Changes in the spatial scale of Beijing UHI and urban development

The seasonal and interannual variations of Beijing urban heat island (UHI) are investigated in this paper using the temperature data from 1960 to 2000 at 20 meteorological stations in the Beijing region, and then the relationship between the intensity and spatial scale of UHI and Beijing urbanization indices is analyzed and discussed. Main conclusions are the followings. First, Beijing UHI shows obvious seasonal variations, and it is strongest in winter, next in spring and autumn, and least in summer. The seasonal variation of the UHI mainly occurs in the urban area. The UHI intensity at the center of Beijing is more than 0.8℃ in winter, and only 0.5℃ in summer. Second, the intensity of Beijing HUI exhibits a clear interannual warming trend with its mean growth rate (MGR) being 0.3088℃/10 a. The MGR of HUI is largest in winter, next in spring and autumn, and least in summer, and the urban temperature increase makes a major contribution to the growth of HUI intensity. Third, since the Reform and Opening, the urbanization indices have grown several ten times or even one hundred times, the intensity of HUI has increased dramatically, and its spatial scale also expanded distinctively along with the expansion of urban architectural complexes. Fourth, the interannual variation of urbanization indices is very similar with that of HUI intensity, and their linear correlation coefficients are significant at a more than 0.001 confidence level.     

Incorporating Spatial Dependence in Predictive Vegetation Models: Residual Interpolation Methods*

Predictive vegetation modeling can be used statistically to relate the distribution of vegetation across a landscape as a function of important environmental variables. Often these models are developed without considering the spatial pattern that is inherent in biogeographical data, resulting from either biotic processes or missing or misspecified environmental variables. Including spatial dependence explicitly in a predictive model can be an efficient way to improve model accuracy with the available data. In this study, model residuals were interpolated and added to model predictions, and the resulting prediction accuracies were assessed. Adding kriged residuals improved model accuracy more often than adding simulated residuals, although some alliances showed no improvement or worse accuracy when residuals were added. In general, the prediction accuracies that were not increased by adding kriged residuals were either rare in the sample or had high nonspatial model accuracy. Regression interpolation methods can be an important addition to current tools used in predictive vegetation models as they allow observations that are predicted well by environmental variables to be left alone, while adjusting over‐ and underpredicted observations based on local factors.     

空间信息网格中动态复制策略的研究

空间信息网格项目数据量大，用户遍及各地，对数据文件的请求延时大、占大量带宽。复制可以节省带宽、减少时延、均衡负载、改善系统可靠性。采用动态复制技术可以使用户有效地获得所需的数据。详细地论述了动态复制策略以及空间信息网格中的复制管理机制、复制服务流程和具体实施。     

空间运动对象与时空数据类型研究

当前时空数据模型多以描述空间实体的离散变化为主。该文中对空间运动对象在抽象层次的无限连续空间、离散层次的有限离散空间上的数据类型进行分析和定义，将其分别划分为时间类型、空间类型和时态类型来研究，并提出支持空间运动对象的表示方法和操作方式。该方法既能表示空间实体的连续运动，也能表示其离散变化，为空间运动对象时空数据模型的建立奠定了基础。     

天津地区表土PAHs含量的多尺度空间相关性

本文应用因子克立格分析方法,分析了天津表土16种PAHs之间多尺度空间结构特征。分析结果显示,天津表土PAHs在空间上可以划分三种尺度:块金尺度(0～5km),小空间尺度(5～10km)和大空间尺度(10～50km)。在不同的空间尺度上,16种PAHs之间表现出不同的空间相关性,具有空间尺度相关性。这种空间尺度相关性,是由于不同的空间污染过程造成的,有助于下一步的多尺度空间污染源的判断和污染物迁移转化规律的识别。     

基于城市体系的长江三角洲旅游地域系统研究

首先分析了长江三角洲旅游资源的分布格局及长三角旅游发展的整体性现状，认为长三角旅游整体发展的协调性不足。然后从城市体系与区域旅游整体发展耦合的角度出发，引入了长江三角洲城市体系的规模结构、空间结构、交通网络结构和城市群内部经济联系的分析研究，并以此划分出了长三角两个层次的旅游地域系统，给出了长三角地域旅游城市网络体系的空间组织。最后提出了长三角区域旅游产业联动发展的对策和建议。     

空间分析与自然地理综合研究

通过对空间分析技术和自然地理综合研究发展历程与趋势的分析，得出两个结论：1)现代地理学的快速发展越来越离不开空间分析的支撑，即空间分析技术在地理学中的应用空间愈来愈广，在自然地理综合研究中尤为如此，其主要原因就是空间分析在综合研究中的优势愈来愈明显；2)空间分析技术随其理论与实践的迅速发展及逐步拓展，愈来愈成为自然地理学发展的巨大推动力，也越来越离不开自然地理综合研究这个促使其发展壮大的空间和能发挥其主导作用的领域。空间分析技术和自然地理综合研究的结合已经成为现代自然地理学以及地球空间信息学研究的主要发展趋势之一。     

Deducing the spatial variability of exchange within a longitudinal channel water balance

Developing an appropriate data collection scheme to infer stream–subsurface interactions is not trivial due to the spatial and temporal variability of exchange flowpaths. Within the context of a case study, this paper presents the results from a number of common data collection techniques ranging from point to reach scales used in combination to better understand the spatial complexity of subsurface exchanges, infer the hydrologic conditions where individual influences of hyporheic and groundwater exchange components on stream water can be characterized, and determine where gaps in information arise. We start with a tracer‐based, longitudinal channel water balance to quantify hydrologic gains and losses at a sub‐reach scale nested within two consecutive reaches. Next, we look at groundwater and stream water surface levels, shallow streambed vertical head gradients, streambed and aquifer hydraulic conductivities, water chemistry, and vertical flux rates estimated from streambed temperatures to provide more spatially explicit information. As a result, a clearer spatial understanding of gains and losses was provided, but some limitations in interpreting results were identified even when combining information collected over various scales. Due to spatial variability of exchanges and areas of mixing, each technique frequently captured a combination of groundwater and hyporheic exchange components. Ultimately, this study provides information regarding technique selection, emphasizes that care must be taken when interpreting results, and identifies the need to apply or develop more advanced methods for understanding subsurface exchanges. Copyright © 2013 John Wiley & Sons, Ltd.     

智能卡和出租车轨迹数据中蕴含城市人群活动模式的差异性分析

当前采用交通流数据量化城市人群活动模式研究已经取得了丰硕的研究成果,但是对于同一区域、同一时段不同类型交通流数据反映城市人群活动模式的共性与差异性仍然知之甚少,直接影响了城市人群活动模式挖掘结果的可解释性与实际应用效果。为此,本文旨在对目前广泛采用的智能卡数据（公交和地铁刷卡）和出租车轨迹数据2种重要的交通流数据,从时空分布模式的差异性、行程距离及距离衰减效应的差异性、空间社团结构的差异性3个方面,探索二者反映城市人群活动模式的差异性：① 采用北京市六环以内区域2016年5月9日至15日的智能卡和出租车轨迹数据进行实验分析,研究发现：① 2种交通流反映出行需求的空间分布呈现出高度相关性,但是在同一空间单元上,2种交通流反映出行需求的时间相关性较低;② 2种交通流的使用率在不同空间位置存在明显差异,仅在城市中心区域使用率较为均衡;③ 2种交通流反映人群行程距离的空间分布、距离衰减效应存在明显差异,公共交通对于促进长距离出行更为重要;④ 从2种交通流发现的空间社团结构都显示了城市的多中心结构特征,但是二者发现社团结构存在的差异性表明两种交通方式对城市空间交互起着不同的作用。本研究有助于深入理解多源交通流反映城市人群活动的内在机理,提升城市人群活动模式在城市规划、交通管理等领域的应用效果。