CRUISEResearchGroup/Massive-STEPS-Melbourne

--- license: apache-2.0 task_categories: - other tags: - poi-recommendation - trajectory-prediction - human-mobility dataset_info: - config_name: default features: - name: user_id dtype: string - name: trail_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 2505811 num_examples: 5504 - name: validation num_bytes: 353884 num_examples: 787 - name: test num_bytes: 723050 num_examples: 1573 download_size: 963371 dataset_size: 3582745 - config_name: tabular features: - name: trail_id dtype: string - name: user_id dtype: int64 - name: venue_id dtype: int64 - name: latitude dtype: float64 - name: longitude dtype: float64 - name: name dtype: string - name: address dtype: string - name: venue_category dtype: string - name: venue_category_id dtype: string - name: venue_category_id_code dtype: int64 - name: venue_city dtype: string - name: venue_city_latitude dtype: float64 - name: venue_city_longitude dtype: float64 - name: venue_country dtype: string - name: timestamp dtype: string splits: - name: train num_bytes: 3022656 num_examples: 15419 - name: validation num_bytes: 422938 num_examples: 2156 - name: test num_bytes: 877365 num_examples: 4475 download_size: 1386830 dataset_size: 4322959 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* - config_name: tabular data_files: - split: train path: tabular/train-* - split: validation path: tabular/validation-* - split: test path: tabular/test-* --- # Massive-STEPS-Melbourne <div align="center"> [](https://huggingface.co/collections/CRUISEResearchGroup/massive-steps-point-of-interest-check-in-dataset-682716f625d74c2569bc7a73) [](https://huggingface.co/papers/2505.11239) [](https://arxiv.org/abs/2505.11239) [](https://github.com/cruiseresearchgroup/Massive-STEPS) </div> ## 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](https://github.com/D2KLab/semantic-trails) and [Foursquare Open Source Places](https://huggingface.co/datasets/foursquare/fsq-os-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 🇮🇩 | [🤗](https://huggingface.co/datasets/CRUISEResearchGroup/Massive-STEPS-Bandung/) | | Beijing 🇨🇳 | [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-Beijing/) | | Istanbul 🇹🇷 | [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-Istanbul/) | | Jakarta 🇮🇩 | [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-Jakarta/) | | Kuwait City 🇰🇼 | [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-Kuwait-City/) | | Melbourne 🇦🇺 | [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-Melbourne/) | | Moscow 🇷🇺 | [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-Moscow/) | | New York 🇺🇸 | [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-New-York/) | | Palembang 🇮🇩 | [🤗](https://huggingface.co/datasets/CRUISEResearchGroup/Massive-STEPS-Palembang/) | | Petaling Jaya 🇲🇾 | [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-Petaling-Jaya/) | | São Paulo 🇧🇷 | [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-Sao-Paulo/) | | Shanghai 🇨🇳 | [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-Shanghai/) | | Sydney 🇦🇺 | [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-Sydney/) | | Tangerang 🇮🇩 | [🤗](https://huggingface.co/datasets/CRUISEResearchGroup/Massive-STEPS-Tangerang/) | | Tokyo 🇯🇵 | [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-Tokyo/) | ### Dataset Sources The dataset is derived from two sources: 1. **Semantic Trails Dataset**: - Repository: [D2KLab/semantic-trails](https://github.com/D2KLab/semantic-trails) - 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)*. 2. **Foursquare Open Source Places**: - Repository: [foursquare/fsq-os-places](https://huggingface.co/datasets/foursquare/fsq-os-places) - Documentation: [Foursquare Open Source Places](https://docs.foursquare.com/data-products/docs/access-fsq-os-places) ## Dataset Structure ```shell . ├── melbourne_checkins_test.csv # test set check-ins ├── melbourne_checkins_train.csv # train set check-ins ├── melbourne_checkins_validation.csv # validation set check-ins ├── melbourne_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 `melbourne_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 2013_859,13,22,-37.801350958185104,144.96374816300963,Rydges On Swanston,701 Swanston St,Hotel,4bf58dd8d48988d1fa931735,271,Carlton,-37.8,144.96667,AU,2012-10-12 09:34:00 2013_859,13,1321,-37.8202543652127,144.95751462533752,The Deck,508 Flinders St,Bar,4bf58dd8d48988d116941735,41,Southbank,-37.8228,144.96434,AU,2012-10-12 10:36:00 2013_862,13,121,-37.81318407201451,144.96107997751233,Charlie's Bar,71 Hardware Ln,Lounge,4bf58dd8d48988d121941735,60,Southbank,-37.8228,144.96434,AU,2013-03-07 08:26:00 2013_862,13,5815,-37.81308997226177,144.96099994659798,Amigos,75 Hardware Ln,Mexican Restaurant,4bf58dd8d48988d1c1941735,204,Southbank,-37.8228,144.96434,AU,2013-03-07 09:12:00 2013_862,13,6811,-37.81644017352219,144.96602093219425,Shebeen,36 Manchester Ln,Cocktail Bar,4bf58dd8d48988d11e941735,55,Southbank,-37.8228,144.96434,AU,2013-03-07 12:34:00 ``` ### 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 | | ----------- | :----: | :-----: | :---: | :-------: | :----: | :---: | :---: | | Melbourne 🇦🇺 | 646 | 7,864 | 7,699 | 22,050 | 5,504 | 787 | 1,573 | ## 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`.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.

搜集汇总

背景与挑战

背景概述

Massive-STEPS是一个大规模语义轨迹数据集，专注于POI签到，涵盖10个国家15个城市的签到数据，数据来源于Semantic Trails Dataset和Foursquare Open Source Places。该数据集强调地理多样性、规模、语义丰富性和可重复性，旨在促进轨迹预测、POI推荐和城市建模等研究领域的发展。