Utilizing Urban Geospatial Data to Understand Heritage Attractiveness in Amsterdam
Bibliographic Data
| ID | 22034995 |
|---|---|
| Authors | Sevim Sezi Karayazı (0000-0003-1100-4042, Eindhoven University of Technology, corresponding author), Gamze Dane (0000-0001-6107-2727, Eindhoven University of Technology), Bauke De Vries (0000-0002-7885-8221, Eindhoven University of Technology) |
| Year | 2021 |
| Volume | 10 |
| Issue | 4 |
| Pages | 198 |
| Publication date | 2021-03-25 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi10040198 |
| OpenAlex | W3138359330 |
| Language | EN |
| Citations received | 7 |
| References cited | 33 |
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
Aesthetics · Archaeology · Attractiveness · Cartography · Cluster analysis · Cultural heritage · Data science · DBSCAN · Fuzzy clustering · Geography · Geospatial analysis · Tourism · Computer Science · Diverse Aspects of Tourism Research · Economic and Environmental Valuation · Land Use and Ecosystem Services · Artificial Intelligence
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| Unique citing works | 7 |
|---|---|
| Citations per year | 1.4 |
| Citation span | 2021 - 2025 (5) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 7 |