Re-examining urban region and inferring regional function based on spatial–temporal interaction

Urban system is shaped by the interactions between different regions and regions planned by the government, then reshaped by human activities and residents’ needs. Understanding the changes of regional structure and dynamics of city function based on the residents’ movement demand are important to evaluate and adjust the planning and management of urban services and internal structures. This paper constructed a probabilistic factor model on the basis of probabilistic latent semantic analysis and tensor decomposition, for purpose of understanding the higher order interactive population mobility and its impact on urban structure changes. First, a four-dimensional tensor of time (T)?×?week (W)?×?origin (O)?×?destination (D) was constructed to identify the day-to-day activities in three time modes and weekly regularity of weekday/weekend pattern. Then we reclassified the urban regions based on the space clustering formed by the space factor matrix and core tensor. Finally, we further analysed the space–time interaction on different time scales to deduce the actual function and connection strength of each region. Our research shows that the application of individual-based spatial–temporal data in human mobility and space–time interaction study can help to analyse urban spatial structure and understand the actual regional function from a new perspective.     

Analysing regional industrialisation in Jiangsu province using geographically weighted regression

 Industry is the most important sector in the Chinese economy. To identify the spatial interaction between the level of regional industrialisation and various factors, this paper takes Jiangsu province of China as a case study. To unravel the existence of spatial nonstationarity, geographically weighted regression (GWR) is employed in this article. Conventional regression analysis can only produce `average' and `global' parameter estimates rather than `local' parameter estimates which vary over space in some spatial systems. Geographically weighted regression (GWR), on the other hand, is a relatively simple, but useful new technique for the analysis of spatial nonstationarity. Using the GWR technique to study regional industrialisation in Jiangsu province, it is found that there is a significant difference between the ordinary linear regression (OLR) and GWR models. The relationships between the level of regional industrialisation and various factors show considerable spatial variability. Received: 4 April 2001 / Accepted: 17 November 2001     

A parallel approach for improving Geo-SPARQL query performance

Geospatial Semantic Web promises better retrieval geospatial information for Digital Earth systems by explicitly representing the semantics of data through ontologies. It also promotes sharing and reuse of geospatial data by encoding it in Semantic Web languages, such as RDF, to form geospatial knowledge base. For many applications, rapid retrieval of spatial data from the knowledge base is critical. However, spatial data retrieval using the standard Semantic Web query language – Geo-SPARQL – can be very inefficient because the data in the knowledge base are no longer indexed to support efficient spatial queries. While recent research has been devoted to improving query performance on general knowledge base, it is still challenging to support efficient query of the spatial data with complex topological relationships. This research introduces a query strategy to improve the query performance of geospatial knowledge base by creating spatial indexing on-the-fly to prune the search space for spatial queries and by parallelizing the spatial join computations within the queries. We focus on improving the performance of Geo-SPARQL queries on knowledge bases encoded in RDF. Our initial experiments show that the proposed strategy can greatly reduce the runtime costs of Geo-SPARQL query through on-the-fly spatial indexing and parallel execution.     

Heterogeneous Space–Time Artificial Neural Networks for Space–Time Series Prediction

Space–time series prediction plays a key role in the domain of geographic data mining and knowledge discovery. In general, the existing methods of space–time series prediction can be divided into two main categories: statistical machine learning methods. Comparatively, machine leaning methods have obvious advantages with respect to handling nonlinear problems. However, space–time dependence and the heterogeneity of space–time data are not well addressed by the existing machine learning methods. Because of this limitation, an accurate prediction of a space–time series is still a challenging problem. Therefore, in this study, both space–time dependence and heterogeneity are incorporated into the feedback artificial neural network, and heterogeneous space–time artificial neural networks (HSTANNs) are developed for space–time series prediction. First, to handle spatial heterogeneity, space–time series clustering is used to divide the study area into a set of homogeneous sub‐areas. Then, a space–time autocorrelation analysis is employed to explore the space–time dependence structure of the dataset. Finally, a HSTANN is established for each sub‐area. Further, HSTANNs are applied to predict the concentrations of fine particulate matter (PM_2.5) in Beijing–Tianjin–Hebei. The experimental results show that when compared with other methods, the accuracy of the forecasting results is considerably improved by using HSTANNs.     

A space-time analysis of knowledge production

Regional growth models often emphasize the importance of research and development activities leading to technological progress. The role of knowledge production and spatiotemporal spillover effects is investigated using a space-time panel data set covering 49 US states over the period 1994–2005. The aim is to test for the existence of regional knowledge spillovers in the context of a space-time dynamic suggested by the knowledge production function. A space-time specification is set forth that can be applied to panel data models that include random effects. We compare alternative models that have been proposed in the panel data literature to provide a better understanding of how new ideas diffuse across space and time. The results indicate that the space-time panel data set is consistent with the presence of strong spatiotemporal regional spillovers of knowledge. The empirical findings are interpreted in light of the existing theoretical and empirical literature on endogenous growth.     

A spatio-temporal econometric model of regional growth in Spain

In this paper, a combined cross-section and time-series econometric analysis of Spanish regional growth is presented. This analysis operates with a database where the number of cross-sectional units is small for a typical panel of data, while the time dimension is clearly dominant. First, using recent techniques in the econometric analysis of panel data (both panel unit root and panel co-integration tests), a co-integrating relationship between the level of regional output and the level of regional input factors is found, and the steady-state equilibrium production function is estimated. Second, a dynamic spatio-temporal panel error correction model is used in order to describe the short-run regional growth adjustment process in space and time. As conclusion, it is possible to identify significant spatial effects in the Spanish regional growth after controlling for temporal variation of the implied variables.     

A bootstrap based space–time surveillance model with an application to crime occurrences

This study proposes a bootstrap-based space–time surveillance model. Designed to find emerging hotspots in near-real time, the bootstrap based model is characterized by its use of past occurrence information and bootstrap permutations. Many existing space–time surveillance methods, using population at risk data to generate expected values, have resulting hotspots bounded by administrative area units and are of limited use for near-real time applications because of the population data needed. However, this study generates expected values for local hotspots from past occurrences rather than population at risk. Also, bootstrap permutations of previous occurrences are used for significant tests. Consequently, the bootstrap-based model, without the requirement of population at risk data, (1) is free from administrative area restriction, (2) enables more frequent surveillance for continuously updated registry database, and (3) is readily applicable to criminology and epidemiology surveillance. The bootstrap-based model performs better for space–time surveillance than the space–time scan statistic. This is shown by means of simulations and an application to residential crime occurrences in Columbus, OH, year 2000.     

Spatial and temporal intercomparison of four global burned area products

We characterize the agreement and disagreement of four publically available burned products (Fire CCI, Copernicus Burnt Area, MODIS MCD45A1, and MODIS MCD64A1) at a finer spatial and temporal scale than previous assessments using a grid of three-dimensional cells defined both in space and in time. Our analysis, conducted using seven years of data (2005–2011), shows that estimates of burned area vary greatly between products in terms of total area burned, the location of burning, and the timing of the burning. We use regional and monthly units for analysis to provide insight into the variation between products that can be lost when considering products yearly and/or globally. Comparison with independent, contemporaneous MODIS active fire observations provides one indication of which products most reasonably capture the burning regime. Our results have implications for the use of global burned area products in fire ecology, management and emissions applications.     

European regional efficiency and geographical externalities: a spatial nonparametric frontier analysis

This paper analyses region-level technical efficiency in nine European countries over the 1995–2007 period. We propose the application of a nonparametric conditional frontier approach to account for the presence of heterogeneous conditions in the form of geographical externalities. Such environmental factors are beyond the control of regional authorities, but may affect the production function. Therefore, they need to be considered in the frontier estimation. Specifically, a spatial autoregressive term is included as an external conditioning factor in a robust order-m model. Thus we can test the hypothesis of non-separability (the external factor impacts both the input–output space and the distribution of efficiencies), demonstrating the existence of significant global interregional spillovers into the production process. Our findings show that geographical externalities affect both the frontier level and the probability of being more or less efficient. Specifically, the results support the fact that the spatial lag variable has an inverted U-shaped non-linear impact on the performance of regions. This finding can be interpreted as a differential effect of interregional spillovers depending on the size of the neighboring economies: positive externalities for small values, possibly related to agglomeration economies, and negative externalities for high values, indicating the possibility of production congestion. Additionally, evidence of the existence of a strong geographic pattern of European regional efficiency is reported and the levels of technical efficiency are acknowledged to have converged during the period under analysis.     

Relationship between value of open space and distance from housing locations within a community

This research uses a sequence of hedonic spatial regressions for a metropolitan housing market in the Southeastern United States to explore a new procedure that establishes the relationship between the value attributable to open space and distance from housing locations (a “distance-decay function”) within a given community. A distance-decay function allows identification of the range of distance over which open space affects housing values and the estimation of a proxy for the value added to nearby houses resulting from hypothetical open space preservation. Ex post analyses of the open-space regression coefficients suggest marginal implicit price functions for three types of open space that decay as open space area increases with respect to house location. After controlling for other factors in the spatial hedonic model, simple distance-decay functional relationships were established between the implicit prices of developed open space, forest-land open space, and agriculture-wetland open space and the buffer radius of the open-space areas surrounding a given housing location. The proposed method may be useful for identifying the range over which preferences for different types of open space are exhibited.     

显著局部空间同位模式自动探测方法

局部空间同位模式挖掘旨在揭示多类地理事件在异质环境下的共生共存规律。已有的方法一方面需要模式筛选的频繁度阈值参数,另一方面需要区域探测的划分参数或聚类参数,参数的不合理设置会导致挖掘结果不可靠甚至出现错误。因此,提出了一种显著局部空间同位模式自动探测方法。首先,基于空间统计思想,采用非参数模式重建方法对空间同位模式进行显著性判别,将全局非显著空间同位模式作为进一步局部探测的候选模式;然后,借助自适应空间聚类方法提取每个候选模式的热点区域;最后,通过不断生长并测试每个热点区域,界定显著局部空间同位模式的有效边界,即空间影响域。通过实验与比较发现,该方法能够客观且有效判别空间同位模式的显著性,并且自适应地提取局部同位模式的空间分布结构,降低了现有方法参数设置的主观性。     

环嵩山地区史前聚落空间数据库设计

空间数据库是分析和挖掘区域复杂时空体系中的聚落分布和传承规律的基础。选取了对研究中原文明乃至中华文明起源具有代表意义的环嵩山地区,从该区域聚落空间数据特点、研究需求入手,分析了环嵩山地区史前聚落数据库的建设思路和数据库的主要功能,提出了聚落空间数据库的设计和实现方案,为该区域聚落考古研究提供数据和技术支持。     

地图学新产品与人类空间认知能力变革

地图是人类空间认知能力的图形化表述,地图如实地记载了人类思维发展的历程。同时人类的空间认知能力也随着地图学新产品的深入开发与普及应用不断地发生着巨大的变革,这是一个相互促进、相辅相成的过程。本文将从四组关键词的角度:1)现实空间、思维空问、虚拟空间;2)抽象思维、形象思维、创造思维;3)空间、时间;4)显性知识、缄默知识(意会知识),详细探讨地图学新产品对人类空间认知能力的重要影响。     

Ionospheric effects on differential GPS applications during auroral substorm activity

The use of the Global Positioning System (GPS) technology has become increasingly incorporated into airborne remote sensing applications over the past decade. While GPS positioning results may prove adequate for several applications at present, users should expect to experience degraded positioning accuracies over the next few years due to auroral substorm activity. Such degraded accuracies will arise from increased spatial decorrelation of ionosphere range delay errors in differential GPS applications, as the ionospheric activity increases during solar maximum. In this paper, the spatial decorrelation of ionospheric range delay is estimated during a substorm event and compared with “quiet” time values. Positional errors (in both vertical and horizontal measurements) in the range 60–80 cm RMSE were observed during a 1997 substorm event that is representative of the activity anticipated at solar maximum around the year 2000.     

网络约束下局部空间同位模式的扫描统计方法

空间同位模式挖掘旨在发现空间数据库中频繁发生在邻近位置的地理事件。由于空间异质性,地理事件在不同区域邻近出现的频繁程度亦存在差异,进而形成局部同位模式。现有局部同位模式挖掘方法多基于欧氏空间的平面假设,难以客观揭示网络空间（如城市道路）内地理事件间的局部同位规律,因此基于空间扫描统计思想,提出了一种网络约束下的局部同位模式挖掘方法。首先,发展了网络约束下的路径扩展方法,识别可能存在局部网络空间同位模式的候选路径;其次,基于网络约束下的二元泊松分布构建显著性检验的零模型,判别候选路径中局部网络空间同位模式的有效性。通过模拟实验与北京市出租车供需模式分析,发现该方法比现有方法得到的结果更精细、更客观,能够有效地挖掘网络约束下的局部同位模式。     

全球导航卫星信号体制知识产权分析

对全球导航卫星的信号体制进行了专利检索分析,综合考虑信号调制、多路复用等技术,分别以导航卫星的信号调制方式、信号生成方式、信号编码方式为检索领域,对全球导航卫星系统申请专利进行全面而系统的检索。在初检基础上,以空间段、星上技术为依据进行复检并深入分析,指出该领域未来发展方向。为我国未来全球导航卫星系统信号体制的设计提出知识产权规避措施和发展建议。     

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.     

Delineating managed land for reporting national greenhouse gas emissions and removals to the United Nations framework convention on climate change

Land use and management activities have a substantial impact on carbon stocks and associated greenhouse gas emissions and removals. However, it is challenging to discriminate between anthropogenic and non-anthropogenic sources and sinks from land. To address this problem, the Intergovernmental Panel on Climate Change developed a managed land proxy to determine which lands are contributing anthropogenic greenhouse gas emissions and removals. Governments report all emissions and removals from managed land to the United Nations Framework Convention on Climate Change based on this proxy, and policy interventions to reduce emissions from land use are expected to focus on managed lands. Our objective was to review the use of the managed land proxy, and summarize the criteria that governments have applied to classify land as managed and unmanaged. We found that the large majority of governments are not reporting on their application of the managed land proxy. Among the governments that do provide information, most have assigned all area in specific land uses as managed, while designating all remaining lands as unmanaged. This designation as managed land is intuitive for croplands and settlements, which would not exist without management interventions, but a portion of forest land, grassland, and wetlands may not be managed in a country. Consequently, Brazil, Canada and the United States have taken the concept further and delineated managed and unmanaged forest land, grassland and wetlands, using additional criteria such as functional use of the land and accessibility of the land to anthropogenic activity. The managed land proxy is imperfect because reported emissions from any area can include non-anthropogenic sources, such as natural disturbances. However, the managed land proxy does make reporting of GHG emissions and removals from land use more tractable and comparable by excluding fluxes from areas that are not directly influenced by anthropogenic activity. Moreover, application of the managed land proxy can be improved by incorporating additional criteria that allow for further discrimination between managed and unmanaged land.     

Simulated responses of soil organic carbon stock to tillage management scenarios in the Northwest Great Plains

Background  

Tillage practices greatly affect carbon (C) stocks in agricultural soils. Quantification of the impacts of tillage on C stocks at a regional scale has been challenging because of the spatial heterogeneity of soil, climate, and management conditions. We evaluated the effects of tillage management on the dynamics of soil organic carbon (SOC) in croplands of the Northwest Great Plains ecoregion of the United States using the General Ensemble biogeochemical Modeling System (GEMS). Tillage management scenarios included actual tillage management (ATM), conventional tillage (CT), and no-till (NT).