Utilizing Urban Geospatial Data to Understand Heritage Attractiveness in Amsterdam
| العنوان: | Utilizing Urban Geospatial Data to Understand Heritage Attractiveness in Amsterdam |
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| المؤلفون: | GZ Gamze Dane, Bauke de Vries, Sevim Sezi Karayazı |
| المساهمون: | Information Systems Built Environment, Urban Systems & Real Estate, EAISI Mobility |
| المصدر: | ISPRS International Journal of Geo-Information, Vol 10, Iss 198, p 198 (2021) ISPRS International Journal of Geo-Information Volume 10 Issue 4 ISPRS International Journal of Geo-Information, 10(4):198. Multidisciplinary Digital Publishing Institute (MDPI) |
| بيانات النشر: | MDPI AG, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Attractiveness, DBSCAN, Geospatial analysis, spatial analysis, Geography, Planning and Development, location-based social media data, lcsh:G1-922, OLS, computer.software_genre, Flickr data, heritage, 0502 economics and business, Earth and Planetary Sciences (miscellaneous), Regional science, urban geospatial data, Relevance (information retrieval), Computers in Earth Sciences, GWR, Built environment, Sustainable tourism, 05 social sciences, SDG 8 - Decent Work and Economic Growth, Geography, SDG 12 – Verantwoordelijke consumptie en productie, SDG 8 – Fatsoenlijk werk en economische groei, Spatial clustering, 050211 marketing, SDG 12 - Responsible Consumption and Production, computer, 050212 sport, leisure & tourism, Tourism, lcsh:Geography (General) |
| الوصف: | 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. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2220-9964 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b0a42cd0bf4f7aa1b0306f817a71ffcc https://www.mdpi.com/2220-9964/10/4/198 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....b0a42cd0bf4f7aa1b0306f817a71ffcc |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 22209964 |
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