scholarly journals Spatial Agglomeration of China’s Forest Products Manufacturing Industry: Measurement, Characteristics and Determinants

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1006
Author(s):  
Zhenhuan Chen ◽  
Hongge Zhu ◽  
Wencheng Zhao ◽  
Menghan Zhao ◽  
Yutong Zhang
Keyword(s):  
Spatial Distribution ◽  
Spatial Data ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Economies Of Scale ◽  
Forest Products ◽  
Spatial Data Analysis ◽  
Forest Protection ◽  
Spatial Agglomeration ◽  
Continuous Space

China’s forest products manufacturing industry is experiencing the dual pressure of forest protection policies and wood scarcity and, therefore, it is of great significance to reveal the spatial agglomeration characteristics and evolution drivers of this industry to enhance its sustainable development. Based on the perspective of large-scale agglomeration in a continuous space, in this study, we used the spatial Gini coefficient and standard deviation ellipse method to investigate the spatial agglomeration degree and location distribution characteristics of China’s forest products manufacturing industry, and we used exploratory spatial data analysis to investigate its spatial agglomeration pattern. The results show that: (1) From 1988 to 2018, the degree of spatial agglomeration of China’s forest products manufacturing industry was relatively low, and the industry was characterized by a very pronounced imbalance in its spatial distribution. (2) The industry has a very clear core–periphery structure, the spatial distribution exhibits a “northeast-southwest” pattern, and the barycenter of the industrial distribution has tended to move south. (3) The industry mainly has a high–high and low–low spatial agglomeration pattern. The provinces with high–high agglomeration are few and concentrated in the southeast coastal area. (4) The spatial agglomeration and evolution characteristics of China’s forest products manufacturing industry may be simultaneously affected by forest protection policies, sources of raw materials, international trade and the degree of marketization. In the future, China’s forest products manufacturing industry should further increase the level of spatial agglomeration to fully realize the economies of scale.

scholarly journals An Empirical Study on Spatial–Temporal Dynamics and Influencing Factors of Tea Production in China

2018 ◽  
Vol 10 (9) ◽  
pp. 3037 ◽  
Author(s):  
Hanchu Liu ◽  
Jie Fan ◽  
Kan Zhou
Keyword(s):  
Spatial Distribution ◽  
Influencing Factors ◽  
Spatial Data ◽  
Temporal Dynamics ◽  
Regional Cooperation ◽  
Labor Cost ◽  
Spatial Data Analysis ◽  
Eastern Region ◽  
Spatial Agglomeration ◽  
Tea Production

Revealing the characteristics of spatial–temporal dynamics and influencing factors is important for optimizing the spatial distribution of tea production. Taking prefecture-level cities as the basic spatial unit, this study uses the Herfindahl index and exploratory spatial data analysis to reveal the spatial–temporal dynamics of China’s tea production from 2000 to 2015. A theoretical analysis framework is established and a spatial econometric model is used to explore its influencing factors. The results show a U-shaped trend in the degree of tea spatial agglomeration, which gradually declined during 2000–2010, and rapidly increased during 2011–2015. The proportion of tea production shifted from the eastern region to the central and western regions, and spatial distribution coverage expanded to the north. Tea production had significant spatial correlation, and spatial agglomeration characteristics were exhibited for similar values (high or low). Tea production had a significant spatial spillover effect. Natural resources, labor cost, specialized production, and policies all affected the spatial–temporal dynamics of tea production somewhat, but the effects of traffic conditions and technological level were insignificant. Finally, this study proposed optimizing four aspects of the tea spatial layout: regional cooperation, comprehensive suitability evaluation of tea cultivation, spatial agglomeration, and distinctive local brands.

scholarly journals Approach to Urban Environmental Justice Using Exploratory Spatial Data Analysis. The Case of Valencia’s Monumental Trees

2020 ◽  
Vol 12 (18) ◽  
pp. 7760
Author(s):  
Alfonso Gallego-Valadés ◽  
Francisco Ródenas-Rigla ◽  
Jorge Garcés-Ferrer
Keyword(s):  
Spatial Distribution ◽  
Data Analysis ◽  
Environmental Justice ◽  
Spatial Data ◽  
Urban Trees ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
The Public ◽  
Object Of Study ◽  
The City

Environmental justice has been a relevant object of analysis in recent decades. The generation of patterns in the spatial distribution of urban trees has been a widely addressed issue in the literature. However, the spatial distribution of monumental trees still constitutes an unknown object of study. The aim of this paper was to analyse the spatial distribution of the monumental-tree heritage in the city of Valencia, using Exploratory Spatial Data Analysis (ESDA) methods, in relation to different population groups and to discuss some implications in terms of environmental justice, from the public-policy perspective. The results show that monumental trees are spatially concentrated in high-income neighbourhoods, and this fact represents an indicator of environmental inequality. This diagnosis can provide support for decision-making on this matter.

scholarly journals An Approach of Identifying and Extracting Urban Commercial Areas Using the Nighttime Lights Satellite Imagery

2020 ◽  
Vol 12 (6) ◽  
pp. 1029
Author(s):  
Xuzhe Duan ◽  
Qingwu Hu ◽  
Pengcheng Zhao ◽  
Shaohua Wang ◽  
Mingyao Ai
Keyword(s):  
Satellite Imagery ◽  
Spatial Data ◽  
Large Scale ◽  
Weather Conditions ◽  
Spatial Data Analysis ◽  
Vegetation Coverage ◽  
Economic Activities ◽  
Traditional Methods ◽  
Detection Model ◽  
Nighttime Lights

Urban commercial areas can reflect the spatial distribution of business activities. However, the scope of urban commercial areas cannot be easily detected by traditional methods because of difficulties in data collection. Considering the positive correlation between business scale and nighttime lighting, this paper proposes a method of urban commercial areas detection based on nighttime lights satellite imagery. First, an imagery preprocess model is proposed to correct imageries and improve efficiency of cluster analysis. Then, an exploratory spatial data analysis and hotspots clustering method is employed to detect commercial areas by geographic distribution metric with urban commercial hotspots. Furthermore, four imageries of Wuhan City and Shenyang City are selected as an example for urban commercial areas detection experiments. Finally, a comparison is made to find out the time and space factors that affect the detection results of the commercial areas. By comparing the results with the existing map data, we are convinced that the nighttime lights satellite imagery can effectively detect the urban commercial areas. The time of image acquisition and the vegetation coverage in the area are two important factors affecting the detection effect. Harsh weather conditions and high vegetation coverage are conducive to the effective implementation of this method. This approach can be integrated with traditional methods to form a fast commercial areas detection model, which can then play a role in large-scale socio-economic surveys and dynamic detection of commercial areas evolution. Hence, a conclusion can be reached that this study provides a new method for the perception of urban socio-economic activities.

scholarly journals Spatial distribution of pedestrian-motor vehicle collisions before and after pedestrian countdown signal installation in Toronto, Canada

2017 ◽  
Vol 25 (2) ◽  
pp. 110-115 ◽  
Author(s):  
Linda Rothman ◽  
Marie-Soleil Cloutier ◽  
Alison K Macpherson ◽  
Sarah A Richmond ◽  
Andrew William Howard
Keyword(s):  
Spatial Distribution ◽  
Spatial Patterns ◽  
Spatial Data ◽  
Motor Vehicle ◽  
Spatial Data Analysis ◽  
Motor Vehicle Collisions ◽  
Vehicle Collisions ◽  
Point Patterns ◽  
Kernel Density Estimates ◽  
Before And After

BackgroundPedestrian countdown signals (PCS) have been installed in many cities over the last 15 years. Few studies have evaluated the effectiveness of PCS on pedestrian motor vehicle collisions (PMVC). This exploratory study compared the spatial patterns of collisions pre and post PCS installation at PCS intersections and intersections or roadways without PCS in Toronto, and examined differences by age.MethodsPCS were installed at the majority of Toronto intersections from 2007 to 2009. Spatial patterns were compared between 4 years of police-reported PMVC prior to PCS installation to 4 years post installation at 1864 intersections. The spatial distribution of PMVC was estimated using kernel density estimates and simple point patterns examined changes in spatial patterns overall and stratified by age. Areas of higher or lower point density pre to post installation were identified.ResultsThere were 14 911 PMVC included in the analysis. There was an overall reduction in PMVC post PCS installation at both PCS locations and non-PCS locations, with a greater reduction at non-PCS locations (22% vs 1%). There was an increase in PMVC involving adults (5%) and older adults (9%) at PCS locations after installation, with increased adult PMVC concentrated downtown, and older adult increases occurring throughout the city following no spatial pattern. There was a reduction in children’s PMVC at both PCS and non-PCS locations, with greater reductions at non-PCS locations (35% vs 48%).ConclusionsResults suggest that the effects of PCS on PMVC may vary by age and location, illustrating the usefulness of exploratory spatial data analysis approaches in road safety. The age and location effects need to be understood in order to consistently improve pedestrian mobility and safety using PCS.

scholarly journals Analysis of the Spatial Distribution Characteristics of Urban Resilience and Its Influencing Factors: A Case Study of 56 Cities in China

2019 ◽  
Vol 16 (22) ◽  
pp. 4442 ◽  
Author(s):  
Zhang ◽  
Chen ◽  
Cai ◽  
Gao ◽  
Zhang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Influencing Factors ◽  
Spatial Data ◽  
Evaluation System ◽  
Negative Impact ◽  
Positive Impact ◽  
Measurement Model ◽  
Spatial Data Analysis ◽  
Urban Resilience ◽  
Pension Insurance

The healthy development of the city has received widespread attention in the world, and urban resilience is an important issue in the study of urban development. In order to better provide a useful reference for urban resilience and urban health development, this paper takes 56 cities in China as the research object, and selects 29 indicators from urban infrastructure, economy, ecology and society. The combination weight method, exploratory spatial data analysis (ESDA) and spatial measurement model are used to explore the spatial distribution of urban resilience and its influencing factors. From 2006 to 2017, the urban resilience of prefecture-level cities in the four provinces showed a wave-like rise. During the study period, the urban resilience values, measured as Moran’s Is, were greater than 0.3300, showing a significantly positive correlation in regard to their spatial distribution. Regarding the local spatial correlation, the urban resilience of the study area had spatial agglomeration characteristics within the province, with a significant distribution of "cold hot spots" in the spatial distribution. From the perspective of the factors that affected urban resilience, the proportion of the actual use of foreign capital in GDP and carbon emissions per 10,000 CNY of GDP had a negative impact and GDP per square kilometer, the proportion of urban pension insurance coverage, the proportion of the population with higher education, and expenditure to maintain and build cities had a positive impact. The development strategy of urban resilience must be combined with the actual situation of the region, and the rational resilience performance evaluation system and the top-level design of urban resilience improvement should be formulated to comprehensively improve urban resilience.

Spatial Econometric Analysis of Cultivated Land Change and its Influencing Factors in the Poyang Lake Eco-Economics Zone

2013 ◽  
Vol 864-867 ◽  
pp. 2659-2664
Author(s):  
Peng Wang ◽  
Qu Liu ◽  
Hua Lin Xie
Keyword(s):  
Spatial Distribution ◽  
Influencing Factors ◽  
Spatial Data ◽  
Poyang Lake ◽  
Spatial Data Analysis ◽  
Cultivated Land ◽  
Land Change ◽  
Spatial Econometric ◽  
Agricultural Growth ◽  
Spatio Temporal

Spatio-temporal pattern of cultivated land change and its influencing factors in the Poyang Lake Ecological Economic Zone were conducted by exploratory spatial data analysis and spatial autocorrelation analysis. Results show that there is an obvious correlation for the spatial distribution of cultivated land in the Poyang Lake Eco-economics Zone. Its value of Morans I reduced from 0.4574 in 2002 to 0.4092 in 2008, and then increased to 0.4352 in 2009, which roughly presented a "U" type distribution. Total population is the most important factor that affecting the change of cultivated areas in the Poyang Lake Eco-economics Zone. Agricultural growth, average wage of urban residents and the fixed assets investment are also the main driving factors. Spatial auto-regression model is an effective tool for revaluating the spatial distribution of regional cultivated land, and revealing the evolution mechanisms of cultivated land.

scholarly journals Ecological Efficiency Evaluation and Spatiotemporal Characteristics Analysis of the Coupling Coordination of the Logistics Industry and Manufacturing Industry

2021 ◽  
Author(s):  
Juan Su ◽  
Tong Shen ◽  
shuxin jin
Keyword(s):  
Carbon Emissions ◽  
Spatial Data ◽  
Manufacturing Industry ◽  
Spatial Differentiation ◽  
Manufacturing Industries ◽  
Spatial Data Analysis ◽  
Sustainable Supply Chain ◽  
Ecological Efficiency ◽  
Logistics Industry ◽  
Positive Effects

Abstract The coupling coordination of the logistics industry and manufacturing industry conducive to the sustainable development of logistics and manufacturing and the stability of sustainable supply chain. The logistics and manufacturing industries are not only the basic industries that support social development, but also the industries with high carbon emissions. Firstly, this paper classifies the carbon emissions from the logistics industry and manufacturing industry as undesirable outputs, evaluates the ecological efficiency of the logistics industry (LEE) and manufacturing industry (MEE) in the Yangtze River Delta from 2006 to 2019 by using the unexpected slacks-based measure (SBM) model. Secondly, the coupling coordination method is used to analyze the coupling coordination scheduling of industrial ecological efficiency. Thirdly, the paper analyzes the spatial differences of the coupling coordination ecological efficiency between logistics industry and manufacturing industry (MLCC) by using the exploratory spatial data analysis method. Finally, the spatial econometric model is used to analyze the driving factors of the MLCC. The results show: The ecological efficiency of the manufacturing industry has steadily improved. The ecological efficiency of the logistics industry presents the rising trend in fluctuation. The level of the coupling coordination development between the logistics and manufacturing industries is high. The results of the spatial heterogeneity analysis show that the spatial differentiation of high-high agglomeration and low-low agglomeration is obvious. The spatial agglomeration characteristics are relatively stable, and the spatial diffusion effect is strong; In space, the MLCC shows a trend of developing from multiple agglomeration areas to one agglomeration area. The results of driving factor analysis show that foreign direct investment(FDI), government intervention(GI) and human capital(HP) have positive effects on the MLCC, while industrial structure(IS), environmental regulation(ER) and energy intensity(EI) have negative effects on the MLCC.

scholarly journals A DISTRIBUTED POLYGON RETRIEVAL ALGORITHM USING MAPREDUCE

2015 ◽  
Vol II-4/W2 ◽  
pp. 51-53
Author(s):  
Q. Guo ◽  
B. Palanisamy ◽  
H. A. Karimi
Keyword(s):  
Data Processing ◽  
Spatial Data ◽  
Large Scale ◽  
Programming Model ◽  
Processing Technique ◽  
Spatial Data Analysis ◽  
Retrieval Algorithm ◽  
Quad Tree ◽  
Parallel Data ◽  
Hadoop Cluster

The burst of large-scale spatial terrain data due to the proliferation of data acquisition devices like 3D laser scanners poses challenges to spatial data analysis and computation. Among many spatial analyses and computations, polygon retrieval is a fundamental operation which is often performed under real-time constraints. However, existing sequential algorithms fail to meet this demand for larger sizes of terrain data. Motivated by the MapReduce programming model, a well-adopted large-scale parallel data processing technique, we present a MapReduce-based polygon retrieval algorithm designed with the objective of reducing the IO and CPU loads of spatial data processing. By indexing the data based on a quad-tree approach, a significant amount of unneeded data is filtered in the filtering stage and it reduces the IO overhead. The indexed data also facilitates querying the relationship between the terrain data and query area in shorter time. The results of the experiments performed in our Hadoop cluster demonstrate that our algorithm performs significantly better than the existing distributed algorithms.

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