A client needing to know how many restaurants can deliver to each populated square kilometre of the US.

Use Case

Delivery coverage mapping

Region

United States

Sources

DoorDash, Uber Eats

Number of sources

Deliverable Restaurants

Cell by Cell

Platform Coverage

State by State

Contact Sales

Key Takeaways

Actowiz Solutions is delivering one-time restaurant delivery availability data for a Norway-based delivery-technology company. For each populated 1 km × 1 km grid cell in the United States, we record which restaurants on DoorDash and Uber Eats can deliver there, producing cell-level counts plus a deduplicated restaurant list.

What did the client need?

The client wanted a national view of delivery supply: for any populated location in the US, how many restaurants can actually deliver there, on each platform? Store lists alone do not answer this, because delivery reach depends on the customer's address.

The project is a one-time snapshot. The inquiry arrived in August 2026 and work started on 22 September 2026. Delivery of the full US dataset is in progress. [Confirm grid unit with client – 1 km vs 1 mile]

What made this hard?

Address-dependent results: both platforms show restaurants based on the delivery address, so every cell needs its own query point.
National scale: a 1 km grid across all populated US areas creates a very large number of cells. [Add populated grid cell count from delivery team]
Heavy overlap: one restaurant usually delivers to many neighbouring cells and may list on both platforms, so deduplication is essential.
Shifting availability: store hours and courier supply change during the day, so collection timing affects counts.
Rural edge cases: low-density cells may return few or no restaurants, which must be recorded as true zeros rather than errors.

How did Actowiz solve it?

We combined a population-based grid with address-level storefront queries and a cross-cell dedupe layer.

Approach

Populated grid: we generate 1 km cells from public US population data, such as 2020 Census centers of population and TIGER/Line block geography, and keep cells with residents. [Verify source used by delivery team]
Centroid queries: each cell centroid is converted to a delivery address and used to query the public storefront of each platform, using our DoorDash and Uber Eats collection pipelines.
Restaurant dedupe: restaurants are matched across cells and platforms by platform ID, name and address, so each venue appears once in the master list.
Controlled timing: queries run in consistent time windows to keep counts comparable between regions. [Confirm time window from delivery team]
Validation: zero-count cells, sudden count jumps and duplicate IDs are flagged for review.

Data Attributes / Fields

Field	Description
grid_cell_id / cell_lat / cell_lon	Cell identifier and centroid coordinates
state / city_county	State and city or county of the cell
platform	DoorDash or Uber Eats
deliverable_restaurant_count	Restaurants able to deliver to the cell centroid
restaurant_id / restaurant_name	Platform ID and display name
cuisine	Cuisine tags shown on the storefront
delivery_fee_shown	Delivery fee displayed at query time
eta_minutes_shown	Estimated delivery time displayed
scrape_timestamp	Date and time of the query

What were the results?

Results are still being delivered, so outcomes are described qualitatively.

Cell-level coverage view: deliverable restaurant counts are organised by populated geographic cell and platform, providing a consistent view of restaurant availability across the target area.
Platform comparison: side-by-side coverage data for DoorDash and Uber Eats enables direct comparison of restaurant availability within the same geographic cells.
Clean restaurant master: a deduplicated restaurant list links each restaurant to every geographic cell it serves, supporting accurate coverage analysis.
Coverage gaps surfaced: low- and zero-coverage cells are identified to help the client evaluate potential gaps in delivery availability and guide market analysis.

Project at a Glance

Metric	Value
Use Case	Delivery coverage mapping for a delivery-technology company
Region	United States (populated areas)
Sources	DoorDash, Uber Eats
Metrics	Deliverable restaurant count per cell, fee and ETA shown, cuisine
Reconciliation / Method	Population grid, centroid address queries, cross-cell and cross-platform dedupe
Cadence	One-time snapshot (kickoff 22 Sep 2026)
Output	[Confirm delivery format, e.g. CSV / Parquet / GeoJSON]

Client Feedback

“Actowiz Solutions is helping us build a comprehensive view of restaurant delivery coverage across the United States using DoorDash and Uber Eats data. The structured, grid-based dataset will help our team analyze restaurant availability across populated areas, identify delivery coverage gaps, and better understand food delivery market penetration across different locations.”

— Data Analytics Manager, Food Delivery Technology Company

Frequently Asked Questions

Q: What is restaurant delivery availability data?

A: It shows which restaurants can deliver to a given location on a delivery platform, usually summarised as a count per area or grid cell.

Q: Why use a 1 km grid instead of ZIP codes?

A: ZIP codes vary widely in size. Equal-sized cells make coverage comparable between dense cities and suburbs.

Q: Can the data be refreshed regularly?

A: Yes. The same grid can be re-run monthly or quarterly to track coverage changes over time.

Q: Can it include menus and prices too?

A: Yes. Coverage data can be combined with menu extraction, as in our Uber Eats and DoorDash data extraction case study.

Q: Is this collection compliant?

A: We use only publicly displayed storefront data, collect no customer personal data, respect site terms and recommend a legal review per use case.

Need US Delivery Coverage Data?

Read our USA food delivery dataset guide or explore food delivery market intelligence. Visit actowizsolutions.com to discuss your data requirement.

Hear It Directly from Our Clients

Watch how businesses like yours are using Actowiz data to drive growth.

▶

1 min

★★★★★

"Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing!"

Thomas Galido

Co-Founder / Head of Product at Upright Data Inc.

▶

2 min

★★★★★

"Actowiz delivered impeccable results for our company. Their team ensured data accuracy and on-time delivery. The competitive intelligence completely transformed our pricing strategy."

Iulen Ibanez

CEO / Datacy.es

▶

1:30

★★★★★

"What impressed me most was the speed — we went from requirement to production data in under 48 hours. The API integration was seamless and the support team is always responsive."

Febbin Chacko

-Fin, Small Business Owner