Assessing Users’ Recreational Demand in Urban Parks in Tehran with the Help of the Artificial Neural Network

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

JAHANI ALI; Khalegh panah Reyhaneh; Gashtasb Hamid; Khorasani Nematallah

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Social Studies in Tourism Year:2020 | Volume:7 | Issue:14 Page(s): 339-362

Download Full-Text

Persian Verion

View:

319

Download:

Cites:

Information Journal Paper

Title

Assessing Users’ Recreational Demand in Urban Parks in Tehran with the Help of the Artificial Neural Network

Author(s)

JAHANI ALI | Khalegh panah Reyhaneh | Gashtasb Hamid | Khorasani Nematallah | Issue Writer Certificate

Keywords

recreational demandQ1

urban parkQ2

artificial neural networkQ2

quality of landscapeQ1

urban tourismQ1

Abstract

The advantages of public green spaces are clear. However, it is hard to estimate which parks provide higher standards for users’ (both tourists and citizens) recreational demands. This study provided a model to assess recreational demands in urban parks with the help of the artificial neural network. The aim was to clarify the rules of satisfaction among the users’ recreational demands in urban parks. In 22 districts of the city of Tehran, 104 local urban parks (with a high diversity of the quality of welfare services and design) were selected. Using the user-centered viewpoint, we assessed the recreational demand. A field study from 1395 to 1396 helped to investigate the role of the urban district and park service variables in increasing the demand for urban parks. Results of trained networks showed that the artificial neural network created the best function of topology optimization with a higher coefficient of determination in three categorists of training, validation, and test data. Sensitivity analysis showed that the number of urban district parks, sports areas, cultural areas, and the quality of landscape with a sensitivity coefficient of 183. 5, 58. 1, 52. 7, and 30. 4, respectively, had the highest effect on the users’ recreational demand in urban parks. The suggested model would be a decision support system in designing urban parks. Such an approach would help improve urban development based on tourism attractions and would develop urban tourism on a broader scale.

Cites

No record.

References

No record.

Cite

APA: Copy

JAHANI, ALI, Khalegh panah, Reyhaneh, Gashtasb, Hamid, & Khorasani, Nematallah. (2020). Assessing Users’ Recreational Demand in Urban Parks in Tehran with the Help of the Artificial Neural Network. JOURNAL OF SOCIAL STUDIED IN TOURISM, 7(14 ), 339-362. SID. https://sid.ir/paper/361073/en

Vancouver: Copy

JAHANI ALI, Khalegh panah Reyhaneh, Gashtasb Hamid, Khorasani Nematallah. Assessing Users’ Recreational Demand in Urban Parks in Tehran with the Help of the Artificial Neural Network. JOURNAL OF SOCIAL STUDIED IN TOURISM[Internet]. 2020;7(14 ):339-362. Available from: https://sid.ir/paper/361073/en

IEEE: Copy

ALI JAHANI, Reyhaneh Khalegh panah, Hamid Gashtasb, and Nematallah Khorasani, “Assessing Users’ Recreational Demand in Urban Parks in Tehran with the Help of the Artificial Neural Network,” JOURNAL OF SOCIAL STUDIED IN TOURISM, vol. 7, no. 14 , pp. 339–362, 2020, [Online]. Available: https://sid.ir/paper/361073/en

Related Journal Papers