Cities Release Datasets to Developers

slfisher 1 309 Views

An increasing number of cities in the United States and Canada are releasing data to developers to use in applications.

While regions have released geographic information system (GIS) data for some time, such data required complicated software to use. Cities are now releasing all kinds of data, some of them as simple as Microsoft Excel spreadsheets.

In October, San Francisco mayor Gavin Newsom required that City departments make all non-confidential datasets under their authority available on DataSF.org. Dozens are available, ranging from city surveys, election statements and ballot measures, and performance measures, as well as a great deal of GIS data.

New York has a similar program called the New York Data Mine, while the District of Columbia's is called the Data Catalog. Portland announced such an initiative in September.

In Canada, Toronto has also made a great deal of city data available, while Vancouver has announced its intention to do so.

Such initiatives enable developers -- not just for computers, but also for smart phones such as the iPhone -- to say "there's an app for that" regarding all sorts of city services, including mashups between them. New York is having a contest to develop interesting applications, while San Francisco has posted a showcase of interesting apps online.

The other advantage, notes the New York Times in an article about the phenomenon, is that if third parties are developing such applications, the cities themselves don't have to.

Dani AI

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Building on 's observation that cities are putting machine-readable datasets into the wild, here are practical, revenue- and operations-focused ways e-commerce teams can use that data to produce measurable results for stores, checkout, and delivery.

Cities publish many useful datasets — think transit and road-status feeds, parking/curb-permit records, business licenses and inspection logs, parcel/address files, event/permit calendars, and 311 requests — and each can be mapped to a common e-commerce need: better local landing pages and search relevance (higher conversions), more accurate delivery ETAs and routing (lower costs, fewer failed deliveries), faster vendor/seller vetting (fraud reduction), and smarter, localized promotions or inventory placement (higher AOV).

A small, practical integration workflow:

  • pick one low-friction dataset (CSV/JSON) and verify the license;
  • normalize addresses and spatial refs, then enrich your customer/product records;
  • precompute spatial joins / nearest-neighbor results and cache them for runtime use;
  • expose the enrichment to the storefront (local store pages, delivery options) and measure lift.

Example nearest-warehouse query (PostGIS-style):

SELECT id, ST_DistanceSphere(geom, ST_MakePoint(:lon, :lat)) AS meters
FROM warehouses
ORDER BY meters
LIMIT 1;

Common pitfalls and quick fixes: feeds are often stale or irregular — implement versioning, update stamps and graceful fallbacks; watch coordinate systems and address formats — normalize and use tolerant fuzzy matching; respect licenses and PII rules before publishing derived content; and rate-limit or cache city APIs to avoid outages. For marketing experiments, A/B test localized content before full rollouts. Thanks to for the thread starter — , start small (one dataset, one use case), measure, then expand.

Member Avatar for MktgRob
MktgRob 7 Nearly a Posting Virtuoso

Very interesting. What would be the benefit to e-commerce developers?

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