Geolocation Data Collection and Submission Guide

Accurate geolocation data is essential for supply chain transparency, regulatory compliance, and effective environmental monitoring. However, collecting and processing this data can be challenging, particularly when relying on suppliers. This guide outlines best practices for geolocation data collection, cleaning, and submission, helping you streamline the process and ensure compliance with necessary regulations.

The Importance of Geolocation Data

Geolocation data, which includes points and polygons representing specific geographic areas, is crucial for various applications, from supply chain management to monitoring deforestation. The precision and accuracy of this data are paramount, especially for meeting the stringent requirements of regulations such as the European Union Deforestation Regulation (EUDR). Proper data collection, cleaning, and formatting are vital to avoid potential issues down the line.

- Points: These are single coordinates that indicate a general location. Points can be useful for small plots (4 hectares or less) but may not be sufficient for larger areas where exact boundaries are needed.

- Polygons: These represent the exact boundaries of a geographic area and are required for larger plots. They are essential for compliance with regulations like EUDR.

- Multipolygons: These are complex shapes consisting of multiple polygons. They are used when an area is disjointed or has multiple distinct boundaries.

Precision and Accuracy

For compliance with regulations like EUDR, it's crucial that geolocation data is precise. This typically means ensuring that coordinates are recorded to at least 6 decimal places. Always double-check the precision of your data before submission. If necessary, reprocess the data to meet the required precision standards.

Addressing Geometry "Noise" and Precision

Geometry "noise" refers to errors or inaccuracies in the geographic data, such as unnecessary holes, rough edges, or overly complex shapes. Use tools within GIS software to simplify and clean up these geometries. For example, you can apply a convex hull to close holes or use polygon simplification tools to reduce unnecessary complexity.

Best Practices for Data Collection

Collecting geolocation data can be done through various methods, depending on the resources available and the level of accuracy required.

- Smartphone Apps: Use GPS-enabled apps to capture coordinates. These are convenient for general location data but may lack the precision needed for detailed mapping.

- Handheld GPS Devices: For higher accuracy, especially in remote areas or complex terrains, handheld GPS devices are preferred. They offer better precision and are less likely to suffer from signal interference.

- Satellite Imagery & AI: Suitable for larger plots, this method uses satellite images and AI to draw polygons. However, this approach may be less reliable for smallholder farms due to the difficulty in accurately defining boundaries from a distance.

File Formats and Standards

When collecting and submitting geolocation data, it's crucial to use formats that are compatible with the intended applications and regulatory requirements.

Supported File Formats:

- GeoJSON (.geojson): Orbify eventually converts all the geometries into geojson format (compatibility with EUDR portal)

- Shapefile (as a zip containing .shp, .shx & .dbf): A widely used format but less recommended due to potential compatibility issues.

- KML/KMZ: Used for visualizing geographic data in applications like Google Earth.

- CSV for Point Data: Simple and useful for storing point coordinates, particularly in small-scale projects, however due to a lack of standardization or clearly defined CSV structure for passing geolocation data, this approach is not advised

Data Attributes

Ensure all necessary attributes, such as plot ID, coordinates, and any relevant metadata, are included in the file. This information will be stored in Orbify’s database and can be reused or referenced in reports, Orbify custom code components or in future projects.

Data Cleaning and Validation

After collecting the data, it is crucial to validate and clean it to ensure its integrity. Errors in geolocation data can lead to significant issues, including non-compliance with regulations and inaccurate monitoring results.

Validation Techniques:

- Visual Checks: Start by visualizing the polygons or points on a map to identify any obvious errors, such as outliers or incorrect coordinates. These can often be spotted easily when the data is overlaid on a map.

- Repairing Geometries: Use GIS tools like QGIS or ArcGIS to run checks on the validity of the polygons. Tools like "Check Validity" and "Fix Geometries" can help repair common issues, such as self-intersecting polygons or holes within polygons.

- Precision Requirements: Ensure that coordinates have a precision of at least 6 decimal places, particularly when submitting data for EUDR compliance. This level of precision is necessary to accurately define plot boundaries and avoid errors in compliance reporting.

Common Data Cleaning Steps

- Removing Outliers: Polygons gathered in the field may contain deviating coordinates due to low GPS accuracy. These outliers can be identified and corrected using visual checks or automated tools.

- Handling Empty Geometries: Sometimes, data may include entries that lack geographic information. These records should be removed but kept in a separate log for future reference or follow-up with the data provider.

- Duplicate Removal: Ensure that there are no duplicate geometries in your dataset. GIS tools offer functionality to detect and remove duplicates, but visual checks are recommended to catch overlaps that automated tools might miss.

Orbify's Capabilities in Handling Geolocation Data

Orbify offers several advanced features to help streamline the management of geolocation data, making it easier for you to prepare your data for submission and analysis.

- Automatic Splitting of Multipolygons: Orbify can automatically split complex multipolygons into separate projects, making it easier to manage large or disjointed areas.

- Grouping by Property: The platform allows you to group multipolygons into projects based on specific properties, such as ownership or land use, simplifying project management.

- Point to Circular Geometry Conversion: If your data includes points, Orbify can automatically convert these into circular geometries, ensuring they meet the required standards for analysis and reporting.

These features are designed to save you time and reduce the complexity of managing geolocation data, allowing you to focus on higher-level tasks such as analysis and decision-making.