基于2030可持续发展目标的珠三角土地消耗率与人口增长率协调关系评价

量化联合国可持续发展目标SDG 11.3.1“土地消耗率与人口增长率的比率（LCRPGR）”有助于了解城市扩张与人口增长之间的关系,为城市国土空间规划和人口城镇化调控提供数据支撑,并且对指导决策者制定城市增长计划至关重要。基于土地利用产品、夜间灯光数据和人口普查数据,提取了城市建成区,利用地理加权回归模型测算了珠三角地区1 km×1 km格网尺度人口密度。依据SDG 11.3.1指标元数据中定义和公式,对珠三角地区可持续发展进行了可靠评价。结果显示：①1990~2010年珠三角建成区面积扩张4.6倍,城区人口增长3.7倍;②1990~2010年珠三角的LCRPGR值由0.71增长到2.01;城市扩张速度与人口增长速度并非是成比例协调增长关系;③建成区的扩张主要是由耕地和农村居民用地转换而来。综上,自2000年后珠三角地区土地消耗率已经超过人口增长率,城市扩张速度与人口增长速度并非是成比例协调增长关系,二者的差异在增大,应当引起一定注意。     

基于GIS的区域医疗卫生综合服务指数计算方法

针对传统方法采用区域内医疗机构数量、医疗机构等级及规模等指标进行区域内的医疗卫生服务水平评价,不能量化、科学地反映出区域内医疗卫生服务能力空间差异性的问题,提出一种能够量化区域内医疗卫生服务能力的方法,该方法基于地理信息系统( GIS)网格划分功能将研究区域划分为多个网格评价单元,并建立医疗卫生机构中心服务指数计算模型、医疗卫生服务指数衰减模型、医疗卫生综合服务指数计算模型,再根据模型算法计算出研究区域内各个评价单元的医疗卫生服务综合指数,最后通过该综合指数实现对研究区域内医疗卫生服务能力的量化。以上海市嘉定区为例,使用该计算方法计算嘉定区医疗卫生机构综合服务指数,量化评价嘉定区内不同区域的医疗卫生服务水平,研究结果表明该计算方法能够实现对区域医疗卫生服务能力空间差异性的量化评价。     

基于模糊熵的城市设施水平综合评价分析

在综合评价时,权重的确定直接影响到评价结果的准确性和科学性.为此,基于粗糙集的模糊熵提出了一种新的综合评价赋权数的方法,有效表达了不同的指标面对不同的评价方案有不同的信息容量,故每个指标对不同的评价方案应赋予不同的权重,使得权系数既易于解释,又更加客观准确.进而对华北、东北、华东三大地区的城市基础设施水平进行TOPSIS方法分析,得出了三大地区的城市基础设施水平的排名,通过数据分析得出三个地区在不同评价指标下存在的优点和缺点,可以为城建部门提供一定的参考意见,查漏补缺.     

差分方程在人口增长预测中的应用研究

在人口增长预测的研究中,关于人口总数、性别、年龄结构等预测,一般预测模型准确性较低,预测结果缺乏指导意义;而双曲模型和微分方程模型计算复杂,很难求解,不便于科技水平较低地区使用.为解决上述问题,基于灰色预测模型,提出差分方程构建人口增长预测模型.模型能仿真出不同年龄、不同类型人口变化情况,能准确预测结构变化、性别比例变化、城镇化情况等.应用模型对中国人口进行仿真预测,得到结果与国家人口发展战略研究人口发展预测相符合,为人口增长提供了较好的预测模型.     

基于混合潮流算法的多能源互补调度仿真研究

充分考虑综合能源系统受外部能源供应条件以及能源设备的影响,为了降低多能源耦合潮流计算复杂度,提升工程应用的可操作性,提出基于混合潮流算法的多能源互补调度仿真.依据经济指标、安全可靠性指标以及清洁性指标建立多能源互补调度优化模型,设定能量转换枢纽单元输出容量约束、能量转换枢纽单元多能流耦合约束、供能平衡约束、设备运行约束四项约束条件,利用多目标最优混合潮流计算平台基于非劣排序遗传算法求解多能源互补调度优化模型,利用所输出最优解实现混合潮流算法的多能源互补调度.仿真分析结果表明,该方法可实现综合能源系统的多能源互补调度,经济性以及静态安全性提升幅度明显.     

基于区间模型的智慧城市社区信息管控平台设计

为提高智慧城市社区管理效率与服务水平,设计基于区间模型的智慧城市社区信息管控平台.通过对非概率可靠性的定义与区间函数计算,缩小非概率可靠性指标可行域,将冗余方法、软件容错技术、双机容错以及集群技术作为平台可靠性重要保障,分析平台设计目标和技术路线,将平台结构分为表现层、业务层与数据层,根据三层结构模型,设计平台登录、房屋与人口管理、信息查询等模块,实现对社区治理信息的综合管控.仿真实验证明,此管控平台的系统故障率较低,提高了用户体验的满意程度.     

基于本体的融合知识测度分析

针对知识融合产生的新知识规模庞大的问题, 提出一个基于本体的融合知识测度指标. 利用默认关系强度分析知识单元之间融合的紧密程度, 根据词汇链的构建规则定义语义相关度, 由概念本体树的语义距离计算概念之间的语义相关度, 并运用最大熵模型分析融合知识的语义熵. 分析知识元素属性值对融合知识的影响, 确定其相应效用权重系数; 综合上述指标构建对融合算法具有特定趋势指导作用的融合知识测度, 并分析该测度指标对称性、确定性、非负性和扩展性等性质. 应用实例表明了所提出指标的有效性, 并进一步说明了融合知识测度在知识评价体系中的重要作用.

     

面向城市特点的新型智慧城市评价指标裁剪模型

论文首先构建新型智慧城市评价指标数据库,针对不同特点的城市,对数据库中的评价指标进行取舍,基于层次分析法及专家赋分,动态设定其权重,完成评价指标的自动裁剪,从而建立有针对性的智慧城市评价模型,并计算出不同城市的综合得分.将该模型应用于2018版新型智慧城市评价体系及中部6省会城市的智慧旅游、智慧教育评价,实践结果表明,论文基于层次分析法构建的模型与专家经验基本吻合,与这些城市的基本特点相符,有助于客观评价不同特点城市的智慧建设水平.     

京津唐地区城市化对植被物候的影响研究

城市化过程使得土地利用、地表植被覆盖发生着显著的变化,如何定量化描述城市化对植被物候的影响越来越受到各方的关注。基于京津唐地区2001~2006年NDVI时间序列影像,得出了京津唐地区植被物候空间分布格局,计算出北京、天津、唐山3个核心城市城区的距离变量与平均植被物候,并分析了城市化对植被物候指标的影响趋势。结果表明:①2001~2006年,北京城市化使得城区及离城区较近的地方植被生长开始时间提前、结束时间推后、生长季周期变长、NDVI振幅减小;②天津和唐山的城市化使得城区及离城区较近的地方植被生长开始时间延后、结束时间提前、生长季周期变短\,NDVI振幅减小;③城市化对植被物候的影响与该地区城市扩张类型存在相关性关系。     

基于数据挖掘的医院综合评价量化建模方法研究

为解决患者就医寻找合适医院难的问题,提出依据医院诊疗数据,建立基于死亡率与综合实力的医院评价模型。在基于死亡率评价模型中,筛选出5个影响治疗效果的病人自身因素并将其量化。以成功治愈样本为基础,计算出各因素对于治愈天数的影响权重,以此计算出所评价医院每一个死亡样本治愈的预估天数。若大于治愈天数的最大阈值,则确定该样本死亡不可避免。否则为可避免,由此得到该医院关于该病症的"不当死亡率",以此评判该医院针对该疾病的诊疗水平。在基于综合实力的评价模型中,对医院综合实力分11个指标进行评价并量化。采用主成分分析法确定各指标在整套评价方案中所占权重,将该指标量化加权后的结果与所有医院相同指标量化加权均值计算相对差,作为该医院该指标得分,加和该医院所有指标得分作为评价分值。采用4家样本医院实际诊疗数据对模型进行了检验,模型评价的结果符合样本医院的实际水平。     

Space-time dynamics of urban systems from satellite images of night lighting. Urban progress scenarios for European metropolitan regions

The aim of this study was to use satellite imagery of metropolitan regions to develop a method for predicting the evolution of Night-Time Light (NTL) and design a mathematical cellular automata model to predict urban progress scenarios. In order to validate the model with real data, we used NTL for 1992–2012 from the European NUTS-2 region of Catalonia. Lighted surface scenarios (as an estimate of urban expansion) were calculated using variations in light intensity (as an estimate of economic activity). In all, we applied the model to twelve NUTS-3 European metropolitan regions. The model gives us three kinds of useful information related to urban surface areas: the main one is a NTL urban surface baseline, which in turn was used in calibration/validation processes; the second one is a regression line for NTL intensity and economic activity, which could be improved with further research; finally, we found a good coarse approximation to urban shape in urban progress scenarios. Since NTL is available for the entire globe, the model could be used to study urbanization dynamics in metropolitan regions for which, in particular, socio-economic data is not available at this territorial scale, and even megaregions emerging as global economic units.     

Exploiting Multiple Correlations Among Urban Regions for Crowd Flow Prediction

Crowd flow prediction has become a strategically important task in urban computing,which is the prerequisite for traffic management,urban planning and public safety.However,due to variousness of crowd flows,multiple hidden correlations among urban regions affect the flows.Besides,crowd flows are also influenced by the distribution of Points-of-Interests(POIs),transitional functional zones,environmental climate,and different time slots of the dynamic urban environment.Thus,we exploit multiple correlations between urban regions by considering the mentioned factors comprehensively rather than the geographical distance and propose multi-graph convolution gated recurrent units(MGCGRU)for capturing these multiple spatial correlations.For adapting to the dynamic mobile data,we leverage multiple spatial correlations and the temporal dependency to build an urban flow prediction framework that uses only a little recent data as the input but can mine rich internal modes.Hence,the framework can mitigate the influence of the instability of data distributions in highly dynamic environments for prediction.The experimental results on two real-world datasets in Shanghai show that our model is superior to state-of-the-art methods for crowd flow prediction.     

The implementation of land use and relief height data in predicting the evolution of the atmospheric boundary-layer within the urban environment

It is vitally important to understand the structure of the atmospheric boundary-layer within the urban environment and the local rural surroundings. Within the local environment the structure of the earth's boundary-layer is highly turbulent and diffusive, with distinctive inner and outer regions. The flow in the inner region is mainly influenced by the physical characteristics of the terrain, such as the relief and the land use, whereas in the outer region larger scale phenomena need to be taken into account. In this paper, novel methods are implemented to understand the structure of the atmospheric boundary-layer within the urban environment. These include a technique that allows the larger scale topographical effects on the atmospheric boundary-layer to be incorporated, yet still enabling fine resolution of the boundary-layer within the smaller scale of the urban environment. This is achieved by manipulating two large data sets for the land use and the relief height within the UK in conjunction with sophisticated mathematical modelling. In this paper a simple but effective algorithm is presented that allows these large data sets to be practically implemented within the framework of an atmospheric boundary-layer fluid flow model.     

A GIS-based method for modeling urban-climate parameters using automated recognition of shadows cast by buildings

Projections for the next thirty years predict a rapid growth in urban population, particularly in the less developed regions. Constructing and updating GIS databases of 3D urban form is essential for analyzing spatial phenomena related to urbanization. One of these phenomena, urban climate, is influenced by urban morphology. A prerequisite for representing urban morphology is 3D data including the height of urban features. In cases where no city plans or field survey data are available, a viable option is to use remotely-sensed data for recognition of urban features. Often, the output of automated object recognition consists of footprints of urban features. A known method for calculating a feature's height for reconstructing 3D urban morphology is to use the shadows cast by those features.A GIS-based method for constructing 3D geodatabases of urban morphology from cast shadows and analyzing geometrical parameters for urban-climate analysis was developed. The method focuses on the aspect-ratio of inner courtyards and its relation to solar access. A case-study is presented to demonstrate the feasibility of this method. Results demonstrate that the method offers a reliable and low-cost process for constructing 3D geodatabases that can be applied to the analysis of urban-climate parameters in less developed regions, where costly data and sophisticated processing practices are less accessible. This method can enhance the understanding of microclimatic conditions and facilitate climate conscious and sustainable future urban planning in sprawling urban centers.     

基于遥感和微博签到数据的住房空置空间识别

房屋空置率是衡量房地产市场健康与否的重要指标。基于夜间灯光遥感数据和全国土地覆盖数据,对房屋空置率进行空间识别。利用微博签到数据,通过基于密度的聚类算法和热力分析对居民活动空间强度进行分析,从“鬼城”指数排名靠前的100个城市中随机选择30个样本城市,进行城市内部房屋空置空间识别。结果表明：2013年全国地级及以上城市基于像元的平均房屋空置率为27.3%。东部地区房屋空置率较低,中西部地区房屋空置现象明显;房屋空置在中小型城市更加突出。     

Study on the Spatial Identification of Housing Vacancy

Housing Vacancy Rate (HVR) is an important index in assessing the healthiness of residential real estate market. Due to lack of clear and effectively evaluation criterion, the understanding of housing vacancy in China is then rather limited. This paper quantitatively analyzed spatial identification and difference pattern of house vacancy at different scale in China by using nighttime light data and micro-blog check-in data, in order to make up the deficiency of traditional methods in the aspects of data missing and differential approach. The nighttime light intensity for non-vacancy area is estimated after removing the nighttime light intensity from non-residential sources of NPP-VIIRS light data and difference of nighttime light caused by the different urban area ratio. Then, the HVR is calculated for the spatial pattern analysis. This paper analyzed the spatial strength of residents activities by using micro-blog check-in data, based on density-based spatial clustering of applications with noise and heat map. The 30 sample cities were selected to identify house vacancy from 100 cities which ghost city index were high. The following conclusions were drawn through the study: The HVR of eastern coastal cities and regions with rapid development of economy were lower, while the phenomenon of house vacancy in central and western regions were more obvious. The HVR increased from eastern coastal regions to inland areas. What’s more, the phenomenon of house vacancy in middle and small cities were more distinct from the aspect of urban scale. The house vacancy of China were divided into five types: industry or resources driven, government planned, epitaxy expansionary, environmental constraint and speculative activate by taking the factors of natural environment, social economic development level, and population density into consideration. This may shed light on policy implications for Chinese urban development.     

A framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data

Many studies are in an effort to explore urban spatial structure, and urban functional regions have become the subject of increasing attention among planners, engineers and public officials. Attempts have been made to identify urban functional regions using high spatial resolution (HSR) remote sensing images and extensive geo-data. However, the research scale and throughput have also been limited by the accessibility of HSR remote sensing data. Recently, big geo-data are becoming increasingly popular for urban studies since research is still accessible and objective with regard to the use of these data. This study aims to build a novel framework to provide an alternative solution for sensing urban spatial structure and discovering urban functional regions based on emerging geo-data – points of interest (POIs) data and an embedding learning method in the natural language processing (NLP) field. We started by constructing the intraurban functional corpus using a center-context pairs-based approach. A word embeddings representation model for training that corpus was used to extract multiprototype vectors in the second step, and the last step aggregated the functional parcels based on an introduced spatial clustering method, hierarchical density-based spatial clustering of applications with noise (HDBSCAN). The clustering results suggested that our proposed framework used in this study is capable of discovering the utilization of urban space with a reasonable level of accuracy. The limitation and potential improvement of the proposed framework are also discussed.     

一种基于规则的脑组织磁共振图像分割新方法

本文将小波算法、分水岭算法及基于区域的模糊C均值算法相结合，提出了一种基于规则的二次分割方法实现对脑组织磁共振图像的分割。首先，采用一种基于小波的滤波嚣去除图像中的噪声；然后采用分水岭算法实现对图像的初始分割。为克服分水岭算法的过度分割问题，本文提出了基于区域的模糊C均值（RFCM）聚类算法实现对过度分割区域的合并。尽管分水岭算法存在过度分割现象，仍有一些区域分割得并不完全，尤其是在脑脊液与灰质，或灰质与白质的过渡区域。为此，本文提出一种局部区域连续性与全局信息相结合的基于规则的多阈值分割方法，对分水岭算法初始分割不完全的区域再次分割。通过对大量模拟数据和真实数据分割的实验证明了此方法的准确性和可靠性。     

逻辑平衡与高速数字电路

在设计者进行系统和电路级设计时,时常会将要实现的逻辑功能或操作较为平均地分配到时序中的各个阶段,称之为逻辑平衡设计。该论文引用了逻辑平衡的方法,将其运用在高速数字部件设计中,以常用运算单元如计数器,有限状态机和乘法器的高性能设计方案为例,分析了逻辑平衡在高速集成电路设计中的应用;并分析了逻辑平衡的方法在减小电路面积,提高电路的性价比和降低电路功耗中的作用。