This table contains data on the percent of residents aged 16 years and older mode of transportation to work for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Census Bureau, Decennial Census and American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Commute trips to work represent 19% of travel miles in the United States. The predominant mode – the automobile - offers extraordinary personal mobility and independence, but it is also associated with health hazards, such as air pollution, motor vehicle crashes, pedestrian injuries and fatalities, and sedentary lifestyles. Automobile commuting has been linked to stress-related health problems. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which is associated with lowering rates of heart disease and stroke, diabetes, colon and breast cancer, dementia and depression. Risk of injury and death in collisions are higher in urban areas with more concentrated vehicle and pedestrian activity. Bus and rail passengers have a lower risk of injury in collisions than motorcyclists, pedestrians, and bicyclists. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience four times the death rate Whites or Asian pedestrians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

The U.S. auto industry sold nearly ************* cars in 2024. That year, total car and light truck sales were approximately ************ in the United States. U.S. vehicle sales peaked in 2016 at roughly ************ units. Pandemic impact The COVID-19 pandemic deeply impacted the U.S. automotive market, accelerating the global automotive semiconductor shortage and leading to a drop in demand during the first months of 2020. However, as demand rebounded, new vehicle supply could not keep up with the market. U.S. inventory-to-sales ratio dropped to its lowest point in February 2022, as Russia's war on Ukraine lead to gasoline price hikes. During that same period, inflation also impacted new and used car prices, pricing many U.S. consumers out of a market with increasingly lower car stocks. Focus on fuel economy The U.S. auto industry had one of its worst years in 1982 when customers were beginning to feel the effects of the 1973 oil crisis and the energy crisis of 1979. Since light trucks would often be considered less fuel-efficient, cars accounted for about ** percent of light vehicle sales back then. Thanks to improved fuel economy for light trucks and cheaper gas prices, this picture had completely changed in 2020. That year, prices for Brent oil dropped to just over ** U.S. dollars per barrel. The decline occurred in tandem with lower gasoline prices, which came to about **** U.S. dollars per gallon in 2020 - and cars only accounted for less than one-fourth of light vehicle sales that year. Four years on, prices are dropping again, after being the highest on record since 1990 in 2022.

The Household Size by Vehicles Available dataset was compiled using information from December 31, 2023 and updated December 12, 2024 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Household Size by Vehicles Available table from the 2023 American Community Survey (ACS) 5-year estimates was joined to 2023 tract-level geographies for all 50 States, District of Columbia and Puerto Rico provided by the Census Bureau. A new file was created that combines the demographic variables from the former with the cartographic boundaries of the latter. The national level census tract layer contains data on the number and percentage of households by household size by number of vehicles available. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529030

This layer shows household size by number of vehicles available. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the count and percentage of households with no vehicle available. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B08201 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Total Vehicle Sales in the United States decreased to 15.30 Million in October from 16.40 Million in September of 2025. This dataset provides the latest reported value for - United States Total Vehicle Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Sales of used light vehicles in the United States came to around **** million units in 2024. In the same period, approximately **** million new light trucks and automobiles were sold here. Declining availability of vehicles In the fourth quarter of 2024, about ***** million vehicles were in operation in the United States, an increase of around *** percent year-over-year. The rising demand for vehicles paired with an overall price inflation lead to a rise in new vehicle prices. In contrast, used vehicle prices slightly decreased. E-commerce: a solution for the bumpy road ahead? Financial reports have revealed how the outbreak of the coronavirus pandemic has triggered a shift in vehicle-buying behavior. With many consumer goods and services now bought online due to COVID-19, the automobile industry has also started to digitally integrate its services online to reach consumers with a preference for contactless test driving amid the global crisis. Several dealers and automobile companies had already begun to tap into online car sales before the pandemic, some of them being Carvana and Tesla.

This dataset provides a comprehensive, state-level view of the key factors influencing electric vehicle (EV) adoption across the United States. Compiled from authoritative sources such as the US Census Bureau, Department of Energy, National Renewable Energy Laboratory (NREL), and others, it includes annual data on EV registrations, socioeconomic indicators, infrastructure availability, policy incentives, and energy prices from multiple years.

The dataset is designed to support research and analysis on the drivers of EV adoption, enabling users to explore questions around policy effectiveness, infrastructure planning, and market dynamics.

Context & Motivation The transition to electric vehicles is a cornerstone of US climate and energy policy, yet EV adoption rates remain highly uneven across states. While states like California lead with robust infrastructure and incentives, other regions-particularly in the Midwest and South-lag behind. Understanding what drives these differences is crucial for policymakers, automakers, and energy providers.

This dataset was created as part of a research project investigating the determinants of EV adoption. By making this data publicly available, I hope to empower further research, foster data-driven policy decisions, and encourage innovation in sustainable transportation.

Data Sources EV Registrations: National Renewable Energy Laboratory (NREL)

Socioeconomic Indicators: US Census Bureau (population, income, education, labor force, unemployment)

Charging Infrastructure & Incentives: Alternative Fuels Data Center (AFDC)

Fuel Economy & Vehicle Registrations: Bureau of Transportation Statistics

Gasoline Prices: American Automobile Association (AAA)

Electricity Prices: Energy Information Administration (EIA)

CO2 Emissions: Bureau of Transportation Statistics Variables Included

Vehicle Thefts by State and Make/Model

Identifying Highest Risk Models and Locations

By Joe Boutros [source]

About this dataset

This dataset compiled by the National Insurance Crime Bureau (NICB) reveals the top ten most stolen cars in each state and across the country during the year 2015. Additionally, this dataset provides information on the top 25 stolen model year cars in 2015. It offers insights into theft trends through valuable data points such as vehicle, make and model, and thefts per state. This dataset can help you gain access to important data which will enable you to make informed decisions about car purchase, insurance rates or areas where extra precaution may be necessary when it comes to protecting your automobile investment

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This dataset provides an in-depth look at the vehicles that are most often stolen in the United States during 2015. It can be used to understand which models and makes of cars are most frequently targeted by thieves on a state-by-state basis.

The two files included in this dataset provide information about both the general trends for all U.S. states and more specific insights about the top 25 stolen 2015 model year cars.

The first file, 2015_State_Top10Report_wTotalThefts.csv, includes detailed information regarding each of the top ten most stolen vehicles per state. Each row contains data such as state and rank, make/model, thefts by number and year of thefts as well as vehicle model year (allowing users to track trends over time). It is important to note that rank specifies which car is most frequently stolen overall within a given state for all years combined (and not just within 2015), whereas thefts only indicate how many times a specific vehicle was reported stolen within that chosen state for the given year (as stated above). Additionally, # of Thefts reflects how many times each model has been reported stolen nationally regardless of individual location or date - it gives an indication to how widespread a particular car's theft rate is across America. All in all this file provides valuable information regarding individual type and make/model combinations across various states in American while being able to pinpoint exact numbers with regards to frequency of theft over time intervals This can help inform users on whether certain makes/models have risen or decreased in desirability among thieves thus impacting their decision making when purchasing or insuring their own cars..

The second file Top25-2015-Models-2015-thefts-for-release.csv offers detailed insight into exactly 25 car models which were reported as being amongst those with highest localised incidence factor when concerning theft reports coming out of various US states throughout 2015 – consequently it may also be called „most popular” list amongst criminal circles when referring specifically NATIONALLY occurring incidents within one given calendar year . The average user can utilize this data along similar means as before - reviewing „popularity” title conferred upon them either locally through investigation into variations between popular target choices between different areas but also compare those against national rates too . It has one slight difference however from previous entry – model years covered revert exclusively towards brand new releases , i,.e maybe user would want

Research Ideas

• Targeted Crime Prevention Programs: Governments, law enforcement, and insurers can use this dataset to target areas with the highest rate of car thefts and create crime prevention programs tailored to those locations.
• Vehicle Security Analysis & Upgrades: Auto manufacturers can use this data to analyze which models are being stolen the most in certain states and adjust security systems accordingly.
• Insurance Loss Modeling & Rates: Insurance providers can use this dataset to refine their loss models by creating actuarial tables based on model type and region, which would help them better define risk profiles for customers in different locations across the country

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material...

Car Production in the United States increased to 11.04 Million Units in August from 10.42 Million Units in July of 2025. This dataset provides - United States Car Production- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This table contains data on the annual number of fatal and severe road traffic injuries per population and per miles traveled by transport mode, for California, its regions, counties, county divisions, cities/towns, and census tracts. Injury data is from the Statewide Integrated Traffic Records System (SWITRS), California Highway Patrol (CHP), 2002-2010 data from the Transportation Injury Mapping System (TIMS) . The table is part of a series of indicators in the [Healthy Communities Data and Indicators Project of the Office of Health Equity]. Transportation accidents are the second leading cause of death in California for people under the age of 45 and account for an average of 4,018 deaths per year (2006-2010). Risks of injury in traffic collisions are greatest for motorcyclists, pedestrians, and bicyclists and lowest for bus and rail passengers. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience 4 times the death rate as Whites or Asians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

The projected change in the market share of compact cars in total light-duty vehicle sales, from the model year 1990 to 2010 based on the business-as-usual scenario is shown on this map. This scenario assumes that there are no major policy changes during the 1990 to 2010 period, which would affect vehicle preferences. Light-duty vehicles include all cars and light trucks. The projection shows that, between the model years 1990 and 2010, the market share of compact cars in Canada would decrease from 27% to 20%, specifically, areas in the Atlantic Provinces and Quebec. There are also significant proportional reductions of market share in some areas of Alberta and British Columbia.

Luxury Car Market Trends | Industry Analysis, Size & Forecast Report

Dataset updated: Jun 27, 2024

Dataset authored and provided by: Mordor Intelligence

License: https://www.mordorintelligence.com/privacy-policy

Time period covered: 2019 - 2029

Area covered: Global

Variables measured: CAGR, Market size, Market share analysis, Global trends, Industry forecast

Description: The Luxury Car Market size is estimated at USD 738.63 billion in 2024, and is expected to reach USD 967.65 billion by 2029, growing at a CAGR of 5.55% during the forecast period (2024-2029).

Quantitative Units: Revenue in USD Billion, Volumes in Units, Pricing in USD

Segments Covered: The luxury car market is segmented by vehicle type, drive type, vehicle class, and geography. By vehicle type, the market is segmented into hatchbacks, sedans, sport utility vehicles, multi-purpose vehicles, and other vehicle types (sports, etc.). By drive type, the market is segmented into internal combustion engines and electric and hybrid. By vehicle class, the market is segmented into entry-level luxury class, mid-level luxury class, and ultra-luxury class.

Regions and Countries Covered: North America, Europe, Asia-Pacific, and Rest of the world

Market Players Covered: Key Players Include Mercedes-Benz, BMW, Volkswagen Group, and Tesla.

Daily vehicle miles traveled (VMT) is a distance- and volume-based measure of driving on roadways for all motorized vehicle types—car, bus, motorcycle, and truck—on an average day. Per capita VMT is the same measure divided by the same area's population for the same year. Per vehicle VMT divides VMT by the number of household vehicles available by residents of that geography in the same year. These three value types can be selected in the dropdown in the first chart below. Use the legend items to explore various geographies. The second chart below shows per capita and total personal vehicles available to the region’s households from the American Community Survey.

Normalizing VMT by a county or region's population, or household vehicles, is helpful for context, but does not have complete parity with what is measured in VMT estimates. People and vehicles come into the region from other places, just as people and vehicles leave the region to visit other places. VMT per capita compares all miles traveled on the region's roads to the region's population (for all ages) from the U.S. Census Bureau's latest population estimates. Vehicle counts for VMT are classified by vehicle types, but not by vehicle ownership. In 2017, statewide estimates for VMT by motorcycles, passenger cars, and two-axle single-unit trucks with four wheels made up 88% of Pennsylvania's VMT, and 95% of New Jersey's. These vehicle types are highly likely to be personal vehicles, owned by households, but a small percent could be fleet vehicles of companies or governments. The remaining VMT is made up of vehicle types like school and commercial buses and trucks with more than two axles so they are highly likely to be commercial vehicles.

Cash-Flow-Per-Share Time Series for Aptiv PLC. Aptiv PLC engages in design, manufacture, and sale of vehicle components for the automotive and commercial vehicle markets in North America, Europe, the Middle East, Africa, the Asia Pacific, South America, and internationally. It operates through two segments, Signal and Power Solutions, and Advanced Safety and User Experience. The Signal and Power Solutions segment designs, manufactures, and assembles vehicle's electrical architecture, including engineered component products, connectors, wiring assemblies and harnesses, cable management products, electrical centers, and high voltage and safety distribution systems. The Advanced Safety and User Experience segment provides critical technologies and services for vehicle safety, security, comfort, and convenience, such as sensing and perception systems, electronic control units, multi-domain controllers, vehicle connectivity systems, cloud-native software platform, application software, autonomous driving technologies, and end-to-end DevOps tools. Aptiv PLC was incorporated in 2011 and is based in Schaffhausen, Switzerland.

Automotive Scenario Database Market Outlook

According to our latest research, the global automotive scenario database market size reached $1.37 billion in 2024, driven by escalating investments in autonomous vehicle development and advanced driver assistance systems (ADAS). With a robust compound annual growth rate (CAGR) of 15.8% projected from 2025 to 2033, the market is forecasted to soar to $4.91 billion by 2033. This impressive growth trajectory is fueled primarily by the increasing complexity of vehicle automation and the need for comprehensive scenario databases to validate, test, and enhance the safety and reliability of next-generation automotive technologies.

One of the most significant growth factors for the automotive scenario database market is the rapid evolution of autonomous vehicles. As the automotive industry shifts towards higher levels of automation, the necessity for extensive and diverse scenario databases becomes paramount. These databases are essential for simulating a wide array of real-world and synthetic driving conditions, enabling manufacturers and developers to test and refine the performance of autonomous systems without the risks and constraints of physical road testing. The proliferation of machine learning and artificial intelligence in automotive applications further accentuates the need for robust scenario datasets, as these technologies rely heavily on large volumes of varied and high-quality data for training and validation.

Another major driver is the tightening regulatory landscape and the growing emphasis on vehicle safety. Regulatory bodies across North America, Europe, and Asia Pacific are introducing stringent standards for the testing and validation of autonomous and semi-autonomous vehicles. Compliance with these regulations necessitates the use of well-structured scenario databases that can replicate a multitude of traffic, weather, and pedestrian scenarios. Automotive OEMs and Tier 1 suppliers are increasingly investing in scenario database solutions to ensure their vehicles meet or exceed regulatory requirements, minimize liability, and enhance consumer trust in automated driving technologies.

The surge in connected vehicle technologies and the integration of ADAS features are also propelling the automotive scenario database market forward. As vehicles become more connected and equipped with advanced sensors, the scope for scenario-based testing expands significantly. Scenario databases enable manufacturers to simulate complex interactions between vehicles, infrastructure, and other road users, supporting the development of sophisticated ADAS functionalities such as collision avoidance, lane keeping, and adaptive cruise control. The ongoing digital transformation of the automotive sector, coupled with the adoption of cloud computing and big data analytics, is further amplifying the demand for scalable and easily accessible scenario database platforms.

From a regional perspective, North America currently holds the largest share of the automotive scenario database market, underpinned by the presence of leading technology companies, automotive OEMs, and regulatory frameworks that support autonomous vehicle testing. Europe follows closely, benefiting from strong government initiatives, a mature automotive industry, and a collaborative ecosystem involving research institutes and regulatory bodies. The Asia Pacific region is emerging as a high-growth market, driven by rapid urbanization, increasing investments in smart mobility, and the expansion of automotive manufacturing hubs. Latin America and the Middle East & Africa are gradually catching up, supported by rising interest in vehicle automation and mobility innovation.

Database Type Analysis

The automotive scenario database market is segmented by database type into simulation scenario databases, real-world scenario databases, and synthetic scenario databases. Simulation scenario databases are pivotal for virtual testing environments, allowi

This dataset tracks annual american indian student percentage from 2003 to 2013 for Crandon Alternative Resource School (Cars) vs. Wisconsin and Crandon School District

WFRC Community Focus Areas (2023)Geographic Representation Units WFRC’s Community Focus Areas (CFAs) are geographic areas for which additional consideration may be given within the planning and programming processes for future transportation, economic development, and other projects administered through WFRC. CFAs are used by WFRC in support of meeting the Council-established goal of promoting “inclusive engagement in transportation planning processes and equitable access to affordable and reliable transportation options.” CFAs are designated from Census block group geographic zones that meet the criteria described below. Census block groups are used as these are the smallest geographic areas for which more detailed household characteristics like employment, income, vehicle ownership, commute trip, and English language proficiency are available. WFRC recognizes the limitations of geography-based analysis, as proper planning work considers together the needs of individuals, groups and sectors, and geographic areas. However, geography-based analyses offer a useful starting point for the consideration and prioritization of projects that will serve specific community needs.2023 Community Focus Area Criteria UpdateFor the 2023 RTP planning cycle, WFRC will use two factors in designating geography-based CFAs: 1) concentration of low-income households and 2) concentration of persons identifying as members of racial and ethnic minority groups. The geography for these factors can be identified from consistent and regularly updated data sources maintained by the U.S. Census Bureau. WFRC will also make data available that conveys, while maintaining individual anonymity, the geographic distribution of additional measures including concentrations of persons with disabilities, households with limited English language proficiency, households that do not own a vehicle, older residents (65+ years of age), and younger residents (0-17 years of age). While the application of these factors within the planning process is less straightforward because of their higher statistical margins of error and comparatively even distribution within the region, these additional factors remain valuable as planning context. Low Income Focus Areas, Methodology for IdentificationThe block group-level data from the 2020 Census American Community Survey (ACS) 5-year dataset (Table C17002: Ratio of Income to Poverty Level), is used to determine the percentage of the population within each block group that are in households that have a ratio of income to federal poverty threshold of equal to or less than 1, i.e., their income is below the poverty level. The federal poverty threshold is set differently for households, considering their household size and age of household members.Census block groups in which more than 20% of the households whose income is less than or equal to the federal poverty threshold are included in the WFRC CFAs and designated as Low-Income focus areas. Racial and Ethnic Minority Focus AreasThe block group-level data from the 2020 ACS 5-year dataset (Table B03002: Hispanic or Latino Origin By Race) is used to determine the percentage of the population that did not self-identify their race and ethnicity as “White alone.” The average census block group area in the Wasatch Front urbanized areas has 24.2% of its population that identifies as Black or African American alone, American Indian, and Alaska Native alone, Asian alone, Native Hawaiian and other Pacific Islander alone, some other race alone, two or more races, or of Hispanic or Latino origin.Census blocks in which more than 40%2 of the population identifies as one or more of the racial or ethnic groups listed above are included in the WFRC CFAs and designated as Racial and Ethnic Minority focus areas.Excluding Predominantly Non-Residential Areas from CFAsSome census block groups that meet one or both of the CFA criteria described above contain large, non-residential areas or low density residential areas. Such census block areas may have small residential neighborhoods surrounded by predominantly commercial or industrial land uses, or large areas of public land or as-yet undeveloped lands. For this reason, WFRC staff may adjust the boundaries of an CFA whose census block group population density is less than 500 persons per square mile, to exclude areas of those block groups that have large, predominantly non-residential land uses.Community Focus Area Update FrequencyThe geography for WFRC CFAs will be updated not less than every four years, preceding the project phasing period of the Regional Transportation Planning update cycle. The update will use the most recent version of the 5 year ACS dataset. The next update is expected in the summer of 2026 (the beginning of the 4th year for the 2027 RTP development process) and is expected to use the 2024 5-year ACS results that average results across 2020-2024.Footnotes:1. The 2019 version of WFRC CFAs used ‘Zero Car Households’ as a third factor. This factor is no longer included because of its geographic and statistical fluctuation over time in data reported by the American Community Survey. Additionally, ‘Zero Car households’ was observed to have a strong relationship with the other two CFA designation factors.2. The percentage threshold specified here is approximately one standard deviation above the regional mean for this indicator. Assuming a statistically normal distribution, approximately 16% of the overall set (i.e. census blocks, in this case) would fall above a one standard deviation threshold.3. Table B03002 includes information from both 'Race' and 'Hispanic or Latino Origin' identification questions asked as part of the Census Bureau's American Community Survey.

Key Features

How to use this dataset

1. Exploratory Data Analysis (EDA): Begin by performing exploratory data analysis to understand the distribution and characteristics of key variables. Utilize visualizations such as histograms, scatter plots, and bar charts to identify patterns and outliers.

2. Feature Engineering: Identify relevant features for your analysis and create new features if needed. Explore relationships between different columns to uncover insights or derive meaningful features.

3. Descriptive Statistics: Compute descriptive statistics such as mean, median, and standard deviation for key numerical features. Analyze categorical variables using frequency counts and percentages.

4. Predictive Modeling: If applicable, consider building predictive models based on the dataset. Utilize machine learning algorithms to predict specific outcomes or trends. Split the dataset into training and testing sets for model evaluation.

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Road Traffic Injuries in California

Pedestrian Fatalities and Injury Rates By Transport Mode, 2002-2010

By Health [source]

About this dataset

This table contains data on the number of annual fatal and severe road traffic injuries per population and per miles traveled by transport mode, for the state of California and its various regions, counties, county divisions, cities/towns, and census tracts. Road traffic injury is an important public health issue in California; it ranks second among leading causes of death for people under 45 in the state with an average of 4,018 fatalities per year (2006-2010). In addition to this terrible statistic are also elevated risks for certain population subgroups; Native American male pedestrians experience 4 times the death rate as Whites or Asians while African-Americans and Latinos experience twice the death rate as Whites or Asians.

This dataset has been generated through a combination of datasets--SWITRS (Statewide Integrated Traffic Records System), CHP (California Highway Patrol), 2002-2010 data from TIMS (Transportation Injury Mapping System)--and presents itself as part of a healthy community indicators project from the Office of Health Equity. By looking at this data users can learn about which communities are bearing a disproportionate share in terms of pedestrian/car fatalities due to road traffic injuries without taking into account additional factors such as socioeconomic status or gender. Through understanding these statistics more accurately we can begin to take steps towards promoting safe transportation practices across all communities

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Welcome to the Road Traffic Injury dataset! This dataset contains information on the annual number of fatal and severe road traffic injuries per population and per miles traveled by transport mode in California from 2002-2010. We hope that this data will be useful to you in understanding trends, evaluating safety policies, and tracking changes in transportation safety over time.

In this guide, we’ll provide an overview of the dataset so you can start making use of it. We’ll cover what each column means and how you can use them for further analysis and exploration.

The columns in this dataset include detailed information about each road traffic injury event: - ind_definition: Definition of the indicator – i.e., whether it is a rate (per population) or a risk ratio (relative to some reference group).
- reportyear: Year of the report; - race_eth_code/name: Race/ethnicity code and name provided;
- geotype/value/name: Type of geographic area included as well as its corresponding value or name; - county_fips/name: FIPS code for counties, as well as their corresponding names;

• region_code/name: Region codes with accompanying region names provided respectively;

• mode: Mode of transportation associated with these events (motorcycles, pedestrians, buses & rail passengers);

• severity : Severity level (fatal or severe);

• • 11): Number of injuries occurring within that time period within each race ethinic category (injuries, totalpop [its total population], poprate [the rate by which there are injuries happening]) ;

12)- 15): Confidence Intervals associated with 95% Lower & Upper Limits (LL 95CI [Lower than 95% range] & UL95CI [Upper than 95% range]) by population rates (poprate) & miles traveled rates (avmtrate)

16): Standard Error Rates calculated by both Population Rate(poprate) & Miles Traveled Rate(amtrate) ; 19), 20), 23)}: Relative Risk Ration Rates providing values compared bottom line across geographic regions respectively {Population Rate(CA RR poprate), Miles Traveled Rate()) ; 21), 22}, 24), 25 => Decile Rankings arranging breakdowns from 1-10 into 10 respective categories calculations

Research Ideas

• The dataset can be used to develop maps that show impact of traffic injuries in different areas by race, geotype and mode.
• It can be used to measure the performance of safety improvement interventions by comparing changes in injury rates at certain county or cities before and after safety tactics have been implemented.
• It could also be used to study the effects of individual driving behaviors on collision related injury rates by analyzing data from counties with disparate levels of enforcement

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

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Exchange-Rate-Changes Time Series for Aptiv PLC. Aptiv PLC engages in design, manufacture, and sale of vehicle components for the automotive and commercial vehicle markets in North America, Europe, the Middle East, Africa, the Asia Pacific, South America, and internationally. It operates through two segments, Signal and Power Solutions, and Advanced Safety and User Experience. The Signal and Power Solutions segment designs, manufactures, and assembles vehicle's electrical architecture, including engineered component products, connectors, wiring assemblies and harnesses, cable management products, electrical centers, and high voltage and safety distribution systems. The Advanced Safety and User Experience segment provides critical technologies and services for vehicle safety, security, comfort, and convenience, such as sensing and perception systems, electronic control units, multi-domain controllers, vehicle connectivity systems, cloud-native software platform, application software, autonomous driving technologies, and end-to-end DevOps tools. Aptiv PLC was incorporated in 2011 and is based in Schaffhausen, Switzerland.