智慧城市时空大数据云平台建设技术大纲研究

基于智慧城市时空大数据与云平台建设技术大纲要求,研究支持智慧城市时空基础设施建设的核心与框架内容,为加强顶层设计、有序推进时空基础设施建设提供有益参考。     

剑门关丹霞地貌类型景观格局时空变化研究

基于遥感和GIS技术,以SPOT数据作为主要的数据源,分别提取了1990年、2000年和2010年剑门关局部地区丹霞地貌类型景观格局信息,通过计算多种景观指数,分析了不同坡度、坡向和时空上丹霞地貌类型景观格局的变化情况。研究结果表明丹霞地貌景观格局变化随坡度、坡向等因子的不同表现出差异性,国家政策和人类活动影响是其景观格局变化的主要因素。     

自然资源基础大数据服务平台研究

基于国家基础大数据、服务平台建设现状和自然资源管理需求,综合盘点自然资源数据家底,通过对自然资源多源异构数据的同构、同化、同步处理,实现不同数据的集成、融合,形成自然资源基础大数据。结合时空大数据和政务大数据主流技术和基础平台,建立了统一的自然资源大数据综合管理与服务平台,为自然资源大数据相关体系架构和标准化建设提供技术参考。     

基于地理格网的复杂路线车辆通行时间估算方法

车辆通行时间隐含了特定时隙的交通状况,准确地汁算该时间在交通监测和路径规划中具有重要意义。现有研究通常利用车辆历史轨迹佔算一定距离内选定路径的通行时间,然而当路径距离较长时,限于很难找到完整穿越指定路径的历史轨迹而无法对其通行时间进行准确估计;此外,海量历史轨迹在估计路径通行时间时会产生巨大的数据管理和计算压力因此,本文引入地理格网,首先构建统一的时空索引,将路网及其历史轨迹分别划分为-系列落在地理格网单;(Cell)中的路段模式及轨迹段;然肩利用一系列频繁共享轨迹在Cell中的停留时间计算车辆在当前路段模式的通行时间;最肩通过一组防史时段相似路径模式的通行时间估算较长路线的车辆通行时间通过对北京市10000辆出租车一周的轨迹数据进行试验,验证了本文方法在处理海量历史轨迹数据上的有效性,以及在估算较氏路径上车辆通行时间的优越性。     

智慧城市时空大数据平台建设的问题思考

针对时空大数据平台早期建设过程中暴露出来的数据汇聚难、更新不及时、服务不智能、应用不聚焦等问题,该文围绕新时期机构改革后测绘地理信息的工作定位以及新型基础测绘体系的技术变革,在变化发现增量更新、多尺度联动更新、网络抓取汇聚更新、平台智能化改造以及示范应用聚焦选取等方面提出了时空大数据平台建设的技术思路。结合正在开展的智慧城市时空大数据平台建设试点,探索了为自然资源“两统一”职责行使提供主动与智能服务,为政府各部门提供基础性公共信息服务的试点建设新模式。     

Effectiveness of Dynamic Point Symbols in Quantitative Mapping

The main focus of this paper is the effectiveness of dynamic point symbols in the presentation of quantitative data. Such symbols are of particular use in the design of animated maps and computer games. The authors examine three existing techniques of using dynamic point symbols to present quantitative data: blinking, pulsation, and rotation. The aim of the study is to compare their effectiveness with that of classical cartographic animation techniques. The results of the study show that in animated map design, dynamic point symbols might be used to present not only qualitative but also quantitative data with comparable effectiveness. The results may serve as the basis for designing dynamic point symbols to be as effective as the classical techniques used in animated cartography.     

Agricultural labor changes and agricultural economic development in China and their implications for rural vitalization

Journal of Geographical Sciences - Based on panel data from 1991, 2000 and 2010 at the county level in China, this study analyzed the coupling characteristics and spatio-temporal patterns of...     

气候变化背景下1981-2010年中国玉米物候变化时空分异（英文）

Spatio-temporal changes in the differentiation characteristics of eight consecutive phenological periods and their corresponding lengths were quantitatively analyzed based on long-term phenological observation data from 114 agro-meteorological stations in four maize growing zones in China. Results showed that average air temperature and growing degree-days (GDD) during maize growing seasons showed an increasing trend from 1981 to 2010, while precipitation and sunshine duration showed a decreasing trend. Maize phenology has significantly changed under climate change: spring maize phenology was mainly advanced, especially in northwest and southwest maize zones, while summer and spring-summer maize phenology was delayed. The delay trend observed for summer maize in the northwest maize zone was more pronounced than in the Huang-Huai spring-summer maize zone. Variations in maize phenology changed the corresponding growth stages length: the vegetative growth period (days from sowing date to tasseling date) was generally shortened in spring, summer, and spring-summer maize, although to different degrees, while the reproductive growth period (days from tasseling date to mature date) showed an extension trend. The entire growth period(days from sowing date to mature date) of spring maize was extended, but the entire growth periods of summer and spring-summer maize were shortened.     

Spatio-temporal evolution and the influencing factors of PM2.5 in China between 2000 and 2015

High concentrations of PM_2.5 are universally considered as a main cause for haze formation. Therefore, it is important to identify the spatial heterogeneity and influencing factors of PM_2.5 concentrations for regional air quality control and management. In this study, PM_2.5 data from 2000 to 2015 was determined from an inversion of NASA atmospheric remote sensing images. Using geo-statistics, geographic detectors, and geo-spatial analysis methods, the spatio-temporal evolution patterns and driving factors of PM_2.5 concentration in China were evaluated. The main results are as follows. (1) In general, the average concentration of PM_2.5 in China increased quickly and reached its peak value in 2006; subsequently, concentrations remained between 21.84 and 35.08 μg/m³. (2) PM_2.5 is strikingly heterogeneous in China, with higher concentrations in the north and east than in the south and west. In particular, areas with relatively high PM_2.5 concentrations are primarily in four regions, the Huang-Huai-Hai Plain, Lower Yangtze River Delta Plain, Sichuan Basin, and Taklimakan Desert. Among them, Beijing-Tianjin-Hebei Region has the highest concentration of PM_2.5. (3) The center of gravity of PM_2.5 has generally moved northeastward, which indicates an increasingly serious haze in eastern China. High-value PM_2.5 concentrations have moved eastward, while low-value PM_2.5 has moved westward. (4) Spatial autocorrelation analysis indicates a significantly positive spatial correlation. The “High-High” PM_2.5 agglomeration areas are distributed in the Huang-Huai-Hai Plain, Fenhe-Weihe River Basin, Sichuan Basin, and Jianghan Plain regions. The “Low-Low” PM_2.5 agglomeration areas include Inner Mongolia and Heilongjiang, north of the Great Wall, Qinghai-Tibet Plateau, and Taiwan, Hainan, and Fujian and other southeast coastal cities and islands. (5) Geographic detection analysis indicates that both natural and anthropogenic factors account for spatial variations in PM_2.5 concentration. Geographical location, population density, automobile quantity, industrial discharge, and straw burning are the main driving forces of PM_2.5 concentration in China.

     

地理大数据挖掘的本质

针对地理大数据的内在本质以及地理大数据挖掘对于地理学研究的意义,本文解释了地理大数据的含义,并在大数据“5V”特征的基础上提出了粒度、广度、密度、偏度和精度等“5度”的特征,揭示了地理大数据的本质特点。在此基础上,从地理大数据的表达方式、地理大数据挖掘的目标、地理模式的叠加与尺度性、地理大数据挖掘与地理学的关系等4个方面阐述了地理大数据挖掘的本质与作用,并从挖掘目标的角度对地理大数据挖掘方法进行分类。未来地理大数据挖掘的研究将面临地理大数据的聚合、挖掘结果的有效性评价以及发现有价值的知识而非常识等几方面的挑战。