Exploring spatiotemporal dynamics in a housing market using the spatial vector autoregressive Lasso: A case study of Seoul,Korea

House prices fluctuate spatiotemporally and when influential changes from a region happen, the effects spread out in space over time. Although many studies have introduced various models to explain the spatiotemporal dynamics in housing markets, it is always challenging to consider both dimensions in a model. Some recent studies have identified spatiotemporal interactions of house prices by combining spatial and temporal models via spatial vector autoregression. The approach, however, assumes spatial homogeneity of the variables due to insufficient degrees of freedom. Since the housing market is generally conceived as heterogeneous, we suggest an alternative model of the spatial vector autoregressive Lasso without the homogeneity assumption. As an empirical example, we examine the spatiotemporal interaction between house sales price and rent in Seoul, Korea. The results show that rent for apartments in Gangnam‐gu, a socioeconomic core of Seoul, has positive impacts on rent for apartments in surrounding suburbs rather than their sales price. Moreover, the suggested model outperforms the classical method in terms of explanation, prediction, and autocorrelation of residuals. This research is expected to provide a methodological guide to explore the interaction between house sales price and rent, and insights into the spatiotemporal dynamics of the housing market in Seoul.     

Analyzing Cognitive Conceptualizations Using Interactive Visual Environments

The conceptualization of spatio-temporal information is an interdisciplinary research area. The focus of this article is on human conceptualizations of spatio-temporal geographic phenomena (also referred to as events). Identifying and understanding human conceptualizations is a crucial component in defining the semantics of spatio-temporal information. However, most research focuses primarily on how humans imbue dynamic phenomena with meaning on a general level. In contrast, this article is concerned with contextual factors (specifically: individual differences) that are too often neglected in general theories and in the analysis of behavioral data. In other words, we are interested in individual or group strategies of participants that are not detected by classical analysis methods. Research on individual difference is gaining widespread attention in cognitive and spatial sciences and it is time to consider individual differences in the area of conceptualizing spatio-temporal information. To understand individual differences in behavioral data on how people conceptualize events, we have developed illustrative software and have combined them with established similarity measures. We demonstrate the feasibility of our approach, its usefulness in analyzing behavioral data, and results that can be obtained through this individualized analysis by reanalyzing four sets of experimental data we previously collected.     

时空Kalman滤波在变形分析中的应用研究

时空Kalman滤波可对变形监测数据进行时空滤波去噪、数据插补和变形预测,本文利用时空Kalman滤波进行变形分析,从模型原理及试验两方面比较分析了Kriged Kalman filter(KKF)、space time Kalman filter(STKF)和spatio-temporal mixed effects(STME) 3种典型时空Kalman滤波模型的性能和适用性。结果表明:3种时空Kalman滤波模型均基于空间基函数及动力学模型组合形式描述时空数据的时空相关性,其主要差异在于空间变异的描述形式不同、空间基函数和状态转移矩阵构造过程不同及模型降维方法不同。在适用性方面,KKF模型更适合于稀疏测站的变形分析,STKF模型及STME模型更适合于海量测站的变形分析。在变形分析应用效果方面,3种时空Kalman滤波模型均具有较高精度的时空滤波去噪、数据插补和变形预测性能,其滤波结果相对于普通Kalman滤波结果的平均改善率为21.1%,其缺失数据插补结果相对于Hermite时间插值结果的平均改善率为42.4%,其空间预测结果相对于Kriging空间插值结果的平均改善率为65.3%,其对已知测站未来变形的时空预测结果相对于普通Kalman滤波时间预测结果的平均改善率为20.6%,其对非观测站点未来变形的时空预测结果相对于Kalman滤波+Kriging组合模型预测结果的平均改善率为20.5%。     

基于事件的时空数据模型应用研究

本文首先分析和比较了几种有代表性时空数据模型的特点和存在的局限性 ,对基于事件的时空数据模型进行了改进 ,并提出了利用亲缘继承关系进行历史回溯查询 ,根据具体的实际应用 ,在现有成熟的ARCGIS平台上进行扩展 ,验证了该模型的有效性。实践表明 :利用该模型可以有效的进行各种时间、空间和属性的互查 ;时间因素和空间对象的空间特征和属性信息分开存储管理 ,便于时空分析 ,适于进行地理事件与空间对象状态因果关系的推理 ;减小了数据的存储空间 ,以及数据的冗余度     

基于Kevin-Ian 3D模型的紧致时空数据模型

利用时间标识的方式来建立时空数据模型，无论是全局标识，还是各种程度的局部标识，都存在着许多问题；把时间当做独特的要素对待，不能较好地实现时空一体化。在研究Kevin-Ian 3D模型的基础上，提出了把时间和空间同等对待的紧致时空数据模型。该模型通过对时空中的点、线、面、体的描述，可以较好地解决时空数据库的数据冗余、时间拓扑等问题，同时具有较强的描述实体变化的能力。     

Area-to-point Kriging in spatial hedonic pricing models

This paper proposes a geostatistical hedonic price model in which the effects of location on house values are explicitly modeled. The proposed geostatistical approach, namely area-to-point Kriging with External Drift (A2PKED), can take into account spatial dependence and spatial heteroskedasticity, if they exist. Furthermore, this approach has significant implications in situations where exhaustive area-averaged housing price data are available in addition to a subset of individual housing price data. In the case study, we demonstrate that A2PKED substantially improves the quality of predictions using apartment sale transaction records that occurred in Seoul, South Korea, during 2003. The improvement is illustrated via a comparative analysis, where predicted values obtained from different models, including two traditional regression-based hedonic models and a point-support geostatistical model, are compared to those obtained from the A2PKED model.     

Modeling Dynamic Polygon Objects in Space and Time: A New Graph-based Technique

The analysis of dynamic spatial systems requires an explicit spatio-temporal data model and spatio-temporal analysis tools. Event-based models have been developed to analyze discrete change in continuous and feature-based spatial data. In this paper, a spatio-temporal graph model is described that supports the analysis of continuous change in feature-based polygon spatial data. The spatio-temporal graph edges, called temporal links, track changes in polygon topology through space and time. The model also introduces the concept of a spatial-interaction region that extends a model's focus beyond short-term local events to encompass long-term regional events. The structure of the spatio-temporal graph is used to classify these events into five types of local polygon events and two types of spatial-interaction region events. To illustrate its utility, the model is applied to the ecological question of how patch size influences longevity in underwater plant communities in Chesapeake Bay, USA. Both a short-term local analysis and a longer-term regional analysis showed that patches of plants, or groups of patches, larger than one to two hectares in size were more likely to persist than smaller patches or groups of patches. Overall, the spatio-temporal graph model approach appears applicable to a variety of spatio-temporal questions.     

面向BDS时空数据的多粒度时空模型构建研究

针对北斗卫星导航系统（BDS）时空数据组织管理过程中数据存储与分析困难的问题,本文面向BDS时空数据,通过分析其数据特征,引入时空粒化思想,从空间粒度、时间粒度、时空粒度三个粒度对BDS时空对象进行多粒度表达．参考面向对象和基态修正模型的方法,建立BDS多粒度时空数据模型的概念模型,提出一种BDS时空对象多粒度表达方法,并实例证明该方法可以实现BDS时空数据的组织与存储．      

顾及信誉的众源时空数据模型

提出了一种顾及信誉度的众源时空数据模型。在分析众源时空数据中地理要素、目标状态、对象版本、贡献者、信誉度、改变现实空间实体或信息空间对象状态的事件等要素间的相互作用机理的基础上,采用面向对象方法设计了一种顾及信誉度的众源时空数据组织方法,用UML对其进行描述,分析了与信誉度相关操作及其联动关系,得出了8条联动规则。开发了顾及信誉度的众源时空数据管理原型系统,验证了所提模型的有效性。     

Cyclone Frame Prediction by Gaussian Mixture Modeling of the Three Penultimate Time-Lapse Frames

A fast cyclone frame prediction is proposed in this paper that fits a Gaussian Mixture model on the spatio-temporal data extracted from the three penultimate time-lapse frames, prior to fuzzy regression. Unlike the previous work in Verma and Pal (In: Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1–8, 2010) that models the entire history on a per pixel basis, a single Gaussian mixture is used for fitting the spatio-temporal data within the time-span of the last three frames, making the process faster and more accurate. The increase in accuracy is attributed to the fact that cyclones evolve over time and thus the recent frames give a more meaningful insight into the predictions for the next frame. The number of components in the Gaussian mixture is determined from the occurrence of equally likely modes that correspond to high entropy peaks. Our results on satellite videos of recent cyclones that hit the Indian seas show a high accuracy of frame prediction.     

时空信息云平台在乱占耕地建房整治上的应用

为满足农村乱占耕地建房问题整治工作的需要,基于Web AppBuilder框架,以拥有宿主寄存技术的智慧黄山时空信息云平台为依托,利用ArcGIS Server服务发布技术结合即用型微件创建了农房核查程序,提供了地图操作、数据编辑、数据汇总统计等功能,为农房核查提供了方便快捷的技术支持。该程序具有易操作、可移植、可交互、响应时间短、用户界面友好等特点。     

面向对象的遥感影像时空特征模型研究

对遥感影像的影像对象进行时空特征分析是遥感影像的智能分析与解译的基础,现有的时空数据模型不能满足影像对象时空特征分析的需要。文中基于遥感影像多尺度分割产生的影像对象用面向对象的方法建立时空特征数据模型,此模型可以满足对象存储和基于时间、空间、特征、属性查询的需要。同时,将此模型应用于遥感影像中海岸线与海岸带地物的时空特征建模,并验证模型的有效性。     

面向对象的超图时空推理模型的研究与应用

通过对各种时空数据模型和时空推理模型的比较，把面向对象技术与实际生产需要相结合，构造了基于超图理论的时空数据模型和时空推理模型，并将该模型应用到云南省红河州数字烟草系统对地块的管理中。实践证明，该时空推理模型实现了时空一体化，具有完整的时空内涵。     

基于时间语义的地籍管理信息系统数据组织与实现

地籍管理信息系统属于GIS范畴,具有很强的时态特性,如何高效地组织时空数据是系统成败的关键。本文在分析地籍管理信息系统必要性以及时间概念及其具体含义的基础上,引入时间语义,提出基于时间语义的数据模型。根据语义对地籍管理信息系统的时空数据组织方法进行了研究。并根据地籍管理对模型的要求,分别从时间点、时间段角度对历史信息进行查询和回溯,验证了模型的可行性。     

一种基于XML数据模型的时空查询代数探讨

时空查询代数是一种抽象的时空查询语言,它利用时空抽象数据类型的一部分基本的谓词、操作、函数来表达时空查询.而在时空微观运算上采用的数据结构是本源XML数据库(Native XML Database,NXD)用来存储半结构化数据的XML数据结构,所以对XML查询代数-XQuery FS的数据类型和查询操作做了时空扩展,并...     

基于加权图的地籍时空数据描述模型研究

以计算机通用的数据结构——图为基础，研究了基于加权图的时空数据组织策略，为表达时空语义提供了一种形式化的描述方法，并分析了以该模型为中间件实现不同时空模型转换的应用前景。     

一种面向海量基础地理信息数据更新的多人协同作业方法

为解决传统面向海量基础地理信息数据多人协同更新技术方案中存在的问题,满足实际生产中对作业效率、过程数据管理等要求,本文提出了一种多人离线协同作业方法。该方法以扩展的时空数据库模型为基础,通过更新过程中多时态数据的集中存储管理,要素的离线编辑及协同状态维护,协同冲突检测与处理,本地编辑内容提交等工艺方法,实现了多人离线协同更新作业。基于该方法研发了协同作业系统,该系统已在多个省级及以上数据库更新项目中得到了应用,应用效果良好。     

一种基于对象和快照的混合地表覆盖时空数据存储模型

时空数据模型是数据库和时空数据分析研究热点之一。由于时空数据变化类型多、分析计算复杂及应用范围广,一种观点认为建立通用的时空数据模型具有高度复杂性。本文针对地表覆盖变化时空数据建库和时空分析应用需求,提出了基于对象和快照的混合时空数据模型。该模型用面向对象描述地表覆盖的斑块对象时空过程,组织管理斑块对象时空事件和空间、属性信息,同时用快照描述地表覆盖整体时空分布,组织管理栅格快照,两者通过基于时间和空间位置的逻辑关联关系形成混合模型。模型在表达对象自身变化的时空事件类型基础上,增加了不同分类对象之间互相转变的时空事件表达。该模型既可以用于对地表覆盖历史观测数据组织存储建库,又可以用于支持时空统计分析、时空演变模拟和时空数据挖掘。通过对黑龙江地区地表覆盖时空数据建库、查询检索、整体格局演变分析和斑块变化时空过程检索与表达的试验,验证了模型的有效性。     

空间信息服务时态数据的管理与应用

本文面向当前空间信息应用的实际需求,以传统GIS为基础,研究建立实用的、易于实现的集矢量与栅格数据为一体的时空数据模型,增加GIS对时态属性的管理和操作。本文研究解决了时态拓扑构建的关键技术,采用面向对象方法开发了一套时态空间数据库管理系统,实现了时空数据的采集、存储、查询和显示,并以中国多个历史时期的行政界线为基础数据,进行了时空数据模型的应用研究,为TGIS发展探索了一条实用的技术路线。     

面向地理信息大数据的时空事件关系可视化分析框架

不同类型的地理信息大数据蕴含了丰富的时空关系,反映个体或者群体时空事件之间的变化模式,在资源、环境、交通等领域的智慧应用中起到关键性作用。然而,地理信息大数据拥有的多尺度和多语义特性,在挖掘地理信息大数据的时空关系时会存在许多困难。如何有效地构建多尺度多语义时空关系,并直观地表达这些时空关系是一个重大挑战。本文设计了一种时空事件关系可视化分析框架,通过定义事件间时空关系的描述,设计了一种多视图协同的可视化分析方法,结合时空关系的可视化协同交互,实现地理信息大数据时空事件可视分析。基于时空轨迹数据,通过试验说明该方法可以探寻时空事件及背景间的时空关系,实现时空事件的交互可视分析。