Note: SafeGraph does not offer mobility or foot traffic data.
Data privacy for location data is fundamental to building trust in geospatial intelligence. As location data increasingly informs decisions across enterprises, research institutions, and public organizations, transparency around how that data is sourced, validated, and protected becomes essential.
Safe Graph’s goal is to be a source of truth for data on physical places. To support this goal, we focus on curating accurate, precise, and up-to-date geospatial datasets that power location analytics for large corporations, small businesses, and academic institutions.
We also recognize that truth in a rapidly changing physical world is aspirational. While we continuously strive for the highest possible accuracy, we are equally committed to being transparent about where our data comes from and how it is evaluated. This commitment includes free access to our data sourcing methodologies, schemas, fill rates, and known limitations.
From the beginning, SafeGraph has been committed to data privacy for location data and to providing access to high-quality places data without compromising consumer trust. Our focus has always been on data tied to latitude and longitude coordinates, never to individual people. We are equally transparent about the limitations of our datasets because trust is built not only through accuracy, but through openness.
Understanding physical locations also requires understanding how people interact with them. To support location intelligence, use cases while maintaining strong data ethics, we partner with trusted data providers to enrich our places data with anonymized foot traffic data and aggregated transaction signals. All such data is privacy-safe, aggregated, and designed to prevent individual identification.
Our earliest datasets consisted of aggregated and anonymized mobile location pings sourced from applications where consumers explicitly opted in to share their location. At the time, this data included latitude, longitude, and timestamp information, but lacked context about what existed at those locations. Customer feedback made this limitation clear. Without knowing what was present at a location, the data was difficult to interpret or use effectively.
To address this gap, we began sourcing points of interest (POI) data to add meaningful context to mobility signals. However, we quickly discovered that high-quality POI data was difficult to obtain. Existing providers updated their data infrequently, relied on inaccurate geocoding, and lacked the level of detail required for reliable location analytics.
As a result, we made a strategic decision to build our own POI database. In 2018, SafeGraph shifted its focus from device-based location data to place-based intelligence. This shift strengthened our commitment to POI data sourcing methodology, transparency, and data quality. Even as we built a more accurate and comprehensive database than what was available in the market, we recognized that complete accuracy is impossible in a constantly changing physical world.
To maintain transparency, we continue to publish known issues, data limitations, and monthly fixes. This approach allows customers to understand not only the strengths of our data, but also its boundaries. You can learn more about how we structure and maintain place intelligence in our POI Data documentation.
Organizations often use location intelligence for mission-critical analytics, which means they need to trust both the data itself and the company that produces it. This is especially true when working with sensitive datasets such as anonymized foot traffic data and other privacy-aware location signals.
When there is uncertainty around data quality or POI data sourcing methodology, the analytics built on top of that data are immediately called into question. Whether insights are used to make important business decisions, advise clients, or inform consumers, the outcome must be trustworthy to be valuable. Transparency around what the data represents, how it is sourced, and where its limitations exist forms the foundation of that trust.
The same principle applies to data providers. If a company is not trusted, neither is the product or service it delivers. Transparency about bugs is one part of building trust, but so is openness around how data is collected, anonymized, and governed. This is particularly important in data ethics within location intelligence, where responsible handling and clear documentation directly influence credibility.
Industry research and policy guidance consistently emphasize that transparency and anonymization are critical safeguards in the use of location data, particularly as geospatial insights play a growing role in enterprise and public-sector decision-making.
For SafeGraph, transparency is especially important because our data often serves as one component within a larger solution. If the integrity of that data is questioned, the reliability of the end solution is also at risk. By maintaining openness around sourcing practices, data quality, and known limitations, we work to protect both the integrity of our datasets and the outcomes our customers depend on.
SafeGraph does not hide anything about its data. We publish our data schema publicly, along with bug fixes and release notes, as part of an open data schema for geospatial intelligence. Our datasets are refreshed monthly to reflect a dynamically changing world. While we strive to create the most accurate places data possible, we recognize that no dataset is ever perfect. We actively encourage user feedback and make it easy to report errors so they can be addressed quickly.
SafeGraph also makes data access straightforward. We provide data free to academics for use in research and education and remain transparent about what data we offer and how it is built. Our goal is to ensure that high-quality location data is accesible to those who need it.
The integrity of our company and data curation practices is a core value across SafeGraph. In location intelligence, privacy is foundational to transparency. That is why we are open about what data we build, how it is built, and the safeguards we use to protect consumer privacy.
Other companies may violate privacy regulations, but what differentiates SafeGraph is that we don't offer any consumer data. Because our focus is entirely on places, we never collaborate with individual-level consumer data. Instead, we curate aggregated and anonymized foot traffic data to provide insights into visit volume and frequency at specific locations.
Industry research and policy guidance consistently emphasize that transparency and anonymization are critical safeguards in the ethical use of location data, particularly as geospatial insights play an increasing role in enterprise and public-sector decision-making.
Across our datasets, we apply a consumer protection methodology to ensure that insights reflect trends at locations rather than individual behaviour, reinforcing trust and responsible data use.
Part of being transparent is clearly defining the data we provide. SafeGraph offers data about physical places. Our POI database (Point of Interest) includes attributes such as latitude and longitude coordinates, open and close dates, and NAICS codes. Geometry data defines structural boundaries and building relationships to support accurate proximity analysis and geofencing. We also offer SafeGraph Spend, which provides anonymized and aggregated debit and credit card transaction data at the business level.
SafeGraph creates its datasets using a combination of machine learning, web crawling, and licensed third-party data. Places and Geometry datasets are built from open store locators, publicly available APIs, and licensed sources, supplemented by proprietary machine learning models to infer additional attributes and spatial boundaries.
SafeGraph Spend is created using licensed third-party transaction data that is aggregated and anonymized at the store level. The purpose of this dataset is not to analyse individual spending behaviour, but to understand how aggregated transactions relate to physical locations, regions, and categories of places.
You can learn more about our full data sourcing process here.
SafeGraph does not provide an SDK or consumer-facing software available in app stores.
One of SafeGraph’s core values is ensuring that data is not hoarded by a small number of organizations. We believe data should be accessible to those who need it to support research, innovation, and informed decision-making. This commitment to openness has been central to SafeGraph since before our product strategy shift in 2018.
We aim to foster an environment where data scientists, academics, and businesses can collaborate using location data to generate meaningful insights.
SafeGraph recognizes that our data is often one component within larger analytical systems. To protect the integrity of those systems and the decisions built on them, our priority is to be a trusted and transparent data partner. By remaining open about our practices, limitations, and safeguards, we work to increase the usability, accessibility, and reliability of geospatial data for critical problem-solving.
If you’d like to learn more about SafeGraph data and our approach to transparent, privacy-safe location intelligence, explore our free datasets or schedule a demo with one of our experts.
SafeGraph builds its datasets using a combination of publicly available sources, licensed third-party data, web crawling, and proprietary machine learning. Places and Geometry data are sourced from open store locators, public APIs, and licensed datasets, with additional attributes inferred using internal models.
SafeGraph does not collect or sell individual-level consumer data, thereby protecting consumer privacy.
SafeGraph focuses exclusively on physical places rather than people. It differentiates itself through transparent data sourcing, publicly available schemas, published bug fixes, frequent updates, and a privacy-first approach to location intelligence.
SafeGraph refreshes its datasets monthly to reflect real-world changes. Updates include new locations, closures, corrections, and known issues, all documented through public release notes to maintain transparency.
SafeGraph provides free data access for academic research and education. Its open data schema and public documentation allow users to understand how the data is structured and sourced, making it easier to evaluate and audit for specific analytical use cases.
