Foursquare OS Places is now a gated dataset on Hugging Face. Read more about why we are making this change here: https://medium.com/@foursquare/evolving-fsq-os-places-fa7a3f5197cd

  Access FSQ OS Places

With Foursquare’s Open Source Places, you can access free data to accelerate geospatial innovation and insights. View the Places OS Data Schemas for a full list of available attributes.

  Prerequisites

In order to access Foursquare's Open Source Places data, it is recommended… See the full description on the dataset page: https://huggingface.co/datasets/foursquare/fsq-os-places.

Dataset

This dataset was created by Theo Viel

Released under CC0: Public Domain

Contents

The dataset used in the paper is Twitter and Foursquare, two real-world aligned heterogeneous social networks.

Dataset

This dataset was created by takaito

Contents

How high is the brand awareness of Foursquare in the United States?When it comes to social media users, brand awareness of Foursquare is at *** in the United States. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Foursquare in the United States?In total, ** of U.S. social media users say they like Foursquare. However, in actuality, among the *** of U.S. respondents who know Foursquare, *** of people like the brand.What is the usage share of Foursquare in the United States?All in all, ** of social media users in the United States use Foursquare. That means, of the *** who know the brand, *** use them.How loyal are the users of Foursquare?Around ** of social media users in the United States say they are likely to use Foursquare again. Set in relation to the ** usage share of the brand, this means that *** of their users show loyalty to the brand.What's the buzz around Foursquare in the United States?In March 2022, about ** of U.S. social media users had heard about Foursquare in the media, on social media, or in advertising over the past four weeks. Of the *** who know the brand, that's ***, meaning at the time of the survey there's little buzz around Foursquare in the United States.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

The Foursquare and Weeplace datasets contain users' check-in histories in the cities of New York, Los Angeles, and Chicago.

A complete list of live websites using the Foursquare Integration technology, compiled through global website indexing conducted by WebTechSurvey.

Context

Dataset includes check-in, tip and tag data of restaurant venues in NYC collected from Foursquare from 24 October 2011 to 20 February 2012. It contains 3,112 users, 3,298 venues with 27,149 check-ins and 10,377 tips.

Content

1. NY_Restauraunts_checkins.csv {Originally dataset_ubicomp2013_checkins.txt} has two columns. Each line represents a check-in event. The first column is user ID, while the second column is venue ID.

2. NY_Restauraunts_tips.csv {Originally dataset_ubicomp2013_tips.txt} has three columns. Each line represents a tip/comment a user left on a venue. The first and second columns are user ID and venue ID, repsectively. The third column is tip text.

3. NY_Restauraunts_tags.csv {Originally dataset_ubicomp2013_tags.txt} has two columns. Each line represents the tags users added to a venue. The first column is venue ID while the second column is tag set of the corresponding venues. Empty tag sets may exist for a venue if no user has ever added a tag to it.

Acknowledgements

Columns headers, details and file formats added manually.

Source: Scraped from Foursquare and downloaded from: https://sites.google.com/site/yangdingqi/home/foursquare-dataset

• Dingqi Yang, Daqing Zhang, Zhiyong Yu and Zhiwen Yu, Fine-Grained Preference-Aware Location Search Leveraging Crowdsourced Digital Footprints from LBSNs. In Proceeding of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2013), September 8-12, 2013, in Zurich, Switzerland.
• Dingqi Yang, Daqing Zhang, Zhiyong Yu and Zhu Wang, A Sentiment-enhanced Personalized Location Recommendation System. In Proceeding of the 24th ACM Conference on Hypertext and Social Media (HT 2013), 1-3 May, 2013, Paris, France. Dingqi Yang, Daqing Zhang, Zhiyong Yu, Zhiwen Yu, Djamal Zeghlache.
• SESAME: Mining User Digital Footprints for Fine-Grained Preference-Aware Social Media Search. ACM Trans. on Internet Technology, (TOIT), 14(4), 28, 2014.

Original README included (note that columns were added).

Inspiration

Interesting questions:

• Linkage to additional data.
• Sentiment analysis.
• Recommender systems, prediction of checkins to related venues or tags.
• Use for augmenting other datasets with geospatial or geotemporal data (for that period).

doublyy/FourSquare-NYC-POI dataset hosted on Hugging Face and contributed by the HF Datasets community

This statistic gives information on the number of check-ins by registered members on Foursquare between August 2011 and August 2017. As of the most recently reported month, the social check-in app community had accumulated over ** billion check-ins worldwide.

Dataset Card for "FourSquare-NYC-POI"

More Information needed

A complete list of live websites using the Wp Foursquare technology, compiled through global website indexing conducted by WebTechSurvey.

This dataset provides information about the number of properties, residents, and average property values for Foursquare Road cross streets in Windsor, VA.

Abstract This aim of this paper is the acquisition of geographic data from the Foursquare application, using data mining to perform exploratory and spatial analyses of the distribution of tourist attraction and their density distribution in Rio de Janeiro city. Thus, in accordance with the Extraction, Transformation, and Load methodology, three research algorithms were developed using a tree hierarchical structure to collect information for the categories of Museums, Monuments and Landmarks, Historic Sites, Scenic Lookouts, and Trails, in the foursquare database. Quantitative analysis was performed of check-ins per neighborhood of Rio de Janeiro city, and kernel density (hot spot) maps were generated The results presented in this paper show the need for the data filtering process - less than 50% of the mined data were used, and a large part of the density of the Museums, Historic Sites, and Monuments and Landmarks categories is in the center of the city; while the Scenic Lookouts and Trails categories predominate in the south zone. This kind of analysis was shown to be a tool to support the city's tourist management in relation to the spatial localization of these categories, the tourists’ evaluations of the places, and the frequency of the target public.

Places of Interest(POI) within the City of Detroit from Foursquare's Open Source Places dataset.With Foursquare’s Open Source Places, you can access free data to accelerate geospatial innovation and insights.https://docs.foursquare.com/data-products/docs/access-fsq-os-places02-06-2025

This dataset includes long-term (about 22 months from Apr. 2012 to Jan. 2014) global-scale check-in data collected from Foursquare, and also two snapshots of user social networks before and after the check-in data collection period (see more details in our paper). The check-in dataset contains 22,809,624 checkins by 114,324 users on 3,820,891 venues. The social network data contains 363,704 (old) and 607,333 (new) friendships. Due to frequent requests, we also include the raw check-in dataset containing 90,048,627 checkins by 2,733,324 users on 11,180,160 venues.Please cite our paper if you publish material based on this dataset:+ Dingqi Yang, Bingqing Qu, Jie Yang, Philippe Cudre-Mauroux, Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach, In Proc. of The Web Conference (WWW'19). May. 2019, San Francisco, USA. + Dingqi Yang, Bingqing Qu, Jie Yang, Philippe Cudre-Mauroux, LBSN2Vec++: Heterogeneous Hypergraph Embedding for Location-Based Social Networks, IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020.

10 millions flickr photos in 10 US & 10 European cities (photo_id, user_id, time_taken, GPS_coordinates, city, if available: associated venue id) 4.5 million checkins (anonymized user_id, venue id, local time, city) 400k venues (venue id, name, GPS_coordinates, city, category id) Please cite our paper if you're using this data in your own work @inproceedings{Thesis15,author = {{Le Falher}, G\'{e}raud and Gionis, Aristides and Mathioudakis, Michael},booktitle = {International AAAI Conference on Web and Social Media},title = {{Where Is the Soho of Rome? Measures and Algorithms for Finding Similar Neighborhoods in Cities}},url = {http://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/view/10514},year = {2015}}

Poster used for Foursquare flash hour promotion

Dataset Card for "FourSquare-TKY-POI"

More Information needed

sabin125/foursquare-os-filtered-us-places dataset hosted on Hugging Face and contributed by the HF Datasets community