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GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

                )

            [continent] => Array
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                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
                        (
                            [de] => Nordamerika
                            [en] => North America
                            [es] => Norteamérica
                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
                        )

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                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [location] => Array
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                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
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            [postal] => Array
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                    [code] => 43215
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                    [geoname_id] => 6252001
                    [iso_code] => US
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                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

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                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

                        )

                )

            [traits] => Array
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                )

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                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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                            [ru] => США
                            [zh-CN] => 美国
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                    [iso_code] => US
                    [names] => Array
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                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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    [traits:protected] => GeoIp2\Record\Traits Object
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                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
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                    [11] => isPublicProxy
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                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
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                    [19] => staticIpScore
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                )

        )

    [city:protected] => GeoIp2\Record\City Object
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                    [geoname_id] => 4509177
                    [names] => Array
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                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

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            [validAttributes:protected] => Array
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        )

    [location:protected] => GeoIp2\Record\Location Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

            [validAttributes:protected] => Array
                (
                    [0] => averageIncome
                    [1] => accuracyRadius
                    [2] => latitude
                    [3] => longitude
                    [4] => metroCode
                    [5] => populationDensity
                    [6] => postalCode
                    [7] => postalConfidence
                    [8] => timeZone
                )

        )

    [postal:protected] => GeoIp2\Record\Postal Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [code] => 43215
                )

            [validAttributes:protected] => Array
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                    [0] => code
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                )

        )

    [subdivisions:protected] => Array
        (
            [0] => GeoIp2\Record\Subdivision Object
                (
                    [record:GeoIp2\Record\AbstractRecord:private] => Array
                        (
                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

                        )

                    [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                            [0] => en
                        )

                    [validAttributes:protected] => Array
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                )

        )

)

 country : United States
 city : Columbus
US
Array
(
    [as_domain] => amazon.com
    [as_name] => Amazon.com, Inc.
    [asn] => AS16509
    [continent] => North America
    [continent_code] => NA
    [country] => United States
    [country_code] => US
)

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  • Home
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  • Murphy USA Location Data Scraping - Fuel Retail Density Patterns

Murphy USA Location Data Scraping Reveals Fuel Retail Density Patterns Across Major US Cities

Discover how Murphy USA Location Data Scraping uncovers fuel retail density patterns across major US cities, enabling strategic insights for market expansion and operations.

Download Case Studies

GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

                )

            [continent] => Array
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                    [names] => Array
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                            [es] => Norteamérica
                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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            [country] => Array
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                    [iso_code] => US
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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                            [zh-CN] => 美国
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                                    [fr] => Ohio
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                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

                        )

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            [traits] => Array
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                    [ip_address] => 216.73.216.213
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                )

        )

    [continent:protected] => GeoIp2\Record\Continent Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [names] => Array
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                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
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                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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                            [ru] => США
                            [zh-CN] => 美国
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                )

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            [validAttributes:protected] => Array
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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            [validAttributes:protected] => Array
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                    [0] => queriesRemaining
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        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [iso_code] => US
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                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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                    [names] => Array
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                            [de] => Columbus
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                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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                    [latitude] => 39.9625
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                    [time_zone] => America/New_York
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            [validAttributes:protected] => Array
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                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
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                                )

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                    [validAttributes:protected] => Array
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)

 country : United States
 city : Columbus
US
Array
(
    [as_domain] => amazon.com
    [as_name] => Amazon.com, Inc.
    [asn] => AS16509
    [continent] => North America
    [continent_code] => NA
    [country] => United States
    [country_code] => US
)
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Navratri Mega Sale Price Tracking

Introduction

Understanding fuel retail distribution is critical for companies aiming to optimize operations and expand strategically. Using Murphy USA Location Data Scraping, Actowiz Solutions collected detailed information on Murphy USA outlets across major US cities, enabling comprehensive insights into fuel retail density and market coverage. The project leveraged Fuel Retail Mapping Using Web Scraping to visualize location clusters, identify underserved areas, and support data-driven expansion decisions. By analyzing real-time location data, businesses can pinpoint high-potential regions, optimize supply chains, and enhance competitive positioning. This case study highlights how advanced scraping and mapping techniques empower fuel retailers to make informed, strategic choices in a fast-moving market.

About the Client

The client is a leading fuel retail company in the United States, operating hundreds of gas stations across multiple states. With a focus on convenience, customer service, and operational efficiency, the company serves millions of consumers annually, including both individual drivers and commercial fleets. Targeting high-traffic urban and suburban locations, the client seeks to expand its footprint strategically while maintaining optimal service levels. Actowiz Solutions helped the client achieve Real-Time Mapping of Retail Locations, providing actionable insights into store density, geographic coverage, and market opportunities. This enabled smarter decision-making for site selection, competitive benchmarking, and expansion planning.

Challenges & Objectives

Challenges
  • Limited Visibility of Fuel Retail Density: Murphy USA needed a clear understanding of how their gas stations were distributed across major US cities. Without accurate mapping of outlets, it was difficult to identify high-density regions versus underserved areas. This lack of visibility made it challenging to prioritize expansion, optimize resource allocation, and assess market saturation.
  • Manual Data Collection Inefficiencies: Tracking hundreds of locations manually from various sources consumed significant time and resources. Errors in data entry and inconsistent information formats slowed decision-making. The client required a streamlined, automated solution that could gather, clean, and standardize data quickly.
  • Competitor Location Blind Spots: Understanding the competitive landscape is crucial for strategic growth. Murphy USA lacked insights into competitor gas station locations, making it difficult to benchmark performance or identify opportunities in underserved regions. Without competitor data, pricing and promotional strategies were less effective.
  • Delayed Decision-Making: Because of outdated or incomplete data, strategic decisions were delayed. Expansion, marketing campaigns, and operational planning relied on incomplete insights, risking missed opportunities in high-potential markets.
Objectives
  • Implement Geolocation Mapping: The goal was to create a detailed map of Murphy USA stations and competitors, providing clear visualization of fuel retail density. This helps pinpoint clusters, gaps, and potential expansion areas.
  • Optimize Expansion Strategy: By analyzing location density and market demand, the client aimed to identify high-potential areas for new outlets and avoid oversaturated regions.
  • Enable Real-Time Monitoring: The project aimed to deliver continuous updates on station openings, closures, and competitor movements, ensuring timely and informed decisions.
  • Support Data-Driven Decisions: The ultimate objective was to provide actionable insights through dashboards and reports, enabling strategic planning, resource allocation, and competitive advantage.

Our Strategic Approach

Comprehensive Data Collection

To deliver precise insights, Actowiz Solutions employed advanced web scraping techniques to perform US Fuel Station Map Analysis from multiple online sources, including government databases, mapping platforms, and competitor websites. The collected data included detailed attributes such as store addresses, operational hours, geocoordinates, and amenities. By standardizing this information, we ensured accuracy and completeness across all urban, suburban, and rural locations. This allowed us to capture not only the client’s outlets but also competitor stations, enabling a holistic understanding of the market landscape. Automated pipelines were set up to continuously refresh the dataset, ensuring that any new openings, closures, or relocations were reflected in real time.

Visualization & Analytical Layer

Once the data was curated, it was integrated into GIS platforms for in-depth spatial analysis. By overlaying demographic data, traffic density, and competitor locations with Murphy USA Gas Station Locations, we generated heatmaps and cluster analyses to identify areas with high growth potential. This visualization allowed the client to assess underserved regions, optimize site selection for expansion, and refine supply chain routes. The analytical layer also provided actionable insights for marketing campaigns, regional performance monitoring, and long-term strategic planning, enabling data-driven decision-making at every level.

Technical Roadblocks

  • Data Standardization: Station information came from heterogeneous sources, requiring extensive cleaning and normalization to standardize addresses, geocoordinates, and attributes.
  • Real-Time Updates: Web sources frequently updated, necessitating a system capable of continuous Location Intelligence monitoring without missing changes or duplicating records.
  • Geographic Accuracy: Ensuring precise geolocation mapping was critical. We implemented verification using mapping APIs and cross-referencing multiple sources to reduce errors.

Our Solutions

Actowiz Solutions implemented a full-scale location data scraping and mapping system. By leveraging Murphy USA Location Data Scraping, we collected accurate station information, validated addresses, and converted it into geospatial datasets. Heatmaps and cluster analyses provided actionable insights for expansion planning and market penetration. Automated pipelines ensured ongoing updates, while dashboards allowed easy visualization of station density and competitor distribution. Our solution enabled the client to quickly identify underserved regions, prioritize high-traffic locations, and optimize logistics and resource allocation.

Results & Key Metrics

  • Coverage Accuracy: 100% of Murphy USA stations mapped across 40+ US states.
  • Expansion Insights: Identified 120 underserved high-potential areas for new outlets.
  • Operational Efficiency: Reduced manual location tracking by 85%, saving ~200 hours/month.
  • Competitor Benchmarking: Full visualization of competitors’ station clusters enabled strategic pricing and promotions.
  • Decision Support: Dashboards allowed real-time monitoring of station density and geospatial trends for improved planning.

Client Feedback

"Actowiz Solutions transformed our approach to market expansion. The US Fuel Station Map Analysis provided accurate, real-time insights that helped us identify high-potential locations efficiently. Their expertise and technology are unmatched."

— Director of Strategy

Why Partner with Actowiz Solutions?

  • Expertise: Proven experience in Location Intelligence for retail and fuel sectors.
  • Technology: Advanced web scraping, geolocation mapping, and GIS integration for actionable insights.
  • Support: End-to-end services from data extraction to dashboard visualization, ensuring operational continuity.
  • Scalability: Automated pipelines accommodate future expansions and dynamic updates of location data.
  • Competitive Edge: Timely insights enable faster, more informed strategic decisions than competitors relying on manual data collection.

Conclusion

Through Murphy USA Location Data Scraping, Actowiz Solutions delivered precise fuel retail density insights, enabling smarter expansion and operational decisions. Using Web scraping API, Custom Datasets, and an instant data scraper, the client now accesses real-time location intelligence to optimize site selection, benchmark competitors, and increase ROI on new outlets.

FAQs

1. What is Murphy USA Location Data Scraping?

It’s the process of collecting accurate geolocation and operational data of Murphy USA stations using automated scraping tools for analysis and mapping.

2. How does this help fuel retailers?

Scraping station locations enables retailers to map density, analyze competitors, identify underserved areas, and make informed expansion decisions.

3. Can the data be updated in real-time?

Yes, our pipelines ensure continuous monitoring and updates for all stations, including new openings or closures.

4. What tools are used in this project?

We employ advanced web scraping APIs, GIS mapping platforms, and data visualization dashboards to generate actionable insights.

5. Can this methodology be applied to other retail sectors?

Absolutely. The approach is scalable and can track location data for convenience stores, grocery chains, quick commerce, or any geographically distributed retail business.

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