---
license: cc-by-nc-4.0
tags:
  - conflict-forecasting
  - geospatial-transformer
  - political-violence
  - political-science
  - ucdp
  - pytorch
---

# UCDP Mini Spatial Event Encoder (ucdp_M)

A small transformer encoder that learns representations of location-months from
UCDP Georeferenced Event Dataset (GED) event histories. Each PRIO-GRID 0.5 degree
cell-month is represented by its trailing 12-month sequence of organized-violence
events (type of violence, dyad sides, fatality bin, month offset), encoded with
masked-event modeling. The CLS embedding is a general-purpose representation for
conflict forecasting and transfer learning: fine-tune it for incidence forecasting,
rebel/state violence, escalation, or related event-based tasks.

- Architecture: field embeddings -> concat-project -> [CLS] + TransformerEncoder
  (4 layers, 8 heads, d=128), masked-event-modeling objective.
- Unit: PRIO-GRID 0.5 deg cell-month; max 200 events per sequence; seed 123.
- Training data: UCDP GED 25.1 (1989-2024), the open (CC BY 4.0) georeferenced
  event dataset of organized violence.

## Usage
```python
from modeling_ucdp import load_encoder
model, cfg = load_encoder(".")
# seq: LongTensor [B, 200, 5] (ids per ucdp_vocab.json; 0 = pad)
# mask: BoolTensor [B, 200] (True = real event)
z = model.encode_sequence(seq, mask)   # [B, 128] cell-month embeddings
```

## License and attribution
Model weights and code: CC BY-NC 4.0. Training data is the UCDP GED (CC BY 4.0).
If you use this model, cite UCDP:

- Davies, S., Pettersson, T., Sollenberg, M., & Oberg, M. (2025). Organized violence
  1989-2024, and the challenges of identifying civilian victims. Journal of Peace
  Research 62(4).
- Sundberg, R. & Melander, E. (2013). Introducing the UCDP Georeferenced Event
  Dataset. Journal of Peace Research 50(4).

## Limitations
- UCDP GED covers organized violence with at least one fatality; protests and
  non-lethal events are not included. Coverage reflects media/source reporting.
- The encoder is a research artifact for representation learning and forecasting
  research. It is not suitable for operational targeting or safety-critical use.
- Embeddings reflect historical reporting patterns and inherit their biases.