Developing Feasible Scenarios for Bioenergy Production in South Africa with Python
summary
BioEnergy Atlas for South Africa models transport and siting costs and produces concrete outputs: a 48 million‑node road graph, millions of least‑cost off‑road paths from SRTM cost surfaces, and 397 cost‑competitive potential facility sites. It implements a virtual roads algorithm (scikit-image, SRTM) and location-allocation via NetworkX to pick facility locations, then calculates CAPEX/OPEX, emissions and jobs with Pandas and GDAL and exposes static maps plus a Dash/Plotly dashboard (large‑data rendering remains a bottleneck).
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chapters (29)
- 0:00Introduction
- 1:15Who are we
- 7:30The problem
- 9:18Methodology
- 11:05Why Python
- 12:08Virtual Rhodes Map
- 12:40Aerial Imagery
- 13:14Digital Elevation Model
- 14:50Windowed Approach
- 17:19Decision Model
- 18:00Results for South Africa
- 18:49Location Allocation
- 22:04Outputs
- 22:39Static Output
- 23:55Dashboards
- 25:50Next Steps
- 26:35Questions
- 26:59Why invest in bioenergy
- 28:30Virtual Roads Algorithm
- 29:24Image Processing
- 30:37Python PSPA
- 32:15PG Routing
- 33:54Data Sets
- 35:55Impact on Biodiversity
- 38:14Offroad Routing
- 39:11Metadata
- 40:03Biodiesel
- 41:05Transporting Biomass
- 42:17Main Feedstock
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