1977-2010年青海湖环湖区土地利用/覆盖变化与土地资源管理

青海湖流域是青海省主要牧场,也是青藏高原东北部重要的生态安全屏障.本文以1977、1987、2000、2004年和2010年遥感影像、MODIS NDVI为基础数据,通过数学模型、一元线性回归、景观格局指数的方法分析了青海湖环湖区土地利用/覆盖时空变化及景观格局特点,探讨了土地利用/覆盖变化的影响因素,最后提出青海湖环湖区土地资源管理建议.研究结果表明:近33年来青海湖环湖区土地利用/覆盖变化显著,草地、林地、水体等生态用地面积总体减少,耕地、建设用地、未利用地面积逐渐增加,土地利用/覆盖变化整体处于不平衡态,单向转换频繁;植被覆盖度总体微弱增加,变化趋势具有空间差异性;由于人类的定向选择,草地景观类型退化,景观格局趋向破碎化;土地利用/覆盖与景观格局变化是环湖区气温升高、降水增加、载畜量超载、旅游等因素共同作用的结果;基于土地利用/覆盖变化特点和影响因素,提出创建“生态友好”型土地资源管理模式,实现人地关系协调发展.     

国内产业转移背景下重庆对外贸易的地理格局

在全球第四次产业转移的背景下,重庆迅速调整产业结构并取得突破,对外贸易的地理格局发生较大变化。本文搜集整理2008～2012年海关统计数据,采用地理信息系统软件（Ar-cGIS9.3）对重庆市对外贸易伙伴的空间分布格局、商品构成状况进行研究。研究表明：①2008年以来重庆进出口商品总量增长迅速,出口集中度逐年提高,而进口集中度逐年降低;②进口贸易伙伴集中分布于亚洲、欧洲和北美洲。贸易伙伴的分布重心向东南亚倾斜。进口商品集中于电子信息产品及机械设备;③出口贸易伙伴分布更加集中于北美洲与欧洲。出口商品以计算机与通信产品、机械设备为主。其中,计算机类电子信息产品已经超过传统汽车、摩托车等产品而位居首位。最后对重庆市对外贸易发展的趋势进行了展望。     

基于GIS的福州应急避难所空间格局评价

日益凸显的突发性自然灾害对城市应急管理提出了更高的要求,应急避难所是其重要基础设施之一.本研究利用福州市社区尺度的人口统计数据建立高精度的人口密度分布图,根据应急避难所的容纳人数与人口密度图动态缓冲区分析计算其服务能力空间范围,对研究区内21个避难所的空间格局进行评价.结果表明,福州城区避难所建设数量不足,空间分布不均,人口服务辐射能力弱.针对福州城区避难所空间分布现状,分析了主要的影响因素,对福州城区应急避难场所选址提出了进一步优化建议,认为在人口分散区的避难所应该增加数量,适当减小规模;而在人口稠密区则可增加规模适中的避难所建设.     

石漠化景观格局对土地利用时空演变的响应

土地利用是人类活动最直接的表现形式,喀斯特地区的石漠化演化与土地利用变化有密切关系,石漠化治理是在人为干预下加快恢复脆弱的喀斯特生态系统.运用3S技术和景观格局空间分析技术相结合的手段,对石漠化综合治理下,紫云县水塘小流域的土地利用及石漠化景观格局时空演变关系进行了探讨.结果表明:1.2004-2010年间水塘小流域土地利用类型以林地及草地变化较多,其他地类变动较少,其中林地面积增加10％,草地面积增加5％;2.2004-2010年间水塘小流域石漠化变化主要类型为,中度石漠化转轻度石漠化面积5％,轻度石漠化转潜在石漠化面积10％;3.2004-2010年间水塘小流域土地利用和石漠化景观格局,多样性指数各提高了0.3和0.12,破碎度指数各减少了0.138和0.16,优势度指数各降低了0.181和0.126,均匀度指数各提高了0.092和0.209,分维度指数各提高了0.02和0.18.研究发现运用土地利用与石漠化转移矩阵与景观格局相结合的分析方法,可为评价石漠化治理成效提供借鉴意义.     

Temporal change in the landscape erosion pattern in the Yellow River Basin,China

Using Landsat TM data from 1995 and 2000, changes in the landscape erosion pattern of the Yellow River Basin, China were analysed. The aim was to improve our understanding of soil‐erosion change so that sustainable land use could be established. First, a soil‐erosion intensity index model was developed to study soil‐erosion intensity change in the study area. Over the 5 years, the areas of weak erosion, moderate erosion, severe erosion, and very severe erosion all increased. The area of weak erosion increased dramatically by 7.94×10⁵ ha, and areas of slight erosion and acute erosion decreased by 1.93×10⁶ ha and 4.50×10⁴ ha, respectively. The results show that while the intensity of soil erosion has gradually been decreasing as a whole, in some regions the soil erosion is becoming more severe. Based on landscape indices, the pattern of changes in soil erosion over the past 5 years was analysed. The changes in landscape pattern of soil erosion resulted from human activities. Analysis showed that human impact increases fragmentation, having three major effects on landscape pattern, reduction in patch area, variations in patch shape, and changes in spatial pattern. In the study area, population growth, farming, governmental policy and forest degradation are the major factors causing soil erosion change over a 5‐year period.     

Evaluating the Temporal and Spatial Urban Expansion Patterns of Guangzhou from 1979 to 2003 by Remote Sensing and GIS Methods

China has experienced and is experiencing expeditious urban expansion in the recent decades, especially in the coastal areas and big cities. Rapid urban expansion and dramatic changes of landscape have caused great economic, environmental and social impacts consequently. It is crucial to understand urban temporal, spatial expansion patterns and their related effects. In this paper, urban expansion of Guangzhou, a rapid growing city in south‐east China, from 1979 to 2003 is studied temporally and spatially. Four time ranges including 1979–1990, 1990–1995, 1995–2000 and 2000–2003 are designed and the urban expansion area, expansion rate and the spatial expansion pattern are discussed by using remote sensing data and Geographical Information System (GIS) tool. Two transects are designed along two axes of Guangzhou expansion and the structural of urban expansion patches at different orientations are compared in order to quantitively understand the urban expansion of Guangzhou during the past 24 years. The gradient analysis integrating multi‐temporal data is performed in order to analyze and compare the spatial and temporal dynamics of urban expansion. Two indices of compactness and fractal dimensional index are used to describe the urban developing pattern in the study time durations. And the influence of different types of traffic roads to urban expansion is evaluated using the buffer analysis of GIS. The results show that: (1) temporally, urban area of Guangzhou increase 296.54 km² from 141.15 km² in 1979 to 437.70 km² in 2003 and the increasing rate is up to 210.08%; (2) spatially, Guangzhou has different urban expanding directions in different stages and the general expanding directions are towards northeast, north, southeast and north in four studied time ranges; (3) transportation lines play a very important role in urban expansion of Guangzhou, but different types of road have different impacts. National roads and highways exhibit stronger control to urban expansion than provincial roads; and (4) expansion of Guangzhou has gradually changed from a compact pattern to leapfrogging and disordering patterns.     

Generating hierarchical strokes from urban street networks based on spatial pattern recognition

Strokes are products of a higher-level aggregation of street segments that can reflect functional importance and perceptual significance that is associated with them in human spatial mental conceptualizations, which is of vital importance for network analysis, street selection, and map generalization. Street properties (e.g., street names) and angles between street segments are the two main elements used for generating street strokes according to the continuity principle of perceptual grouping into networks. However, it is difficult to automatically generate strokes with good continuity from street networks with multiple lanes such as dual carriageways or complex street junctions. This article proposes a method for generating street strokes that maintain good continuity across multiple lanes and complex street junctions. The proposed method first detects dual carriageways and complex junctions in street networks and then generates strokes according to the continuity principle of perceptual grouping. Finally, it groups the generated street strokes across the dual carriageways and complex street junctions to maintain good continuity. Moreover, the generated strokes are hierarchically ranked based on stroke length and centrality measurements. Experimental studies demonstrate the validity and effectiveness of the proposed method. The result shows that the generated street strokes maintain good continuity and reflect well the hierarchical structure of the street networks.     

Massively parallel spatial point pattern analysis: Ripley’s K function accelerated using graphics processing units

This study presents a massively parallel spatial computing approach that uses general-purpose graphics processing units (GPUs) to accelerate Ripley’s K function for univariate spatial point pattern analysis. Ripley’s K function is a representative spatial point pattern analysis approach that allows for quantitatively evaluating the spatial dispersion characteristics of point patterns. However, considerable computation is often required when analyzing large spatial data using Ripley’s K function. In this study, we developed a massively parallel approach of Ripley’s K function for accelerating spatial point pattern analysis. GPUs serve as a massively parallel platform that is built on many-core architecture for speeding up Ripley’s K function. Variable-grained domain decomposition and thread-level synchronization based on shared memory are parallel strategies designed to exploit concurrency in the spatial algorithm of Ripley’s K function for efficient parallelization. Experimental results demonstrate that substantial acceleration is obtained for Ripley’s K function parallelized within GPU environments.     

Predicting human mobility with activity changes

Human mobility patterns can provide valuable information in understanding the impact of human behavioral regularities in urban systems, usually with a specific focus on traffic prediction, public health or urban planning. While existing studies on human movement have placed huge emphasis on spatial location to predict where people go next, the time dimension component is usually being treated with oversimplification or even being neglected. Time dimension is crucial to understanding and detecting human activity changes, which play a negative role in prediction and thus may affect the predictive accuracy. This study aims to predict human movement from a spatio-temporal perspective by taking into account the impact of activity changes. We analyze and define changes of human activity and propose an algorithm to detect such changes, based on which a Markov chain model is used to predict human movement. The Microsoft GeoLife dataset is used to test our methodology, and the data of two selected users is used to evaluate the performance of the prediction. We compare the predictive accuracy (R²) derived from the data with and without implementing the activity change detection. The results show that the R² is improved from 0.295 to 0.762 for the user with obvious activity changes and from 0.965 to 0.971 for the user without obvious activity changes. The method proposed by this study improves the accuracy in analyzing and predicting human movement and lays the foundation for related urban studies.