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CRUISEResearchGroup/Massive-STEPS-Tokyo

Massive-STEPS-Tokyo Dataset Summary Massive-STEPS is a large-scale dataset of semantic trajectories intended for understanding POI check-ins. The dataset is derived from the Semantic Trails Dataset and Foursquare Open Source Places, and includes check-in data from 15 cities across 10 countries. The dataset is designed to facilitate research in various domains, including trajectory prediction, POI recommendation, and urban modeling. Massive-STEPS emphasizes… See the full description on the dataset page: https://huggingface.co/datasets/CRUISEResearchGroup/Massive-STEPS-Tokyo.

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Dataset Card

Massive-STEPS-Tokyo

![huggingface](https://huggingface.co/collections/CRUISEResearchGroup/massive-steps-point-of-interest-check-in-dataset-682716f625d74c2569bc7a73) ![huggingface](https://huggingface.co/papers/2505.11239) ![arXiv](https://arxiv.org/abs/2505.11239) ![GitHub](https://github.com/cruiseresearchgroup/Massive-STEPS)

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Dataset Summary

[Massive-STEPS](https://github.com/cruiseresearchgroup/Massive-STEPS) is a large-scale dataset of semantic trajectories intended for understanding POI check-ins. The dataset is derived from the Semantic Trails Dataset and Foursquare Open Source Places, and includes check-in data from 15 cities across 10 countries. The dataset is designed to facilitate research in various domains, including trajectory prediction, POI recommendation, and urban modeling. Massive-STEPS emphasizes the importance of geographical diversity, scale, semantic richness, and reproducibility in trajectory datasets.

City	URL
Bandung 🇮🇩	🤗
Beijing 🇨🇳	🤗
Istanbul 🇹🇷	🤗
Jakarta 🇮🇩	🤗
Kuwait City 🇰🇼	🤗
Melbourne 🇦🇺	🤗
Moscow 🇷🇺	🤗
New York 🇺🇸	🤗
Palembang 🇮🇩	🤗
Petaling Jaya 🇲🇾	🤗
São Paulo 🇧🇷	🤗
Shanghai 🇨🇳	🤗
Sydney 🇦🇺	🤗
Tangerang 🇮🇩	🤗
Tokyo 🇯🇵	🤗

Dataset Sources

The dataset is derived from two sources:

1.Semantic Trails Dataset:
2.Repository: D2KLab/semantic-trails
3.Paper: Monti, D., Palumbo, E., Rizzo, G., Troncy, R., Ehrhart, T., & Morisio, M. (2018). Semantic trails of city explorations: How do we live a city. arXiv preprint [arXiv:1812.04367](https://arxiv.org/abs/1812.04367).
4.Foursquare Open Source Places:
5.Repository: foursquare/fsq-os-places
6.Documentation: Foursquare Open Source Places

Dataset Structure

shell

.
├── tokyo_checkins_test.csv # test set check-ins
├── tokyo_checkins_train.csv # train set check-ins
├── tokyo_checkins_validation.csv # validation set check-ins
├── tokyo_checkins.csv # all check-ins
├── data # trajectory prompts
│   ├── test-00000-of-00001.parquet
│   ├── train-00000-of-00001.parquet
│   └── validation-00000-of-00001.parquet
└── README.md

Data Instances

An example of entries in tokyo_checkins.csv:

csv

trail_id,user_id,venue_id,latitude,longitude,name,address,venue_category,venue_category_id,venue_category_id_code,venue_city,venue_city_latitude,venue_city_longitude,venue_country,timestamp
2018_18009,901,1425,35.59492076661418,139.34504702908666,JR 橋本駅 (JR Hashimoto Sta.),緑区橋本6-1-25,Train Station,4bf58dd8d48988d129951735,59,Hachiōji,35.65583,139.32389,JP,2017-10-03 14:55:00
2018_18009,901,191,35.595137562150846,139.34373266637422,京王 橋本駅 (KO45),緑区橋本2-3-2,Train Station,4bf58dd8d48988d129951735,59,Hachiōji,35.65583,139.32389,JP,2017-10-03 14:59:00
2018_18010,901,38,35.64444469921653,139.35434304296533,北野駅 (Kitano Sta.) (KO33),打越町335-1,Train Station,4bf58dd8d48988d129951735,59,Hachiōji,35.65583,139.32389,JP,2017-10-04 11:57:00
2018_18010,901,3,35.65808032639735,139.34275103595496,京王八王子駅 (Keiō-hachiōji Sta.),明神町3-27-1,Train Station,4bf58dd8d48988d129951735,59,Hachiōji,35.65583,139.32389,JP,2017-10-04 12:00:00
2018_18010,901,2684,35.65829873991196,139.34315085411072,ローソン 京王八王子駅前店,明神町4-6-13,Convenience Store,4d954b0ea243a5684a65b473,215,Hachiōji,35.65583,139.32389,JP,2017-10-04 12:04:00

Data Fields

Field	Description
`trail_id`	Numeric identifier of trail
`user_id`	Numeric identifier of user
`venue_id`	Numeric identifier of POI venue
`latitude`	Latitude of POI venue
`longitude`	Longitude of POI venue
`name`	POI/business name
`address`	Street address of POI venue
`venue_category`	POI category name
`venue_category_id`	Foursquare Category ID
`venue_category_id_code`	Numeric identifier of category
`venue_city`	Administrative region name
`venue_city_latitude`	Latitude of administrative region
`venue_city_longitude`	Longitude of administrative region
`venue_country`	Country code
`timestamp`	Check-in timestamp

Dataset Statistics

City	Users	Trails	POIs	Check-ins	#train	#val	#test
Tokyo 🇯🇵	764	5,482	4,725	13,839	3,836	549	1,097

Additional Information

License

Copyright 2024 Foursquare Labs, Inc. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and limitations under the License.

🔖 Citation

If you find this repository useful for your research, please consider citing our paper:

bibtex

@misc{wongso2025massivestepsmassivesemantictrajectories,
  title         = {Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks},
  author        = {Wilson Wongso and Hao Xue and Flora D. Salim},
  year          = {2025},
  eprint        = {2505.11239},
  archiveprefix = {arXiv},
  primaryclass  = {cs.LG},
  url           = {https://arxiv.org/abs/2505.11239}
}

Contact

If you have any questions or suggestions, feel free to contact Wilson at w.wongso(at)unsw(dot)edu(dot)au.