Spatial aggregations for spatial based decision making in spatial data warehouses

For spatial based decision making such as choice of best place to construct a new department store, spatial data warehousing system is required more and more previous spatial data warehousing systems; however, provided decision making of non-spatial data on a map and so those cannot support enough spatial based decision making. The spatial aggregations are proposed for spatial based decision making in spatial data warehouses. The meaning of aggregation operators for applying spatial data was modified and new spatial aggregations were defined. These aggregations can support hierarchical concept of spatial measure. Using these aggregations, the spatial analysis classified by non-spatial data is provided. In case study, how to use these aggregations and how to support spatial based decision making are shown.

Fast Web - Based Data Transmission

Since web based GIS processes large size spatial geographic information on internet, we should try to improve the efficiency of spatial data query processing and transmission. This paper presents two efficient methods for this purpose: division transmission and progressive transmission methods. In division transmission method, a map can be divided into several parts, called “tiles”, and only tiles can be transmitted at the request of a client. In progressive transmission method, a map can be split into several phase views based on the significance of vertices, and a server produces a target object and then transmits it progressively when this spatial object is requested from a client. In order to achieve these methods, the algorithms, “tile division”, “priority order estimation” and the strategies for data transmission are proposed in this paper, respectively. Compared with such traditional methods as “map total transmission” and “layer transmission”, the web based GIS data transmission, proposed in this paper, is advantageous in the increase of the data transmission efficiency by a great margin.

Over-sampling algorithm for imbalanced data classification

For imbalanced datasets, the focus of classification is to identify samples of the minority class. The performance of current data mining algorithms is not good enough for processing imbalanced datasets. The synthetic minority over-sampling technique(SMOTE) is specifically designed for learning from imbalanced datasets, generating synthetic minority class examples by interpolating between minority class examples nearby. However, the SMOTE encounters the overgeneralization problem. The densitybased spatial clustering of applications with noise(DBSCAN) is not rigorous when dealing with the samples near the borderline.We optimize the DBSCAN algorithm for this problem to make clustering more reasonable. This paper integrates the optimized DBSCAN and SMOTE, and proposes a density-based synthetic minority over-sampling technique(DSMOTE). First, the optimized DBSCAN is used to divide the samples of the minority class into three groups, including core samples, borderline samples and noise samples, and then the noise samples of minority class is removed to synthesize more effective samples. In order to make full use of the information of core samples and borderline samples,different strategies are used to over-sample core samples and borderline samples. Experiments show that DSMOTE can achieve better results compared with SMOTE and Borderline-SMOTE in terms of precision, recall and F-value.

Using ALS raster data in forest planning

Raster type of forest inventory data with site and growing stock variables interpreted for small squareshaped grid cells are increasingly available for forest planning.In Finland,there are two sources of this type of lattice data:the multisource national forest inventory and the inventory that is based on airborne laser scanning(ALS).In both cases,stand variables are interpreted for 16 m×16 m cells.Both data sources cover all private forests of Finland and are freely available for forest planning.This study analyzed different ways to use the ALS raster data in forest planning.The analyses were conducted for a grid of 375×375 cells(140,625 cells,of which 97,893 were productive forest).The basic alternatives were to use the cells as calculation units throughout the planning process,or aggregate the cells into segments before planning calculations.The use of cells made it necessary to use spatial optimization to aggregate cuttings and other treatments into blocks that were large enough for the practical implementation of the plan.In addition,allowing premature cuttings in a part of the cells was a prerequisite for compact treatment areas.The use of segments led to 5–9%higher growth predictions than calculations based on cells.In addition,the areas of the most common fertility classes were overestimated and the areas of rare site classes were underestimated when segments were used.The shape of the treatment blocks was more irregular in cell-based planning.Using cells as calculation units instead of segments led to 20 times longer computing time of the whole planning process than the use of segments when the number of grid cells was approximately 100,000.

Soil polygon disaggregation through similarity-based prediction with legacy pedons

Conventional soil maps generally contain one or more soil types within a single soil polygon.But their geographic locations within the polygon are not specified.This restricts current applications of the maps in site-specific agricultural management and environmental modelling.We examined the utility of legacy pedon data for disaggregating soil polygons and the effectiveness of similarity-based prediction for making use of the under-or over-sampled legacy pedon data for the disaggregation.The method consisted of three steps.First,environmental similarities between the pedon sites and each location were computed based on soil formative environmental factors.Second,according to soil types of the pedon sites,the similarities were aggregated to derive similarity distribution for each soil type.Third,a hardening process was performed on the maps to allocate candidate soil types within the polygons.The study was conducted at the soil subgroup level in a semi-arid area situated in Manitoba,Canada.Based on 186 independent pedon sites,the evaluation of the disaggregated map of soil subgroups showed an overall accuracy of 67% and a Kappa statistic of 0.62.The map represented a better spatial pattern of soil subgroups in both detail and accuracy compared to a dominant soil subgroup map,which was commonly used in practice.Incorrect predictions mainly occurred in the agricultural plain area and the soil subgroups that are very similar in taxonomy,indicating that new environmental covariates need to be developed.We concluded that the combination of legacy pedon data with similarity-based prediction is an effective solution for soil polygon disaggregation.

地理信息技术赋能国土空间规划实施监督和体检评估预警

针对地理信息技术赋能国土空间规划实施监督和体检评估预警进行了研究论述,围绕强化规划权威和规划实施监督管理工作需要,从汇聚规划成果、强化实施监督、赋能体检评估3个角度进行深入研究,引入了新技术赋能国土空间规划实施监督与动态感知,完善了国土空间规划体检评估预警体系。

“一网统管”城市运行一张图设计与实现

基于智慧城市和数字政府建设对城市精细化管理提出的更高要求,为夯实“一网统管”数字底座基础,结合常州实际,设计了城市运行一张图的总体框架,建立全市统一的空间数据底座,形成资源展示、数据共享、场景配置、二次开发、后台管理等能力。通过应用实践,有效加快了城市空间、城市部件、城市运行动态的数字化进程,促进数据共享、治理转型,支撑政府部门各类智能化应用,推动数字政府转型和城市建设智慧化升级。

基于改进DBSCAN和距离共识评估的分段点云去噪方法

针对点云数据中噪声点的剔除问题,提出了一种基于改进DBSCAN(density-based spatial clustering of applications with noise)算法的多尺度点云去噪方法。应用统计滤波对孤立离群点进行预筛选,去除点云中的大尺度噪声;对DBSCAN算法进行优化,减少算法时间复杂度和实现参数的自适应调整,以此将点云分为正常簇、疑似簇及异常簇,并立即去除异常簇;利用距离共识评估法对疑似簇进行精细判定,通过计算疑似点与其最近的正常点拟合表面之间的距离,判定其是否为异常,有效保持了数据的关键特征和模型敏感度。利用该方法对两个船体分段点云进行去噪,并与其他去噪算法进行对比,结果表明,该方法在去噪效率和特征保持方面具有优势,精确地保留了点云数据的几何特性。

城镇化推进模式与我国农业低碳全要素生产率——来自双源夜间灯光证据

本文利用卫星监测的数据构造夜间灯光复合指数表征城镇化水平,运用Superefficiency Ray Slacks-Based Measure(Super-RSBM)模型和Global Malmquist-Luenberger(GML)指数测算2000—2021年我国农业低碳全要素生产率(TFP),实证检验城镇化对我国农业低碳TFP的影响及其作用机制,并考察紧凑集约型和规模扩张型两种城镇化推进模式对农业低碳TFP的异质性影响。研究发现,从全国来看,城镇化推进与农业低碳TFP之间具有显著的U型关系,且邻近地区农业低碳TFP的提升对本地区产生示范效应;分区域来看,这种U型关系主要体现在农业适度发展区,而农业优化发展区的城镇化与农业低碳TFP之间呈现显著的正向线性关系,表明农业优化发展区应发挥“领头羊”作用,带动适度发展区早日跨越U型曲线的拐点,实现城镇化带动农业绿色发展;紧凑集约型的城镇化深度推进模式能够显著提升农业低碳TFP,而规模扩张型的城镇化广度推进模式降低了农业低碳TFP;农业低碳技术进步、农村劳动力转移、规模效应、农业产业链延伸和农村居民可支配收入增加是城镇化影响农业低碳TFP的主要途径。

基于机器学习空间聚类的出租车停靠站点布局规划

针对出租车随意停靠给城市交通带来的负面影响,为规范出租车营运秩序、改善出租车营运环境和居民乘车条件,提出一种将出租车出行空间信息与机器学习算法相结合的出租车停靠站点布局规划方法。首先利用出租车GPS轨迹数据提取出租车出行起点,然后采用HDBSCAN聚类算法对起点进行空间密度聚类,形成聚类簇后以其中心点作为出租车停靠站点布局的备选点。最后,为验证所提方法的可行性和有效性,选取重庆市中心城区一土地利用类型丰富、人口密度高的典型区域进行案例分析。结果显示,107个备选点主要分布于商业中心区和居住集中区,与出租车出行高需求区域的空间分布基本吻合;布局的出租车停靠站点在300 m范围内的覆盖率达到76.0%,未覆盖区域主要为城市绿地和水体。研究表明,机器学习算法可实现出租车停靠站点的高效布局规划,但在规划和实施阶段,停靠站点的设置还应结合邻近区域的建成环境特点综合考虑。

面向自然资源要素三维管理的关键技术研究与应用

针对省级自然资源要素三维管理中时空数据处理传统方法存在数据处理耗时长、工序复杂、三维浏览效率低下等问题,开展自然资源要素三维管理关键技术研究。基于自然资源相关多源异构数据,研究基于镶嵌数据集的TB级海量影像管理技术、基于CRF压缩存储技术的影像快速切片技术、支持并行的三维场景实时渲染技术等技术方法,提高了自然资源要素三维时空数据处理、可视化效率;并研发自然资源要素三维管理系统,实现自然资源要素数据三维空间基底,为自然资源管理相关业务应用提供支撑。

“一带一路”沿线国家工程建设信息系统的建设

海外工程建设面临诸多地质风险,本文提出了一种以空间数据库和地理信息系统(GIS)技术为支撑的信息系统建设方式,将已收集的“一带一路”沿线国家数百项工程、上千个地质钻孔、6种特殊岩土及多种专题地质图件等数据进行统一入库管理,并集成了海外工程建设标准规范、施工工艺等非结构化数据,实现了工程建设多源异构数据源的一体化管理。该信息系统基于缓冲区分析和叠加分析等GIS空间分析技术,可快速获取在建工程项目及周边场地的综合地质信息,为工程建设全过程提供数据支撑,提高了数据的应用效率。通过将各类数量巨大、来源分散、格式多样的工程建设数据进行统一采集、存储、关联分析及智能应用,打破了数据孤岛,不仅可为单一工程建设提供服务,还可为大区域范围内的数据挖掘、知识发现及智能分析等应用打下基础。

基于Intranet的地热地理信息系统的设计与实现

通过建立Intranet网络及地热地理信息系统,为地热资源的可持续开发利用提供帮助。该系统具有数据存储、数据查询、数据更新功能,涉及资源管理、技术开发、开采监测等三个方面;具有空间查询及分析功能,图形信息与属性信息可以相互查询,也可以独立查询,空间分析功能包括资源量计算、资源开发评价等;自动制图功能,可自动绘制井身结构图、地层柱状图等多种标准图。

空间数据挖掘及其与智能系统的集成框架

空间数据挖掘是指从空间数据库中抽取隐含的知识、空间关系和非显式地存储在空间数据库中有意义的特征或模式 .它在遥感、地理信息系统、医疗影像、信息融合系统等领域具有广阔的应用前景 ,因此日渐受到关注和重视 .本文从知识发现、认知科学与智能系统交叉结合的角度 ,提出了基于数据库和知识库双库协同机制的空间数据挖掘模型 ,并系统地介绍了从空间数据库中可发现的知识类型及挖掘方法 ,然后提出了基于空间数据挖掘的新型智能系统总体框架和系统开发基本原则 ,最后探讨了空间数据挖掘的发展方向 .

多专题多比例尺水系河网管理模型研究

随着水利行业GIS系统的快速发展和对性能要求的不断提高,迫切需要解决多比例尺水系河网信息统一访问、关系维护等问题。本文提出多专题多比例尺地图管理模型,通过对地图信息依据不同比例尺和专题进行目录层次和专题层次的划分,实现水系河网信息的全国分布式统一管理和共享系统。文中给出了该模型下的水系河网表示和专题中对象间关系,分析了为形成一套基于空间数据库和空间化分析方法的分布式水系河网信息共享系统所需进行的下一步工作。该模型下的全国水系河网管理系统具有较好的可扩展性,支持有效获取不同比例尺的地图,较少信息冗余和保证信息最大可能。

利用OCI与OLE开发Oracle空间数据库

简要阐述了Oracle存储空间数据的基本原理,探讨了利用OCI(Oracle Call Interface)和OLE(ObjectLink Embed)技术开发空间数据库的方法,并对这两种方法进行了分析比较,解决了在应用平台上开发空间数据库并实现地图化功能的问题,进一步阐明了两种开发方式的不同特点。

重庆市地质灾害数据库设计与建设

灾害信息管理在减灾工作中占有重要地位。世界各国非常重视灾害数据库的建设。我国灾害数据库研究较为薄弱。重庆市地质灾害频繁,但灾害信息的管理缺乏有效的管理方法。因此,在这一地区设计和建立标准统一、功能完善的的地质灾害时空数据库无疑具有十分重要的意义。论文从数据库的概念性设计、逻辑设计和数据库实体建设等方面详细介绍了重庆市地质灾害数据库的设计和建设过程。概念性设计中对重庆市地质灾害数据库的内容、灾害信息的分类编码、数据库的宏观地理定义进行了设计;逻辑设计中侧重对数据库中空间数据和属性数据的结构以及图形属性数据库一体化进行设计;最后,文章从图形数据库和属性数据库2个方面详细介绍了如何进行重庆市地质灾害数据库的实体建设。

数字环境下的数据缩编更新方法

随着地理空间信息资源建设的不断推进与广泛应用,测绘工作的重心正在由初始的数据生产向数据更新与服务进行转变。在目前实际生产的各种更新方法中,由于利用较大比例尺数据更新较小比例尺数据的经济快速,日益引起了人们的重视。本文从目前空间数据更新生产工程的实际需求出发,分析了综合利用多种现势数据资料缩编更新空间数据库的需求与技术难点,以目前的空间数据的数字化生产工艺为基础,分析探讨了缩编更新的关键技术,实现了适应数字环境下的空间数据缩编更新。根据该方法开发的缩编更新系统——精图(GenTool),在生产中得到了初步应用,并获得了良好的效果。

空间数据库引擎的R树索引

介绍了空间数据库引擎（SDBE）的R树索引结构，给出系统使用R树索引的方式，并描述了利用R树索引实现最近邻居查询的分支-限界算法,包括代价函数及其上、下界函数的定义，以及算法的伪码形式。

基于对象存储的海量空间数据存储与管理

提出了一种基于对象存储的空间对象集群存储系统(geospace object store,GOS),为空间数据的存储与管理提供了一种新的解决方法。在GOS系统中,实现了在逻辑层对面向对象空间数据模型的支持,使得数据模型映射得到简化。同时,对象存储本身具有的并行性使得系统具有并行的查询处理功能,使得空间数据查询处理这一计算密集型操作在GOS中得到很好的解决。