Geospatial boundaries are polygons representing the (sometimes disputed) extents of national borders and their sub-regions, postal delivery areas, statistical distributions of people and their demographics, or local designations of neighborhoods and places. These areas are constantly changing around the world, with new boundaries being created, shifting, or being removed entirely. When boundaries in the real world change, the maps to understand the people, places, and things become inaccurate or misleading without corresponding changes in the data that people rely upon.

Boundaries quality

The output of analytics and the decisions made upon it are only as good as the data that is being used.  Geospatial data quality, and data quality in general, can be thought of in terms of accuracy, validity, currency, and completeness, and encompasses not only the geometry of a record, but the metadata associated with it.

Data quality as a user-facing concept can be summed up in a single sentence — the ability to make decisions based upon the data with confidence.

Accuracy

Accurate data matches the truth on the ground, or what exists in the real world. An example would be a boundary line that perfectly follows a set of surveyed corners or the centerline of a river that marks the border between states. Negative examples would be a county line drawn such that it crosses into an adjacent state or a district that has been assigned the wrong name. Often we live in a world of quasi-subjective reality, where there may be several “correct” interpretations of a given area: districts that are known by a local name as well as an official one, neighborhoods with no canonically defined boundary, national borders that are disputed by the adjacent parties, etc.

Accuracy is a difficult metric to measure because ground truth is often hard to ascertain. Care should be taken when choosing a comparative authority for establishing ground truth, and truly canonical sources should be chosen whenever possible.

Validity

For geospatial data, validity is often considered in terms of its underlying geometry, specifically whether the data is topologically correct, meaning there are no gaps, overlaps, or incorrect connections between features. Gaps and overlaps create obvious issues with geospatial analysis, allowing comparative features to “fall through the cracks” between regions or be double-counted when areas are incorrectly coincident.

Validity also applies to metadata. Names, codes, and other attributes should be correctly formatted and cased, which may include regional variations. Postcodes are formatted differently depending on the country, and dropping or adding hyphens, spaces, or capitalization could change whether they refer to the correct region or be recognized properly.

Currency

Currency in this case does not refer to money, but rather the temporal element of data quality — how up-to-date the data is today. The data that people use, whether addresses, POIs, boundaries, or places, is discrete and finite in nature. Every place on Earth is at some point established and likely at some point will no longer exist or be recognized by the people who once knew it. New houses are built and old houses are torn down, cities are constantly expanding or changing with new subdivisions, lots, streets, and numbering systems, and around 137,000 new businesses are started each day across the globe.

In relation to geospatial boundary data, the Mapbox team looks for whether the most recent changes due to redistricting, geopolitical shifts, or administrative authority have been applied. This again requires comparison to ground truth, and when looking at the data on a global scale, is quite a massive undertaking.

Completeness

Completeness is the comparison between the data on hand vs the total body of data that should exist. This applies on both a regional level (is postal data available in X country) and within a given region (are all postcode areas completely filled within X country or only a subset). Some boundaries are more obscure than others, and some are simply not available in a format that can be used for mapping.  Completeness is always relative to what is needed in order to answer a question or solve a problem.

This includes more facets than just the existence of a record. Completeness is not just about whether a record exists but also about the quality of its attributes. Records should be free of missing, incomplete, or low-quality properties and may require remedial actions to correct them.

Key aspects of Mapbox Boundaries Data

Global coverage and data freshness 

With over 5 million boundaries covering political, statistical, state, county, city, and zip code areas worldwide, Mapbox boundaries data is expertly curated for each country. To stay current with geographic, administrative, and political changes — like redrawn district lines or updated zip codes — boundaries data is regularly refreshed, typically on a quarterly or annual schedule. Mapbox recently released Boundaries 4.3 and Boundaries 4.4, ensuring data accuracy and alignment with the latest developments.

Expertly curated for accuracy 

Our boundaries data features edge-matched layers forming a seamless, topologically correct hierarchical structure. Backed by meticulous research and expert curation, boundary lines are manually checked prior to release to ensure consistency across layers. Our geospatial experts enrich each dataset with insights from our broader data ecosystem, such as customized postal layers derived from our geocoding address data — often offering more precise postal boundary information than even official postal services.

Flexible data formats 

Mapbox Boundaries data is available in several formats to suit different use cases:

• Vector Tiles
  
  • Ideal for visual maps
  • High-performance, easy to implement
  • Enables simple interactivity and data joins
• Flat Files (New)
  
  • Perfect for geospatial analysis
  • High-throughput geocoding and data combination
  • Supports advanced operations like intersection and derivation

Curious about Mapbox Boundaries flat file format? Watch our short demo to learn more:

Business benefit of using Mapbox Boundaries

‍“Companies using data-driven decision-making are 20% more likely to exceed performance goals. Specifically, when location data is incorporated, businesses saw a 15% improvement in sales planning efficiency and a 12% increase in forecast accuracy.”  - Dana Therrien, VP of the Sales Performance Management & Revenue Advisory Practice at Anaplan

Anaplan, a cloud-based planning and performance management platform, is a Business Intelligence (BI) platform that uses Mapbox Boundaries to help businesses make better, faster decisions across functions like finance, sales, supply chain, HR, and marketing. As a keynote speaker at BUILD with Mapbox 2024, Anaplan’s VP of Sales Performance Management & Revenue Advisory Practice, Dana Therrien, shared reflections on how Anaplan uses Mapbox Boundaries to build smarter BI capabilities for their customers.

“Mapbox Boundaries grows alongside our clients as their needs evolve. Whether they’re adding geographies or different boundary types, we ensure they have the data needed to support their business goals.” - Dana Therrien, VP of the Sales Performance Management & Revenue Advisory Practice at Anaplan

Building together for innovation

Mapbox has long been a trusted partner for our customers, constantly listening closely to customer needs, providing support, and sharing expertise to help customers succeed. 

“Having a partner who collaborates with us not only on technical implementation but also in redefining our product strategy, the Mapbox team has been instrumental in helping us push the boundaries of what is possible in location intelligence, and they've been a fantastic partner in driving innovation.” - Dana Therrien, VP of the Sales Performance Management & Revenue Advisory Practice at Anaplan 

Get started with Mapbox Boundaries

Explore what is included within the Mapbox Boundaries datasets using the Mapbox Boundaries Explorer or request free sample data today.