基于作息空间思想的人口统计数据空间化方法

探讨了基于作息空间思想的人口统计数据空间化方法。定义居住和工作的活动空间范围为作息空间,并结合土地利用类型数据和泰森多边形构建了作息空间,以作息空间人口密度作为人口空间分布的表达方式。对辽宁省2005年人口统计数据进行了实例分析,精度优于传统的面积权重内插法,结果的地图渲染能够表现受自然因素和社会经济因素影响的人口分布特征。该方法不涉及复杂的权重计算过程,也可以避免主观因素的干扰,简单易行,有较强的实用性。     

多年平均气温数据空间化误差的尺度效应

属性数据空间化是利用矢量数据生成栅格数据产品的有效方法,它有助于不同来源、不同格式之间的数据的综合分析。空间化是一种必然有误差伴随的过程,为探讨空间化误差与数据源密度、空间化模型方法,以及空间化分辨率之间的关系,本文利用7种水平的气象站点密度、5种空间化模型方法和19种栅格分辨率分析多年平均气温数据空间化误差与这3类影响因子之间的关系。分析发现：（1）气象站点密度的降低导致多年平均气温数据的空间化误差增加;（2）在IDW、Kriging、Adjusted IDW、Regression和Anusplin 5种空间化模型方法中,Adjusted IDW、Regression、Anusplin比IDW、Kriging的精度高;（3）随着栅格分辨率的变粗,多年平均气温数据空间化误差增大;（4）在影响空间化精度的3类因子中,空间化模型方法对空间化精度的影响最大,栅格分辨率次之,气象站点密度的影响最小。通过多元回归分析,建立了多年平均气温数据空间化误差与这3类影响因子之间的定量模型,可为空间化技术方案的制定提供参考和依据。     

Making a Mark: a computational and visual analysis of one researcher’s intellectual domain

Dr. David Mark is widely regarded as a path-breaking researcher in geographic information science. What are the structural and temporal characteristics of his intellectual contributions, as seen through the eyes of the broader academic community? Aiming to answer that question, this article presents a scientometric analysis of publications that have been cited alongside David Mark’s papers. In deliberate contrast to the widespread focus on using citation data to condense scientific impact into a handful of indicators, the methodological contribution of this study lies in its mix of computational and visualization approaches. In the search for latent domain structures, state-of-the-art practices in information science, bibliometrics, and network visualization are combined and extended. An initial network of 50,000+ publications and 4,000,000+ document co-citations undergoes a series of transformations reducing it to 9000 publications that are then clustered in a two-stage process, leading to 678 communities whose co-citation linkages are used to delineate 19 super-communities. To enable replication of this approach for other studies, much focus in this article is on detailed discussion of that workflow as well as on highlighting the reasoning behind the choices made among data sources and analytical methods. The topical evolution of David Mark’s domain of influence is explored in some detail, based on tabular and graphic representations of extracted community structures. Results confirm not only the enormous overall breadth of his influence but also how lasting and recurrent it has been in some areas.     

Geo-scape,a granularity depended spatialization tool for visualizing multidimensional data sets

Recently,the expertise accumulated in the field of geovisualization has found application in the visualization of abstract multidimensional data,on the basis of methods called spatialization methods.Spatialization methods aim at visualizing multidimensional data into low-dimensional representational spaces by making use of spatial metaphors and applying dimension reduction techniques.Spatial metaphors are able to provide a metaphoric framework for the visualization of information at different levels of gran...     

基于生态足迹的格网化生态承载力评价——以衡水市为例

针对行政单元生态承载力评价不能精细反映区域内部生态状况的差异和局部细节特征的问题,难以满足精细管控生态承载力的需要,本文提出了格网化生态承载力评价方法。基于生态足迹模型,结合人口空间分布模型,借助GIS空间分析技术,从格网角度对生态足迹和生态承载力进行定量估算与空间格局分析,并以衡水市为例进行试验验证。结果表明,格网尺度生态承载力评价方法与区域生态承载力评价方法相比更符合真实情况。从格网尺度看,衡水市生态足迹分布圈层特点,生态承载力呈离散分布特征。生态赤字状况表现出明显的异质性,沿滏东排河和滏阳河两侧呈条带状分布。衡水市大部分地区处于生态赤字临界状态,生态状况亟须得到保护。     

百度地图位置解析技术在地震应急基础数据库的应用

分析百度地图位置解析技术功能及特点,提出使用地址解析及坐标转换功能,对地震应急基础属性数据进行解析,实现基础属性数据空间化,设计地理坐标纠偏算法,提高地震应急基础数据的可用性,为地震应急工作提供数据保障。该算法操作简单,运行快捷,解决了地震应急基础数据库中属性数据及时更新的关键技术问题。     

基于居民点密度的人口密度空间化

提出了一种基于居民点密度的人口密度空间化方法。以江苏省人口数据空间化为例,在采用核密度估计法得到江苏省镇(乡、街道)居民点密度后,运用基于居民点密度的人口密度空间化计算公式获得江苏省1 000m×1 000m的人口密度格网图,结果表明:江苏省人口分布的热点地区正是在建的三大都市圈(苏锡常都市圈、南京都市圈和徐州都市圈),其中苏锡常都市圈的人口分布呈现各向异性,而南京和徐州都市圈的人口分布则表现为各向同性;人口密度不仅取决于居民点密度,还与平均每个居民点的人口数密切相关,居民点密度大的区域的人口密度不一定大,居民点密度小的区域的人口密度也不一定小。     

基于随机森林模型的珠江三角洲30 m格网人口空间化

人口空间化是实现人口统计数据与其他环境资源空间数据融合分析的有效途径。本文选取夜间灯光数据、道路网数据、水域分布数据、建成区数据、数字高程模型和地形坡度数据作为影响珠江三角洲人口分布的变量因子,利用随机森林模型对珠江三角洲2010年人口数据进行了30 m格网空间化,并将模拟结果与三个公开数据集作精度对比,最后基于随机森林模型的变量因子重要性分析珠江三角洲人口空间分布的影响因素。结果表明：本文模拟整体精度达到82.32%,均优于WorldPop数据集以及中国公里网格人口数据集,接近GPW数据集,而且在人口密度中等区域模拟精度最高;通过对变量因子重要性进行度量,发现夜间灯光强度是珠江三角洲人口分布的最重要指示性指标,到水域的距离、到建成区的距离和路网密度对珠江三角洲人口分布均具有重要作用。利用随机森林模型结合多源信息能够实现高空间分辨率的人口空间化,可为精细化城市管理提供重要数据源,也可为相关政策决策制定提供支持。     

DEVELOPMENT OF EARTHQUAKE EMERGENCY DISASTER INFORMATION PRE-EVALUATION DATA BASED ON KM GRID

After an earthquake, earthquake emergency response and rescue is one of the effective ways to reduce casualties from the earthquake. Earthquake emergency disaster information is one of crucial factors to effectively guide the rescue work. However, there is a "black box effect" on the emergency disaster information acquisition after an earthquake, which means real-time earthquake disaster information is insufficient. Hazard estimates are usually used as a substitute for the real-time disaster information in the "black box" period. However, it is subject to the accuracy and speed of the estimation. The development of the km grid technology provides good prospect to solve this problem. The paper suggests to develop earthquake disaster information pre-estimation data with the support of the km grid technology. The definition and source of the pre-estimation data are introduced and its possibility in improving the estimation speed and accuracy are analyzed theoretically. Then, we elaborate the calculation model of the pre-estimation data. The framework of the model includes disaster-bearing body data, disaster-causing factors used in calculation and calculation formula. The disaster-bearing body data in km grid format are introduced, including population data in km grid format and building data in km grid format. Then the four elements of the earthquake(earthquake occurrence time, earthquake location, earthquake magnitude and focal depth)are selected as disaster-causing factors for calculation. Map algebra method is used to realize the calculation model in which calculation parameters are associated with base map in the km grid format. So the pre-estimation data are developed by python and ArcGIS, which includes building damage dataset(100 layers), death toll dataset(10 layers)and direct economic loss dataset(5 layers). Finally, the pre-estimation data based method for earthquake emergency disaster information estimation is presented. With the support of this method, two real earthquake cases are used to validate the effect of the pre-estimation data. The validation results show the pre-estimation data can not only significantly improve the speed of the estimation but also greatly improve the accuracy of the estimation. Another good result is found in the validation process that with the support of the pre-estimation data, the estimated result can display the spatial distribution of the disaster information, which will effectively aid earthquake emergency response and rescues.     

地理国情普查数据支持下的人口空间化方法

针对传统的人口空间化方法存在数据源多、模型复杂、忽视居住建筑类型的影响等问题,提出了一种以居住建筑作为人口分布参考的人口空间化方法。在地理国情普查数据的支持下,文章选取株洲市境内的18个乡镇为研究区。首先,将研究区内的居住建筑重分类为多层房屋、低矮房屋和废弃房屋,采用最小二乘法求算各类居住建筑的居住密度。然后,根据各乡镇人口估计值的相对误差对各乡镇各类居住建筑的居住密度进行修正。最后,运用GIS的叠加分析技术得到250m格网人口密度图。检验结果表明,人口估计值与人口统计值的线性拟合斜率为0.965,另外基于居住建筑重分类的人口空间化方法使得人口估计精度较未重分类时有了明显提高。