Study on changes of urban spatial pattern and heterogeneity of driving factors in the Su-Xi-Chang region

2022 ◽  
Author(s):  
Xiaoxiao Wang ◽  
Ying Zhou
Keyword(s):  
Spatial Pattern ◽  
Driving Factors

Thirty-year expansion of construction land in Xi'an: Spatial pattern and potential driving factors

2017 ◽  
Vol 53 ◽  
pp. 309-321 ◽  
Author(s):  
Xiaofeng Wang ◽  
Feiyan Xiao ◽  
Yuan Zhang ◽  
Lichang Yin ◽  
Muchu Lesi ◽  
...  
Keyword(s):  
Spatial Pattern ◽  
Driving Factors ◽  
Construction Land

scholarly journals Analysis on Spatial Pattern and Driving Factors of Carbon Emission in Urban–Rural Fringe Mixed-Use Communities: Cases Study in East Asia

2020 ◽  
Vol 12 (8) ◽  
pp. 3101 ◽  
Author(s):  
Xiaoqing Zhu ◽  
Tiancheng Zhang ◽  
Weijun Gao ◽  
Danying Mei
Keyword(s):  
Spatial Pattern ◽  
East Asia ◽  
Carbon Emissions ◽  
Industrial Development ◽  
Carbon Emission ◽  
East Asian ◽  
Driving Factors ◽  
Material Consumption ◽  
Mixed Use ◽  
Urban Rural

Urban-intensive areas are responsible for an estimated 80% of greenhouse gas emissions, particularly carbon dioxide. The urban–rural fringe areas emit more greenhouse gases than urban centers. The purpose of this study is to analyze the spatial pattern and driving factors of carbon emissions in urban–rural fringe mixed-use communities, and to develop planning methods to reduce carbon emissions in communities. This study identifies mixed-use communities in East Asian urban–rural fringe areas as industrial, commercial, tourism, and rental-apartment communities, subsequently using the emission factor method to calculate carbon emissions. The statistical information grid analysis and geographic information systems spatial analysis method are employed to analyze the spatial pattern of carbon emission and explore the relationship between established space, industrial economy, material consumption, social behavior, and carbon emission distribution characteristics by partial least squares regression, ultimately summing up the spatial pattern of carbon emission in the urban–rural fringe areas of East Asia. Results show that (1) mixed-use communities in the East Asian urban–rural fringe areas face tremendous pressure to reduce emissions. Mixed-use community carbon emissions in the late urbanization period are lower than those the early urbanization. (2) Mixed-use community carbon emission is featured by characteristics, such as planning structure decisiveness, road directionality, infrastructure directionality, and industrial linkage. (3) Industrial communities produce the highest carbon emissions, followed by rental-apartment communities, business communities, and tourism communities. (4) The driving factor that most affects the spatial distribution of carbon emissions is the material energy consumption. The fuel consumption per unit of land is the largest driver of carbon emissions. Using the obtained spatial pattern and its driving factors of carbon emissions, this study provides suggestions for planning and construction, industrial development, material consumption, and convenient life guidance.

Spatial pattern and temporal trend of prehistoric human sites and its driving factors in Henan Province, Central China

2015 ◽  
Vol 25 (9) ◽  
pp. 1109-1121 ◽  
Author(s):  
Zhongxuan Li ◽  
Cheng Zhu ◽  
Guoxi Wu ◽  
Chaogui Zheng ◽  
Pengju Zhang
Keyword(s):  
Spatial Pattern ◽  
Temporal Trend ◽  
Henan Province ◽  
Driving Factors ◽  
Central China

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.

Sign in / Sign up

Export Citation Format

Share Document