Spatial Agglomeration Pattern Research Articles

Research on the Spatial Agglomeration Characteristics and Influencing Factors of Express Delivery Station Based on DNN.

In this paper, the POI data of 736 Cainiao stations in Nanjing is taken as the research sample. With the help of ArcGIS software, the standard deviation ellipse, spatial autocorrelation, average nearest neighbor, cold and hot spot analysis, nuclear density estimation, and other spatial analysis models are used to quantitatively characterize its business mode, spatial distribution characteristics, and equilibrium. Based on DNN, the spatial agglomeration characteristics and distribution directions of the Cainiao station in Nanjing were sorted out, the cold spots and hot spots of the spatial layout were identified, and the spatial differentiation rules and agglomeration patterns were revealed. Finally, the geographically weighted regression analysis model is used to analyze the influencing factors of the spatial agglomeration of the Cainiao station in Nanjing. The research found that Firstly, the proportion of Nanjing Cainiao station operating mode adopting the exclusive mode is 59.1%, the proportion adopting the concurrent operation mode is 33.7%, and the rest adopting the joint operation mode of cooperation with other logistics enterprises. Secondly, Nanjing Cainiao Station gathers in the central city area, forming a “central hot spot.” The urban fringe area does not form a “peripheral cold spot area,” and the whole presents a “1 + 4” five-core agglomeration model across the river. Thirdly, Regional GDP, population density, and the number of convenience stores/supermarkets are the main factors affecting the spatial agglomeration of the Cainiao station in Nanjing.

Spatial distribution analysis of seismic activity based on GMI, LMI, and LISA in China

Abstract Recently, all kinds of geological disasters happen frequently on the earth. In China, there are countless earthquakes every year, which greatly affect the country’s economic level and development as well as the people’s life and health. The analysis of seismic activity is becoming more and more significant. In this article, the spatial distribution of China’s seismic activities was analyzed by using the provincial seismic data from 1970 to 2013. On the basis of spatial autocorrelation analysis theory, Global Moran’s I, Local Moran’s I, and the Local Indicators of Spatial Association are used to measure the geospatial distribution characteristics of China’s seismic activities. The research results show that earthquakes in mainland China have significant global autocorrelation characteristics as a whole, and the global autocorrelation coefficients are all positive. And the Z-value test (P < 0.05) shows that earthquakes in mainland China present a spatial agglomeration pattern. Furthermore, we observed a reduction trend in disparities of seismic activity among regions in China.

Assessing equity in park accessibility using a travel behavior-based G2SFCA method in Nanjing, China

Spatial equity of parks is a major concern in environmental justice studies. While many measurements have been used to evaluate access to parks, few studies have considered the impact of travel behavior on park accessibility. This study aims to establish a travel behavior-based Gaussian two-step floating catchment area (TB-G2SFCA) method to assess spatial equity of parks in the Nanjing region, and Local Moran's I index is applied to identify spatial agglomeration patterns in assessing equity. The results demonstrate that (1) the traditional single-mode model maybe cannot provide accurate approach to evaluating park accessibility while the TB-G2SFCA method can provide a more realistic park accessibility evaluation; (2) the inhabitants of most communities distributed south of the Yangtze River can obtain better park services than the northern areas; (3) the spatial disparities of equity in park accessibility are severe in three suburban districts, relatively minor in four central districts, and insignificant in two central districts. These findings may assist urban planners and policy makers to frame more reasonable policy and planning to improve spatial equity to parks in urban areas.

Multi-scale simulation and dynamic coordination evaluation of water-energy-food and economy for the Pearl River Delta city cluster in China

In this research, an integrated approach is developed through the incorporation of systems dynamics (SD), catastrophe progression (CP), and coupled coordination degree (CCD) analysis within a general modelling framework. The developed SD-CP-CCD is then employed to simulate and evaluate relationships among the water-energy-food (WEF) and the economic systems. It will improve traditional methods for WEF nexus analysis through a) incorporating urban WEF connections and changes into the cross-system coupling analysis framework, b) evaluating its coordination with economic growth and a multi-scale and regional disparity, and c) promoting policy recommendations to ensure the guarantee of urban WEF under economic growth. The approach is then verified through WEF nexus system of Urban Agglomerations of Pearl River Delta City Cluster (PRDCC) in China. Temporal and spatial characteristics of CCD in the region were analyzed. The results revealed their performance in cities with multiple economic backgrounds. Also, the fragile factors of each city were reflected by multi-scale exploration. Regional inequality and spatial agglomeration effects were considered. The results showed that CCD has noticeable temporal and spatial differences. Geographically, CCD varied from uncoordinated to high-level coordination. The eastern cities were much better coordinated than the western cities. Due to the increasing pressure of population and economic growth in the future, the upward trend of CCD would occur in most cities. The vulnerability factors of each city were explored. Simultaneously, apparent spatial inequality and agglomeration patterns were observed, with a downward trend over time. The temporal and spatial patterns of CCD revealed in this study indicated that PRDCC policymakers should formulate policies that suit the characteristics of cities and reduce the targeted regional imbalances.

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

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.

Spatiotemporal patterns and modifiable areal unit problems of the landscape ecological risk in coastal areas: A case study of the Shandong Peninsula, China

Ecological risk assessment is the basis for sustainable land use and ecological protection and management in coastal areas. The research scale of ecological risk assessment is usually determined by empirical methods, and existing research focused on coastal areas is relatively limited. This paper aims to explore the spatiotemporal pattern of ecological risk in coastal areas, taking the Shandong Peninsula as the study area, and to determine the optimal scale of ecological risk assessment based on semivariogram analysis. The results show that the waterbodies and unused land had a relatively high landscape loss index, while the cropland had the least value. The optimal scale for landscape risk assessment in the Shandong Peninsula was 4 km. The ecological risk was generally low in the Shandong Peninsula, with the lowest risk regions and lower risk regions accounting for more than 70% of the total area. During 1990–2018, the average ecological risk dropped from 0.0819 to 0.0698, indicating a lower probability of adverse ecological effects caused by landscape changes. The spatial agglomeration pattern of the ecological risk showed low–low agglomeration in the central and northeast areas and high–high agglomeration in the northwest along the sea. Scale effects had a significant influence on the uncertainty of the assessment of ecological risks, including the study extent, size of risk units, risk level classification, and data resolutions. It is suggested that protecting cropland was important for both food safety and the stability of the ecosystem in the Shandong Peninsula. Waterbodies in this area are vulnerable due to their fragmentation and close distribution with built-up land and agricultural land. This study could offer useful information for coastal ecological risk control, and provide new perspectives regarding optimal scale selection when conducting local ecological risk assessment.

Investigating the spatiotemporal variability and driving factors of China's building embodied carbon emissions.

Rapid and large scale construction activities consume significant resources and make impacts on the environment. To support policy for emission reduction and route of low-carbon society development, this paper estimated building stocks and building embodied carbon emissions (BECEs) in China's 31 provinces from 1997 to 2016 by material flow analysis (MFA). Furthermore, global and local Moran's indices were employed to investigate the geographical clustering patterns, and temporal and spatial decomposition models were proposed to identify the driving forces. The results reveal the total BECEs has boomed from 9.67 billion tons in 1997 to 28.99 billion tons in 2016. BECEs in 31 provinces have experienced consistent increase but obvious differences in growth rate, and are spatially inclined to decrease from eastern coastal regions to western inland regions. The change of spatial agglomeration pattern is complex and variable. It presents that a long and narrow "H-L agglomeration" is located in the two northernmost provinces and the other 29 provinces enforce a sequence arrangement with an order of "H-H", "L-H", "H-L", and "L-L" from east to west. Temporal decomposition results show that investment scale, economic level, and population density are the main driving forces for the increase of BECEs from both national and provincial levels, while the main reasons for the decrease are technical level and return on investment. Spatial decomposition results demonstrate that population density and provincial area are the main driving forces for the difference between provincial and national average, and others cause the difference among provinces.

Spatial Agglomeration Pattern of Homestay Inn and Influencing Factors in Shanghai Central District

Spatial Agglomeration Pattern of Homestay Inn and Influencing Factors in Shanghai Central District

Spatial Agglomeration Pattern and Driving Factors of Grain Production in China since the Reform and Opening Up

Since the reform and opening up, regional imbalances in the development of market economy and urbanization have significantly changed the spatial agglomeration pattern of grain production (GP) in China. To characterize GP by the yield, we used the gravity center (GC) and standard deviation ellipse (SDE) to investigate the evolution characteristics of the spatial agglomeration pattern of GP and its three major crops with wheat, maize and rice in China, then establish a spatial econometric model to explore the natural and socioeconomic drivers of GP at the national level, at different crops and at different regions. The research results indicate that the GC of GP gradually shifts and expands to the northeast and presents a spatial distribution pattern in a “northeast–southwest” direction. The GC of wheat production mainly expands upwards in the east–west direction. The GC of maize production shifts in a direction that is more consistent with grain production, and there is an expansion trend in the east–west direction, while the GC of rice production has the largest north–south span, and continues to expand upward to the north by the east. The status of grain production in Northeast China and Northwest China is rising and the importance of grain production in the Southeast coastal areas are decreasing. The importance of wheat and corn production in North China continues to strengthen, and Northeast China is becoming more important for rice production, but the middle and lower reaches of the Yangtze River are still important rice producing regions. Changes in GP with significant spatial dependence are jointly affected by many factors, such as natural and socio-economic factors, and there are obvious differences among different food crops and different division regions, with the most prominent positive effects being the multiple crop index (MCI), arable land per capita (AL) and agricultural mechanization (MECH), while economic growth and urbanization are significantly negative.

Agglomeration centrality examination of cities: an urban transport perspective

Cities are complicated dynamic systems that result in the agglomerations of streets, population and socioeconomic activities. In this paper, we use the partition method of regular grid model to divide a city into small areas, and introduce the concept of agglomeration centrality for describing agglomeration characteristics of local areas in a city. A new measure of agglomeration centrality is defined considering the penetrability of roads within a local area from the 2-dimension plane perspective. Based on this, we introduce the concept of overall agglomeration intensity to illustrate the agglomeration pattern of the city from macro level. Experiments on a set of cities show that the agglomeration centrality distribution of local areas in a city present a power law phenomenon. Besides, the results reveal a crucial important phenomenon that the power law exponent is related to city area and partition scale. The exponent can be described using a power function with two terms and has an upper limit. Moreover, we find that the overall agglomeration intensity indicator is useful to reflect the spatial agglomeration pattern of street networks. In summary, this study develops a new centrality measure from 2-dimension perspective and demonstrates its effectiveness for understanding the spatial agglomeration pattern of cities.

Assessing the coordination between economic growth and urban climate change in China from 2000 to 2015

The balance between economic growth and environmental protection has been a critical concern for sustainable urban development. However, among the multiple research efforts exploring the coordination between the two aspects, the widespread urban climate change has rarely been considered. This study encompasses urban climate change into the cross-system coupling analysis framework to assess its coordination with economic growth using the Coupling Coordination Degree (CCD) model. The two aspects are respectively represented using indicators of Surface Urban Heat Island Intensity (SUHII) and Gross Domestic Product (GDP). Specifically, China is selected as case study, and a total of 259 cities from the 2000–2015 period are analyzed. The spatio-temporal patterns of CCD are investigated through time series clustering to uncover its performance under diversified economic and climatic contexts. The regional inequality and spatial agglomeration effects are also examined. Results reveal significant spatio-temporal heterogeneity of CCD. Geographically, CCD varies from uncoordinated to high-level coordination. Wealthier cities in the eastern coastal region are significantly better coordinated than their inland counterparts. Temporally, the uptrend of CCD is not significant for most cities due to the relatively insufficient emphasis on urban heat island (UHI) mitigation in previous efforts. Evident spatial inequality and agglomeration patterns are also observed with slight downtrends. The spatio-temporal patterns of CCD revealed in this study indicate great necessity for the central government to develop policies suiting cities' special characteristics and contexts, and more efforts should be targeted on reducing regional imbalance. Hence, a nation-city-community policy skeleton is last outlined to enhance the pursuit of a more climate-friendly urban environment under rapid economic development. Overall, this study advances the understanding of economy-urban climate interactions and facilitates the future pursuit of better sustainable cities. The proposed workflow can be utilized for other countries with diversified urbanization processes and potentially used for comparison among different countries.

Spatial Association Pattern of Air Pollution and Influencing Factors in the Beijing–Tianjin–Hebei Air Pollution Transmission Channel: A Case Study in Henan Province

The Beijing–Tianjin–Hebei (BTH) air pollution transmission channel and its surrounding areas are of importance to air pollution control in China. Based on daily data of air quality index (AQI) and air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) from 2015 to 2016, this study analyzed the spatial and temporal characteristics of air pollution and influencing factors in Henan Province, a key region of the BTH air pollution transmission channel. The result showed that non-attainment days and NAQI were slightly improved at the provincial scale during the study period, whereas that in Hebi, Puyang, and Anyang became worse. PM2.5 was the largest contributor to the air pollution in all cities based on the number of non-attainment days, but its mean frequency decreased by 21.62%, with the mean occurrence of O3 doubled. The spatial distribution of NAQI presented a spatial agglomeration pattern, with high-high agglomeration area varying from Jiaozuo, Xinxiang, and Zhengzhou to Anyang and Hebi. In addition, the NAQI was negatively correlated with sunshine duration, temperature, relative humidity, wind speed, and positively to atmospheric pressure and relative humidity in all four clusters, whereas relationships between socioeconomic factors and NAQI differed among them. These findings highlight the need to establish and adjust regional joint prevention and control of air pollution as well as suggest that it is crucially important for implementing effective strategies for O3 pollution control.

Analysis on net primary productivity change of forests and its multi–level driving mechanism – A case study in Changbai Mountains in Northeast China

It is significant to clarify the driving mechanism of the forest ecosystem changes at different scales in Northeast China with serious forest degradation. With Changbai Mountains in Northeast China as the study area, this study integrated multi–source data to explore the spatio–temporal changes of Net Primary Productivity (NPP) and its spatial agglomeration patterns, and probed its multi–level driving mechanism based on the Hierarchical Linear Model (HLM). The results showed the overall NPP in the study area had a gradual declining trend from southeast to northwest from 2001 to 2015. Besides, the ecological risk regions, including Low-Low (L–L) and High–Low (H–L) cluster types, expanded from 27.56% during 2001–2008 to 28.21% during 2008–2015, suggesting the local departments should focus on optimizing these regions and strengthen the construction of complex forests with large age differences to make the ecological environment healthier. In addition, results from the HLM suggested that key driving factors, e.g., the precipitation and vegetation coverage rate, had significant promoting effects on NPP at the grid scale. Whereas the soil organic matter content, distance to the highway, irrigation rate, percentage of the disaster area had significant inhibitory effects (p<0.01) on ecological environment at the watershed scale. Finally, the increase of the total investment in ecological engineering might directly promote the ecological recovery at the county scale. Those results could provide a reasonable scientific basis for the rational development and utilization of regional forest resources, and sustainable socio–economic development.

Spatial agglomeration pattern of homestay inn and influencing factorsbased on the comparison of Hangzhou, Huzhou, and Enshi cities

Spatial agglomeration pattern of homestay inn and influencing factorsbased on the comparison of Hangzhou, Huzhou, and Enshi cities

A spatio-temporal perspective of China's industrial circular economy development

The circular economy pattern is the key to solve the contradiction between industrial development and resource protection. Based on the principle of the “3Rs” of the circular economy (reduction, reuse, and resource utilization), and combined with the characteristics of industrial and provincial circular economy development from the perspective of material flow analysis, this study constructs an evaluation index system of industrial circular economy in China's provinces. A combination of the global entropy and the coefficient of variation methods is used to calculate the comprehensive index of industrial circular economy development. Furthermore, this study analyzes the development of China's provincial industrial circular economy from the two dimensions of time and space. The results show that China's industrial circular economy developed rapidly during the Eleventh and Twelfth Five-Year Plan periods. Resource output, resource consumption, and resource reuse and waste disposal have improved over time, but the outlook for emissions of major pollutants is not optimistic. China's industrial circular economy has significant spatial correlation and obvious regional differences, since the development of the circular economy requires a large amount of capital input, and the capacity of capital input fundamentally depends on the regional economic situation. Therefore, the spatial agglomeration pattern of China's industrial circular economy is roughly the same as that of China's economic development. Eastern China has the fastest development, followed by the central and northeast regions, while the western region, especially the northwest, lags the other regions.

Investigation of a spatial coupling relationship between carbon emission performance and regional urbanization in China.

In light of the problem of environmental pollution caused by fossil fuel combustion, and its association with rapid urbanization, China is grappling with the question of how to reduce carbon emissions through more efficient energy consumption while simultaneously advancing its economic development. We applied a directional distance function to estimate the carbon emission performance of 30 provinces in China during the period 2000–2016. We selected an index system to assess urbanization processes in these provinces and conducted a spatial analysis to investigate the relationship between urbanization and carbon emission performance. We obtained the following results. First, the carbon emission performance of the eastern region, valued at 0.853, was relatively higher than the corresponding values of 0.810, 0.804, and 0.843 in the central, western, and northeastern regions, respectively. However, during this period, disparities among provinces increased. Second, the average urbanization value for each province showed an upward trend during the study period, and urbanization assumed a “striped” spatial agglomeration pattern. A third finding was that carbon emission performance and urbanization demonstrated a relationship of positive spatial dependence. The average value of their coordinated coupling indicated an upward trend, with an annual increase of 0.85%. Last, we found that efforts to reduce carbon emissions that are solely based on carbon emission performance do not yield reliable results. Accordingly, measurements of urbanization values can enable more detailed differentiation. In conclusion, reasonable measures should be implemented to improve carbon emission performance and urbanization that are in alignment with the actual situation within a given region.

Hourly PM2.5 Estimates from a Geostationary Satellite Based on an Ensemble Learning Algorithm and Their Spatiotemporal Patterns over Central East China

Satellite-derived aerosol optical depths (AODs) have been widely used to estimate surface fine particulate matter (PM2.5) concentrations over areas that do not have PM2.5 monitoring sites. To date, most studies have focused on estimating daily PM2.5 concentrations using polar-orbiting satellite data (e.g., from the Moderate Resolution Imaging Spectroradiometer), which are inadequate for understanding the evolution of PM2.5 distributions. This study estimates hourly PM2.5 concentrations from Himawari AOD and meteorological parameters using an ensemble learning model. We analyzed the spatial agglomeration patterns of the estimated PM2.5 concentrations over central East China. The estimated PM2.5 concentrations agree well with ground-based data with an overall cross-validated coefficient of determination of 0.86 and a root-mean-square error of 17.3 μg m−3. Satellite-estimated PM2.5 concentrations over central East China display a north-to-south decreasing gradient with the highest concentration in winter and the lowest concentration in summer. Diurnally, concentrations are higher in the morning and lower in the afternoon. PM2.5 concentrations exhibit a significant spatial agglomeration effect in central East China. The errors in AOD do not necessarily affect the retrieval accuracy of PM2.5 proportionally, especially if the error is systematic. High-frequency spatiotemporal PM2.5 variations can improve our understanding of the formation and transportation processes of regional pollution episodes.

Research on the Influencing Factors of Producer Services Agglomeration—Based on the Yangtze River Delta Urban Group

Based on the panel data of 16 cities between 2003 and 2018 in China City Statistical Yearbook, this paper analyses the current situation and the influencing factors of Producer Services Agglomeration in the Yangtze River Delta. The research adopts spatial econometric methods with spatial weight matrix. The empirical result shows that the agglomeration trend of producer services in Yangtze River Delta is obvious, and the spatial agglomeration pattern is gradually formed. Opening degree and human capital have positive impacts on producer services agglomeration. The agglomeration is negative. And city scale has no effect on the agglomeration. At last, the paper puts forward some suggestions to improve the level of producer services agglomeration in the Yangtze River Delta.

The Effect of Urbanization and Farmland Transfer on the Spatial Patterns of Non-Grain Farmland in China

Rapid urbanization has accelerated farmland transfer in China, which in turn has increased the cultivation intensity of non-grain farmland (NGF). The aim of this paper was to analyze the spatial patterns of non-grain farmland (NGF) at municipal-level cities in China from 2000 to 2014, and explore the influence of urbanization and farmland transfer on the spatial patterns of non-grain farmland (NGF). The study found that both NGF area and share have a consistent variation trend which increased first, then decreased, and increased again. There were great differences between NGF area and share, which had various spatial patterns. Global Moran’s I of NGF area and share had strong positive spatial autocorrelations in 2000, 2005, 2010, and 2014, and revealed remarkable clustered spatial patterns. Local spatial autocorrelation analysis found that the spatial agglomeration pattern of the NGF area and the share changed significantly from 2000 to 2014. The spatial patterns of NGF were significantly influenced by regional differences in urbanization and farmland transfer. These results provide critical insights for formulating differential farmland transfer policies and policies for sustainable development of agriculture and rural economy in China.

The Effect of Economic Cooperation on China’s Outward Foreign Direct Investment—A Spatial Panel Data Approach

ABSTRACTThe study investigates the factors influencing the distribution of Chinese outward foreign direct investment (OFDI) across different host countries. Although economic cooperation between the host country and home country is an important driving factor of OFDI, it is largely ignored in the empirical literature. In this study, we first investigate this issue by employing spatial econometric techniques using Chinese OFDI data between 2003 and 2014. We find that Chinese OFDI to the host country is positively associated with China’s economic cooperation with it after considering the traditional determinants of OFDI, and its effect is robust with a different sub-sample. We also find strong support for the argument that Chinese OFDI shows a significant spatial agglomeration pattern. Furthermore, the host country’s market size, openness to FDI, and well-developed infrastructure attract Chinese OFDI, while the effect of geographic distance to China, and the cultural proximity to China do not hold using different samples.