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1.
Landscape pattern is an important determinant of soil contamination at multiple scales, and a proper understanding of their relationship is essential for alleviating soil contamination and making decisions for land planners. Both soil contamination and landscape patterns are heterogeneous across spaces and scale-dependent, but most studies were carried out on a single scale and used the conventional multivariate analyses (e.g. correlation analysis, ordinary least squared regression-OLS) that ignored the issue of spatial autocorrelation. To move forward, this paper examined spatially varying relationships between agricultural soil trace metal contamination and landscape patterns at three block scales (i.e. 5 km × 5 km, 10 km × 10 km, 15 km × 15 km) in the Pearl River Delta (PRD), south China, using geographically weighted regression (GWR). This paper found that GWR performed better than OLS in terms of increasing R square of the model, lowering Akaike Information Criterion values and reducing spatial autocorrelation. GWR results revealed great spatial variations in the relationships across scales, with an increasing explanatory power of the model from small to large block scales. Despite a few negative correlations, more positive correlations were found between soil contamination and different aspects of landscape patterns of water, urban land and the whole landscape (i.e. the proportion, mean patch area, the degree of landscape fragmentation, landscape-level structural complexity, aggregation/connectivity, road density and river density). Similarly, more negative correlations were found between soil contamination and landscape patterns of forest and the distance to the river and industry land (p < 0.05). Furthermore, most significant correlations between soil contamination and landscape variables occurred in the western PRD across scales, which could be explained by the prevailing wind, the distribution of pollutant sources and the pathway of trace metal inputs. 相似文献
2.
Rapid urbanization leads to losses in arable land; quantitatively analyzing the impact of urbanization on arable land is significant for arable land management. However, changes in arable land due to urbanization with scale and neighborhood effects remains poorly understood at the town scale. In this study, high-resolution historical land use data, landscape metric analysis and spatial regression were integrated to quantify the impacts of urbanization on arable land use change (abandonment and conversion) at spatial scales of 300 m–3300 m using a block size increment of 200 m and at the catchment scale in the town of Jinjing in subtropical central China. Arable land abandonment was the predominant type of arable land change and presented strong spatial autocorrelations at each spatial scale. Arable land was converted to tea fields because agricultural structure transformations were occurring around the urban cores, and the amount of arable land converted to residential land accounted for only a small proportion of the total arable land loss and had no spatial autocorrelation. The significance and robustness of the arable land changes impacted by urbanization had obvious scale effects and strong neighborhood effects in nearby regions. Compared with block scales, the catchment scale is an optimal scale for assessing the influence of urbanization and applying planning policy. Our results highlight the significance of incorporating spatial interactions in urbanization research, which can generate less biased estimations and consequently lead to proper policy implication and recommendations. In addition, multi-scale comparisons are helpful for better understanding the relationships between arable land changes and urbanization and provide further insights into the harmonious development of rural settlements and urban cores to preserve arable land. 相似文献
3.
基于地理加权回归的漫湾库区景观破碎化及影响因子分析 总被引：2，自引：0，他引：2
应用地理加权回归模型分析漫湾库区景观破碎化指数——有效筛网大小与相关因子之间的空间关系。选取的解释变量分别是距道路的距离、距乡村的距离、距河流的距离、坡度。结果表明：大坝修建后4种解释变量与有效筛网大小呈现较显著的正相关性。与线性回归模型相比,地理加权回归模型的拟合效果显著提高。1974~1988年,有效筛网大小对各影响因子最敏感的区域面积呈现显著的时空变化这为确定水电站建设及其他因素对景观破碎化影响的大小,并进一步改善库区景观破碎化的现状提供了依据。 相似文献
4.
Analyzing the spatial determinants of urban growth is helpful for urban planning and management. In a case study of urban agglomeration around Hangzhou Bay (China), four landscape metrics (total area, total edge, landscape shape index and aggregation index) were used to describe the landscape characteristics of the urban growth at two block scales (4 km and 7 km) during two temporal intervals (1994–2003 and 2003–2009). Spatial autocorrelation regression was employed to identify the geographic determinants of the urban landscape changes. The results indicated that the urban landscapes became more dominant, unstable, irregular and compact, especially in the centers of cities. These changes exhibited notable spatial variations and spatial autocorrelation at the two block scales. The distances to national and provincial roads influenced the urban pattern changes. The impacts of the urban centers on urban expansion gradually declined with the urbanization progress. The slope factor was the most influential determinant of urban growth. Our study emphasized the importance of considering the autocorrelation and scale effects when analyzing the determinants of urban growth. These findings may help land planners create policies and strategies for future urban development. 相似文献
5.
多元统计回归及地理加权回归方法在多尺度人口空间化研究中的应用 总被引：3，自引：2，他引：1
对统计型人口数据进行格网形式的空间化可更直观地展示人口的空间分布,但不同的人口空间化建模方法和不同的格网尺度在表达人口空间化结果方面存在差异。本文在人口特征分区的基础上,引入DMSP/OLS夜间灯光对城镇用地进行再分类,采用多元统计回归和地理加权回归方法(GWR),开展人口统计数据空间化多尺度模型研究,生成1 km、5 km和10 km等3个尺度的2010年安徽省人口空间数据,并对3个尺度下2个模型结果进行精度评价与比较。结果表明：人口空间数据精度不仅与建模所用方法关系密切,还受到建模格网尺度大小的影响。基于多元统计回归方法的模型估计人口数与实际人口的平均相对误差值随着尺度的增加而降低,而基于GWR方法获得的人口空间数据误差值随着尺度的增加而升高。整体来看,基于GWR方法的1 km研究尺度的人口空间数据平均相对误差最低(22.31%)。区域地形地貌条件与人口空间数据误差有较强的关联,地貌类型复杂的山区人口空间数据误差较大。 相似文献
6.
深圳市近20年城市景观格局演变及其驱动因素 总被引：1，自引：0，他引：1
基于深圳市1996—2015年土地利用数据,利用景观指数、景观转移矩阵和景观扩张指数等方法探究了深圳市近20年景观格局时空变化、主要景观类型转移和建筑用地扩张模式,最后使用Binary Logit模型考察了市级和区级建筑用地景观扩张的主要驱动因素。结果表明：① 1996—2015年,深圳市建筑用地景观优势性逐步增强,面积增加15.81%,以蔓延式（61.9%）和填充式（36.27%）扩张为主;② 1996—2006年为城市化快速扩张期,建筑用地扩张呈集中开发形态,景观多样性和均匀性增加,城市扩张中心略微向北部和东部移动,2006—2015为城市化低速过渡期,景观破碎化加剧,城市扩张重心向北部和西部偏移;③ 在市级尺度上,GDP密度和人口密度对建筑用地景观扩张有显著正影响,生态控制线、高程、坡度和至道路的距离有着显著负影响。每单位生态控制线范围、坡度的增加分别将使建筑用地景观扩张的机会比率将平均减少到原来0.8168倍、0.8841倍。各驱动因素表现出区域和尺度差异性,GDP对宝安区、南山区和坪山区,人口增长对宝安区、龙华区,以及交通可达性对大鹏新区、龙岗区驱动分别最为突出。研究结果可以为中国城市快速扩张过程中的景观格局变化提供科学实践。 相似文献
7.
中国亚热带丘陵山区植被沿海拔梯度分布格局(英文) 总被引：3，自引：0，他引：3
Knowledge of vegetation distribution patterns is very important.Their relationships with topography and climate were explored through a geographically weighted regression(GWR) framework in a subtropical mountainous and hilly region,Minjiang River Basin of Fujian in China.The HJ-1 satellite image acquired on December 9,2010 was utilized and NDVI index was calculated representing the range of vegetation greenness.Proper analysis units were achieved through segregation based on small sub-basins and altitudinal bands.Results indicated that the GWR model was more powerful than ordinary linear least square(OLS) regression in interpreting vegetation-environmental relationship,indicated by higher adjusted R 2 and lower Akaike information criterion values.On one side,the OLS analysis revealed dominant positive influence from parameters of elevation and slope on vegetation distribution.On the other side,GWR analysis indicated that spatially,the parameters of topography had a very complex relationship with the vegetation distribution,as results of the various combinations of environmental factors,vegetation composition and also anthropogenic impact.The influences of elevation and slope generally decreased,from strongly positive to nearly zero,with increasing altitude and slope.Specially,most rapid changes of coefficients between NDVI and elevation or slope were observed in relatively flat and low-lying areas.This paper confirmed that the non-stationary analysis through the framework of GWR could lead to a better understanding of vegetation distribution in subtropical mountainous and hilly region.It was hoped that the proposed scale selection method combined with GWR framework would provide some guidelines on dealing with both spatial(horizontal) and altitudinal(vertical) non-stationarity in the dataset,and it could easily be applied in characterizing vegetation distribution patterns in other mountainous and hilly river basins and related research. 相似文献
8.
Huang Jixian Mao Xiancheng Chen Jin Deng Hao Dick Jeffrey M. Liu Zhankun 《Natural Resources Research》2020,29(1):439-458
Exploring the spatial relationships between various geological features and mineralization is not only conducive to understanding the genesis of ore deposits but can also help to guide mineral exploration by providing predictive mineral maps. However, most current methods assume spatially constant determinants of mineralization and therefore have limited applicability to detecting possible spatially non-stationary relationships between the geological features and the mineralization. In this paper, the spatial variation between the distribution of mineralization and its determining factors is described for a case study in the Dingjiashan Pb–Zn deposit, China. A local regression modeling technique, geological weighted regression (GWR), was leveraged to study the spatial non-stationarity in the 3D geological space. First, ordinary least-squares (OLS) regression was applied, the redundancy and significance of the controlling factors were tested, and the spatial dependency in Zn and Pb ore grade measurements was confirmed. Second, GWR models with different kernel functions in 3D space were applied, and their results were compared to the OLS model. The results show a superior performance of GWR compared with OLS and a significant spatial non-stationarity in the determinants of ore grade. Third, a non-stationarity test was performed. The stationarity index and the Monte Carlo stationarity test demonstrate the non-stationarity of all the variables throughout the area. Finally, the influences of the degree of non-stationary of all controlling factors on mineralization are discussed. The existence of significant non-stationarity of mineral ore determinants in 3D space opens up an exciting avenue for research into the prediction of underground ore bodies.
相似文献9.
Binbin Lu Chris Brunsdon Martin Charlton Paul Harris 《International journal of geographical information science》2017,31(5):982-998
Geographically weighted regression (GWR) is an important local technique to model spatially varying relationships. A single distance metric (Euclidean or non-Euclidean) is generally used to calibrate a standard GWR model. However, variations in spatial relationships within a GWR model might also vary in intensity with respect to location and direction. This assertion has led to extensions of the standard GWR model to mixed (or semiparametric) GWR and to flexible bandwidth GWR models. In this article, we present a strongly related extension in fitting a GWR model with parameter-specific distance metrics (PSDM GWR). As with mixed and flexible bandwidth GWR models, a back-fitting algorithm is used for the calibration of the PSDM GWR model. The value of this new GWR model is demonstrated using a London house price data set as a case study. The results indicate that the PSDM GWR model can clearly improve the model calibration in terms of both goodness of fit and prediction accuracy, in contrast to the model fits when only one metric is singly used. Moreover, the PSDM GWR model provides added value in understanding how a regression model’s relationships may vary at different spatial scales, according to the bandwidths and distance metrics selected. PSDM GWR deals with spatial heterogeneities in data relationships in a general way, although questions remain on its model diagnostics, distance metric specification, and computational efficiency, providing options for further research. 相似文献
10.
中国省域犯罪率影响因素的空间非平稳性分析 总被引：4，自引：2，他引：2
收入差距和流动人口是影响犯罪率的两个重要因素, 以往研究基于OLS模型, 在假设地域空间为均质的前提下分析其对犯罪率的影响, 但现实世界的空间单元往往难以满足“均质”的假设, 多数表现为“空间异质”。以OLS计量空间异质会造成计量结果出现偏差, 同时无法了解不同空间单元的不同影响。而地理加权回归模型通过将空间结构嵌入线性回归模型中, 很好的解决了空间异质的计量问题。利用地理加权回归模型研究2008 年中国大陆省域单元犯罪率的影响因素, 结果表明:① 犯罪率的影响因素表现出空间非平稳性, 流动人口与犯罪率显著相关, 但各个省份相关程度并不相同, 影响关系随空间位置变化而变化;② 地理加权回归模型的计量精度和拟合度比OLS模型有大幅提高 相似文献
11.
Stefano Mainardi 《International journal of geographical information science》2013,27(3):387-411
Public interventions in support of public health and housing in developing countries could benefit from better understanding of spatial heterogeneity and anisotropy. Estimation of directional variation within geographically weighted regression (GWR) faces problems of local parameter instability, border effects and, if extended to non- spatial attributes, potential endogeneity. This study formulates a GWR model where anisotropy is filtered out based on information from directional variograms. Along with classical regressions, the approach is applied to investigate child anaemia and its associations with household characteristics, sanitation and basic infrastructure in 173 regions of sub-Saharan Africa. Based on ordinary least squares (OLS) results, anaemia prevalence rates are up to three times more responsive to child morbidity (related to malaria and other diseases) than to other covariates. GWR estimates provide similar indications, but also point to poor sanitation facilities as a cofactor of severe anaemia particularly in east and southern Africa. The anisotropy-adjusted GWR is spatially stationary in residuals, and its estimated local parameters are less collinear than GWR with no adjustment. However, similar explanatory power and lack of significant bias in parameters estimated by the latter suggest that directional variation is largely captured by modelled co-movements among the variables. 相似文献
12.
Jane Southworth Graeme S. Cumming Matt Marsik Michael W. Binford 《The Professional geographer》2006,58(4):406-420
Anthropogenic, ecological, and land‐surface processes interact in landscapes at multiple spatial and temporal scales to create characteristic patterns. The relationships between temporally and spatially varying processes and patterns are poorly understood because of the lack of spatiotemporal observations of real landscapes over significant stretches of time. We report a new method for observing joint spatiotemporal landscape variation over large areas by analyzing multitemporal Landsat data. We calculate the spatiotemporal variation of the Normalized Difference Vegetation Index (NDVI) in the area covered by one Landsat scene footprint in north central Florida, over spatial windows of 104–108 m2 and time steps of two to sixteen years. The correlations, slopes, and intercepts of spatial versus temporal regressions in the real landscape all differ significantly from results obtained using a null model of a randomized landscape. Spatial variances calculated within windows of 105–107 m2 had the strongest relationships with temporal variances (regressions with both larger and smaller windows had lower coefficients of determination), and the relationships were stronger with longer time steps. Slopes and y‐intercepts increased with window size and decreased with increased time step. The spatial and temporal scales at which NDVI signals are most strongly related may be the characteristic scales of the processes that most strongly determine landscape patterns. For example, the important time and space windows correspond with areas and timing of fires and tree plantation harvests. Observations of landscape dynamics will be most effective if conducted at the characteristic scales of the processes, and our approach may provide a tool for determining those scales. 相似文献
13.
景观生态风险研究进展及基于生态系统服务的评价框架: ESRISK 总被引：14，自引：0，他引：14
在快速城市化过程中,高强度人为活动对生态系统结构和服务功能造成极大扰动,进而产生一定生态风险。近年来,景观生态风险评价的兴起为景观格局—生态过程互馈研究提供了新的视角,可有效支持生态系统管理。本文在探讨生态风险、区域生态风险与景观生态风险联系与区别的基础上,重点评述了景观指数法和风险“源—汇”法等主要景观生态风险评价方法,归纳了基于生态系统服务的景观生态风险研究进展。进一步地,以生态系统服务退化为损失表征,从地形、人为胁迫、生态恢复力、景观脆弱性等维度构建概率表征指标体系,提出了基于生态系统服务的景观生态风险评价（ESRISK）框架,以期为景观生态风险研究提供一个更为完善、综合的评价方法参考,并辅助支持风险减缓策略制定和有限资源的高效分配。最后,从生态系统服务权衡与协同关系、评价结果不确定性分析、景观生态风险与景观格局多尺度关联关系、脆弱性研究深化等方面做出展望。 相似文献
14.
Zhenhong Du Zhongyi Wang Sensen Wu Feng Zhang Renyi Liu 《International journal of geographical information science》2020,34(7):1353-1377
ABSTRACT Geographically weighted regression (GWR) is a classic and widely used approach to model spatial non-stationarity. However, the approach makes no precise expressions of its weighting kernels and is insufficient to estimate complex geographical processes. To resolve these problems, we proposed a geographically neural network weighted regression (GNNWR) model that combines ordinary least squares (OLS) and neural networks to estimate spatial non-stationarity based on a concept similar to GWR. Specifically, we designed a spatially weighted neural network (SWNN) to represent the nonstationary weight matrix in GNNWR and developed two case studies to examine the effectiveness of GNNWR. The first case used simulated datasets, and the second case, environmental observations from the coastal areas of Zhejiang. The results showed that GNNWR achieved better fitting accuracy and more adequate prediction than OLS and GWR. In addition, GNNWR is applicable to addressing spatial non-stationarity in various domains with complex geographical processes. 相似文献
15.
This paper reflects on collaborative landscape research conducted in Reunion Island, an outermost region of the European Union. On this 2,500 km2 tropical island also considered a major international biodiversity hotspot, land-use planners must address important challenges, especially growing population densities and urban sprawl that cause important pressure on agricultural land and natural ecosystems. While progress has been made towards land-use zoning and planning at the island scale, entrenched interests and a lack of communication between the agricultural, urban and environmental sectors continue to hinder the design and implementation of integrated land-use plans at the local level. This paper presents an approach to territorial foresight where urban development scenarios and spatial models were co-constructed with a collective of institutional actors in order to facilitate dialogue on future urbanization patterns and impacts on landscapes. It describes how spatially explicit models and simulations of urban development, first used as demonstrators, have raised individual interests and expectations and facilitated the structuring of a collaborative research network. Models and scenarios were then questioned, redesigned collectively and used as boundary objects to facilitate a shift away from statistical and sectorial readings towards more territorialized and integrated perspectives. Analysing inputs, reactions and feedback from the actors involved in the research, this paper discusses the role and potential value of landscape modelling and simulation in mediating debates among planning stakeholders and creating social learning situations. 相似文献
16.
基于景观生态学的城市化背景下洪灾风险评估 总被引：1，自引：0，他引：1
以秦淮河流域为例,构建HEC-HMS模型,采用统计学分析方法,在两期历史景观分布情况下,研究洪水特征值（洪量、洪峰）与不同景观的景观格局指数间的响应关系,提出并构建基于景观格局的洪水生态风险（洪水—景观生态风险）指数,结合空间分析方法,进行全流域风险的时空变化分析和特征子流域间的对比分析。结果表明：不同类型景观的景观格局与洪水特征值间均具有一定程度的响应关系,景观类型不同,其响应指标与程度不同;2003—2017年,研究区风险程度有所增加,空间差异明显;景观格局对区域洪水影响显著,避免景观的大面积聚集发展,增加各类景观周边的景观丰富度,提高景观间的接触面积,有助于减弱城市化进程中建设用地景观面积增加带来的洪水危害,发挥景观的生态正效应。 相似文献
17.
中国城市化与非农就业增长的空间格局及关系类型 总被引：2，自引：2，他引：0
城市化与就业是中国目前经济社会发展中的热点问题,充分认识城市化与就业之间相互作用关系对于指导中国城市化与就业健康有序发展具有现实意义。在梳理中国城市人口与就业统计口径变更的基础上,选用第五、第六次人口普查及相关统计年鉴数据,利用莫兰指数和地理加权回归探讨中国地级行政单元尺度城市化与非农就业增长的空间特征及相互关系。研究表明：① 2000-2010 年间中国城市化与非农就业均呈现出快速增长的趋势,绝对增长均表现为大城市主导,相对增长则表现出向中心城市周边扩散的趋势;② 莫兰指数揭示出中国2000-2010 年间城市化与非农就业增长空间分布呈现出极化趋势,且两者空间分布格局类似;③ 通过对地理加权模型回归系数划分得到各区域城市化与非农就业增长的关系类型,全国大部分地区城市化与非农就业呈现出协调发展的趋势,城市化滞后区域主要有青海、甘肃中东部及四川盆地中部,重庆、新疆以及部分省会城市出现了一定程度的城市化超前。据此提出,各地区应根据自身实际情况,制订差异化的城市化发展战略。 相似文献
18.
黄土高原地区NDVI与气候因子空间尺度依存性及非平稳性研究 总被引：6，自引：3，他引：3
基于MODIS传感器的植被指数产品(MOD13Q1)及50年气候数据,通过地理加权回归与普通最小二乘回归模型对比,对中国黄土高原地区NDVI与气候因子间的空间尺度依存性及非平稳性进行研究,以期准确建立二者间关系.结果表明:① 研究区域内,NDVI与气候因子间存在很强的空间尺度依存关系,相同空间尺度下,年均降水较年均温对NDVI影响的波动性更大;② 与普通最小二乘回归模型相比,地理加权回归模型能够更准确地展现二者间关系;③气候因子对该地区NDVI的影响差异明显,降水存在直接正向影响,而温度的影响则较复杂;④ NDVI与气候因子间沿东北--西南的分布格局体现出区域内不同植被--气候区差异特征.二者间的异质情况还反映出除气候外,人类活动,地形等其他因素对NDVI的影响. 相似文献
19.
Understanding how landscape pattern determines population or ecosystem dynamics is crucial for managing our landscapes. Urban areas are becoming increasingly dominant social-ecological systems, so it is important to understand patterns of urbanization. Most studies of urban landscape pattern examine land-use maps in two dimensions because the acquisition of 3-dimensional information is difficult. We used Brista software based on Quickbird images and aerial photos to interpret the height of buildings, thus incorporating a 3-dimensional approach. We estimated the feasibility and accuracy of this approach. A total of 164,345 buildings in the Liaoning central urban agglomeration of China, which included seven cities, were measured. Twelve landscape metrics were proposed or chosen to describe the urban landscape patterns in 2- and 3-dimensional scales. The ecological and social meaning of landscape metrics were analyzed with multiple correlation analysis. The results showed that classification accuracy compared with field surveys was 87.6%, which means this method for interpreting building height was acceptable. The metrics effectively reflected the urban architecture in relation to number of buildings, area, height, 3-D shape and diversity aspects. We were able to describe the urban characteristics of each city with these metrics. The metrics also captured ecological and social meanings. The proposed landscape metrics provided a new method for urban landscape analysis in three dimensions. 相似文献
20.
长江三角洲生态用地破碎度及其城市化关联 总被引：12，自引：3，他引：9
破碎化是当今地表自然景观演化的重要形式。基于县(区) 行政单元,利用GIS 技术和景观生态方法,定量探讨了长江三角洲生态用地破碎分区及其城市化关联,研究结论有：① 建立了生态用地破碎度综合模型,并与城市化规模和空间构型等参数聚类,将全区划为2 个 高破碎区、3 个中等破碎区和2 个低破碎区;② 基于区县尺度和30 m 分辨率影像的生态用 地破碎度是城市化、生态用地特征及其他干扰源的综合表现,与城镇化水平和城镇用地比例 等规模参数相关性不明显,相关系数分别为0.303、0.432,但与城镇用地聚合度呈现明显的负相关,相关系数为-0.807;③ 破碎分区及破碎度的城市化关联为不同生态用地空间战略的选择提供依据,对面向生态保护的城市化空间引导和规划具有指导意义。 相似文献