Effects of spatial heterogeneity on pseudo-static stability of coal mine overburden dump slope,using random limit equilibrium and random finite element methods:A comparative study

Sudden and unforeseen seismic failures of coal mine overburden(OB)dump slopes interrupt mining operations,cause loss of lives and delay the production of coal.Consideration of the spatial heterogeneity of OB dump materials is imperative for an adequate evaluation of the seismic stability of OB dump slopes.In this study,pseudo-static seismic stability analyses are carried out for an OB dump slope by considering the material parameters obtained from an insitu field investigation.Spatial heterogeneity is simulated through use of the random finite element method(RFEM)and the random limit equilibrium method(RLEM)and a comparative study is presented.Combinations of horizontal and vertical spatial correlation lengths were considered for simulating isotropic and anisotropic random fields within the OB dump slope.Seismic performances of the slope have been reported through the probability of failure and reliability index.It was observed that the RLEM approach overestimates failure probability(P_(f))by considering seismic stability with spatial heterogeneity.The P_(f)was observed to increase with an increase in the coefficient of variation of friction angle of the dump materials.Further,it was inferred that the RLEM approach may not be adequately applicable for assessing the seismic stability of an OB dump slope for a horizontal seismic coefficient that is more than or equal to 0.1.

Influence of Transportation Accessibility on Urban-rural Income Disparity and Its Spatial Heterogeneity

Transportation accessibility has been treated as an important means of reducing the urban-rural income disparity.However,only a few studies have examined the effects of different types of transportation accessibility on urban-rural income disparity and their spatial heterogeneity.Based on data from 285 prefecture-level(and above)Chinese cities in 2000,2005,2010,2015,and 2020,this study uses spatial econometric models to examine how highway accessibility and railway accessibility influence the urban-rural income disparity and to identify their spatial heterogeneity.The result reveals that highway accessibility and railway accessibility have‘coreperiphery’ring-like circle structures.The urban-rural income disparity exhibits strong spatial clustering effects.Both highway accessibility and railway accessibility are negatively associated with urban-rural income disparity,and the former having a greater effect size.Moreover,there is a substitution effect between highway accessibility and railway accessibility in the whole sample.Furthermore,these associations differ in geographic regions.In the central region,highway accessibility is more important in reducing the urban-rural income disparity,but its effect is weakened with the increase of railway accessibility.In the western region,railway accessibility has a larger effect on narrowing the urban-rural income disparity,and this effect is strengthened by the increase of highway accessibility.We conclude that improving transportation accessibility is conducive to reducing the urban-rural income disparity but its effect is spatial heterogenetic.Highways and railways should be developed in a coordinated manner to promote an integrated transport network system.

Spatial Heterogeneity and Risk Factors of Dental Caries in 12-Year-Old Children in Shanxi Province,China

Objective This study aimed to explore the spatial heterogeneity and risk factors for dental caries in 12-year-old children in Shanxi province,China.Methods The data encompassed 3,721 participants from the two most recent oral health surveys conducted across 16 districts in Shanxi Province in 2015 and 2018.Eighteen specific variables were analyzed to examine the interplay between socioeconomic factors,medical resources and environmental conditions.The Geo-detector model was employed to assess the impacts and interactions of these ecological factors.Results Socioeconomic factors(Q=0.30,P<0.05)exhibited a more substantial impact compared to environmental(Q=0.19,P<0.05)and medical resource factors(Q=0.25,P<0.05).Notably,the urban population percentage(UPP)demonstrated the most significant explanatory power for the spatial heterogeneity in caries prevalence,as denoted by its highest q-value(q=0.51,P<0.05).Additionally,the spatial distribution’s heterogeneity of caries was significantly affected by SO2 concentration(q=0.39,P<0.05)and water fluoride levels(q=0.27,P<0.05)among environmental factors.Conclusion The prevalence of caries exhibited spatial heterogeneity,escalating from North to South in Shanxi Province,China,influenced by socioeconomic factors,medical resources,and environmental conditions to varying extents.

Quantifying the impact of earthquakes and geological factors on spatial heterogeneity of debris-flow prone areas:A case study in the Hengduan Mountains

Understanding the spatial heterogeneity of debris-flow-prone areas holds significant implications for regional risk management, particularly in seismically active regions with geological faults. Despite the significance of this knowledge, a comprehensive quantification of the influence of regional topographical and geological factors on the spatial heterogeneity of debris-flow-prone areas has been lacking. This study selected the Hengduan Mountains, an earthquake-prone region characterized by diverse surface conditions and complex landforms, as a representative study area. An improved units zoning and objective factors identification methodology was employed in earthquake and fault analysis to assess the impact of seismic activity and geological factors on spatial heterogeneity of debrisflow prone areas. Results showed that the application of GIS technology with hydrodynamic intensity and geographical units analysis can effectively analyze debris-flow prone areas. Meanwhile, earthquake and fault zones obviously increase the density of debrisflow prone catchments and make them unevenly distributed. The number of debris-flow prone areas shows a nonlinear variation with the gradual increase of geomorphic factor value. Specifically, the area with 1000 m-2500 m elevation difference, 25°-30° average slope, and 0.13-0.15 land use index is the most favorable conditions for debris-flow occurrence;The average annual rainfall from 600 to 1150 mm and landslides gradient from 16° to 35° are the main causal factors to trigger debris flow. Our study sheds light on the quantification of spatial heterogeneity in debris flow-prone areas in earthquake-prone regions, which can offer crucial support for post-debris flow risk management strategies.

Study on the spatial and temporal evolutionary characteristics of ozone concentration and the impact of human activities in China in 2019

Ozone(O_(3))pollution has a profound impact on human health,vegetation development,and the ecological environment,making it a critical focus of global academic research.In recent years,O_(3)pollution in China has been on a steady rise,with ozone emerging as the sole conventional pollutant to consistently increase in concentration without any decline.This study conducted a quantitative analysis of O_(3)concentrations across 367 Chinese cities in 2019,examining spatial autocorrelation and local clustering of O_(3)levels,and investigated the diverse relationships between human activity factors and O_(3)concentration.The seasonal fluctuation of O_(3)exhibited the“M-type”pattern,with peak concentrations in winter and the lowest levels in summer.The center of O_(3)pollution migrated southeastward,with the area of highest concentration progressively shifting south along the eastern coast.Moreover,O_(3)concentration showed a strong positive correlation with population density,road freight volume,and industrial emissions,suggesting that human activities,vehicle emissions,and industrial operations are significant contributors to O_(3)production.The results provide comprehensive information on the characteristics,causes,and occurrence mechanism of O_(3)in Chinese cities that can be utilized by global government departments to formulate strategies to prevent and control O_(3)pollution.

Spatial patterns and driving forces of urban vegetation greenness in China:A case study comprising 289 cities

Urban vegetation in China has changed substantially in recent decades due to rapid urbanization and dramatic climate change.Nevertheless,the spatial differentiation of greenness among major cities of China and its evolution process and drivers are still poorly understood.This study examined the spatial patterns of vegetation greenness across 289 cities in China in 2000,2005,2010,2015,and 2018 by using spatial autocorrelation analysis on the Normalized Difference Vegetation Index(NDVI);then,the influencing factors were analyzed by using the optimal parameters-based geographical detector(OPGD)model and 18 natural and anthropogenic indicators.The findings demonstrated a noticeable rise in the overall greenness of the selected cities during 2000-2018.The cities in northwest China and east China exhibited the rapidest and slowest greening,respectively,among the six sub-regions.A significant positive spatial correlation was detected between the greenness of the 289 cities in different periods,but the correlation strength weakened over time.The hot and very hot spots in southern and eastern China gradually shifted to the southwest.While the spatial pattern of urban greenness in China is primarily influenced by wind speed(WS)and precipitation(PRE),the interaction between PRE and gross domestic product(GDP)has the highest explanatory power.The explanatory power of most natural factors decreased and,conversely,the influence of anthropogenic factors generally increased.These findings emphasize the variations in the influence strength of multiple factors on urban greenness pattern,which should be taken into account to understand and adapt to the changing urban ecosystem.

Contour Farming Suitability of the Black Soil Region in Northeast China and Its Spatial Characteristics

Contour farming technology plays a key role in reducing soil erosion,enhancing water use efficiency,and fostering sustain-able agricultural development,Despite being a straightforward yet efficacious farming technique,it has not seen widespread implement-ation in China.Considering the deteriorating quality of arable lands in the Black Soil Region of Northeast China(BSR-NEC),it is ne-cessary to investigate spatial patterns and identify suitable areas for contour farming in this region.To achieve this objective,spatial autocorrelation and grouping analysis methods were employed to classify the land into four categories of suitability for contour farming:highly suitable,moderately suitable,generally suitable,and unsuitable.The results reveal that:1)the contour farming suitable area in BSR-NEC covers 89861.32 km^(2),accounting for 21.59%of arable land as of 2020.Heilongjiang Province owns the largest suitable area of 32853.68 km^(2),and Inner Mongolia has the highest proportion of 28.89%.2)In terms of the spatial distribution,regions with higher suitability for contour farming are concentrated in the Da Hinggan Mountains region,particularly Nenjiang City(Heilongjiang Province),which has the highest area of 2593.07 km^(2).Areas with a high proportion of suitable arable lands for contour farming are mainly found in the Da Hinggan Mountains and Changbai Mountains regions,with Ergun City(Inner Mongolia)having the highest pro-portion at 47.2%.Regions with higher suitability and proportion are concentrated in the Da Hinggan Mountains region,primarily cover-ing the Inner Mongolia and Heilongjiang.3)Regarding spatial clustering,both the area and proportion of suitable contour farming areas exhibit noticeable clustering effects,though not entirely consistent.4)Group analysis results designate 148 counties in BSR-NEC as highly suitable areas,predominantly located in the Changbai Mountains region,Liaodong Peninsula,Hulun Buir Plateau,and the north and south regions of the Da Hinggan Mountains.The zoning of suitable areas for contour farming in BSR-NEC informs the strategic de-velopment of policies and measures,allowing for the implementation of targeted policies in distinct areas suitable for contour farming.This provides a valuable reference for promoting contour farming technology more effectively and efficiently.re effectively and effi-ciently.

Spatiotemporal Characteristics of Typical Ecosystem Services and Their Spatial Responses to Driving Factors in Ecologically Fragile Areas in Upper Yellow River,China

The identification of dominant driving factors for different ecosystem services(ESs)is crucial for ecological conservation and sustainable development.However,the spatial heterogeneity of the dominant driving factors affecting various ESs has not been adequately elucidated,particularly in ecologically fragile regions.This study employed the integrated valuation of ESs and trade-offs(InVEST)model to evaluate four ESs,namely,water yield(WY),soil conservation(SC),habitat quality(HQ),and carbon storage(CS),and then to identify the dominant driving factors of spatiotemporal differentiation of ES and further to characterize the spatial heterogeneity characteristics of the dominant driving factors in the eco-fragile areas of the upper Yellow River,China from 2000 to 2020.The results demonstrated that WY exhibited northeast-high and northwest-low patterns in the upper Yellow River region,while high values of SC and CS were distributed in central forested areas and a high value of HQ was distributed in vast grassland areas.The CS,WY,and SC exhibited decreasing trends over time.The most critical factors affecting WY,SC,HQ,and CS were the actual evapotranspiration,precipitation,slope,and normalized difference vegetation index,respectively.In addition,the effects of different factors on various ESs exhibited spatial heterogeneity.These results could provide spatial decision support for eco-protection and rehabilitation in ecologically fragile areas.

Prediction of Porous Media Fluid Flow with Spatial Heterogeneity Using Criss-Cross Physics-Informed Convolutional Neural Networks

Recent advances in deep neural networks have shed new light on physics,engineering,and scientific computing.Reconciling the data-centered viewpoint with physical simulation is one of the research hotspots.The physicsinformedneural network(PINN)is currently the most general framework,which is more popular due to theconvenience of constructing NNs and excellent generalization ability.The automatic differentiation(AD)-basedPINN model is suitable for the homogeneous scientific problem;however,it is unclear how AD can enforce fluxcontinuity across boundaries between cells of different properties where spatial heterogeneity is represented bygrid cells with different physical properties.In this work,we propose a criss-cross physics-informed convolutionalneural network(CC-PINN)learning architecture,aiming to learn the solution of parametric PDEs with spatialheterogeneity of physical properties.To achieve the seamless enforcement of flux continuity and integration ofphysicalmeaning into CNN,a predefined 2D convolutional layer is proposed to accurately express transmissibilitybetween adjacent cells.The efficacy of the proposedmethodwas evaluated through predictions of several petroleumreservoir problems with spatial heterogeneity and compared against state-of-the-art(PINN)through numericalanalysis as a benchmark,which demonstrated the superiority of the proposed method over the PINN.

Spatial-temporal heterogeneity and controlling factors of evapotranspiration in Nujiang River Basin based on Variable Infiltration Capacity(VIC)model

Land surface evapotranspiration(ET)is a critical component in the hydrological cycle but has not well been understood in data-scarce areas especially in river basins,like Nujiang River(NRB)which is characterized by large elevation gradient and different vegetation zones with complex processes of water and energy exchange.The quality of ET from optical remote sensing is constrained by cloud cover which is common in the NRB in the monsoon seasons.To understand factors controlling the spatial-temporal heterogeneity of ET in NRB,we employed the Variable Infiltration Capacity(VIC)hydrological model by parameter optimization with support of quality controlled remote sensing ET product and observed river runoff series in the river.The modeled ET has increased during 1984-2018,which might be one of the reasons for the runoff decrease but precipitation increase in the same period.ET increase and runoff decrease tended to be quicker within altitudinal band of 2000-4000 m than in other areas in NRB.We observed that ET variation in different climatic zones were controlled by different factors.ET is generally positively correlated with precipitation,temperature,and shortwave radiation but negatively with relative humidity.In the Tundra Climate(Et)zone in the upper reach of NRB,ET is controlled by precipitation,while it is controlled by shortwave radiation in the snow climate with dry winter(Dw)zone.ET increase is influenced by the increase of temperature,wind speed,and shortwave radiation in the middle and downstream of NRB with warm temperate climate,fully humid(Cf)and warm temperate climate with dry winter(Cw).

Spatial Analysis of the Aging Population and Socio-economic Factors of China:Global and Local Perspectives

Population aging has become an inevitable trend and exerted profound influences on socio-economic development in China.In this study,we utilized data from national population census and statistical yearbooks in 2010 and 2020 to explore spatio-temporal patterns of aging population and its coupling correlations with socio-economic factors from both global and local perspectives.The results from Local Indicators of Spatial Association(LISA)uncover notable spatial disparities in aging population rates,with higher rates concentrated in the eastern regions and lower rates in the western areas of the Chinese mainland.The results from the global correlation analysis with the changes in aging population rates show significant positive correlations with government interventions and industrial structures,but negatively correlated with economic development,social consumption,and medical facilities.From a local perspective,a Geographically Weighted(GW)correlation analysis is employed to uncover local correlations between aging trends and socio-economic factors.The insights gained from this technique not only underscore the complexity and diversity of economic implications stemming from population aging,but also provide invaluable guidance for crafting region-specific economic policies tailored to various stages of population aging.

Spatial Heterogeneity Modeling Using Machine Learning Based on a Hybrid of Random Forest and Convolutional Neural Network (CNN)

Spatial heterogeneity refers to the variation or differences in characteristics or features across different locations or areas in space. Spatial data refers to information that explicitly or indirectly belongs to a particular geographic region or location, also known as geo-spatial data or geographic information. Focusing on spatial heterogeneity, we present a hybrid machine learning model combining two competitive algorithms: the Random Forest Regressor and CNN. The model is fine-tuned using cross validation for hyper-parameter adjustment and performance evaluation, ensuring robustness and generalization. Our approach integrates Global Moran’s I for examining global autocorrelation, and local Moran’s I for assessing local spatial autocorrelation in the residuals. To validate our approach, we implemented the hybrid model on a real-world dataset and compared its performance with that of the traditional machine learning models. Results indicate superior performance with an R-squared of 0.90, outperforming RF 0.84 and CNN 0.74. This study contributed to a detailed understanding of spatial variations in data considering the geographical information (Longitude & Latitude) present in the dataset. Our results, also assessed using the Root Mean Squared Error (RMSE), indicated that the hybrid yielded lower errors, showing a deviation of 53.65% from the RF model and 63.24% from the CNN model. Additionally, the global Moran’s I index was observed to be 0.10. This study underscores that the hybrid was able to predict correctly the house prices both in clusters and in dispersed areas.

Spatial heterogeneity of urban land-cover landscape in Guangzhou from 1990 to 2005

Urbanization has boon the most important process that changed land cover landscape in Guangzhou since reformation, especially since 1990. It is essential for monitoring and assessing ecological consequences of urbanization to understand landscape quantitative characteristics and its changes. Based on four land-cover type maps interpreted from remote sensing TM images of 1990, 1995, 2000 and 2005, combining gradient analysis with landscape metrics, the quantified spatial pattern and its dynamics of urbanization in Guangzhou was got. Three landscape metrics were computed within different regional areas including the whole study area, two transects along two highways （one N-S and the other W-E） and radiation zones with equal distance outwards the city center were set. Buffer zones for transects N-S and W-E were outlined along highways. The following questions should be answered in this paper： What responses were implied with changing spatial grain size or extent for landscape pattern analysis？ Could gradient progress of urbanization be characterized by landscape pattern analysis？ Did landscape metrics reveal urban expanding gradually？ Were there directional differences in land cover landscape pattern during urbanizing development？ The results gave some affirmative answers. Landscape pattern exhibited obviously scale-dependent to grain size and extent. The landscape metrics with gradient analysis could quantitatively approach spatial pattern of urbanization. A precise location for urbanized area, like city center and sub-center, could be identified by multiple landscape metrics. Multiple adjunctive centers occurred as indicated by analysis of radiation zones around the city center. Directional differences of landscape pattern along the two transects （N-S and W-E） came into being. For example, fragmentation of landscape in the transect W-E was obviously higher than that in the transect N-S. All in all, some interesting and important ecological implications were revealed under landscape patterns of two transects or radiation zones, and that was the important step to link pattern with processes in urban ecological studies and the basis to improve urban environment.

Spatial pattern of plant species diversity and the influencing factors in a Gobi Desert within the Heihe River Basin, Northwest China

Understanding the spatial pattern of plant species diversity and the influencing factors has important implications for the conservation and management of ecosystem biodiversity. The transitional zone between biomes in desert ecosystems, however, has received little attention in that regard. In this study, we conducted a quantitative field survey （including 187 sampling plots） in a 40-km2 study area to determine the spatial pattern of plant species diversity and analyze the influencing factors in a Gobi Desert within the Heihe River Basin, Northwest China. A total of 42 plant species belonging to 16 families and 39 genera were recorded. Shrub and semi-shrub species generally represented the major part of the plant communities （covering 90% of the land surface）, while annual and perennial herbaceous species occupied a large proportion of the total recorded species （71%）. Patrick richness index （R）, Shannon-Wiener diversity index （H）, Simpson＇s dominance index （D）, and Pielou＇s evenness index （I） were all moderately spadaUy variable, and the variability increased with increasing sampling area. The semivariograms for R and H＇ were best fitted with Gaussian models while the semivariograms for D andJ were best fitted with exponential models. Nugget-to-still ratios indicated a moderate spatial autocorrelation for R, H＇, and D while a strong spatial autocorrelation was observed for J. The spatial patterns of R and H＇ were closely related to the geographic location within the study area, with lower values near the oasis and higher values near the mountains. However, there was an opposite trend for D. R, H＇, and D were significantly correlated with elevation, soil texture, bulk density, saturated hydraulic conductivity, and total porosity （P〈0.05）. Generally speaking, locations at higher elevations tended to have higher species richness and diversity and the higher elevations were characterized by higher values in sand and gravel contents, bulk density, and saturated hydraulic conductivity and also by lower values in total porosity. Furthermore, spatial variability of plant species diversity was dependent on the sampling area.

A Method for Spatial Heterogeneity Evaluation on Landscape Pattern of Farmland Shelterbelt Networks:A Case Study in Midwest of Jilin Province,China

On the basis of landscape ecology, combining the Spot 5 high resolution satellite imagery with GIS, a method evaluating the spatial heterogeneity of shelterbelts distribution at landscape scale is put forward in this paper. The distance coefficients of reasonable and existing landscape indexes of farmland shelterbelt networks were com-puted, and then through the classification of the distance coefficients, and the establishment of evaluation rules, the spatial heterogeneity of farmland shelterbelts was evaluated. The method can improve the evaluating system of previ-ous studies on shelterbelts distribution, resolve the disadvantages of lacking spatiality of overall evaluation, and make the evaluation results have more directive significance for shelterbelt management. Based on this method, spatial het-erogeneity of shelterbelt networks was evaluated in the midwest of Jilin Province, China. The results show that the re-gions with fewer shelterbelts and no closed network account for 34.7% of the total area, but only 4.9% of the area has relative reasonable pattern of shelterbelt networks. Many problems exist in the distribution pattern of shelterbelts, therefore, much attention should be paid to construct farmland shelterbelts in the study area.

Impact factors identification of spatial heterogeneity of herbaceous plant diversity on five southern islands of Miaodao Archipelago in North China

Herbaceous plants are widely distributed on islands and where they exhibit spatial heterogeneity.Accurately identifying the impact factors that drive spatial heterogeneity can reveal typical island biodiversity patterns.Five southern islands in the Miaodao Archipelago,North China were studied herein.The spatial distribution of herbaceous plant diversity on these islands was analyzed,and the impact factors and their degree of impact on spatial heterogeneity were identified using CCA ordination and ANOVA.The results reveal 114 herbaceous plant species,belonging to 94 genera from 34 families in the50 plots sampled.The total species numbers on different islands were significantly positively correlated with island area,and the average a diversity was correlated with human activities,while the(3 diversity among islands was more affected by island area than mutual distances.Spatial heterogeneity within islands indicated that the diversities were generally high in areas with higher altitude,slope,total nitrogen,total carbon,and canopy density,and lower moisture content,pH,total phosphorus,total potassium,and aspect.Among the environmental factors,pH,canopy density,total K,total P,moisture content,altitude,and slope had significant gross effects,but only canopy density exhibited a significant net effect.Terrain affected diversity by restricting plantation,plantation in turn influenced soil properties and the two together affected diversity.Therefore,plantation was ultimately the fundamental driving factor for spatial heterogeneity in herbaceous plant diversity on the five islands.

Vulnerability of Water Resources and Its Spatial Heterogeneity in Haihe River Basin, China

To manage water resources effectively, a multiscale assessment of the vulnerability of water resources on the basis of political boundaries and watersheds is necessary. This study addressed issues on the vulnerability of water resources and provided a multiscale comparison of spatial heterogeneity under a climate change background. Using improved quantitative evaluation methods of vulnerabil- ity, the Theil index and the Shannon-Weaver index, we evaluated the vulnerability of water resources and its spatial heterogeneity in the Haihe River Basin in four scales, namely, second-class water resource regions （Class II WRRs）, third-class water resource regions （Class III WRRs）, Province-Class II WRRs, and Province-Class III WRRs. Results show that vulnerability enhances from the north to south in the different scales, and shows obvious spatial heterogeneity instead of moving toward convergence in multiscale assessment results. Among the Class II WRRs, the Tuhai-Majia River is the most vulnerable area, and the vulnerability of the Luanhe River is lower than that of the north of the Haihe River Basin, which in turn is lower than that of the south of the Haihe River Basin. In the scales of Class III WRRs and Province-Class III WRRs, the vulnerability shows obvious spatial heterogeneity and diversity measured by the Theil index and the Shannon-Weaver index. Multiscale vulnerability assessment results based on political boundaries and the watersheds of the Haihe River Basin innovatively provided in this paper are important and useful to characterize the real spatial pattern of the vulnerability of water resources and improve water resource management.

Spatial heterogeneity of soil chemical properties between Haloxylon persicum and Haloxylon ammodendron populations

Spatial heterogeneity is a ubiquitous feature in natural ecosystems, especially in arid regions. Different species and their discontinuous distribution, accompanied by varied topographic characteristics, result in soil resources distributed differently in different locations, and present significant spatial heterogeneity in desert ecosystems. In this study, conventional and geostatistical methods were used to identify the heterogeneity of soil chemical properties in two desert populations, Haloxylon persicum Bunge ex Boss., which dominates on the slopes and tops of sand dunes and Haloxylon ammodendron (C. A. Mey.) Bunge, which inhabits interdunes in the Gurbantunggut Desert of Xinjiang, China. The results showed that soil pH, electrical conductivity (EC), soil organic carbon (SOC), available nitrogen (AN) and available phosphorus (AP) were significantly higher in H. ammodendron populations than that in H. persicum. The coefficient of variation (CV) indicated that (1) most parameters presented a moderate degree of variability (10% 【 CV 【 100%) except pH in both plots, (2) the variability of soil pH, EC and AP in H. ammodendron populations was higher than that in H. persicum populations, and (3) SOC and AN in H. ammodendron populations were lower than that in H. persicum populations. Geostatistical analysis revealed a strong spatial dependence (C0/(C0+C) 【 25%) within the distance of ranges for all tested parameters in both plots. The Kriging-interpolated figures showed that the soil spatial distribution was correlated with the vegetation distribution, individual size of plants, and the topographic features, especially with the plants nearest to sampling points and the topographic features. In each plot, soil EC, SOC, AN and AP presented similar distributions, and fertile islands and salt islands occurred in both plots but did not affect every individual plant, since the sampling distance was larger than the size of such fertile islands. The results of topographic effects on soil heterogeneity suggested significant differences between the interdunes and dune-tops. Different topographic characteristics (physical factors) between plots result in the differences in SOC, AN and AP, while the heterogeneity of soil pH and EC arise from plant species and their distribution (biotic factor). Such biotic and physical factors did not occur in isolation, but worked together on soil heterogeneity, and played important parts in improving the soil properties. Hence these factors were ecologically valuable in the highly resource-stressed arid study area.

Spatial Pattern Evolution and Casual Analysis of County Level Economy in Changsha-Zhuzhou-Xiangtan Urban Agglomeration, China

In order to evaluate whether or not the county units′ economy in the Changsha-Zhuzhou-Xiangtan(Chang-Zhu-Tan) Urban Agglomeration was growing as expected, this study analyzed the spatial economy pattern at county-level by using the Exploratory Spatial Data Analysis(ESDA) method. In this process, the global Moran′s I and local Getis-Ord G*i indexes were employed to analyze indicators including per capita GDP and three industrials(i.e. primary, secondary and tertiary industry) from 2000 to 2010. The results show that: 1) the county units′ economy in the Chang-Zhu-Tan Urban Agglomeration has exhibited a strong spatial autocorrelation and an accelerated integration trend since 2008(Moran′ s I increased from 0.26 to 0.56); 2) there is a significant difference in economy development between the northern and southern county units in the Chang-Zhu-Tan Urban Agglomeration: the hotspot zone with high economic level was formed among the northern county units whereas the coldspot zone with low economic level was located in the southern areas. This difference was caused primarily by the increasingly prominent economic radiation effect of Changsha ′upheaval′; 3) town density, secondary industry, and the integration policy are the major contributors driving the evolution of the spatial economy pattern in the Chang-Zhu-Tan Urban Agglomeration.

Impact of the Built Environment on the Spatial Heterogeneity of Regional Innovation Productivity:Evidence from the Pearl River Delta,China

With the global economy increasingly dependent on innovation,urban discourse has shifted to consider what kinds of spatial designs may best nurture innovation.We examined the relationship between the built environment and the spatial heterogeneity of regional innovation productivity(RIP)using the example of China's Pearl River Delta(PRD).Based on a spatial database of 522546 patent data from 2017,this study proposed an innovation-based built environment framework with the following five aspects:healthy en-vironment,daily interaction,mixed land use,commuting convenience,and technology atmosphere.Combining negative binomial regression and Geodetector to examine the impact of the built environment on RIP,the results show that the spatial distribution of innovation productivity in the PRD region is extremely uneven.The negative binomial regression results show that the built environment has a significant impact on the spatial differentiation of RIP,and,specifically,that healthy environment,mixed land use,commuting convenience,and technology atmosphere all demonstrate significant positive impacts.Meanwhile,the Geodetector results show that the built environment factor impacts the spatial heterogeneity of RIP to varying degrees,with technology atmosphere demonstrating the greatest impact intensity.We conclude that as regional development discourse shifts focus to the knowledge and innovation economy,the innovation-oriented design and updating of built environments will become extremely important to policymakers.