenizing disease risk across space – by altering the demography and behavior of vectors.