基于混合过滤的地学数据个性化推荐方法设计与实现

推荐系统是帮助互联网用户克服信息过剩的有效工具。在地学数据共享领域,较其他物品的内容属性,地学数据具有更加丰富的时空属性,这也给地学数据推荐带来挑战。针对地学数据的特点,为地学数据共享推荐服务开发了一种动态加权的混合过滤方法。该方法分别采用协同过滤和基于内容过滤算法预测用户对数据的兴趣度,再以训练模型计算最优加权权重,计算最终预测评分。在数据获取阶段,通过用户访问日志数据,采用Jenks Natural Break算法分析用户访问记录获取用户的数据兴趣度。在基于内容过滤部分,通过数据的空间、时间及内容属性计算数据相似度,并以用户历史行为为依据计算用户兴趣。在协同过滤和基于内容过滤中分别采用k-NN算法计算用户对未访问数据的预测评分,并进行加权求和。通过训练集,对理想权重值及用户的共同评价度（co-rating level）进行建模,拟合二者的关系。该模型被应用于混合过滤的权重调整,以获得最优的加权方程。测试结果显示,结合数据时空属性的混合过滤方法的准确度和召回率,较单一的协同过滤或基于内容过滤方法有显著提高。     

A data model and algorithms for a spatial data marketplace

A data marketplace is a platform that facilitates online data trading. It gives sellers the capabilities of publishing data, defining the price function, the authorization rules and the data contract. It gives customers the capabilities to search for data services, to query for their price, to invoke them and to pay for the usage. As data are more and more emphasized as a payable asset, the need for data marketplaces rises. There is a lack of marketplace proposals for spatial data. This paper contributes to fill-in this gap. We propose a data model for representing the different types of data involved. The logical representation of data is a quadtree, and the physical representation fits into a key-value store. The model is thus logically simple and extensible, and physically scalable and highly available. We discuss price queries in detail and propose evaluation algorithms.     

Generating vague neighbourhoods through data mining of passive web data

Neighbourhoods have been described as ‘the building blocks of public services society’. Their subjective nature, however, and the resulting difficulties in collecting data, means that in many countries there are no officially defined neighbourhoods either in terms of names or boundaries. This has implications not only for policy but also business and social decisions as a whole. With the absence of neighbourhood boundaries many studies resort to using standard administrative units as proxies. Such administrative geographies, however, often have a poor fit with those perceived by residents. Our approach detects these important social boundaries by automatically mining the Web en masse for passively declared neighbourhood data within postal addresses. Focusing on the United Kingdom (UK), this research demonstrates the feasibility of automated extraction of urban neighbourhood names and their subsequent mapping as vague entities. Importantly, and unlike previous work, our process does not require any neighbourhood names to be established a priori.     

Non-current data and geographical information systems A case for data retention

Inter-urban income disparities reflect differences between individual urban localities in the average incomes of their residents. The present paper discusses different ways of visualizing such disparities on thematic maps. The approach we propose is based on the transformation of distances between individual localities and a reference city (e.g. a major population centre of a country) in proportion to the actual differences in the income levels. The general principle of such a transformation is to bring closer to the reference city places with higher incomes, while moving away localities with low income levels. Three alternative approaches to the implementation of this transformation technique are discussed. According to the ‘actual distance’ method, the spatial ‘shift’ of a locality on the map is set proportional to both the relative difference in incomes and the aerial distance between a locality and the reference city. In the ‘proportional increment’ transformation, the distance between a locality and the reference city is adjusted by a parameter whose values are proportional to income disparities between the two. Lastly, according to the ‘concentric circle’ transformation, localities with identical levels of incomes are positioned at a certain distance from the reference city, forming concentric circles around it. Both advantages and disadvantages of these transformation techniques are discussed, and the ‘proportional increment’ method is chosen as the best-performing visualization technique. The performance of this technique is demonstrated using income data for urban localities in Israel in 1991 and 1999. As analysis indicates, the proposed method helps to illustrate both the existing patterns of inter-urban income disparities and their dynamics over time.     

Combining spatial data with survey data improves predictions of boundaries between settlements

Studies of land-use change often require the combination of socioeconomic survey data with spatially continuous maps of land-cover change. One approach is to define maps of land ownership, assuming that all land-use change can be attributed to the owners or managers of each parcel of land. Unfortunately, records of administrative boundaries between towns and villages are commonly unavailable in developing countries and prohibitively costly or time consuming to map for individual projects. However, point locations of the settlements themselves can be obtained easily from existing maps or remotely-sensed imagery. In this paper we compare three methods – circular buffers, unweighted Voronoi polygons (sometimes referred to as Thiessen polygons) and multiplicatively weighted Voronoi polygons – for estimating boundaries between villages in an agricultural landscape in West Africa. The benefits and limitations of each approach are discussed, and their accuracy assessed using 98 independently collected GPS coordinates of village boundaries. We present a novel method for generating and optimising weights for multiplicatively weighted Voronoi polygons using survey data of village sizes from a subset of villages. By using both spatial information and survey data from villages, we show that multiplicatively weighted Voronoi polygons outperform other methods of predicting village boundaries, and increase the correlation coefficient between surveyed village area and mapped areas from 0.18 to 0.68 compared with more commonly used unweighted Voronoi polygons. Our method of weighting Voronoi polygons can be implemented with data and software commonly available to researchers and non-governmental organisations.     

Enabling smart data selection based on data completeness measures: a quality-aware approach

Geographic information system (GIS) users rely heavily on the versatile operations of GIS software and the abundant variety of geospatial data from different resources to satisfy their application requirements. However, the convenient use of GIS software has resulted in users easily ignoring the threat of data misuse because of the lack of understanding of data quality. Here we argue that data quality considerations must be coherently assimilated into the GIS operation design to visually present helpful information and ensure the accuracy of data for decision making. Data completeness is selected in this paper to demonstrate how the use of data quality information opens a new dimension to the design of future GIS software. We propose a new model for the representation, analysis, and visualization of data completeness information. With the brand new quantitative measures and informative visual approach, understanding of the data completeness of the illustrated contents in the map interface is enhanced, and inappropriate dataset selection can be effectively prevented. Thus, this paper presents an innovative, integrated and geospatial concept of future GIS operation design, where users are constantly aware of the continuously changing status of data quality based on formalized and quantitative data quality theories.     

从传统数据到大数据+的人居环境研究进展

伴随着城市化的快速推进,环境问题日益突出,人居环境研究已成为学术界关注的热点。论文以CNKI和Web of Science数据库中关于人居环境的文献为基础,结合CiteSpace软件分析国内外人居环境研究的热点领域,探讨其数据获取方式、研究方法与技术、研究视角3个方面。结果表明：① 数据获取方式由传统的抽样调查和官方统计向卫星遥感、电子设备感知及互联网等大数据靠拢,实现传统统计数据向网络大数据获取的转变;② 研究方法日趋完善,注重问卷调查、新地理计量模型与GIS相结合,同时引入物联网大数据、人工智能等先进技术,使研究方法更加多样、研究结果更加准确;③ 社会感知、乡村振兴、拟态人居等成为近年来的研究方向。今后,人居环境研究应综合利用传统方式和现代化技术手段获取的数据,深度学习挖掘大数据,加强乡村振兴、虚拟人居环境等方面的研究。     

沙漠地质记录和人类活动遗迹数据汇交规范化

数据汇交是实现数据共享的前提,是实现科学数据的持续累积、科学整合和有效管理可持续发展的重要基础.分析了沙漠地质记录和人类活动遗迹数据汇交规范化存在的问题,强调了制定沙漠地质记录和人类活动遗迹数据汇交规范的必要性,根据沙漠地质记录和人类活动遗迹资料的数据特性,对数据分类并建立不同专题数据库,因地制宜采纳国际和国内现行的规...     

The GEO VIEW design A relational data base approach to geographical data handling

Abstract

Digital map data are currently available based on a variety of data structures, depending on the uses to which the data are to be put Within the major categories of vector and raster data, as well as other structures, there is a multiplicity of data formats. Further to this, data for digital map coordinates are frequently stored in a different way from attribute data pertaining to points, lines and polygons. Given these problems, this paper investigates the possibility of handling different kinds of data structures, as well as both coordinate and attribute information within a unified conceptual scheme. This scheme is expressed in terms of the design of an integrated geographical information system called GEO VIEW, which can be implemented in a relational data base environment. The structure of the tables in the data base is outlined, together with the methodology for coding different kinds of data structure into a standard form. Examples of queries are provided, using the SQL query language, to indicate how the system might be used and problems in optimizing spatial searching on a data base of this kind are addressed.     

Mine your data: open data,digital strategies and entrepreneurial governance by code

Investment in the release of open data has become increasingly central to the implementation of smart city programs by governments around the world. Though originally arising out of a push towards “open government” and the pursuit of more transparent decision-making by public authorities at multiple scales, open data programs have more recently been adopted by municipal governments to support entrepreneurial goals of enhanced competitive positioning and attracting investment. As urban scholars now subject the smart city project to critical scrutiny for its role in advancing urban entrepreneurialism, this article considers the relevance of the open data agenda as it shapes wider understandings of the smart city. In particular, I address the collection of policy practices, aspirations, stakeholders and entrepreneurs active in framing the opportunities and values of open data for urban governments. Both the momentum of support for open data, along with a recent shift in the rhetorical aspirations of the open data movement away from the values of openness and transparency and towards a more confined focus on value generation, raise important critical questions for urban geographers concerned with the nature of urban governance in an age of big data.     

Spatiotemporal data model for network time geographic analysis in the era of big data

There has been a resurgence of interest in time geography studies due to emerging spatiotemporal big data in urban environments. However, the rapid increase in the volume, diversity, and intensity of spatiotemporal data poses a significant challenge with respect to the representation and computation of time geographic entities and relations in road networks. To address this challenge, a spatiotemporal data model is proposed in this article. The proposed spatiotemporal data model is based on a compressed linear reference (CLR) technique to transform network time geographic entities in three-dimensional (3D) (x, y, t) space to two-dimensional (2D) CLR space. Using the proposed spatiotemporal data model, network time geographic entities can be stored and managed in classical spatial databases. Efficient spatial operations and index structures can be directly utilized to implement spatiotemporal operations and queries for network time geographic entities in CLR space. To validate the proposed spatiotemporal data model, a prototype system is developed using existing 2D GIS techniques. A case study is performed using large-scale datasets of space-time paths and prisms. The case study indicates that the proposed spatiotemporal data model is effective and efficient for storing, managing, and querying large-scale datasets of network time geographic entities.     

An event-based spatiotemporal data model (ESTDM) for temporal analysis of geographical data

Abstract

Representations historically used within GIS assume a world that exists only in the present. Information contained within a spatial database may be added-to or modified over time, but a sense of change or dynamics through time is not maintained. This limitation of current GIS capabilities has recently received substantial attention, given the increasingly urgent need to better understand geographical processes and the cause-and-effect interrelationships between human activities and the environment. Models proposed so-far for the representation of spatiotemporal data are extensions of traditional raster and vector representations that can be seen as location- or feature-based, respectively, and are therefore best organized for performing either location-based or feature-based queries. Neither form is as well-suited for analysing overall temporal relationships of events and patterns of events throughout a geographical area as a temporally-based representation.

In the current paper, a new spatio-temporal data model is proposed that is based on time as its organizational basis, and is thereby intended to facilitate analysis of temporal relationships and patterns of change through time. This model is named the Event-based Spatio Temporal Data Model (ESTDM). It is shown that temporally-based queries relating to locations can be implemented in an efficient and conceptually straightforward manner using ESTDM by describing algorithms for three fundamental temporally-based retrieval tasks based on this model: (1) retrieving location(s) that changed to a given value at a given time, (2) retrieving location(s) that changed to a given value over a given temporal interval, and (3) calculation of the total area that has changed to a given value over a given temporal interval. An empirical comparison of the space efficiency of ESTDM and compressed and uncompressed forms of the ‘snapshot’ model is also given, showing that ESTDM is also a compact representation of spatio-temporal information.     

地理学时空数据分析方法

随着地理空间观测数据的多年积累,地球环境、社会和健康数据监测能力的增强,地理信息系统和计算机网络的发展,时空数据集大量生成,时空数据分析实践呈现快速增长。本文对此进行了分析和归纳,总结了时空数据分析的7类主要方法,包括：时空数据可视化,目的是通过视觉启发假设和选择分析模型;空间统计指标的时序分析,反映空间格局随时间变化;时空变化指标,体现时空变化的综合统计量;时空格局和异常探测,揭示时空过程的不变和变化部分;时空插值,以获得未抽样点的数值;时空回归,建立因变量和解释变量之间的统计关系;时空过程建模,建立时空过程的机理数学模型;时空演化树,利用空间数据重建时空演化路径。通过简述这些方法的基本原理、输入输出、适用条件以及软件实现,为时空数据分析提供工具和方法手段。     

Quality assessment of linear data

This paper presents methods to evaluate the geometric quality of spatial data. Firstly, a point‐based method is presented, adapting conventional assessment methods whereby common points between datasets are compared. In our approach, initial matches are established automatically and refined further through interactive editing. Second, a line‐based method which uses correspondences between line segments is proposed. Here, the geometry of line segments in vector is transformed into a set of rasterized values so that their combination at each pixel can restore their original vector geometry. Matching is performed on rasterized line segments and their matching lengths and displacements are measured. Experimental results show that the line‐based approach proposed is efficient to evaluate the geometric quality of spatial data without requirements of topological relationships among line features.     

空间信息分析技术

在GIS技术日趋成熟和空间数据极大丰富的今天,通过分析空间数据探索空间过程机理正变得日益迫切。空间信息分析技术至少包括以下六个主要方面:(1)空间数据获取和预处理;(2)属性数据空间化和空间尺度转换;(3)空间信息探索分析;(4)地统计;(5)格数据分析;(6)复杂信息反演和预报。本文提出了解决具体应用问题一般的空间数据分析计算、结果解释和反馈程序。认为空间过程的一般共性和作为共同的研究对象,各种不同的方法技术最终可能导致空间数学(spatialmathematics)的产生,同时发展鲁棒的空间分析软件包对于普及空间数学是必要的。