试论中国NSDI建设的若干问题

国家空间数据基础设施是“数字地球”的基础。要发展中国数字地球及其应用,就必须加在ＮＳＤＩ的建设力度,文中主要从（地球）空间数据框架,空间数据标准,空间数据交换网络和空间数据协调管理机构４个方面,介绍了中国ＮＳＤＩ的有关进展,分析了其中存在的一些问题,讨论了发展方向。     

可视化交互空间数据挖掘技术的探讨

随着地理信息获取技术飞速发展,使得当前存储在空间数据库中的空间数据的深度和广度得到了前所未有的发展,传统的空间统计方法和空间分析方法已经难以有效而迅速地处理和分析它们,如何有效而及时地分析和处理空间数据变得越来越迫切。空间数据挖掘作为上个世纪90年代逐步发展起来的新兴技术,逐渐在研究和实践中显示出它的优势。与此同时,地理可视化技术也逐步走向成熟,二者的结合催生出新型空间数据分析技术———可视化交互空间数据挖掘。本文就该技术的相关问题进行了一些研究探讨。     

GIS- and RS-based spatial decision support: structure of a spatial environmental information system (SEIS)

Abstract

The development of spatial decision support for environmental resource management, e.g. forest and agroecosystem management, biodiversity conservation, or hydrological planning, started in the 1980s and was the focus of many research groups in the 1990s. The combined availability of spatial data and communication, computing, positioning, geographic information system (GIS)- and remote sensing (RS)-technologies has been responsible for the implementation of complex SDSS since the late 1990s. The regional GIS-based modelling of environmental resources, and therefore ecosystems in general, requires setting-up an extensive geo and model database. Spatial data on topography, soil, climate, land use, hydrology, flora, fauna and anthropogenic activities have to be available. Therefore, GIS- and RS-technologies are of central importance for spatial data handling and analysis. In this context, the structure of spatial environmental information systems (SEIS) is introduced. In SEIS, the input data for environmental resource management are organised in at least seven sub-information systems: base geodata information system (BGDIS), climate information system (CIS), soil information system (SIS), land use information system (LUIS), hydrological information system (HIS), spatial/temporal biodiversity information system (STBIS), forest/agricultural management information system (FAMIS). The major tasks of a SEIS are to (i) provide environmental resource information on a regional level, (ii) analyse the impact of anthropogenic activities and (iii) simulate scenarios of different impacts.     

Extracting spatial patterns in bicycle routes from crowdsourced data

Much is done nowadays to provide cyclists with safe and sustainable road infrastructure. Its development requires the investigation of road usage and interactions between traffic commuters. This article is focused on exploiting crowdsourced user‐generated data, namely GPS trajectories collected by cyclists and road network infrastructure generated by citizens, to extract and analyze spatial patterns and road‐type use of cyclists in urban environments. Since user‐generated data shows data‐deficiencies, we introduce tailored spatial data‐handling processes for which several algorithms are developed and implemented. These include data filtering and segmentation, map‐matching and spatial arrangement of GPS trajectories with the road network. A spatial analysis and a characterization of road‐type use are then carried out to investigate and identify specific spatial patterns of cycle routes. The proposed analysis was applied to the cities of Amsterdam (The Netherlands) and Osnabrück (Germany), proving its feasibility and reliability in mining road‐type use and extracting pattern information and preferences. This information can help users who wish to explore friendlier and more interesting cycle patterns, based on collective usage, as well as city planners and transportation experts wishing to pinpoint areas most in need of further development and planning.     

Behavior mining of spatial objects with data field

The advanced data mining technologies and the large quantities of remotely sensed Imagery provide a data mining opportunity with high potential for useful results. Extracting interesting patterns and rules from data sets composed of images and associated ground data can be of importance in object identification, community planning, resource discovery and other areas. In this paper, a data field is presented to express the observed spatial objects and conduct behavior mining on them. First, most of the important aspects are discussed on behavior mining and its implications for the future of data mining. Furthermore, an ideal framework of the behavior mining system is proposed in the network environment. Second, the model of behavior mining is given on the observed spatial objects, including the objects described by the first feature data field and the main feature data field by means of the potential function. Finally, a case study about object identification in public is given and analyzed. The experimental results show that the new model is feasible in behavior mining.     

BIM及空间大数据技术支持下的铁路隧道施工数据管理

针对铁路隧道施工项目海量异构数据存储困难、信息不共享、展示不直观、数据价值利用不充分等问题,本文利用BIM及空间大数据技术,研究探讨了隧道施工项目海量异构数据的分布式存储、网络共享、三维查询、智能分析决策等技术,实现了隧道施工项目高效的信息化管理;提出了铁路隧道BIM三维模型动态建立的技术方案,设计了隧道施工管理空间大数据的云平台框架,研发了一套铁路隧道施工空间大数据管理应用云平台;通过对平台的成功开发与应用,验证了框架体系的可行性,提高了隧道施工项目管理的信息化水平。     

基于场论的空间聚类算法

从空间数据场的角度出发,提出了一种适用于空间聚类的场——凝聚场,并给出了一种新的空间聚类度量指标(即凝聚力)。进而,提出了一种基于场论的空间聚类算法(简称FTSC算法)。该算法根据凝聚力的矢量计算获取每个实体的邻近实体,通过递归搜索的策略,生成一系列不同的空间簇。通过模拟实验验证、经典算法比较和实际应用分析,发现所提出的算法具有3个方面的优势:(1)不需要用户输入参数;(2)能够发现任意形状的空间簇;(3)能够很好适应空间数据分布不均匀的特性。     

Data Integration Issues for a Farm Decision Support System

This paper explores data integration and compatibility issues raised during the development of a prototype spatial decision support system (SDSS) as a support tool for the farm manager of the University of Central Lancashire's farm at Newton Rigg and as a teaching resource for staff and students on campus. Metadata concerns and interoperability problems are addressed in detail. The paper outlines the proposed model for the SDSS and issues identified during the investigation of the users’ requirements and the analysis of the underlying spatial data sets. The initial data issues relate to the identification of existing and missing data sets ( Parker et al. 1996 ) and the creation of metadata describing the data sets. The second area to be explored concerns interoperability issues. This is relevant when users must access more than one dataset using distributed computing resources ( Sondheim et al. 1999 ).     

基于数据场的空间目标行为挖掘(英文)

The advanced data mining technologies and the large quantities of remotely sensed Imagery provide a data mining opportunity with high potential for useful results. Extracting interesting patterns and rules from data sets composed of images and associated ground data can be of importance in object identification, community planning, resource discovery and other areas. In this paper, a data field is presented to express the observed spatial objects and conduct behavior mining on them. First, most of the important aspects are discussed on behavior mining and its implications for the future of data mining. Furthermore, an ideal framework of the behavior mining system is proposed in the network environment. Second, the model of behavior mining is given on the observed spatial objects, including the objects described by the first feature data field and the main feature data field by means of the potential function. Finally, a case study about object identification in public is given and analyzed. The experimental results show that the new model is feasible in behavior mining. Supported by the National 973 Program of China(No.2006CB701305,No.2007CB310804), the National Natural Science Fundation of China (No.60743001), the Best National Thesis Fundation (No.2005047), the National New Century Excellent Talent Fundation (No.NCET-06-0618).     

北京市城市扩展空间格局变化监测与分析

本文基于地理国情监测数据,结合同期城市经济、人口等统计数据,利用高分辨率卫星遥感影像数据提取北京市建成区边界要素。采用地理国情与城市空间扩展相融合的监测分析模型及相关评价指标开展空间格局变化分析研究,揭示北京市2000—2017年间城市扩展的空间格局及其变化规律,并对其变化原因进行分析。其成果可为北京市科学规划与可持续发展提供决策依据。     

面向国土空间规划的测绘地理信息技术及数据成果服务应用展望

新时代国土空间规划体系的顶层设计和“四梁八柱”建设及未来的智慧型国土空间规划需要数字化、信息化技术支撑。以空间数据获取与分析为主要特征的测绘地理信息,能为国土空间规划的编制、审批、修改和实施监督各环节提供有效的技术服务和基于数据成果的定制服务。而融合现状数据、规划数据、管理数据,实现统一“底图”、统一“底数”、统一“底线”、统一“底板”的国土空间规划“一张图”,以及支撑国土空间规划动态监测评估预警的国土空间规划“一张图”实施监督信息系统,是可感知、能学习、善治理和自适应的智慧型国土空间规划发展的重要保障。本文在梳理“五级三类”国土空间规划体系基本构成和特征的基础上,从技术体系和数据成果定制体系两个方面,研究了面向国土空间规划的测绘地理信息服务体系,展望了面向智慧型国土空间规划的测绘地理信息应用。     

关于地理信息系统与决策支持系统的探讨

首先对GIS和DSS的定义、结构及其基本功能作了简要分析 ,分析了二者进行集成的必要性和可行性 ,在此基础上提出了基于GIS的空间决策支持系统 (SDSS)作为DSS的一个新的研究分支 ,是一个解决与空间有关问题的决策支持系统。举例说明了SDSS在各领域的应用。还提出了研究知识工程、空间数据挖掘等技术对SDSS具有的重要意义     

建筑用途的城市职住空间组织特征研究

针对现有职住空间关系的研究难以在微观维度上有效促进大城市职住功能空间的均衡发展以及规划政策与现状发展存在的时序错位问题,该文以北京市为例,从房屋建筑使用用途的角度切入,运用空间自相关分析模型、热点分析模型和职住用地比三个评价方法,同时借用ArcGIS软件平台进行空间分析与可视化表达,探究北京职住空间在乡镇尺度下的组织特征。研究发现:北京市职住空间表现为聚类分布的空间格局特征,以首都功能核心区为中心,大致呈现出环状圈层分布,北京市职住空间的“热点区”在空间分布上存在差异,而“冷点区”在空间分布上基本相同,职住空间关系存在失衡,与规划目标存在一定的偏差。提出的研究方法从微观维度分析了城市职住空间的组织特征,促进了城市规划与发展时序的有效结合,可在特大城市的职住空间关系的研究中进行推广应用。     

An integrated environment for developing knowledge-based spatial decision support systems

Knowledge-based spatial decision support systems (KBSDSS) incorporate heuristic reasoning, and analytical and spatial modelling capabilities to support decision making in resource and environmental management. They are able to provide the assistance for both the structuring and formulation of the spatial problem, and the design and execution of a solution process by automatically integrating different types of models and data. This paper presents a KBSDSS development environment, which connects a GIS tool (ARC/INFO) with an expert system tool (CLIPS) and a hypertext diagramming tool (HARDY). ARC/INFO provides capabilities for spatial data handling. CLIPS is used to develop knowledge bases and heuristic reasoning. HARDY is used to represent spatial problems in terms of diagrams that can then be translated into a CLIPS knowledge base for evaluation. The development environment allows users to structure their problems and develop models for particular requirements and domains.     

Expanding the SDI environment: comparing current spatial data infrastructure with emerging indoor location-based services

The authors compare key elements of the emerging field of Indoor Location-Based Services (Indoor LBS) to those currently found in spatial data infrastructure (SDI) programs. After a brief review of SDIs and Location-Based Services, the corresponding drivers, characteristics and emerging issues within the field of Indoor LBS are introduced and discussed. A comparative framework relates the two in terms of the criteria ‘People’, ‘Data', ‘Technologies', ‘Standards' and ‘Policies/Institutional Arrangements'. After highlighting key similarities and differences, the authors suggested three areas – definition of common framework datasets in Indoor LBS, more effective use of volunteered geographic information by SDI programs and development of appropriate privacy policies by both communities – that may benefit from sharing ‘lessons learned'.     

SDI planning using the system dynamics technique within a community of practice: lessons learnt from Tanzania

There exist major challenges in accelerating the spatial data infrastructure (SDI) planning process in the developing countries as well as advocating for politicians to support the development of SDI, due to the high complexity of SDI, lack of knowledge and experience, and limited insight in the benefits. To address these challenges, a methodology for SDI planning in Tanzania, based on the system dynamics technique and the communities of practice concept, was adopted and applied within a community consisting of experts from stakeholder organizations. The groups gathered to develop an SDI plan, while they shared their knowledge and discussed their ideas that helped their understanding of SDI. By running the system dynamics model, the development of SDI over time could be simulated that gave the planning community an insight about the future effects of today’s plans and decisions. Finally, an optimum model could be developed by refinements and improvements done with the consensus of the SDI stakeholders. This model included the components and policies that are essential for a successful SDI implementation in Tanzania and can be used as a basis for SDI planning and help to gain political support. Lessons learnt from this research were promising regarding the usability of the methodology for SDI planning in comparable countries.     

基于Web服务的数字城市空间数据资源共享研究

数据异构、处理复杂、海量管理、部门专有等问题制约了空间数据在数字城市中广泛的共享与应用。本文以Web服务技术为核心,提出了一个包括管理层、数据层、服务层以及应用层的数字城市空间数据资源共享框架。研究了共享数据服务平台层基本服务的实现,以及服务的组合、服务的注册与发布、服务的Qos等关键技术。最后通过共享服务平台支撑下的数字城市应用系统的实现,验证了本文提出的方法在数字城市建设中的实用性和有效性。     

A framework for identifying spatiotemporal hot spots of speculative land transactions using prospective monitoring procedures: the case of South Korea

Abnormally high-priced transactions in urban land speculation bring detrimental effects on economy, environment, and society. Governmental agencies around the world are striving hard to monitor and control land speculation by introducing various policy objectives and tools for an efficient urban development planning. One of the major challenges in controlling land speculation is to quickly identify the spatiotemporal locations of concern (hot spots) by monitoring the spatial clustering pattern changes over time and to alert the appropriate decision-making agencies for timely policy intervention. In this paper, we introduce a framework to rapidly detect the spatiotemporal hot spots of speculative land transactions in near-real-time data by exploiting the prospective monitoring procedures. We applied this method in the city of Hwasung, Republic of Korea, as an empirical illustration and found that the locations Jeongnam, Bongdam, Mado, and Dongtan were identified as hot spots with high, concentrated transaction values. The results indicate that the proposed framework is a capable tool for capturing prospective temporal indicators and pinpointing the localities of land speculation.     

A novel spatial index using spatial analyses and hierarchical fuzzy expert system for obtaining green TOD: a case study in Tehran city

Sustainable development is a vital and challenging factor for managing urban growth smartly. This factor contains three main components, namely economic growth, ecological protection and social justice. Green Transit-Oriented Development (GTOD) is a consummate planning approach in line with those components. Implementation of GTOD in an urban area is underpinned by its quantification. Therefore, a quantitative spatial index based on several indicators related to TOD and Green urbanism concepts should be developed. In this study, Geo-spatial Information Science and hierarchical fuzzy inference system (HFIS) were employed to calculate the indicators and aggregate them, respectively. In order to showcase the feasibility of the proposed method, it was implemented in a case study area in the City of Tehran, Iran. The result of this method is an integrated spatial GTOD index, which measures the neighbourhoods’ GTOD levels. These measurements specify weaknesses and strengths of neighbourhoods’ factors. Therefore, this index helps decision-makers to plan neighbourhoods based on land use and public transit views. Additionally, the HFIS method helps decision-makers to consider criteria and indicators with their inherent uncertainties and aggregate them with much fewer rules. For evaluating the results, the developed GTOD index was assessed with municipal action planning and attraction maps. According to the outcomes of the assessment, it is concluded that the proposed method is adequately robust and efficient for smart and sustainable urban planning.     

基于GIS和空间句法的空间形态一致性测度

少有学者测度规划道路系统分析出的空间形态与规划师空间形态规划意图的一致性。为更好地落实规划师的空间形态规划意图,以安厚镇为例,提出"基于GIS和空间句法的空间形态一致性测度"方法。主要思路为:首先,基于规划的城镇道路系统用GIS空间分析技术及集成度、智能度等句法变量分析新一轮城镇空间形态,然后,测度分析出空间形态与空间形态规划意图的一致性。测度结果表明经分析出的城镇空间形态与规划师本意的一致性有待改善,主要表现为城镇发展轴整体智能度较低、可理解性不高,城镇空间拓展主轴较大程度偏离原规划拓展方向,城镇发展次轴对空间结构的引导效用高于主轴。