Daily utilization metrics for data.lacity.org and geohub.lacity.org. Updated monthly

This is a dataset of Tor cell file extracted from browsing simulation using Tor Browser. The simulations cover both desktop and mobile webpages. The data collection process was using WFP-Collector tool (https://github.com/irsyadpage/WFP-Collector). All the neccessary configuration to perform the simulation as detailed in the tool repository.The webpage URL is selected by using the first 100 website based on: https://dataforseo.com/free-seo-stats/top-1000-websites.Each webpage URL is visited 90 times for each deskop and mobile browsing mode.

Attributes of sites in Hamilton City which collect anonymised data from a sample of vehicles. Note: A Link is the section of the road between two sites

Column_InfoSite_Id, int : Unique identiferNumber, int : Asset number. Note: If the site is at a signalised intersection, Number will match 'Site_Number' in the table 'Traffic Signal Site Location'Is_Enabled, varchar : Site is currently enabledDisabled_Date, datetime : If currently disabled, the date at which the site was disabledSite_Name, varchar : Description of the site locationLatitude, numeric : North-south geographic coordinatesLongitude, numeric : East-west geographic coordinates

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Disclaimer

Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works.

Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data.

While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data:

‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'

The dataset provides 12 months (August 2016 to August 2017) of obfuscated Google Analytics 360 data from the Google Merchandise Store , a real ecommerce store that sells Google-branded merchandise, in BigQuery. It’s a great way analyze business data and learn the benefits of using BigQuery to analyze Analytics 360 data Learn more about the data The data includes The data is typical of what an ecommerce website would see and includes the following information:Traffic source data: information about where website visitors originate, including data about organic traffic, paid search traffic, and display trafficContent data: information about the behavior of users on the site, such as URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions on the Google Merchandise Store website.Limitations: All users have view access to the dataset. This means you can query the dataset and generate reports but you cannot complete administrative tasks. Data for some fields is obfuscated such as fullVisitorId, or removed such as clientId, adWordsClickInfo and geoNetwork. “Not available in demo dataset” will be returned for STRING values and “null” will be returned for INTEGER values when querying the fields containing no data.This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery

Construction site coordination in Hamburg The preservation of the infrastructure is of fundamental importance for the development of Hamburg. Therefore, construction sites in the street space are part of the normal picture - to the chagrin of local residents and road users. In many cases, however, it is not work on the road itself that leads to disabilities, but the many supply and disposal lines in the road body or the construction projects of private individuals. Approximately 25,000 jobs per year on Hamburg's road network, of which over 3,700 are on major roads, therefore require careful coordination to minimise obstacles to traffic flow. This is the task of the Traffic Optimization Department at the Department of Transport and Mobility Transition. Here, the incoming information of all road construction departments, pipeline companies and private builders is collected and evaluated. The information for the most important construction sites is published with a 7-day preview on the Internet at www.hamburg.de/baustellen. When coordinating construction sites, the aim is to prevent simultaneous construction sites, e.g. on important parallel roads, so that traffic has trouble-free alternative routes. However, no matter how good coordination can absolutely prevent congestion. The Hamburg road network is partly busy and partly overloaded in the morning and evening rush hour. Therefore, we recommend every road user to inform himself about the current traffic situation before starting the journey and only then to choose a suitable means of transport including route.

If you have any questions about construction sites in Hamburg, please contact the construction site hotline on 040 428 28 2020 or by post to

Free and Hanseatic City of Hamburg Transport and Mobility Transition Authority Old stone path 4 20459 Hamburg

Unlock the Power of Behavioural Data with GDPR-Compliant Clickstream Insights.

Swash clickstream data offers a comprehensive and GDPR-compliant dataset sourced from users worldwide, encompassing both desktop and mobile browsing behaviour. Here's an in-depth look at what sets us apart and how our data can benefit your organisation.

User-Centric Approach: Unlike traditional data collection methods, we take a user-centric approach by rewarding users for the data they willingly provide. This unique methodology ensures transparent data collection practices, encourages user participation, and establishes trust between data providers and consumers.

Wide Coverage and Varied Categories: Our clickstream data covers diverse categories, including search, shopping, and URL visits. Whether you are interested in understanding user preferences in e-commerce, analysing search behaviour across different industries, or tracking website visits, our data provides a rich and multi-dimensional view of user activities.

GDPR Compliance and Privacy: We prioritise data privacy and strictly adhere to GDPR guidelines. Our data collection methods are fully compliant, ensuring the protection of user identities and personal information. You can confidently leverage our clickstream data without compromising privacy or facing regulatory challenges.

Market Intelligence and Consumer Behaviuor: Gain deep insights into market intelligence and consumer behaviour using our clickstream data. Understand trends, preferences, and user behaviour patterns by analysing the comprehensive user-level, time-stamped raw or processed data feed. Uncover valuable information about user journeys, search funnels, and paths to purchase to enhance your marketing strategies and drive business growth.

High-Frequency Updates and Consistency: We provide high-frequency updates and consistent user participation, offering both historical data and ongoing daily delivery. This ensures you have access to up-to-date insights and a continuous data feed for comprehensive analysis. Our reliable and consistent data empowers you to make accurate and timely decisions.

Custom Reporting and Analysis: We understand that every organisation has unique requirements. That's why we offer customisable reporting options, allowing you to tailor the analysis and reporting of clickstream data to your specific needs. Whether you need detailed metrics, visualisations, or in-depth analytics, we provide the flexibility to meet your reporting requirements.

Data Quality and Credibility: We take data quality seriously. Our data sourcing practices are designed to ensure responsible and reliable data collection. We implement rigorous data cleaning, validation, and verification processes, guaranteeing the accuracy and reliability of our clickstream data. You can confidently rely on our data to drive your decision-making processes.

Vehicle travel time and delay data on sections of road in Hamilton City, based on Bluetooth sensor records. To get data for this dataset, please call the API directly talking to the HCC Data Warehouse: https://api.hcc.govt.nz/OpenData/get_traffic_link_stats?Page=1&Start_Date=2021-06-02&End_Date=2021-06-03. For this API, there are three mandatory parameters: Page, Start_Date, End_Date. Sample values for these parameters are in the link above. When calling the API for the first time, please always start with Page 1. Then from the returned JSON, you can see more information such as the total page count and page size. For help on using the API in your preferred data analysis software, please contact dale.townsend@hcc.govt.nz. NOTE: Anomalies and missing data may be present in the dataset.

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Disclaimer

Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works.

Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data.

While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data:

‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'

This dataset is comprised of NetFlow records, which capture the outbound network traffic of 8 commercial IoT devices and 5 non-IoT devices, collected during a period of 37 days in a lab at Ben-Gurion University of The Negev. The dataset was collected in order to develop a method for telecommunication providers to detect vulnerable IoT models behind home NATs. Each NetFlow record is labeled with the device model which produced it; for research reproducibilty, each NetFlow is also allocated to either the "training" or "test" set, in accordance with the partitioning described in: Y. Meidan, V. Sachidananda, H. Peng, R. Sagron, Y. Elovici, and A. Shabtai, A novel approach for detecting vulnerable IoT devices connected behind a home NAT, Computers & Security, Volume 97, 2020, 101968, ISSN 0167-4048, https://doi.org/10.1016/j.cose.2020.101968. (http://www.sciencedirect.com/science/article/pii/S0167404820302418) Please note: The dataset itself is free to use, however users are requested to cite the above-mentioned paper, which describes in detail the research objectives as well as the data collection, preparation and analysis. Following is a brief description of the features used in this dataset. # NetFlow features, used in the related paper for analysis 'FIRST_SWITCHED': System uptime at which the first packet of this flow was switched 'IN_BYTES': Incoming counter for the number of bytes associated with an IP Flow 'IN_PKTS': Incoming counter for the number of packets associated with an IP Flow 'IPV4_DST_ADDR': IPv4 destination address 'L4_DST_PORT': TCP/UDP destination port number 'L4_SRC_PORT': TCP/UDP source port number 'LAST_SWITCHED': System uptime at which the last packet of this flow was switched 'PROTOCOL': IP protocol byte (6: TCP, 17: UDP) 'SRC_TOS': Type of Service byte setting when there is an incoming interface 'TCP_FLAGS': Cumulative of all the TCP flags seen for this flow # Features added by the authors 'IP': Prefix of the destination IP address, representing the network (without the host) 'DURATION': Time (seconds) between first/last packet switching # Label 'device_model':

Access high-fidelity consumer data powered by our proprietary modeling technology that provides the most comprehensive consumer intelligence, accurate targeting, first-party data enrichment, and personalization at scale. Our deterministic dataset, anchored in the purchasing habits of over 140 million U.S. consumers, delivers superior targeting performance with proven 70% increase in ROAS.

Core Data Assets Transactional Data Foundation: Real purchasing behavior from over 140 million U.S. consumers with 8.5 billion behavioral signals across 250 million adults. Seven years of daily credit card and debit card purchase data aggregated from all major credit cards sourced from more than 300 national banks, capturing $2+ trillion in annual discretionary spending.

Consumer Demographics & Lifestyle: Comprehensive profiles including age, income, household composition, geographic distribution, education, employment, and lifestyle indicators. Our proprietary taxonomy organizes consumer spending across 8,000+ brands and 2,500+ merchants, from major retailers to emerging direct-to-consumer brands.

Behavioral Segmentation: 150+ custom consumer communities including demographic groups (Gen Z, Millennials, Gen X), lifestyle segments (Health & Fitness Enthusiasts, Tech Early Adopters, Luxury Shoppers), and behavioral categories (Deal Seekers, Brand Loyalists, Premium Service Users, Streaming Subscribers). Purchase Intelligence: Deep insights into consumer spending patterns across entertainment, fitness, fashion, technology, travel, dining, and retail categories. Our models identify cross-category purchasing behaviors, seasonal trends, and brand switching patterns to optimize targeting strategies. Advanced Modeling Technology

Our proprietary consumer intelligence engine combines deterministic transaction-based data with Smart Audience Engineering that transforms first-party signals from anonymized website traffic, behavioral indicators, and CRM enrichment into precision-modeled segments. Unlike traditional data providers who sell static lists, our AI-powered predictive modeling continuously learns and optimizes for unprecedented precision and superior conversion outcomes.

Performance Advantages: Audiences built on user-level transactional data deliver 70% increase in ROAS compared to traditional targeting methods. Weekly-optimized audiences with performance narratives eliminate wasted ad spend by 20-30%, while our deterministic AI models analyze hundreds of attributes and conversion-validated signals to identify prospects with genuine purchase intent, not just lookalike behaviors.

A log of dataset alerts open, monitored or resolved on the open data portal. Alerts can include issues as well as deprecation or discontinuation notices.

INFO ABOUT THE BOT-IOT DATASET, NOTE: only the csv files stated in the description are used

The BoT-IoT dataset can be downloaded from HERE. You can also use our new datasets: the TON_IoT and UNSW-NB15.

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The BoT-IoT dataset was created by designing a realistic network environment in the Cyber Range Lab of UNSW Canberra. The network environment incorporated a combination of normal and botnet traffic. The dataset’s source files are provided in different formats, including the original pcap files, the generated argus files and csv files. The files were separated, based on attack category and subcategory, to better assist in labeling process.

The captured pcap files are 69.3 GB in size, with more than 72.000.000 records. The extracted flow traffic, in csv format is 16.7 GB in size. The dataset includes DDoS, DoS, OS and Service Scan, Keylogging and Data exfiltration attacks, with the DDoS and DoS attacks further organized, based on the protocol used.

To ease the handling of the dataset, we extracted 5% of the original dataset via the use of select MySQL queries. The extracted 5%, is comprised of 4 files of approximately 1.07 GB total size, and about 3 million records.

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Free use of the Bot-IoT dataset for academic research purposes is hereby granted in perpetuity. Use for commercial purposes should be agreed by the authors. The authors have asserted their rights under the Copyright. To whom intent the use of the Bot-IoT dataset, the authors have to cite the following papers that has the dataset’s details: .

Koroniotis, Nickolaos, Nour Moustafa, Elena Sitnikova, and Benjamin Turnbull. "Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset." Future Generation Computer Systems 100 (2019): 779-796. Public Access Here.

Koroniotis, Nickolaos, Nour Moustafa, Elena Sitnikova, and Jill Slay. "Towards developing network forensic mechanism for botnet activities in the iot based on machine learning techniques." In International Conference on Mobile Networks and Management, pp. 30-44. Springer, Cham, 2017.

Koroniotis, Nickolaos, Nour Moustafa, and Elena Sitnikova. "A new network forensic framework based on deep learning for Internet of Things networks: A particle deep framework." Future Generation Computer Systems 110 (2020): 91-106.

Koroniotis, Nickolaos, and Nour Moustafa. "Enhancing network forensics with particle swarm and deep learning: The particle deep framework." arXiv preprint arXiv:2005.00722 (2020).

Koroniotis, Nickolaos, Nour Moustafa, Francesco Schiliro, Praveen Gauravaram, and Helge Janicke. "A Holistic Review of Cybersecurity and Reliability Perspectives in Smart Airports." IEEE Access (2020).

Koroniotis, Nickolaos. "Designing an effective network forensic framework for the investigation of botnets in the Internet of Things." PhD diss., The University of New South Wales Australia, 2020.

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A dataset providing information of the vehicle types and counts in several locations in Leeds. Purpose of the project The aim of this work was to examine the profile of vehicle types in Leeds, in order to compare local emissions with national predictions. Traffic was monitored for a period of one week at two Inner Ring Road locations in April 2016 and at seven sites around the city in June 2016. The vehicle registration data was then sent to the Department for Transport (Dft), who combined it with their vehicle type data, replacing the registration number with an anonymised ‘Unique ID’. The data is provided in three folders:- Raw Data – contains the data in the format it was received, and a sample of each format. Processed Data – the data after processing by LCC, lookup tables, and sample data. Outputs – Excel spreadsheets summarising the data for each site, for various time/dates. Initially a dataset was received for the Inner Ring Road (see file “IRR ANPR matched to DFT vehicle type list.csv”), with vehicle details, but with missing / uncertain data on the vehicles emissions Eurostandard class. Of the 820,809 recorded journeys, from the pseudo registration number field (UniqueID) it was determined that there were 229,891 unique vehicles, and 31,912 unique “vehicle types” based on the unique concatenated vehicle description fields. It was therefore decided to import the data into an MS Access database, create a table of vehicle types, and to add the necessary fields/data so that combined with the year of manufacture / vehicle registration, the appropriate Eurostandard could be determined for the particular vehicle. The criteria for the Eurostandards was derived mainly from www.dieselnet.com and summarised in a spreadsheet (“EuroStandards.xlsx”). Vehicle types were assigned to a “VehicleClass” (see “Lookup Tables.xlsx”) and “EU class” with additional fields being added for any modified data (Gross Vehicle Weight – “GVM_Mod”; Engine capacity – “EngineCC_mod”; No of passenger seats – “PassSeats”; and Kerb weight – “KerbWt”). Missing data was added from the internet lookups, extrapolation from known data, and by association – eg 99% of cars with an engine size Additional data was then received from the Inner Ring Road site, giving journey date/time and incorporating the Taxi data for licensed taxis in Leeds. Similar data for Sites 1-7 was also then received, and processed to determine the “VehicleClass” and “EU class”. A mixture of Update queries, and VBA processing was then used to provide the Level 1-6 breakdown of vehicle types (see “Lookup Tables.xlsx”). The data was then combined into one database, so that the required Excel spreadsheets could be exported for the required time/date periods (see “outputs” folder).

1. DATASET We created various types of network attacks in Internet of Things (IoT) environment for academic purpose. Two typical smart home devices -- SKT NUGU (NU 100) and EZVIZ Wi-Fi Camera (C2C Mini O Plus 1080P) -- were used. All devices, including some laptops or smart phones, were connected to the same wireless network. The dataset consists of 42 raw network packet files (pcap) at different time points.

The packet files are captured by using monitor mode of wireless network adapter. The wireless headers are removed by Aircrack-ng.

All attacks except Mirai Botnet category are the packets captured while simulating attacks using tools such as Nmap. The case of Mirai Botnet category, the attack packets were generated on a laptop and then manipulated to make it appear as if it originated from the IoT device.

Revision History

Three more packet files of Mirai Botnet -- Host Discovery and Telnet Bruteforce -- were added on September 20, 2019.

1. SUMMARY OF OUR DATASET

2. DOWNLOADS You can download our dataset from IEEE dataport page: https://ieee-dataport.org/open-access/iot-network-intrusion-dataset

3. CİTATİON Please cite our dataset's page when you use this dataset as follows.

Hyunjae Kang, Dong Hyun Ahn, Gyung Min Lee, Jeong Do Yoo, Kyung Ho Park, and Huy Kang Kim, "IoT Network Intrusion Dataset.", http://ocslab.hksecurity.net/Datasets/iot-network-intrusion-dataset, 2019

or

Hyunjae Kang, Dong Hyun Ahn, Gyung Min Lee, Jeong Do Yoo, Kyung Ho Park, and Huy Kang Kim, "IoT network intrusion dataset", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/q70p-q449. Accessed: Sep. 30, 2019.

1. CONTACT Hyunjae Kang (trifle19@korea.ac.kr) or Huy Kang Kim (cenda@korea.ac.kr)

2. SEE ALSO See also our another dataset containing IoT traffic: IoT-Environment-Dataset

Please read [IoT-Environment-Dataset > 1.3 Comparison with IoT Network Intrusion Dataset] for more details.

cenda at korea.ac.kr | 로봇융합관 304 | +82-2-3290-4898

The Motor Vehicle Collisions crash table contains details on the crash event. Each row represents a crash event. The Motor Vehicle Collisions data tables contain information from all police reported motor vehicle collisions in NYC. The police report (MV104-AN) is required to be filled out for collisions where someone is injured or killed, or where there is at least $1000 worth of damage (https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/ny_overlay_mv-104an_rev05_2004.pdf). It should be noted that the data is preliminary and subject to change when the MV-104AN forms are amended based on revised crash details.For the most accurate, up to date statistics on traffic fatalities, please refer to the NYPD Motor Vehicle Collisions page (updated weekly) or Vision Zero View (updated monthly). Due to success of the CompStat program, NYPD began to ask how to apply the CompStat principles to other problems. Other than homicides, the fatal incidents with which police have the most contact with the public are fatal traffic collisions. Therefore in April 1998, the Department implemented TrafficStat, which uses the CompStat model to work towards improving traffic safety. Police officers complete form MV-104AN for all vehicle collisions. The MV-104AN is a New York State form that has all of the details of a traffic collision. Before implementing Trafficstat, there was no uniform traffic safety data collection procedure for all of the NYPD precincts. Therefore, the Police Department implemented the Traffic Accident Management System (TAMS) in July 1999 in order to collect traffic data in a uniform method across the City. TAMS required the precincts manually enter a few selected MV-104AN fields to collect very basic intersection traffic crash statistics which included the number of accidents, injuries and fatalities. As the years progressed, there grew a need for additional traffic data so that more detailed analyses could be conducted. The Citywide traffic safety initiative, Vision Zero started in the year 2014. Vision Zero further emphasized the need for the collection of more traffic data in order to work towards the Vision Zero goal, which is to eliminate traffic fatalities. Therefore, the Department in March 2016 replaced the TAMS with the new Finest Online Records Management System (FORMS). FORMS enables the police officers to electronically, using a Department cellphone or computer, enter all of the MV-104AN data fields and stores all of the MV-104AN data fields in the Department’s crime data warehouse. Since all of the MV-104AN data fields are now stored for each traffic collision, detailed traffic safety analyses can be conducted as applicable.

⭐ OBAT PENGGUGUR KANDUNGAN 087776558899 ⭐ APOTEK OBAT PENGGUGUR KANDUNGAN 087776558899 ⭐ CARA MENGGUGURKAN KANDUNGAN 087776558899 ⭐ PENJUAL OBAT PENGGUGUR KANDUNGAN 087776558899 ⭐ TEMPAT OBAT PENGGUGUR KANDUNGAN 087776558899 ⭐ LOKASI OBAT PENGGUGUR KANDUNGAN 087776558899 ⭐ JUAL OBAT PENGGUGUR KANDUNGAN 087776558899

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.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 smartphone users in countries like Australia & Oceania and Asia.

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.

IL Coverage of the Gateway camera snapshots. The Gateway provides camera snapshot images throughout its coverage area in the form of camera icons on its maps and images in its camera report. With a free subscription, users can also access the Gateway ftp server which contains the most up to date versions of the images available.ImgPath - this is a link to the travelmidwest.com/lmiga/showCamera.jsp popup window that allows the user to select another direction, if availableCameraLocation - a text description of where the camera is locatedCameraDirection - the direction the camera is facing (NONE, N, E, S, W, NE, NW, SE, or SW)y - latitude in decimal degreesx - longitude in decimal degreesSnapShot - public URL of camera's image file that is suitable for placement in a tag, for instanceWarningAge - "true" if the camera is more than 10 minutes old, false otherwiseTooOld - "true" if more than 30 minutes old, "false" otherwiseAgeInMinutes - integer age of camera image in minutes

PS: Data update until June 30, 2021.

Context

The Polícia Rodoviária Federal - PRF (federal highway police) was created, with the initial denomination "Police of Roads", by President Washington Luís on July 24, 1928, through Decree No. 18,323 - which defined the traffic rules at the time.

With the advent of the Brazil's 1988 Constitution, the PRF was institutionalized and integrated into the National Public Security System. Inserted in Art. 144, in Title V - Defense of the State and Democratic Institutions, Chapter III - Public Security, the PRF definitely gains the status of permanent State institution, acting in policing and inspecting highways and areas of Union interest.

PRF's headquarters are located in Brasília, Distrito Federal. Currently, it has 27 regional superintendencies and is responsible for the inspection and policing of more than 70 thousand kilometers of federal highways and various areas of interest to the Union.

His mission is to "Promote the Nation's prosperity by guaranteeing public security and mobility in Brazil".

(The text above was taken from the Polícia Rodoviária Federal website)

Content

This dataset covers the period from 2007 to 2021 and were extracted directly from the website of the Federal Highway Police, which are available through this electronic address: https://www.gov.br/prf/pt-br/acesso-a-informacao/dados-abertos/dados-abertos-acidentes

These data are divided into annual files (.CSV format) and updated monthly in the last file (current year).

The records are available in three main groups: 1. Grouped by occurrence - (files like datatran20XX.csv) 2. Grouped by person - (files like acidentes20XX.csv) 3. Grouped by person - All causes and types of accidents (starting from 2017) - (files like acidentes20XX_todas_causas_tipos)

The available data comes from the BR-Brasil and BAT systems, with the following considerations:

• The BR-Brasil system was used at a national level between 2007 and 2016. The accidents were recorded using the BR-Brazil system. In this system the police officer responsible for the occurrence inserts data related to those involved, the location, the vehicles and the dynamics of the accident.

• In January 2017, the BR-Brasil system was discontinued and the PRF started using a new system to record traffic accident occurrences, the BAT system.

The data dictionary was provided by the PRF and was added as this dataset.

License

I asked the Brazil's Federal Highway Police (owner of the data) about which license of use he would be tied to, but I still haven't had an answer. On their website, they say that the license of use is free:

"Open Data is data published in a machine-readable format and without restrictions on licenses, patents or control mechanisms, so that they are freely available to be used and redistributed at will".

Acknowledgements

• This dataset was provided by the Polícia Rodoviária Federal do Brasil (Federal Highway Police) https://www.gov.br/prf/
• Banner image by Matthew T Rader on Unsplash

The map layers in this service provide color-coded maps of the traffic conditions you can expect for the present time (the default). The map shows present traffic as a blend of live and typical information. Live speeds are used wherever available and are established from real-time sensor readings. Typical speeds come from a record of average speeds, which are collected over several weeks within the last year or so. Layers also show current incident locations where available. By changing the map time, the service can also provide past and future conditions. Live readings from sensors are saved for 12 hours, so setting the map time back within 12 hours allows you to see a actual recorded traffic speeds, supplemented with typical averages by default. You can choose to turn off the average speeds and see only the recorded live traffic speeds for any time within the 12-hour window. Predictive traffic conditions are shown for any time in the future.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. 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.Data sourceEsri’s typical speed records and live and predictive traffic feeds come directly from HERE (www.HERE.com). HERE collects billions of GPS and cell phone probe records per month and, where available, uses sensor and toll-tag data to augment the probe data collected. An advanced algorithm compiles the data and computes accurate speeds. The real-time and predictive traffic data is updated every five minutes through traffic feeds.Data coverageThe 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. Look at the coverage map to learn whether a country currently supports traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, visit the directions and routing documentation and the ArcGIS Help.SymbologyTraffic 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%To view live traffic only—that is, excluding typical traffic conditions—enable the Live Traffic layer and disable the Traffic layer. (You can find these layers under World/Traffic > [region] > [region] Traffic). To view more comprehensive traffic information that includes live and typical conditions, disable the Live Traffic layer and enable the Traffic layer.ArcGIS Online organization subscriptionImportant Note:The World Traffic map service is available for users with an ArcGIS Online organizational subscription. To access this map service, you'll need to sign in with an account that is a member of an organizational subscription. If you don't have an organizational subscription, you can create a new account and then sign up for a 30-day trial of ArcGIS Online.