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نتیجه جستجو - Geographically weighted regression analysis

تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 Relationship among land price, entrepreneurship, the environment, economics, and social factors in the value assessment of Japanese cities
رابطه قیمت زمین ، کارآفرینی ، محیط زیست ، اقتصاد و عوامل اجتماعی در ارزیابی ارزش شهرهای ژاپن-2019
Assessing the value of local areas and cities, including examining the entrepreneurial, environmental, economic, and social dimensions of sustainability, has become key to fostering development. Such assessments can be conducted using a land-price function. This study analysed the land-price function using a geographically weighted regression model of land price, and explanatory variables related to entrepreneurial, environmental, economic, and social factors. To consider these factors, specific data scores were taken into account. A correlation analysis showed that six variables are essential for future value analyses: these are entrepreneurship, nature conservation, resource recycling, social vitality, the local governments financial viability, and environmental quality. Using these six variables to conduct preliminary regression analyses via four models, the study found that entrepreneurship, social vitality, and environmental quality have a positive impact on land prices in local areas and cities, while nature conservation, resource recycling, and the local governments financial viability have a negative impact. The result of a geographically weighted regression analysis showed that the areas around large cities in Japan have benefitted more from entrepreneurship and social vitality than have the areas around small and mid-sized cities. While large cities may be better equipped to promote environmental policies, small and mid-sized cities could promote entrepreneurship and social vitality policies using their environmental advantages
Keywords: Entrepreneurship | Environment | Economy | Society | Land-price function | Geographically weighted regression model
مقاله انگلیسی
2 Using a spatial hedonic analysis to evaluate the effect of sea view on hotel prices
استفاده از یک تحلیل فضایی لذتی برای بررسی تاثیر چشم انداز دریایی روی قیمت های هتل-2018
This paper attempts to examine the effect of sea view to room rates alongside other structural and locational attributes. Specifically, it aims to test whether rooms with a sea view are priced higher than others, thus trying to quantify the associated aesthetic values of coastal areas where tourism-related development is a key economic activity. For this purpose, a sample of 557 rooms in Halkidiki, Greece was collected through an online database during the summer tourist season. Subsequently, these data were integrated into a GIS-system in order to apply a spatial hedonic model. A semi-parametric geographically weighted regression model was used to assess the local effects, as well as, to investigate the spatial variability of the selected attributes. The results exhibited a significant spatial variability concerning the effect of sea view to room rates, indicating that local natural and/or tourism resources may have a substantial role in aesthetic values.
keywords: Coastal tourism |Sea view |Accommodation price |Hedonic pricing method |Geographically weighted regression analysis
مقاله انگلیسی
3 Spatial variations in urban public ridership derived from GPS trajectories and smart card data
تغییرات فضایی در سواری عمومی شهری حاصل از مسیرهای GPS و داده های کارت هوشمند -2018
Understanding urban public ridership is essential for promoting public transportation. However, limited efforts have been made to reveal the spatial variations of multi-modal public ridership (such as buses, metro systems, and taxis) and the underlying controlling factors. This study explores multi-modal public ridership and compares the similarities and differences of the associated factors. Daily bus, metro, and taxi ridership patterns are first extracted from multiple sources of big transportation data, including vehicle (bus and taxi) GPS trajectories and smart card data. Multivariate regression analysis and geographically weighted regression analysis are used to reveal the associations between these data and demographic, land use, and transportation factors. An empirical study in Shenzhen, China, suggests that employment, mixed land use, and road density have significant effects on the ridership of each mode; however, some effects vary from negative to positive across the city. The results also indicate that road density, income, and metro accessibility do not have significant effects on metro, transit or bus ridership. These findings suggest that the effects of the associated factors vary depending on the mode of travel being considered and that the city should carefully consider which factors to emphasize in formulating future transport policy.
Keywords: Ridership ، Big data ، Trajectory ، Smart card data ، Geographically weighted regression ، Transit ، Taxi
مقاله انگلیسی
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