Base data • Name/Brand • Adress • Geocoordinates • Opening Hours • Phone • ...

25+ Fuel Types like • Regular • Mid-Grade • Premium • Diesel • DEF • CNG •...

30+ Services and characteristics like • Carwash • Shop • Restaurant • Toilet • ATM • Pay at Pump •...

20+ Payment options • Cash • Visa • MasterCard • Fueling Cards • Google Pay • ...

Xavvy fuel is the leading source for Gas Station Location Data and Gasoline Price data worldwide and specialized in data quality and enrichment. Xavvy provides POI Data of gas stations at a high quality level for the United States. Next to base information like name/brand, address, geo-coordinates or opening hours, there are also detailed information about available fuel types, accessibility, special services, or payment options for each station. The level of information to be provided is highly customizable. One-time or regular data delivery, push or pull services, and any data format – we adjust to our customer’s needs.

Total number of stations per country or region, distribution of market shares among competitors or the perfect location for new gas stations, charging stations or hydrogen dispensers - our gas station data and gasoline price data provides answers to various questions and offers the perfect foundation for in-depth analyses and statistics. In this way, our data helps customers from various industries to gain more valuable insights into the fuel market and its development. Thereby providing an unparalleled basis for strategic decisions such as business development, competitive approach or expansion.

In addition, our data can contribute to the consistency and quality of an existing dataset. Simply map data to check for accuracy and correct erroneous data.

Especially if you want to display information about gas stations on a map or in an application, high data quality is crucial for an excellent customer experience. Therefore, our processing procedures are continuously improved to increase data quality:

• regular quality controls • Geocoding systems correct and specify geocoordinates • Data sets are cleaned and standardized • Current developments and mergers are taken into account • The number of data sources is constantly expanded to map different data sources against each other

Integrate the largest database of Retail Gas Station Location Data, Amenities and accurate Diesel and Gasoline Price Data in Europe and North America into your business. Check out our other Data Offerings available, and gain more valuable market insights on gas stations directly from the experts!

Base data • Name/Brand • Adress • Geocoordinates • Opening Hours • Phone •...

25+ Fuel Types like • Super E5 • Super 98 • Diesel • AdBlue • LPG • CNG •...

60+ Services and characteristics like • Carwash • Shop • Restaurant • Toilet • ATM • Toll •...

300+ Payment options • Cash • Visa • MasterCard • Fueling Cards •...

We are the leading source for Gas Station Location Data and Petrol Price Data worldwide and specialized in data quality and enrichment. We provide high quality POI Data of gas stations for all European countries.

The gas station location data is delivered country by country and the level of information to be provided is highly customizable One-time or regular data delivery, push or pull services, and any data format – we adjust to our customer’s needs.

Total number of stations per country or region, distribution of market shares among competitors or the perfect location for new gas stations, charging stations or hydrogen dispensers - our data provides answers to various questions and offers the perfect foundation for in-depth analyses and statistics. In this way, our gas station location data and petrol price data helps customers from various industries to gain more valuable insights into the fuel market and its development. Thereby providing an unparalleled basis for strategic decisions such as business development, competitive approach or expansion.

In addition, our data can contribute to the consistency and quality of an existing dataset. Simply map data to check for accuracy and correct erroneous data.

200+ sources including governments, petroleum companies, fuel card providers and crowd sourcing enable xavvy to provide various information. Next to base information like name/brand, address, geo-coordinates or opening hours, there are also detailed information about available fuel types, accessibility, special services, or payment options for each station:

Especially if you want to display information about gas stations on a map or in an application, high data quality is crucial for an excellent customer experience. Therefore, processing procedures are continuously improved to increase data quality:

• regular quality controls (e.g. via monitoring dashboards) • Geocoding systems correct and specify geocoordinates • Data sets are cleaned and standardized • Current developments and mergers are taken into account • The number of data sources is constantly expanded to map different data sources against each other

Check out our other Data Offerings available and gain more valuable market insights on gas stations directly from the experts!

Number of Service Station: by Operation: PTT Retail Management data was reported at 153.000 Unit in Sep 2018. This stayed constant from the previous number of 153.000 Unit for Jun 2018. Number of Service Station: by Operation: PTT Retail Management data is updated quarterly, averaging 148.000 Unit from Mar 2011 (Median) to Sep 2018, with 31 observations. The data reached an all-time high of 153.000 Unit in Sep 2018 and a record low of 145.000 Unit in Mar 2015. Number of Service Station: by Operation: PTT Retail Management data remains active status in CEIC and is reported by Department of Energy Business. The data is categorized under Global Database’s Thailand – Table TH.RB012: Number of Fuel Distribution Service Station.

Number of Service Station: by Operation: Shell data was reported at 511.000 Unit in Sep 2018. This stayed constant from the previous number of 511.000 Unit for Jun 2018. Number of Service Station: by Operation: Shell data is updated quarterly, averaging 510.000 Unit from Mar 2011 (Median) to Sep 2018, with 31 observations. The data reached an all-time high of 548.000 Unit in Mar 2011 and a record low of 486.000 Unit in Sep 2015. Number of Service Station: by Operation: Shell data remains active status in CEIC and is reported by Department of Energy Business. The data is categorized under Global Database’s Thailand – Table TH.RB012: Number of Fuel Distribution Service Station.

There is no description for this dataset.

The Oil and Gas Infrastructure Mapping (OGIM) database is a global, spatially explicit, and granular dataset of oil and gas infrastructure. It is developed by Environmental Defense Fund (EDF) (www.edf.org) and MethaneSAT, LLC (www.methanesat.org), a wholly owned subsidiary of EDF. The OGIM database helps fill a crucial geospatial data need, by supporting the quantification and source characterization of oil and gas methane emissions. The database is developed via acquisition, analysis, curation, integration, and quality-assurance (performed at EDF) of publicly available geospatial data sources. These oil and gas facility datasets are reported by governments, industry, academics, and other non-government entities.

OGIM is a collection of data tables within a GeoPackage. Each data table within the GeoPackage includes locations and facility attributes of oil and gas infrastructure types that are important sources of methane emissions, including: oil and gas production wells, offshore production platforms, natural gas compressor stations, oil and natural gas processing facilities, liquefied natural gas facilities, crude oil refineries, and pipelines. OGIM v2.7 includes approximately 6.7 million features, including 4.5 million point locations of oil and gas wells and over 1.2 million kilometers of oil and gas pipelines.

Please see the PDF document in the “Files” section of this page for more information about this version, including attribute column definitions, key changes since the previous version, and more. Full details on database development and related analytics can be found in the following Earth System Science Data (ESSD) journal paper. Please cite this paper when using any version of the database:

Omara, M., Gautam, R., O'Brien, M., Himmelberger, A., Franco, A., Meisenhelder, K., Hauser, G., Lyon, D., Chulakadabba, A., Miller, C., Franklin, J., Wofsy, S., and Hamburg, S.: Developing a spatially explicit global oil and gas infrastructure database for characterizing methane emission sources at high resolution, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-15-3761-2023, 2023.

Important note: While the results section of this manuscript is specific to v1 of the OGIM, the methods described therein are the same methods used to develop and update v2.7. Additionally, while we describe our data sources in detail in the manuscript above, and include maps of all acquired datasets, this open-access version of the OGIM database does not include the locations of about 300 natural gas compressor stations in Russia. Future updates may include these locations when appropriate permissions to make them publicly accessible are obtained.

OGIM v2.7 is based on public-domain datasets reported in February 2025 or prior. Each record in OGIM indicates a date (SRC_DATE) when the original source of the record was published or last updated. Some records may contain out-of-date information, for example, if a facility’s status has changed since we last visited a data source. We anticipate updating the OGIM database on a regular cadence and are continually including new public domain datasets as they become available.

Point of Contact at Environmental Defense Fund and MethaneSAT, LLC: Madeleine O’Brien (maobrien@methanesat.org) and Mark Omara (momara@edf.org).

In the rapidly evolving energy landscape, strategic planning and investment decisions require accurate, comprehensive data. dataplor's Global Fuel and Charging Station Dataset empowers businesses with a detailed view of fuel stations and charging infrastructure worldwide, providing a critical advantage in this transformative market.

Precise Data for Strategic Insights:

-Station Identification: Official names of fuel stations and charging networks, reflecting global variations, and unique identifiers for seamless tracking.

-Fuel Type and Charging Standards: Clear categorization by fuel types (e.g., gasoline, diesel, LPG, CNG) and charging standards.

-Exact Location Information: Street addresses, city, state/province, postal codes, country, and geographic coordinates for precise mapping and analysis.

-Operational Status: Real-time information on open/closed status and availability of specific fuel types or charging connectors.

Applications Across the Energy Sector:

-Market Landscape Analysis: Evaluate the distribution and density of fuel stations and charging stations globally to identify gaps and opportunities.

-Infrastructure Planning: Optimize the placement of new fuel stations or charging infrastructure based on demand and demographics.

-EV Adoption Strategies: Understand the charging infrastructure landscape to develop effective policies and incentives for electric vehicle adoption.

-Fuel Retail Optimization: Analyze competitor locations and fuel offerings to make informed decisions about pricing, promotions, and site upgrades.

-Supply Chain Efficiency: Streamline fuel delivery and EV charging network operations based on station locations.

By providing non-PII mobility data paired with the most comprehensive location data for our fuel and charging stations, our product ensures businesses can act with confidence while maintaining data privacy standards.

dataplor's datasets include 55+ attributes such as:

• Unique, Static dataplor ID
• Business Name
• Main/Sub/Business Categories
• Chain ID/Name
• Home Address
• Neighborhood
• City, State, Postal, Country
• Latitude/Longitude
• Open/Closed Status
• Visit Counts
• Confidence Scoring
• First Opened
• Popularity Indices
• Sentiment Indices, and more

Hamme et al. (2019) Global noble gas and N2/Ar database, version 1.0. These data are a compilation of dissolved noble gas and N2/Ar ratio measurements collected from 1998-2016 in locations spanning the globe.

This database contains the data on dissolved gas measurements published in:
Hamme, R. C., Nicholson, D. P., Jenkins, W. J., & Emerson, S. R. (2019). Using Noble Gases to Assess the Ocean’s Carbon Pumps. Annual Review of Marine Science, 11(1), 75–103. doi:10.1146/annurev-marine-121916-063604

Data Originators: Roberta Hamme, William Jenkins, Steven Emerson, David Nicholson, Rachel Stanley

Date contributed to BCO-DMO: 17 January 2022

Version 2.0 corrects an incorrect sign in the longitude for cruise 33KI20040814:HOT162 in version 1.0. The error in the database does not affect any figures in the publication.

This data is provided free for educational and non-profit research purposes. We ask that you appropriately cite Hamme et al. (2019) Annual Review of Marine Science in any work that uses this database. Please also send an e-mail to rhamme@uvic.ca, letting her know that you have downloaded the data, so that she can keep you apprised of any further corrections or changes. If you discover what you believe to be an error in the database, it is your responsibility to send an e-mail to me at rhamme@uvic.ca before using the data in a publication.

Both MATLAB .mat databases and comma-delimited .csv text files were provided to BCO-DMO. For the flat, ASCII version (csv) use the "Get Data" button on the BCO-DMO metadata landing page. For convenience, the MATLAB file is also provided as a Supplemental File: Global_Hammeetal2019.mat (400 kb)

These two formats contain identical information. Different cruises can be identified by the sequence number, cruisename, or date.

Secondary data - On some cruises, Ar concentration and N2/Ar ratio measurements were performed at two different labs on separate samples, for inter-calibration purposes. In these cases, data from both labs is given separately with data from the second lab labeled "secondary".

Indonesia Number of Customers: City Gas: Fuel Gas Filling Stations data was reported at 16.000 Person in 2017. This records an increase from the previous number of 14.000 Person for 2015. Indonesia Number of Customers: City Gas: Fuel Gas Filling Stations data is updated yearly, averaging 18.000 Person from Dec 2010 (Median) to 2017, with 7 observations. The data reached an all-time high of 43.000 Person in 2014 and a record low of 14.000 Person in 2015. Indonesia Number of Customers: City Gas: Fuel Gas Filling Stations data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.RBE008: City Gas: Sales.

The following dataset was collected from a set of cybersecurity experiments conducted in an Electricity and Natural Gas environment. The architecture was instantiated within the powerNET testbed at Pacific Northwest National Laboratory, and is comprised of both simulated components and hardware-in-the-loop devices. The test environment consisted of a substation and control center network representative of electrical systems. In addition, it also contained a compressor station, and an odorizer and pressure regulation station representative of oil and natural gas systems. The various devices on the electrical and gas systems were organized into multiple networks to mimic real-world deployments. There were 14 testing scenarios overall that covered a wide variety of cybersecurity and infrastructure events.

In the ALE/GAGE/AGAGE global network program, continuous high frequency gas chromatographic measurements of four biogenic/anthropogenic gases (methane, CH4; nitrous oxide, N2O; hydrogen, H2; and carbon monoxide, CO) and several anthropogenic gases that contribute to stratospheric ozone destruction and/or to the greenhouse effect have been carried out at five globally distributed sites for several years. The program, which began in 1978, is divided into three parts associated with three changes in instrumentation: the Atmospheric Lifetime Experiment (ALE), which used Hewlett Packard HP5840 gas chromatographs; the Global Atmospheric Gases Experiment (GAGE), which used HP5880 gas chromatographs; and the present Advanced GAGE (AGAGE). AGAGE uses two types of instruments: a gas chromatograph with multiple detectors (GC-MD), and a gas chromatograph with mass spectrometric analysis (GC-MS). Beginning in January 2004, an improved cryogenic preconcentration system (Medusa) replaced the absorption-desorption module in the GC-MS systems at Mace Head and Cape Grim; this provided improved capability to measure a broader range of volatile perfluorocarbons with high global warming potentials. The Medusa GC-MS systems were subsequently used at other AGAGE stations (Trinidad Head, Barbados, American Samoa, Zeppelin, Jungfraujoch, and Goan) after the initial setup at Mace Head and Cape Grim. More information may be found on the AGAGE home page: https://agage.mit.edu/instruments.Data from the AGAGE and affiliated stations (total of 10 sites) between August 1993 and December 2022 are provided in “Agage_gcmd_gcms_data_2023_11_15.tar.gz” (compressed tar file). The metadata file has information on each station and currently released species. The standard scales used in archived species are listed in "AGAGE_scale_2023_v2.pdf". Additional information can be found on the AGAGE website (https://agage.mit.edu).

This data set is used in the Master's thesis: "A Comparison of Price Fluctuations Between Brent Crude Oil and Retail Fuel Prices in Stavanger - An Algorithmic Model for Refueling" by Ola Nes (2021) The data set contains the fuel prices collected (Excel and CSV files), and the Python code which contains all functions used in the thesis. Abstract for thesis: "This thesis investigates and compares the volatility in the retail fuel market in Stavanger and Brent crude oil. Gasoline and diesel prices have been collected from gas stations in Stavanger in 2020 and 2021, and are used for the thesis’ main goal of developing an algorithmic mathematical model for refueling vehicles at optimal times for consumers that could be used in practice. The collected data suggests that there is higher volatility in the retail fuel market in Stavanger compared to the Brent crude oil market. Gas stations follow a characteristic Edgeworth cycle pattern that have price spikes occur when restarting their price cycles. These occur for the most part at the same time across all gas stations monitored in Stavanger. This pattern can be difficult for consumers to predict. Therefore, a practical refueling algorithm could be useful. There are many factors that go in to such a model to make it efficient such as price spike analysis from the Edgeworth cycle pattern found in retail fuel markets and estimating volatility using GARCH(1,1) method."

Thailand Number of Service Station: by Operation: PTT data was reported at 75.000 Unit in Sep 2018. This records a decrease from the previous number of 1,799.000 Unit for Jun 2018. Thailand Number of Service Station: by Operation: PTT data is updated quarterly, averaging 1,489.000 Unit from Mar 2011 (Median) to Sep 2018, with 31 observations. The data reached an all-time high of 1,799.000 Unit in Jun 2018 and a record low of 75.000 Unit in Sep 2018. Thailand Number of Service Station: by Operation: PTT data remains active status in CEIC and is reported by Department of Energy Business. The data is categorized under Global Database’s Thailand – Table TH.RB012: Number of Fuel Distribution Service Station. Since Sept 2018, restructuring of PTT consisting of a transfer of the oil business unit from PTT to PTT Oil and Retail Business Company Limited (“PTTOR”).

This dataset accompanies a journal article with the title 'Limits to Paris-compatibility of CO2 capture and utilisation' which is expected to feature in the February 2022 isssue of the journal One Earth. The dataset includes the greenhouse gas emissions of CO2 capture and utilisation technologies for 2030 and 2050, as well as source data for the results figures in the article. Date Submitted: 2022-01-24

The information provided in this dataset is categorized/symbolized as either 1) the depth range to which a well or borehole has been drilled or 2) the aquifer or activity status of monitor wells having attached time series data. Only the locations of well/borehole stations currently registered in the SFWMD DBHYDRO database are available for viewing through this mapping application. The inventory of stations in DBHYDRO include wells installed and boreholes drilled by or on behalf of the SFWMD, United States Geological Survey (USGS), United States Army Corps of Engineers (USACE), state, county and local government agencies, regulated utilities, and oil & gas companies for various purposes. NOTE: This data-set was compiled for the purpose of aiding groundwater resource evaluations.Many of the wells/boreholes owned and/or operated by SFWMD or other entities are included in DBHYDRO, but it is not, and should not be viewed as a comprehensive well inventory. The web layer is created daily using an Extract-Transform-Load (ETL) on the SFWMD wrep Oracle database view “DMDBASE.WELL_MAP_ROLLUP_VW” which itself consolidates information from multiple database tables to produce the map view. The web layer symbolized by the monitoring status and depth range can be customized by filtering on specific attributes. Fields for attributes include, but are not limited to: water supply planning region, county, watershed, water management district, agency, purpose, status (active or inactive), aquifer system or specific aquifer within a system. Each well location is linked to its corresponding station in DBHYDRO from which a variety of data and information on a well can be obtained. These data and information include water level and water quality time series monitoring data, depth, well construction, hydrostratigraphy, lithology, geophysical logs, hydraulic characteristics, and technical reports.The following fields are available for filtering:- Station: The DBHYDRO database station name associated with the well.- Agency: The agency primarily responsible for the monitoring data (The agency to whom data is most recently attributed)- Aquifer: The specific aquifer monitored if applicable- Aq_System: The more general aquifer system monitored if applicable- Depth Drilled: The depth to which the borehole was drilled. It may have been backfilled before long-term monitoring began- Regnl_Network: Indicates whether or not this well is part of the so-called Regional Network - Status: The current status of monitoring if applicable- USGS_Coop_Prgm: Indicates whether or not this well is part of the United States Geological Survey cooperative monitoring program- Core_Lab: Is there core lab data for this well- Litho_Logs: Are there lithological logs for this well- Geophys_Logs: Are there geophysical logs for this well- Hydraulic_Properties: Are there hydraulic properties for this well- Construction: Is there construction information for this well- Hydrostratigraphy: Is there hydrostratigraphy data for this well- Formation: Is there formation data for this well- Flow: Is there flow data for this well- Tracer_Tests: Is there tracer test data for this well- Attachments: Are there documents available for download attached to this well- Video: Are there down-hole videos for this well- Open_Hole: Is this an open hole- Time Series Data: Are there time series data at this well- WQ_Samples: Are there water quality samples at this well- WQ_Chlorides: Are there chloride data at this well- Purposes: The purpose(s) for which this well was drilled- AQ_Perf_Test: Are there aquifer performance test (APT) data for this well- ASR: Is the purpose of this well for Aquifer Storage and Recovery - Destroyed: Was this well destroyed or plugged- Drain: is this a drain well- Exploratory: Is this an exploratory well- Inject Monitor: Is this an injection monitoring well- Inject Well: Is this an injection well- Mine: is this a mine- Observation: is this an observation well- Oil_Gas: Is this an oil or gas well- Recharge: Is this a recharge well- Test_Well: is this a test well- Unused: is this an unused well- Withdrawal: Are there withdrawals from this well- County: The coordinate-derived county within which the station resides- Region: The coordinate-derived Water Supply Planning Region within which the station resides- Watershed: The coordinate-derived SFWMD ArcHydro database watershed within which the station resides- WMD: The coordinate-derived Florida Water Management District boundary within which the station resides.

Version 2. This study is a joint effort by the University of Wyoming (UW), the UW Engineering Department (UW-ENG), and Idaho National Laboratories (INL) and the United States Geological Survey (USGS) to describe rare earth element concentrations in oil and gas produced waters. In this work we present the Rare Earth Element (REE) and trace metal character of produced water in several oil and gas fields and three coal fired power stations.

Thailand Number of Service Station: by Operation: Siam Gas data was reported at 461.000 Unit in Sep 2018. This records an increase from the previous number of 457.000 Unit for Jun 2018. Thailand Number of Service Station: by Operation: Siam Gas data is updated quarterly, averaging 418.000 Unit from Mar 2011 (Median) to Sep 2018, with 31 observations. The data reached an all-time high of 461.000 Unit in Sep 2018 and a record low of 310.000 Unit in Mar 2011. Thailand Number of Service Station: by Operation: Siam Gas data remains active status in CEIC and is reported by Department of Energy Business. The data is categorized under Global Database’s Thailand – Table TH.RB012: Number of Fuel Distribution Service Station.

Liquefied Natural Gas (LNG): Market Main Price: Gas Station: Shandong: Jinan data was reported at 4.780 RMB/kg in 25 Apr 2025. This records a decrease from the previous number of 4.800 RMB/kg for 18 Apr 2025. Liquefied Natural Gas (LNG): Market Main Price: Gas Station: Shandong: Jinan data is updated daily, averaging 4.713 RMB/kg from Mar 2019 (Median) to 25 Apr 2025, with 322 observations. The data reached an all-time high of 9.500 RMB/kg in 25 Feb 2022 and a record low of 2.650 RMB/kg in 31 Jul 2020. Liquefied Natural Gas (LNG): Market Main Price: Gas Station: Shandong: Jinan data remains active status in CEIC and is reported by Shandong Longzhong Information Technology Co., Ltd.. The data is categorized under China Premium Database’s Energy Sector – Table CN.RBP: Liquefied Natural Gas (LNG): Market Price: Gas Station.

Thailand Number of Service Station: by Operation: Thai Gas data was reported at 33.000 Unit in Sep 2018. This records an increase from the previous number of 32.000 Unit for Jun 2018. Thailand Number of Service Station: by Operation: Thai Gas data is updated quarterly, averaging 33.000 Unit from Mar 2013 (Median) to Sep 2018, with 23 observations. The data reached an all-time high of 44.000 Unit in Mar 2017 and a record low of 11.000 Unit in Mar 2013. Thailand Number of Service Station: by Operation: Thai Gas data remains active status in CEIC and is reported by Department of Energy Business. The data is categorized under Global Database’s Thailand – Table TH.RB012: Number of Fuel Distribution Service Station.

Gas Station: Natural Gas Consumption: A. & N. Islands data was reported at 0.000 Cub m in 2022. This stayed constant from the previous number of 0.000 Cub m for 2021. Gas Station: Natural Gas Consumption: A. & N. Islands data is updated yearly, averaging 0.000 Cub m from Mar 1996 (Median) to 2022, with 26 observations. The data reached an all-time high of 0.000 Cub m in 2022 and a record low of 0.000 Cub m in 2022. Gas Station: Natural Gas Consumption: A. & N. Islands data remains active status in CEIC and is reported by Central Electricity Authority. The data is categorized under Global Database’s India – Table IN.RBC019: Electricity: Gross Generation: Utilities: Gas Station.