Using Foursquare place data for estimating building block use

摘要

Information about the Land Use (LU) of built-up areas is required for the comprehensive planning and management of cities. However, due to the high cost of the LU surveys, LU 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 LU mapping purposes. As main case study, we used Foursquare place data for estimating non-residential Building Block Use (BBU) in the city of Amsterdam. Based on the Foursquare place categories, we estimated the use of 9,827 building blocks, and we compared the classification results with a reference BBU 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 LU categories “hotels, restaurants & cafes” (0.76) and “retail” (0.65). The lowest kappa coefficients were found for the LU categories “industries” and “storage & 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 LU categories.