mapsdatarecommendation

Comparing Map Data Sources: Building a Location Recommendation Engine Like a Dining App

UUnknown

2026-02-14

11 min read

Technical guide to fuse Google, Waze, and OpenStreetMap POI data for high-quality, low-cost dining recommendations in micro apps.

Building a dining-style location recommendation engine? Fix the data first.

If you’ve built micro apps or small dining recommendation services, you know the hard part isn't the UI or the ML model — it's the messy, inconsistent world of POI and map data. You need accurate locations, normalized addresses, up-to-date opening hours, and low-cost or open licensing so your micro app doesn't blow the budget or violate terms. In 2026 the landscape has evolved: OpenStreetMap (OSM) tooling and community growth have matured, commercial platforms tightened pricing and terms, and new realtime telemetry sources have changed how we weigh recency. This guide compares Google Maps, Waze, and OpenStreetMap as POI sources, and gives a pragmatic, production-ready approach for data fusion that powers a robust recommendation engine for micro apps.

Quick verdict (what to pick and when)

Google Maps / Google Places — Best for breadth and rich metadata (reviews, photos), but expensive for scale and restrictive for redistribution. Use when you need turnkey quality and you can absorb API cost and licensing limits.
Waze — Best for traffic and live event/incident telemetry, not as strong for POI completeness. Use for freshness signals (closures, temporary events) and routing-aware context.
OpenStreetMap (OSM) — Best for open licensing, offline use, and local correctness; coverage varies but tooling (Nominatim, osm2pgsql, Overpass) and community contributions have significantly improved through 2024–2026. Use as the base layer for micro apps that need low-cost redistribution or offline-first behavior.

2026 trends that should shape your architecture

Open data goes mainstream: OSM contributor numbers and mapper tools saw renewed growth in 2025 thanks to better mobile editors and AI-assisted tagging. Expect higher baseline quality for urban POI by 2026.
Cost sensitivity & micro apps: After successive pricing changes among commercial map providers (2023–2025), builders increasingly default to hybrid strategies to keep costs predictable.
Real-time telemetry matters: Live incident streams (Waze, regional traffic APIs) are now commonly fused into recommendation scoring to avoid recommending closed or congested venues — see playbooks on capturing and ingesting edge event streams.
AI-assisted enrichment: Late-2025 tooling for extracting hours, menu URLs, and categories from web pages and social profiles has become common. Use these enrichment steps to fill gaps in POI metadata; see practical tool guides like AI summarization for extraction workflows.

Feature-by-feature comparison: Google vs Waze vs OSM

Coverage and POI density

Google generally has the largest global POI index with commercial vetting and user-contributed content (reviews, photos). Waze focuses largely on road network telemetry and incident reports; its POI coverage is typically weaker. OSM coverage is uneven — excellent in many cities and regions where local mappers are active, and weaker in others — but it often contains details (entrances, footpaths, building footprints) that commercial providers omit.

Metadata richness

Google Places: Ratings, reviews, photos, popular times, permanent/temporarily closed flags, detailed address components, phone, website.
Waze: Primarily incident/traffic metadata, some place markers but limited user-facing POI metadata.
OSM: Variable — tags can include opening_hours, phone, website, cuisine, wheelchair, but completeness varies by area.

Data freshness and live signals

Waze excels at live incidents and dynamic events (accidents, closures). Google has good freshness for high-traffic POIs via user edits and third-party data partners. OSM's freshness depends on community edits, but with AI-assisted suggestions (2025+) and faster edit pipelines, updates are improving.

Licensing and redistribution

Google: Strict — caching and local redistribution are limited, and attribution plus billing constraints matter. Commercial license for high-volume or embedded redistribution is often required.
Waze: Data sharing via the Connected Citizens Program is possible for governments and partners but has usage constraints. Waze does not provide freely redistributable POI dumps for commercial products.
OSM: Open Database License (ODbL) — you can use, copy, and redistribute with attribution and share-alike obligations on derived databases. This is the practical choice for micro apps that require local datasets or offline support.

Cost & rate limits

Google's unit costs can be significant for high-volume recommendations; Waze access is constrained and often free only in partner programs; OSM is free but you pay for hosting, processing, and enrichment. Hybrid architectures reduce per-request costs by caching and limiting commercial API calls to enrichment only.

Architectural patterns for fusing map / POI sources

In production you'll rarely pick a single source. The best outcome comes from a hybrid, layered approach that treats each source as a signal with different strengths. Here’s a recommended pipeline suited for micro apps and small teams:

1) Source selection & ingestion

Base layer: OSM (full planet extracts, regional extracts, or Overpass queries) for authoritative geometries and permission to redistribute.
Enrichment layer: Google Places API for high-value POIs where you need ratings, photos, and consistent categories. Use sparingly for items you plan to promote.
Realtime layer: Waze incident feeds / regional traffic APIs for closures, congestion and event detection.

2) Normalization & canonicalization

Different sources will provide the same real-world place with slightly different names, coordinates, or types. Normalization is essential.

Normalize textual fields: lowercase, remove punctuation, strip stop-phrases ("the", branch indicators), and expand common abbreviations (St -> Street).
Normalize address units via libpostal or similar libraries to get canonical address components.
Normalize geometry: convert all input to WGS84 lat/lon and represent POIs as a point with optional footprint polygon.

3) Entity resolution (dedupe)

Run a deterministic and fuzzy matching pipeline to merge duplicates. Techniques that work well:

Spatial proximity: cluster points within a small radius (10–30m for urban POIs). Use PostGIS ST_DWithin or a KD-tree for fast neighbor searches.
Name similarity: Jaro-Winkler or token-set ratio (fuzzywuzzy) tuned for place names.
Address overlap: shared house number + street wins.
Type/category similarity: bakery vs cafe is closer than bakery vs hospital.

// Simple JS pseudo-code to merge candidate POIs
function mergeCandidates(a, b) {
  const nameScore = tokenSetRatio(a.name, b.name);
  const dist = haversine(a.lat, a.lon, b.lat, b.lon);
  const addressMatch = a.house_number && a.house_number === b.house_number && a.street === b.street;
  return (nameScore > 85 && dist < 30) || addressMatch;
}

4) Scoring and confidence

After merging, compute a composite confidence score to decide which metadata to trust and what to show to users. Example weighted score:

Source trust (Google: 0.9, OSM: 0.75, Waze: 0.6)
Recency (last edit timestamp normalized)
Local validation (has photos, multiple sources agree, or user feedback)

const score = 0.5*sourceTrust + 0.3*recencyScore + 0.2*localValidation;

5) Enrichment

Fill missing fields using targeted operations:

Website & menu: scrape or use AI extractors (respect robots.txt and terms of service).
Opening hours: infer from OSM tags, Google Places, or published web pages; apply heuristics for common patterns.
Cuisines & categories: unify to your ontology (e.g., "italian" -> "Italian") using a category mapping layer.

6) Real-time adjustments

Use Waze / traffic streams and social signals for short-term penalties in recommendations — e.g., avoid recommending a restaurant near a major incident or that is temporarily closed.

7) Serving & caching

Precompute vector tiles and a lightweight recommendation index for client-side micro apps to minimize API calls. Use Tippecanoe to build vector tiles from merged POIs and host them on a CDN. For small micro apps consider shipping a filtered dataset (size-limited) in the app bundle.

Practical implementation: a minimal fusion pipeline

Below is a pragmatic stack and example flow suitable for a micro app or prototype team:

Data storage: PostGIS for spatial queries and dedupe.
Geocoding: Nominatim or Pelias for OSM forward/reverse; reserve Google Geocoding for edge cases that matter.
Realtime: ingest Waze event streams or a regional traffic API into Kafka; mark affected POIs with temporary flags.
Search index: Elastic/Opensearch for textual and geo-distance queries with custom scoring.
Tiles & client: Tippecanoe + CDN; lightweight client that hits a small serverless function for personalized ranking.

// Example: query pipeline pseudo-code
1) client sends bbox + user prefs
2) server fetches candidate POIs from Elastic (text + geo)
3) server applies freshness penalties (waze incidents) and boosts (user favorites)
4) server returns top-K with confidence and provenance metadata

Geocoding nuances you must handle

Geocoding is not just lat/lon; for a dining app you care about:

Place vs address: POIs represent places; addresses represent units. When users search "Tony's Pizza", prefer POI matches instead of address interpolations.
Ambiguity: Short queries need contextual hints (user location, recent history) to disambiguate.
Polygon containment: For venues inside malls or campuses, use building footprints or address polygons to decide whether two entries refer to the same sub-place.

Privacy, compliance, and licensing checklist

Check Google Maps Platform Terms for caching & redistribution. Avoid embedding Google imagery or Places blobs in datasets meant to be redistributed.
With OSM, comply with ODbL requirements: provide attribution and share derivatives under the same license if you publish a derived database.
For scraped enrichment, obey robots.txt and privacy laws (don’t store or expose personal data).
Implement rate-limiting and budget controls to prevent runaway costs from commercial APIs; treat operational controls like patching and CI/CD policies in the same way as other infra (see playbooks on automating virtual patching).

Evaluation: how to measure if fusion improves recommendations

Set up A/B experiments where the control uses a single source (e.g., Google-only or OSM-only) and the variant uses fused data. Key metrics:

Recommendation acceptance rate (user taps / shown recommendations)
Click-to-navigation rate (did users navigate to suggested venue)
Conversion or reservation rate (if integrated with booking)
Error/fallthrough rate (user searches after a failed recommendation)
Time-to-first-relevant-result (latency + relevance)

Optimizations for micro apps

Trim your data: Ship only the bounding box or category slice your micro app needs.
Edge compute: Use edge regions and serverless functions for on-demand enrichment and keep the client light.
Cache aggressively: Cache merged POIs and tile layers; refresh incrementally by change feeds or Overpass diff tiles. See guidance on edge caching and micro-fulfilment optimizations.
Graceful degrade: If commercial APIs throttle, fall back to OSM results with clear attribution.
Attribution UI: Display clear attribution to satisfy OSM/Google requirements and build trust with users. Also consider how authority and discoverability affect results (teachings on discoverability).

Case study: a 2026 micro dining app flow

Imagine Where2Dine, a micro app built in 2026 for friend groups. The team uses:

OSM as the canonical dataset for offline-first base POIs.
Google Places (limited quota) to add top-50 high-traffic venues’ photos and ratings.
Waze as a live signal for incidents and construction — causes temporary de-prioritization in the ranking.

Result: The app can run on-device for nearby recommendations using a precomputed vector tile of ~2MB for a city neighborhood, call a serverless endpoint only for personalization, and reserve paid Google calls for confirmation screens (e.g., show reviews for a selected restaurant).

Example scoring function (practical)

Below is a concrete scoring formula you can tune. Give each POI a score between 0–1:

score = clamp(0,1,
  0.4 * sourceTrust +        // Google:0.9, OSM:0.8
  0.2 * recencyFactor +      // normalized last-updated age
  0.2 * popularity +         // check-ins, ratings (normalized)
  0.1 * categoryMatch +      // user preference match
  0.1 * realtimePenalty      // -ve if an incident nearby
)

Provenance is critical: always return source and confidence for each field (e.g., name_from: 'OSM', rating_from: 'Google'). That transparency helps debugging and drives trust with users.

Tooling & libraries worth adopting (2026)

PostGIS — spatial DB for dedupe & containment
Pelias / Nominatim — open geocoders for OSM
Tippecanoe — vector tile generation
Elastic / OpenSearch — text + geo search with custom scoring
libpostal — address parsing & normalization
AI enrichment toolkits (2025–26) — for extracting menu URLs and hours (use responsibly)

Actionable checklist to start fusing today

Download a regional OSM extract or configure an Overpass query for your target area.
Run libpostal to normalize addresses and index into PostGIS.
Implement a dedupe pipeline (spatial + fuzzy name matching) and tag merged entities with provenance.
Integrate a limited Google Places quota for top-N enrichment; reserve Waze incident ingestion for live penalties.
Build vector tiles and ship a small region to your micro app; implement fallback to serverless ranking when needed.
Run an A/B trial comparing fused results vs a single-source baseline and monitor your KPI set.

Final recommendations

For micro apps in 2026, the pragmatic approach is hybrid: OSM as the exportable base, Google for selective enrichment, and Waze for real-time penalties. Build a strong normalization/deduping layer, enforce licensing compliance, and optimize for offline and cost-sensitive operation. Prioritize confidence and provenance in your API responses — developers and users both want to know why a recommendation was made.

“Data fusion wins: single-source convenience is tempting, but combining open data, selective commercial enrichment, and realtime telemetry gives the best tradeoff of quality, cost, and compliance.”

Next steps — get a starter kit

If you’re building a dining micro app or a location recommendation microservice, start with a lightweight starter kit: an OSM regional extract, a configured PostGIS schema, a simple dedupe script, and a Tippecanoe pipeline to produce vector tiles. If you'd like, grab our curated starter repo with scripts and tuned matching thresholds (optimized for urban dining apps) to accelerate your first prototype.

Ready to reduce guesswork and ship a better recommendation engine? Download the starter kit, run the fusion checklist above on a small neighborhood, and let the data guide your UX. Share your results or ask for an architecture review — we’ll help you tune scores and scale safely.

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