Database of IPv4 address networks with their respective geographical location.

Based on GeoLite2 Country Free Downloadable Databases as of Apr 21, 2015 http://dev.maxmind.com/geoip/geoip2/geolite2/...

The US Consumer IP Address file contains information on the location and observation dates of IP addresses tied to individuals in the Consumer Database.

Start.io's mobile IP database is one of the largest and most comprehensive out there. Used by some of the largest location and device-graph companies in the world, this data is linked with MAIDs and timestamps, offering insights into billions of devices and events.

Use cases : - Device graph enrichment - Fraud detection - Geolocation services - Customer journey mapping - Ad-targeting

This dataset offers a comprehensive collection of Telegram users' geolocation data, including IP addresses, with full user consent, covering 50,000 records. This data is specifically tailored for use in AI, ML, DL, and LLM models, as well as applications requiring Geographic Data and Social Media Data. The dataset provides critical geospatial information, making it a valuable resource for developing location-based services, targeted marketing strategies, and more.

What Makes This Data Unique? This dataset is unique due to its focus on geolocation data tied to Telegram users, a platform with a global user base. It includes IP to Geolocation Data, offering precise geospatial insights that are essential for accurate geographic analysis. The inclusion of user consent ensures that the data is ethically sourced and legally compliant. The dataset's broad coverage across various regions makes it particularly valuable for AI and machine learning models that require diverse, real-world data inputs.

Data Sourcing: The data is collected through a network of in-app tasks across different mini-apps within Telegram. Users participate in these tasks voluntarily, providing explicit consent to share their geolocation and IP information. The data is collected in real-time, capturing accurate geospatial details as users interact with various Telegram mini-apps. This method of data collection ensures that the information is both relevant and up-to-date, making it highly valuable for applications that require current location data.

Primary Use-Cases: This dataset is highly versatile and can be applied across multiple categories, including:

IP to Geolocation Data: The dataset provides precise mapping of IP addresses to geographical locations, making it ideal for applications that require accurate geolocation services. Geographic Data: The geospatial information contained in the dataset supports a wide range of geographic analysis, including regional behavior studies and location-based service optimization. Social Media Data: The dataset's integration with Telegram users' activities provides insights into social media behaviors across different regions, enhancing social media analytics and targeted marketing. Large Language Model (LLM) Data: The geolocation data can be used to train LLMs to better understand and generate content that is contextually relevant to specific regions. Deep Learning (DL) Data: The dataset is ideal for training deep learning models that require accurate and diverse geospatial inputs, such as those used in autonomous systems and advanced geographic analytics. Integration with Broader Data Offering: This geolocation dataset is a valuable addition to the broader data offerings from FileMarket. It can be combined with other datasets, such as web browsing behavior or social media activity data, to create comprehensive AI models that provide deep insights into user behaviors across different contexts. Whether used independently or as part of a larger data strategy, this dataset offers unique value for developers and data scientists focused on enhancing their models with precise, consented geospatial data.

A collection of router interface IP addresses geolocated to the city level. 11,857 IP addressed geolocated based on DNS names and 4,838 IP addresses geolocated based on RTT proximity to RIPE Atlas probes. The DNS-based data was created on May 15, 2016. The RTT-proximity data was created from measurements collected on May 25, 2016. The total number of addresses in the dataset is 16586 (109 addresses found to be common between the two sources of data with very similar locations). Data supplement for paper M. Gharaibeh, A. Shah, B. Huffaker, H. Zhang, R. Ensafi, and C. Papadopoulos, A Look at Router Geolocation in Public and Commercial Databases, Proc. Internet Measurement Conference (IMC), Nov 2017.

The dataset stands out due to its exceptional accuracy, zero-duplication rate, and a specialized focus on underserved Hispanic markets, with an emphasis on Puerto Rico. This geographic precision offers businesses a competitive advantage by delivering highly targeted insights for market expansion and customer engagement, particularly in areas where accurate geolocation data is often lacking.

The data is primarily collected from mobile devices, using real-time timestamps, IP to geolocation, and accuracy metrics. This comprehensive sourcing approach ensures up-to-date tracking of user movements, providing a reliable foundation for analysis and decision-making.

| Use Cases | 1. Optimizing delivery logistics by leveraging precise location data to improve route planning and efficiency. 2. Location-based marketing for better targeting of Hispanic consumers in underserved areas. 3. Customer behavior analysis, enabling businesses to understand and respond to movement patterns and preferences. 4. Supporting retail and e-commerce operations by enhancing supply chain management and operational decisions.

This device location data integrates seamlessly with other consumer behavior datasets, contributing to a well-rounded understanding of customer actions and preferences. It enhances the broader data offering by delivering high-quality, geographically specific insights that are crucial for targeting and growth in key markets, such as Puerto Rico, where Hispanic products are in high demand but underrepresented.

| Delivery Options | Choose from various delivery options such as flat files, databases, APIs, and more, tailored to your needs. JSON, XLS, CSV

| Other key features | Free data samples

Tags: Device Location Data, B2B2C Platform, Hispanic Grocers, Authentic Hispanic Food, User Engagement, Latam User Base, Ecommerce Dataset, Mobile Application Insights, User Behavior, User Experiences, Strategic Decisions, Hispanic Food Market Landscape, Geolocation Services, Delivery Optimization, Retail Insights, Market Expansion.

Dataset

This dataset was created by jiahaoYU

Released under Data files © Original Authors

Contents

IP Geolocation dataset contains the location information of IP addresses of 9 /8s (1 2 12 14 24 31 121 192 196) and also the raw probing data.

Aurora:GeoStudio® is a powerful geospatial analysis platform that effectively leverages IP Address Data to support a wide range of security and network management objectives. IP Address Data encompasses critical information such as the geographical location of an IP address, the internet service provider (ISP) associated with it, and the organization that owns the IP address range. This data is instrumental in enhancing targeted advertising, fraud prevention, network security, and website analytics.

Core Features and Data Formats:

  1.    Network Graph:
  •    HTML Network Graph: Aurora:GeoStudio® presents IP address scan results in an interactive HTML network graph, providing a visual representation of network connections and relationships.
  •    CSV Formatted Scan Results: The platform also offers CSV formatted results for detailed analysis and integration with other tools.
  2.    Full Scan:
  •    JSON Formatted Results: A comprehensive full scan of IP addresses is available in JSON format, detailing all aspects of the scan, including open ports, services, and detected vulnerabilities.
  3.    Basic Scan:
  •    GeoJSON and CSV Formatted Results: Basic scan results are provided in GeoJSON and CSV formats, offering essential information about IP addresses, including geographical data and ownership details.

Applications and Benefits:

  1.    Targeted Advertising:
  •    Geographical Location: By identifying the geographical location of IP addresses, Aurora:GeoStudio® enables businesses to deliver targeted advertisements based on the location of potential customers, increasing the relevance and effectiveness of marketing campaigns.
  2.    Fraud Prevention:
  •    IP Reputation: The platform can detect IP addresses associated with VPNs, proxies, or anonymous networks, helping businesses identify potentially fraudulent activities and take preventive measures.
  •    ISP and Ownership Details: Understanding the ISP and ownership details of IP addresses aids in verifying the legitimacy of transactions and interactions.
  3.    Network Security:
  •    Vulnerability Detection: Aurora:GeoStudio® scans IP addresses to identify open ports, running services, and potential vulnerabilities. This information is crucial for securing networks against unauthorized access and cyber threats.
  •    Intrusion Detection: By monitoring network traffic and IP address activity, the platform helps in detecting unusual patterns that may indicate security breaches or malicious activities.
  4.    Website Analytics:
  •    Visitor Analysis: IP Address Data allows website owners to analyze the geographical distribution of their visitors, understand traffic patterns, and optimize content delivery.
  •    Performance Monitoring: By tracking IP addresses, businesses can monitor website performance, identify issues, and enhance user experience.

Additional Uses and Capabilities:

  1.    Network Management:
  •    IP Inventory: Aurora:GeoStudio® maintains an up-to-date inventory of all devices connected to a network, helping administrators manage IP address allocation and resolve conflicts.
  •    Health Monitoring: Regular scanning provides insights into the health and performance of the network, enabling proactive maintenance and optimization.
  2.    Compliance and Policy Enforcement:
  •    Regulatory Compliance: The platform ensures that network devices comply with organizational policies and industry regulations by identifying unauthorized or non-compliant devices.
  •    Policy Enforcement: Scanning IP addresses helps enforce security policies, such as restricting access to sensitive resources and ensuring proper device configuration.

Aurora:GeoStudio®’s ability to scan and analyze IP Address Data provides a comprehensive toolset for enhancing network security, managing networks efficiently, and leveraging IP information for business insights. By offering detailed scan results in various formats (Network Graph, Full Scan, and Basic Scan), the platform enables users to detect vulnerabilities, optimize network performance, and make informed decisions based on accurate IP address data. This powerful integration of geospatial and network analytics makes Aurora:GeoStudio® an indispensable resource for businesses and organizations aiming to secure their networks and optimize their operations.

The Near Real Time (NRT) VIIRS/JPSS2 Imagery Resolution Terrain Corrected Geolocation 6-Min L1 Swath, short-name VJ203IMG_NRT is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21) platform-derived NASA VIIRS L1 terrain-corrected geolocation product and contains the derived line-of-sight (LOS) vectors for each of the 375-m image-resolution or I-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform's ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It produces geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VJ203IMG product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, and quality flag for every pixel location. VJ203IMG provides a fundamental input to derive a number of VIIRS I-band higher-level products.

The J2 VIIRS geolocation underwent an on-orbit validation. Geolocation errors of about 350 m in the along-scan direction and about 165 m in the along-track direction were corrected for the image-resolution bands and moderate-resolution bands. The Day-Night band (DNB) geolocation error of about 2000 m was corrected. Further, the geolocation biases in the scan profile were also corrected. All these corrections bring the geolocation uncertainties for the J2 L1 products to within 75 m (1-sigma) in both the along-scan and along-track directions.

This dataset does not contain any resources hosted on data.gov.au. It provides a link to the location of the IP Australia Freedom of Information (FOI) disclosure log to aide in information and data …Show full descriptionThis dataset does not contain any resources hosted on data.gov.au. It provides a link to the location of the IP Australia Freedom of Information (FOI) disclosure log to aide in information and data discovery. You can find the FOI Disclosure log here and the Agency's Information Publication Scheme here. The data.gov.au team is not responsible for the contents of the above linked pages.

CESNET-TimeSeries24: The dataset for network traffic forecasting and anomaly detection

The dataset called CESNET-TimeSeries24 was collected by long-term monitoring of selected statistical metrics for 40 weeks for each IP address on the ISP network CESNET3 (Czech Education and Science Network). The dataset encompasses network traffic from more than 275,000 active IP addresses, assigned to a wide variety of devices, including office computers, NATs, servers, WiFi routers, honeypots, and video-game consoles found in dormitories. Moreover, the dataset is also rich in network anomaly types since it contains all types of anomalies, ensuring a comprehensive evaluation of anomaly detection methods.Last but not least, the CESNET-TimeSeries24 dataset provides traffic time series on institutional and IP subnet levels to cover all possible anomaly detection or forecasting scopes. Overall, the time series dataset was created from the 66 billion IP flows that contain 4 trillion packets that carry approximately 3.7 petabytes of data. The CESNET-TimeSeries24 dataset is a complex real-world dataset that will finally bring insights into the evaluation of forecasting models in real-world environments.

Please cite the usage of our dataset as:

Josef Koumar, Karel Hynek, Tomáš Čejka, Pavel Šiška, "CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting", arXiv e-prints (2024): https://doi.org/10.48550/arXiv.2409.18874 @misc{koumar2024cesnettimeseries24timeseriesdataset, title={CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting}, author={Josef Koumar and Karel Hynek and Tomáš Čejka and Pavel Šiška}, year={2024}, eprint={2409.18874}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2409.18874}, }

Time series

We create evenly spaced time series for each IP address by aggregating IP flow records into time series datapoints. The created datapoints represent the behavior of IP addresses within a defined time window of 10 minutes. The vector of time-series metrics v_{ip, i} describes the IP address ip in the i-th time window. Thus, IP flows for vector v_{ip, i} are captured in time windows starting at t_i and ending at t_{i+1}. The time series are built from these datapoints.

Datapoints created by the aggregation of IP flows contain the following time-series metrics:

Simple volumetric metrics: the number of IP flows, the number of packets, and the transmitted data size (i.e. number of bytes)

Unique volumetric metrics: the number of unique destination IP addresses, the number of unique destination Autonomous System Numbers (ASNs), and the number of unique destination transport layer ports. The aggregation of \textit{Unique volumetric metrics} is memory intensive since all unique values must be stored in an array. We used a server with 41 GB of RAM, which was enough for 10-minute aggregation on the ISP network.

Ratios metrics: the ratio of UDP/TCP packets, the ratio of UDP/TCP transmitted data size, the direction ratio of packets, and the direction ratio of transmitted data size

Average metrics: the average flow duration, and the average Time To Live (TTL)

Multiple time aggregation: The original datapoints in the dataset are aggregated by 10 minutes of network traffic. The size of the aggregation interval influences anomaly detection procedures, mainly the training speed of the detection model. However, the 10-minute intervals can be too short for longitudinal anomaly detection methods. Therefore, we added two more aggregation intervals to the datasets--1 hour and 1 day.

Time series of institutions: We identify 283 institutions inside the CESNET3 network. These time series aggregated per each institution ID provide a view of the institution's data.

Time series of institutional subnets: We identify 548 institution subnets inside the CESNET3 network. These time series aggregated per each institution ID provide a view of the institution subnet's data.

Data Records

The file hierarchy is described below:

cesnet-timeseries24/

 |- institution_subnets/

 |   |- agg_10_minutes/<id_institution>.csv

 |   |- agg_1_hour/<id_institution>.csv

 |   |- agg_1_day/<id_institution>.csv

 |   |- identifiers.csv

 |- institutions/

 |   |- agg_10_minutes/<id_institution_subnet>.csv

 |   |- agg_1_hour/<id_institution_subnet>.csv

 |   |- agg_1_day/<id_institution_subnet>.csv

 |   |- identifiers.csv

 |- ip_addresses_full/

 |   |- agg_10_minutes/<id_ip_folder>/<id_ip>.csv

 |   |- agg_1_hour/<id_ip_folder>/<id_ip>.csv

 |   |- agg_1_day/<id_ip_folder>/<id_ip>.csv

 |   |- identifiers.csv

 |- ip_addresses_sample/

 |   |- agg_10_minutes/<id_ip>.csv

 |   |- agg_1_hour/<id_ip>.csv

 |   |- agg_1_day/<id_ip>.csv

 |   |- identifiers.csv

 |- times/

 |   |- times_10_minutes.csv

 |   |- times_1_hour.csv

 |   |- times_1_day.csv

 |- ids_relationship.csv   |- weekends_and_holidays.csv

The following list describes time series data fields in CSV files:

id_time: Unique identifier for each aggregation interval within the time series, used to segment the dataset into specific time periods for analysis.

n_flows: Total number of flows observed in the aggregation interval, indicating the volume of distinct sessions or connections for the IP address.

n_packets: Total number of packets transmitted during the aggregation interval, reflecting the packet-level traffic volume for the IP address.

n_bytes: Total number of bytes transmitted during the aggregation interval, representing the data volume for the IP address.

n_dest_ip: Number of unique destination IP addresses contacted by the IP address during the aggregation interval, showing the diversity of endpoints reached.

n_dest_asn: Number of unique destination Autonomous System Numbers (ASNs) contacted by the IP address during the aggregation interval, indicating the diversity of networks reached.

n_dest_port: Number of unique destination transport layer ports contacted by the IP address during the aggregation interval, representing the variety of services accessed.

tcp_udp_ratio_packets: Ratio of packets sent using TCP versus UDP by the IP address during the aggregation interval, providing insight into the transport protocol usage pattern. This metric belongs to the interval <0, 1> where 1 is when all packets are sent over TCP, and 0 is when all packets are sent over UDP.

tcp_udp_ratio_bytes: Ratio of bytes sent using TCP versus UDP by the IP address during the aggregation interval, highlighting the data volume distribution between protocols. This metric belongs to the interval <0, 1> with same rule as tcp_udp_ratio_packets.

dir_ratio_packets: Ratio of packet directions (inbound versus outbound) for the IP address during the aggregation interval, indicating the balance of traffic flow directions. This metric belongs to the interval <0, 1>, where 1 is when all packets are sent in the outgoing direction from the monitored IP address, and 0 is when all packets are sent in the incoming direction to the monitored IP address.

dir_ratio_bytes: Ratio of byte directions (inbound versus outbound) for the IP address during the aggregation interval, showing the data volume distribution in traffic flows. This metric belongs to the interval <0, 1> with the same rule as dir_ratio_packets.

avg_duration: Average duration of IP flows for the IP address during the aggregation interval, measuring the typical session length.

avg_ttl: Average Time To Live (TTL) of IP flows for the IP address during the aggregation interval, providing insight into the lifespan of packets.

Moreover, the time series created by re-aggregation contains following time series metrics instead of n_dest_ip, n_dest_asn, and n_dest_port:

sum_n_dest_ip: Sum of numbers of unique destination IP addresses.

avg_n_dest_ip: The average number of unique destination IP addresses.

std_n_dest_ip: Standard deviation of numbers of unique destination IP addresses.

sum_n_dest_asn: Sum of numbers of unique destination ASNs.

avg_n_dest_asn: The average number of unique destination ASNs.

std_n_dest_asn: Standard deviation of numbers of unique destination ASNs)

sum_n_dest_port: Sum of numbers of unique destination transport layer ports.

avg_n_dest_port: The average number of unique destination transport layer ports.

std_n_dest_port: Standard deviation of numbers of unique destination transport layer ports.

Moreover, files identifiers.csv in each dataset type contain IDs of time series that are present in the dataset. Furthermore, the ids_relationship.csv file contains a relationship between IP addresses, Institutions, and institution subnets. The weekends_and_holidays.csv contains information about the non-working days in the Czech Republic.

The results of a Top10VPN.com investigation into the privacy risks associated with 20 of the most popular Android proxy apps. The data table indicates which apps have high-risk permissions, share data with third-parties, share user IP addresses, contain location tracking code and which apps share data with Yandex or Bytedance.

The Near Real Time (NRT) VIIRS/JPSS2 Day/Night Band Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m, short-name VJ203DNB_NRT is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21) platform-based NASA VIIRS L1 terrain-corrected geolocation product, and contains the derived line-of-sight (LOS) vectors for the single panchromatic Day-Night band (DNB). The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform's ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It provides geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VJ203DNB product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, lunar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, moon illumination fraction and phase angle, and quality flag for every pixel location.

The J2 VIIRS geolocation underwent an on-orbit validation. Geolocation errors of about 350 m in the along-scan direction and about 165 m in the along-track direction were corrected for the image-resolution bands and moderate-resolution bands. The Day-Night band (DNB) geolocation error of about 2000 m was corrected. Further, the geolocation biases in the scan profile were also corrected. All these corrections bring the geolocation uncertainties for the J2 L1 products to within 75 m (1-sigma) in both the along-scan and along-track directions.

VIIRS Level-1B Geolocation ATBD

https://ladsweb.modaps.eosdis.nasa.gov/internal/deploy/web-stage/ladsweb/api/v2/content/archives/Document%20Archive/Science%20Data%20Product%20Documentation/Product%20Generation%20Algorithms/NASARevisedVIIRSGeolocationATBD2014.pdf

This respository contains the CLUE-LDS (CLoud-based User Entity behavior analytics Log Data Set). The data set contains log events from real users utilizing a cloud storage suitable for User Entity Behavior Analytics (UEBA). Events include logins, file accesses, link shares, config changes, etc. The data set contains around 50 million events generated by more than 5000 distinct users in more than five years (2017-07-07 to 2022-09-29 or 1910 days). The data set is complete except for 109 events missing on 2021-04-22, 2021-08-20, and 2021-09-05 due to database failure. The unpacked file size is around 14.5 GB. A detailed analysis of the data set is provided in [1]. The logs are provided in JSON format with the following attributes in the first level:

id: Unique log line identifier that starts at 1 and increases incrementally, e.g., 1. time: Time stamp of the event in ISO format, e.g., 2021-01-01T00:00:02Z. uid: Unique anonymized identifier for the user generating the event, e.g., old-pink-crane-sharedealer. uidType: Specifier for uid, which is either the user name or IP address for logged out users. type: The action carried out by the user, e.g., file_accessed. params: Additional event parameters (e.g., paths, groups) stored in a nested dictionary. isLocalIP: Optional flag for event origin, which is either internal (true) or external (false). role: Optional user role: consulting, administration, management, sales, technical, or external. location: Optional IP-based geolocation of event origin, including city, country, longitude, latitude, etc. In the following data sample, the first object depicts a successful user login (see type: login_successful) and the second object depicts a file access (see type: file_accessed) from a remote location:

{"params": {"user": "intact-gray-marlin-trademarkagent"}, "type": "login_successful", "time": "2019-11-14T11:26:43Z", "uid": "intact-gray-marlin-trademarkagent", "id": 21567530, "uidType": "name"}

{"isLocalIP": false, "params": {"path": "/proud-copper-orangutan-artexer/doubtful-plum-ptarmigan-merchant/insufficient-amaranth-earthworm-qualitycontroller/curious-silver-galliform-tradingstandards/incredible-indigo-octopus-printfinisher/wicked-bronze-sloth-claimsmanager/frantic-aquamarine-horse-cleric"}, "type": "file_accessed", "time": "2019-11-14T11:26:51Z", "uid": "graceful-olive-spoonbill-careersofficer", "id": 21567531, "location": {"countryCode": "AT", "countryName": "Austria", "region": "4", "city": "Gmunden", "latitude": 47.915, "longitude": 13.7959, "timezone": "Europe/Vienna", "postalCode": "4810", "metroCode": null, "regionName": "Upper Austria", "isInEuropeanUnion": true, "continent": "Europe", "accuracyRadius": 50}, "uidType": "ipaddress"} The data set was generated at the premises of Huemer Group, a midsize IT service provider located in Vienna, Austria. Huemer Group offers a range of Infrastructure-as-a-Service solutions for enterprises, including cloud computing and storage. In particular, their cloud storage solution called hBOX enables customers to upload their data, synchronize them with multiple devices, share files with others, create versions and backups of their documents, collaborate with team members in shared data spaces, and query the stored documents using search terms. The hBOX extends the open-source project Nextcloud with interfaces and functionalities tailored to the requirements of customers. The data set comprises only normal user behavior, but can be used to evaluate anomaly detection approaches by simulating account hijacking. We provide an implementation for identifying similar users, switching pairs of users to simulate changes of behavior patterns, and a sample detection approach in our github repo. Acknowledgements: Partially funded by the FFG project DECEPT (873980). The authors thank Walter Huemer, Oskar Kruschitz, Kevin Truckenthanner, and Christian Aigner from Huemer Group for supporting the collection of the data set. If you use the dataset, please cite the following publication: [1] M. Landauer, F. Skopik, G. Höld, and M. Wurzenberger. "A User and Entity Behavior Analytics Log Data Set for Anomaly Detection in Cloud Computing". 2022 IEEE International Conference on Big Data - 6th International Workshop on Big Data Analytics for Cyber Intelligence and Defense (BDA4CID 2022), December 17-20, 2022, Osaka, Japan. IEEE. [PDF]

Irys specializes in collecting and curating high-quality geolocation signals from millions of connected devices across the globe. Our real-time and historical foot traffic data, categorized under Map Data, is sourced through partnerships with tier-1 mobile applications and app developers. The advanced aggregated location data covers the entire world, providing valuable insights for diverse use-cases related to Transport and Logistic Data, Mobile Location Data, Mobility Data, and IP Address Data.

Our commitment to privacy compliance is paramount. We ensure that all data is collected in accordance with privacy regulations, accompanied by clear and compliant privacy notices. Our opt-in/out management allows for transparent control over data collection, use, and distribution to third parties.

Discover the power of foot traffic data with Irys – where precision meets privacy.

Irys specializes in collecting and curating high-quality GPS signals from millions of connected devices worldwide. Our Mobile Location Data insights are sourced through partnerships with tier-1 app developers and a unique data collection method. The low-latency delivery ensures real-time insights, setting us apart and providing unparalleled benefits and use cases for Location Data, Places Data, Mobility Data, and IP Address Data.

Our commitment to privacy compliance is unwavering. Clear and compliant privacy notices accompany our data collection process. Opt-in/out management empowers users over data distribution.

Discover the precision of our Mobile Location Data insights with Irys – where quality meets innovation.

Thailand Cargo Volume: IP: Thai Prosperity Terminal Co. Ltd (TPT) data was reported at 122,018.721 Metric Ton in Jun 2018. This records a decrease from the previous number of 162,431.279 Metric Ton for May 2018. Thailand Cargo Volume: IP: Thai Prosperity Terminal Co. Ltd (TPT) data is updated monthly, averaging 177,625.943 Metric Ton from Oct 2010 (Median) to Jun 2018, with 93 observations. The data reached an all-time high of 274,650.833 Metric Ton in May 2012 and a record low of 64,030.941 Metric Ton in Dec 2010. Thailand Cargo Volume: IP: Thai Prosperity Terminal Co. Ltd (TPT) data remains active status in CEIC and is reported by Map Ta Phut Industrial Port Office. The data is categorized under Global Database’s Thailand – Table TH.TA023: Port Statistics: Map Ta Phut Industrial of Thailand.

Additional file 7. Raw Data. Raw Data gathered and used to support the conclusions in this article. Data has been de-identified by redacting: 1. Submission IP addresses; 2. Location longitude and latitude; 3. Participant-submitted email addresses.

泰国 Cargo Volume: IP: Rayong Bulk Terminal Co. Ltd (RBT)在2018-06达0.000 公吨，相较于2018-05的0.000 公吨保持不变。泰国 Cargo Volume: IP: Rayong Bulk Terminal Co. Ltd (RBT)数据按月度更新，2010-10至2018-06期间平均值为0.000 公吨，共93份观测结果。该数据的历史最高值出现于2010-10，达195,721.117 公吨，而历史最低值则出现于2018-06，为0.000 公吨。CEIC提供的泰国 Cargo Volume: IP: Rayong Bulk Terminal Co. Ltd (RBT)数据处于定期更新的状态，数据来源于Map Ta Phut Industrial Port Office，数据归类于Global Database的泰国 – Table TH.TA023: Port Statistics: Map Ta Phut Industrial of Thailand。