Rank-based strategies for cleaning inconsistent spatial databases

A spatial data set is consistent if it satisfies a set of integrity constraints. Although consistency is a desirable property of databases, enforcing the satisfaction of integrity constraints might not be always feasible. In such cases, the presence of inconsistent data may have a negative effect on the results of data analysis and processing and, in consequence, there is an important need for data-cleaning tools to detect and remove, if possible, inconsistencies in large data sets. This work proposes strategies to support data cleaning of spatial databases with respect to a set of integrity constraints that impose topological relations between spatial objects. The basic idea is to rank the geometries in a spatial data set that should be modified to improve the quality of the data (in terms of consistency). An experimental evaluation validates the proposal and shows that the order in which geometries are modified affects both the overall quality of the database and the final number of geometries to be processed to restore consistency.     

A regionalization method for clustering and partitioning based on trajectories from NLP perspective

ABSTRACT

Regionalization attempts to group units into a few subsets to partition the entire area. The results represent the underlying spatial structure and facilitate decision-making. Massive amounts of trajectories produced in the urban space provide a new opportunity for regionalization from human mobility. This paper proposes and applies a novel regionalization method to cluster similar areal units and visualize the spatial structure by considering all trajectories in an area into a word embedding model. In this model, nodes in a trajectory are regarded as words in a sentence, and nodes can be clustered in the feature space. The result depicts the underlying socio-economic structure at multiple spatial scales. To our knowledge, this is the first regionalization method from trajectories with natural language processing technology. A case study of mobile phone trajectory data in Beijing is used to validate our method, and then we evaluate its performance by predicting the next location of an individual’s trajectory. The case study indicates that the method is fast, flexible and scalable to large trajectory datasets, and moreover, represents the structure of trajectory more effectively.     

大样本降水空间插值研究--以2009年中国年降水为例

以2009年全国2203个气象台站累积降水数据为例,采取逐步抽稀方法,定量分析大样本的数据样本量、样本空间分布、以及不同空间插值方法对插值结果的影响。研究表明：①在随机抽样中,总体而言,平均绝对误差（MAE）、均方根误差（RMSE）随着插值样本量的减小而增加、相关系数递减,特别当抽样比〈20%时,MAE、RMSE显著增加,R2显著减少;②以Thiessen多边形剖分的方式检验随机抽样、等间隔抽样、分区单站点控制面积约束抽样分布的均匀性,经交叉验证后知,样本空间分布对降水空间插值的结果影响比较复杂,并非越均匀越好;③对随机组中抽样比4%的数据和等间隔组,采用Kriging方法插值,插值结果优于IDW方法。以等间隔分布的（50%,50%）（、20%,80%）数据为例,采用IDW、Kriging方法,得到2009年全国降水空间分布图,降水空间分布规律与中国2009年实际降水量分布吻合。     

Automatic analysis of positional plausibility for points of interest in OpenStreetMap using coexistence patterns

In the past decade, Volunteered Geographic Information (VGI) has emerged as a new source of geographic information, making it a cheap and universal competitor to existing authoritative data sources. The growing popularity of VGI platforms, such as OpenStreetMap (OSM), would trigger malicious activities such as vandalism or spam. Similarly, wrong entries by unexperienced contributors adds to the complexities and directly impact the reliability of such databases. While there are some existing methods and tools for monitoring OSM data quality, there is still a lack of advanced mechanisms for automatic validation. This paper presents a new recommender tool which evaluates the positional plausibility of incoming POI registrations in OSM by generating near real-time validation scores. Similar to machine learning techniques, the tool discovers, stores and reapplies binary distance-based coexistence patterns between one specific POI and its surrounding objects. To clarify the idea, basic concepts about analysing coexistence patterns including design methodology and algorithms are covered in this context. Furthermore, the results of two case studies are presented to demonstrate the analytical power and reliability of the proposed technique. The encouraging results of this new recommendation tool elevates the need for developing reliable quality assurance systems in OSM and other VGI projects.     

The CartACom model: transforming cartographic features into communicating agents for cartographic generalisation

Our research is concerned with automated generalisation of topographic vector databases in order to produce maps. This article presents a new, agent-based generalisation model called CartACom (Cartographic generalisation with Communicating Agents), dedicated to the treatment of areas of low density but where rubber sheeting techniques are not sufficient because some eliminations or aggregations are needed. In CartACom, the objects of the initial database are modelled as agents, that is, autonomous entities, that choose and apply generalisation algorithms to themselves in order to increase the satisfaction of their constraints as much as possible. The CartACom model focuses on modelling and treating the relational constraints, defined as constraints that concern a relation between two objects. In order to detect and assess their relational constraints, the CartACom agents are able to perceive their spatial surroundings. Moreover, to make the good generalisation decisions to satisfy their relational constraints, they are able to communicate with their neighbours using predefined dialogue protocols. Finally, a hook to another agent-based generalisation model – AGENT – is provided, so that the CartACom agents can handle not only their relational constraints but also their internal constraints. The CartACom model has been applied to the generalisation of low-density, heterogeneous areas like rural areas, where the space is not hierarchically organised. Examples of results obtained on real data show that it is well adapted for this application.     

探索性空间分析及其与GIS集成模式探讨

探索性空间分析基于让数据说话的理念,可以避免野值或非典型观测值的误导。在对探索性空间分析的基本原理和概念界定的基础上,探讨交互式和动态空间数据分析、地学可视化及可视化空间分布、确认性空间分析、空间数据挖掘等探索性空间分析的主要技术。由于统计分析软件和GIS的数据格式差异很大,直接将二者简单集成存在一定困难,因此切实可行的集成方式是采用对象连接和嵌入(OLE)技术,分别调用统计分析软件的探索性分析功能(或者函数)以及GIS的地图显示和空间分析功能,并进行必要开发,实现二者的集成。最后对探索性空间分析的发展方向进行展望。     

A methodology with a distributed algorithm for large-scale trajectory distribution prediction

In this paper, we propose a method for predicting the distributions of people’s trajectories on the road network throughout a city. Specifically, we predict the number of people who will move from one area to another, their probable trajectories, and the corresponding likelihoods of those trajectories in the near future, such as within an hour. With this prediction, we will identify the hot road segments where potential traffic jams might occur and reveal the formation of those traffic jams. Accurate predictions of human trajectories at a city level in real time is challenging due to the uncertainty of people’s spatial and temporal mobility patterns, the complexity of a city level’s road network, and the scale of the data. To address these challenges, this paper proposes a method which includes several major components: (1) a model for predicting movements between neighboring areas, which combines both latent and explicit features that may influence the movements; (2) different methods to estimate corresponding flow trajectory distributions in the road network; (3) a MapReduce-based distributed algorithm to simulate large-scale trajectory distributions under real-time constraints. We conducted two case studies with taxi data collected from Beijing and New York City and systematically evaluated our method.     

Fusing remote sensing with sparse demographic data for synthetic population generation: an algorithm and application to rural Afghanistan

We develop a new algorithm for population synthesis that fuses remote-sensing data with partial and sparse demographic surveys. The algorithm addresses non-binding constraints and complex sampling designs by translating population synthesis into a computationally efficient procedure for constrained network growth. As a case, we synthesize the rural population of Afghanistan, validate the algorithm with in-sample and out-of-sample tests, examine the variability of algorithm outputs over k-nearest neighbor manifolds, and show the responsiveness of our algorithm to additional data as a constraint on marginal population counts.