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.     

Abstract databases as information sources in economic geography

The limitations of existing information sources on mergers/acquisitions within the EU are a serious obstacle to the study of the restructuring of the European production system. The experience of using a computerized abstracting service as a source of information on mergers/acquisitions in the chemical industry is reviewed. This experience informs an assessment of the strengths and weaknesses of similar databases as information sources in economic geography. The proliferation of such databases, together with advances in text management software, suggests that their potential has not been fully appreciated. This potential is considerable in providing easy access to detailed factual information on economic events and processes at continental and global scales. It is, however, strictly limited when it comes to interpreting the corporate strategies and motivations responsible for these developments.     

中国西部亚高山针叶林资源查询系统

在搜集了中国西部亚高山针叶林主要分布区的行政区划信息及各针叶树种的分布信息的基础上，利用流行桌面GIS软件AreView V3．2的Avenue开发出中国西部亚高山针叶林资源查询系统，该系统既可以查询任意一个属或种的分布情况，又可以查询多个属、种(同属的和不同属)的交叉分布情况，并且查询结果可以立即反映到电子地图上，图例也能立即更新。并且在查询单一种的分布情况时，还可以查询其相关图像、文本、视频等信息，大大丰富了种的查询内容。同时，该系统也可查询任意一个或多个省级行政区或县级行政区的属种分布及其组合情况，从而使得行政区和亚高山针叶林属、种的双向查询变得非常简单。另外，本系统具有很强的可扩展性，可以轻松实现亚高山针叶树种、行政区及图像、文本、视频的扩充，并且扩充的内容可以立即使用，达到随时扩充、随时使用的目的。     

An exploration of spatial dispersion, pattern, and association of socio-economic functional units in an urban system

Hypotheses based on a set of socio-economic, cultural, and political functional units are examined. The geographic techniques employed in the study include spatial mean, standard distance, standard deviational ellipse, nearest neighbor analysis, and spatial analysis on a network: cross K function and network cross K function. The study not only explores the spatial pattern, distribution, and association of key socio-economic, cultural, and political units which could reveal internal structures and activities of an urban system, but also demonstrates a number of operational procedures that permit applications of traditional and advanced spatial analysis approaches in the study of real urban systems.     

基于地理信息技术油松(Pinus tabuliformis)根长密度估算及空间分布特征分析

 定量研究植物根系的生长发育及时空分布特征是构建根系吸水模型、计算根系吸水量的基础。选择陇中黄土高原定西安家沟流域为研究区,对研究区内的油松(Pinus tabuliformis)吸水根系空间分布特征进行研究。利用剖面法获取不同空间点上的吸水根系,基于地理信息系统技术估算油松吸水根系的根长密度。结果表明：在水平方向上,油松根长密度呈二次曲线分布,吸水根系主要集中在0～400 cm范围内;在垂直方向上,油松吸水根系呈对数分布,吸水根系主要集中在0～100 cm范围内。并构建了油松吸水根系的二维空间分布方程。     

A relative spatial access assessment approach for analyzing potential spatial access to colorectal cancer services in Texas

The usefulness of gravity-based spatial access models is limited because of the uncertainty introduced by the range of values of the impedance coefficient. To solve this problem, this paper proposes the concept of spatial access ratio (SPAR) derived from the enhanced 2-step floating catchment area (E2SFCA) method — a recent extension of the gravity model — to assess potential spatial access. First, a sensitivity assessment is conducted to verify the effectiveness of SPAR and its advantages in overcoming the uncertainty problem. Then, the E2SFCA method and the shortest travel time method are employed to measure potential spatial access to colorectal cancer (CRC) prevention and treatment services in Texas based on data at the census tract level. The socio-demographic and geographic distributions of potential spatial access to CRC services are also examined. The sensitivity assessment reveals substantial fluctuations in the values of the spatial access index calculated directly by the E2SFCA method under different values of the impedance coefficient. However, the values of SPAR remain stable under different values of the coefficient. A comparative analysis indicates that potential spatial access to primary care physicians (PCPs), CRC screening facilities, and oncologists varied among different racial/ethnic and socioeconomic population groups as well as in different geographic regions in Texas. Non-Hispanic blacks, Asians, and people in affluent areas had a geographical advantage in accessing CRC services than other groups. The urban/rural difference was more obvious and serious than those of different racial/ethnic groups and groups with different socio-economic statuses, as metropolitan residents had more than three times the potential spatial access than isolated rural residents.     

Fundamental causes and spatial heterogeneity of deforestation in Legal Amazon

This study explores the main direct and underlying causes of deforestation in Brazil's Legal Amazon region by considering spatial differences. The computation of localized parameters is based on geographically weighted regression (GWR). The novelty of this paper lies in its incorporation of economic, rather than Euclidean, distances into the GWR. Economic distances are measured by travel time, sourced from Google Inc. A global approach revealed several important factors that affect deforestation, including: rural population, GDP (suggesting a U-shaped environmental Kuznets curve), forest stock, cattle ranching, timber value, and road networks (both official and unofficial). Local analysis uncovered patterns not seen under global models, especially in the state of Pará. Most notably, crop cultivation was found to accelerate deforestation in southeastern Pará and northeastern Mato Grosso, while in some regions (especially in the northeastern corner of Pará), the area covered by crop plantations was negatively associated with deforestation. For Pará, rural credit constraints, larger territories designated as sustainable use areas and indigenous lands, and higher levels of precipitation inhibit deforestation. Further, rural population has a very heterogeneous impact on deforestation across Legal Amazon: it is not a significant factor of deforestation in northern Pará and Amapá, but it has a relatively strong effect in the western parts of Mato Grosso and Rondônia. Also, official and illegal roads create significantly more pressure on forests in remote regions compared to developed areas. Finally, the use of economic distances, as opposed to Euclidean distances, leads to notably different GWR results.     

Modelling spatial patterns of urban growth in Africa

The population of Africa is predicted to double over the next 40 years, driving exceptionally high urban expansion rates that will induce significant socio-economic, environmental and health changes. In order to prepare for these changes, it is important to better understand urban growth dynamics in Africa and better predict the spatial pattern of rural-urban conversions. Previous work on urban expansion has been carried out at the city level or at the global level with a relatively coarse 5–10 km resolution. The main objective of the present paper was to develop a modelling approach at an intermediate scale in order to identify factors that influence spatial patterns of urban expansion in Africa. Boosted Regression Tree models were developed to predict the spatial pattern of rural-urban conversions in every large African city. Urban change data between circa 1990 and circa 2000 available for 20 large cities across Africa were used as training data. Results showed that the urban land in a 1 km neighbourhood and the accessibility to the city centre were the most influential variables. Results obtained were generally more accurate than results obtained using a distance-based urban expansion model and showed that the spatial pattern of small, compact and fast growing cities were easier to simulate than cities with lower population densities and a lower growth rate. The simulation method developed here will allow the production of spatially detailed urban expansion forecasts for 2020 and 2025 for Africa, data that are increasingly required by global change modellers.     

Testing for similarity in area-based spatial patterns: A nonparametric Monte Carlo approach

The proliferation of geographic information systems and point data has made the analysis of spatial point patterns of increasing interest in a variety of disciplines. Though early forms of spatial point pattern analysis were limited in their scope, current forms have been developed that provide significant insight into underlying data generating processes. This paper builds on the spatial point pattern analysis literature through the development of a nonparametric Monte Carlo spatial point pattern test (and corresponding index) to measure the degree of similarity between two spatial point patterns. The applicability of this new test is then shown using crime data.     

Integrating algebraic multigrid method in spatial aggregation of massive trajectory data

The advanced technologies in location-based services and telecom have yield large volumes of trajectory data. Understanding these data effectively requires intuitive yet accurate visual analysis. The visual analysis of massive trajectory data is challenged by the numerous interactions among different locations, which cause massive clutter. This paper presents a new methodology for visual analysis by integrating algebraic multigrid (AMG) method in data aggregation. The non-parametric method helps to build a multi-layer node representation from a graph which is extracted from trajectory data. The comparison with AMG and other methods shows that AMG method is more advanced in both the spatial representation and the importance of nodes. The new method is tested with real-world dataset of cell-phone signalling records in Beijing. The results show that our method is suitable for processing and creating abstraction of massive trajectory dataset, revealing inherent patterns and creating intuitive and vivid flow maps.     

基于ESDA的河南艾滋病空间格局

以2008 年河南省地级市艾滋病分布数据为基础,利用ESDA空间数据挖掘的方法,对河南省艾滋病空间格局进行全面分析。结果表明：① 综合考虑河南省18 个地级市艾滋病确诊人数数据的空间自相关分析结果和不同传播途径感染单因素空间格局的结果,将河南艾滋病感染区域分为以FPD感染途径为主的豫南、豫东南高度流行区和以高危行为传播途径为主的豫北低度流行区;② 河南省艾滋病感染途径分布中,FPD感染、母婴感染、异性传播感染和注射吸毒传播具有明显的空间正相关,而同性传播途径与输血感染则显示出较大的空间随机分布特征;③ 与FPD 感染存在空间正相关的为注射吸毒传播、异性传播以及母婴传播方式,输血、同性以及其他方式传播途径与FPD感染途径分布不存在空间相关性。     

Interaction between scale and scheduling choices in simulations of spatial agents

Spatial simulations are a valuable tool in understanding dynamic spatial processes. In developing these simulations, it is often required to make decisions about how to represent features in the environment and how events unfold in time. These spatial and temporal choices have been shown to significantly alter model outcomes, yet their interaction is less well understood. In this paper, we make use of a simple group foraging model and systematically vary how features are represented (cell size of the landscape) as well as how events unfold in time (order in which foragers take action) to better understand their interaction. Our results show similar nonlinear responses to changes in spatial representation found in the literature, and an effect of the order in which agents were processed. There was also a clear interaction between how features are represented and how events unfold in time, where, under certain environmental representations results were found to be more sensitive to the order in which individuals were processed. Furthermore, the effects of feature representation, scheduling of agents, and their interaction were all found to be influenced by the heterogeneity of the spatial surface (food), suggesting that the statistical properties of the underlying spatial variable will additionally play a role. We suggest that navigating these interactions can be facilitated through a better understanding of how these choices affect the decision landscape(s) on which agents operate. Specifically, how changes to representation affect aggregation and resolution of the decision surface, and thereby the degree to which agents interact directly or indirectly. We suggest that the challenges of dealing with spatial representation, scheduling, and their interaction, while building models could also present an opportunity. As explicitly including alternate representations and scheduling choices during model selection can aid in identifying optimal agent–environment representations. Potentially leading to improved insights into the relationships between spatial processes and the environments in which they occur.     

A density-based approach for detecting network-constrained clusters in spatial point events

Existing spatial clustering methods primarily focus on points distributed in planar space. However, occurrence locations and background processes of most human mobility events within cities are constrained by the road network space. Here we describe a density-based clustering approach for objectively detecting clusters in network-constrained point events. First, the network-constrained Delaunay triangulation is constructed to facilitate the measurement of network distances between points. Then, a combination of network kernel density estimation and potential entropy is executed to determine the optimal neighbourhood size. Furthermore, all network-constrained events are tested under a null hypothesis to statistically identify core points with significantly high densities. Finally, spatial clusters can be formed by expanding from the identified core points. Experimental comparisons performed on the origin and destination points of taxis in Beijing demonstrate that the proposed method can ascertain network-constrained clusters precisely and significantly. The resulting time-dependent patterns of clusters will be informative for taxi route selections in the future.     

An exploratory spatial analysis of western medical services in Republican Beijing

In this paper, we reconstructed the spatial organization of Western medical services in Beijing city during the Republican period using a recently completed Republican Beijing GIS dataset. The primary objective is to explore the utility of spatial analytical methods, such as hotspot analysis and geographically weighted regression (GWR), in studying the spatial patterns of Western medical services. Our study is successful in depicting the spatial structure of Western medical services in the city. In addition, our analysis offers a preliminary but holistic view of the spatial relationships between Western medical services in the city and traditional Chinese medicine, population distribution, temple locations and industry-commerce patterns.     

The spatial structure of employment in the metropolitan region of Tokyo: A scale-view

Despite Tokyo’s size and position in the global urban system, studies of its employment distribution have been few. This research identified the locations and ranks of the employment centers in the entire metropolitan region and analyzed the characteristics of the economic sectors in those centers by using fine scale economic census data. Minimum sizes for a center were explored at a range from as small as 250 by 250 m to 3,000 by 3,000 m, and the Local Indicators of Spatial Association technique was utilized for detecting centers in different ranks. The results showed that, from a regional view, Tokyo has a typical monocentric or concentric spatial structure. However, from a fine scale view, the distribution was revealed to be expanding along the railroads to the suburbs, and both the main central business district and the suburbs are polycentric. Through this research, the authors argue that rather than using thresholds of total employment and minimum densities as seen in the previous studies, centers can be effectively detected by comparing with the surrounding areas at different scales. This “scale-view” approach of center identification may provide a new perspective to other metropolitan regions of the world as well.     

The price for subway access: spatial econometric modelling of office rental rates in London

ABSTRACT

The relative location of commercial properties as the distance to the metro station as well as its absolute location as the address in the Central Business District (CBD) is expected to influence its valuation. We apply the advanced spatial econometric methodology, based on micro geo-located data, to the office rental transaction data from London in 2015. We use different spatial information on the local and global neighbourhood in order to estimate the spatial hedonic valuation model and test the strength of spatial spillovers and location effects on office fees. For offices in London in 2015, geographical factors may raise the price by 50%, as every next 100 m to the metro cost an additional 0.7 ? per ft² per year and the CBD location raises the cost for ca. 20 ? per ft² per year.     

双核型空间结构模式及其在世界地理研究中的应用

作者在我国沿海和沿江地区的空间结构进行深入研究的基础上,发现了一种新的空间结构模式,即双核型空间结构模式。本文在对这一模式进行理论分析的基础上,初步探讨也该模式在世界地理研究中的应用：①指出全球最大的双核结构由全球最大的港口城市鹿特丹和全球最大的内河港口城市杜伊斯堡所组成;②作为推动美国区域经济发展的最重要的一个轴线－“美国经济地理横轴”,是一由芝加哥和纽约组成的双核结构;③新旧首都构成一特殊的双