The map data and summary statistics data are sourced from Johns Hopkins University and Esri’s Living Atlas. The charts are being sourced from a database created by Timmons Group GIS that leverages the temporal data provided by JHU on github.

Why did we do this?

1. The JHU dashboard is focused on Global and one can only drill down to a country-level for charting and summary statistics
2. We wanted to create a US Centric dashboard that one could drill down to the State level and County level for charting and summary statistics

How did we do this?

The raw data from JHU does not support the temporal charting at the State level or County level, so we created a data pipeline to leverage JHU’s source data files and transforms their raw data into our data model

Key features:

1. The only US centric dashboard with State and County level temporal charts of COVID-19 data
2. Ability to select multiple States or Counties and have maps and charts reflect the aggregate of those states/counties
3. Truly responsive design web-app; our dashboard works on desktop/tablet/phone without the need for users to select multiple apps
4. Ability to see the hardest impact States from the State table and exploring their associated charts
5. Ability to see the hardest impacted counties by the County table and exploring their associated charts
6. Ability to see the hardest impacted counties per State by selecting a State and exploring their associated charts

Check out our other ArcGIS Dashboard powered by the new ArcGIS Experience Builder to explore the COVID-19 curves at the country level around the world - Explore the COVID-19 Curve

For additional information, please contact:

• Mike Wiley
• Brian Kingery, GISP (@briankingery87)
• Jenn Kennedy

The net job and business growth indicator measures the annual change in both the number of firms and the number of employees between 1978 and 2022. The data is categorized by the size of the firm: those with 1-19 employees, those with between 20 and 499 employees, and those with more than 500 employees.

This data contributes to the big picture of economic conditions in Champaign County. More firms and larger employment numbers are generally positive economic indicators, but any strictly economic indicator should be considered in the context of other factors.

The number of firms and number of employees show very different trends.

Historically, there have been significantly more firms with 1-19 employees than firms in the larger two size categories. The number of firms with 1-19 employees has also been relatively consistent until 2021: there were 95 fewer such firms in 2021 than 1978, and the largest year-to-year change in that 43-year period of analysis was a -3.2% decrease between 1979 and 1980. However, there were 437 fewer such firms in 2022 than 1978. There was a decrease in these firms of 12.5% from 2021 to 2022, the only double-digit year-to-year change and the largest year-to-year change over 44 years.

The larger two size categories have shown an increasing trend over the period of analysis. There were 43 more firms with 20-499 employees in 2022 than 1978, a total increase of 9%. The number of firms with more than 500 employees almost doubled, increasing by 206 firms from 212 in 1978 to 418 in 2022, a total increase of 97.2%.

The trends of employment also vary based on firm size. Firms with 1-19 employees have consistently, and unsurprisingly, accounted for less of the total employment than the larger two categories. Employment in firms with 1-19 employees has also remained relatively consistent over the period of analysis. Employment in firms with more than 500 employees saw an overall trend of growth, interrupted by brief and intermittent decreases, between 1978 and 2022. Employment in the middle category (firms with between 20 and 499 employees) was also greater in 2022 than in 1978.

This data is from the U.S. Census Bureau’s Business Dynamics Statistics Data Tables. This data is at the geographic scale of the Champaign-Urbana Metropolitan Statistical Area (MSA), which is comprised of Champaign and Piatt Counties, or a larger area than the cities or Champaign County.

Source: U.S. Census Bureau; 2022 Business Dynamics Statistics Data Tables; "BDSFSIZE - Business Dynamics Statistics: Firm Size: 1978-2022"; retrieved 21 October 2024.

The main stock market index in the United States (US500) decreased 176 points or 2.99% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on March of 2025.

No description is available. Visit https://dataone.org/datasets/6bd4cd3262872a9d1511a0c73554d203 for complete metadata about this dataset.

Author: ANN WURST, educator, NGS TEACHER CONSULTANTGrade/Audience: grade 1, grade 2, grade 3, grade 4, grade 5, grade 6, grade 7, grade 8, high school, ap human geography, post secondary, professional developmentResource type: warm_upSubject topic(s): geographic thinkingRegion: worldStandards: (19) Social studies skills. The student applies critical-thinking skills to organize and use information acquired through established research methodologies from a variety of valid sources, including technology. The student is expected to: (A) analyze information by sequencing, categorizing, identifying cause-and-effect relationships, comparing, contrasting, finding the main idea, summarizing, making generalizations and predictions, and drawing inferences and conclusions;

(D) analyze and evaluate the validity of information, arguments, and counterarguments from primary and secondary sources for bias, propaganda, point of view, and frame of reference;

(E) evaluate government data using charts, tables, graphs, and maps. Objectives: Students will keep a list of the toolkit 'helpers' in their notebook and use the elements to process/apply information in various formats such as short answers responses, tickets out the door, setting up writing samples for World Cultures, World Geo, AP Human Geography and other courses involving the study of geographic concepts. Summary: Students can use these 'hooks' in their study of geography, can be applied in every unit where geography is studied. Helps further critical thinking skills. These specific helpers are for reading charts and graphs.

This is a collection of Excel spreadsheet tables in support of the article "A pan-genome method to determine core regions of the Bacillus subtilis and Escherichia coli genomes" DOI (10.12688/f1000research.51873.1). The tables show characteristics of pan-genome graph (PGG) determined core genes for Bacillus subtilis and Escherichia coli.Table 1. Pan-genome graph statistics for B. subtilis and E. coli.Table 2. The number of deleted genes from B. subtilis reduced strains which are noncore versus core.Table 3. Large noncore regions which have not been deleted from any of the strains delta 6, IIG-Bs27-47-24, or PG10, PS38.Supplementary Table 1. All B. subtilis genes compared to the PGG based annotation of core regions for the type strain genome used by Kobayashi et al and Koo et al. (strain 168, GenBank sequence AL009126.3, BioSample SAMEA3138188, Assembly ASM904v1/GCF_000009045.1). Columns 1-5 are the start, stop, strand, OGC, and OGC size for the PGG annotation. Columns 6-11 are the gene type, start, stop, strand, locus tag, and gene symbol/name for the GenBank annotation. Column 12 is a list of synonyms for B. subtilis genes associated with the Koo and Kobayashi genes. Column 13 is the Koo et al. gene symbol/name. Columns 14-15 are the Kobayashi et al. gene symbol/name and evidence type (from Supporting Table 4 “RB, reference to study with Bacillus subtilis; RO, reference to study with other bacteria; TW, this work; TW*, inactivation failed but IPTG mutant could not be made”). Columns 16-17 are the GenBank protein product accession and name. Column 18 is the PGG core or non-core region the gene is contained in. Column 19 indicates if the gene is in MiniBacillus. Columns 20-23 show genes deleted for strains delta 6, IIG-Bs27-47-24, PG10, and PS38 respectively. Supplementary Table 2. The 305 B. subtilis genes deemed essential by either Kobayashi et al. or Koo et al. These genes are compared to the PGG based annotation of core regions for the type strain genome used by Kobayashi et al. and Koo et al. (strain 168, GenBank sequence AL009126.3, BioSample SAMEA3138188, Assembly ASM904v1/GCF_000009045.1). Columns 1-5 are the start, stop, strand, OGC, and OGC size for the PGG annotation. Columns 6-11 are the gene type, start, stop, strand, locus tag, and gene symbol/name for the GenBank annotation. Column 12 is a list of synonyms for B. subtilis genes associated with the Koo and Kobayashi genes. Column 13 is the Koo et al. gene symbol/name. Columns 14-15 are the Kobayashi et al. gene symbol/name and evidence type (from Supporting Table 4 “RB, reference to study with Bacillus subtilis; RO, reference to study with other bacteria; TW, this work; TW*, inactivation failed but IPTG mutant could not be made”). Columns 16-17 are the GenBank protein product accession and name. Column 18 is the PGG core or non-core region the gene is contained in. Supplementary Table 3. The 258 B. subtilis core regions identified through the pan-genome graph. Supplementary Table 4. All B. subtilis tRNA and rRNA genes for the type strain genome (strain 168, GenBank sequence AL009126.3, BioSample SAMEA3138188, Assembly ASM904v1/GCF_000009045.1) compared to the refined PGG. Columns 1-5 are the start, stop, strand, OGC, and OGC size for the PGG annotation. Columns 6-11 are the gene type, start, stop, strand, locus tag, and gene symbol/name for the GenBank annotation. Supplementary Table 5. The 108 B. subtilis genomes used in the study. Data is from GenBank RefSeq: BioSample ID, Assembly ID, GenBank Species, GenBank Strain, Genome SIaze, and whether the genome is a type strain. Supplementary Table 6. The 414 E. coli genes deemed essential by Goodall et al., Baba et al., or Yamazaki et al. These genes are compared to the PGG based annotation of core regions for the K-12 BW25113 strain used by Goodall (GenBank sequence CP009273.1, Assembly ASM75055v1/GCA_000750555.1, BioSample SAMN03013572). Columns 1-5 are the start, stop, strand, OGC, and OGC size for the PGG annotation. Columns 6-11 are the gene type, start, stop, strand, locus tag, and gene symbol/name for the GenBank annotation. Column 12 is a list of gene synonyms for the gene from GenBank. Columns 13-21 are from Goodall et al.: 13-15 from Table S1 (normal essentiality), 16-18 from Table S4 (essentiality after outgrowth), 19-20 from Table S3 (outlier discrepancies), and 21 from Table S2 (comparison of data sets). Column 22 is the PGG core or non-core region the gene is contained in.Supplementary Table 7. The 521 E. coli core regions identified through the pan-genome graph.Supplementary Table 8. All E. coli genes compared to the PGG based annotation of core regions for the K-12 BW25113 strain used by Goodall (GenBank sequence CP009273.1, Assembly ASM75055v1/GCA_000750555.1, BioSample SAMN03013572). Columns 1-5 are the start, stop, strand, OGC, and OGC size for the PGG annotation. Columns 6-11 are the gene type, start, stop, strand, locus tag, and gene symbol/name for the GenBank annotation. Column 12 is a list of gene synonyms for the gene from GenBank. Columns 13-21 are from Goodall et al.: 13-15 from Table S1 (normal essentiality), 16-18 from Table S4 (essentiality after outgrowth), 19-20 from Table S3 (outlier discrepancies), and 21 from Table S2 (comparison of data sets). Column 22 is the PGG core or non-core region the gene is contained in.

Supplementary Table 9. The 971 E. coli genomes used in the study. Data is from GenBank RefSeq: BioSample ID, Assembly ID, GenBank Species, GenBank Strain, Genome SIaze, and whether the genome is a type strain.

Tables and charts have long been seen as effective ways to convey data. Much attention has been focused on improving charts, following ideas of human perception and brain function. Tables can also be viewed as two-dimensional representations of data, yet it is only fairly recently that we have begun to apply principles of design that aid the communication of information between the author and reader. In this study, we collated guidelines for the design of data and statistical tables. These guidelines fall under three principles: aiding comparisons, reducing visual clutter, and increasing readability. We surveyed tables published in recent issues of 43 journals in the fields of ecology and evolutionary biology for their adherence to these three principles, as well as author guidelines on journal publisher websites. We found that most of the over 1,000 tables we sampled had no heavy grid lines and little visual clutter. They were also easy to read, with clear headers and horizontal orient..., Once we had established the above principles of table design, we assessed their use in issues of 43 widely read ecology and evolution journals (SI 2). Between January and July 2022, we reviewed the tables in the most recent issue published by these journals. For journals without issues (such as Annual Review of Ecology, Evolution, and Systematics, or Biological Conservation), we examined the tables in issues published in a single month or in the entire most recent volume if few papers were published in that journal on a monthly basis. We reviewed only articles in a traditionally typeset format and published as a PDF or in print. We did not examine the tables in online versions of articles. Having identified all tables for review, we assessed whether these tables followed the above-described best practice principles for table design and, if not, we noted the way in which these tables failed to meet the outlined guidelines. We initially both reviewed the same 10 tables to ensure that we a..., , # Design of tables for the presentation and communication of data in ecological and evolutionary biology

Once we had established the above principles of table design, we assessed their use in issues of 43 widely read ecology and evolution journals (SI 2). Between January and July 2022, we reviewed the tables in the most recent issue published by these journals. For journals without issues (such as Annual Review of Ecology, Evolution, and Systematics, or Biological Conservation), we examined the tables in issues published in a single month or in the entire most recent volume if few papers were published in that journal on a monthly basis. We reviewed only articles in a traditionally typeset format and published as a PDF or in print. We did not examine the tables in online versions of articles.

Having identified all tables for review, we assessed whether these tables followed the above-described best practice principles for table design and, if not, we noted the way in which these ...

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset contains time series figures (shown in the report) generated for baseline and crdp mine footprints , which represent the footprints used in the surface water modelling. The footprints are contained within a single shapefile (HUN Mine footprints for timeseries) and the timelines contained within the the spreadhseet (HUN mine time series tables v01). Dataset History The footprints are contained within a single shapefile (HUN Mine footprints for timeseries) and the timelines contained within the the spreadsheet (HUN mine time series tables v01). Timelines for all mines were assembled into the spreadsheet Mine_files_summary_Final.xlsx. The script MineFootprint_TimeSeries_Final.m reads the data from the spreadsheet and creates the time series figures in png format which form the dataset. Dataset Citation Bioregional Assessment Programme (XXXX) HUN Mine Footprints Timeseries Graph v01. Bioregional Assessment Derived Dataset. Viewed 22 June 2018, http://data.bioregionalassessments.gov.au/dataset/11493517-df5f-49ed-84dc-23afdbe00c5e. Dataset Ancestors Derived From HUN Groundwater footprint polygons v01 Derived From HUN mine time series tables v01 Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012 Derived From HUN Historical Landsat Images Mine Foot Prints v01 Derived From Historical Mining footprints DTIRIS HUN 20150707 Derived From HUN Mine footprints for timeseries Derived From Climate model 0.05x0.05 cells and cell centroids Derived From HUN Historical Landsat Derived Mine Foot Prints v01 Derived From HUN SW footprint shapefiles v01 Derived From Mean Annual Climate Data of Australia 1981 to 2012

Historical dataset of the daily level of the federal funds rate back to 1954. The fed funds rate is the interest rate at which depository institutions (banks and credit unions) lend reserve balances to other depository institutions overnight, on an uncollateralized basis. The Federal Open Market Committee (FOMC) meets eight times a year to determine the federal funds target rate.

After detemining that there is no direct connection to the port in the network diagram, get the direct connection distance between ports through the port.sol.com.cn、SeaRates.com and McDistance shipping calculation tool. If there is a big difference between the three query data, the average value method is used for optimization, get the table Port distance.
Using the Floyd algorithm, the path between two ports in the port network graph is solved on the basis of the table Port Distance, there maybe multiple shortest paths between two ports, but this situation is not considered here, the only result will be the result of Python simulation, get the table Port Shortest Path.
After get the Port Shortest Path, calculate the value of the shortest path between two ports, get the table Port Shortest Path Value.
According to the shortest path between two ports, count the number of routes for each port, then use the K-Medoids, construting the model of strategic importance of ports, get the table Port Passes Number Group.
According to the principle of the Betweenness Centrality model, the Betweenness Centrality of each port in the whole network is obtained by the table Port Shortest Path, and then use the K-Medoids, get the table Betweenness Centrality Group.
The values and contents of the table Port Passes Number Group and the table Betweenness Centrality Group are combined together to get the table Total Group to facilitate data search.

The code will run on an installation of R with the add on packages lattice, dplyr, lattice, and latticeExtra. The output is a graph (Fig. 2) and a table showing likelihood ratios of run chart rules for identification of non-random variation in simulated run charts of different length with or without a shift in process mean. (R)

"The Statistical Abstract of the United States, published since 1878, is the standard summary of statistics on the social, political, and economic organization of the United States. It is designed to serve as a convenient volume for statistical reference and as a guide to other statistical publications and sources. The latter function is served by the introductory text to each section, the source note appearing below each table, and Appendix I, which comprises the Guide to Sources of Statisti cs, the Guide to State Statistical Abstracts, and the Guide to Foreign Statistical Abstracts. The Statistical Abstract sections and tables are compiled into one Adobe PDF named StatAbstract2009.pdf. This PDF is bookmarked by section and by table and can be searched using the Acrobat Search feature. The Statistical Abstract on CD-ROM is best viewed using Adobe Acrobat 5, or any subsequent version of Acrobat or Acrobat Reader. The Statistical Abstract tables and the metropolitan areas tables from Appendix II are available as Excel(.xls or .xlw) spreadsheets. In most cases, these spreadsheet files offer the user direct access to more data than are shown either in the publication or Adobe Acrobat. These files usually contain more years of data, more geographic areas, and/or more categories of subjects than those shown in the Acrobat version. The extensive selection of statistics is provided for the United States, with selected data for regions, divisions, states, metropolitan areas, cities, and foreign countries from reports and records of government and private agencies. Software on the disc can be used to perform full-text searches, view official statistics, open tables as Lotus worksheets or Excel workbooks, and link directly to source agencies and organizations for supporting information. Except as indicated, figures are for the United States as presently constituted. Although emphasis in the Statistical Abstract is primarily given to national data, many tables present data for regions and individual states and a smaller number for metropolitan areas and cities.Statistics for the Commonwealth of Puerto Rico and for island areas of the United States are included in many state tables and are supplemented by information in Section 29. Additional information for states, cities, counties, metropolitan areas, and other small units, as well as more historical data are available in various supplements to the Abstract. Statistics in this edition are generally for the most recent year or period available by summer 2006. Each year over 1,400 tables and charts are reviewed and evaluated; new tables and charts of current interest are added, continuing series are updated, and less timely data are condensed or eliminated. Text notes and appendices are revised as appropriate. This year we have introduced 72 new tables covering a wide range of subject areas. These cover a variety of topics including: learning disability for children, people impacted by the hurricanes in the Gulf Coast area, employees with alternative work arrangements, adult computer and Internet users by selected characteristics, North America cruise industry, women- and minority-owned businesses, and the percentage of the adult population considered to be obese. Some of the annually surveyed topics are population; vital statistics; health and nutrition; education; law enforcement, courts and prison; geography and environment; elections; state and local government; federal government finances and employment; national defense and veterans affairs; social insurance and human services; labor force, employment, and earnings; income, expenditures, and wealth; prices; business enterprise; science and technology; agriculture; natural resources; energy; construction and housing; manufactures; domestic trade and services; transportation; information and communication; banking, finance, and insurance; arts, entertainment, and recreation; accommodation, food services, and other services; foreign commerce and aid; outlying areas; and comparative international statistics." Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

This table contains mortality indicators by sex for Canada and all provinces except Prince Edward Island. These indicators are derived from three-year complete life tables. Mortality indicators derived from single-year life tables are also available (table 13-10-0837). For Prince Edward Island, Yukon, the Northwest Territories and Nunavut, mortality indicators derived from three-year abridged life tables are available (table 13-10-0140).

No description is available. Visit https://dataone.org/datasets/143db554688ee9555f0e4c5d3f0e98b9 for complete metadata about this dataset.

The housing affordability measure illustrates the relationship between income and housing costs. A household that spends 30% or more of its collective monthly income to cover housing costs is considered to be “housing cost-burden[ed].”[1] Those spending between 30% and 49.9% of their monthly income are categorized as “moderately housing cost-burden[ed],” while those spending more than 50% are categorized as “severely housing cost-burden[ed].”[2]

How much a household spends on housing costs affects the household’s overall financial situation. More money spent on housing leaves less in the household budget for other needs, such as food, clothing, transportation, and medical care, as well as for incidental purchases and saving for the future.

The estimated housing costs as a percentage of household income are categorized by tenure: all households, those that own their housing unit, and those that rent their housing unit.

Throughout the period of analysis, the percentage of housing cost-burdened renter households in Champaign County was higher than the percentage of housing cost-burdened homeowner households in Champaign County. All three categories saw year-to-year fluctuations between 2005 and 2023, and none of the three show a consistent trend. However, all three categories were estimated to have a lower percentage of housing cost-burdened households in 2023 than in 2005.

Data on estimated housing costs as a percentage of monthly income was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Housing Tenure.

[1] Schwarz, M. and E. Watson. (2008). Who can afford to live in a home?: A look at data from the 2006 American Community Survey. U.S. Census Bureau.

[2] Ibid.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (22 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (30 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).;U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; 16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).

The Statistical Abstract is the Nation's best known and most popular single source of statistics on the social, political, and economic organization of the country. The print version of this reference source has been published since 1878 while the compact disc version first appeared in 1993. This disc is designed to serve as a convenient, easy-to-use statistical reference source and guide to statistical publications and sources. The disc contains over 1,400 tables from over 250 different gove rnmental, private, and international organizations. The 1998 Statistical Abstract on CD-ROM, like the book, is a statistical reference and guide to over 250 statistical publications and sources from government and private organizations. This compact disc (CD) has 1,500 tables and charts from over 250 sources. Text and tables can be viewed or searched with the software. Tables and charts cover these subjects in 31 sections and 2 appendices: Population, Vital Statistics, Health and Nutrition, Education, Law Enforcement, Courts and Prisons, Geography and Environment, Parks, Recreation and Travel, Elections, State and Local Government, Finances and Employment, Federal Government, Finances and Employment, National Defense and Veterans Affairs, Social Insurance and Human Services, Labor Force, Employment and Earnings, Income, Expenditure and Wealth, Prices, Banking, Finance and Insurance, Business Enterprise, Communications, Energy, Science, Transportation -- Land, Transportation -- Air and Water, Agriculture, Forests and Fisheries, Mining and Mineral Products, Construction and Housing, Manufactures, Domestic Trade and Services, Foreign Commerce and Aid, Outlying Areas, Comparative International Statistics, State Rankings, Population of MSAs, Congressional District Profiles. There are changes this year in both the content of the information on the disc and software used for accessing and installing the information. As usual, updates have been made to most of the more than 1,500 tables and charts that were on the previous disc with new or more recent data. The spreadsheet files which are available in both the Excel or Lotus formats for these ta bles will usually have more information than displayed in the book or Adobe Acrobat files. There are also 93 new tables on such subjects as family planning, women's health, persons with disabilities, health insurance coverage, ambulatory surgery, school violence, household use of public libraries, public library of the Internet, toxic chemical releases, leisure activity, NCAA sports and high school athletic programs, voter registration, licensed child care centers, foster care, home-based businesses, employee benefits, home equity debt, use of debit credit cards, alcohol-related fatal accidents, computer shipments, and foreign stock market indices. See Appendix V on the disc for a complete list of the new tables presented. In the software area, a new opening screen using the DemoShield software has been added. This provide better access to the electronic version of the booklet which is available from the opening screen, the new tutorial step the user through the principal ways to search for information on this disc and other related helpful information. It will also facilitate the installation process for the Adobe Acrobat Reader, the new Microsoft Excel Viewer, and QuickTime for viewing movies. The Adobe Acrobat Reader and Search engine, version 3.01, is on the disc. The Acrobat Reader allows users to view, navigate, search, and print on demand any of the pages from the book. Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.