Next Article in Journal
Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain)
Next Article in Special Issue
CrimeVec—Exploring Spatial-Temporal Based Vector Representations of Urban Crime Types and Crime-Related Urban Regions
Previous Article in Journal
Depth Contours and Coastline Generalization for Harbour and Approach Nautical Charts
Previous Article in Special Issue
Landscape Visual Sensitivity Assessment of Historic Districts—A Case Study of Wudadao Historic District in Tianjin, China
Article

Utilizing Urban Geospatial Data to Understand Heritage Attractiveness in Amsterdam

Department of the Built Environment, Information Systems in the Built Environment, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editors: Wolfgang Kainz and Maria Antonia Brovelli
ISPRS Int. J. Geo-Inf. 2021, 10(4), 198; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040198
Received: 31 December 2020 / Revised: 28 February 2021 / Accepted: 22 March 2021 / Published: 25 March 2021
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
Touristic cities are home to historical landmarks and irreplaceable urban heritages. Although tourism brings financial advantages, mass tourism creates pressure on historical cities. Therefore, “attractiveness” is one of the key elements to explain tourism dynamics. User-contributed and geospatial data provide an evidence-based understanding of people’s responses to these places. In this article, the combination of multisource information about national monuments, supporting products (i.e., attractions, museums), and geospatial data are utilized to understand attractive heritage locations and the factors that make them attractive. We retrieved geotagged photographs from the Flickr API, then employed density-based spatial clustering of applications with noise (DBSCAN) algorithm to find clusters. Then combined the clusters with Amsterdam heritage data and processed the combined data with ordinary least square (OLS) and geographically weighted regression (GWR) to identify heritage attractiveness and relevance of supporting products in Amsterdam. The results show that understanding the attractiveness of heritages according to their types and supporting products in the surrounding built environment provides insights to increase unattractive heritages’ attractiveness. That may help diminish the burden of tourism in overly visited locations. The combination of less attractive heritage with strong influential supporting products could pave the way for more sustainable tourism in Amsterdam. View Full-Text
Keywords: location-based social media data; urban geospatial data; Flickr data; heritage; spatial analysis; DBSCAN; OLS; GWR location-based social media data; urban geospatial data; Flickr data; heritage; spatial analysis; DBSCAN; OLS; GWR
Show Figures

Figure 1

MDPI and ACS Style

Karayazi, S.S.; Dane, G.; Vries, B.d. Utilizing Urban Geospatial Data to Understand Heritage Attractiveness in Amsterdam. ISPRS Int. J. Geo-Inf. 2021, 10, 198. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040198

AMA Style

Karayazi SS, Dane G, Vries Bd. Utilizing Urban Geospatial Data to Understand Heritage Attractiveness in Amsterdam. ISPRS International Journal of Geo-Information. 2021; 10(4):198. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040198

Chicago/Turabian Style

Karayazi, Sevim S., Gamze Dane, and Bauke d. Vries 2021. "Utilizing Urban Geospatial Data to Understand Heritage Attractiveness in Amsterdam" ISPRS International Journal of Geo-Information 10, no. 4: 198. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040198

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop