The annual bike and pedestrian count is a volunteer data collection effort each fall that helps the City understand where and how many people are biking and walking in Somerville, and how those numbers are changing over time. This program has been taking place each year since 2010. Counts are collected Tuesday, Wednesday, or Thursday for one hour in the morning and evening using a “screen line” method, whereby cyclists and pedestrians are counted as they pass by an imaginary line across the street and sidewalks. Morning count sessions begin between 7:15 and 7:45 am, and evening count sessions begin between 4:45 and 5:15 pm. Bike counts capture the number of people riding bicycles, so an adult and child riding on the same bike would be counted as two counts even though it is only one bike. Pedestrian counts capture people walking or jogging, people using a wheelchair or assistive device, children in strollers, and people using other micro-mobility devices, such as skateboards, scooters, or roller skates. While the City and its amazing volunteers do their best to collect accurate and complete data each year and the City does quality control to catch clear errors, it is not possible to ensure 100% accuracy of the data and not all locations have been counted every year of the program. There are also several external factors impacting counts that are not consistent year-to-year, such as nearby construction and weather. For these reasons, the counts are intended to be used to observe high-level trends across the city and at count locations, and not to extrapolate that biking and walking in Somerville has changed by a specific percentage or number. Data in this dataset are available at the location count level. To request data at the movement level, please contact transportation@somervillema.gov.

About Dataset

This case study is a part of Google Data Analytics course. Cyclistic is a fictional bike-sharing company, however, the data is real. It encompasses information about bike-sharing stations in Chicago and total rides with rented bikes during more than 10 years, from 2013 until February 2023.

The business task is to help design the marketing strategy. The project owner aims at converting casual riders into annual members. To achieve that goal the marketing team needs to better understand how annual members and casual riders differ in using rented bikes.

My specific task was to analyze the available data of rides and provide 3 main recommendation for the marketing strategy, based on the data analysis.

The requirement was to analyze the data for the last 12 months. However, I decided to use the whole dataset, since it was openly available for the whole period of operations.

Data License Agreement

Lyft Bikes and Scooters, LLC (“Bikeshare”) operates the City of Chicago’s (“City”) Divvy bicycle sharing service. Bikeshare and the City are committed to supporting bicycling as an alternative transportation option. As part of that commitment, the City permits Bikeshare to make certain Divvy system data owned by the City (“Data”) available to the public, subject to the terms and conditions of this License Agreement (“Agreement”). By accessing or using any of the Data, you agree to all of the terms and conditions of this Agreement.

License. Bikeshare hereby grants to you a non-exclusive, royalty-free, limited, perpetual license to access, reproduce, analyze, copy, modify, distribute in your product or service and use the Data for any lawful purpose (“License”). Prohibited Conduct. The License does not authorize you to do, and you will not do or assist others in doing, any of the following

Use the Data in any unlawful manner or for any unlawful purpose; Host, stream, publish, distribute, sublicense, or sell the Data as a stand-alone dataset; provided, however, you may include the Data as source material, as applicable, in analyses, reports, or studies published or distributed for non-commercial purposes; Access the Data by means other than the interface Bikeshare provides or authorizes for that purpose; Circumvent any access restrictions relating to the Data; Use data mining or other extraction methods in connection with Bikeshare's website or the Data; Attempt to correlate the Data with names, addresses, or other information of customers or Members of Bikeshare; and State or imply that you are affiliated, approved, endorsed, or sponsored by Bikeshare. Use or authorize others to use, without the written permission of the applicable owners, the trademarks or trade names of Lyft Bikes and Scooters, LLC, the City of Chicago or any sponsor of the Divvy service. These marks include, but are not limited to DIVVY, and the DIVVY logo, which are owned by the City of Chicago. No Warranty. THE DATA IS PROVIDED “AS IS,” AS AVAILABLE (AT BIKESHARE’S SOLE DISCRETION) AND AT YOUR SOLE RISK. TO THE MAXIMUM EXTENT PROVIDED BY LAW BIKESHARE DISCLAIMS ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING THE IMPLIED WARRANTIES OF MERCHANTABILITY FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. BIKESHARE FURTHER DISCLAIMS ANY WARRANTY THAT THE DATA WILL MEET YOUR NEEDS OR WILL BE OR CONTINUE TO BE AVAILABLE, COMPLETE, ACCURATE, TIMELY, SECURE, OR ERROR FREE.

Limitation of Liability and Covenant Not to Sue. Bikeshare, its parent, affiliates and sponsors, and their respective directors, officers, employees, or agents will not be liable to you or anyone else for any loss or damage, including any direct, indirect, incidental, and consequential damages, whether foreseeable or not, based on any theory of liability, resulting in whole or in part from your access to or use of the Data. You will not bring any claim for damages against any of those persons or entities in any court or otherwise arising out of or relating to this Agreement, the Data, or your use of the Data. In any event, if you were to bring and prevail on such a claim, your maximum recovery is limited to $100 in the aggregate even if you or they had been advised of the possibility of liability exceeding that amount. Ownership and Provision of Data. The City of Chicago owns all right, title, and interest in the Data. Bikeshare may modify or cease providing any or all of the Data at any time, without notice, in its sole discretion. No Waiver. Nothing in this Agreement is or implies a waiver of any rights Bikeshare or the City of Chicago has in the Data or in any copyrights, patents, or trademarks owned or licensed by Bikeshare, its parent, affiliates or sponsors. The DIVVY trademarks are owned by the City of Chicago. Termination of Agreement. Bikeshare may terminate this Agreement at any time and for any reason in its sole discretion. Termination will be effective ...

The District Department of Transportation (DDOT) maintains a system of automated counters to measure the number of people walking and biking. DDOT began installing these counters in 2014, and now has 18 in operation. Counters have been installed in both bicycle lanes and trails. One location counts only pedestrians; 10 locations count only bikes; and 7 locations count people biking and walking. DDOT monitors the continuous data stream to analyze trends in walking and biking, assess the value of its facility investments, and apply this data to plan for new bike lanes and trails. Data will sometimes contain errors or contain gaps because the dashboard presents "raw" data direct from the system server and the devices in the field.

Context

During the Google Data Analytics Specialization course on Coursera, I had to do case study to apply all the skills that I have learned during the course. I decided to do it using this data on bike sharing. I was provided with a case study and this was the scenario: You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations. Note: Cyclistic is a fictional company. For the purpose of this case study, the datasets are appropriate and will enable you to answer the business questions.

Content

The dataset contains data from the company that operates bike sharing services in Chicago city. The data below is for the year of 2021. Following are the columns in the dataset and what they represent: - ride_id : the unique id to refer to each trip - rideable_type : the type of the bike used for the trip - started_at : date-time for when the trip started - ended_at : date-time for when the trip ended - start_station_name : name of the station from where the trip started - start_station_id : unique id of the station from where the trip started - end_station_name : name of the station where the trip ended - end_station_id : unique id of the station where the trip ended - start_lat : latitude of the start station - start_lng : longitude of the start station - end_lat : latitude of the end station - end_lng : longitude of the end station - member_casual : member denotes the users who have subscribed to annual membership, and casual denotes the users who haven't

Acknowledgements

The data was made available by Motivate International Inc. under this license. Credits to Google Data Analytics team too, for sharing this information.

Inspiration

Some of the questions that you can answer using this dataset: - How do annual members and casual riders use Cyclistic bikes differently? - Why would casual riders buy Cyclistic annual memberships? - How can Cyclistic use digital media to influence casual riders to become members?

The Bicycle and E-Mobility Element of the STP will help create a safer, more bikeable Seattle. It provides a foundation for the City of Seattle to grow our investment in bicycling and e-mobility to achieve STP goals. The STP and the Bicycle and E-Mobility Element build on and supersede the 2014 Bicycle Master Plan (BMP). The bicycle and e-mobility network serves not only people riding traditional bicycles, but also people using adaptive bikes, cargo bicycles for both personal use and deliveries, trikes, scooters, skateboards, roller skates, wheelchairs or other wheeled mobility devices, and “e-mobility” devices, which refers to personal and shared electric-powered bicycles, scooters, and other electric-powered devices. It serves people bicycling and taking e-mobility to serve a variety of trip purposes, such as getting to work, school, transit, the gym or doctor's office, recreating, making urban goods deliveries, and more.The Bike+ network consists of bikeways suitable for people of all ages and abilities (AAA), including protected bike lanes, Neighborhood Greenways, Healthy Streets, and bike lanes where vehicle speeds and volumes are sufficiently low. The Bike+ network is envisioned to seamlessly integrate with the multi-use trail network, which provides connections through or on the edges of parks and opens spaces, where an off-street connection is preferred, or is more feasible than an on-street connection. Diagram of an umbrella titled "What is Bike+?" Underneath the umbrella, the following are bulleted - protected bike lane, bike lane (if vehicle speed and volumes low. See Table 4), neighborhood greenway, and healthy street. Many planned projects from the 2014 BMP have been implemented and are shown on the existing bicycle and e-mobility network map. The Bike+ network shows existing and proposed AAA bikeways on Seattle’s arterial and non-arterial (i.e., neighborhood streets) networks.Refresh Cycle: None, Static. Manually as required.Original Publish: 5/23/2024Update Publish: 7/11/2024 per Policy and Planning teamContact: Policy and Planning team

The spatial data set contains the statistical evaluation of the city cycling competition in the city of Freiburg from the year 2024. The number of trips per direction, the number of trips per section and a heat map of cycling are available. The data is "open data". The ‘Heatmap’ shows the recorded GPS points of the bike rides. The representation works like the image of a thermal camera, the ‘warmer’ (red colours) the representation, the more points were recorded at this point. If a place is shown as particularly ‘warm’, many GPS points were transmitted at this point. This can have two different causes: 1) Particularly many people pass through this place. 2) Few people stay in this place for a particularly long time.

Case Study 1- Bike Sharing Introduction: In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown to a fleet of 5,824 bicycles that are geotracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system anytime. There are two types of members are sharing bike differently! 1.) Annual members- who bought annual membership. 2.) Casual members- who bought or buying single-ride passes, full-day passes.

Phase_1- Ask- 1. Identify the business task- • How do annual members and casual riders use Cyclistic bikes differently? • Why would casual riders buy Cyclistic annual memberships? • How can Cyclistic use digital media to influence casual riders to become members? 2. Consider key stakeholders- Lily Moreno: The director of marketing and manager, Cyclistic marketing analytics team, Cyclistic executive team.

Phase_2- Prepare--
I downloaded and store it in my excel sheet, I am using only one month (April_2020) data, and using excel for solving task, I am also sorting and filtering my data according to requirement. I downloaded data from public source and it’s fully reliable, unbiased. Data is also, complete, consistent and accurate. Phase_3- Process— • I downloaded 202004-divvy-tripdata.cvs data and I unzip the file and converted into .xls file, here I am using only April data because this case study is my first case study and only for my learning, so I want to keep it simple. I am using excel this time because I am more comfortable with excel then other tools. I also want to perform good analysis and don’t want to lost in multiple sheets & large dataset, in initial stage.

• I Checked the data errors, and corrected some errors, I also did some calculation in my sheet, and try to clean data, so I can use sheet appropriately, Phase_4- analyze— I organize my data, performed sorting and filtering multiple time as I needed, did some calculation, add few pivots table and try to analyze data properly, also try to Identify trends and relationships.

Phase_5- Share— • After completing my analysis, I used some charts to present my findings. First, I found Total count of ride is 16383 and annual members took 11552 count of ride what is 71% of total ride, and casual riders took only 29% of ride which is 4831.

• I also found that casual riders using ride for some times but members are taking ride anytime no matter if they need bike for long time or short time, they are taking ride without any second thought, because after buying annual pass they no need to pay (any extra money or) every time.

• Clark St & Elm St is a most bike rented point, people took 180 bikes from this station, and 132 are the annual member from that. Also, I found other station where we need more bikes. Likewise, we also can find station name where most people end their ride, so they have plenty space for bikes. Phase_6- Act— Feeling happy to share my finding with you, feeling little confident after completing my first case study.

This table contains data on the percent of population aged 16 years or older whose commute to work is 10 or more minutes/day by walking or biking for California, its regions, counties, and cities/towns. Data is from the U.S. Census Bureau, American Community Survey, and from the U.S. Department of Transportation, Federal Highway Administration, and National Household Travel Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Active modes of transport, bicycling and walking alone and in combination with public transit, offer opportunities to incorporate physical activity into the daily routine. Physical activity is associated with lowering rates of heart disease and stroke, diabetes, colon and breast cancer, dementia and depression. Automobile commuting is associated with health hazards, such as air pollution, motor vehicle crashes, pedestrian injuries and fatalities, and sedentary lifestyles. Consequently the transition from automobile-focused transport to public and active transport offers environmental health benefits, including reductions in air pollution, greenhouse gases and noise pollution, and may lead to greater overall safety in transportation. More information about the data table and a data dictionary can be found in the About/Attachments section.

London’s Cycling Infrastructure Database (CID) is the world’s largest and most comprehensive database of cycling infrastructure, containing comprehensive details of cycling infrastructure in the Capital. The CID is intended to address barriers to cycling by providing Londoners with clear and accurate information about cycling infrastructure, helping them plan cycle journeys with confidence. The CID is a core part of our Cycling Action Plan , which sets out how TfL, boroughs and others will work together to make London the world’s best big city for cycling.

To create the database, TfL have surveyed every street in every London borough to collect information on over 240,000 pieces of infrastructure, covering an area of 1,595 square kilometres.

The database also contains 480,000 photographs of cycling infrastructure, allowing users to see exactly what can be found on street. For example, cycle parking users will be able to see what type of parking is available. TfL collected data of 146,000 cycle parking spaces across London, as well as gathering information on 2,000km of cycle routes and 58,000 wayfinding signs.

An update to TfL's own Journey Planner means that people using the planner for cycle journeys can now see the nearest and most convenient place to park for every journey. Third party developers will be able to use the data for their own journey planning tools, which will make it simpler for Londoners to plan cycle journeys using their preferred apps. We’re excited to see how developers can use the data to help make cycling in the Capital easier, and to kick-start this we will invite app developers to a hackathon this autumn to see how this data can be maximised to benefit people cycling.

As well as making it easier for Londoners to plan cycle journeys, the database will help TfL and boroughs to plan future cycling investment. For example, the database has already been used to develop TfL's Cycle Parking Implementation Plan, which sets out how TfL will work with partners across the capital to deliver 50,000 cycle parking spaces over the next six years where they are needed most, to meet the growing demand for safe places to park cycles.

The following types of asset are included in the database:

• Cycle lanes and tracks – including whether they are segregated or painted lanes
• Cycle parking, including the type and capacity of parking
• Signalised crossings for cycles
• Restricted route - Modal filters and traffic gates which allow cycles to pass but restrict car traffic
• Traffic calming, including the location of all speed humps in Greater London
• Advanced stop lines – boxes at junctions for people cycling
• Signals - early-release signals at junctions
• Signage - Signed cycle routes and other wayfinding
• Restricted Points – points where people cycling will have to dismount Paths through parks and other green spaces that can, and cannot, be cycled on

The Principal Bicycle Network (PBN) is a network of proposed and existing cycle routes that help people cycle for transport, and provide access to major destinations in Victoria The Principal Bicycle Network (PBN) is a network of proposed and existing cycle routes that help people cycle for transport, and provide access to major destinations in Victoria. Cycling for transport includes riding bicycles to work, to school, shopping, visiting friends etc. The PBN is also a 'bicycle infastructure planning tool' to guide State investment in the development of transport bicycle network. The PBN is one of a number of network planning tools. (other examples include individual Council networks) Together these networks make up the developing cycle infrastructure of Victoria. The PBN makes use of many local roads and off-road paths, as well as State arterial roads. New bicycle facilities on the PBN are designed with the principle of increasing separation between cyclists and motorists, and giving priority to cyclists at key intersections.

The spatial data set contains the statistical evaluation of the city cycling competition in the city of Freiburg from 2021. Available is the average speed of cyclists, the number of trips per direction, the number of trips per section and a heat map of cycling. The data is "open data". The ‘Heatmap’ shows the recorded GPS points of the bike rides. The representation works like the image of a thermal camera, the ‘warmer’ (red colours) the representation, the more points were recorded at this point. If a place is shown as particularly ‘warm’, many GPS points were transmitted at this point. This can have two different causes: 1) Particularly many people pass through this place. 2) Few people stay in this place for a particularly long time.

Cyclist June 2022 to May 2023 Analisys Project Yolanda Aguilar 07/10/2023

Cyclistic Full Year Analysis

This analysis is based on the Cyclistic case study of the Google Analytics Professional Certificate. https://www.coursera.org/learn/completa-un-caso-practico/supplement/7PGIT/caso-practico-1-como-lograr-el-exito-rapido-de-un-negocio-de-bicicletas Introduction The case study I work for a not real bike-sharing company in Chicago. The scenario: You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The task: The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations.” The question that the stakeholders want to answer in this scenario are: Which differences exist between the members and casual users.

This dataset contains cycling volume, movement flow and demographic data for the 'Super Sunday' Recreation Bike Count event over the years 2011 to 2018. The counts are presented as individual points which relate to the location where the counts were visually undertaken by volunteers. The data covers the following Local Government Areas (LGAs):

Boroondara

Monash

Moonee Valley

Moreland

Whitehorse

Yarra

Bicycle Network's 'Super Counts' programs count the volumes of bicycles and other active travellers, and their movements on roads and paths. The Super Counts program comprises two counting events: 'Super Tuesday' commuter count and the 'Super Sunday' recreation count. Super Tuesday is a two-hour count conducted during a Tuesday morning peak period in March (7-9am); Super Sunday is a four-hour count conducted during a Sunday morning-afternoon period in November (9-1pm). For more information please visit Bicycle Network Bike Counts. Bicycle counts help track progress, and identify areas of improvement to inform active transport policy with the goal of making it easier for more people to choose active modes everyday. The data is available for researchers and policymakers to plan and build cycling infrastructure to promote increased cycling activity, and to gather longitudinal insights of cycling volumes and trends. AURIN has converted dates to ISO 8601 format, and have spatially enabled this dataset by creating geometries from the latitude and longitude columns with PostGIS.

Active Travel – Walking and Cycling CountsThis data set is sourced from Dundee City Council’s Public Space Camera Surveillance System. It shows a count of people walking and cycling in 4 specified areas across Dundee. The data set shows a snapshot of people and cyclists within these areas every Monday, Wednesday and Saturday during the period 8am-9am.This data is experimental and subject to further refinement. Please note that due the nature of CCTV cameras at times data may not be collected as specified above. Therefore, caution should be exercised when analysing data and drawing conclusions from this data set.CCTV datasets contain information on object detections taken from a selection of the CCTV cameras throughout Dundee City. CCTV images are translated into object counts, objects counted include ‘person’, ‘car’, ‘bicycle’, ‘bus’, ‘motorcycle', 'truck, ‘pickup truck 'and ‘van’. The data is generated and owned by Dundee City Council. Copyright © Dundee City Council 2022. This dataset is available for use under the Open Government Licence.Background information about the Dundee CCTV cameras including a map showing the location of the cameras is available on the Dundee City Council website and can be accessed using the following link:https://www.dundeecity.gov.uk/service-area/city-development/sustainable-transport-and-roads/dundees-public-space-camera-surveillance-system

The spatial data set contains the statistical evaluation of the city cycling competition in the city of Freiburg from 2023. The number of trips per direction, the number of trips per section and a heat map of cycling are available. The data is "open data". The ‘Heatmap’ shows the recorded GPS points of the bike rides. The representation works like the image of a thermal camera, the ‘warmer’ (red colours) the representation, the more points were recorded at this point. If a place is shown as particularly ‘warm’, many GPS points were transmitted at this point. This can have two different causes: 1) Particularly many people pass through this place. 2) Few people stay in this place for a particularly long time.

This dataset contains cycling volume, movement flow and demographic data for the 'Super Tuesday' Commuter Bike Count event in September 2015. The counts are presented as individual points which relate to the location where the counts were visually undertaken by volunteers. The data covers 47 Local Government Areas (LGAs) across ACT, NSW, SA, TAS, VIC and WA. 'Super Tuesday' is Australia's largest visual bike count, where volunteer counters observe and record cyclist numbers across Australia. The Bicycle Network data is collected within 97 councils and over 1,500 sites, the 'Super Counts' are Australia's largest cycling surveys of their kind. Bicycle Network have used a consistent methodology across Australia to collect consistent figures on cycling participation. The data can be used to compliment other traffic surveys, such as automated systems by providing a broader picture of cycling trends at both a local and national scale. For more information please visit Bicycle Network Bike Counts. Bicycle counts help track progress, and identify areas of improvement to inform active transport policy with the goal of making it easier for more people to choose active modes everyday. The data is available for researchers and policymakers to plan and build cycling infrastructure to promote increased cycling activity, and to gather longitudinal insights of cycling volumes and trends. AURIN has spatially enabled this dataset by creating geometries from the latitude and longitude columns with PostGIS.

The spatial data set contains the statistical evaluation of the city cycling competition in the city of Freiburg from 2022. The number of trips per direction, the number of trips per section and a heat map of cycling are available. The data is "open data". The ‘Heatmap’ shows the recorded GPS points of the bike rides. The representation works like the image of a thermal camera, the ‘warmer’ (red colours) the representation, the more points were recorded at this point. If a place is shown as particularly ‘warm’, many GPS points were transmitted at this point. This can have two different causes: 1) Particularly many people pass through this place. 2) Few people stay in this place for a particularly long time.

In Pittsburgh, Autonomous Vehicle (AV) companies have been testing autonomous vehicles since September 2016. However, the tech is new, and there have been some high-profile behavior that we believe warrants a larger conversation. So in early 2017, we set out to design a survey to see both how BikePGH donor-members, and Pittsburgh residents at large, feel about about sharing the road with AVs as a bicyclist and/or as a pedestrian. Our survey asked participants how they feel about being a fellow road user with AVs, either walking or biking. We also wanted to collect stories about people’s experiences interacting with this nascent technology. We are unaware of any public surveys about people’s feelings or understanding of this new technology. We hope that our results will help add to the body of data and help the public and politicians understand the complexity of possible futures that different economic models AV technology can bring to our cities and towncenters.

We conducted our 2017 survey in two parts. First, we launched the survey exclusively to donor-members, yielding 321 responses (out of 2,900) via email. Once we closed the survey, we launched it again, but allowed the general public to take it. Through promoting it on our website, social media channels, and a few news articles, we yielded 798 responses (mostly from people in the Pittsburgh region), for a combined total of 1,119 responses.

Regarding the 2019 survey: In total, 795 people responded. BikePGH solicited responses from their blog, website, and email list. There were also a few local news articles about the survey. While many questions were kept similar to the 2017 survey, BikePGH wanted to dig a bit deeper into regulations as well as demographics this time around.

The 2019 follow up survey also aims to see how the landscape has changed, and how specifically, Pittsburghers on bike and on foot feel about sharing the road with AVs so that we’re all better prepared to deal with this new reality and help make sure that it is introduced as safely as humanly possible.

This dataset contains counts of bicycles from permanent detectors installed on Toronto streets and multi-use paths. The City uses the data to monitor trends in people cycling and the seasonal use of bicycle lanes. Additional new count stations are being added to the system and the data will be brought online when data validation is completed. The following files showing different views of the data are available: Detector Locations (cycling_permanent_counts_locations): Location and metadata of each detector. This table references the City of Toronto's Street Centreline dataset. Daily Counts (cycling_permanent_counts_daily): Total daily counts by location and direction 15-minute Counts (cycling_permanent_counts_15min): 15-minute counts by direction and location. 1-hour counts are provided if 15-minute counts are not available. Readme and Data Dictionary (cycling_permanent_counts_readme): Provides a detailed definition of each data table and it's corresponding columns.

Policy-makers are looking to promote the uptake of bicycling as a healthy mode of travel that reduces the negative effects of traditional motorised transport (physical inactivity, air pollution, traffic congestion) and achieves sustainability goals. As an active form of mobility, bicycling improves physical and mental health and has long-term public health benefits. However, there are a number of barriers that prevent people from riding a bike, including fears about riding alongside motor vehicle traffic and the lack of safe and appropriate bicycling infrastructure. For the strategic installation of safer bicycling infrastructure or the improvement of existing infrastructure, rigorous evidence-informed scientific studies are necessary, which in turn rely on high-quality bicycling data, which is scarce. In this regard, one of the prerequisites is understanding the different types of bicycling infrastructure that exist in an urban area and create an inventory dataset that can form the basis of future bicycling-related research. OpenStreetMap (OSM) is a valuable open-source map database that contains transport infrastructure data among other things and has spatial coverage for almost the entire planet. Hence, it is used extensively by researchers and planners and it helps develop methods that are transferable and thus can be replicated irrespective of the study area. We, the Sustainable Mobility and Safety Research Group (SMSR) at Monash University, Australia, have developed a classification process to classify existing bicycling infrastructure across Greater Melbourne, Australia. We have derived knowledge from existing studies and calibrated our classification system to suit local tagging practices.