I5m41L/gowalla_checkins_stpp

Gowalla Check-ins STPP Benchmark Dataset

A benchmark-ready Spatio-Temporal Point Process (STPP) dataset derived from Gowalla Check-ins (~6.4 Million records), following the standard split semantics for Neural STPP evaluation.

Dataset Description

Each record represents a sequence of user check-in events. The dataset covers historical check-in incidents, partitioned sequentially into train / val / test subsets (70% / 15% / 15% ratio).

Source Format

Raw data was obtained from Stanford SNAP. Each sequence maps to a (N, 3) float64 array with columns [t, x, y].

Sequence Unit

One sequence corresponds to a chunk of contiguous events. No new windowing or segmentation was applied. The dataset unit aligns with benchmark STPP formulations.

Event Schema

Field	Type	Description
t	float	Time of event
x	float	Longitude or X coordinate
y	float	Latitude or Y coordinate

Values are exported as-is — no normalization applied. The Neural STPP codebase applies StdScaler normalization at training time, not during preprocessing.

Split Semantics

Split	Sequences	Events	Ratio
train	45,101	4,510,024	70%
val	9,665	966,435	15%
test	9,665	966,433	15%

Split logic mirrors a sequential temporal split sequential_split_ratio_(0.7, 0.15, 0.15) — no random splitting, no reshuffling.

File Structure

gowalla_checkins/
├── train.jsonl        # 45101 sequences
├── val.jsonl          # 9665 sequences
├── test.jsonl         # 9665 sequences
├── splits.json        # {"train": [...], "val": [...], "test": [...]}
├── dataset_meta.json  # Task/schema metadata
└── README.md

JSONL Row Schema

Each line in a .jsonl file is a JSON object:

{
  "sequence_id": "seq_0",
  "events": [
    {"t": 1.062, "x": -122.419, "y": 37.774},
    {"t": 2.318, "x": -122.420, "y": 37.775}
  ]
}

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"]        # list of {"t", "x", "y"} dicts
        t = [e["t"] for e in events]
        x = [e["x"] for e in events]
        y = [e["y"] for e in events]

Source & License

Source data: Stanford SNAP URL: https://snap.stanford.edu/data/loc-gowalla_totalCheckins.txt.gz

Version 1.0.0 Time Ordering Fix

Version 1.0.0 applies a deterministic data-level repair: events are stable-sorted by t globally before chunking. Validation ensures non-decreasing timestamps for every sequence in train/val/test.