深圳市道路网络结构特征的成因及其景观格局效应

本文以我国典型快速城市化地区深圳市为例,综合使用GIS技术、道路网络结构特征分析、景观格局分析和相关分析方法研究其道路网络结构特征的成因及其景观生态效应。在确定了24个独立的空间研究单元的基础上,重点分析了深圳市道路网络结构特征的相关关系、城市化水平差异对道路网络结构特征的影响和道路格局特征的景观整体及重要组分的格局效应。结果表明:城市建设用地密度的增加导致交通用地密度、节点和廊道储量增加,道路网络结构复杂程度、格局指数降低;资源条件、环境和生态保护约束是导致道路网络复杂性增加、结构发育水平下降、网络格局指数不断降低的主要原因;深圳市的道路网络格局特征对全市景观整体格局没有表现显著的约束性影响,对建设用地显示出环境保护约束和空间吸引两个方面的综合效应,对于林地则表现出生态保护约束、空间排斥和物理分割三个方面的综合效应。     

道路网络生态干扰测度指数的改进及其空间分异特征——以闽江上游为例

以闽江上游地区为例,在传统指数的基础上,提出并采用改进后的道路网络线密度和道路网络影响域面密度指数,结合空间自相关分析方法,从线上和面上综合探索道路网络对生态干扰的空间分异格局;并分析指数间的相关性。结果表明：① 道路干扰程度具有明显的地区差异,在研究区中部、东部和南部存在空间集聚效应;② 考虑到坡度影响,同等级的道路缓冲带宽度并不是一个固定值;③ 所有指数均呈显著正相关,同类指数相关性大,不同类指数相关性小。若仅采用单一的线密度或面密度指数,则将导致信息不全;虽然同类指数间相关性很大,相互印证,但改进后的指数更加符合客观事实,建议采用。     

Relationships of Agricultural Land Use to an Expanded Road Network within Tropical Forest Landscapes of Cameroon and Republic of the Congo

Road building in Congo Basin forests has increased due to expansion of commercial logging, with potential to expose intact forests to greater establishment of agriculture. We developed new knowledge of agriculture clearing sizes, spatial patterns, and relationships with roads in seven case study sites comprising 7,529 km². Using very high spatial resolution satellite imagery, we mapped roads and rivers, plus clearings for agriculture, settlements, and logging. Mapped clearings (N = 1,781) ranged in size from 0.008 ha to more than 300 ha; most were smallholder agriculture, with 64 percent ≤ 1 ha. Statistical tests of spatial pattern confirmed that agriculture occurred in an inhomogeneous-aggregated pattern, suggesting interactions with other landscape elements. Proximity analyses showed that 76 percent of clearings were within 1 km of a road or river. Thirty-five percent of agriculture clearings were within 1 km of main public roads built before 1990, compared to 17 percent for logging roads built after 2000. Less than 6 percent of agriculture clearings were within 1 km of logging roads with overgrown canopies, suggesting transient relationships. Results based on fine-scale data provide new empirical support for understanding the interactions between agriculture and roads in one of the remaining relatively intact forest areas of the Congo Basin.     

A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data

Kernel density estimation (KDE) is a classic approach for spatial point pattern analysis. In many applications, KDE with spatially adaptive bandwidths (adaptive KDE) is preferred over KDE with an invariant bandwidth (fixed KDE). However, bandwidths determination for adaptive KDE is extremely computationally intensive, particularly for point pattern analysis tasks of large problem sizes. This computational challenge impedes the application of adaptive KDE to analyze large point data sets, which are common in this big data era. This article presents a graphics processing units (GPUs)-accelerated adaptive KDE algorithm for efficient spatial point pattern analysis on spatial big data. First, optimizations were designed to reduce the algorithmic complexity of the bandwidth determination algorithm for adaptive KDE. The massively parallel computing resources on GPU were then exploited to further speed up the optimized algorithm. Experimental results demonstrated that the proposed optimizations effectively improved the performance by a factor of tens. Compared to the sequential algorithm and an Open Multiprocessing (OpenMP)-based algorithm leveraging multiple central processing unit cores for adaptive KDE, the GPU-enabled algorithm accelerated point pattern analysis tasks by a factor of hundreds and tens, respectively. Additionally, the GPU-accelerated adaptive KDE algorithm scales reasonably well while increasing the size of data sets. Given the significant acceleration brought by the GPU-enabled adaptive KDE algorithm, point pattern analysis with the adaptive KDE approach on large point data sets can be performed efficiently. Point pattern analysis on spatial big data, computationally prohibitive with the sequential algorithm, can be conducted routinely with the GPU-accelerated algorithm. The GPU-accelerated adaptive KDE approach contributes to the geospatial computational toolbox that facilitates geographic knowledge discovery from spatial big data.     

基于GWR模型的道路网络对森林碳密度干扰的地理变异

以闽江上游地区为例,在分析三明市2007—2016年森林碳密度时空动态的基础上,采用常规的以及改进后的道路网络测度指标,应用缓冲区分析方法和地理加权回归（Geographically Weighted Regression,GWR）模型,从线上和面上分别探讨道路网络对森林碳密度干扰的地理变异规律。结果表明：① 碳密度受到道路网络的较大影响,路网影响域内外碳密度的大小排序为：路网影响域内＜整个研究区＜路网影响域外;多条道路影响域重叠区的碳密度(26.330 Mg/hm²)明显低于单条道路影响域的碳密度(37.406 Mg/hm²);不同等级道路影响域的碳密度由大到小依次为县道＞高速＞省道＞其它道路＞国道＞乡道。道路网络对2007—2016年碳密度的降低也有明显影响。② GWR模型的分析结果表明,路网对碳密度的影响程度随着样点的变化而变化,具有“空间非平稳性”。碳密度随着路网密度的增加而降低,而随着离道路距离的增加而增加。③ 研究区西北部和中部,GWR的回归系数及相关系数均较大,表明这2个区域道路对碳密度影响大且解释力皆较强。     

A network Kernel Density Estimation for linear features in space–time analysis of big trace data

ABSTRACT

Kernel Density Estimation (KDE) is an important approach to analyse spatial distribution of point features and linear features over 2-D planar space. Some network-based KDE methods have been developed in recent years, which focus on estimating density distribution of point events over 1-D network space. However, the existing KDE methods are not appropriate for analysing the distribution characteristics of certain kind of features or events, such as traffic jams, queue at intersections and taxi carrying passenger events. These events occur and distribute in 1-D road network space, and present a continuous linear distribution along network. This paper presents a novel Network Kernel Density Estimation method for Linear features (NKDE-L) to analyse the space–time distribution characteristics of linear features over 1-D network space. We first analyse the density distribution of each linear feature along networks, then estimate the density distribution for the whole network space in terms of the network distance and network topology. In the case study, we apply the NKDE-L to analyse the space–time dynamics of taxis’ pick-up events, with real road network and taxi trace data in Wuhan. Taxis’ pick-up events are defined and extracted as linear events (LE) in this paper. We first conduct a space–time statistics of pick-up LE in different temporal granularities. Then we analyse the space–time density distribution of the pick-up events in the road network using the NKDE-L, and uncover some dynamic patterns of people’s activities and traffic condition. In addition, we compare the NKDE-L with quadrat method and planar KDE. The comparison results prove the advantages of the NKDE-L in analysing spatial distribution patterns of linear features in network space.     

Time series assessment on landslide occurrences in an area undergoing development

This study assesses the influence of development on landslide occurrences in a rapidly developing area, Kota Kinabalu in Sabah Malaysia, across three assessment years (1978, 1994 and 2010). Two development indicators, land use and road density, were used to measure the influence of development on landslide occurrence. Land use was classified into four categories (barren, forest, developed and other), and road density was classified into low (< 50 m/40 000 m²), moderate (50–150 m/40 000 m²) and high (> 150 m/40 000 m²). Landslide density analysis was used to calculate the concentration of landslide for the different land use and road density categories. The number of landslides in developed areas increased from 19 landslides/100 km² in 1978, to 29 landslides/100 km² in 1994 and to 50 landslides/100 km² in 2010, mirroring an increase in land use for development purposes from 8 per cent in 1978 to 27 per cent in 2010. Landslide density also gradually increased in the high road density class from 10 landslides/100 km² in 1978, to 30 landslides/100 km² in 1994 and 62 landslides/100 km² in 2010. These results show that road construction activities influence landslide occurrences.     

不同道路类型对澜沧江流域景观的生态影响

生境破碎化引发一系列潜在的生态效应,定量表达道路建设造成破碎化特征对于道路生态系统管理具有重要意义。针对澜沧江流域,利用GIS和FragStat软件,通过情景分析,分析了不同级别道路建设对景观的影响。结果表明:澜沧江流域一级路所影响面积最大,三级路最小,而影响的斑块数目则为一级路>二级路>三级路>高速路;生态系统影响面积为林地>草地>旱地>灌丛>水田>建设用地,斑块数目以旱地最高,其次为林地。情景分析表明,随着道路的建设和规划方案的实施,影响域内斑块平均面积减少,斑块数目、平均分维数增加,但总体上,高速路建设对生境破碎化的贡献率较小。土壤侵蚀分布结果表明,侵蚀面积为一级路>二级路>高速路>三级路。     

Quantitative estimation and spatial reconstruction of urban and rural construction land in Jiangsu Province, 1820–1985

Land cover is the most evident landscape signal to characterize the influence of human activities on terrestrial ecosystems. Since the industrial revolution, the expansion of construction land has profoundly changed the status of land use coverage and changes. This study is proposed to reconstruct the spatial pattern of construction land (urban construction land and rural settlement land) for five historical periods over the past 200 years in Jiangsu Province with 200 m × 200 m grids on the basis of quantitative estimation. Urban construction land is estimated based on data about city walls, four gates along walls, and other socio-economic factors. Rural settlement land is calculated based on the rural population and per capita housing allowance. The spatial pattern of historical construction land is simulated based on the distribution of modern construction land in 1985 with a quantitative-boundarysuitability control method and thorough consideration over connectivity of different land use types. The study concludes that: (1) the amount of construction land in Jiangsu Province is estimated at 963.46 km² in 1820, 1043.46 km² in 1911, 1672.40 km² in 1936, 1980.34 km² in 1952 and 10,687.20 km² in 1985; and (2) the spatial distribution of construction land features the great proclivity to water bodies and main roads and the strong polarization of existent residence. The results are verified directly and indirectly by applying the trend verification of construction land changes and patterns, the correlation analysis between rural settlement land and local arable land, and quantitative accuracy test of the reconstructed construction land to actual historical survey maps covering four sample regions in 1936.     

上海市城市景观生态网络连接度评价

城市是典型的人工景观系统,具有景观破碎化现象严重、高连通性低连接度等特征。本文运用景观生态网络连接度理论,探讨我国生态城市建设中景观生态连通性和连接度的内涵和特征,在城市景观生态网络构成分析的基础上,采用连接度指数、廊道密度和引入交通网络指数等建立起景观生态连接度评价方法和体系,对上海市城市景观生态网络中道路网络、绿地网络、水系网络及其形成的"三网合一"的复合网络连接度进行评价。目的在于为我国生态城市发展建设和城市景观生态网络规划提供理论基础。从评价结果来看,单个网络的连接度较高但特征各不相同。将水系网络、路网和绿地网络进行复合的一体化网络的连接度却急剧下降,揭示出上海城市景观生态网络存在分割多、连接少的网络平面化特征。     

道路建设对秦岭山区社会—生态系统的影响——一个社区恢复力的视角

社会—生态系统具有不可预期、自组织和多稳态体制等特征。道路建设是区域发展重要的干扰驱动因素,对局域社会—生态系统产生多要素和跨尺度的影响。基于社会—生态系统恢复力的相关概念和分析框架,以道路建设为切入点,借鉴社会经济连通度概念,以商洛市为例,从区域（local）和社区（community）两个尺度,分析道路建设对欠发达山区社会—生态系统的综合影响。将社会—生态系统分解为经济增长、生态景观格局和乡村发展3个维度,选取商洛市统计年鉴、商州区遥感影像和典型农户调查3类数据,结合数理模型、遥感与GIS技术和问卷统计分析,对道路建设与经济发展,道路网络与景观破碎度变化以及高速公路（国道）建设对乡村社区恢复力的影响进行了研究。主要结论包括：① 道路扩张成为减贫的原因,却并非经济增长的动因,而经济增长却是道路网络扩展的原因;② 道路建设增加了当地生态景观的破碎度,却提高了社会连通度,并且景观破碎度与社会连通度之间呈正相关;③ 在乡村社区尺度上,道路建设与社区恢复力之间呈现复杂关系,乡村社区恢复力可以从集体记忆、生计多样性和适应能力3个准则层选取相应的替代指标,通过对农户调查来分析和解释,运用逐步回归分析,识别了乡村社区恢复力的影响因素。最后,对基础设施建设影响下的欠发达山区乡村恢复力的研究方向进行了探讨。     

武汉市城市空间集聚要素的分布特征与模式

不同城市要素的集聚现象具有不同的空间模式,定量化研究城市要素集聚模式的差异和联系对于理解城市发展机理、合理制定城市规划具有重要意义。本文以武汉市为例,获取不透水表面、商业服务业网点（POI）、人口、容积率、城市道路等城市要素数据;采用核密度估计法识别城市主次中心,从城市要素的分布形态、集聚程度、集聚模式等来研究城市要素的空间分布格局特征。研究发现,武汉市呈现“一主七副”的多中心结构,各城市要素从城市中心向外呈反S型的圈层递减。采用集聚度指数衡量城市要素的集聚水平,结果显示商业POI、人口密度的集聚程度最大,其次是容积率、道路密度、不透水表面占比。高度集聚的商业POI、人口密度呈现出点状模式与带状模式相结合的分布模式,中度集聚的道路密度、容积率呈现点状模式、环状模式与轴状模式相结合的分布模式,低度集聚的不透水表面密度主要表现为环状模式。城市中心的吸引、立体空间开发等促进了城市要素的向心集聚,交通干线、稀缺景观资源的廊道效应等重塑了城市要素的空间分布形态。     

青藏公路对区域土地利用和景观格局的影响——以格尔木至唐古拉山段为例

根据1995年和2000年土地利用数据,运用ARC/INFO软件和景观生态学方法,研究了青藏公路沿线格尔木、曲麻莱、治多3个县(市)的土地利用变化和景观格局变化,修订了单一土地利用类型动态度计算公式,重点分析了公路缓冲带的土地利用变化、景观格局变化和建设用地缓冲带的土地利用变化。主要结论如下:①研究区建设用地增加迅速,林地、水域、未利用土地增加,耕地大量减少,草地退化严重,其中曲麻菜县草地减少22.8%,未利用土地增加 43.96%;②景观破碎化程度加剧,分维数、多样性指数和景观破碎度均增加;③与其它交通干线对沿线的 “轴向”影响不同,该段青藏公路以“点”型的辐射效应为标志,且仅仅影响建设用地周围的区域,格尔木市的影响范围是3 km,五道梁的影响范围是1km; ④K=(Ua+Ub－2Uc)Ua－1T-1×100%,可准确地反映单一土地利用类型变化特点。     

Exploring spatially varying and scale-dependent relationships between soil contamination and landscape patterns using geographically weighted regression

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.     

丝绸之路经济带交通运输的时空演化特征及对经济发展的影响

采用交通网络维数法、相关系数法和格兰杰因果检验分析法等定量统计方法,系统分析了自1998-2015年丝绸之路经济带九省区交通运输投资额、交通网络发育程度、交通运输能力的时空变化特征及对国民经济发展的影响。结果表明：丝绸之路经济带九省区交通运输投资额总体呈增长趋势,交通网络密度明显低于全国平均水平;交通网络发育程度不高,但增长发育速度较快,道路密度和网络维数均有很大幅度的提升,公路网的通达性水平及增速明显优于铁路网;交通设施建设对经济发展的影响显著,对国民生产总值的促进作用时滞长度为两年左右,但各省区之间的交通基础设施建设造成了不明显的空间溢出效应,体现出丝绸之路经济带各省区之间的经济联系尚处薄弱阶段。     

基于二分网络的北京公交线路布局的空间依赖性

公交网络运行系统的结构性缺陷常常致使其成为城市交通拥堵的罪魁祸首。传统公交网络空间研究皆将其视为一个独立的交通运输方式,忽视了城市道路网形态与结构的内生作用。本文基于二分网络的思想,通过构建公交线路—城市道路的空间依赖矩阵,引入系列空间依赖度测度指标从局域、全局两个尺度探讨了北京市的公交线路的空间依赖度及依赖的核心空间,并通过社团识别对北京市公交线路的依赖空间进行了划分。结果发现：① 局域依赖度方面,北京市公交线路布局高度依赖度少数城市主干道和城—郊区快速干道,其空间依赖格局形成了以市中心为核心,以城—郊区快速干道为通道的中心—外围的廊道扩散格局;② 全局依赖度方面,北京市公交线路空间布局结构失衡,比较脆弱,受城市道路路面路况影响较大,同时以四环为模糊边界的市中心区域成为北京市公交线路依赖的核心空间;③ 依赖空间划分上,北京市公交线路布局与城市地域空间呈现良好的对应性,朝阳、海淀、三环内（东城和西城）是公交线路布局规模最密的3个区域。     

暴雨内涝影响下的城市道路交通拥挤特征识别

近年来,暴雨内涝频繁发生,常引发严重的城市交通拥堵问题。本文利用自主开发的宏观交通模拟工具,模拟了上海市中心城区50年一遇和100年一遇暴雨强度情景下每条路段的小时交通量,通过计算道路饱和度,研究了不同强度暴雨内涝对中心城区高架出入口和重要道路拥挤程度的影响。结果表明：①100年一遇暴雨内涝对上海市中心城区道路交通服务能力影响显著,可导致7个高架道路出入口关闭,部分出入口严重拥堵;②暴雨内涝对道路拥堵状况影响的差异性明显,变拥挤路段占道路总里程的13.35%,其中一级道路的拥挤程度变化最为明显,如：大连路、武宁路,周家嘴路和长寿路等主要路段服务水平下降。     

基于浮动车数据的旅游景点周边路网容量研究——以厦门岛为例

随着旅游业的快速发展,旅游城市在法定节假日出现了交通需求的大幅增长,导致不同程度的交通问题。本文利用厦门市的浮动车数据(FCD),分别选取2014年10月1日与10月14日作为节假日与工作日的样本进行研究,首先通过OD点核密度估计判断节假日与工作日出行在时空上的差异,确定研究的时空范围;其次,通过对FCD数据进行核密度估计分析旅游景点周边的车流量变化,并与路段的等级、车道数等道路设施属性进行对比,推测路网的容量;最后进一步通过计算路网中路段行程车速验证方法的可行性。结果显示：节假日期间的旅游交通与工作日的通勤交通在时空分布上存在差异,同时基于FCD数据研究旅游景点周边的路网容量是有效的,厦大—南普陀景点周边路网在节假日期间无法满足车流量的需求。本文对旅游景点周边路网容量的评估结果可为旅游旺季旅游路网的合理规划与管控提供依据。     

基于路网形态的上海住宿业空间布局特征

采用中心性模型、空间自相关理论、核密度法等,从城市路网形态角度分析上海市住宿业的空间格局特征及其与路网结构的空间响应关系。结果发现,上海市住宿业分布具有空间集聚性、类型分异性和距离衰减性,集聚核心与路网节点密度核心一致,主要围绕中心城区形成一级集聚核,星级酒店集聚分布在中心城区,经济型酒店和旅馆在一级集聚核外形成多个散状集聚核心,旅馆集聚依附性较为明显,民宿集聚度最高。不同类型的住宿设施分布存在较显著的空间相关性,对路网依赖性具有多元性,受路网影响的程度不同,道路连接值较高的区域交通可达性较好,住宿设施密度也相应较高。经济型酒店受到城市路网相对可达性和直线型交通效率的依赖最大,旅馆和民宿对于路网节点间通达性的依赖相对较大。住宿业发展要在产品偏好设计的前提下兼顾城市路网结构和消费需求进行合理选址,以实现综合效益最大化。     

Network-scape metric analysis: a new approach for the pattern analysis of urban road networks

The spatial patterns of road networks reflect the morphological and structural characteristics of cities. Previous studies have focused mainly on seeking universal laws in road networks rather than explaining their differences. A new approach for road network pattern analysis is proposed that has been inspired by landscape metric analysis. The utility of this approach is illustrated through (but is not limited to) the extraction of the main factors in network landscapes, or network-scapes for short. Twenty-four metrics were calculated for network-scapes of 100 cities worldwide, before an exploratory analysis is performed to detect the main factors. Four main factors were revealed and may be regarded as the characteristic indicators of road networks, which were identified to be evenness, richness-density, shape irregularity, and size and shape variation. The meanings of these factors are explained, and their spatial distributions are illustrated. Compared to existing road network analytics, these factors depict better the characteristic differences of road networks. The proposed approach provides a new framework for road network pattern analysis from a cellular perspective.