Introduction

This repository hosts the Testing Roads for Autonomous VEhicLes (TRAVEL) dataset. TRAVEL is an extensive collection of virtual roads that have been used for testing lane assist/keeping systems (i.e., driving agents) and data from their execution in state of the art, physically accurate driving simulator, called BeamNG.tech. Virtual roads consist of sequences of road points interpolated using Cubic splines.

Along with the data, this repository contains instructions on how to install the tooling necessary to generate new data (i.e., test cases) and analyze them in the context of test regression. We focus on test selection and test prioritization, given their importance for developing high-quality software following the DevOps paradigms.

This dataset builds on top of our previous work in this area, including work on

test generation (e.g., AsFault, DeepJanus, and DeepHyperion) and the SBST CPS tool competition (SBST2021),

test selection: SDC-Scissor and related tool

test prioritization: automated test cases prioritization work for SDCs.

Dataset Overview

The TRAVEL dataset is available under the data folder and is organized as a set of experiments folders. Each of these folders is generated by running the test-generator (see below) and contains the configuration used for generating the data (experiment_description.csv), various statistics on generated tests (generation_stats.csv) and found faults (oob_stats.csv). Additionally, the folders contain the raw test cases generated and executed during each experiment (test..json).

The following sections describe what each of those files contains.

Experiment Description

The experiment_description.csv contains the settings used to generate the data, including:

Time budget. The overall generation budget in hours. This budget includes both the time to generate and execute the tests as driving simulations.

The size of the map. The size of the squared map defines the boundaries inside which the virtual roads develop in meters.

The test subject. The driving agent that implements the lane-keeping system under test. The TRAVEL dataset contains data generated testing the BeamNG.AI and the end-to-end Dave2 systems.

The test generator. The algorithm that generated the test cases. The TRAVEL dataset contains data obtained using various algorithms, ranging from naive and advanced random generators to complex evolutionary algorithms, for generating tests.

The speed limit. The maximum speed at which the driving agent under test can travel.

Out of Bound (OOB) tolerance. The test cases' oracle that defines the tolerable amount of the ego-car that can lie outside the lane boundaries. This parameter ranges between 0.0 and 1.0. In the former case, a test failure triggers as soon as any part of the ego-vehicle goes out of the lane boundary; in the latter case, a test failure triggers only if the entire body of the ego-car falls outside the lane.

Experiment Statistics

The generation_stats.csv contains statistics about the test generation, including:

Total number of generated tests. The number of tests generated during an experiment. This number is broken down into the number of valid tests and invalid tests. Valid tests contain virtual roads that do not self-intersect and contain turns that are not too sharp.

Test outcome. The test outcome contains the number of passed tests, failed tests, and test in error. Passed and failed tests are defined by the OOB Tolerance and an additional (implicit) oracle that checks whether the ego-car is moving or standing. Tests that did not pass because of other errors (e.g., the simulator crashed) are reported in a separated category.

The TRAVEL dataset also contains statistics about the failed tests, including the overall number of failed tests (total oob) and its breakdown into OOB that happened while driving left or right. Further statistics about the diversity (i.e., sparseness) of the failures are also reported.

Test Cases and Executions

Each test..json contains information about a test case and, if the test case is valid, the data observed during its execution as driving simulation.

The data about the test case definition include:

The road points. The list of points in a 2D space that identifies the center of the virtual road, and their interpolation using cubic splines (interpolated_points)

The test ID. The unique identifier of the test in the experiment.

Validity flag and explanation. A flag that indicates whether the test is valid or not, and a brief message describing why the test is not considered valid (e.g., the road contains sharp turns or the road self intersects)

The test data are organized according to the following JSON Schema and can be interpreted as RoadTest objects provided by the tests_generation.py module.

{ "type": "object", "properties": { "id": { "type": "integer" }, "is_valid": { "type": "boolean" }, "validation_message": { "type": "string" }, "road_points": { §\label{line:road-points}§ "type": "array", "items": { "$ref": "schemas/pair" }, }, "interpolated_points": { §\label{line:interpolated-points}§ "type": "array", "items": { "$ref": "schemas/pair" }, }, "test_outcome": { "type": "string" }, §\label{line:test-outcome}§ "description": { "type": "string" }, "execution_data": { "type": "array", "items": { "$ref" : "schemas/simulationdata" } } }, "required": [ "id", "is_valid", "validation_message", "road_points", "interpolated_points" ] }

Finally, the execution data contain a list of timestamped state information recorded by the driving simulation. State information is collected at constant frequency and includes absolute position, rotation, and velocity of the ego-car, its speed in Km/h, and control inputs from the driving agent (steering, throttle, and braking). Additionally, execution data contain OOB-related data, such as the lateral distance between the car and the lane center and the OOB percentage (i.e., how much the car is outside the lane).

The simulation data adhere to the following (simplified) JSON Schema and can be interpreted as Python objects using the simulation_data.py module.

{ "$id": "schemas/simulationdata", "type": "object", "properties": { "timer" : { "type": "number" }, "pos" : { "type": "array", "items":{ "$ref" : "schemas/triple" } } "vel" : { "type": "array", "items":{ "$ref" : "schemas/triple" } } "vel_kmh" : { "type": "number" }, "steering" : { "type": "number" }, "brake" : { "type": "number" }, "throttle" : { "type": "number" }, "is_oob" : { "type": "number" }, "oob_percentage" : { "type": "number" } §\label{line:oob-percentage}§ }, "required": [ "timer", "pos", "vel", "vel_kmh", "steering", "brake", "throttle", "is_oob", "oob_percentage" ] }

Dataset Content

The TRAVEL dataset is a lively initiative so the content of the dataset is subject to change. Currently, the dataset contains the data collected during the SBST CPS tool competition, and data collected in the context of our recent work on test selection (SDC-Scissor work and tool) and test prioritization (automated test cases prioritization work for SDCs).

SBST CPS Tool Competition Data

The data collected during the SBST CPS tool competition are stored inside data/competition.tar.gz. The file contains the test cases generated by Deeper, Frenetic, AdaFrenetic, and Swat, the open-source test generators submitted to the competition and executed against BeamNG.AI with an aggression factor of 0.7 (i.e., conservative driver).

    Name
    Map Size (m x m)
    Max Speed (Km/h)
    Budget (h)
    OOB Tolerance (%)
    Test Subject




    DEFAULT
    200 × 200
    120
    5 (real time)
    0.95
    BeamNG.AI - 0.7


    SBST
    200 × 200
    70
    2 (real time)
    0.5
    BeamNG.AI - 0.7

Specifically, the TRAVEL dataset contains 8 repetitions for each of the above configurations for each test generator totaling 64 experiments.

SDC Scissor

With SDC-Scissor we collected data based on the Frenetic test generator. The data is stored inside data/sdc-scissor.tar.gz. The following table summarizes the used parameters.

    Name
    Map Size (m x m)
    Max Speed (Km/h)
    Budget (h)
    OOB Tolerance (%)
    Test Subject




    SDC-SCISSOR
    200 × 200
    120
    16 (real time)
    0.5
    BeamNG.AI - 1.5

The dataset contains 9 experiments with the above configuration. For generating your own data with SDC-Scissor follow the instructions in its repository.

Dataset Statistics

Here is an overview of the TRAVEL dataset: generated tests, executed tests, and faults found by all the test generators grouped by experiment configuration. Some 25,845 test cases are generated by running 4 test generators 8 times in 2 configurations using the SBST CPS Tool Competition code pipeline (SBST in the table). We ran the test generators for 5 hours, allowing the ego-car a generous speed limit (120 Km/h) and defining a high OOB tolerance (i.e., 0.95), and we also ran the test generators using a smaller generation budget (i.e., 2 hours) and speed limit (i.e., 70 Km/h) while setting the OOB tolerance to a lower value (i.e., 0.85). We also collected some 5, 971 additional tests with SDC-Scissor (SDC-Scissor in the table) by running it 9 times for 16 hours using Frenetic as a test generator and defining a more realistic OOB tolerance (i.e., 0.50).

Generating new Data

Generating new data, i.e., test cases, can be done using the SBST CPS Tool Competition pipeline and the driving simulator BeamNG.tech.

Extensive instructions on how to install both software are reported inside the SBST CPS Tool Competition pipeline Documentation;

Dataset shows an individual’s statistical area 3 (SA3) of usual residence and the SA3 of their workplace address, for the employed census usually resident population count aged 15 years and over, by main means of travel to work from the 2018 and 2023 Censuses.

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The main means of travel to work categories are:

• Work at home
• Drive a private car, truck, or van
• Drive a company car, truck, or van
• Passenger in a car, truck, van, or company bus
• Public bus
• Train
• Bicycle
• Walk or jog
• Ferry
• Other.

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Main means of travel to work is the usual method which an employed person aged 15 years and over used to travel the longest distance to their place of work.

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Workplace address refers to where someone usually works in their main job, that is the job in which they worked the most hours. For people who work at home, this is the same address as their usual residence address. For people who do not work at home, this could be the address of the business they work for or another address, such as a building site.

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Workplace address is coded to the most detailed geography possible from the available information. This dataset only includes travel to work information for individuals whose workplace address is available at SA3 level. The sum of the counts for each region in this dataset may not equal the total employed census usually resident population count aged 15 years and over for that region. Workplace address – 2023 Census: Information by concept has more information.

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This dataset can be used in conjunction with the following spatial files by joining on the SA3 code values:

• Statistical area 3 2023 (generalised)

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Download data table using the instructions in the Koordinates help guide.

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Footnotes

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Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city. 

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Population counts

Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts. 

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data).

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Workplace address time series

Workplace address time series data should be interpreted with care at lower geographic levels, such as statistical area 2 (SA2). Methodological improvements in 2023 Census resulted in greater data accuracy, including a greater proportion of people being counted at lower geographic areas compared to the 2018 Census. Workplace address – 2023 Census: Information by concept has more information.

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Working at home

In the census, working at home captures both remote work, and people whose business is at their home address (e.g. farmers or small business owners operating from their home). The census asks respondents whether they ‘mostly’ work at home or away from home. It does not capture whether someone does both, or how frequently they do one or the other.

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Rows excluded from the dataset

Rows show SA3 of usual residence by SA3 of workplace address. Rows with a total population count of less than six have been removed to reduce the size of the dataset, given only a small proportion of SA3-SA3 combinations have commuter flows.

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

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Main means of travel to work quality rating

Main means of travel to work is rated as moderate quality.

Main means of travel to work – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Workplace address quality rating

Workplace address is rated as moderate quality.

Workplace address – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Percentages

To calculate percentages, divide the figure for the category of interest by the figure for ‘Total stated’ where this applies.

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Symbol

-999 Confidential

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Inconsistencies in definitions

Please note that there may be differences in definitions between census classifications and those used for other data collections.

Context

The bike-share industry is rapidly becoming popular as a primary use of transportation, especially in urban areas and campuses. In the past, people cycled primarily as a form of sport or exercise, but now, households and individuals make use of bicycles to transport themselves.

Due to the pandemic, the bike-share industry experienced a decline in the number of bike-share customers, leading to a huge dip in revenue, but it is now gradually recovering as cities and businesses reopen. Research on bike and scooter rental markets predicts that the revenue for the bike-share industry will increase from $2.5 billion in 2019 to $10.1 billion in 2027, which is due to people’s need for easy commuting and increasing use in the food delivery industry to avoid traffic.

Bearing the high initial capital expenditure and ongoing depreciation expense, the bike sharing companies are facing risks of loss and a problem on how to maximize allocation efficiency to gain profits.

Content

The dataset is inspired by the past Bike Sharing Demand Competition. The data is hourly from 2018.1.1 to 2021.08.31. Data in 2020 April is missing since Capital Bikeshare does not provide the information on its website.

• datetime: Date and hour of the bike share data
• count: Count of total rentals
• holiday: Whether it is a holiday
• workingday: Whether it is a working day (Mon-Fri except holiday)
• temp: Temperature in Celsius
• feels_like: “Feels like” Temperature in Celsius
• temp_min: Lowest temperature
• temp_max: Highest temperature
• pressure: Air pressure
• humidity: Relative humidity
• wind_speed: Wind speed
• wind_deg: Wind direction degree
• rain_1h: Precipitation in the last 1 hour (missing when it did not rain)
• snow_1h: Snow volume for the last 1 hour (missing when it did not snow)
• cloud_all: Cloudiness in percentage
• weather_main: Weather types (Rain, Snow, Extreme etc.)

Acknowledgements

Original dataset: https://www.kaggle.com/c/bike-sharing-demand Capital Bikeshare trip data: http://capitalbikeshare.com/system-data Weather Information: https://openweathermap.org/history Holiday Schedule: http://dchr.dc.gov/page/holiday-schedule

Inspiration

Bearing the high initial capital expenditure and ongoing depreciation expense, the bike sharing companies are facing risks of loss. How can we better predict the bike share demand in order to help the companies maximize allocation efficiency?

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.

This commuter mode share data shows the estimated percentages of commuters in Champaign County who traveled to work using each of the following modes: drove alone in an automobile; carpooled; took public transportation; walked; biked; went by motorcycle, taxi, or other means; and worked at home. Commuter mode share data can illustrate the use of and demand for transit services and active transportation facilities, as well as for automobile-focused transportation projects.

Driving alone in an automobile is by far the most prevalent means of getting to work in Champaign County, accounting for over 69 percent of all work trips in 2023. This is the same rate as 2019, and the first increase since 2017, both years being before the COVID-19 pandemic began.

The percentage of workers who commuted by all other means to a workplace outside the home also decreased from 2019 to 2021, most of these modes reaching a record low since this data first started being tracked in 2005. The percentage of people carpooling to work in 2023 was lower than every year except 2016 since this data first started being tracked in 2005. The percentage of people walking to work increased from 2022 to 2023, but this increase is not statistically significant.

Meanwhile, the percentage of people in Champaign County who worked at home more than quadrupled from 2019 to 2021, reaching a record high over 18 percent. It is a safe assumption that this can be attributed to the increase of employers allowing employees to work at home when the COVID-19 pandemic began in 2020.

The work from home figure decreased to 11.2 percent in 2023, but which is the first statistically significant decrease since the pandemic began. However, this figure is still about 2.5 times higher than 2019, even with the COVID-19 emergency ending in 2023.

Commuter mode share data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

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

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

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Means of Transportation to Work.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (18 September 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (10 October 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (14 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (26 March 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (26 March 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).

About this dataset

Context

The dataset tabulates the data for the De Tour Village, MI population pyramid, which represents the De Tour Village population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for De Tour Village, MI, is 12.3.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for De Tour Village, MI, is 103.5.
• Total dependency ratio for De Tour Village, MI is 115.8.
• Potential support ratio, which is the number of youth (working age population) per elderly, for De Tour Village, MI is 1.0.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the De Tour Village population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the De Tour Village for the selected age group is shown in the following column.
• Population (Female): The female population in the De Tour Village for the selected age group is shown in the following column.
• Total Population: The total population of the De Tour Village for the selected age group is shown in the following column.

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 De Tour Village Population by Age. You can refer the same here

The dataset typically includes several features that provide insights into employee characteristics, job satisfaction, and performance. While the exact features may vary, here's a general list of common features you might find in such a dataset:

Employee ID: A unique identifier for each employee.

Age: The age of the employee.

Attrition: A binary variable indicating whether the employee has left the company (1) or is still employed (0).

Business Travel: The frequency and nature of business-related travel (e.g., "Travel_Rarely," "Travel_Frequently," "Non-Travel").

Department: The department to which the employee belongs (e.g., "Sales," "Research & Development," "Human Resources").

Distance From Home: The distance of the employee's residence from the workplace.

Education: The employee's level of education (e.g., "1: 'Below College'," "2: 'College'," "3: 'Bachelor'," "4: 'Master'," "5: 'Doctor').

Education Field: The field in which the employee's education lies (e.g., "Life Sciences," "Medical," "Marketing").

Environment Satisfaction: The level of satisfaction with the work environment on a scale.

Gender: The gender of the employee.

Job Involvement: The degree to which the employee is involved in their job.

Job Level: The level or rank of the employee's position.

Job Role: The specific role or title of the employee's job.

Job Satisfaction: The level of satisfaction with the job on a scale.

Marital Status: The marital status of the employee.

Monthly Income: The monthly salary of the employee.

Num Companies Worked: The number of companies the employee has worked for.

Over Time: Whether the employee works overtime or not.

Performance Rating: The performance rating of the employee.

Relationship Satisfaction: The level of satisfaction with relationships at the workplace.

Stock Option Level: The level of stock options provided to the employee.

Total Working Years: The total number of years the employee has been working.

Training Times Last Year: The number of training sessions the employee attended last year.

Work-Life Balance: The balance between work and personal life.

Years At Company: The number of years the employee has been with the current company.

Years In Current Role: The number of years the employee has been in their current role.

Years Since Last Promotion: The number of years since the last time the employee was promoted.

Years With Current Manager: The number of years the employee has been working under the current manager.

Please note that this is a general list, and the actual dataset might include additional features or variations. It's essential to explore the dataset thoroughly to understand the specifics of each feature and its relevance to the analysis.

This table contains information regarding the mobility of the residents of the Netherlands aged 6 or older in private households, so excluding residents of institutions and homes. The table contains per person per day /year an overview of the average number of trips, the average distance travelled and the average time travelled. These are regular trips on Dutch territory, including domestic holiday mobility. The distance travelled is based on stage information. Excluded in this table is mobility based on series of calls trips. The mobility behaviour is broken down by modes of travel, purposes of travel, population and region characteristics. The data used are retrieved from The Dutch National travel survey named Onderweg in Nederland (ODiN). Data available from: 2018

Status of the figures: The figures in this table are final.

Changes as of 4 July 2024: The figures for year 2023 are added.

When will new figures be published? Figures for the 2024 research year will be published in mid-2025

Insights from City Supply and Demand Data This data project has been used as a take-home assignment in the recruitment process for the data science positions at Uber.

Assignment Using the provided dataset, answer the following questions:

1. Which date had the most completed trips during the two week period?
2. What was the highest number of completed trips within a 24 hour period?
3. Which hour of the day had the most requests during the two week period?
4. What percentages of all zeroes during the two week period occurred on weekend (Friday at 5 pm to Sunday at 3 am)? Tip: The local time value is the start of the hour (e.g. 15 is the hour from 3:00pm - 4:00pm)
5. What is the weighted average ratio of completed trips per driver during the two week period? Tip: "Weighted average" means your answer should account for the total trip volume in each hour to determine the most accurate number in whole period.
6. In drafting a driver schedule in terms of 8 hours shifts, when are the busiest 8 consecutive hours over the two week period in terms of unique requests? A new shift starts in every 8 hours. Assume that a driver will work same shift each day.
7. True or False: Driver supply always increases when demand increases during the two week period. Tip: Visualize the data to confirm your answer if needed.
8. In which 72 hour period is the ratio of Zeroes to Eyeballs the highest?
9. If you could add 5 drivers to any single hour of every day during the two week period, which hour should you add them to? Hint: Consider both rider eyeballs and driver supply when choosing
10. True or False: There is exactly two weeks of data in this analysis
11. Looking at the data from all two weeks, which time might make the most sense to consider a true "end day" instead of midnight? (i.e when are supply and demand at both their natural minimums) Tip: Visualize the data to confirm your answer if needed.

Data Description To answer the question, use the dataset from the file dataset_1.csv. For example, consider the row 11 from this dataset:

Date Time (Local) Eyeballs Zeroes Completed Trips Requests Unique Drivers

2012-09-10 16 11 2 3 4 6

This means that during the hour beginning at 4pm (hour 16), on September 10th, 2012, 11 people opened the Uber app (Eyeballs). 2 of them did not see any car (Zeroes) and 4 of them requested a car (Requests). Of the 4 requests, only 3 complete trips actually resulted (Completed Trips). During this time, there were a total of 6 drivers who logged in (Unique Drivers)

Travel accident:

Accident occurring during the journey between the place of work and home or the place of catering, at standard time. The data presented concerns civil servants and contract workers. They indicate, by community, by body, by sex, the number of commuting accidents as well as the number of days of work stoppage associated with them.

Service-work accident:

Duty accident for civil servants and work accident for agents non-holders. These accidents occur when the agent is under the authority of the employer, during the time and in the workplace. Mission accidents that occur while traveling as part of professional obligations are included in work accidents. The data presented concerns civil servants and contract workers. They indicate, by community, by body, by gender, the number of on-duty accidents as well as the number of days of sick leave associated with them.

The social report of the Parisian community provides, each year, quantitative but also qualitative information on the human resources of the City of Paris. It traces the evolution of the main indicators relating to human resources policy, whether it concerns staff numbers, remuneration, health and safety, careers and training or even working conditions and social relations.

Link to the dataset Social Report - Work accident and journey by body - Since 2016

This collection automatically captures metadata, the source of which is the GOVERNMENT OF THE REPUBLIC OF SLOVENIA STATISTICAL USE OF THE REPUBLIC OF SLOVENIA and corresponding to the source database entitled “Day number of journeys per working and non-working day by travel purpose, Slovenia, multiannually”.

Actual data are available in Px-Axis format (.px). With additional links, you can access the source portal page for viewing and selecting data, as well as the PX-Win program, which can be downloaded free of charge. Both allow you to select data for display, change the format of the printout, and store it in different formats, as well as view and print tables of unlimited size, as well as some basic statistical analyses and graphics.

Dataset population: Households with at least one person working or studying (persons in employment the week before the census and full-time students)

Car or van availability

The number of cars or vans that are owned, or available for use, by one or more members of a household. This includes company cars and vans that are available for private use. It does not include motorbikes or scooters, or any cars or vans belonging to visitors.

Households with 10 to 20 cars or vans were counted as having only 1. Responses indicating a number of cars or vans greater than 20 were treated as invalid and a value was imputed.

The count of cars or vans in an area relates only to households. Cars or vans used by residents of communal establishments were not counted.

Means of travel to place of work or study (alternate classification)

The method of travel used for the longest part, by distance, of the usual journey to work or place of study (including school).

Number of people in household working or studying

Provides a count of the number of people working or studying in a household.

Description

TLC Trip Record Data Yellow and green taxi trip records include fields capturing pick-up and drop-off dates/times, pick-up and drop-off locations, trip distances, itemized fares, rate types, payment types, and driver-reported passenger counts. The data used in the attached datasets were collected and provided to the NYC Taxi and Limousine Commission (TLC) by technology providers authorized under the Taxicab & Livery Passenger Enhancement Programs (TPEP/LPEP). The trip data was not created by the TLC, and TLC makes no representations as to the accuracy of these data.

For-Hire Vehicle (“FHV”) trip records include fields capturing the dispatching base license number and the pick-up date, time, and taxi zone location ID (shape file below). These records are generated from the FHV Trip Record submissions made by bases. Note: The TLC publishes base trip record data as submitted by the bases, and we cannot guarantee or confirm their accuracy or completeness. Therefore, this may not represent the total amount of trips dispatched by all TLC-licensed bases. The TLC performs routine reviews of the records and takes enforcement actions when necessary to ensure, to the extent possible, complete and accurate information.

Proposed tasks

• Identify the routes with the lowest price/km, time/km, and price/time ratios.
• Visualize the evolution of the average travel time and distance throughout the day.
• Calculate the probability of traveling from one zone to another in less than X minutes (where X is easily modifiable).
• Analyze the zones where it is most likely to take a taxi based on the time of day.
• Determine the best time of day to travel to the airport.
• Design a model that predicts the travel duration and cost given the hour, origin, and destination zone. Show the relevance of the dataset attributes.

Features

VendorID: An ID code indicating the taxi vendor, 1 for Creative Mobile Technologies, LLC and 2 for VeriFone Inc. lpep_pickup_datetime: The date and time when the taxi ride started. lpep_dropoff_datetime: The date and time when the taxi ride ended. store_and_fwd_flag: Indicates whether the trip record was held in vehicle memory before sending to the vendor, Y=store and forward; N=not a store and forward trip. RatecodeID: The rate code for the trip, 1=Standard rate, 2=JFK, 3=Newark, 4=Nassau or Westchester, 5=Negotiated fare, 6=Group ride PULocationID: The pickup location ID, corresponding to the taxi zone where the taximeter was engaged. DOLocationID: The dropoff location ID, corresponding to the taxi zone where the taximeter was disengaged. passenger_count: The number of passengers in the vehicle. trip_distance: The distance of the trip in miles. fare_amount: The metered fare for the trip. extra: Extra charges. Currently, this only includes the 0.5 dollars and 1 dollar rush hour and overnight charges. mta_tax: The 0.50 dollars MTA tax that is automatically triggered based on the metered rate in use. tip_amount: Tip amount – This field is automatically populated for credit card tips. Cash tips are not included. tolls_amount: Total amount of all tolls paid in trip. ehail_fee: This is a $1.00 surcharge that is automatically applied to every trip booked through the ehail platform. improvement_surcharge: 0.30 dollars improvement surcharge assessed trips at the flag drop. The improvement surcharge began being levied in 2015. total_amount: The total amount charged to passengers. This field includes the metered fare, extra charges, mta_tax, tip_amount and tolls_amount plus any improvement_surcharge or ehail_fee. payment_type: A numeric code indicating the payment method: 1= Credit card, 2= Cash, 3= No charge, 4= Dispute, 5= Unknown, 6= Voided trip. trip_type: A code indicating whether the trip was a street-hail or a dispatch that is automatically assigned based on the metered rate in use but can be overridden by the driver. congestion_surcharge: 2.75 dollars congestion surcharge assessed trips in yellow and green taxis in Manhattan south of 96th St. The surcharge began being levied in 2019.

License

The data from the New York City Taxi and Limousine Commission (TLC) Trip Record Data website is available to the public under the Open Data Commons Open Database License (ODbL). This license allows for the use, sharing, and modification of the data as long as attribution is given to the original source and any derivative works are also licensed under the ODbL.

Citation

New York City Taxi and Limousine Commission (2019). TLC Trip Record Data. Retrieved from https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page

About

The Synthetic Sweden Mobility (SySMo) model provides a simplified yet statistically realistic microscopic representation of the real population of Sweden. The agents in this synthetic population contain socioeconomic attributes, household characteristics, and corresponding activity plans for an average weekday. This agent-based modelling approach derives the transportation demand from the agents’ planned activities using various transport modes (e.g., car, public transport, bike, and walking).

This open data repository contains four datasets:

(1) Synthetic Agents,

(2) Activity Plans of the Agents,

(3) Travel Trajectories of the Agents, and

(4) Road Network (EPSG: 3006)

(OpenStreetMap data were retrieved on August 28, 2023, from https://download.geofabrik.de/europe.html, and GTFS data were retrieved on September 6, 2023 from https://samtrafiken.se/)

The database can serve as input to assess the potential impacts of new transportation technologies, infrastructure changes, and policy interventions on the mobility patterns of the Swedish population.

Methodology

This dataset contains statistically simulated 10.2 million agents representing the population of Sweden, their socio-economic characteristics and the activity plan for an average weekday. For preparing data for the MATSim simulation, we randomly divided all the agents into 10 batches. Each batch's agents are then simulated in MATSim using the multi-modal network combining road networks and public transit data in Sweden using the package pt2matsim (https://github.com/matsim-org/pt2matsim).

The agents' daily activity plans along with the road network serve as the primary inputs in the MATSim environment which ensures iterative replanning while aiming for a convergence on optimal activity plans for all the agents. Subsequently, the individual mobility trajectories of the agents from the MATSim simulation are retrieved.

The activity plans of the individual agents extracted from the MATSim simulation output data are then further processed. All agents with negative utility score and negative activity time corresponding to at least one activity are filtered out as the ‘infeasible’ agents. The dataset ‘Synthetic Agents’ contains all synthetic agents regardless of their ‘feasibility’ (0=excluded & 1=included in plans and trajectories). In the other datasets, only agents with feasible activity plans are included.

The simulation setup adheres to the MATSim 13.0 benchmark scenario, with slight adjustments. The strategy for replanning integrates BestScore (60%), TimeAllocationMutator (30%), and ReRoute (10%)— the percentages denote the proportion of agents utilizing these strategies. In each iteration of the simulation, the agents adopt these strategies to adjust their activity plans. The "BestScore" strategy retains the plan with the highest score from the previous iteration, selecting the most successful strategy an agent has employed up until that point. The "TimeAllocationMutator" modifies the end times of activities by introducing random shifts within a specified range, allowing for the exploration of different schedules. The "ReRoute" strategy enables agents to alter their current routes, potentially optimizing travel based on updated information or preferences. These strategies are detailed further in W. Axhausen et al. (2016) work, which provides comprehensive insights into their implementation and impact within the context of transport simulation modeling.

Data Description

(1) Synthetic Agents

This dataset contains all agents in Sweden and their socioeconomic characteristics.

The attribute ‘feasibility’ has two categories: feasible agents (73%), and infeasible agents (27%). Infeasible agents are agents with negative utility score and negative activity time corresponding to at least one activity.

File name: 1_syn_pop_all.parquet

Column

Description

Data type

Unit

PId

Agent ID

Integer

-

Deso Zone code of Demographic statistical areas (DeSO)1

String

kommun

Municipality code

Integer

marital

Marital Status (single/ couple/ child)

String

sex

Gender (0 = Male, 1 = Female)

Integer

age

Age

Integer

HId

A unique identifier for households

Integer

HHtype

Type of households (single/ couple/ other)

String

HHsize

Number of people living in the households

Integer

num_babies

Number of children less than six years old in the household

Integer

employment Employment Status (0 = Not Employed, 1 = Employed)

Integer

studenthood Studenthood Status (0 = Not Student, 1 = Student)

Integer

income_class Income Class (0 = No Income, 1 = Low Income, 2 = Lower-middle Income, 3 = Upper-middle Income, 4 = High Income)

Integer

num_cars Number of cars owned by an individual

Integer

HHcars Number of cars in the household

Integer

feasibility

Status of the individual (1=feasible, 0=infeasible)

Integer

1 https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/

(2) Activity Plans of the Agents

The dataset contains the car agents’ (agents that use cars on the simulated day) activity plans for a simulated average weekday.

File name: 2_plans_i.parquet, i = 0, 1, 2, ..., 8, 9. (10 files in total)

Column

Description

Data type

Unit

act_purpose

Activity purpose (work/ home/ school/ other)

String

-

PId

Agent ID

Integer

-

act_end

End time of activity (0:00:00 – 23:59:59)

String

hour:minute:seco

nd

act_id

Activity index of each agent

Integer

-

mode

Transport mode to reach the activity location

String

-

POINT_X

Coordinate X of activity location (SWEREF99TM)

Float

metre

POINT_Y

Coordinate Y of activity location (SWEREF99TM)

Float

metre

dep_time

Departure time (0:00:00 – 23:59:59)

String

hour:minute:seco

nd

score

Utility score of the simulation day as obtained from MATSim

Float

-

trav_time

Travel time to reach the activity location

String

hour:minute:seco

nd

trav_time_min

Travel time in decimal minute

Float

minute

act_time

Activity duration in decimal minute

Float

minute

distance

Travel distance between the origin and the destination

Float

km

speed

Travel speed to reach the activity location

Float

km/h

(3) Travel Trajectories of the Agents

This dataset contains the driving trajectories of all the agents on the road network, and the public transit vehicles used by these agents, including buses, ferries, trams etc. The files are produced by MATSim simulations and organised into 10 *.parquet’ files (representing different batches of simulation) corresponding to each plan file.

File name: 3_events_i.parquet, i = 0, 1, 2, ..., 8, 9. (10 files in total)

Column

Description

Data type

Unit

time

Time in second in a simulation day (0-86399)

Integer

second

type

Event type defined by MATSim simulation*

String

person

Agent ID

Integer

link

Nearest road link consistent with the road network

String

vehicle

Vehicle ID identical to person

Integer

from_node

Start node of the link

Integer

to_node

End node of the link

Integer

• One typical episode of MATSim simulation events: Activity ends (actend) -> Agent’s vehicle enters traffic (vehicle enters traffic) -> Agent’s vehicle moves from previous road segment to its next connected one (left link) -> Agent’s vehicle leaves traffic for activity (vehicle leaves traffic) -> Activity starts (actstart)

(4) Road Network

This dataset contains the road network.

File name: 4_network.shp

Column

Description

Data type

Unit

length

The length of road link

Float

metre

freespeed

Free speed

Float

km/h

capacity

Number of vehicles

Integer

permlanes

Number of lanes

Integer

oneway

Whether the segment is one-way (0=no, 1=yes)

Integer

modes

Transport mode

String

from_node

Start node of the link

Integer

to_node

End node of the link

Integer

geometry

LINESTRING (SWEREF99TM)

geometry

metre

Additional Notes

This research is funded by the RISE Research Institutes of Sweden, the Swedish Research Council for Sustainable Development (Formas, project number 2018-01768), and Transport Area of Advance, Chalmers.

Contributions

YL designed the simulation, analyzed the simulation data, and, along with CT, executed the simulation. CT, SD, FS, and SY conceptualized the model (SySMo), with CT and SD further developing the model to produce agents and their activity plans. KG wrote the data document. All authors reviewed, edited, and approved the final document.

The Florida Department of Transportation (FDOT or Department) has identified processed, authoritative datasets to support the preliminary spatial analysis of equity considerations. These processed datasets are available at larger geographies, such as the United States Census Bureau tract or county-level; however, additional raw datasets from other sources can be used to identify equity considerations. Most of this raw data is available at the Census block group, parcel, or point-level—but additional processing is required to make suitable for spatial analysis. For more information, contact Dana Reiding with the FDOT Forecasting and Trends Office (FTO).The Job Accessibility by Auto dataset represents a “cumulative opportunity” metric—it reflects the total number of jobs reachable by auto within a travel time threshold. The FDOT Source Book reports the number of 2018 and 2019 jobs accessible by transit within 10-, 20-, 30-, 40-, 50-, and 60-minute automobile commute thresholds. Travel times are calculated using a detailed road network and speed data that reflect typical conditions for an 8 a.m. Wednesday morning departure. Additionally, the accessibility results for 8 a.m. are compared with the maximum accessibility results across the 24-hour period to estimate the impact of road and highway congestion on job accessibility. Estimates for 2018 and 2019 are shown by county and state boundaries. This data is owned and managed by FDOT FTO. Data Link: https://fdotsourcebook.com/performance-measures/auto/job-accessibility-by-auto Available Geography Levels: State, County Owner/Managed By: FDOT FTO Point of Contact:Dana Reiding, ManagerForecasting and Trends OfficeFlorida Department of TransportationDana.Reiding@dot.state.fl.us605 Suwannee Street, Tallahassee, Florida 32399850-414-4719

HR Employee Attrition

**Through various efforts, a significant amount of money is spent on maintaining existing staff. We want to reduce the cost of staff retention. We propose that the incentives be limited to only those employees who are in danger of leaving. We need Identify patterns in the characteristics of employees who leave the organization and use this information to predict if an employee is at risk of attrition.

Attrition is the departure of employees from the organization for any reason (voluntary or involuntary), including resignation, termination, death or retirement.

Main Objective:

To identify the different factors that drive attrition

To make a model to predict if an employee will attrite or not

Dataset: The data contains demographic details, work-related metrics and attrition flag.

EmployeeNumber - Employee Identifier Attrition - Did the employee attrite? Age - Age of the employee BusinessTravel - Travel commitments for the job DailyRate - Data description not available** Department - Employee Department DistanceFromHome - Distance from work to home (in km) Education - 1-Below College, 2-College, 3-Bachelor, 4-Master,5-Doctor EducationField - Field of Education EnvironmentSatisfaction - 1-Low, 2-Medium, 3-High, 4-Very High Gender - Employee's gender HourlyRate - Data description not available** JobInvolvement - 1-Low, 2-Medium, 3-High, 4-Very High JobLevel - Level of job (1 to 5) JobRole - Job Roles JobSatisfaction - 1-Low, 2-Medium, 3-High, 4-Very High MaritalStatus - Marital Status MonthlyIncome - Monthly Salary MonthlyRate - Data description not available** NumCompaniesWorked - Number of companies worked at Over18 - Over 18 years of age? OverTime - Overtime? PercentSalaryHike - The percentage increase in salary last year PerformanceRating - 1-Low, 2-Good, 3-Excellent, 4-Outstanding RelationshipSatisfaction - 1-Low, 2-Medium, 3-High, 4-Very High StandardHours - Standard Hours StockOptionLevel - Stock Option Level TotalWorkingYears - Total years worked TrainingTimesLastYear - Number of training attended last year WorkLifeBalance - 1-Low, 2-Good, 3-Excellent, 4-Outstanding YearsAtCompany - Years at Company YearsInCurrentRole - Years in the current role YearsSinceLastPromotion - Years since the last promotion YearsWithCurrManager - Years with the current manager

This service shows the proportion of commuters using sustainable transportation for Canada by 2016 census subdivision. The data is from the data table Main Mode of Commuting (10), Commuting Duration (7), Time Leaving for Work (7), Sex (3) and Age (5) for the Employed Labour Force Aged 15 Years and Over Having a Usual Place of Work or No Fixed Workplace Address, in Private Households of Canada, Provinces and Territories, Census Divisions and Census Subdivisions, 2016 Census - 25% Sample Data, Statistics Canada Catalogue no. 98-400-X2016324. 'Main mode of commuting' refers to the main mode of transportation a person uses to travel between his or her home and his or her place of work. 'Sustainable transportation' refers to persons using public transit (bus; subway or elevated rail; light rail, streetcar or commuter train; or passenger ferry) or active transport (walked or bicycle). For additional information refer to 'Main mode of commuting' in the 2016 Census Dictionary. For additional information refer to 'Main mode of commuting' in the 2016 Census Dictionary. To have a cartographic representation of the ecumene with this socio-economic indicator, it is recommended to add as the first layer, the “NRCan - 2016 population ecumene by census subdivision” web service, accessible in the data resources section below.

Envestnet®| Yodlee®'s Bank Statement Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis