江西省入境旅游流时空演变研究

采用旅游流年际变动指数、年际集中指数、地理集中指数、基尼系数及空间重心模型,对2000年至2012年江西入境旅游流时空动态演变进行分析。结果表明:1入境旅游流年际波动较大,港澳客源市场表现最为稳定,其次是台湾和外国客源市场,港澳台近程入境旅游客源市场仍具有重要的开发价值,外国入境客源市场潜力还要进一步激发。2年际集中指数较高的美日韩等国,应在稳定基数效应的基础上,加强针对性营销;年际集中指数较低的新加坡、法国、俄罗斯等国,应进行重点开发,培育新兴市场。3港澳游客地理集中度高,外国游客呈现分散-集中趋势,为分散风险应加大对亚洲邻国市场的开拓力度;而城市入境旅游流空间分布相对均衡,客源结构更加完善,但由于交通、区位、资源禀赋、知名度及其客源国消费结构和水平等因素的影响,旅游流重心移动轨迹存在空间错位。     

县域尺度下能源产区能源消费碳排放强度空间分异——以陕西省榆林市为例

以能源产区陕西省榆林市的12个区县为研究单元,在Geo Da和Arc GIS等软件的支持下,结合趋势分析和空间统计方法,分析2005—2010年榆林县域尺度下的能源消费碳排放强度的空间格局特征与空间分异机制。榆林各区县能源消费碳排放强度水平差异明显,总体差异呈扩大趋势,两极分化加剧。县域碳排放强度南北走向上表现出逐渐减小趋势,东西走向上具有微弱的"U"型变化态势。总体上,东北部区县碳排放强度高于西南部。各区县碳排放强度具有正的空间关联分布特征,且处于低水平非均衡空间分布状态,空间依赖性明显。结合相关性分析,榆林区县碳排放强度具有极化区、扩散区、塌陷区和传染区4个特征。"热点"主要分布在东北部三县,"冷点"主要分布于南部六县。能源资源分布不均、经济总量以及产业结构差异是造成榆林碳排放强度空间分布不均的主要原因。     

2000年以来广东省经济极化时空演变

选择崔-王指数、变异系数和空间自相关等3种方法,以地级市为基本评价单元,对2000年以来广东省的经济极化的时空演变进行了分析。研究表明：（1）广东省经济极化程度呈上升趋势;（2）2000以来,广东省极化现象明显,但是经济总量的极化现象大于人均经济总量;（3）广东省人均GDP和GDP的Moran＇s I均为正值且呈现增大趋势,经济空间集聚程度呈增大趋势,但是人均GDP的空间集聚程度大于GDP;（4）从局部空间自相关关系来看,广东省经济极化格局2000年以来处于稳定状态。     

滇西北高原降水量时空变化特征

利用滇西北高原1961-2009年逐月降水量资料,采用多种统计分析方法,研究滇西北高原降水量的时空变化特征。结果表明:滇西北高原冬季、夏季和年平均降水量空间分布的主要特征是一致多雨或少雨型,且均具有经向分布特征,其次为"西北部-东南部"或者"西部-东部"反位相振荡型。冬季、夏季和年平均降水量的两种主要空间分布型所对应的时间系数均以年际变化为主,周期变化主要集中在4年以下的高频振荡时域内,其次是周期为12年的年代际变化。近48年来,滇西北高原冬季和年平均降水量随时间变化总体上均以增加趋势为主,增加趋势不明显,夏季降水量变化则呈减少趋势,其中香格里拉县夏季降水量减少趋势明显。     

基于上市公司网络的长三角城市网络空间结构研究

公司内部网络研究是当前城市经济网络研究的重要内容.本文利用2005、2010年长三角A股上市公司企业网络数据库,运用社会网络分析方法、位序—规模分析法等,分析了2005、2010年长三角城市网络空间结构演变及其主要影响因素.研究发现:①长三角上市公司总部沿沪宁—沪杭—杭甬Z字形轴线集聚,从一主(上海)二副(杭州、南京)三中心向一主(上海)三副(杭州、苏州、南京)四中心空间结构演变;苏州超过南京成为第三大企业总部集聚中心.②长三角城市网络核心—边缘结构整体延续,Z字形轴线城市维持核心地位,具备强大的网络权力与威望.③长三角城市网络除等级扩散、邻近扩散等特征之外.还具有明显的行政地域性.主要体现在省会南京、杭州分别成为江苏、浙江省内外上市公司子公司的重要据点;尤其是南京凭借省会优势,中心度仍然高于总部职能较强的苏州.④在长三角经济一体化背景下,跨行政地域性的联系有所加强,特别是上海、苏南向苏中、苏北的扩散明显;浙西南仍是网络相对封闭、孤立的地带,中心度相对较低.⑤行政等级和区划,市场容量等是长三角城市网络空间结构的主要影响因素,而到上海高速公路时间距离、劳动力成本和土地成本的影响在统计上未通过显著性检验.     

重力模型参数空间差异研究——以中国城市间铁路客流为例

重力模型是重要的空间相互作用模型,已经在相关研究和实践中得到了广泛应用.在重力模型应用中,结果对参数的敏感性较高,不同的参数设置就可能导致结果体系的巨大差异,故精确的参数是重力模型应用的基础.许多研究关注了重力模型参数的提取,但限于数据等因素,参数在空间上的差异性尚未得到充分的重视.本文采用2010年城市间铁路客运量作为城市间相互作用强度的反映,以城市市辖区居民总可支配收入作为城市的“质量”,刻画城市本身的吸引力,进而通过回归分析考察重力模型的参数结果;并结合地理信息系统的空间插值工具,分析重力模型中各个参数的空间差异并得到可视化结果.相比于既有研究,本文在变量选取中增加了城市间列车交流频次这一变量,与平均运行时间结合共同反映城市之间的“距离”,使模型拟合结果得到较大改进.结果表明,重力模型参数取值在空间上存在显著差异,对不同区域使用统一的重力模型参数可能使结果出现较大偏差,因此将重力模型应用到具体实践中时,不能忽略参数的空间异质性.     

Evaluating the fitness for use of spatial data sets to promote quality in ecological assessment and monitoring

This article proposes and illustrates a practical methodological framework to evaluate the fitness for use of spatial data sets for environmental and ecological applications, focusing on user requirements for specified application contexts. The methodology is based on the use of metadata to analyze similarity between the data characteristics and the user’s needs or expectations for several quality indicators. Additionally, the concept of ‘critical factors’ is introduced in this framework, allowing users to define which quality indicators have greater importance given their own requirements or expectations and the specified application contexts. The proposed methodology further allows integrating and interconnecting the spatial data quality (SDQ) evaluation methodology with metadata geoportals in WebGIS platforms, facilitating its operation by users from non-spatial disciplines and with often limited expertise on this subject. Examples of the evaluation of fitness for use for specific application contexts within the project BIO_SOS (‘Biodiversity Multi-SOurce Monitoring System: From Space To Species’ FP7 project) are presented. By providing a prompt and straightforward evaluation tool, the proposed methodology can encourage the implementation of SDQ evaluation routines in ecological assessment and monitoring programs, promoting a more adequate use of geospatial data and ultimately contributing to well-supported policy and management decisions.     

An algorithm for converting weather radar data into GIS polygons and its application in severe weather warning systems

An algorithm was developed for converting radar data from matrix format into polygons that can be easily visualized, processed, and analyzed using Geographic Information Systems. Spatial operators can be used to overlap radar polygons with land surface features represented by points, lines, and polygons to meet the demands of severe weather identification and tracking and risk recognition. Application and testing of the algorithm demonstrate that the converted radar polygons are suitable for use in weather modification, risk assessments of flash floods in urban areas, and the identification of lightning activity for lightning risk recognition, all of which are essential in real-time severe weather monitoring and warning.     

Tagging-the-triangle algorithm for partitioning features with inconsistent boundaries

In spatial data sets, gaps or overlaps among features are frequently found in spatial tessellations due to the non-abutting edges with adjacent features. These non-abutting edges in loose tessellations are also called inconsistent boundaries or slivers; polygons containing at least one inconsistent boundary are called inconsistent polygons or sliver polygons. The existing algorithms to solve topological inconsistencies in sliver polygons suffer from one or more of three major issues, namely determination of tolerances, excessive CPU processing time for large data sets and loss of vertex history. In this article, we introduce a new algorithm that mitigates these three issues. Our algorithm efficiently searches the features with inconsistent polygons in a given spatial data set and logically partitions them among adjacent features. The proposed algorithm employs the constrained Delaunay triangulation technique to generate labelled triangles from which inconsistent polygons with gaps and overlaps are identified using label counts. These inconsistent polygons are then partitioned using the straight skeleton method. Moreover, each of these partitioned gaps or overlaps is distributed among the adjacent features to improve the topological consistency of the spatial data sets. We experimentally verified our algorithm using the real land cadastre data set. The comparison results show that the proposed algorithm is four times faster than the existing algorithm for data sets with 200,000 edges.