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1.
The real estate market has long provided an active application area for spatial–temporal modeling and analysis and it is well known that house prices tend to be not only spatially but also temporally correlated. In the spatial dimension, nearby properties tend to have similar values because they share similar characteristics, but house prices tend to vary over space due to differences in these characteristics. In the temporal dimension, current house prices tend to be based on property values from previous years and in the spatial–temporal dimension, the properties on which current prices are based tend to be in close spatial proximity. To date, however, most research on house prices has adopted either a spatial perspective or a temporal one; relatively little effort has been devoted to situations where both spatial and temporal effects coexist. Using ten years of house price data in Fife, Scotland (2003–2012), this research applies a mixed model approach, semiparametric geographically weighted regression (GWR), to explore, model, and analyze the spatiotemporal variations in the relationships between house prices and associated determinants. The study demonstrates that the mixed modeling technique provides better results than standard approaches to predicting house prices by accounting for spatiotemporal relationships at both global and local scales. 相似文献
2.
COVID-19疫情不断蔓延为国际政治、外交关系等带来深刻影响。目前基于复杂网络方法的国际关系研究较少考虑节点的空间属性,难以探索国际关系的动态演化模式及其空间分布特征。该文提出一种结合时间序列聚类与空间统计的国家关系交互网络演化模式探测方法。基于2020年1月-2021年3月的GDELT数据构建国家关系交互网络,基于节点的演化特征,应用K-means聚类算法将节点划分为6种类型,结合局部连接统计方法分析节点演化模式的空间分布特征。研究表明:面对疫情冲击,各国为控制疫情蔓延倾向于参与合作交互事件;国家关系交互网络中的不同时序演化模式总体按照节点的点度中心性强度由高到低分布;疫情防控期间网络中始终处于边缘地位的节点在空间分布上呈现聚集特征,而核心节点空间分布较分散。通过研究网络节点的时序演化模式及空间分布特征可为公共卫生危机事件期间国际关系与地缘政治研究提供新思路,对于危机事件期间制定外交政策与应对策略具有一定参考价值。 相似文献
3.
Rapid response to fire incidents is critical as delays in the departure and arrival at the scene can have significant consequences in terms of damage, injury and death. Research on the dynamics of residential fire incident response times has barely begun, a situation arguably underpinned by limited access to disaggregate command and control data. In this paper we draw on unit record data and employ quantile regression to examine the role that socio-demographic, infrastructure characteristics and temporal factors play on response times. Results reveal that response times are slower during the winter, in locales with larger numbers of children (aged 14 years and below) and low socioeconomic households, and in areas that have more complex street layouts. We conclude through emphasising the importance of these findings in their capacity to contribute to a new evidence base to inform policy decisions from a resource allocation perspective through the spatial allocation of finite fire resources. 相似文献
4.
Julian Hagenauer Marco Helbich 《International journal of geographical information science》2013,27(10):2026-2042
Spatial sciences are confronted with increasing amounts of high-dimensional data. These data commonly exhibit spatial and temporal dimensions. To explore, extract, and generalize inherent patterns in large spatiotemporal data sets, clustering algorithms are indispensable. These clustering algorithms must account for the distinct special properties of space and time to outline meaningful clusters in such data sets. Therefore, this research develops a hierarchical method based on self-organizing maps. The hierarchical architecture permits independent modeling of spatial and temporal dependence. To exemplify the utility of the method, this research uses an artificial data set and a socio-economic data set of the Ostregion, Austria, from the years 1961 to 2001. The results for the artificial data set demonstrate that the proposed method produces meaningful clusters that cannot be achieved when disregarding differences in spatial and temporal dependence. The results for the socio-economic data set show that the proposed method is an effective and powerful tool for analyzing spatiotemporal patterns in a regional context. 相似文献
5.
为实现国家自主贡献承诺,如期达到“碳达峰、碳中和”目标,中国服务业的低碳发展是必然趋势。基于多种空间分析方法,从时空交互视角研究了中国服务业碳强度差异格局、空间关联、动态演化及跃迁机制。结果表明：(1) 2005—2019年中国服务业碳强度的总体差异存在动态收敛趋势,在空间上也呈现显著的聚类现象,且空间集聚水平逐渐趋于稳定。(2)在服务业碳强度局部空间结构与依赖方向上,西北与东北地区波动性较强,东部沿海地区相对稳定;在碳强度时空跃迁的过程中整体表现出一定的转移惰性,具有较强的空间依赖或路径锁定特征,其中中部、西部的多数地区始终保持高碳强度属性,是制约中国服务业协同减排的关键区域。(3)服务业碳强度的时空网络格局主要以正向关联为主,表现出较强的空间整合性,但少数邻接省域仍存在一定程度的时空竞争。(4)各地区服务业碳强度时空跃迁的驱动模式存在差异,其中,东部沿海省份主要受人口—城镇化制约模式的影响,西北、西南和东北的多数地区主要受技术—规制驱动模式的影响。自东南至西北,中国服务业碳强度的跃迁模式逐渐呈现出“同向制约—反向发展—同向发展”的阶梯递变格局。因此,政府减排政策的制定不仅应统筹考虑... 相似文献
6.
Donna J. Peuquet Anthony C. Robinson Samuel Stehle Franklin A. Hardisty Wei Luo 《International journal of geographical information science》2013,27(9):1588-1611
Pattern analysis techniques currently common within geography tend to focus either on characterizing patterns of spatial and/or temporal recurrence of a single event type (e.g., incidence of flu cases) or on comparing sequences of a limited number of event types where relationships between events are already represented in the data (e.g., movement patterns). The availability of large amounts of multivariate spatiotemporal data, however, requires new methods for pattern analysis. Here, we present a technique for finding associations among many different event types where the associations among these varying event types are not explicitly represented in the data or known in advance. This pattern discovery method, known as T-pattern analysis, was first developed within the field of psychology for the purpose of finding patterns in personal interactions. We have adapted and extended the T-pattern method to take the unique characteristics of geographic data into account and implemented it within a geovisualization toolkit for an integrated computational-geovisual environment we call STempo. To demonstrate how T-pattern analysis can be employed in geographic research for discovering patterns in complex spatiotemporal data, we describe a case study featuring events from news reports about Yemen during the Arab Spring of 2011–2012. Using supplementary data from the Global Database of Events, Language, and Tone, we briefly summarize and reference a separate validation study, then evaluate the scalability of the T-pattern approach. We conclude with ideas for further extensions of the T-pattern technique to increase its utility for spatiotemporal analysis. 相似文献
7.
Sonja Pravilovic Donato Malerba 《International journal of geographical information science》2018,32(1):191-212
Managing geophysical data generated by emerging spatiotemporal data sources (e.g. geosensor networks) presents a growing challenge to Geographic Information System science. The presence of correlation poses difficulties with respect to traditional spatial data analysis. This paper describes a novel spatiotemporal analytical scheme that allows us to yield a characterization of correlation in geophysical data along the spatial and temporal dimensions. We resort to a multivariate statistical model, namely CoKriging, in order to derive accurate spatiotemporal interpolation models. These predict unknown data by utilizing not only their own geosensor values at the same time, but also information from near past data. We use a window-based computation methodology that leverages the power of temporal correlation in a spatial modeling phase. This is done by also fitting the computed interpolation model to data which may change over time. In an assessment, using various geophysical data sets, we show that the presented algorithm is often able to deal with both spatial and temporal correlations. This helps to gain accuracy during the interpolation phase, compared to spatial and spatiotemporal competitors. Specifically, we evaluate the efficacy of the interpolation phase by using established machine-learning metrics (i.e. root mean squared error, Akaike information criterion and computation time). 相似文献
8.
Alan Kwok Lun Cheung David O’sullivan Gary Brierley 《International journal of geographical information science》2013,27(4):580-605
The structure of computational spatial analysis has mostly built on data lattices inherited from cartography, where visualization of information takes priority over analysis. In these framings, spatial relationships cannot easily be encoded into traditional data lattices. This hinders spatial analysis that emphasizes how interactions among spatial entities reflect mutual inter-relationships. This paper explores how graph theoretic principles can support spatiotemporal analysis by enabling assessment of spatial and temporal relationships in landscape monitoring. 相似文献
9.
主观幸福感是目前国内外研究热点,与提升居民生活质量和建设宜居城市密切相关。已有大部分文献侧重单一空间尺度的研究,分析社会经济属性和地理环境要素(包括建成环境、社会环境、环境污染)对主观幸福感的影响;也有部分研究关注居民日常出行属性和活动特征对主观幸福感的作用机制,探讨长期幸福感与短期幸福感的内在关系。论文对上述研究进行较为系统的梳理与评价,综合考虑地理环境、时空行为与主观幸福感的复杂关系,构建主观幸福感的理论研究框架,总结时空行为视角下多尺度、多维度地理环境要素对主观幸福感的影响机制以及作用路径,并探讨主观幸福感的时空动态规律以及微观行为机制,为改善城市人居环境、优化居民行为模式提供科学依据和政策建议。 相似文献
10.
食物系统认知进展及其地理学研究范式探讨 总被引：1，自引：0，他引：1
食物关系国计民生,中国食物系统面临诸多挑战,耕地资源趋减、环境压力临界、农业劳动力流失、消费需求快速转型等对食物系统功能提出更高要求,地理学的综合性和系统性思维为应对这些问题提供了有效的工具和视角。尽管食物系统得到世界银行、联合国粮农组织以及其他国际各方的高度关注,但目前国内对食物系统的研究却严重不足。论文对食物系统的认知进展进行了深入分析,归纳了食物系统的概念认知历程、类型、特征,梳理出食物系统的研究脉络,包括从“概念”存在到“方法”存在、从线性认知到系统认知、从经济活动到食物景观、从现象描述到时空嵌入等,以此凝练出食物系统的核心内涵与认知进展;在科学哲学范式、人地关系范式、空间范式和系统科学范式指导下,论文遵从“格局—结构—过程—机理”由表及里的研究脉络,进一步探讨了食物系统的时空格局、要素结构、演化过程和发展机理,尝试构建了食物系统的地理学研究范式,研究结论旨在为推动食物系统视角基础研究和实践应用提供参考和借鉴。 相似文献
11.
地理学时空数据分析方法 总被引：13，自引：4，他引：9
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。 相似文献
12.
犯罪热点时空分布研究方法综述 总被引：5，自引：3，他引：2
犯罪在地理时空内并不是均匀分布的,而是表现出明显的时空聚集特性,这种聚集性常用“犯罪热点”表述.基于对犯罪热点的理解,从犯罪热点时空分布模式、犯罪热点成因分析以及犯罪热点时空转移及预测等3 个方面总结了当前国内外犯罪热点时空分布相关研究方法的进展.最后,对该领域研究进行了总结与展望.总体上,国内相关研究较少,尚需进一步结合中国国情,提出适用方法.另外,也需要通过相关犯罪理论的深入研究以及其他领域研究方法的借鉴,实现犯罪热点时空分布研究方法的突破与创新. 相似文献
13.
社区生活圈的新时间地理学研究框架 总被引：5，自引：5，他引：0
社区生活圈从居民日常活动及行为视角考察城市社区,是城市地理学和城市相关学科的研究前沿,也是中国国土空间规划体系创新的重要组成部分,以及中国城市社会可持续发展的重要抓手。伴随着流动性和信息化的不断深入,社区生活圈的主体日益多元化、社区活动和居民时空行为日益多样化、社区空间的功能与意义日益丰富化,亟需城市地理学的研究创新与实践引导。时间地理学是理解人与环境关系的社会—技术—生态综合方法,为早期基于时空行为与生活空间的社区生活圈研究提供了重要基础。新时间地理学重视家庭及其他组织企划的交互与时空组合,可为社区生活圈内个体—家庭—社区之间的复杂互动关系研究、时空行为的社会文化制约与多情境分析及模拟提供重要支撑。论文基于新时间地理学方法,从理论、方法和实证3个维度提出社区生活圈的新时间地理学研究框架,具体包括构建社区生活圈的时空行为理论,揭示社区生活圈的时空间结构;创新社区生活圈的时空行为分析和模拟方法;从社区生活圈时空行为优化、社区交往生活圈、社区安全生活圈等方面创新中国城市规划与管理等研究内容。 相似文献
14.
Two areas still need further examination in the ecological study of inequality and mortality. First, the evidence for the relationship between income inequality and mortality remains inconclusive, particularly when the analytic unit is small (e.g., county in the U.S.). Second, most previous studies are cross-sectional and are unable to address the recent diverging patterns whereby mortality has decreased and income inequality increased. This study aims to contribute to both topic areas by studying the relationship between inequality and mortality via a spatiotemporal approach that simultaneously considers the spatial structure and the temporal trends of inequality and mortality using county panel data between 1990 and 2010 for the conterminous U.S. Using both spatial panel random effect and spatial panel fixed effect models, we found that (a) income inequality was not a significant factor for mortality after taking into account the spatiotemporal structure and the most salient factors for mortality (e.g., socioeconomic status); (b) the spatial panel fixed effect model indicated that income inequality was negatively associated with mortality over the time, a relationship mirroring the diverging patterns; and (c) the significant spatial and temporal fixed effects suggested that both dimensions are critical factors in understanding the inequality-mortality relationship in the U.S. Our findings lend support to the argument that income inequality does not affect mortality and suggest that the cross-sectional findings may be a consequence of ignoring the temporal trends. 相似文献
15.
To study the development of spatial and social behavior of preschool children, micro-level spatiotemporal data were collected for the first time in both spatial and social context using a novel behavioral coding system. These unique behavioral data enable us to explore the group-level, dynamic, spatial, and social patterns of preschool children's playing behavior from a hybrid geographic and social perspective. In this research, GIS and exploratory spatial data analysis (ESDA) techniques are employed together to study group-level spatial and social behavior emerging from children's everyday activities and interactions. ESDA with social weights is proposed to explore spatial and social patterns of preschool children's behavior at the same time. The results highlight the utility of this approach for studying the relationships between preschool children's playing behavior and preschool's environmental settings and the relationships between preschool children's personal activities and the formation of their social network space. 相似文献
16.
Yizhao Gao Anand Padmanabhan Junjun Yin Guofeng Cao 《International journal of geographical information science》2018,32(3):425-449
Tracking spatial and temporal trends of events (e.g. disease outbreaks and natural disasters) is important for situation awareness and timely response. Social media, with increasing popularity, provide an effective way to collect event-related data from massive populations and thus a significant opportunity to dynamically monitor events as they emerge and evolve. While existing research has demonstrated the value of social media as sensors in event detection, estimating potential time spans and influenced areas of an event from social media remains challenging. Challenges include the unstable volumes of available data, the spatial heterogeneity of event activities and social media data, and the data sparsity. This paper describes a systematic approach to detecting potential spatiotemporal patterns of events by resolving these challenges through several interrelated strategies: using kernel density estimation for smoothed social media intensity surfaces; utilizing event-unrelated social media posts to help map relative event prevalence; and normalizing event indicators based on historical fluctuation. This approach generates event indicator maps and significance maps explaining spatiotemporal variations of event prevalence to identify space-time regions with potentially abnormal event activities. The approach has been applied to detect influenza activity patterns in the conterminous US using Twitter data. A set of experiments demonstrated that our approach produces high-resolution influenza activity maps that could be explained by available ground truth data. 相似文献
17.
Jane Southworth Graeme S. Cumming Matt Marsik Michael W. Binford 《The Professional geographer》2006,58(4):406-420
Anthropogenic, ecological, and land‐surface processes interact in landscapes at multiple spatial and temporal scales to create characteristic patterns. The relationships between temporally and spatially varying processes and patterns are poorly understood because of the lack of spatiotemporal observations of real landscapes over significant stretches of time. We report a new method for observing joint spatiotemporal landscape variation over large areas by analyzing multitemporal Landsat data. We calculate the spatiotemporal variation of the Normalized Difference Vegetation Index (NDVI) in the area covered by one Landsat scene footprint in north central Florida, over spatial windows of 104–108 m2 and time steps of two to sixteen years. The correlations, slopes, and intercepts of spatial versus temporal regressions in the real landscape all differ significantly from results obtained using a null model of a randomized landscape. Spatial variances calculated within windows of 105–107 m2 had the strongest relationships with temporal variances (regressions with both larger and smaller windows had lower coefficients of determination), and the relationships were stronger with longer time steps. Slopes and y‐intercepts increased with window size and decreased with increased time step. The spatial and temporal scales at which NDVI signals are most strongly related may be the characteristic scales of the processes that most strongly determine landscape patterns. For example, the important time and space windows correspond with areas and timing of fires and tree plantation harvests. Observations of landscape dynamics will be most effective if conducted at the characteristic scales of the processes, and our approach may provide a tool for determining those scales. 相似文献
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19.
移动定位大数据视角下的人群移动模式及城市空间结构研究进展 总被引：5，自引：3，他引：2
了解城市人群移动行为和空间结构对城市规划、交通管理、应急响应等具有重要的意义。近年来,随着信息技术(ICT)的快速发展,采集大规模、长时间序列的人群移动定位大数据变得容易,为人群移动行为研究带来了新的机遇和挑战。本文首先介绍了目前用于城市人群移动行为和空间结构研究的主要数据源及其特征,并分别从人群移动行为、城市空间结构2个方面对近3年国内外相关研究进行归纳总结。目前的研究主要从移动定位大数据中挖掘人群移动模式,理解人群移动时空规律,进一步透视城市的空间结构特征;而对城市空间结构与人群移动行为影响的研究较少。未来可通过融合多源时空数据,综合研究人群移动行为与城市空间结构之间的相互作用,发展大规模群体移动行为时空分析理论和模型,进一步深入理解人群移动行为与城市空间结构的耦合关系。 相似文献
20.
Yukio Sadahiro 《International journal of geographical information science》2019,33(2):215-239
The appearance and disappearance of immovable points are important spatiotemporal events in geographical information science. They represent phenomena such as the birth and death of trees in forests, construction and destruction of buildings in cities and openings and closures of shops and restaurants. This paper proposes a new method for analyzing the appearance and disappearance of points. The method helps analysts capture the overall picture and regional variation of event pattern and detecting significant local patterns. Four measures are defined that indicate the intensity of spatial and temporal patterns of events. The measures are visualized as grid maps. A statistical test is used to evaluate the significance of the measures to extract the regions of significant patterns. The proposed method is applied in an analysis of shops and restaurants in Shibuya, Tokyo. Technical soundness of the method is discussed along with empirical findings. 相似文献