VITAL SIGNS INDICATOR Fatalities From Crashes (EN4-6)

FULL MEASURE NAME Fatalities from crashes (traffic collisions)

LAST UPDATED May 2022

DESCRIPTION Fatalities from crashes refers to deaths as a result of injuries sustained in collisions. The California Highway Patrol includes deaths within 30 days of the collision that are a result of injuries sustained as part of this metric. This total fatalities dataset includes fatality counts for the region and counties, as well as individual collision data and metropolitan area data.

DATA SOURCE National Highway Safety Administration: Fatality Analysis Reporting System

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) The data is reported by the California Highway Patrol (CHP) to the Statewide Integrated Traffic Records System (SWITRS), which was accessed via SafeTREC’s Transportation Injury Mapping System (TIMS). The data was tabulated using provided categories specifying injury level, individuals involved, causes of collision, and location/jurisdiction of collision (for more: http://tims.berkeley.edu/help/files/switrs_codebook.doc).

For more regarding reporting procedures and injury classification, see the California Highway Patrol Manual (https://one.nhtsa.gov/nhtsa/stateCatalog/states/ca/docs/CA_CHP555_Manual_2_2003_ch1-13.pdf).

Dataset Description: This dataset is an amended version of the UC Berkeley Transportation Injury Mapping System (TIMS) collision records from the six SCAG Counties (Imperial, Los Angeles, Orange, Riverside, San Bernardino, and Ventura) of all collisions between January 1, 2015 and December 31, 2019, downloaded from the TIMS webpage on March 23, 2022. SCAG developed this collection of collisions to determine the Regional High-Injury network. This dataset represents collisions between 2015 and 2019 located in the SCAG region that resulted in serious injury or fatality and have a clear mode type (Auto-Auto, Auto-Pedestrian, or Auto-Bicycle). Some collisions from the original dataset needed to be manually verified for location. Data development/processing methodology:SCAG prepared the collision data by filtering out collisions from the data downloaded from TIMS that did not fall into the scope of this analysis per the location, collision severity, and mode parameters. For more details on the methodology, please contact the point of contact.

Vital Signs: Injuries From Crashes by County (2022) DRAFT

VITAL SIGNS INDICATOR Injuries From Crashes (EN7-9)

FULL MEASURE NAME Serious injuries from crashes (traffic collisions)

LAST UPDATED May 2022

DESCRIPTION Injuries from crashes refers to serious but not fatal injuries sustained in a collision. The California Highway Patrol classifies a serious injury as any combination of the following: broken bones; dislocated or distorted limbs; severe lacerations; skull, spinal, chest or abdominal injuries that go beyond visible injuries; unconsciousness at or when taken from the scene; or severe burns. This injuries dataset includes serious injury counts for the region and counties, as well as individual collision data.

DATA SOURCE California Highway Patrol: Statewide Integrated Traffic Records System

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) The data is reported by the California Highway Patrol (CHP) to the Statewide Integrated Traffic Records System (SWITRS), which was accessed via SafeTREC’s Transportation Injury Mapping System (TIMS). The data was tabulated using provided categories specifying injury level, individuals involved, causes of collision, and location/jurisdiction of collision (for more: http://tims.berkeley.edu/help/files/switrs_codebook.doc).

For more regarding reporting procedures and injury classification, see the California Highway Patrol Manual (https://one.nhtsa.gov/nhtsa/stateCatalog/states/ca/docs/CA_CHP555_Manual_2_2003_ch1-13.pdf).

Redirect Notice: The website https://transbase.sfgov.org/ is no longer in operation. Visitors to Transbase will be redirected to this page where they can view, visualize, and download Traffic Crash data.

A. SUMMARY This table contains all crashes resulting in an injury in the City of San Francisco. Fatality year-to-date crash data is obtained from the Office of the Chief Medical Examiner (OME) death records, and only includes those cases that meet the San Francisco Vision Zero Fatality Protocol maintained by the San Francisco Department of Public Health (SFDPH), San Francisco Police Department (SFPD), and San Francisco Municipal Transportation Agency (SFMTA). Injury crash data is obtained from SFPD’s Interim Collision System for 2018 through the current year-to-date, Crossroads Software Traffic Collision Database (CR) for years 2013-2017 and the Statewide Integrated Transportation Record System (SWITRS) maintained by the California Highway Patrol for all years prior to 2013. Only crashes with valid geographic information are mapped. All geocodable crash data is represented on the simplified San Francisco street centerline model maintained by the Department of Public Works (SFDPW). Collision injury data is queried and aggregated on a quarterly basis. Crashes occurring at complex intersections with multiple roadways are mapped onto a single point and injury and fatality crashes occurring on highways are excluded.

The crash, party, and victim tables have a relational structure. The traffic crashes table contains information on each crash, one record per crash. The party table contains information from all parties involved in the crashes, one record per party. Parties are individuals involved in a traffic crash including drivers, pedestrians, bicyclists, and parked vehicles. The victim table contains information about each party injured in the collision, including any passengers. Injury severity is included in the victim table.

For example, a crash occurs (1 record in the crash table) that involves a driver party and a pedestrian party (2 records in the party table). Only the pedestrian is injured and thus is the only victim (1 record in the victim table).

To learn more about the traffic injury datasets, see the TIMS documentation

B. HOW THE DATASET IS CREATED Traffic crash injury data is collected from the California Highway Patrol 555 Crash Report as submitted by the police officer within 30 days after the crash occurred. All fields that match the SWITRS data schema are programmatically extracted, de-identified, geocoded, and loaded into TransBASE. See Section D below for details regarding TransBASE.

C. UPDATE PROCESS After review by SFPD and SFDPH staff, the data is made publicly available approximately a month after the end of the previous quarter (May for Q1, August for Q2, November for Q3, and February for Q4).

D. HOW TO USE THIS DATASET This data is being provided as public information as defined under San Francisco and California public records laws. SFDPH, SFMTA, and SFPD cannot limit or restrict the use of this data or its interpretation by other parties in any way. Where the data is communicated, distributed, reproduced, mapped, or used in any other way, the user should acknowledge TransBASE.sfgov.org as the source of the data, provide a reference to the original data source where also applicable, include the date the data was pulled, and note any caveats specified in the associated metadata documentation provided. However, users should not attribute their analysis or interpretation of this data to the City of San Francisco. While the data has been collected and/or produced for the use of the City of San Francisco, it cannot guarantee its accuracy or completeness. Accordingly, the City of San Francisco, including SFDPH, SFMTA, and SFPD make no representation as to the accuracy of the information or its suitability for any purpose and disclaim any liability for omissions or errors that may be contained therein. As all data is associated with methodological assumptions and limitations, the City recommends that users review methodological documentation associated with the data prior to its analysis, interpretation, or communication.

This dataset can also be queried on the TransBASE Dashboard. TransBASE is a geospatially enabled database maintained by SFDPH that currently includes over 200 spatially referenced variables from multiple agencies and across a range of geographic scales, including infrastructure, transportation, zoning, sociodemographic, and collision data, all linked to an intersection or street segment. TransBASE facilitates a data-driven approach to understanding and addressing transportation-related health issues, informed by a large and growing evidence base regarding the importance of transportation system design and land use decisions for health. TransBASE’s purpose is to inform public and private efforts to improve transportation system safety, sustainability, community health and equity in San Francisco.

E. RELATED DATASETS Traffic Crashes Resulting in Injury: Parties Involved Traffic Crashes Resulting in Injury: Victims Involved TransBASE Dashboard iSWITRS TIMS

VITAL SIGNS INDICATOR Injuries From Crashes (EN7-9)

FULL MEASURE NAME Serious injuries from crashes (traffic collisions)

LAST UPDATED October 2017

DESCRIPTION Injuries from crashes refers to serious but not fatal injuries sustained in a collision. The California Highway Patrol classifies a serious injury as any combination of the following: broken bones; dislocated or distorted limbs; severe lacerations; skull, spinal, chest or abdominal injuries that go beyond visible injuries; unconsciousness at or when taken from the scene; or severe burns. This injuries dataset includes serious injury counts for the region and counties, as well as individual collision data.

DATA SOURCE California Highway Patrol: Statewide Integrated Traffic Records System

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) The data is reported by the California Highway Patrol (CHP) to the Statewide Integrated Traffic Records System (SWITRS), which was accessed via SafeTREC’s Transportation Injury Mapping System (TIMS). The data was tabulated using provided categories specifying injury level, individuals involved, causes of collision, and location/jurisdiction of collision (for more: http://tims.berkeley.edu/help/files/switrs_codebook.doc). Fatalities were normalized over historic population data from the US Census and American Community Surveys and vehicle miles traveled (VMT) data from the Federal Highway Administration.

For more regarding reporting procedures and injury classification, see the California Highway Patrol Manual (http://www.nhtsa.gov/nhtsa/stateCatalog/states/ca/docs/CA_CHP555_Manual_2_2003_ch1-13.pdf).

SACOG Regional Transportation Collision Dataset Transportation Injury Mapping System data, includes years 2012-2022.Provisional years 2023 and2024From TIMS"We geocoded the 2023-2024 provisional SWITRS data and uploaded it into TIMS. We have started including partial year SWITRS data for the current year with a 3-month delay. Currently, TIMS includes SWITRS data up to crashes that occurred on December 31, 2024. Please email tims_info@berkeley.edu if you have any follow up questions regarding this update.Please note that the provisional data are subject to change and only contain records added to the I-SWITRS website by CHP up until March 11 2025. Therefore, they should not be directly compared to the number of crashes in previous years."

All of the data for the injury collision maps came from TIMS – the Transportation Injury Mapping System. TIMS is a project of UC Berkeley’s Safe Transportation Research and Education Center (SafeTREC). It takes data from another mapping system called SWITRS (State-Wide Integrated Traffic Records System), which is compiled by the CHP from law enforcement agencies throughout the state.

Data Source: Statewide Integrated Traffic Records System (SWITRS), Transportation Injury Mapping System (TIMS),https://tims.berkeley.edu/Bicycle and pedestrian traffic collisions in Santa Clara County between 2008-2018.

The project needs data for macroscopic statistical modeling, which are OTS rankings and historical crash data. OTS crash ranking data California Office of Traffic Safety (OTS) provides a crash ranking dataset that was developed so that individual cities could compare their city’s traffic safety statistics to those of other cities with similar-sized populations. The OTS crash rankings are based on the Empirical Bayesian Ranking Method. It adds weights to different crash statistical categories including observed crash counts, population and daily vehicle miles traveled (DVMT). In addition, the OTS crash rankings include different types of crashes with larger percentages of total victims and areas of focus for the OTS grant program. In conjunction with the research context, two types of crash rankings are focused on, namely pedestrians and bicyclists. SWITRS crash data The Transportation Injury Mapping System (TIMS) to provide the project quick, easy, and free access to California crash data provided by the Statewide Integrated Traffic Records System (SWITRS). The crash data includes bicycle and pedestrian collisions with vehicles resulting in injuries from 2014 to 2018. Besides, this crash database provides detailed accident reports including information on casualties, vehicle mode, accident reason, accident location, and road condition. With this information on crashes, we will select crashes between vehicles and VRUs at signalized intersections, which is the scope of this study. To avoid misunderstanding, the crashes in the following content will only refer to accidents between vehicles and VRUs. Besides, we will also collect historical weather data (including daily temperature, wind speed, rainfall, humidity, and visibility) and road condition data. All these data will be used for the next crash feature analysis. The data is publicly available and no commitment is required from SafeTREC. SUMO Source Code (Modified) This repository also includes the modified SUMO source code for traffic simulation. The modification is done in two aspects. First, a series of parameters of junction-control models are added to the set of vehicle type parameters, such that the simulation scenarios for different IVTs are defined by changing the values of vehicle type parameters. Second, a filtering logic is inserted into vehicles’ interaction processes. It determines whether a potential foe object is in the blind spot areas; whether the subject vehicle’s driver is distracted in this time step; and whether the equipped IVT can compensate for the visual limitations. The section below lists all the added parameters and functions. The Safe Transportation Research and Education Center (SafeTREC) at the University of California, Berkeley, develops the Transportation Injury Mapping System (TIMS) to provide a quick, easy and free access to California crash data provided by the Statewide Integrated Traffic Records System (SWITRS). We collect five-year-long crash data, which are from 01/01/2014 to 12/31/2018. The crash data includes bicyclist and pedestrian collisions with vehicles resulting in injuries across four types of crash severity: fatal, severe injury, visible injury, and complaint of injury. The data consists of three tables including the collision dataset, the involved parties dataset, and the victims dataset. In particular, we use the collision and parties datasets that contain enough information for modeling. The rows in the crash data are built based on each case of a crash and includes information such as weather, road surface, road condition, control device, and lighting. The parties dataset includes information specific to each vehicle or VRU such as age and sex. In order to perform a party-by-party analysis, we attach the datasets of each crash to every pair of VRU and vehicle that involved in a specific collision. The data files can be viewed by Excel.

A. SUMMARY This table contains all parties involved in a traffic crash resulting in an injury in the City of San Francisco. Fatality year-to-date crash data is obtained from the Office of the Chief Medical Examiner (OME) death records, and only includes those cases that meet the San Francisco Vision Zero Fatality Protocol maintained by the San Francisco Department of Public Health (SFDPH), San Francisco Police Department (SFPD), and San Francisco Municipal Transportation Agency (SFMTA). Injury crash data is obtained from SFPD’s Interim Collision System for 2018 to YTD, Crossroads Software Traffic Collision Database (CR) for years 2013-2017 and the Statewide Integrated Transportation Record System (SWITRS) maintained by the California Highway Patrol for all years prior to 2013. Only crashes with valid geographic information are mapped. All geocodable crash data is represented on the simplified San Francisco street centerline model maintained by the Department of Public Works (SFDPW). Collision injury data is queried and aggregated on a quarterly basis. Crashes occurring at complex intersections with multiple roadways are mapped onto a single point and injury and fatality crashes occurring on highways are excluded.

The crash, party, and victim tables have a relational structure. The traffic crashes table contains information on each crash, one record per crash. The party table contains information from all parties involved in the crashes, one record per party. Parties are individuals involved in a traffic crash including drivers, pedestrians, bicyclists, and parked vehicles. The victim table contains information about each party injured in the collision, including any passengers. Injury severity is included in the victim table.

For example, a crash occurs (1 record in the crash table) that involves a driver party and a pedestrian party (2 records in the party table). Only the pedestrian is injured and thus is the only victim (1 record in the victim table).

B. HOW THE DATASET IS CREATED Traffic crash injury data is collected from the California Highway Patrol 555 Crash Report as submitted by the police officer within 30 days after the crash occurred. All fields that match the SWITRS data schema are programmatically extracted, de-identified, geocoded, and loaded into TransBASE. See Section D below for details regarding TransBASE.

C. UPDATE PROCESS After review by SFPD and SFDPH staff, the data is made publicly available approximately a month after the end of the previous quarter (May for Q1, August for Q2, November for Q3, and February for Q4).

D. HOW TO USE THIS DATASET This data is being provided as public information as defined under San Francisco and California public records laws. SFDPH, SFMTA, and SFPD cannot limit or restrict the use of this data or its interpretation by other parties in any way. Where the data is communicated, distributed, reproduced, mapped, or used in any other way, the user should acknowledge TransBASE.sfgov.org as the source of the data, provide a reference to the original data source where also applicable, include the date the data was pulled, and note any caveats specified in the associated metadata documentation provided. However, users should not attribute their analysis or interpretation of this data to the City of San Francisco. While the data has been collected and/or produced for the use of the City of San Francisco, it cannot guarantee its accuracy or completeness. Accordingly, the City of San Francisco, including SFDPH, SFMTA, and SFPD make no representation as to the accuracy of the information or its suitability for any purpose and disclaim any liability for omissions or errors that may be contained therein. As all data is associated with methodological assumptions and limitations, the City recommends that users review methodological documentation associated with the data prior to its analysis, interpretation, or communication.

This dataset can also be queried on the TransBASE Dashboard. TransBASE is a geospatially enabled database maintained by SFDPH that currently includes over 200 spatially referenced variables from multiple agencies and across a range of geographic scales, including infrastructure, transportation, zoning, sociodemographic, and collision data, all linked to an intersection or street segment. TransBASE facilitates a data-driven approach to understanding and addressing transportation-related health issues, informed by a large and growing evidence base regarding the importance of transportation system design and land use decisions for health. TransBASE’s purpose is to inform public and private efforts to improve transportation system safety, sustainability, community health and equity in San Francisco.

E. RELATED DATASETS Traffic Crashes Resulting in Injury Traffic Crashes Resulting in Injury: Victims Involved TransBASE Dashboard iSWITRS TIMS

Live tracking of recent earthquakes near Berkeley, California from the past 30 days. Real-time updates of M1.5+ quakes with interactive map visualization.

A. SUMMARY This table contains all fatalities resulting from a traffic crash in the City of San Francisco. Fatality year-to-date crash data is obtained from the Office of the Chief Medical Examiner (OME) death records, and only includes those cases that meet the San Francisco Vision Zero Fatality Protocol maintained by the San Francisco Department of Public Health (SFDPH), San Francisco Police Department (SFPD), and San Francisco Municipal Transportation Agency (SFMTA). Injury crash data is obtained from SFPD’s Interim Collision System for 2018 to YTD, Crossroads Software Traffic Collision Database (CR) for years 2013-2017 and the Statewide Integrated Transportation Record System (SWITRS) maintained by the California Highway Patrol for all years prior to 2013. Only crashes with valid geographic information are mapped. All geocodable crash data is represented on the simplified San Francisco street centerline model maintained by the Department of Public Works (SFDPW). Collision injury data is queried and aggregated on a quarterly basis. Crashes occurring at complex intersections with multiple roadways are mapped onto a single point and injury and fatality crashes occurring on highways are excluded.

The fatality table contains information about each party injured or killed in the collision, including any passengers.

B. HOW THE DATASET IS CREATED Traffic crash injury data is collected from the California Highway Patrol 555 Crash Report as submitted by the police officer within 30 days after the crash occurred. All fields that match the SWITRS data schema are programmatically extracted, de-identified, geocoded, and loaded into TransBASE. See Section D below for details regarding TransBASE. This table is filtered for fatal traffic crashes.

C. UPDATE PROCESS After review by SFPD and SFDPH staff, the data is made publicly available approximately a month after the end of the previous quarter (May for Q1, August for Q2, November for Q3, and February for Q4).

D. HOW TO USE THIS DATASET This data is being provided as public information as defined under San Francisco and California public records laws. SFDPH, SFMTA, and SFPD cannot limit or restrict the use of this data or its interpretation by other parties in any way. Where the data is communicated, distributed, reproduced, mapped, or used in any other way, the user should acknowledge the Vision Zero initiative and the TransBASE database as the source of the data, provide a reference to the original data source where also applicable, include the date the data was pulled, and note any caveats specified in the associated metadata documentation provided. However, users should not attribute their analysis or interpretation of this data to the City of San Francisco. While the data has been collected and/or produced for the use of the City of San Francisco, it cannot guarantee its accuracy or completeness. Accordingly, the City of San Francisco, including SFDPH, SFMTA, and SFPD make no representation as to the accuracy of the information or its suitability for any purpose and disclaim any liability for omissions or errors that may be contained therein. As all data is associated with methodological assumptions and limitations, the City recommends that users review methodological documentation associated with the data prior to its analysis, interpretation, or communication.

TransBASE is a geospatially enabled database maintained by SFDPH that currently includes over 200 spatially referenced variables from multiple agencies and across a range of geographic scales, including infrastructure, transportation, zoning, sociodemographic, and collision data, all linked to an intersection or street segment. TransBASE facilitates a data-driven approach to understanding and addressing transportation-related health issues, informed by a large and growing evidence base regarding the importance of transportation system design and land use decisions for health. TransBASE’s purpose is to inform public and private efforts to improve transportation system safety, sustainability, community health and equity in San Francisco.

E. RELATED DATASETS Traffic Crashes Resulting in Injury Traffic Crashes Resulting in Injury: Parties Involved Traffic Crashes Resulting in Injury: Victims Involved iSWITRS TIMS

About this dataset

Context

The dataset tabulates the Berkeley population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Berkeley across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Berkeley was 118,962, a 0.21% increase year-by-year from 2022. Previously, in 2022, Berkeley population was 118,715, an increase of 3.55% compared to a population of 114,647 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Berkeley increased by 16,026. In this period, the peak population was 124,205 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Berkeley is shown in this column.
• Year on Year Change: This column displays the change in Berkeley population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Berkeley Population by Year. You can refer the same here

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 Berkeley. 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 Berkeley, the median income for all workers aged 15 years and older, regardless of work hours, was $59,074 for males and $42,731 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 28% between the median incomes of males and females in Berkeley. With women, regardless of work hours, earning 72 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Berkeley.

- Full-time workers, aged 15 years and older: In Berkeley, among full-time, year-round workers aged 15 years and older, males earned a median income of $112,208, while females earned $94,526, leading to a 16% gender pay gap among full-time workers. This illustrates that women earn 84 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.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Berkeley, showcasing a consistent income pattern irrespective of employment status.

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 Berkeley median household income by race. You can refer the same here

Graph and download economic data for Mean Commuting Time for Workers (5-year estimate) in Berkeley County, WV (B080ACS054003) from 2009 to 2023 about Berkeley County, WV; Hagerstown; commuting time; WV; workers; average; 5-year; and USA.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Berkeley County. The dataset can be utilized to gain insights into gender-based income distribution within the Berkeley County population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Berkeley County, among individuals aged 15 years and older with income, there were 86,668 men and 83,168 women in the workforce. Among them, 53,724 men were engaged in full-time, year-round employment, while 40,080 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 7.18% fell within the income range of under $24,999, while 13.81% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 23.69% of men in full-time roles earned incomes exceeding $100,000, while 9.51% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

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.

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 Berkeley County median household income by race. You can refer the same here

Mean Commuting Time for Workers (5-year estimate) in Berkeley County, SC was 29.11214 Minutes in January of 2023, according to the United States Federal Reserve. Historically, Mean Commuting Time for Workers (5-year estimate) in Berkeley County, SC reached a record high of 29.11214 in January of 2023 and a record low of 25.51892 in January of 2013. Trading Economics provides the current actual value, an historical data chart and related indicators for Mean Commuting Time for Workers (5-year estimate) in Berkeley County, SC - last updated from the United States Federal Reserve on July of 2025.

Estos datos representan 10 años de datos de colisión de tráfico dentro de la ciudad de Salinas, condado de Monterey, California. Los datos de colisión son recopilados por el Sistema de Registros Integrados de Tráfico (SWITRS) de la Patrulla de Caminos de California (CHP) y un producto derivado está a disposición del público del Sistema de Mapeo de Lesiones por Transporte (TIMS) de UC Berkeley. Nuestros datos de colisión se actualizan cada tres o cuatro meses y pueden tener diferencias en las ubicaciones de colisión en comparación con los datos de TIMS. Para obtener más detalles sobre los datos, consulte la documentación de SWITRS.

Mean Commuting Time for Workers (5-year estimate) in Berkeley County, WV was 30.91390 Minutes in January of 2023, according to the United States Federal Reserve. Historically, Mean Commuting Time for Workers (5-year estimate) in Berkeley County, WV reached a record high of 31.71044 in January of 2013 and a record low of 29.55310 in January of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for Mean Commuting Time for Workers (5-year estimate) in Berkeley County, WV - last updated from the United States Federal Reserve on July of 2025.

Description

This dataset consists of 18 days' worth of HTTP traces gathered from the Home IP service offered by UC Berkeley to its students, faculty, and staff Home IP provides dial-up PPP/SLIP IP connectivity using 2.4 kb/s, 9.6 kb/s, 14.4 kb/s, or 28.8 kb/s wireline modems, or Metricom Ricochet (approximately 20-30 kb/s) wireless modems. These client traces were unobtrusively gathered through the use of a packet sniffing machine placed at the head-end of the Home IP modem bank; the tracing program used was a custom module written on top of the Internet Protocol Scanning Engine (IPSE) created by Ian Goldberg. Only traffic destined for port 80 was traced; all non-HTTP protocols and HTTP connections for other ports were excluded from these traces.

The traces contain the following information:

• a total of 9,244,728 references spanning the period from Friday, November 1st, 1996 at 15:18:59 PST through Tuesday, November 19th, 1996 at 05:52:03 PST. 8,377 unique clients were seen in the traces.
• the time at which the client made the request
• the time at which the first byte of the server response was seen
• the time at which the last byte of the server response was seen
• the client IP address (suitably anonymized)
• the client port
• the server IP address (suitably anonymized)
• the server port (always 80 for these traces)
• the presence of the no-cache, keep-alive, cache-control, if-modified-since, and unless client headers.
• the presence of the no-cache, cache-control, expires, and last-modified server headers.
• the values of the client if-modified-since, the server expires, and the server last-modified headers, if present.
• the length of the response HTTP header
• the length of the response data
• the request URL (suitably anonymized)

Format

For the sake of storage efficiency, the (gzipped) traces are stored in a binary representation. This archive of tools includes the following code to parse and manipulate the archives:

• showtrace: this program will print out a human readable ASCII representation of what is in the traces. To use, type:

  gzcat

  Take a look at the source file showtrace.c to see how you can use logparse.[ch] to write code that parses and manipulates the traces. All times displayed are as reported by the gettimeofday() system call.

• anon_clients: this is the program that we used to anonymize the traces. I include this program under the principle that the anonymization used is strong enough that distributing the anonymization code cannot help anybody break the anonymization.

• timeconvert: a program that accepts a calendar time (i.e. time in seconds since the Epoch, as reported by showtrace and as used in the trace filenames) and outputs the local time corresponding to that calendar time.

The showtrace tool will display lines in the following format:

848278028:829593 848278028:893670 848278028:895350 23.240.8.98:1462
207.36.205.194:80 2 8 4294967295 4294967295 835418853 170 844
37 GET 9168504434183313441..gif HTTP/1.0

• 848278028:829593 is the time at which the client made the request
• 848278028:893670 is the time at which the first byte of the server response was seen
• 848278028:895350 is the time at which the last byte of the server response was seen
• 23.240.8.98:1462 is the anonymized client IP address and the client port number
• 207.36.205.194:80 is the anonymized server IP address and the server port number
• 2 is the decimal representation of the client headers bitfield
• 8 is the decimal representation of the server headers bitfield
• the first 4294967295 is the if-modified-since client header value (note that 4294967295 is 0xFFFFFFFF, which means this header value was not present for this entry)
• the second 4294967295 is the expires server header value (again not present)
• 835418853 is the last-modified server header value
• 170 is the length of the HTTP response header
• 844 is the length of the response data
• 37 is the length of the anonymized request URL
• "GET 9168504434183313441..gif HTTP/1.0" is the anonymized request URL.

The interpretation of the client and server header bitfields are as defined in the logparse.h header in the tools code.

The tools code has been tested on both Linux and Solaris. The provided Makefile assumes Solaris - you may have to play with the LIBS definition for other platforms. HPUX is a mess; I didn't even try, but it should be possible to get these tools to work with little effort. If you do, please let me know what you did so that I can make your changes available to the world.

Measurement

The Home IP population gains IP connectivity using PPP or SLIP across their 2.4 kb/s, 9.6 kb/s, 14.4kb/s or 28.8kb/s wireline modem, or their (approximately) 20-30kb/s wireless Metricom Ricochet modem. There are a total of roughly 600 modems available via the Home IP bank. All traffic from these modems ends up feeding over a single 10Mb/s shared Ethernet segment, on which we placed a network monitoring computer (a Pentium Pro 200Mhz running Linux 2.0.27). The monitor was running the IPSE user-level packet scanning engine and a custom-written HTTP module that reconstructed HTTP connections from the gathered IP packets on-the-fly and emitted an unanonymized trace file. Each trace file was then anonymized and transmitted to our research workstations for further postprocessing and analysis.

The trace gathering engine was brought down and restarted approximately every 4 hours (for administrative and address-space-growth reasons). This implies that there are two weaknesses in these traces that you should be aware of:

1. any connection active when the engine was brought down will have a possibly incorrect timestamp for the last byte seen from the server, and a possibly incorrect reported size. We estimate that no more than 150 such entries (out of roughly 90000-100000) are misreported for each 4 hour period.

2. any connection that was forged in the very small time window (about 300 milliseconds) between when the engine was shut down and restarted will not appear in the logs. We estimate that no more than 30 such drops occur for each 4 hour period.

The packet capture tool reported no packet drops. Considering that a Pentium Pro 200MHz was used to capture the traces on a 10 Mb/s Ethernet segment, it is virtually certain that no trace drops besides those mentioned above occurred. There may be periods of uncharacteristically low activity in the traces - these correspond to network outages from Berkeley's ISP, rather than trace failures.

The traces do contain entries for requests issued by the client but that weren't completed (because, for instance, the user pressed the STOP button and the TCP connection was shut down before the request completed). Unknown timestamps in the traces contain the value 0xFFFFFFFF (reported by showtrace as 4294967295), and incomplete requests contain header and data length values that report as much header/data was seen.

The trace data is sorted by completion time (i.e. the time at which the last bye of the server response was seen, or the time at which the connection was dropped). However, because of inaccuracies and apparent time travel in the Linux system clock, some trace entries appear slightly out of order.

All timestamps within the traces are as reported by the gettimeofday() system call, so these timestamps ostensibly have microsecond resolution.

Privacy

To maintain the privacy of each individual Home IP user, we have stripped identity information out of the traces through a post-processing phase. Because it is very trivial to identify a user based solely on the pages that the user has visited, we were forced to anonymize the URL and destination IP address of each web request as well as the source IP address. All anonymization was done using a keyed MD5 hash of the data (32 bits for client and server IP addresses, 64 bits for URLs). We ourselves do not know the key used to salt the MD5 hash, so don't bother asking us for it. Similarly, don't bother asking us for unanonymized traces.

In order to preserve some information about the URLs, the post-processed URLs have the following format:

COMMAND URLHASH.[flags][.suffix] [HTTPVERS]

where:

• COMMAND is one of GET, HEAD, POST, or PUT,

  <p> </p>
  </li>
  <li><strong><code>URLHASH</code></strong> is the string representation of the 64-bit MD5 hash of the URL,
  <p> </p>
  </li>
  <li><strong><code>flags</code></strong> contains the character <strong>q</strong> to indicate that a question mark was seen in the URL, and the character <strong>c</strong> to indicate that the string <strong>CGI</strong> or <strong>cgi</strong> was seen in the URL,
  <p> </p>
  </li>
  <li><strong><code>suffix</code></strong> is the filename suffix, if present, and
  <p> </p>
  </li>
  <li><strong><code>HTTPVERS</code></strong> is the HTTP version field of the HTTP command issued by the client,