The relevance of newly available big data and urban heritage tourism

This thesis presents an exploratory approach to identify the most popular/attractive urban
heritage areas with their temporal distribution. For this research, geotagged photographs
from Flickr are used. 285.130 geotagged photos are harvested from Flickr and the most
photographed locations are defined using a density-based algorithm (DBSCAN). A method is
processed to define the most concentrated areas in Amsterdam. The temporal distribution of
tourists and locals is analysed per POIs and per heritage types to define differences regarding
time stamp. Clusters generated by DBSCAN are used to find heritage distribution by using
geoprocessing tools in QGIS. The results from POI analysis and heritage analysis are evaluated
by comparing promoted tourist map. Also, eating-drinking points and tram-metro stops from
Amsterdam City Data are processed to investigate the relation between urban facilities and
the most attractive/popular urban heritage areas. It is concluded that newly available datasets
are useful sources to investigate spatio-temporal pattern of tourists and locals in the urban
heritage areas. It provides a better understanding of distribution of people in time and space.