The Spatial Pattern of Ski Areas and Its Driving Factors in China: A Strategy for Healthy Development of the Ski Industry

2020 ◽  
Author(s):  
Hongmin An ◽  
Cunde Xiao ◽  
Minghu Ding
Keyword(s):  
Spatial Pattern ◽  
Spatial Autocorrelation ◽  
Economic Benefits ◽  
Driving Factors ◽  
Economic Factors ◽  
Mountain Areas ◽  
Ski Resorts ◽  
Ski Industry ◽  
Ski Areas ◽  
Clustered Distributions

<p>The development of ski areas would bring socio-economic benefits to mountain regions. At present, the ski industry in China is developing rapidly, and the number of ski areas is increasing dramatically. However, the understanding of the spatial pattern and driving factors for these ski areas is limited. This study collected detailed data about ski areas and their surrounding natural and economic factors in China. Criteria for classification of ski areas were proposed, and a total of 589 alpine ski areas in China were classified into three types: ski resorts for vacationing (va-ski resorts), ski areas for learning (le-ski areas) and ski parks to experience skiing (ex-ski parks), with proportions of 2.1%, 15.4% and 82.5%, respectively, which indicated that the Chinese ski industry was still dominated by small-sized ski areas. The overall spatial patterns of ski areas were clustered with a nearest neighbor indicator (NNI) of 0.424, in which ex-ski parks and le-ski areas exhibited clustered distributions with NNIs of 0.44 and 0.51, respectively, and va-ski resorts were randomly distributed with an NNI of 1.04. The theory and method of spatial autocorrelation were first used to analyze the spatial pattern and driving factors of ski areas. The results showed that ski areas in cities had a positive spatial autocorrelation with a Moran’s index value of 0.25. The results of Local Indications of Spatial Association (LISA) showed that ski areas were mainly concentrated in 3 regions: the Beijing-centered Yanshan-Taihang Mountains and Shandong Hill areas, the Harbin-centered Changbai Mountain areas and the Urumqi-centered Tianshan-Altay Mountain areas. The first location was mainly driven by socio-economic factors, and the latter two locations were mainly driven by natural factors. Ski tourism in China still faces many challenges. The government sector should strengthen supervision, develop a ski industry alliance, and promote the healthy and sustainable development of the ski industry in the future.</p>

scholarly journals The Spatial Pattern of Ski Areas and Its Driving Factors in China: A Strategy for Healthy Development of the Ski Industry

2019 ◽  
Vol 11 (11) ◽  
pp. 3138 ◽  
Author(s):  
Hongmin An ◽  
Cunde Xiao ◽  
Minghu Ding
Keyword(s):  
Spatial Pattern ◽  
Spatial Autocorrelation ◽  
Economic Benefits ◽  
Driving Factors ◽  
Economic Factors ◽  
Mountain Areas ◽  
Ski Resorts ◽  
Ski Industry ◽  
Ski Areas ◽  
Clustered Distributions

The development of ski areas would bring socio-economic benefits to mountain regions. At present, the ski industry in China is developing rapidly, and the number of ski areas is increasing dramatically. However, the understanding of the spatial pattern and driving factors for these ski areas is limited. This study collected detailed data about ski areas and their surrounding natural and economic factors in China. Criteria for classification of ski areas were proposed, and a total of 589 alpine ski areas in China were classified into three types: ski resorts for vacationing (va-ski resorts), ski areas for learning (le-ski areas) and ski parks to experience skiing (ex-ski parks), with proportions of 2.1%, 15.4% and 82.5%, respectively, which indicated that the Chinese ski industry was still dominated by small-sized ski areas. The overall spatial patterns of ski areas were clustered with a nearest neighbor indicator (NNI) of 0.424, in which ex-ski parks and le-ski areas exhibited clustered distributions with NNIs of 0.44 and 0.51, respectively, and va-ski resorts were randomly distributed with an NNI of 1.04. The theory and method of spatial autocorrelation were first used to analyze the spatial pattern and driving factors of ski areas. The results showed that ski areas in cities had a positive spatial autocorrelation with a Moran’s index value of 0.25. The results of Local Indications of Spatial Association (LISA) showed that ski areas were mainly concentrated in 3 regions: the Beijing-centered Yanshan-Taihang Mountains and Shandong Hill areas, the Harbin-centered Changbai Mountain areas and the Urumqi-centered Tianshan-Altay Mountain areas. The first location was mainly driven by socio-economic factors, and the latter two locations were mainly driven by natural factors. Ski tourism in China still faces many challenges. The government sector should strengthen supervision, develop a ski industry alliance, and promote the healthy and sustainable development of the ski industry in the future.

scholarly journals Spatial Patterns of China’s Ski Resorts and Their Influencing Factors: A Geographical Detector Study

2021 ◽  
Vol 13 (8) ◽  
pp. 4232
Author(s):  
Yan Fang ◽  
Yiyi Jiang ◽  
Chin-Hsun Ken Tsai ◽  
Binghao Luo ◽  
Ming-Hsiang Chen
Keyword(s):  
Northwest China ◽  
Influential Factors ◽  
Cold Spot ◽  
Winter Olympics ◽  
Ski Resorts ◽  
Geographical Detector ◽  
Ski Industry ◽  
Low Latitudes ◽  
Ski Areas ◽  
Main Factors

This study uses geographic information systems (GIS) and geographical detector techniques to explore the national and regional pattern of the spatial distribution of China’s ski resorts, and quantitatively identifies the main factors that influence their location. Results show that although China’s ski areas are geographically clustered, ski resorts are more likely to be located at high latitudes (northeast and northwest China) than at low latitudes (central and south China). Among the most influential factors are the winter sporting mega-events that explain 70% of the location of China’s ski areas; the 2022 Winter Olympics accounted for 14%. The main factors that contribute to the distribution of ski areas depend on the regions and types of ski resorts. Implications for the ski resorts industry, such as the different practice for hot and cold spot areas of China’s ski resorts, and the future development direction of ski industry, are discussed.

scholarly journals Using Spatial Pattern Analysis to Explore the Relationship between Vulnerability and Resilience to Natural Hazards

2021 ◽  
Vol 18 (11) ◽  
pp. 5634
Author(s):  
Chien-Hao Sung ◽  
Shyue-Cherng Liaw
Keyword(s):  
Spatial Pattern ◽  
Spatial Autocorrelation ◽  
Natural Hazards ◽  
Socioeconomic Development ◽  
Spatial Pattern Analysis ◽  
Spatial Difference ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Mountain Areas ◽  
Urban Regions

This research aims to explore the spatial pattern of vulnerability and resilience to natural hazards in northeastern Taiwan. We apply the spatially explicit resilience-vulnerability model (SERV) to quantify the vulnerability and resilience to natural hazards, including flood and debris flow events, which are the most common natural hazards in our case study area due to the topography and precipitation features. In order to provide a concise result, we apply the principal component analysis (PCA) to aggregate the correlated variables. Moreover, we use the spatial autocorrelation analysis to analyze the spatial pattern and spatial difference. We also adopt the geographically weighted regression (GWR) to validate the effectiveness of SERV. The result of GWR shows that SERV is valid and unbiased. Moreover, the result of spatial autocorrelation analysis shows that the mountain areas are extremely vulnerable and lack enough resilience. In contrast, the urban regions in plain areas show low vulnerability and high resilience. The spatial difference between the mountain and plain areas is significant. The topography is the most significant factor for the spatial difference. The high elevation and steep slopes in mountain areas are significant obstacles for socioeconomic development. This situation causes consequences of high vulnerability and low resilience. The other regions, the urban regions in the plain areas, have favorable topography for socioeconomic development. Eventually, it forms a scenario of low vulnerability and high resilience.

scholarly journals Seasonal Variability of the Acoustic Climate of Ski Resorts in the Aosta Valley Territory

Environments ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 18 ◽  
Author(s):  
Christian Tibone ◽  
Marco Masoero ◽  
Filippo Berlier ◽  
Giovanni Tabozzi ◽  
Daniele Crea ◽  
...  
Keyword(s):  
Environmental Protection Agency ◽  
Winter Season ◽  
Noise Pollution ◽  
Sound Sources ◽  
Mountain Areas ◽  
Mountain Area ◽  
North West ◽  
Ski Resorts ◽  
Acoustic Quality ◽  
High Level

The Aosta Valley is an alpine region in north-west Italy that is characterized by a high level of naturalness, with extensive uninhabited areas that are distant from artificial sound sources. The Aosta Valley Regional Environmental Protection Agency (ARPA-VdA) has been particularly sensitive to the preservation of the soundscape, which is considered an integral part of the landscape, since the laws on noise pollution were first introduced. The nature of the ski areas in the Aosta mountains, which undergoes changes throughout the year, is surely of great importance, especially during the winter season, when the number of visitors is particularly high. In fact, during the winter, the sounds of nature are replaced by those produced by recreation and sports activities. Mountain and snow tourism, which are developed in sensitive environmental contexts in the Aosta Valley, are sectors of immense social and economic importance. Much of this tourism takes place in ski resorts. Three mountain areas with different characteristics, in terms of attendance and recreational/sport activities, have been examined in this paper, as part of a collaboration between ARPA-VdA and the Politecnico di Torino. Acoustic measurements were performed in order to identify the seasonal variations of sound emissions from both natural and anthropic sound sources. In addition to the standard environmental acoustic descriptors foreseen by European legislation (LAeq, Ln, Lden, etc.), the harmonica (IH) index, which provides a quantitative evaluation of the acoustic quality on a zero to ten numerical scale, was used to qualify the acoustic climate of the three areas. The results presented in the paper provide useful information on a relevant subject—the preservation of the acoustic quality of a mountain area of touristic importance—which has been scarcely investigated so far.

Accuracy Analysis of DEM Based on Coons Surface

2011 ◽  
Vol 65 ◽  
pp. 214-217
Author(s):  
Yao Ge Wang ◽  
Peng Yuan Wang
Keyword(s):  
Spatial Pattern ◽  
Spatial Autocorrelation ◽  
Boundary Curve ◽  
Digital Elevation ◽  
Grid Points ◽  
Elevation Model ◽  
Global Spatial Autocorrelation ◽  
Core Problem ◽  
Dem Model ◽  
Better Than

Interpolation is the core problem of Digital Elevation Model (DEM). The Coons DEM model is better than bilinear interpolation and moving surface fitting. It is constructed by grid boundary curve, the curve interpolates by some adjoining grid points. Its spatial pattern of error is random in global area, there is no significant global spatial autocorrelation, but it is an increasing trend along with the terrain average gradient increases.There is significant local spatial autocorrelation, the spatial pattern of error converges strongly in local areas.

scholarly journals Socio-economic Factors Influencing Profitability of Rice Seed Production in Selected Areas of Bangladesh

2014 ◽  
Vol 12 (1) ◽  
pp. 33-40
Author(s):  
MZ Hoque ◽  
ME Haque
Keyword(s):  
Seed Production ◽  
Crop Production ◽  
Information Sources ◽  
Economic Benefits ◽  
Influential Factor ◽  
Farm Size ◽  
Economic Factors ◽  
Rice Seed ◽  
The Government ◽  
Socio Economic Factors

Seed is the foundation of agriculture for enhancing crop production. The economic benefits from the improved quality seed production help scaling up the livelihood standard as well as nutritional status of the common people. The study was carried out in three districts namely Jamalpur, Gazipur and Manikganj to identify the socio-economic dimensions of the government seed production project beneficiaries persuading profitability of rice seed production. Data were collected through intensive survey of randomly selected 211 sample respondents using pre-tested interview schedule. To examine the profitability of rice seed production, the gross margin and cost benefit analysis were carried out. Co-efficient of correlation and multiple stepwise regressions were employed to find out the determinants of profitability in rice seed production. Rice seed production was not found to be so profitable as investment in rice seed cultivation can produce average BCR of only up to 1.44, where highest BCR was found in Jamalpur (1.58) compared to Manikganj (1.48) and Gazipur (1.26). The results revealed that socio-economic factors have a profound influence on profitability of rice seed production as these factors combined explained 54.9 percent variation. Farm size, contact with information sources, knowledge on quality rice production and age of the respondents were identified as significant contributors in profitability of rice seed production, whereas contact with information sources was the single most influential factor (24.6%). Therefore, steps may be taken so that the seed-growers could directly be linked with more information sources dealing with seed production and marketing through the government initiatives to boost up the production as well as to ensure appropriate price of the farmers’ home grown seed. DOI: http://dx.doi.org/10.3329/agric.v12i1.19578 The Agriculturists 2014; 12(1) 33-40

scholarly journals Analysis on the Ski Industry in China

2021 ◽  
Vol 251 ◽  
pp. 01008
Author(s):  
Yuyan Wang
Keyword(s):  
Rapid Development ◽  
Current Situation ◽  
Living Standard ◽  
Ski Resorts ◽  
Ski Industry

As the living standard increased, more and more people join this sport. The rapid development of skiing equipment and ski resorts stimulates multiple people to experience skiing. This paper analyzes the current situation and development of the ski industry in China. Based on the case study, the author analyzes the prospect and provides suggestions in the end for ski companies.

scholarly journals Seasonal evolution of a ski slope under natural and artificial snow: detailed observations and modelling

2016 ◽  
Author(s):  
Pierre Spandre ◽  
Hugues François ◽  
Emmanuel Thibert ◽  
Samuel Morin ◽  
Emmanuelle George-Marcelpoil
Keyword(s):  
Water Mass ◽  
Differential Gps ◽  
Seasonal Evolution ◽  
Ski Resorts ◽  
Ski Industry ◽  
Physically Based ◽  
Snow Conditions ◽  
Projected Changes ◽  
Pm 10 ◽  
The Impact

Abstract. The production of Machine Made (MM) snow is now generalized in ski resorts and represents the most common adaptation method to mitigate the impacts of both the natural variability and projected changes of the climate on the snow conditions to guarantee suitable conditions for skiing. Most investigations of the impact of snow conditions on the economy of the ski industry under past, present or projected climate focus on the production of MM snow. So far, none of them accounted for the efficiency of the snowmaking process i.e. the actual MM snow mass that can be recovered from a given water mass used for snowmaking. The present study consisted in observations and interpolation on a 0.5 × 0.5 m grid of snow conditions (depth and mass) using a Differential GPS method and snow density coring, after single sessions of production (prior to MM snow spreading by grooming machines) and on the ski slope as opened to skiers, on a beginner trail in Les Deux Alpes ski resort (French Alps). A detailed physically based snowpack model accounting for grooming and snowmaking was used to address the seasonal evolution of the snowpack and compared to the observations. Our results show that approximately 30 % of the water mass can be recovered as MM snow within 10 m from the center of a MM snow pile after the production and 50 % within 20 m. The observations and simulations on the ski slope were relatively consistent with 60 % (±10 %) of the water mass used for snowmaking within the edge of the ski slope. We also addressed the losses due to thermodynamic effects resulting in less than 10 % of the total water mass in the present case. The main uncertainty pertains to the surface of observations: the surface of the ski slope opened to skiers changed along the season and objective uncertainties exist, in particular from man-made decisions. These results suggest that even in the ideal conditions for production a significant fraction of the water used for snowmaking can not be found as MM snow within the edge of the ski slope with most of the lost fraction of water due to site dependent characteristics (e.g. meteorological conditions, topography, human decisions).

scholarly journals Spatiotemporal Differentiation of the School-Age Migrant Population in Liaoning Province, China, and Its Driving Factors

Land ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1036
Author(s):  
Wenwen Xu ◽  
Chunrui Song ◽  
Dongqi Sun ◽  
Baochu Yu
Keyword(s):  
Spatial Autocorrelation ◽  
Hot Spot ◽  
Driving Factors ◽  
Liaoning Province ◽  
Teaching Staff ◽  
School Age ◽  
Migrant Population ◽  
Spot Area ◽  
Quality Of Teaching

This study analyzed the spatiotemporal distribution and driving factors of the floating school-age population in Liaoning Province, China from 2008 to 2020 using county-level statistical education data combined with spatial autocorrelation and the multiscale geographically weighted regression model. The major findings are as follows. From 2008 to 2020, the distribution of the school-age migrant population exhibited obvious spatial imbalance characteristics both in terms of the number and proportion of school-age migrants. Specifically, the school-age migrant population was concentrated in the municipal districts of large and medium-sized cities and continued to increase over time in the suburbs of large and medium-sized cities. Over the past 12 years, the distribution of the school-age migrant population in Liaoning Province exhibited significant spatial autocorrelation. From the number of school-age migrants, the cold and hot spot area expanded. Conversely, from the proportion of school-age migrants, the cold and hot spot area decreased gradually, whereas the cold spot area became more diffuse. Regarding the driving factors, the quantity and quality of teaching staff, the quality of teaching equipment and conditions, and the quality of the education environment played a role in promoting or restraining the differentiation of the school-age migrant population in Liaoning Province. Moreover, the degree of influence of the driving factors exhibited substantial spatial differences.

Incorporating spatial autocorrelation and settlement type segregation to improve the performance of an urban growth model

2019 ◽  
Vol 47 (7) ◽  
pp. 1184-1200 ◽  
Author(s):  
Chao Xu ◽  
Didit O Pribadi ◽  
Dagmar Haase ◽  
Stephan Pauleit
Keyword(s):  
Logistic Regression ◽  
Markov Chain ◽  
Spatial Autocorrelation ◽  
Urban Growth ◽  
Driving Factors ◽  
Autologistic Regression ◽  
Cellular Automata Model ◽  
Urban Growth Model ◽  
Ordinary Logistic Regression ◽  
Settlement Growth

As rapid urbanization and population growth have become global issues, urban growth modeling has become an essential tool for decision-makers to understand how urban growth works in overall dense environments and to assess the sustainability of current urban forms. However, in urban growth models (particularly when incorporating quantitative approaches to include driving factors of urban growth), spatial autocorrelation may influence the overall model performance. In this paper, an empirical study was conducted in the region of Munich, and an integrated urban growth model was tested to explain current urban growth. The modeling contributes to advances in the state of the art by combining a range of driving factors using autologistic regression with a transition probability matrix from the Markov chain method in a cellular automata model simulation. The autologistic regression employed here addresses the impact of spatial autocorrelation compared to ordinary logistic regression. Furthermore, this study compared modeling of overall settlement growth with modeling high- and low-density settlement types separately. Incorporating spatial dependency into the model through application of autologistic regression showed improvements when compared to the ordinary logistic regression model. The Kappa indexes were higher when separately modeling the two types of settlement density compared to modeling overall settlement growth since the driving factors of settlement growth of different densities might be different. From an urban planning perspective, this novel autologistic regression-Markov chain-based cellular automata model is a powerful tool that offers an opportunity for planners and government authorities to gain a more precise understanding of the different urban growth processes that might occur in an urban region similar to the one tested here. It should allow for a better assessment of the potential costs, benefits, and risks of corresponding planning strategies.

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