Using Foursquare place data for estimating building block use
Bibliographic Data
| ID | 21247331 |
|---|---|
| Authors | Spyridon Spyratos (0000-0002-1675-9738, University of Thessaly, corresponding author), Demetris Stathakis (0000-0003-2411-7249, University of Thessaly), Michael Lutz (Joint Research Centre), Chrisa Tsinaraki (0000-0002-6012-0835, Joint Research Centre) |
| Year | 2017 |
| Volume | 44 |
| Issue | 4 |
| Pages | 693-717 |
| Publication date | 2017-07-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environment and Planning B Urban Analytics and City Science (JOURNAL) |
| Journal identifiers | ISSN: 2399-8083 • E-ISSN: 2399-8091 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/0265813516637607 |
| OpenAlex | W2328176884 |
| Language | EN |
| Citations received | 4 |
| References cited | 19 |
Information about the land use of built-up areas is required for the comprehensive planning and management of cities. However, due to the high cost of the land use surveys, land use data is out-dated or not available for many cities. Therefore, we propose the reuse of up-to-date and low-cost place data from social media applications for land use mapping purposes. As main case study, we used Foursquare place data for estimating nonresidential building block use in the city of Amsterdam. Based on the Foursquare place categories, we estimated the use of 9827 building blocks, and we compared the classification results with a reference building block use dataset. Our evaluation metric is the kappa coefficient, which determines if the classification results are significantly better than a random guess result. Using the optimal set of parameter values, we achieved the highest kappa coefficient values for the land use categories “ hotels, restaurants and cafes” (0.76) and “ retail” (0.65). The lowest kappa coefficients were found for the land use categories “ industries” and “ storage and unclear”. We have also applied the methodology in another case study area, the city of Varese in Italy, where we had similar accuracy results. We therefore conclude that Foursquare place data can be trusted only for the estimation of particular land use categories
Civil engineering · Cohen's kappa · Data mining · Data set · Land use · Machine learning · Operations management · Reuse · Computer Science · Engineering · Geographic Information Systems Studies · Human Mobility and Location-Based Analysis · Mathematics · Urban Transport and Accessibility · Artificial Intelligence
Space and Place
A new insight into land use classification based on aggregated mobile phone data
A review on buildings energy consumption information
A review of assessing the accuracy of classifications of remotely sensed data
Many Pathways from Land Use to Health
Space and place
A Coefficient of Agreement for Nominal Scales
Citizens as sensors
On the Price of Land and the Value of Amenities
Land Use and Violent Crime
Evaluating the Impact of Land-Use Density and Mix on Spatiotemporal Urban Activity Patterns
| Unique citing works | 4 |
|---|---|
| Citations per year | 0,44 |
| Citation span | 2017 - 2022 (6) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 4 |