Using Foursquare place data for estimating building block use

摘要

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 However, due to the high cost of the land use surveys, land use data is outdated or not available for many cities.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.