seahorse-stpp/covid-stpp

COVID-19 NJ STPP Benchmark Dataset

A benchmark-ready Spatio-Temporal Point Process (STPP) dataset derived from COVID-19 case data for New Jersey, following the official split semantics of the Neural STPP paper.

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Dataset Description

Each record represents a sequence of COVID-19 case-report events for a specific reporting unit on a specific date in 2020 (March–July). Events capture the time and location of reported cases.

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Source Format

Raw data was stored in a NumPy .npz archive (covid_nj_cases.npz).
Each key is a string of the form YYYYMMDD_XXX, where:

• YYYYMMDD is the date (e.g. 20200315)
• XXX is a zero-padded county/region index (e.g. 000–049)

Each key maps to a (N, 3) float64 array with columns [t, x, y].

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Sequence Unit

One .npz key = one sequence.
No new windowing or segmentation was applied.

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Event Schema

Field	Type	Description
t	float	Time of event — fractional hours within the day
x	float	Longitude of reported case location (NJ range ≈ −74.80 to −73.84)
y	float	Latitude of reported case location (NJ range ≈ 40.34 to 41.09)

Values are exported as-is — no normalization applied.

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File Structure

covid-stpp/
├── train.jsonl        # 1450 sequences
├── val.jsonl          # 100 sequences
├── test.jsonl         # 100 sequences
├── splits.json        # {"train": [...], "val": [...], "test": [...]}
├── dataset_meta.json  # Task/schema metadata
└── README.md

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JSONL Row Schema

{
  "sequence_id": "20200315_000",
  "events": [
    {"t": 3.42, "x": -74.23, "y": 40.71},
    ...
  ]
}

Example (Python)

import json

with open("train.jsonl") as f:
    for line in f:
        seq = json.loads(line)
        sid    = seq["sequence_id"]
        events = seq["events"]
        t = [e["t"] for e in events]
        x = [e["x"] for e in events]
        y = [e["y"] for e in events]

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Source & License

• Source data: COVID-19 case data for New Jersey, 2020
• Processing code reference: facebookresearch/neural_stpp (MIT License)