It contains nodes of USA with latitude, longitude co-ordinates of locations along with travel distance and duration.

Nodes : NodeID Longitude Latitutde

Edges : NodeID1 NodeID2 Distance Duration

Updates are delayed due to technical difficulties. How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our new mobility statistics. The Trips by Distance data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day. Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air. The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed. These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

Pairwise distance between all US counties.

This is a symmetrical matrix of distances between counties in kilometers

Each row corresponds to a county's (represented by its FIPS code) distance in kilometers to every other US county. Diagonal is zero as the distance between a county and itself is zero.

A global data set of distances from the nearest coastline. Negative distances represent locations over land (including land-locked bodies of water), while positive distances represent the ocean. NASA's Ocean Biology Processing Group (OBPG) generated this data set using the Generic Mapping Tools (GMT) software package. Distances were computed with GMT using its intermediate-resolution coastline and then gridded globally at a spatial resolution of 0.04 degrees. Bilinear interpolation was then applied to increase the spatial resolution to 0.01 degrees. There is an uncertainty of up to 1 km in the computed distance at any given point.

Land area (sq. km) in United States was reported at 9147420 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Land area (sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

A global data set of ocean distances from the nearest coastline. NASA's Ocean Biology Processing Group (OBPG) generated this data set using the Generic Mapping Tools (GMT) software package. Distances were computed with GMT using its intermediate-resolution coastline and then gridded globally at a spatial resolution of 0.04 degrees. Bilinear interpolation was then applied to increase the spatial resolution to 0.01 degrees. There is an uncertainty of up to 1 km in the computed distance at any given point.

United States US: Land Area data was reported at 9,147,420.000 sq km in 2017. This stayed constant from the previous number of 9,147,420.000 sq km for 2016. United States US: Land Area data is updated yearly, averaging 9,158,960.000 sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 9,161,920.000 sq km in 2007 and a record low of 9,147,420.000 sq km in 2017. United States US: Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Land Use, Protected Areas and National Wealth. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.; ; Food and Agriculture Organization, electronic files and web site.; Sum;

We studied >500 golden eagles tracked by telemetry over a 10-year period in western North America, of which 160 engaged in non-routine, long-distance (>300 km) movements. We identified spatial and temporal correlates of those movements at both small and large scales, and we quantified movement timing and direction. We further tested which age and sex classes of eagles were more likely to engage in these movements. This dataset includes data on daily distances and their correlates, long-distance event distances and durations and their correlates, event timing and directions, and eagle ages and sexes.

Population density (people per sq. km of land area) in United States was reported at 36.43 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

This table contains data on the percent of population residing within ½ mile of a major transit stop for four California regions and the counties, cities/towns, and census tracts within the regions. The percent was calculated using data from four metropolitan planning organizations (San Diego Association of Governments, Southern California Association of Governments, Metropolitan Transportation Commission, and Sacramento Council of Governments) and the U.S. Census Bureau. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. A strong and sustainable transportation system supports safe, reliable, and affordable transportation opportunities for walking, bicycling, and public transit, and helps reduce health inequities by providing more opportunities for access to healthy food, jobs, health care, education, and other essential services. Active and public transportation promote health by enabling individuals to increase their level of physical activity, potentially reducing the risk of heart disease and obesity, improving mental health, and lowering blood pressure. More information about the data table and a data dictionary can be found in the About/Attachments section.

United States US: Density of Road: km per One Hundred sq. km data was reported at 73.847 km/100 sq km in 2022. This records an increase from the previous number of 73.671 km/100 sq km for 2021. United States US: Density of Road: km per One Hundred sq. km data is updated yearly, averaging 71.126 km/100 sq km from Dec 1994 (Median) to 2022, with 29 observations. The data reached an all-time high of 73.847 km/100 sq km in 2022 and a record low of 68.638 km/100 sq km in 1998. United States US: Density of Road: km per One Hundred sq. km data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.ITF: Transport Infrastructure, Investment and Maintenance: OECD Member: Annual. [COVERAGE] LENGTH OF ROADS The road network is all roads in a given area. A road is a line of communication (travelled way) open to public traffic, primarily for the use of road motor vehicles, using a stabilised base other than rails or air strips. Paved roads and other roads with a stabilised base, e.g. gravel roads, are included. Roads also cover streets, bridges, tunnels, supporting structures, junctions, crossings and interchanges. Toll roads are also included. Dedicated cycle lanes are not included. [COVERAGE] LENGTH OF ROADS Road refers to the US definition of either roadway or traffic way. Roadway (travelled portion of road) and shoulder, if an, make up the road. Trafficway is the entire right-of-way (or land way set outside) containing one or more roads for traffic in the same or opposite directions. [STAT_CONC_DEF] LENGTH OF ROADS The length of the road is the distance between its start and end point. If one of the directions of the carriageway is longer than the other then the length is calculated as the sum of half of the distances of each direction of the carriageway from first entry point to last exit point.

This dataset is sourced from the U.S. Department of Transportation Bureau of Transportation Statistics. All data and metadata is sourced from the page linked below. Metadata is not updated automatically; data updates weekly.

Source Data Link: https://data.bts.gov/Research-and-Statistics/Trips-by-Distance/w96p-f2qv

How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our new mobility statistics.

The Trips by Distance data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day.

Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air.

The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed.

These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Far Hills, NJ, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 21,779, while the mean income for the highest quintile (20% of households with the highest income) is 943,938. This indicates that the top earners earn 43 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 1,717,043, which is 181.90% higher compared to the highest quintile, and 7883.94% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Far Hills median household income. You can refer the same here

Rail lines (total route-km) in United States was reported at 148553 total route-km in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Rail lines (total route-km) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

kokolamba/city-distance-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Accurate representation of stream networks at various scales in a hydrogeologic system is integral to modeling groundwater-stream interactions at the continental scale. To assess the accurate representation of stream networks, the distance of a point on the land surface to the nearest stream (DS) has been calculated. DS was calculated from the 30-meter Multi Order Hydrologic Position (MOHP) raster datasets for 18 watersheds in the United States that have been prioritized for intensive monitoring and assessment by the U.S. Geological Survey. DS was calculated by multiplying the 30-meter MOHP Lateral Position (LP) datasets by the 30-meter MOHP Distance from Stream Divide (DSD) datasets for stream orders one through five. DS was calculated for the purposes of considering the spatial scale needed for accurate representation of groundwater-stream interaction at the continental scale for a grid with 1-kilometer cell spacing. The data are available as Comma-Separated Value formatted files.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Far Hills. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Far Hills. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Far Hills, the median household income stands at $182,188 for householders within the 25 to 44 years age group, followed by $159,896 for the 45 to 64 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $79,583.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Far Hills median household income by age. You can refer the same here

United States US: Population Density: People per Square Km data was reported at 35.608 Person/sq km in 2017. This records an increase from the previous number of 35.355 Person/sq km for 2016. United States US: Population Density: People per Square Km data is updated yearly, averaging 26.948 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 35.608 Person/sq km in 2017 and a record low of 20.056 Person/sq km in 1961. United States US: Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.; ; Food and Agriculture Organization and World Bank population estimates.; Weighted average;

synthetic-distance

This is a dataset containing synthetic RGB images of 3D cubes along with text prompts and 4×4 transformation matrices representing object poses relative to the camera.

  Each entry includes:

• image: an RGB image rendered from a camera looking down at a 3D object.
• prompt: a natural language instruction describing the object and its dimensions.
• transform: a flattened 4×4 transformation matrix (16 values).
• distance: a float representing the distance… See the full description on the dataset page: https://huggingface.co/datasets/jwgcurrie/synthetic-distance.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Far Hills. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Far Hills, the median income for all workers aged 15 years and older, regardless of work hours, was $112,000 for males and $25,833 for females.

These income figures highlight a substantial gender-based income gap in Far Hills. Women, regardless of work hours, earn 23 cents for each dollar earned by men. This significant gender pay gap, approximately 77%, underscores concerning gender-based income inequality in the borough of Far Hills.

- Full-time workers, aged 15 years and older: In Far Hills, among full-time, year-round workers aged 15 years and older, males earned a median income of $119,167, while females earned $91,563, leading to a 23% gender pay gap among full-time workers. This illustrates that women earn 77 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Far Hills.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Far Hills median household income by race. You can refer the same here