# Google Geocoding Batch Scraper (`seemuapps/google-geocoding-batch-scraper`) Actor Convert a list of addresses to latitude/longitude (forward geocoding) or coordinates to addresses (reverse geocoding) in bulk using the official Google Geocoding API. - **URL**: https://apify.com/seemuapps/google-geocoding-batch-scraper.md - **Developed by:** [Andrew](https://apify.com/seemuapps) (community) - **Categories:** Lead generation, SEO tools, Travel - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $8.00 / 1,000 geocode results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Google Geocoding Batch Scraper Convert a bulk list of addresses to latitude/longitude — or coordinates back to clean, normalized addresses — using the official Google Geocoding API. Stop running one-off cURL calls; paste your list and export. ### What you get - **Forward geocoding** — address → lat/lng, placeId, formatted address, and split components (street, locality, country, postal code, etc.) - **Reverse geocoding** — lat/lng → formatted address, placeId, and the full component breakdown - **One dataset row per result** — Google sometimes returns multiple candidates per input; each one is its own row with a `rank` - **All address components extracted** — street number, route, neighborhood, locality, admin areas, country, country code, postal code, plus code - **Per-input error handling** — bad inputs get a row with `status` set to `INVALID_INPUT`, `ZERO_RESULTS`, etc.; the run continues - **Region & component biasing** — pin forward geocoding to a country, restrict by admin area, or filter reverse geocoding by location type ### Use cases - **CRM cleanup** — turn a column of free-text addresses into normalized fields with lat/lng for mapping - **Real estate / property data** — resolve street addresses to coordinates for distance, school zone, and amenity analysis - **Logistics & routing** — pre-geocode delivery addresses before bulk routing - **Reverse-lookup analytics** — turn GPS pings into city/neighborhood for cohort analysis - **Address validation** — flag inputs that return `partial_match: true` or `ZERO_RESULTS` for cleanup ### How to use 1. Pick a **Mode**: - **Forward** — address strings, one per line. - **Reverse** — `lat,lng` pairs (e.g. `40.7128,-74.006`), one per line. 2. Paste your list into **Inputs**. 3. (Optional) Set **Language** for the formatted address (`en`, `es`, `fr`, …). 4. (Forward only, optional) Set **Region** to bias by country (`us`, `gb`, `de`) and **Components** to constrain (`country:US`). 5. (Reverse only, optional) Set **Result type** and **Location type** filters. 6. Run — every result becomes its own dataset row. ### Forward vs reverse examples **Forward input:** ```` 1600 Amphitheatre Pkwy, Mountain View, CA 221B Baker Street, London ``` **Reverse input:** ``` 37.4220,-122.0841 51.5237,-0.1585 ```` ### Output schema | Field | Type | Description | |---|---|---| | `input` | string | Echo of the original input | | `mode` | string | `forward` or `reverse` | | `rank` | number | 1-based rank when Google returns multiple candidates | | `status` | string | `OK`, `ZERO_RESULTS`, `INVALID_INPUT`, `OVER_QUERY_LIMIT`, `REQUEST_DENIED`, `INVALID_REQUEST`, `ERROR` | | `formattedAddress` | string \| null | Google's canonical address string | | `latitude` / `longitude` | number \| null | Coordinates | | `placeId` | string \| null | Google place ID — stable identifier | | `locationType` | string \| null | `ROOFTOP`, `RANGE_INTERPOLATED`, `GEOMETRIC_CENTER`, `APPROXIMATE` | | `partialMatch` | boolean \| null | `true` if Google guessed at part of the input | | `types` | string[] | Place types (e.g. `street_address`, `locality`) | | `streetNumber` | string \| null | Street number component | | `route` | string \| null | Street name | | `neighborhood` | string \| null | Neighborhood | | `subLocality` | string \| null | Sub-locality (e.g. NYC borough) | | `locality` | string \| null | City | | `adminArea1Long` / `adminArea1Short` | string \| null | State / region (full and abbreviated) | | `adminArea2Long` | string \| null | County | | `country` | string \| null | Country name | | `countryCode` | string \| null | Two-letter country code | | `postalCode` | string \| null | ZIP / postal code | | `plusCodeGlobal` | string \| null | Global plus code | | `plusCodeCompound` | string \| null | Compound plus code | | `error` | string \| null | Error message if `status` is not `OK` | ### Tips - **Use Components** (`country:US`) to prevent ambiguous addresses from matching the wrong country (e.g. "Springfield" matches dozens of places worldwide). - **`locationType` matters** — `ROOFTOP` is a precise address match; `APPROXIMATE` means Google fell back to a city center. - **`partialMatch: true`** is a strong signal the input had typos or missing parts — flag these for human review. - Google rate-limits aggressive batches; if you see `OVER_QUERY_LIMIT` consistently, slow down or raise the quota in Google Cloud. - For reverse geocoding of a point with multiple result types, the first row is the most specific (street address) and later rows broaden up to country. # Actor input Schema ## `mode` (type: `string`): Forward converts addresses to coordinates. Reverse converts 'lat,lng' pairs to formatted addresses and components. ## `inputs` (type: `array`): Forward mode: one address per line. Reverse mode: one 'lat,lng' pair per line (e.g. 40.7128,-74.006). ## `language` (type: `string`): BCP-47 language code for the formatted address (e.g. en, es, fr). ## `region` (type: `string`): Two-letter ccTLD region bias for forward geocoding (e.g. us, gb, de). ## `components` (type: `string`): Component filter for forward geocoding, e.g. 'country:US' or 'country:GB|locality:London'. ## `resultType` (type: `string`): Pipe-separated reverse-geocoding result types, e.g. 'street_address|locality|country'. ## `locationType` (type: `string`): Pipe-separated reverse-geocoding location types, e.g. 'ROOFTOP|RANGE_INTERPOLATED'. ## Actor input object example ```json { "mode": "forward", "inputs": [ "1600 Amphitheatre Pkwy, Mountain View, CA", "1 Apple Park Way, Cupertino, CA" ], "language": "en" } ```` # Actor output Schema ## `results` (type: `string`): One record per input: input, mode, rank, status, formattedAddress, latitude, longitude, placeId, locationType, partialMatch, types, streetNumber, route, neighborhood, subLocality, locality, adminArea1Long, adminArea1Short, adminArea2Long, country, countryCode, postalCode, plusCodeGlobal, plusCodeCompound, error. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "inputs": [ "1600 Amphitheatre Pkwy, Mountain View, CA", "1 Apple Park Way, Cupertino, CA" ] }; // Run the Actor and wait for it to finish const run = await client.actor("seemuapps/google-geocoding-batch-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "inputs": [ "1600 Amphitheatre Pkwy, Mountain View, CA", "1 Apple Park Way, Cupertino, CA", ] } # Run the Actor and wait for it to finish run = client.actor("seemuapps/google-geocoding-batch-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "inputs": [ "1600 Amphitheatre Pkwy, Mountain View, CA", "1 Apple Park Way, Cupertino, CA" ] }' | apify call seemuapps/google-geocoding-batch-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seemuapps/google-geocoding-batch-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Geocoding Batch Scraper", "description": "Convert a list of addresses to latitude/longitude (forward geocoding) or coordinates to addresses (reverse geocoding) in bulk using the official Google Geocoding API.", "version": "1.0", "x-build-id": "f4ggf2uQ6D2LKUYz6" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seemuapps~google-geocoding-batch-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seemuapps-google-geocoding-batch-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seemuapps~google-geocoding-batch-scraper/runs": { "post": { "operationId": "runs-sync-seemuapps-google-geocoding-batch-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seemuapps~google-geocoding-batch-scraper/run-sync": { "post": { "operationId": "run-sync-seemuapps-google-geocoding-batch-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "inputs" ], "properties": { "mode": { "title": "Mode", "enum": [ "forward", "reverse" ], "type": "string", "description": "Forward converts addresses to coordinates. Reverse converts 'lat,lng' pairs to formatted addresses and components.", "default": "forward" }, "inputs": { "title": "Inputs", "type": "array", "description": "Forward mode: one address per line. Reverse mode: one 'lat,lng' pair per line (e.g. 40.7128,-74.006).", "items": { "type": "string" } }, "language": { "title": "Language", "type": "string", "description": "BCP-47 language code for the formatted address (e.g. en, es, fr).", "default": "en" }, "region": { "title": "Region (forward only)", "type": "string", "description": "Two-letter ccTLD region bias for forward geocoding (e.g. us, gb, de)." }, "components": { "title": "Components (forward only)", "type": "string", "description": "Component filter for forward geocoding, e.g. 'country:US' or 'country:GB|locality:London'." }, "resultType": { "title": "Result type (reverse only)", "type": "string", "description": "Pipe-separated reverse-geocoding result types, e.g. 'street_address|locality|country'." }, "locationType": { "title": "Location type (reverse only)", "type": "string", "description": "Pipe-separated reverse-geocoding location types, e.g. 'ROOFTOP|RANGE_INTERPOLATED'." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```