Predicting demand for 311 non-emergency municipal services: An adaptive space-time kernel approach

Many cities in the United States and Canada offer a 311 helpline to their residents for submitting requests for non-emergency municipal services. By dialing 311, urban residents can report a range of public issues that require governmental attention, including potholes, graffito, sanitation complaints, and tree debris. The demand for these municipal services fluctuates greatly with time and location, which poses multiple challenges to effective deployment of limited resources. To address these challenges, this study uses a locally adaptive space-time kernel approach to model 311 requests as an inhomogeneous Poisson process and presents an analytical framework to generate predictions of 311 demand in space and time. The predictions can be used to optimally allocate resources and staff, reduce response time, and allow long-term dynamic planning. We use a bivariate spatial kernel to identify the spatial structure and weigh each kernel by corresponding past observations to capture the temporal dynamics. Short-term serial dependency and weekly temporality are modeled through the temporal weights, which are adaptive to local community areas. We also transform the computation-intensive parameter estimation procedure to a low dimensional optimization problem by fitting to the autocorrelation function of historical requests. The presented method is demonstrated and validated with sanitation service requests in Chicago. The results indicate that it performs better than common industry practice and conventional spatial models with a comparable computational cost.     

江西省宗教场所时空格局及演变模式研究

基于江西省宗教场所数据,以市县作为基本研究单元,以佛教、基督教、道教和伊斯兰教为研究对象,综合运用传统数理统计方法、ESDA以及空间分析方法,对江西省主要宗教场所的时空演变进行分析并总结出宗教发展阶段演变模式。结果表明:建国后特别是改革开放以来,江西省宗教在时间和空间上发展皆不均衡,时间上表现为改革开放以来四大宗教场所总量攀升,佛教数量增加最为明显,基督教增长速率最快,佛教和基督教在空间呈现正的相关性,并形成集聚趋势;空间上,优势宗教的区域空间格局变化明显,优势宗教在空间分布较为分散,并且形成多种优势宗教共同占优区域,同时四大宗教密集区从南往北扩散形成南北带状分布;宗教发展阶段演变模式表明宗教的发展演变有其内部动力机制,并受到诸多因素的影响,宗教之间的竞争融合和相互制衡使宗教的发展演变经历宗教离散阶段、优势宗教集聚阶段并最终形成宗教大融合。     

新疆优质旅游资源空间格局及影响机制

旅游资源空间格局是旅游资源开发战略选择的依据,也是旅游地发展的基础。以新疆优质旅游资源为核心,基于Arc GIS技术,采用核密度和可达性分析法研究其空间格局分布特征和空间可达性。结果表明:新疆旅游资源空间分布呈现一轴一带格局,一轴即亚欧第二大陆桥,一带即新疆沿边境带;新疆优质旅游资源空间可达性较低,尤其是南疆;计算以乌鲁木齐为原点出发到达新疆各优质旅游资源点的时间,有116个景点在6 h可达圈内,占研究景点的44.96%,有62个景点在10 h可达圈外,占研究景点的24.03%。基于核密度和可达性分析结果及其形成的影响机制,提出新疆旅游业空间发展战略。     

非参数统计方法在水文水资源中的应用与展望

非参数统计方法在水文水资源系统中的应用和研究已取得了一些进展,依研究内容不同可分三类:(1)在水文频率分析中的应用和研究;(2)在水文水资源预报中的应用和研究;(3)在水文水资源模拟中的应用和研究.对非参数统计方法在水文水资源系统中的应用和研究现状作了较全面的介绍,并展望了今后的研究趋势和发展方向.     

Nighttime convection in the interior of a dense Douglas fir forest

Infrared radiative surface temperatures as observed over a dense Douglas fir forest during stable atmospheric conditions are analyzed. It is shown that the concept of a single surface temperature to describe both the thermal coupling and the radiative coupling between atmosphere and forest fails when longwave cooling is large and wind speeds are low. In such cases a decoupling of the radiative surface temperature from the air temperature aloft is observed. Conditions for decoupling are formulated in terms of an appropriate Richardson number. It is shown that a convective surface temperature comes into play that is coupled to the forest interior air temperature. Observed radiative surface temperature is then composed of this convective surface temperature and the aerodynamic surface temperature. Forest interior air temperature during nighttime is, in general, lower than air temperature above the canopy. A simple two-layer canopy model is used to explain this phenomenon in terms of the distribution of atmospheric sensible heat flux and storage heat flux over the two canopy layers.     

地震油气储层的小样本卷积神经网络学习与预测

地震储层预测是油气勘探的重要组成部分,但完成该项工作往往需要经历多个环节,而多工序或长周期的研究分析降低了勘探效率.基于油气藏分布规律及其在地震响应上所具有的特点,本文引入卷积神经网络深度学习方法,用于智能提取、分类并识别地震油气特征.卷积神经网络所具有的强适用性、强泛化能力,使之可以在小样本条件下,对未解释地震数据体进行全局优化提取特征并加以分类,即利用有限的已知含油气井段信息构建卷积核,以地震数据为驱动,借助卷积神经网络提取、识别蕴藏其中的地震油气特征.将本方案应用于模型数据及实际数据的验算,取得了预期效果.通过与实际钻井信息及基于多波地震数据机器学习所预测结果对比,本方案利用实际数据所演算结果与实际情况有较高的吻合度.表明本方案具有一定的可行性,为缩短相关环节的周期提供了一种新的途径.     

A model‐based data‐driven dictionary learning for seismic data representation

Planar waves events recorded in a seismic array can be represented as lines in the Fourier domain. However, in the real world, seismic events usually have curvature or amplitude variability, which means that their Fourier transforms are no longer strictly linear but rather occupy conic regions of the Fourier domain that are narrow at low frequencies but broaden at high frequencies where the effect of curvature becomes more pronounced. One can consider these regions as localised “signal cones”. In this work, we consider a space–time variable signal cone to model the seismic data. The variability of the signal cone is obtained through scaling, slanting, and translation of the kernel for cone‐limited (C‐limited) functions (functions whose Fourier transform lives within a cone) or C‐Gaussian function (a multivariate function whose Fourier transform decays exponentially with respect to slowness and frequency), which constitutes our dictionary. We find a discrete number of scaling, slanting, and translation parameters from a continuum by optimally matching the data. This is a non‐linear optimisation problem, which we address by a fixed‐point method that utilises a variable projection method with ?₁ constraints on the linear parameters and bound constraints on the non‐linear parameters. We observe that slow decay and oscillatory behaviour of the kernel for C‐limited functions constitute bottlenecks for the optimisation problem, which we partially overcome by the C‐Gaussian function. We demonstrate our method through an interpolation example. We present the interpolation result using the estimated parameters obtained from the proposed method and compare it with those obtained using sparsity‐promoting curvelet decomposition, matching pursuit Fourier interpolation, and sparsity‐promoting plane‐wave decomposition methods.     

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kernel分析方法的河南省城镇密集区研究

基于kernel城镇集聚区密度的计算方法,借助于ArcGIS软件的计算和空间分析功能,分析河南省城镇密集区的历史演变和空间变化,进一步探讨河南省城镇密集区和中原城市群的关系。     

采用非正定OCSVM 的高光谱影像地物检测

高斯径向基核函数是基于光谱向量间欧氏距离的度量,对于因光照强度变化而引起的地物光谱变异敏感,当同类地物光谱发生变异时,基于高斯径向基核的高光谱影像地物检测算法的性能下降.为了解决该问题,基于光谱曲线形状相似性描述提出了光谱角度余弦核测度这一非正定核函数,并应用于一种非正定OCSVM 方法的高光谱影像地物检测.最后利用两幅高光谱影像进行了实验分析,实验结果证明了本文算法的有效性.