# Section 368 Energy Corridor Screener - Federal ROW Siting Flag (`malonestar/energy-corridor-368-screener`) Actor Section 368 West-wide Energy Corridor API: per lat/lon site, is it inside a designated BLM/USFS federal transmission or pipeline corridor - corridor name, designated use, width, agency, status - plus a streamlined-ROW siting flag. For transmission/pipeline routing and federal-land energy siting. - **URL**: https://apify.com/malonestar/energy-corridor-368-screener.md - **Developed by:** [Kyle Maloney](https://apify.com/malonestar) (community) - **Categories:** Real estate, Developer tools, Agents - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.85 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Section 368 Energy Corridor Screener — Federal ROW Siting Flag Batch **coordinate screener** for the designated **Section 368 West-wide Energy Corridors** — the federal transmission and pipeline rights-of-way that BLM and USFS pre-approved on public land in the 2008 *Designation of Energy Corridors on Federal Land in the 11 Western States* Programmatic EIS (under Section 368 of the Energy Policy Act of 2005). Give it a list of sites (lat/lon) and it returns, for each one, whether the site falls **inside** a designated corridor, the **nearest corridor** — segment ID, designated use (electric / pipeline / multi-modal), width, managing agency, status — the **distance**, and a **`row_streamlining_flag`** that signals when a route benefits from the pre-completed federal siting analysis. Routing a transmission line or pipeline inside a Section 368 corridor can mean a faster, narrower federal right-of-way review, because the corridor-level NEPA siting work was already done. Routing outside one means the project needs its own project-specific siting review from scratch. That distinction is a real early-stage cost/schedule input for transmission, pipeline and West-wide energy siting teams — and this actor is the first tool on Apify to turn the raw Argonne/BLM/USFS corridor layer into a per-site verdict instead of a flat map export. Data is the multi-agency **Argonne National Laboratory / BLM / USFS Section 368 Energy Corridor FeatureServer** — a static, authoritative snapshot (last updated 2017-12-12) of every corridor designated across the 11 western states. ### Who it's for - **Transmission & pipeline developers / ROW applicants** — check candidate routes against pre-designated federal corridors before committing to a siting study. - **West-wide energy siting analysts & NEPA consultants** — fast batch screen of a route or project portfolio for corridor overlap. - **Federal-land energy developers** (wind, solar, BESS interconnection routing) — corridors are a natural collector-line / gen-tie routing option. - **Land, real estate & Phase-I due-diligence teams** — flag a parcel that sits inside a federal energy ROW corridor. - **AI agents & data pipelines** — a clean, chainable geospatial due-diligence tool via MCP. ### Example input ```json { "assets": [ { "lat": 41.673613, "lon": -107.700677, "label": "S. Wyoming route point A", "radiusMiles": 10 }, { "lat": 39.7392, "lon": -104.9903, "label": "Denver, CO metro", "radiusMiles": 10 }, { "lat": 36.1699, "lon": -115.1398, "label": "Las Vegas, NV metro", "radiusMiles": 10 }, { "lat": 40.014986, "lon": -105.270546, "label": "Boulder, CO foothills", "radiusMiles": 15 }, { "lat": 41.8781, "lon": -87.6298, "label": "Chicago, IL (outside 11-state West)", "radiusMiles": 10 } ], "radiusMiles": 10, "maxAssets": 500 } ```` Each asset is `{ lat, lon, label?, radiusMiles? }` (radius in **miles**, optional per-asset override — corridors are sparse and narrow, so the default is wider than a typical proximity screen). `"lat,lon"` strings and `[lat, lon]` arrays are also accepted. One dataset row (one billed result) is produced per asset; a bad entry yields an ERROR row and the run continues. ### Output fields | Field | Description | |-------|-------------| | `asset_label`, `asset_lat`, `asset_lon` | The screened site. | | `radius_miles` | Search radius used for this asset. | | `in_corridor` | Authoritative point-in-polygon test: true when the site falls inside a designated corridor. | | `corridor_count_within_radius` | Count of distinct corridor polygons (containing + nearby) within the radius. | | `nearest_corridor_id` / `nearest_corridor_name` | Source ID and segment identifier (a route code like `78-138`, not a place name). | | `nearest_designated_use` / `nearest_designated_use_label` | Raw code (`Electric-only`, `All`, `Pipeline`, ...) and a plain-English label. | | `nearest_width_feet` | Designated corridor width in feet (already reflected in the polygon boundary — no manual buffer needed). | | `nearest_managing_agency` | BLM or USFS. | | `nearest_sec368_status` / `nearest_sec368_designated` | Raw status text and a true/false/unknown reading of it. | | `nearest_distance_miles` | Distance to the nearest corridor boundary; 0 when inside one. | | `row_streamlining_flag` | True when the site is inside a formally Designated corridor — a federal-ROW streamlining signal. | | `row_streamlining_note` | Plain-English explanation of the flag for this specific site. | | `error`, `disclaimer`, `checked_at`, `source_url` | Diagnostics + provenance. | ### Use as an MCP tool This actor is callable by AI agents (Claude, Cursor, etc.) via **mcp.apify.com**. The field-level output descriptions make it a clean, chainable **energy-siting due-diligence tool**: feed it candidate route points and it returns a structured Section 368 corridor verdict per site — pairs naturally with grid-proximity, sage-grouse habitat, and other federal-land siting screeners in the same portfolio. ### FAQ **Is this a permit guarantee?** No. `row_streamlining_flag` is an editorial siting signal based on the source's `Sec368Stat` field. Project-specific NEPA and agency ROW approval are still required — confirm current corridor status with BLM/USFS before relying on it. **How is distance measured?** Point-to-polygon-boundary distance (nearest edge of the corridor polygon) in miles; 0 when the site is inside the corridor. **Why is "corridor name" just a number like `78-138`?** The Argonne dataset labels corridors by their PEIS route-segment ID, not a descriptive place name — that's the authoritative identifier used in the Records of Decision. **Which areas are covered?** Only the 11 western states from the 2008 PEIS (AZ, CA, CO, ID, MT, NV, NM, OR, UT, WA, WY). A site elsewhere (e.g. most of the eastern US) returns a valid `in_corridor: false` CLEAR row — that's expected, not missing data. **What if a site has no corridor nearby?** You still get a billed row with `in_corridor: false`, `row_streamlining_flag: false`, and null corridor fields — a clean "no corridor" record. ### Pricing Pay-per-result: one billed row per screened asset. Empty/no-corridor sites still return a row. *Source: Argonne National Laboratory / BLM / USFS Section 368 Energy Corridor FeatureServer (2008 PEIS + Records of Decision, updated 2017-12-12). Public federal data. This tool is informational and not a substitute for an agency ROW determination.* # Actor input Schema ## `assets` (type: `array`): Sites to screen for point-in-polygon / proximity to a designated Section 368 West-wide Energy Corridor. Each entry is an object { "lat": number, "lon": number, "label": "optional name", "radiusMiles": optional number }. Also accepts "lat,lon" strings or \[lat, lon] arrays. The optional per-asset "radiusMiles" overrides the global search radius. One dataset row (one billed result) is produced per asset; a bad entry yields an ERROR row and the run continues. ## `radiusMiles` (type: `integer`): Default radius, in miles, around each asset to search for a designated Section 368 Energy Corridor (1-50; internally clamped down to 0.25 if a smaller per-asset value is supplied). Designated corridors are sparse and narrow, so a wider default than typical proximity screens is used. Overridden by a per-asset "radiusMiles". Default 10. ## `maxAssets` (type: `integer`): Maximum number of assets processed in one run (1-2000). One result row is emitted (and billed) per asset. Default 500. ## Actor input object example ```json { "assets": [ { "lat": 41.673613, "lon": -107.700677, "label": "S. Wyoming — corridor 78-138 (multi-modal, BLM, Designated)", "radiusMiles": 10 }, { "lat": 39.7392, "lon": -104.9903, "label": "Denver, CO metro (no mapped corridor)", "radiusMiles": 10 }, { "lat": 36.1699, "lon": -115.1398, "label": "Las Vegas, NV metro", "radiusMiles": 10 }, { "lat": 40.014986, "lon": -105.270546, "label": "Boulder, CO (checking foothills west of I-25)", "radiusMiles": 15 }, { "lat": 41.8781, "lon": -87.6298, "label": "Chicago, IL (outside the 11-state West footprint)", "radiusMiles": 10 } ], "radiusMiles": 10, "maxAssets": 500 } ``` # Actor output Schema ## `results` (type: `string`): The default dataset (one row per asset). # 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 = { "assets": [ { "lat": 41.673613, "lon": -107.700677, "label": "S. Wyoming — corridor 78-138 (multi-modal, BLM, Designated)", "radiusMiles": 10 }, { "lat": 39.7392, "lon": -104.9903, "label": "Denver, CO metro (no mapped corridor)", "radiusMiles": 10 }, { "lat": 36.1699, "lon": -115.1398, "label": "Las Vegas, NV metro", "radiusMiles": 10 }, { "lat": 40.014986, "lon": -105.270546, "label": "Boulder, CO (checking foothills west of I-25)", "radiusMiles": 15 }, { "lat": 41.8781, "lon": -87.6298, "label": "Chicago, IL (outside the 11-state West footprint)", "radiusMiles": 10 } ], "radiusMiles": 10, "maxAssets": 500 }; // Run the Actor and wait for it to finish const run = await client.actor("malonestar/energy-corridor-368-screener").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 = { "assets": [ { "lat": 41.673613, "lon": -107.700677, "label": "S. Wyoming — corridor 78-138 (multi-modal, BLM, Designated)", "radiusMiles": 10, }, { "lat": 39.7392, "lon": -104.9903, "label": "Denver, CO metro (no mapped corridor)", "radiusMiles": 10, }, { "lat": 36.1699, "lon": -115.1398, "label": "Las Vegas, NV metro", "radiusMiles": 10, }, { "lat": 40.014986, "lon": -105.270546, "label": "Boulder, CO (checking foothills west of I-25)", "radiusMiles": 15, }, { "lat": 41.8781, "lon": -87.6298, "label": "Chicago, IL (outside the 11-state West footprint)", "radiusMiles": 10, }, ], "radiusMiles": 10, "maxAssets": 500, } # Run the Actor and wait for it to finish run = client.actor("malonestar/energy-corridor-368-screener").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 '{ "assets": [ { "lat": 41.673613, "lon": -107.700677, "label": "S. Wyoming — corridor 78-138 (multi-modal, BLM, Designated)", "radiusMiles": 10 }, { "lat": 39.7392, "lon": -104.9903, "label": "Denver, CO metro (no mapped corridor)", "radiusMiles": 10 }, { "lat": 36.1699, "lon": -115.1398, "label": "Las Vegas, NV metro", "radiusMiles": 10 }, { "lat": 40.014986, "lon": -105.270546, "label": "Boulder, CO (checking foothills west of I-25)", "radiusMiles": 15 }, { "lat": 41.8781, "lon": -87.6298, "label": "Chicago, IL (outside the 11-state West footprint)", "radiusMiles": 10 } ], "radiusMiles": 10, "maxAssets": 500 }' | apify call malonestar/energy-corridor-368-screener --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=malonestar/energy-corridor-368-screener", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Section 368 Energy Corridor Screener - Federal ROW Siting Flag", "description": "Section 368 West-wide Energy Corridor API: per lat/lon site, is it inside a designated BLM/USFS federal transmission or pipeline corridor - corridor name, designated use, width, agency, status - plus a streamlined-ROW siting flag. For transmission/pipeline routing and federal-land energy siting.", "version": "1.0", "x-build-id": "dNrMdKgl0ZPXa2fsb" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/malonestar~energy-corridor-368-screener/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-malonestar-energy-corridor-368-screener", "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/malonestar~energy-corridor-368-screener/runs": { "post": { "operationId": "runs-sync-malonestar-energy-corridor-368-screener", "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/malonestar~energy-corridor-368-screener/run-sync": { "post": { "operationId": "run-sync-malonestar-energy-corridor-368-screener", "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", "properties": { "assets": { "title": "Assets (sites / coordinates to screen)", "type": "array", "description": "Sites to screen for point-in-polygon / proximity to a designated Section 368 West-wide Energy Corridor. Each entry is an object { \"lat\": number, \"lon\": number, \"label\": \"optional name\", \"radiusMiles\": optional number }. Also accepts \"lat,lon\" strings or [lat, lon] arrays. The optional per-asset \"radiusMiles\" overrides the global search radius. One dataset row (one billed result) is produced per asset; a bad entry yields an ERROR row and the run continues." }, "radiusMiles": { "title": "Search radius (miles)", "minimum": 1, "maximum": 50, "type": "integer", "description": "Default radius, in miles, around each asset to search for a designated Section 368 Energy Corridor (1-50; internally clamped down to 0.25 if a smaller per-asset value is supplied). Designated corridors are sparse and narrow, so a wider default than typical proximity screens is used. Overridden by a per-asset \"radiusMiles\". Default 10.", "default": 10 }, "maxAssets": { "title": "Max assets to screen", "minimum": 1, "maximum": 2000, "type": "integer", "description": "Maximum number of assets processed in one run (1-2000). One result row is emitted (and billed) per asset. Default 500.", "default": 500 } } }, "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 } } } } } } } } } } ```