1. Background:
Vi Agroforestry has two geodatabases (i.e. database1 and database2) that support monitoring and reporting in one of our projects. Database1 constituting about 12,000 farm polygons have errors derived from erroneous data collection and include a lack of coordinate reference system (CRS), errors in specific data points coordinates, overlaps and distortions. Database2 constituting about 27,000 farm polygons exhibit overlaps and distortion with some polygons lacking necessary attribute information. The accuracy and reliability of these geodatabases is crucial for Vi Agroforestry operations, and therefore, we seek the expertise of a GIS consultant to review, correct and merged these geodatabases using manual and/or machine learning techniques. The consultant will support, and quality assure the process of collecting farm polygons from farmers spread in Kitale and Kisumu areas whose farm polygons are missing from the clean and merged geodatabase.
2. Objectives:
The primary objective of this project is to generate spatial data that correctly represent farms of about 30,000 project farmers in Kisumu and Kitale areas by efficiently and cost-effectively correcting the existing databases and mapping farmers farms with missing farm polygons.
3. Scope of Work
The GIS Consultant will be responsible for the following tasks:
- Conducting a thorough review of the existing geodatabases to identify areas of concern, including missing coordinate reference systems and coordinate errors, overlapping features, geometric distortions such as misalignments, gaps, and irregular shapes.
- Design, develop and deploy efficient and cost-effective solutions for correcting issues in the existing geodatabases and merging the cleaned polygons from the two geodatabases to form one clean geodatabase.
- Identify project farmers who are missing from the merged clean geodatabase and develop a data collection and ground truthing plan that includes mobile data collection and functional web GIS application.
- Support Vi Agroforestry and partner organisations in collecting missing farm tracks spread in Kisumu and Kitale.
- Validating the corrected polygons to ensure accuracy and consistency with real-world data.
- Providing documentation of the correction process, including details of the machine learning methods used and any adjustments made to the database.
4. Deliverables
The GIS Consultant will deliver the following outputs:
- A merged geodatabase with clean farm polygons derived from the existing geodatabases.
- A comprehensive report detailing the findings of the review, including identified inaccuracies and errors in the database.
- A solution based on Kobotoolbox for farm mapping and collection of farmers’ attributes data. The solution should be integrated to a functional web GIS application for real-time visualization and quality assurance.
- A complete geodatabase that includes clean farm polygons from the merged databases and newly collected farm polygons from the mapping exercise.
- Recommendations for maintaining the accuracy and integrity of the polygon database in the future.
5. Timeline:
The project is expected to be completed within 30 days with regular progress updates provided to the project manager.
6. The Team and Qualifications
The ideal consultant should have a team of 7 personnel (1 team leader and 6 experts) with the following expertise.
Team leader
- An advanced degree in Geographic Information Systems, Computer Science, or a related field.
- Over 5 years proven experience in designing, building, and deploying GIS solutions.
- Strong programming skills, preferably in languages such as Python or R.
- Excellent communication and documentation skills.
Team Members
The 6 experts will be expected to implement error collection and mapping solutions respective geographical areas. The team member should possess the following qualifications and expertise:
- A degree in Geographic Information Systems, Computer Science, or a related field.
- Proven experience in GIS data management, analysis, and mapping, particularly with polygon datasets.
- Experience conducting GIS data collection using mobile phones.
- Knowledge of GQIS
7. Budget:
The budget for this project will be negotiated based on the qualifications and experience of the selected candidate.
8. Evaluation Criteria:
Candidates will be evaluated based on the following criteria:
- Relevant experience and qualifications in GIS and machine learning.
- Demonstrated ability to work with polygon datasets and correct spatial data errors.
- Clarity and thoroughness of the proposed methodologies, strategies and approaches
9. Point of Contact:
For inquiries or clarification, please contact peter.wachira@viagroforestry.org
10. Confidentiality:
All information shared during this project is considered confidential and should not be disclosed to third parties without prior consent.
11. Amendment of Terms:
These terms of reference are subject to amendment or modification with the agreement of both parties.
12. Acceptance of Terms:
By accepting this project, the GIS consultant agrees to abide by the terms and conditions outlined in this document.
How to apply
Candidates are expected to submit.
- Technical Proposal: A short technical detailing methodologies, strategies and approaches that can be used to solve the problem.
- Financial proposal: The financial proposal should contain both the cost of personnel and the cost of supporting infrastructure. It should clearly show the daily rate for the GIS team leader and team members.
- Additional documents: Include CVs of the team and report/sample on previous works.
The submission should be emailed to procurement@viagroforestry.org, and copied to peter.wachira@viagroforestry.org