Agroecological Space Module¶

File: computing/misc/agroecological_space.py

Overview¶

This module generates agroecological zone data for specific administrative blocks by clipping pan-India agroecological classification data to the region of interest (ROI).

Purpose¶

Extracts agroecological zone information for micro-watersheds (MWS) and distributes to: - Google Earth Engine (GEE) - Vector asset storage - GeoServer - Visualization via WMS/WFS (workspace: agroecological) - Database - Layer metadata storage

Architecture¶

flowchart TD
    A[Celery Task Triggered] --> B[ee_initialize]
    B --> C[Build ROI Asset ID]
    C --> D[Load MWS FeatureCollection]
    D --> E[Load Pan-India Agroecological Data]
    E --> F[Filter by ROI bounds]
    F --> G[Spatial Join with ROI]
    G --> H[Add UID to features]
    H --> I[Export to GEE Asset]
    I --> J[Check Task Status]
    J --> K{Asset Exists?}
    K -->|Yes| L[save_layer_info_to_db]
    L --> M[make_asset_public]
    M --> N[sync_fc_to_geoserver]
    N --> O{Success?}
    O -->|Yes| P[Update sync status]
    P --> Q[Return True]
    O -->|No| R[Return False]

Components¶

Main Task: generate_agroecological_data()¶

Location: Line 22

Parameters: | Parameter | Type | Description | |-----------|------|-------------| | state | str | State name | | district | str | District name | | block | str | Block/tehsil name | | gee_account_id | int | GEE account identifier |

Returns: bool - Whether layer was successfully synced to GeoServer

Processing Logic¶

1. ROI Definition¶

roi_asset_id = get_gee_asset_path(state, district, block) + "filtered_mws_{district}_{block}_uid"

2. Data Source¶

pan_india_asset_id = f"{GEE_EXT_DATASET_PATH}/Agroecological_space_pan_india"

3. Spatial Processing¶

flowchart LR
    A[Pan-India Data] --> B[filterBounds]
    C[MWS ROI] --> B
    B --> D[Clipped Features]
    D --> E[Spatial Join]
    E --> F[Add UID]
    F --> G[Final FeatureCollection]

Spatial Join Logic: - Uses ee.Filter.intersects() with leftField=".geo" and rightField=".geo" - Joins clipped data with ROI features to associate each agroecological feature with its containing MWS - Extracts uid from matched ROI feature and adds to clipped feature

4. UID Assignment¶

def add_uid(feature):
    feature = ee.Feature(feature)
    roi_match = ee.Feature(feature.get("roi_match"))
    uid = roi_match.get("uid")
    return feature.set("uid", uid).set("roi_match", None)

Integration Points¶

computing/misc/agroecological_space.py
├── computing.utils
│   ├── sync_fc_to_geoserver()    # GeoServer sync
│   ├── save_layer_info_to_db()   # Database persistence
│   └── update_layer_sync_status() # Status tracking
├── utilities.gee_utils
│   ├── ee_initialize()           # GEE authentication
│   ├── valid_gee_text()          # Text sanitization
│   ├── check_task_status()       # Task monitoring
│   ├── make_asset_public()       # ACL management
│   ├── is_gee_asset_exists()     # Asset existence check
│   ├── export_vector_asset_to_gee() # Vector export
│   └── get_gee_asset_path()      # Asset path generation
└── utilities.constants
    └── GEE_EXT_DATASET_PATH      # External dataset path

Output¶

Dataset Name: Agroecological

Usage¶

# Trigger via Celery
from computing.misc.agroecological_space import generate_agroecological_data

result = generate_agroecological_data.delay(
    state="Rajasthan",
    district="Jaipur",
    block="Sanganer",
    gee_account_id=1
)

Pattern Classification¶

This module follows the Pan-India Clip Pattern:

flowchart TD
    subgraph Pattern
        A[Load Pan-India Data] --> B[Load ROI]
        B --> C[Filter by Bounds]
        C --> D[Spatial Join]
        D --> E[Add UID]
        E --> F[Export to GEE]
        F --> G[Sync to GeoServer]
    end

This pattern is shared with: - factory_csr.py - green_credit.py - lcw_conflict.py - mining_data.py

Dependencies¶

• ee (Google Earth Engine Python API)
• Celery - Distributed task queue