Using Foursquare place data for estimating building block use

Spyratos, Spyridon; Stathakis, Demetris; Lutz, Michael; Tsinaraki, Chrisa

doi:10.1177/0265813516637607

Cited by 18 publications

(28 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [17], a real-time Google Maps-based arterial traffic information system for urban streets is presented. In [18], the authors proposed the reuse of up-to-date and low-cost place data from social media applications for land use mapping purposes by Foursquare place data. In [19], the study aimed to explore Foursquare mobility networks and investigate the phenomena of clustering venues across the cities.…”

Section: Related Workmentioning

confidence: 99%

A GNSS-Based Crowd-Sensing Strategy for Specific Geographical Areas

Lin

Hung

Hsu

et al. 2020

Sensors

View full text Add to dashboard Cite

Infectious diseases, such as COVID-19, SARS, MERS, etc., have seriously endangered human safety, economy, and education. During the spread of epidemics, restricting the range of activities of personnel is one of the options for the prevention and treatment of infectious diseases. A global navigation satellite system (GNSS), it can provide accurate coordinates of latitude and longitude to targets with GNSS receivers. However, it is not common to use GNSS coordinates to represent positions in social life. For epidemic management, it is important to know the locations (and addresses) of targets, especially in social life. When there are many targets, it is not easy to efficiently map these coordinates to locations. Therefore, we propose a GNSS-based crowd-sensing strategy for specific geographical areas that can be used to calculate how many targets are in specific geographical areas or whether a target is in a specific area. This strategy is based on the coordinates of latitude and longitude provided by GNSS to find the locations of these coordinates. As simulated data, the data records containing latitude and longitude in a well-known social networking service platform are used. The strategy is also available for mining hot spots or hot areas.

show abstract

Section: Related Workmentioning

confidence: 99%

A GNSS-Based Crowd-Sensing Strategy for Specific Geographical Areas

Lin

Hung

Hsu

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Other researchers have characterised LULC, human activites and place semantics using social sensing (Liu et al 2015). The employed data range from spatio-temporal mobile phone data (Jacobs-Crisioni et al 2014;Pei et al 2014;Ríos and Muñoz 2017), Foursquare data (Aubrecht et al 2017;Spyratos et al 2017), geotweets (Frias-Martinez and Frias-Martinez 2014; Lloyd and Cheshire 2017), and georeferenced photos (Feick and Robertson 2015;Antoniou et al 2016). Each of the social sensing approaches presents a considerable challenge for pursuing at a continental scale, due to data access, computational demand, or spatially and demographically biased user bases.…”

Section: Related Workmentioning

confidence: 99%

Increasing the detail of European land use/cover data by combining heterogeneous data sets

Rosina

Silva

Vizcaino

et al. 2018

International Journal of Digital Earth

View full text Add to dashboard Cite

Data on land use and land cover (LULC) are a vital input for policy-relevant research, such as modelling of the human population, socioeconomic activities, transportation, environment, and their interactions. In Europe, CORINE Land Cover has been the only data set covering the entire continent consistently, but with rather limited spatial detail. Other data sets have provided much better detail, but either have covered only a fraction of Europe (e.g. Urban Atlas) or have been thematically restricted (e.g. Copernicus High Resolution Layers). In this study, we processed and combined diverse LULC data to create a harmonised, ready-to-use map covering 41 countries. By doing so, we increased the spatial detail (from 25 to one hectare) and the thematic detail (by seven additional LULC classes) compared to the CORINE Land Cover. Importantly, we decomposed the class 'Industrial and commercial units' into 'Production facilities', 'Commercial/service facilities' and 'Public facilities' using machine learning to exploit a large database of points of interest. The overall accuracy of this thematic breakdown was 74%, despite the confusion between the production and commercial land uses, often attributable to noisy training data or mixed land uses. Lessons learnt from this exercise are discussed, and further research direction is proposed.

show abstract

“…LBS often use spatial information derived from Point of Interest (POI) information, for example when recommending overnight accommodations based on user reviews. The tech industry utilizes POIs in geo-gaming and mapping applications (Juhász & Hochmair, 2017;Juhász, Novack, Hochmair, & Qiao, 2020), and to derive detailed land use/land cover information (Spyratos, Stathakis, Lutz, & Tsinaraki, 2017). Apart from being a static collection of places, POI data combined with visitor patterns can be used to study urban dynamics and user markets.…”

Section: Introductionmentioning

confidence: 99%

Studying Spatial and Temporal Visitation Patterns of Points of Interest Using SafeGraph Data in Florida

Juhász¹,

Hochmair²

2020

giforum

View full text Add to dashboard Cite

SafeGraph is a commercial provider of massive Point of Interest (POI) data, including visitation patterns in North America. Although the data source does not share specific travel trajectories, the data available includes daily and monthly POI visitation numbers for over 160 categories, as well as information about where visitors come from and which other POI categories they visit. This allows analysts to gain insight into travel behavior in a geographic region over time. This study analyzes various aspects of visitation patterns that can be derived from the SafeGraph dataset for Florida. Using three major Florida cities, namely Miami, Orlando and Jacksonville, temporal patterns of daily and monthly visit numbers are correlated between various POI categories, and the effect of a short event (Hurricane Irma) on daily visitation numbers around the event is explored. In addition, travel distances from home to POIs are compared between different POI categories, and Ordinary Least Squares (OLS) regression models are used to identify factors associated with increased or decreased distance between home and a specific POI category. The study concludes that the aggregated data provided on the SafeGraph platform helps the GIScience community to learn more about travel patterns in both the spatial and the temporal domains.

show abstract

Using Foursquare place data for estimating building block use

Cited by 18 publications

References 19 publications

A GNSS-Based Crowd-Sensing Strategy for Specific Geographical Areas

A GNSS-Based Crowd-Sensing Strategy for Specific Geographical Areas

Increasing the detail of European land use/cover data by combining heterogeneous data sets

Studying Spatial and Temporal Visitation Patterns of Points of Interest Using SafeGraph Data in Florida

Contact Info

Product

Resources

About