The resource-location extension for CKAN enhances data resources by automatically adding latitude and longitude coordinates to CSV files containing address data, using provided address, city and zipcode columns. This simplifies geocoding and location-based analysis directly within CKAN. The extension requires CKAN version 2.7.2 or higher. Key Features: Automated Geocoding: Automatically converts address data within CSV files into latitude and longitude coordinates during resource upload. Address Field Configuration: Allows users to specify the CSV column numbers corresponding to address, city, and zipcode fields. Coordinate Appending: Adds new columns to the CSV file containing the calculated latitude and longitude coordinates, preserving the original data. CSV Processing during Upload: Geocoding process is integrated directly into the resource upload workflow. Language Management: Offers translation support and instructions for adding new translations. How It Works: During CSV resource upload, the user is prompted to input column numbers corresponding to the address, city, and zipcode. Upon submission of the upload form, the extension processes the file, geocodes the addresses using these column values, and appends latitude and longitude as new columns to the CSV. This modified CSV file, now containing geographic coordinates, is stored as the resource. Benefits & Impact: By automatically adding geographic coordinates, the resource-location extension simplifies tasks such as mapping and spatial analysis of tabular data. This automated geocoding process enhances the usability and value of address-based datasets within CKAN.

Geocoding API Market Outlook

According to our latest research, the global Geocoding API market size reached USD 1.45 billion in 2024, reflecting robust demand across diverse industries. The market is expected to grow at a CAGR of 13.2% from 2025 to 2033, with the total market value forecasted to reach USD 4.22 billion by 2033. This remarkable growth is primarily driven by the surging adoption of location-based services, the proliferation of IoT devices, and the increasing need for real-time geospatial analytics. As per our latest research, the Geocoding API market is witnessing transformative shifts owing to advancements in cloud computing, machine learning integration, and the expanding scope of digital transformation initiatives globally.

A primary growth factor for the Geocoding API market is the exponential rise in mobile device usage and the integration of geospatial data in everyday applications. Modern businesses, from retail to logistics, are increasingly relying on geocoding solutions to enhance operational efficiency, optimize delivery routes, and improve customer engagement through personalized location-based services. The widespread adoption of smartphones and the ubiquity of GPS-enabled devices have made geospatial data a critical asset, fueling the demand for robust and scalable Geocoding APIs. This trend is further reinforced by the growing popularity of ride-sharing, food delivery, and other on-demand services that require precise location mapping and real-time address resolution.

Another significant driver is the rapid digital transformation across industries, which necessitates the integration of advanced mapping and geospatial analytics into enterprise workflows. Organizations in sectors such as transportation, real estate, and government are leveraging Geocoding APIs to streamline asset tracking, urban planning, and emergency response systems. The ability to convert physical addresses into geographic coordinates and vice versa enables businesses to gain actionable insights, enhance resource allocation, and deliver superior customer experiences. Moreover, the proliferation of big data and IoT devices has intensified the need for real-time, accurate geospatial information, further propelling the adoption of Geocoding API solutions.

The evolution of cloud computing and advancements in artificial intelligence are also catalyzing the growth of the Geocoding API market. Cloud-based deployment models offer unparalleled scalability, cost-effectiveness, and ease of integration, making them the preferred choice for enterprises of all sizes. Additionally, the integration of AI and machine learning algorithms into geocoding platforms has significantly improved the accuracy, speed, and contextual relevance of geospatial data processing. These technological advancements are enabling organizations to unlock new use cases, such as predictive analytics, geofencing, and automated asset management, thereby expanding the addressable market for Geocoding APIs.

In the context of these technological advancements, the role of Location Verification API has become increasingly significant. This API facilitates the accurate verification of physical addresses, ensuring that businesses and services can reliably reach their intended destinations. By integrating Location Verification API into their operations, companies can enhance the precision of their geocoding processes, reducing errors and improving customer satisfaction. This is particularly crucial for industries such as logistics and delivery services, where the timely and accurate delivery of goods is paramount. The API not only supports the validation of addresses but also assists in maintaining up-to-date location databases, which is essential for real-time geospatial analytics and decision-making.

From a regional perspective, North America continues to dominate the Geocoding API market, accounting for a substantial share of global revenues in 2024. The region's leadership is underpinned by the presence of major technology vendors, high digital adoption rates, and a mature ecosystem for location-based services. Europe and Asia Pacific are also witnessing robust growth, fueled by increasing investments in smart city initiatives, expanding e-commerce sectors, and government-led digitalization programs. The Asia Pacific region, in particular, is poised for the faste

This dataset contains key characteristics about the data described in the Data Descriptor Geocoding of worldwide patent data. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format 


      Versioning Note:Version 2 was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata.

Insurance Geocoding Solutions Market Outlook

According to our latest research, the global Insurance Geocoding Solutions market size reached USD 1.92 billion in 2024. The market is exhibiting robust momentum, with a compound annual growth rate (CAGR) of 13.2% projected over the forecast period. By 2033, the market is expected to attain a value of USD 5.65 billion. This significant growth is primarily attributed to the increasing need for accurate location intelligence in insurance operations, digital transformation initiatives, and the rising importance of risk prevention and mitigation strategies in the insurance sector.

One of the principal growth drivers for the Insurance Geocoding Solutions market is the escalating demand for precision in risk assessment and underwriting processes. Insurers are increasingly leveraging geocoding solutions to map and analyze geographic data, enabling them to assess property risks more accurately, price policies more competitively, and reduce fraudulent claims. The integration of advanced geospatial analytics and artificial intelligence within these solutions allows insurers to visualize risk exposures in real-time, which greatly enhances decision-making capabilities. Additionally, the growing frequency and severity of climate-related events and natural disasters have heightened the need for insurers to adopt sophisticated geocoding tools to assess and manage risks proactively, further propelling market growth.

Another key factor fueling the expansion of the Insurance Geocoding Solutions market is the ongoing digital transformation across the insurance industry. Insurers are increasingly adopting cloud-based technologies and big data analytics to streamline operations, enhance customer experience, and comply with regulatory requirements. Geocoding solutions, when integrated with policy administration and claims management systems, enable insurers to automate workflows, improve customer targeting, and optimize resource allocation. The proliferation of Internet of Things (IoT) devices and telematics is also contributing to the market’s growth by providing insurers with real-time location data, which can be harnessed for more dynamic and personalized insurance offerings.

Furthermore, the growing emphasis on customer-centricity and personalized insurance products is pushing insurers to adopt advanced geocoding technologies. By utilizing granular location data, insurance providers can offer tailored products and services that align with the unique needs of individual policyholders. This not only enhances customer satisfaction and loyalty but also opens up new revenue streams for insurers. The increasing collaboration between insurance companies and technology providers is resulting in the development of innovative geocoding platforms with enhanced capabilities, such as 3D mapping and spatial analytics, which are expected to further drive market growth in the coming years.

Regionally, North America continues to dominate the Insurance Geocoding Solutions market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The high adoption of advanced technologies, presence of major insurance companies, and stringent regulatory requirements in North America have contributed to its leadership position. However, Asia Pacific is witnessing the fastest growth, driven by rapid urbanization, increasing insurance penetration, and the rising need for disaster risk management solutions. Europe is also experiencing significant growth, supported by the digitalization of insurance processes and the growing focus on sustainability and climate resilience. Latin America and the Middle East & Africa are gradually emerging as promising markets, propelled by the expansion of the insurance sector and increasing awareness of the benefits of geocoding solutions.

Component Analysis

The Component segment of the Insurance Geocoding Solutions market is bifurcated into software and services, each playing a pivotal role in the overall ecosystem. The software segment is currently the dominant force, capturing a substantial share of the market in 2024. This dominance is largely attributed to the increasing demand for advanced geospatial analytics platforms that can process vast amounts of location-based data with high accuracy and speed. Modern geocoding software solutions are equipped with features such as real-time mapping, risk visualization, a

Improving geolocation accuracy in text data has long been a goal of automated text processing. We depart from the conventional method and introduce a two-stage supervised machine learning algorithm that evaluates each location mention to be either correct or incorrect. We extract contextual information from texts, i.e., N-gram patterns for location words, mention frequency, and the context of sentences containing location words. We then estimate model parameters using a training dataset and use this model to predict whether a location word in the test dataset accurately represents the location of an event. We demonstrate these steps by constructing customized geolocation event data at the subnational level using news articles collected from around the world. The results show that the proposed algorithm outperforms existing geocoders even in a case added post hoc to test the generality of the developed algorithm.

(English version below) Ce jeu de données est une version étendue de la base de données GeoVirus, qui comprend 229 articles de WikiNews sur les épidémies mondiales, dont les entités spatiales nommées (SNE) sont manuellement annotées par des experts, avec leurs coordonnées et noms. Nous avons intégré un processus automatique d'extraction et de désambiguïsation des SNE, lequel a été aligné avec les données annotées par les experts. Ce processus a impliqué une corrélation de 1,360 SNE identifiées à la fois dans notre extraction automatique et dans l'ensemble de données annoté par les experts, garantissant ainsi précision et cohérence dans l'identification spatiale. Le corpus résultant est une extension de la base GeoVirus originale, enrichie de trois colonnes supplémentaires présentant les annotations spatiales automatiques. GeoVirus dataset: Gritta, Milan, Mohammad Taher Pilehvar, and Nigel Collier. "Which melbourne? augmenting geocoding with maps." Association for Computational Linguistics, 2018. Ce jeu de données amélioré non seulement préserve l'intégrité des annotations expertes, mais démontre également l'efficacité de notre processus automatique, comme en témoigne le taux de rappel de 0.911 de notre approche, surpassant nettement le taux de rappel de 0.871 du géocodeur standard Geonames. Ce jeu de données comprend un seul fichier: sne_data.csv. Il est constitué des colonnes suivantes: source : Lien url WikiNews fourni dans les données GeoVirus input_sne: Entité nommée spatiale extraite avec Spacy à partir des données brutes (document). Utilisée comme entrée pour Geonames true_country_code: Code pays obtenu par géocodage inverse, basé sur la latitude et la longitude fournies output_sne: Entité nommée spatiale renvoyée par Geonames predicted_country_code: Code pays correspondant à l'output_sne après désambiguïsation disamb_phase: Correspond à la phase de désambiguïsation qui a aidé à désambiguïser l'entité nommée spatiale saisie -------- This dataset is an extended version of the GeoVirus database, which includes 229 WikiNews articles on global epidemics. Named Spatial Entities (SNEs) in these articles are manually annotated by experts, complete with their coordinates and names. We integrated an automated process for SNE extraction and disambiguation, aligning it with the data annotated by experts. This involved correlating 1,360 SNEs identified both in our automatic extraction and the expert-annotated dataset, ensuring precision and consistency in spatial identification. The resulting corpus is an extension of the original GeoVirus base, enriched with three additional columns presenting automatic spatial annotations. GeoVirus dataset reference: Gritta, Milan, Mohammad Taher Pilehvar, and Nigel Collier. "Which Melbourne? Augmenting geocoding with maps." Association for Computational Linguistics, 2018. This enhanced dataset not only preserves the integrity of expert annotations but also demonstrates the effectiveness of our automated process, evidenced by the recall rate of 0.911 in our approach, significantly surpassing the standard Geonames geocoder's recall rate of 0.871. The dataset consists of a single file: sne_data.csv. It includes the following columns: source: WikiNews URL provided in the GeoVirus data. input_sne: Spatial Named Entity extracted with Spacy from raw data (document). Used as input for Geonames. true_country_code: Country code obtained through reverse geocoding based on provided latitude and longitude. output_sne: Spatial Named Entity returned by Geonames. predicted_country_code: Country code corresponding to output_sne after disambiguation. disamb_phase: Corresponds to the disambiguation phase that aided in disambiguating the entered spatial named entity.

Listing by date of registered businesses. This listing updates daily via automated processes from on-premise data sources.Point features and Lat/Long fields are calculated from the physical address via geocoding processes.

More and more, social scientists are using (big) digital behavioral data for their research. In this context, the social network and microblogging platform Twitter is one of the most widely used data sources. In particular, geospatial analyses of Twitter data are proving to be fruitful for examining regional differences in user behavior and attitudes. However, ready-to-use spatial information in the form of GPS coordinates is only available for a tiny fraction of Twitter data, limiting research potential and making it difficult to link with data from other sources (e.g., official statistics and survey data) for regional analyses. We address this problem by using the free text locations provided by Twitter users in their profiles to determine the corresponding real-world locations. Since users can enter any text as a profile location, automated identification of geographic locations based on this information is highly complicated. With our method, we are able to assign over a quarter of the more than 866 million German tweets collected to real locations in Germany. This represents a vast improvement over the 0.18% of tweets in our corpus to which Twitter assigns geographic coordinates. Based on the geocoding results, we are not only able to determine a corresponding place for users with valid profile locations, but also the administrative level to which the place belongs. Enriching Twitter data with this information ensures that they can be directly linked to external data sources at different levels of aggregation. We show possible use cases for the fine-grained spatial data generated by our method and how it can be used to answer previously inaccessible research questions in the social sciences. We also provide a companion R package, nutscoder, to facilitate reuse of the geocoding method in this paper.

The Postal Code Conversion File (PCCF) is a digital file which provides a correspondence between the Canada Post Corporation (CPC) six-character postal code and Statistics Canada's standard geographic areas for which census data and other statistics are produced. Through the link between postal codes and standard geographic areas, the PCCF permits the integration of data from various sources. The Single Link Indicator provides one best link for every postal code, as there are multiple records for many postal codes. Getting started guide To obtain the postal code conversion file or for questions, consult the DLI contact at your educational institution. The geographic coordinates attached to each postal code on the PCCF are commonly used to map the distribution of data for spatial analysis (e.g., clients, activities). The location information is a powerful tool for planning, or research purposes. In April 1983, the Geography Division released the first version of the PCCF, which linked postal codes to 1981 Census geographic areas and included geographic coordinates. Since then, the file has been updated on a regular basis to reflect changes. For this release of the PCCF, the vast majority of the postal codes are directly geocoded to 2006 Census geography. This improves precision of the file over the previous conversion process used to align postal code linkages to new geographic areas after each census. About 94% of the postal codes were linked to geographic areas using the new automated process. A quality indicator for the confidence of this linkage is available in the PCCF.

Context

Incidents responded to by the Baton Rouge Animal Control and Rescue Center (ACRC).

ACRC is responsible for carrying out duties related to animal-related situations, including: administering the anti-rabies vaccination, licensing, and tag program; investigating animal cruelty incidents; investigating dog fighting; resolving dangerous animal situations; rescuing injured animals; investigating abandoned animal cases; investigating occult, animal sacrifice, and bestiality cases; resolving stray animal situations; enforcing the leash law and owned animal problems; assisting law enforcement with narcotics, evictions, and DWI cases; enforcing barking dog cases; inspecting dog yards/pens; chaining or tethering compliance; assisting animal welfare groups with feral interventions; and conducting educational programs.

As many of the incidents included within this data set involve active cases that are currently under investigation and computerized system limitations do not allow for automated screening of open/closed cases, the identity of animal owners is redacted to protect the privacy of the animal owner. Members of the public interested in the identity of a specific incident may contact ACRC directly to inquire about the incident and, if it is closed, ACRC will release a copy of the file to the person requesting it. However, location data regarding where the incident was reported or occurred is included within this data set, which may or may not be the same location as the animal owner's home or property.

In addition, to protect the identity of the complainant (person filing the complaint or alerting ACRC to a potential incident), only the complainant's street name is included as part of this data set.

Finally, while all incidents are updated on a daily basis, incidents involving animal cruelty are updated based on a rolling 30-day delay to allow for ACRC to investigate the incident

Content

The data was pulled from the City of Baton Rouge Open Data website on 2019-11-18. A lot of the older calls from when they began recording data did not have much, if any, information attached to them so I filtered those out. The remaining data was cleaned up with various Python scripts and R then the addresses were ran through the Census.gov geocoding service. Some addresses would not work on there, so the remaining were ran through Google's geocoding service. There appears to be a bug somewhere in ggmap or Google (I haven't looked into it further) as it fails to process addresses with the "#" symbol in them (101 Main St. Apt. #200). I then created another Python script to format addresses with "#" in them and ran those through Google's geocoding again. Some addresses that were in their database simply do not exist (or used to exist at one time) and those failed geocoding completely. In total, about 20,000 rows had to be discarded leaving us with the below data, which is still quite a bit of calls.

Two columns to be aware of:

• Breed: Some of them have an X in front of the breed. This is not explained on their website and I could not find any solid correlation with the rest of the data for this.
• Disposition: TRANS TO CAA means the animal was brought to a shelter (CAA).

Acknowledgements

D. Kahle and H. Wickham. ggmap: Spatial Visualization with ggplot2. The R Journal, 5(1), 144-161.

http://journal.r-project.org/archive/2013-1/kahle-wickham.pdf

Inspiration

This data details what kind of animals they responded to, including breed, sex, size, age, condition, and temperament. It would be interesting to see if a trend can be identified by a certain type of animal based on the area in the city. What about the vast waterways and woodlands in the area? Louisiana has lots of hunting areas close to the city, could these be tied in and connected with wildlife calls?

The Address Points dataset shows Utah address points for all twenty-nine Utah counties. An address point represents a geographic location that has been assigned a US Postal Service (USPS) address by the local address authority (i.e., county or municipality) but does not necessarily receive mail. Address points may include several pieces of information about the structure or location that’s being mapped, such as:the full address (i.e., the USPS mailing address, if the address is for a physical location [rather than a PO box]);the landmark name; whether the location is a building;the type of unit;the city and ZIP code; unique code identifiers of the specific geographic location, including the Federal Information Processing Standard Publication (FIPS) county code and the US National Grid (USNG) spatial address;the address source; andthe date that the address point was loaded into the map layer.This dataset is mapping grade; it is a framework layer that receives regular updates. As with all our datasets, the Utah Geospatial Resource Center (UGRC) works to ensure the quality and accuracy of our data to the best of our abilities. Maintaining the dataset is now an ongoing effort between UGRC, counties, and municipalities. Specifically, UGRC works with each county or municipality’s Master Address List (MAL) authority to continually improve the address point data. Counties have been placed on an update schedule depending on the rate of new development and change within them. Populous counties, such as Weber, Davis, Salt Lake, Utah, and Washington, are more complete and are updated monthly, while rural or less populous counties may be updated quarterly or every six months.The information in the Address Points dataset was originally compiled by Utah counties and municipalities and was aggregated by UGRC for the MAL grant initiative in 2012. The purpose of this initiative was to make sure that all state entities were using the same verified, accurate county and municipal address information. Since 2012, more data has been added to the Address Points GIS data and is used for geocoding, 911 response, and analysis and planning purposes. The Address Point data is also used as reference data for the api.mapserv.utah.gov geocoding endpoint, and you can find the address points in many web mapping applications. This dataset is updated monthly and can also be found at: https://gis.utah.gov/data/location/address-data/.

Address Validation for Shipping Market Outlook

According to our latest research, the global Address Validation for Shipping market size reached USD 786.5 million in 2024, with a robust CAGR of 11.2% projected from 2025 to 2033. By the end of 2033, the market is forecasted to achieve a value of USD 2,030.4 million. This growth trajectory is primarily driven by the exponential rise in e-commerce transactions, the demand for efficient last-mile delivery, and the increasing adoption of digital transformation initiatives across logistics and retail sectors. The market is witnessing significant technological advancements in data validation and geocoding, further fueling expansion.

The primary growth factor for the Address Validation for Shipping market is the surging volume of e-commerce transactions worldwide. As online shopping becomes a dominant retail channel, businesses are under pressure to ensure accurate and timely deliveries. Address validation solutions play a critical role in reducing failed deliveries, minimizing returns due to incorrect addresses, and enhancing customer satisfaction. The integration of advanced algorithms, artificial intelligence, and machine learning into address validation software is enabling companies to provide real-time, automated verification of shipping addresses, thereby streamlining logistics operations and reducing operational costs. Additionally, the proliferation of omnichannel retailing and cross-border e-commerce is creating a greater need for robust address validation systems that can handle diverse address formats and languages.

Another significant driver is the increasing focus on operational efficiency and cost reduction within logistics and supply chain management. Companies are leveraging address validation solutions to optimize route planning, reduce fuel consumption, and lower the risk of lost or misrouted shipments. The integration of address validation with other supply chain management tools enables seamless data exchange and enhances visibility across the delivery process. With the growing emphasis on sustainability, businesses are also utilizing these solutions to decrease the environmental impact associated with failed deliveries and unnecessary transportation. Furthermore, regulatory compliance requirements, such as Know Your Customer (KYC) and anti-fraud mandates in sectors like banking and finance, are pushing organizations to adopt more sophisticated address validation tools.

Technological advancements are playing a pivotal role in shaping the Address Validation for Shipping market. The adoption of cloud-based platforms, APIs, and microservices architecture is making it easier for businesses to integrate address validation into their existing systems. Real-time address correction, geocoding, and mapping capabilities are now standard features, offering enhanced accuracy and speed. Vendors are investing in research and development to support multilingual address parsing, address standardization, and global coverage. The emergence of artificial intelligence-driven solutions is also enabling predictive validation, where potential address errors are flagged before they lead to delivery issues. These innovations are particularly beneficial for enterprises operating in multiple markets with complex address structures.

From a regional perspective, North America currently holds the largest share of the Address Validation for Shipping market, driven by the mature e-commerce sector, high adoption of automation technologies, and the presence of major logistics providers. Europe follows closely, with significant investments in digital infrastructure and regulatory mandates supporting address validation initiatives. The Asia Pacific region is expected to register the fastest growth during the forecast period, fueled by rapid urbanization, booming e-commerce, and increased demand for efficient logistics solutions in countries like China, India, and Japan. Latin America and the Middle East & Africa are also witnessing steady adoption, albeit at a slower pace, due to improving internet penetration and growing cross-border trade activities.

Component Analysis

The Component segment of the Address Validation for Shipping market is bifurcated into software and services. Address validation software constitutes the backbone of this market, offering functionalities such as address parsing, standardization, verification, and geocoding. The softwar

ABSTRACT OBJECTIVE Analyze the spatial distribution of classical dengue and severe dengue cases in the city of Rio de Janeiro. METHODS Exploratory study, considering cases of classical dengue and severe dengue with laboratory confirmation of the infection in the city of Rio de Janeiro during the years 2011/2012. The georeferencing technique was applied for the cases notified in the Notification Increase Information System in the period of 2011 and 2012. For this process, the fields “street” and “number” were used. The ArcGis10 program’s Geocoding tool’s automatic process was performed. The spatial analysis was done through the kernel density estimator. RESULTS Kernel density pointed out hotspots for classic dengue that did not coincide geographically with severe dengue and were in or near favelas. The kernel ratio did not show a notable change in the spatial distribution pattern observed in the kernel density analysis. The georeferencing process showed a loss of 41% of classic dengue registries and 17% of severe dengue registries due to the address in the Notification Increase Information System form. CONCLUSIONS The hotspots near the favelas suggest that the social vulnerability of these localities can be an influencing factor for the occurrence of this aggravation since there is a deficiency of the supply and access to essential goods and services for the population. To reduce this vulnerability, interventions must be related to macroeconomic policies.

Data Collection Methodology: USA

Data Collection Methods

The data in the Alternative Fueling Station Locator are gathered and verified through a variety of methods. The National Renewable Energy Laboratory (NREL) obtains information about new stations from trade media, Clean Cities coordinators, the Submit New Station form on the Station Locator website, and through collaborating with infrastructure equipment and fuel providers, original equipment manufacturers (OEMs), and industry groups.

Users submitting updates through the "Submit New Station" or "Report a Change" forms will receive an email confirmation of their submittal. NREL will verify station details before the station is added or updated in the Station Locator. The turnaround time for updates will depend on the completeness of the information provided, as well as the responsiveness of the station or point of contact.

NREL regularly compares its station data with those of other relevant trade organizations and websites. Differences in methodologies, data confirmation, and inclusion criteria may result in slight variations between NREL's database and those maintained by other organizations. NREL also collaborates with alternative fuel industry groups to identify discrepancies in data and develop data sharing processes and best practices. NREL and its data collection subcontractor are currently collaborating with natural gas, electric drive, biodiesel, ethanol, hydrogen, and propane industry groups to ensure best practices are being followed for identifying new stations and confirming station changes in the most-timely manner possible.

Station Update Schedule

Existing stations in the database are contacted at least once a year on an established schedule to verify they are still operational and providing the fuel specified. Based on an established data collection schedule, the database is updated on an ongoing basis. Stations that are no longer operational or no longer provide alternative fuel are removed from the database as they are identified.

Beginning in 2021, public, non-networked electric vehicle (EV) charging stations will be proactively verified every other year, with half of the EV charging stations verified each year. This adjustment is to accommodate the growing number of EV charging stations in the Station Locator. NREL will continue to make updates to any station record if changes are reported.

Mapping and Counting Methods

Each point on the map is counted as one station in the station count. A station appears as one point on the map, regardless of the number of fuel dispensers or electric vehicle supply equipment (EVSE) ports at that location. Station addresses are geocoded and mapped using an automatic geocoding application. The geocoding application returns the most accurate location based on the provided address. Station locations may also be provided by external sources (e.g., station operators) and/or verified in a geographic information system (GIS) tool. This information is considered highly accurate, and these coordinates override any information generated using the geocoding application.

Data Collection Methodology: Canada

Data Collection Methods

The data in the Alternative Fueling Station Locator are gathered and verified through a variety of methods. National Resources Canada (NRCan) obtains information about new stations from trade media, the Submit New Station form on the Station Locator website, and through collaborating with infrastructure equipment and fuel providers, original equipment manufacturers (OEMs), and industry groups.

Users submitting updates through the "Submit New Station" or "Report a Change" forms will receive an email confirmation of their submittal. NRCan will verify station details before the station is added or updated in the Station Locator. The turnaround time for updates will depend on the completeness of the information provided, as well as the responsiveness of the station or point of contact.

NRCan regularly compares its station data with those of other relevant trade organizations and websites. Differences in methodologies, data confirmation, and inclusion criteria may result in slight variations between NRCan's database and those maintained by other organizations. NRCan also collaborates with alternative fuel industry groups to identify discrepancies in data and develop data sharing processes and best practices. NRCan and its data collection subcontractor are currently collaborating with alternative fuel industry groups to ensure best practices are being followed for identifying new stations and confirming station changes in the most-timely manner possible.

Station Update Schedule

Existing stations in the database are contacted at least once a year on an established schedule to verify they are still operational and providing the fuel specified. Based on an established data c...

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information If you have questions about the underlying data stored here, please contact the Office of Energy Efficiency and Renewable Energy at eere.webmaster@ee.doe.gov. If you have questions or recommendations related to this metadata entry and extracted data, please contact the CAFE Data Management team at: climatecafe@bu.edu. "Data Collection Methods The data in the Alternative Fueling Station Locator are gathered and verified through a variety of methods. The National Renewable Energy Laboratory (NREL) obtains information about new stations from trade media, Clean Cities and Communities coalitions, the Submit New Station form on the Station Locator website, and through collaborating with infrastructure equipment and fuel providers, original equipment manufacturers (OEMs), and industry groups. Users submitting updates through the "Submit New Station" or "Report a Change" forms will receive an email confirmation of their submittal. NREL will verify station details before the station is added or updated in the Station Locator. The turnaround time for updates will depend on the completeness of the information provided, as well as the responsiveness of the station or point of contact. NREL regularly compares its station data with those of other relevant trade organizations and websites. Differences in methodologies, data confirmation, and inclusion criteria may result in slight variations between NREL's database and those maintained by other organizations. NREL also collaborates with alternative fuel industry groups to identify discrepancies in data and develop data sharing processes and best practices. NREL and its data collection subcontractor are currently collaborating with natural gas, electric drive, biodiesel, ethanol, hydrogen, and propane industry groups to ensure best practices are being followed for identifying new stations and confirming station changes in the most-timely manner possible. Station Update Schedule Most existing stations in the database are contacted at least once a year on an established schedule to verify they are still operational and providing the fuel specified. Based on an established data collection schedule, the database is updated on an ongoing basis. Stations that are no longer operational or no longer provide alternative fuel are removed from the database as they are identified. Public and private non-networked electric vehicle (EV) charging stations are proactively verified every other year, with half of the EV charging stations verified each year. Additionally, all private EV charging stations at multi-family housing are verified every other year. This difference in the update schedule for non-networked EV charging stations accommodates the growing number of EV charging stations in the Station Locator. NREL will continue to make updates to any station record if changes are reported. Mapping and Counting Methods Each point on the map is counted as one station in the station count. A station appears as one point on the map, regardless of the number of fuel dispensers or electric vehicle supply equipment (EVSE) ports at that location. Station addresses are geocoded and mapped using an automatic geocoding application. The geocoding application returns the most accurate location based on the provided address. Station locations may also be provided by external sources (e.g., station operators) and/or verified in a geographic information system (GIS) tool. This information is considered highly accurate, and these coordinates override any information generated using the geocoding application. Notes about Specific Station Types Private Stations The Station Locator defaults to searching only for public stations. To include private stations in the search, use the Station button on the "Advanced Filters" tab. Stations with an access listing of "Private - Fleet customers only" may allow other entities to fuel through a business-to-business arrangement. For more information, fleet customers should refer to the information listed in the details section for that station and contact the station directly. The Station Locator includes information on private fleet fueling stations (e.g., transit bus fueling facilities, other medium- and heavy-duty fueling and charging infrastructure), workplace charging stations, and multi-family housing charging stations. Note that information on these stations is not always published online or in the data download but may be tracked only in the backend Station Locator database. Information tracked only in the backend database may be provided by request to the webmaster listed in the "More Information" section below. Public Restricted Access Stations Stations that are reserved for patrons of a business, such as guests of a hotel, visitors of a museum, or customers of a retail store, are...

Welcome to the "Bangalore Chain Restaurants: Ratings, Reviews, Categories, and Locations" dataset! This comprehensive collection provides valuable insights into the dynamic restaurant landscape of Bangalore, India. Whether you're a data enthusiast, a foodie seeking the best dining spots, or a researcher investigating restaurant trends, this dataset is a valuable resource.

Dataset Overview:

This dataset contains information on various chain restaurants operating in Bangalore, showcasing a diverse array of food categories and sub-categories. The data includes key metrics such as ratings, review counts, as well as geographical data comprising latitude and longitude coordinates.

Data Collection Process:

Data Source Selection: Google Maps was chosen as the primary data source due to its comprehensive coverage of restaurants and its accessibility for users.

Web Scraping: Automated web scraping tools were employed to systematically navigate through Google Maps and extract data from multiple pages. The scraper was programmed to follow links, access individual restaurant pages, and extract specific details.

Data Extraction: Key data points, including restaurant names, ratings, review counts, food categories, sub-categories, latitude, and longitude, were extracted from each restaurant's page.

Data Cleaning: The extracted data was subjected to a rigorous cleaning process to handle missing values, remove duplicates, and ensure uniformity in formatting. Cleaning also involved standardizing categories and sub-categories for consistency.

Geocoding: Geocoding was performed on the restaurant addresses to derive latitude and longitude coordinates. This step facilitated precise geospatial analysis of the restaurants' locations.