Web traffic statistics for the several City-Parish websites, brla.gov, city.brla.gov, Red Stick Ready, GIS, Open Data etc. Information provided by Google Analytics.

Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

This traffic-count data is provided by the City of Pittsburgh's Department of Mobility & Infrastructure (DOMI). Counters were deployed as part of traffic studies, including intersection studies, and studies covering where or whether to install speed humps. In some cases, data may have been collected by the Southwestern Pennsylvania Commission (SPC) or BikePGH.

Data is currently available for only the most-recent count at each location.

Traffic count data is important to the process for deciding where to install speed humps. According to DOMI, they may only be legally installed on streets where traffic counts fall below a minimum threshhold. Residents can request an evaluation of their street as part of DOMI's Neighborhood Traffic Calming Program. The City has also shared data on the impact of the Neighborhood Traffic Calming Program in reducing speeds.

Different studies may collect different data. Speed hump studies capture counts and speeds. SPC and BikePGH conduct counts of cyclists. Intersection studies included in this dataset may not include traffic counts, but reports of individual studies may be requested from the City. Despite the lack of count data, intersection studies are included to facilitate data requests.

Data captured by different types of counting devices are included in this data. StatTrak counters are in use by the City, and capture data on counts and speeds. More information about these devices may be found on the company's website. Data includes traffic counts and average speeds, and may also include separate counts of bicycles.

Tubes are deployed by both SPC and BikePGH and used to count cyclists. SPC may also deploy video counters to collect data.

NOTE: The data in this dataset has not updated since 2021 because of a broken data feed. We're working to fix it.

Below you’ll find a month by month breakdown of traffic on the australia.gov.au website along the following lines:

• Pageviews
• Visits
• Pages per visit
• Average time on page
• Devices

This data is generated using Google analytics.

Please Note: This is an initial version of the data only. We’re looking forward to hearing your feedback on what other metrics are of interest to you. Please let us know by sending an email to data@digital.gov.au.

This map contains a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from TomTom (www.tomtom.com). Historical traffic is based on the average of observed speeds over the past year. The live and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The traffic map layer contains two sublayers: Traffic and Live Traffic. The Traffic sublayer (shown by default) leverages historical, live and predictive traffic data; while the Live Traffic sublayer is calculated from just the live and predictive traffic data only. A color coded traffic map can be requested for the current time and any time in the future. A map for a future request might be used for planning purposes. The map also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

pNEUMA is an open large-scale dataset of naturalistic trajectories of half a million vehicles that have been collected by a one-of-a-kind experiment by a swarm of drones in the congested downtown area of Athens, Greece. A unique observatory of traffic congestion, a scale an-order-of-magnitude higher than what was not available until now, that researchers from different disciplines around the globe can use to develop and test their own models.

How are the .csv files organized?

For more details about the pNEUMA dataset, please check our website at https://open-traffic.epfl.ch

With more than 44,000 Portable Traffic Count (PTC) Stations located throughout North Carolina, Traffic Survey has adopted a collection schedule. Please see our website: https://www.ncdot.gov/projects/trafficsurvey/for further details. The data in this file was digitized referencing the available NCDOT Linear Referencing System (LRS) and is not the result of using GPS equipment in the field, nor latitude and longitude coordinates. The referencing provided is based on the 2015 Quarter 1 publication of the NCDOT Linear Referencing System (LRS). Some differences will be found when using different quarterly publications with this data set. The data provided is seasonally factored to an estimate of an annual average of daily traffic. The statistics provided are: CVRG_VLM_I: Traffic Survey's seven digit unique station identifier COUNTY: County NameROUTE: Numbered route identifier, or local name if not State maintainedLOCATION: Description of the Annual Average Daily Traffic station location AADT_2015: Estimated Annual Average Daily Traffic in vehicles per day for 2015AADT_2014: Estimated Annual Average Daily Traffic in vehicles per day for 2014AADT_2013: Estimated Annual Average Daily Traffic in vehicles per day for 2013 AADT_2012: Estimated Annual Average Daily Traffic in vehicles per day for 2012 AADT_2011: Estimated Annual Average Daily Traffic in vehicles per day for 2011 AADT_2010: Estimated Annual Average Daily Traffic in vehicles per day for 2010 AADT_2009: Estimated Annual Average Daily Traffic in vehicles per day for 2009 AADT_2008: Estimated Annual Average Daily Traffic in vehicles per day for 2008 AADT_2007: Estimated Annual Average Daily Traffic in vehicles per day for 2007 AADT_2006: Estimated Annual Average Daily Traffic in vehicles per day for 2006 AADT_2005: Estimated Annual Average Daily Traffic in vehicles per day for 2005 AADT_2004: Estimated Annual Average Daily Traffic in vehicles per day for 2004 AADT_2003: Estimated Annual Average Daily Traffic in vehicles per day for 2003 AADT_2002: Estimated Annual Average Daily Traffic in vehicles per day for 2002 Note: A value of zero in the AADT field indicates no available AADT data for that year. Please note the following: Not ALL roads have PTC stations located on them. With the exception of Interstate, NC and US routes, NCDOT County Maps refer to roads using a four digit Secondary Road Number, not a road’s local name. If additional information is needed, or an issue with the data is identified, please contact the Traffic Survey Group at 919 814-5116. Disclaimer related to the spatial accuracy of this file: Data in this file was digitized referencing the available NCDOT GIS Data Layer, LRS Arcs Shapefile Format from Quarter 1 release and is not the result of using GPS equipment in the field.North Carolina Department of Transportation shall not be held liable for any errors in this data. This includes errors of omission, commission, errors concerning the content of data, and relative positional accuracy of the data. This data cannot be construed to be a legal document.

The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of internet users in countries like the Americas and Asia.

For further information about traffic counters - see the City of York Council website

1st July 2016 Update

WebTRIS Phase 1 is now available and can be accessed at http://webtris.highwaysengland.co.uk

We are in the process of updating the way that traffic flow data is made available to our external users to replace the old TRADS website. The new platform will deliver a more modern experience, utilising Google Maps with count site overlays and bespoke downloadable reporting capabilities. This new service will be referred to as ‘WebTRIS’.

The new development will contain all of the elements users are already familiar with; searching on Site ID’s and reviewing reports based on Site ID’s etc. but will also modernise the look and feel of the product and allow users to select an area of interest by clicking on a map.

Development began in early February 2016 and is expected to be complete in July 2016.

This is a Phase 1 release. A Phase 2 development is planned to take into account user feedback.

On-going updates will be released here with videos showing the product as it grows. There will also be live demonstrations as the product nears go-live and opportunities to take part in User Acceptance Testing and feedback sessions.

We are working hard to improve the level of service that we provide and thank you for your patience while we do so. We will keep you informed on progress with the next update due in May.

This data series provides average journey time, speed and traffic flow information for 15-minute periods since April 2015 on all motorways and 'A' roads managed by Highways England, known as the Strategic Road Network, in England.

Journey times and speeds are estimated using a combination of sources, including Automatic Number Plate Recognition (ANPR) cameras, in-vehicle Global Positioning Systems (GPS) and inductive loops built into the road surface.

Please note that journey times are derived from real vehicle observations and imputed using adjacent time periods or the same time period on different days. Further information is available in 'Field Descriptions' at the bottom of this page.

This data replaces the data previously made available via the Hatris and Trads websites.

Please note that Traffic Flow and Journey Time data prior to April 2015 is still available on the HA Traffic Information (HATRIS) website which can be found at https://www.hatris.co.uk/

This dataset is composed of the URLs of the top 1 million websites. The domains are ranked using the Alexa traffic ranking which is determined using a combination of the browsing behavior of users on the website, the number of unique visitors, and the number of pageviews. In more detail, unique visitors are the number of unique users who visit a website on a given day, and pageviews are the total number of user URL requests for the website. However, multiple requests for the same website on the same day are counted as a single pageview. The website with the highest combination of unique visitors and pageviews is ranked the highest

In accordance with Law No. 92-1444 of 31 December 1992 on noise control and the Environmental Code (Articles L. 571-10 and R. 571-32 to R. 571-43), in each department, the Prefect identifies and classifies land transport infrastructure according to its noise and traffic characteristics. On the basis of this classification, it shall determine, after consulting the municipalities, the sectors affected by noise, the levels of noise to be taken into account for the construction of buildings and the technical requirements likely to reduce them. The sectors thus determined and the requirements relating to the acoustic characteristics applicable to them are set out in the annexes to the local planning plans (LDPs) of the municipalities concerned. Article R. 571-33 of the Environmental Code specifies the infrastructures concerned by the sound classification: — roadways where the annual average daily traffic, or provided for in the study or the impact statement, exceeds 5000 vehicles per day; — intercity rail lines with average daily traffic exceeding 50 trains; — clean public transport lines with an average daily traffic of more than 100 buses; — urban rail lines with average daily traffic exceeding 100 trains.

The sound classification map and the prefectural decrees can be found on the website of the state departments in the department.

Only the documents annexed to the Prefectural Orders are authentic.

This dataset contains the traffic volumes & Speeds in the area of the city of Münster, which were recorded as part of the campaign “Stadtradeln” of the Klima-Bündnis e.V. by users of the Stadtradeln-App in a 3-week period.

Data is available as heatmaps in hexagon cells (H12) according to Uber’s system for the years 2018 to 2020. This data was processed in the course of the research project MOVEBIS at TU Dresden. This is a mFUND project of the Federal Ministry of Transport and Digital Infrastructure.

The data are licensed under CC-BY-NC, i.e. they may not be re-used for commercial purposes, and the author must be named: “Grubitzsch P., Lißner S., Huber S., Springer T., [2021] Technische Universität Dresden, Chair of Computer Networks and Chair of Transport Ecology”

In this dataset there are only the data for the Münster area. The Germany-wide data can be found at: https://www.mcloud.de/web/guest/suche/-/results/suche/relevance/stadtradeln/0/detail/3096DB7A-9EE4-4C14-B2AA-79E33A7FFF01 Included in some cases are geocoordinates outside the federal territory.

Further data sets of the action “Stadtradeln” from the years 2018-2020 can be found on the mFUND website at the following link: https://www.mcloud.de/web/guest/suche/-/results/suche/relevance/stadtradeln/0

https://opendata.stadt-muenster.de/sites/default/files/vorschau-stadtradeln-heatmap-2018.png" style="width: 559px; height: 400px;"/> (Heatmap visualisation of cycling volumes 2018, produced by Gerald Pape)

Information on the volume of cycling data For easier further use, the raw data originally available as CSV was additionally converted to the GeoJSON format. Thank you for this conversion to Gerald Pape. For more information, see CodeForMünster’s Github repository: https://github.com/codeformuenster/stadtradeln-vis

Information on the speed data The speed data was prepared by the MITFAHR|DE|ZENTRALE. An interactive visualisation of the data can be found at: https://heatview.de/?kreis=05515 The source code of this visualisation can be found at https://github.com/mfdz/heatview-website

This dataset provides information on motor vehicle operators (drivers) involved in traffic collisions occurring on county and local roadways. The dataset reports details of all traffic collisions occurring on county and local roadways within Montgomery County, as collected via the Automated Crash Reporting System (ACRS) of the Maryland State Police, and reported by the Montgomery County Police, Gaithersburg Police, Rockville Police, or the Maryland-National Capital Park Police. This dataset shows each collision data recorded and the drivers involved. Please note that these collision reports are based on preliminary information supplied to the Police Department by the reporting parties. Therefore, the collision data available on this web page may reflect: -Information not yet verified by further investigation -Information that may include verified and unverified collision data -Preliminary collision classifications may be changed at a later date based upon further investigation -Information may include mechanical or human error This dataset can be joined with the other 2 Crash Reporting datasets (see URLs below) by the State Report Number. * Crash Reporting - Incidents Data at https://data.montgomerycountymd.gov/Public-Safety/Crash-Reporting-Incidents-Data/bhju-22kf * Crash Reporting - Non-Motorists Data at https://data.montgomerycountymd.gov/Public-Safety/Crash-Reporting-Non-Motorists-Data/n7fk-dce5 Update Frequency : Weekly

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

This dataset contains the locations of the City's traffic signals. Data currencyThis data is updated frequently in the normal course of business, however priorities and resources determine how fast a change in reality is reflected in the database. The extract on this website is updated weekly. Data accuracyTraffic signal locations are in the approximate centre of the intersection.

This dataset contains information about bikeways in City of Vancouver. NoteSome fields may be blank or have zero values if the information is not available. Data currencyThis data are updated frequently in the normal course of business, however priorities and resources determine how fast a change in reality is reflected in the database. The extract on this website is updated weekly. Data accuracyThese bikeways follow street centrelines so their placement in the street right of way is approximate. This dataset is maintained manually.This dataset includes data on shorter bikeway segments which can be different than how the bikeways are dispayed in the Vancouver Cycling Map. Websites for further informationCycling routes and maps

The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Caribbean and Europe.

This dataset consists of 107 days of vessel tracking using the Automatic Identification System (AIS) at 1 hour intervals extracted for the Queensland region from the Spatial@AMSA Historic Vessel Tracking website (AMSA 2013). It has been converted to Shapefile format and contains just under 1 million points.

Note: The Spatial@AMSA Historic Vessel Tracking website is no longer available, however similar and more recent data is now available from Spatial@AMSA Vessel Tracking Data website (https://www.operations.amsa.gov.au/Spatial/DataServices/DigitalData).

Vessel tracking data is used to support coastal traffic management, search and rescue response and to meet requirements for safety and protection of the maritime environment. A valuable data set for marine use studies.

The Automatic Identification System (AIS) is an automatic tracking system used on ships and by vessel traffic services (VTS) for identifying and locating vessels by electronically exchanging data with other nearby ships, AIS base stations, and satellites. Each vessel regularly transmits its position ranging from 3 minutes for anchored or moored vessels, to 2 seconds for fast moving or manoeuvring vessels.

This dataset only contains vessel positions at approximately 1 hour internals. Even so each vessel can contribute many data points.

Class A transceivers have been mandated by the International Maritime Organization (IMO) for vessels of 300 gross tonnage and upwards engaged on international voyages, cargo ships of 500 gross tonnage and upwards not engaged on international voyages, as well as passenger ships (more than 12 passengers), irrespective of size. Class A transceivers are stronger, have priority transmissions and transmit more frequently then Class B transceivers.

Class B transceivers provide limited functionality and is intended for non-SOLAS vessels. It is not mandated by the International Maritime Organization (IMO) and has been developed for non-SOLAS commercial and recreational vessels.

The Historic Vessel Tracking Spatial@AMSA website provides a data download of historic vessel positions from April 2009 to current time minus 2 weeks.

The original data was downloaded through the Spatial@AMSA Historic Vessel Tracking Request website (https://www.operations.amsa.gov.au/Spatial/DataServices/CraftTrackingRequest). Due to limitations in the maximum size of the download, the data was requested in 3 day lots, using CSV download format and "Select by State" of QLD. All of the CSV files were compiled together using Notepad++, then loaded into ArcMap using Add Data / Add XY. This was then exported as a shapefile. This resulted in a very large shapefile as each of the columns were made excessively large (254 characters) by this process. To reduce the size of the shapefile a duplicate column was setup for each of the text attributes, except this time the size was set to just fit the data in. The data was copied to the new column using the field calculator and the original field deleted. This process reduced the database file of the shapefile from 1.4 GB to 170 MB.

Note that due to a limitation of the shapefile format the high resolution time stamps of vessels did not come out in the shapefile. This information is however available in the CSV version.

This dataset only contains information available from the Historic Vessel Tracking Spatial@AMSA website and only contains the ships course, speed, heading, ship name and if it is piloted. It does not contain information about the ship's length, breadth, cargo or status.

The eAtlas has not confirmed what types of vessels this dataset contains however it probably contains most AIS Class A and some Class B vessels.

Format:

This dataset is available in Comma Separated Value (CSV) (80 MB) and Shapefile format (178 MB).

Data Dictionary:

CSV file: Not a lot is known about the fields of this dataset as they come from AMSA undocumented. Values in brackets are typical values. - CourseDegrees: (0, 331.3, 184) - CraftType: (Vessel) - FixTime: (9/05/2013 21:13) - Heading: Heading of the vessel in integer degrees, sometime there is no value (284) - IsPilotedVoyage: Boolean (FALSE, TRUE) - Latitude, Longitude: Vessel position in decimal degrees (-23.75092333, 151.1676367) - Name: Vessel id (NOMADIC MILDE, SMIT KULLAROO, HYUNDAI SUCCESS) - ReportingAgentName: (AMSA, AIS) - Speed: unknown units (0, 13.2)

Shapefile: These are the same values as for the CSV but renamed to fit limitations of shapefiles: CouseDegr, Heading, Latitude, Longitude, Speed, NameB, IsPiloted, FixTimeB, Reporting.

References:

Australian Maritime Safety Authority. (2013) Historical Vessel Tracking. Spatial@AMSA, [CSV data file]. License: Creative Commons Attribution-Noncommercial 3.0 Australia. Available: https://www.operations.amsa.gov.au/Spatial/DataServices/CraftTrackingRequest. Accessed 6 September 2013

This dataset is available on Brisbane City Council’s open data website – data.brisbane.qld.gov.au. The site provides additional features for viewing and interacting with the data and for downloading the data in various formats.

The Brisbane City Council parking occupancy forecasting data is provided to be accessed by third party web or app developers to develop tools to provide Brisbane residents and visitors with likely parking availability within a paid parking area.

The parking occupancy forecasting data is compiled using advanced analytics and machine learning to estimate paid parking availability. The solution uses parking occupancy survey data, parking meter transaction data and other traffic and environmental data.

This dataset is linked to the open data called Parking — Meter locations. The field called MOBILE_ZONE is used to link the datasets. MOBILE_ZONE is a seven-digit mobile payment zone number that may include one or many parking meter numbers.

Additional information on parking meters can be found on the Brisbane City Council website.

The Brisbane City Council parking occupancy forecasting data includes parking data for all of Council’s parking meters. The data attributes used in this resource and their descriptions can be found in the Parking — Occupancy forecasting — metadata — CSV resource in this dataset.

The Data and resources section of this dataset contains further information for this dataset.