Table of Contents

    What is map data?

    Map data is a part of geospatial data. It data includes geographic information such as political boundaries, weather patterns, infrastructure information, real-time traffic, and points of interest. Map data is typically in an interactive visual format. 

    Individuals, businesses, and governmental organizations use map data for geocoding and accessing a wide variety of information in real time.

    Where does the data come from?

    Map data comes from data mapping tools, satellite images, and public records. It is also augmented in real-time by users, when they report traffic issues or update store opening times. When used for a specific purpose, the users enrich it with relevant information. For example, logistics companies add their fleet locations for tracking the shipments (this is data integration of different datasets).

    What types of attributes should I expect?

    The typical attributes of map data are:

    • Topographical information such as isochrones
    • Current and historical political boundaries
    • Infrastructure details
    • Points of interest such as monuments and restaurants
    • Weather information along with alerts if any
    • Real-time traffic
    • Any current important information and metadata that needs to be made available on maps

    Map data is an interactive image and can additionally have data points in columns.   

    How should I test the quality of the data?

    Testing map data with a lot of freely available data can confirm its accuracy. For maps providing real-time updates, the critical test is in ensuring that the streaming is uninterrupted. 

    For specialized map data such as tracking a shipment, you may need to ascertain the quality of the service provider.

    Ensure that the data conforms to the relevant standards. For example, the FGDC (Federal Geographic Data Committee) standards and guidelines are applicable for geological data.

    To test the quality of the data:

    • Validate the data accuracy by comparing it with other data sources.
    • Confirm that the data is continuously updated in real time.
    • Ensure that the data meets the relevant standards.

    Who uses map data?

    Map data is used in a wide range of applications. Government agencies use it to examine and upgrade infrastructure. They also use GIS, crime data, point of interest, and other information to assess the local economy and take measures to boost it.  Businesses use map data to locate possible opportunities for new stores or new business ventures and can use google map apis as part of their workflows and operations. Map data is often used for data visualization and to enrich other datasets. Combining the right data provides a competitive edge. 

    Individuals use this data for planning their daily commute,  longer trips, visits to monuments and other places of interest, shopping, eating out, and other excursions. They also use real-time traffic information and weather conditions to make their travel comfortable.

    What are the common challenges when buying the data?

    Data accuracy is not a major challenge for map data, as it can be easily confirmed using data freely available from other sources. Data timeliness is a challenge, as several attributes of map data get updated in real-time. Obsolete data or even a day old data can affect the result of the analysis and the decisions it drives. Data completeness for the intended use and consistency across diverse sources are other common challenges when buying map data.

    • Data timeliness: Map data is constantly updated for real-time information, such as weather, traffic, or shops open for business. It also gets periodically updated as point of interest or holiday details change. Vendor data updated as required is a necessity to ensure the accuracy of the actionable insights. 
    • Data completeness: Map data can have different attributes for different requirements. The completeness of map data depends on the use cases. For example, travel planning needs complete weather information, including forecasts for the travel period. Fleet tracking requires accurate locations of the fleet vehicles at any given time. Any missing information can present inaccurate status that can lead to wrong decisions.
    • Data consistency: There are many data layers collected from various sources, and they may present inconsistencies or overlaps. Good data management is crucial. For trusted business decisions and healthy business operations, data consistency across all the sources is important. For example, if a store location has different coordinates in different sources, visitors may not choose that store.

    What are similar data types?

    Map data is similar to location data, GPS data, point of interest data, satellite data, and other categories of geospatial data.

    You can find a variety of examples of geospatial data in the Explorium Data Gallery.

    Sign up for Explorium’s free trial to access the data available on the platform.               


    What are the most common use cases?

    The use case depends on who is using it. For example, individuals use it for travel planning, while the government uses it for infrastructure assessment and revamping.   

    Some of the common use cases include store location planning for retail businesses, fleet management for transport companies, rent price prediction for real estate companies, and traffic management.  It is commonly used to enrich other datasets to build predictive models. 

    • Store Location Planning: The most important factor of store performance is its location. Retailers leverage a variety of data categories to decide on the store location. Good access, proximity to points of interest, and similar information can be derived using map data. A good model for store location planning uses geospatial data such as map data, demographics, and other data categories to arrive at the potential locations.
    • Fleet Management: Commercial transport and shipping companies manage their fleet of vehicles with the modern technology of IoT (Internet of Things). Using the IoT data and the location data of all vehicles, they can estimate the delivery time, adjust the schedule, and provide assistance in an emergency. Fleet management assures optimized use of a fleet of cars, trucks, or ships to get a better ROI. Police and military also use fleet management to improve their response while ensuring safety.
    • Rent Price Prediction: A good model of rent price prediction uses several factors to predict a range of potential prices. Real estate agents use these prices to guide their clients in renting out their houses or apartments. Map data provides valuable information about the location, transport facilities, and points of interest nearby to optimize the model. 
    • Traffic Management: Especially in cities, efficient traffic management helps prevent congestion and reduce accidents. Traffic management systems rely on several data categories, such as map data, to locate the bottlenecks and suggest alternate arrangements. Map data is also used to provide traffic information to the users, recommend the availability of public transportation, and alert them of potential delays on congested routes.   

    Which industries commonly use this type of data?

    Most Industries use map data for a variety of use cases. Those industries include tourism, sports, entertainment, travel, hospitality, leisure, map, CPG, healthcare, financial service providers, insurance providers, banking, manufacturing, and hi-tech among others.   

    How can you judge the quality of your vendors?

    Vendors collect map data from a number of sources and deliver the aggregated, cleansed data. A good way of judging the vendor capabilities, including data quality, committed delivery, and custom datasets, is to use the information available on their websites. After shortlisting vendors based on the website information, interaction with the vendor reps helps assess if the vendor is suitable for your requirements.

    • Customer reviews and ratings: The majority of vendor websites list customer reviews describing the experience of customers. These reviews provide valuable information about the reliability and quality of the vendors. They may also mention the industry vertical, size of the project, and the categories of datasets used, which you can leverage for shortlisting. Customer ratings typically indicate the overall capabilities.
    • Customer testimonials and case studies: Customer testimonials highlight the vendor’s strengths, which help assess how they will impact your project. Case studies go into much more detail about the industry, project size, challenges, solutions, ease of integration, and the datasets used. Case studies also demonstrate the vendor’s capacity to deliver custom datasets and the level of engagement in the projects.
    • Demos: Vendor websites sometimes provide demos, which enable you to evaluate the data quality and ease of integration. Some vendors deliver a recorded demo on request or arrange a live demo for you. Demos support watching data in action to understand how it powers the analysis and actionable insights. With demos, it is easier to shortlist vendors and move to the final selection stage.

    Interacting with vendor reps: At the final selection stage, direct discussion with the vendor reps is the most reliable way to decide the right vendors for your project. You can explain your requirements and assess the vendors’ knowledgeability. You can also review the types of datasets available and the range of attributes. It is an opportunity to judge the vendor interest and commitment, along with the possibility of building a long-term vendor relationship.