北京房地产中介空间格局演化特征及驱动因素分析

本文基于微观企业数据,采用泰森多边形、核密度及空间自相关方法,多尺度考察2001-2010年北京房地产中介门店空间格局演化特征,通过构建地理加权回归模型（GWR）,探究2010年房地产中介空间格局驱动因素。研究表明：①房地产中介集中分布在城六区,数量增速最快功能区是城市发展新区。②空间格局不均衡性增大,空间集聚态势加强。③集聚主核心点出现由"东城"向"朝阳"方向的东推位移,集聚程度加深,主核心区团块面积扩大,次级核心点增多,"两轴、两带、多中心"基本版图形成。④空间集聚现象明显,热点区域扩张,但始终集中在区域中心。⑤驱动效应最大的因素为常住人口,多种驱动因素效应均比较强的街区（乡镇）基本上属于各区县经济水平相对发达的区域。此外,政府政策对其影响也较大。     

Evaluating Polygon Overlay to Support Spatial Optimization Coverage Modeling

Minimizing costs and maximizing coverage are important goals in many planning contexts. These goals often necessitate an abstraction of a continuous demand region, resulting in potential errors when applying traditional coverage models. To reduce coverage errors caused by spatial abstraction, a number of spatial representation schemes have been proposed and applied. A new representation scheme using polygon overlay recently received much attention because potentially it can eliminate representation errors in coverage modeling. However, this overlay‐based approach is computationally challenging in terms of both the generation of demand units and the complexity of the resulting coverage model. This article investigates the operational and computational challenges of polygon overlay for delineating continuous demand in coverage models, an issue that has yet to be fully explored. We present a theoretical evaluation of the computational complexity associated with representation using polygon overlay in coverage modeling. Evaluations of two study regions provide empirical support for the computational complexity analysis. The analysis results provide insight regarding expected problem size and computational requirements if polygon overlay is relied upon to delineate demand unit boundaries in coverage modeling. La minimización de costos y la maximización de la cobertura espacial son objetivos importantes en muchos contextos de planificación. Estas metas a menudo requieren una abstracción de una región continua de demanda , dando lugar a posibles errores en la aplicación de modelos de cobertura tradicionales. Para reducir los errores de cobertura provocadas por la abstracción, la comunidad académica ha propuesto y aplicado una serie de esquemas de representación espacial. Recientemente un nuevo esquema de representación que utiliza la superposición de polígonos ha recibido mucha atención porque potencialmente puede eliminar los errores de representación en el modelado de la cobertura. Sin embargo, este enfoque es computacionalmente difícil, tanto en términos de la generación de unidades de demanda, como en la complejidad del modelo de cobertura resultante. Este artículo investiga los retos operacionales y de cómputo de la superposición de polígonos para delinear la región continua de demanda en los modelos de cobertura, un problema que aún no se ha explorado a fondo. Se presenta una evaluación teórica de la complejidad computacional asociada a la representación mediante superposición de polígonos en el modelado de cobertura espacial. Se presentan evaluaciones de dos regiones de estudio como apoyo empírico para el análisis de la complejidad computacional. Los resultados del análisis proporcionan información sobre el tamaño del problema esperado y los requerimientos computacionales en los casos en que el método de superposición de polígonos es usado para delinear límites de la región de demanda para el modelado de cobertura espacial 最小成本和最大区域覆盖是许多规划情境研究中的重要目标。实现这些目标通常需要对连续需求区域进行抽象,而这又会导致在应用传统覆盖模型时出现潜在误差。为减小由空间抽象引起的覆盖误差,已提出了一系列空间表达方案并得到应用。一种新型的利用多边形覆盖的表达方案,因其或可消除覆盖建模过程的表达误差,近来得到较多的关注。然而,这种基于覆盖的方法在需求单元生成与覆盖模型结果复杂性等方面面临着计算挑战。本文提出了一种在覆盖建模中采用多边形叠加表征的计算复杂度的理论评估方法。两个研究区域的评估为计算复杂度分析提供了经验支撑,如果在覆盖建模中多边形叠加依赖于描述需求单元边界时,该分析结果有助于深入考察预期问题规模大小及计算需求。     

Historiographical layers in the relationship between science and technology

Abstract

This paper aims to examine historiographical layers in the historical narrative on the relationship between science and technology, a topic which has been exhaustively discussed without consensus by both historians of science and technology. I will first examine two extreme positions concerning this issue, and analyze the underlying historiographical standpoints behind them. I will then show that drawing implications for the relationship between science and technology from a few case studies is frequently misleading. After showing this, I .will move to the “macrohistory” of the relationship between science and technology, which reveals a long process in which the barriers between them gradually became porous. Here, I will examine the historical formation of three different kinds of “boundary objects” between science and technology, which facilitated their interactions by making their borders more permeable: instruments as a material boundary object; new institutions, laboratories, and departments as a spatial boundary or boundary space; and new mediators as a human hybrid.     

Parameterization and Visualization of the Errors in Areal Interpolation

Areal interpolation involves the transfer of data from one zonation of a region to another, where the two zonations of space are geographically incompatible. By its very nature this process is fraught with errors. However, only recently have there been specific attempts to quantify these errors. Fisher and Langford (1995) employed Monte Carlo simulation methods, based on modifiable areal units, to compare the errors resulting from selected areal interpolation techniques. This paper builds on their work by parameterizing and visualizing the errors resulting from the areal weighting and dasymetric methods of areal interpolation. It provides the basis for further research by developing the methodology to produce predictive models of the errors in areal interpolation. Random aggregation techniques are employed to generate multiple sets of source zones and interpolation takes place from these units onto a fixed set of randomly generated target zones. Analysis takes place at the polygon, or target zone level, which enables detailed analysis of the error distributions, basic visualization of the spatial nature of the errors and predictive modeling of the errors based on parameters of the target zones. Correlation and regression analysis revealed that errors from the areal weighting technique were related to the geometric parameters of the target zones. The dasymetric errors, however, demonstrated more association with the population or attribute characteristics of the zones. The perimeter, total population, and population density of the target zones were shown to be the strongest predictive parameters.     

空间视角下农村社会关系变迁研究——以山东省枣庄市L村“村改居”为例

以空间为视角,以"村改居"为案例,说明了农村居住空间改观对农村社会关系嬗变的影响。首先,农民集中居住后,公寓楼的阶梯设置造成了垂直方向上行走的高差区分。由于楼梯对身体的规训以及安全性差,导致高差间交往的不便;其次,公寓楼单元化的"门-房"一体结构导致公共空间与私人空间的界限明朗化,缺少了半私密性空间的过渡,空间闭合与开放的矛盾变得不可调和,农村居民户内交往意愿降低;再次,公共空间相对于私人生活区的外设与远离,导致公共空间使用率下降。农村居民间规模化和混合型交往缺失,公共空间没有承担起公共交往的功能。农村社会关系因此从"分散的不规则集中"变为"集中的均匀分散"。     

不同尺度乡村聚落景观的空间集聚性分形特征及影响因素分析——以江苏省镇江市为例

为分析乡村聚落景观空间结构的分形特征,以GIS为平台、土地调查数据为主要数据源,通过集聚维数,分别在县域、镇域和村域三个尺度上,分析了聚落体系空间集聚性的分形特征及其影响因素。结果表明:乡村聚落景观在空间结构上具有比较明显的分形特征;空间集聚性存在不同尺度分形特征的差异,同时丹阳市的镇和中心村的集聚性则明显小于句容市;不同尺度聚落集聚性出现明显的尺度特征,即中心村 > 镇 > 县城;地形是影响乡村聚落景观空间集聚性分形特征,表现为腹地内坡度越大,集聚性越强;交通网络的完善降低了乡村聚落对空间直线距离的依赖性。     

Accuracy of Count Data Estimated by the Point‐in‐Polygon Method

This paper analyzes the accuracy of count data estimated by the point‐in‐polygon method. A point‐in‐polygon interpolation model is proposed, based on a stochastic distribution of points and the target zone, in order to represent a variety of situations. The accuracy of estimates is numerically investigated in relation to the size of the target zone and the distribution of points, and the optimal location of representative points is discussed. The major findings of this paper are as follows: (1) though the relative accuracy of estimates generally increases monotonously with the size of the target zone, the monotoneity is often disturbed by the periodicity in the spatial configuration of source zones and the point distribution; (2) the point‐in‐polygon and the areal weighting interpolation methods have the same accuracy of estimates when points are concentrated in less than 12–15 percent area around the representative point in source zones; (3) the point‐in‐polygon method is not so robust against the locational gap between points and the representative point; (4) the optimal location of representative points is given by the spatial median of points.     

THE IMPACTS OF TRANSPORTATION INFRASTRUCTURE ON PROPERTY VALUES: A HIGHER-ORDER SPATIAL ECONOMETRICS APPROACH

ABSTRACT.  We evaluate the impacts of enhanced transportation systems on property values for U.S. manufacturing firms, allowing for higher-order spatial error correlation. We use a state-level model of production cost and input demand that recognizes the productive contribution of public transportation infrastructure stocks. Our findings include significant impacts on property shadow values and input composition from both public highway and airport investment. We also find that these effects have a spatial dimension that depends on the proximity of the transport system; at least one and as many as three spatial error lags are significant in our estimating equations. Further, recognizing production growth from transportation system improvements augments the associated incentives for private capital investment.     

Endogenous Fractal Dynamics at Alpine Treeline Ecotones

Many of the hypotheses proposed to explain ecotones are based on an individualistic paradigm and are essentially static. While they include local feedbacks, they ignore the interactions between pattern and process across scales. These feedbacks in ecotones are nonlinear in nature and complicate the relations of pattern and process in vegetation, which, combined with observed fractal patterns, suggests a complexity science approach to investigate ecotone dynamics. A cellular automaton of alpine treeline, including nonlinear, local, positive, and negative feedbacks in tree establishment and mortality, as reported in field studies, is used as a model system. Fourier analysis of simulated alpine treelines shows fractal patterns across the treeline landscapes, which are created by patch development. The temporal evolution of the spatial pattern is also fractal. Landscape scale linear correlations between spatial pattern and the rate of advance of trees into tundra arise from localized nonlinear interactions. A tree-patch-landscape scale explanation of pattern–process interaction is proposed in which the endogenous feedbacks determine the spatial and temporal fractal properties of the ecotone. The simulated treeline advance exhibits self-organized complexity and may indicate a potential strategy for monitoring change.     

The accumulation of stochastic copying errors causes drift in culturally transmitted technologies: Quantifying Clovis evolutionary dynamics

The archaeological record is the empirical record of human cultural evolution. By measuring rates of change in archaeological data through time and space it is possible to estimate both the various evolutionary mechanisms that contribute to the generation of archaeological variation, and the social learning rules involved in the transmission of cultural information. Here we show that the recently proposed accumulated copying error model [Eerkens, J.W., Lipo, C.P., 2005. Cultural transmission, copying errors, and the generation of variation in material culture and the archaeological record. Journal of Anthropology archaeology 24, 316–334.] provides a rich, quantitative framework with which to model the cultural transmission of quantitative data. Using analytical arguments, we find that the accumulated copying error model predicts negative drift in quantitative data due to the proportional nature of compounded copying errors (i.e., neutral mutations), and the multiplicative process of cultural transmission. Further, we find that the theoretically predicted rate of drift in long-lived technologies is remarkably close to the observed reduction of Clovis projectile point size through time and space across North America.     

La dynamique spatiale des marchés locaux de l'emploi au sein du champ métropolitain de Québec, 1981–2001

Spatial Dynamics of Local Labour Markets in the Québec City Metropolitan Field, 1981–2001
This research analyzes the spatial dynamics (from 1981 to 2001) of local labour markets at an infra-regional scale, namely the Québec metropolitan field, with particular emphasis on interactions between the metropolitan region and its hinterland. It seeks to better understand the factors underlying this evolution. Centrographic analyses were performed to characterize the evolution of the spatial configuration of local labour markets (displacement of gravity centre, shape change, evolution of dispersion indices and of workforce preferential distribution axes). Between 1981 and 2001, almost all employment poles experienced an increase in the mean-distance tied to their recruitment area, that being particularly true for peri-metropolitan poles which employ an increasing part of their workforce inside the metropolitan labour basin, where a more qualified and diversified labour force is available; thus, giving rise to significant reverse commuting. In addition to the influence of distance to metropolitan area, a multiple regression model shows that factors such as manufacturing specialization and employment growth within job centres also play a crucial role in the spatial dynamics of local labour markets in the Québec City metropolitan field.     

Investigating DEM Error Patterns by Directional Variograms and Fourier Analysis

In many spatial analyses and GIS applications, a Digital Elevation Model (DEM) is often used to derive a variety of new variables and parameters. Previous research shows that the accuracy of derived variables is affected, not merely by the magnitude of DEM errors and the algorithms applied to derive these variables, but also by the spatial structure of DEM errors. However, the lack of knowledge and understanding of the spatial structure of DEM errors often handicaps the analysis of error propagation. This paper investigates the spatial autocorrelation and anisotropic pattern of DEM error by using directional variograms in the spatial domain and Fourier analysis in the frequency domain. Based on an empirical study, it is concluded that the spatial autocorrelation pattern of DEM errors is anisotropic and scale-dependent, and that the maximum direction and range of the autocorrelation depends upon the orientation and wavelength of the terrain features. For a smooth terrain, the magnitude of DEM errors is correlated to surface slope. For a rugged terrain, the elevation values in DEMs tend to be underestimated in ridges, and overestimated in valleys, but the correlation between the DEM error and surface slope is quite low.     

Geostatistical Prediction and Simulation of Point Values from Areal Data

The spatial prediction and simulation of point values from areal data are addressed within the general geostatistical framework of change of support (the term support referring to the domain informed by each measurement or unknown value). It is shown that the geostatistical framework (i) can explicitly and consistently account for the support differences between the available areal data and the sought-after point predictions, (ii) yields coherent (mass-preserving or pycnophylactic) predictions, and (iii) provides a measure of reliability (standard error) associated with each prediction. In the case of stochastic simulation, alternative point-support simulated realizations of a spatial attribute reproduce (i) a point-support histogram (Gaussian in this work), (ii) a point-support semivariogram model (possibly including anisotropic nested structures), and (iii) when upscaled, the available areal data. Such point-support-simulated realizations can be used in a Monte Carlo framework to assess the uncertainty in spatially distributed model outputs operating at a fine spatial resolution because of uncertain input parameters inferred from coarser spatial resolution data. Alternatively, such simulated realizations can be used in a model-based hypothesis-testing context to approximate the sampling distribution of, say, the correlation coefficient between two spatial data sets, when one is available at a point support and the other at an areal support. A case study using synthetic data illustrates the application of the proposed methodology in a remote sensing context, whereby areal data are available on a regular pixel support. It is demonstrated that point-support (sub-pixel scale) predictions and simulated realizations can be readily obtained, and that such predictions and realizations are consistent with the available information at the coarser (pixel-level) spatial resolution.     

A Simulation Study on Specifying a Regression Model for Spatial Data: Choosing between Autocorrelation and Heterogeneity Effects

In this simulation study, regressions specified with autocorrelation effects are compared against those with relationship heterogeneity effects, and in doing so, provides guidance on their use. Regressions investigated are: (1) multiple linear regression, (2) a simultaneous autoregressive error model, and (3) geographically weighted regression. The first is nonspatial and acts as a control, the second accounts for stationary spatial autocorrelation via the error term, while the third captures spatial heterogeneity through the modeling of nonstationary relationships between the response and predictor variables. The geostatistical‐based simulation experiment generates data and coefficients with known multivariate spatial properties, all within an area‐unit spatial setting. Spatial autocorrelation and spatial heterogeneity effects are varied and accounted for. On fitting the regressions, that each have different assumptions and objectives, to very different geographical processes, valuable insights to their likely performance are uncovered. Results objectively confirm an inherent interrelationship between autocorrelation and heterogeneity, that results in an identification problem when choosing one regression over another. Given this, recommendations on the use and implementation of these spatial regressions are suggested, where knowledge of the properties of real study data and the analytical questions being posed are paramount.     

A new Umkehr inversion algorithm

In this paper, we describe a new Umkehr algorithm for the estimation of the vertical ozone profile from observations of the umkehr effect of Götz. The algorithm uses new ozone absorption coefficients, which result in a change in the scale of measurement, and their temperature dependence. Better a priori ozone profiles are possible because of the very large increase in the archive of ozone profile measurements. The characterization of the retrieved ozone profiles and the error analysis follow the recipe of Rodgers. The differences between the new algorithm and the old 1964 algorithm are discussed. For both algorithms, only the retrievals for layers 4–8 (1943 km) are suitable for use in ozone trend analysis. For these layers, the retrieval resolution is trivially improved over that in the 1964 algorithm. For other layers, the retrievals either respond mainly to real ozone changes in adjacent or distant layers, or are a function of low-amplitude changes over a very broad range of layers. The effect of atmospheric aerosol on the retrieved profiles has been determined, and found to be generally similar to the effect for the old algorithm, but differing in some details.     

flowAMOEBA: Identifying Regions of Anomalous Spatial Interactions

This study aims at developing a data‐driven and bottom‐up spatial statistic method for identifying regions of anomalous spatial interactions (clusters of extremely high‐ or low‐value spatial flows), based on which it creates a spatial flow weights matrix. The method, dubbed flowAMOEBA, upgrades a multidirectional optimum ecotope‐based algorithm (AMOEBA) from areal data to spatial flow data through a proper spatial flow neighborhood definition. The method has the potential to dramatically change the way we study spatial interactions. First, it breaks the convention that spatial interaction data are always collected and modeled between spatial entities of the same granularity, as it delineates the OD region of anomalous spatial interactions, regardless of the size, shape, scale, or administrative level. Second, the method creates an empirical spatial flow weights matrix that can handle network autocorrelation embedded in spatial interaction modeling, thus improving related policy‐making or problem‐solving strategies. flowAMOEBA is tested and demonstrated on a synthetic data set as well as a county‐to‐county migration data set.     

A spherical model of wave generation in the atmosphere by the solar terminator

With the help of equations written in a spherical coordinate system moving with the terminator and which describe acoustic-gravitational wave generation, the general solution for pressure field perturbation is obtained. A spherical model of the solar terminator is presented. Types of nonstationary waves generated by the terminator when it crosses atmosphere inhomogeneities have been studied.     

Designs for Detecting Spatial Dependence

The aim of this article is to find optimal or nearly optimal designs for experiments to detect spatial dependence that might be in the data. The questions to be answered are: how to optimally select predictor values to detect the spatial structure (if it is existent) and how to avoid to spuriously detect spatial dependence if there is no such structure. The starting point of this analysis involves two different linear regression models: (1) an ordinary linear regression model with i.i.d. error terms—the nonspatial case and (2) a regression model with a spatially autocorrelated error term, a so-called simultaneous spatial autoregressive error model. The procedure can be divided into two main parts: The first is use of an exchange algorithm to find the optimal design for the respective data collection process; for its evaluation an artificial data set was generated and used. The second is estimation of the parameters of the regression model and calculation of Moran's I , which is used as an indicator for spatial dependence in the data set. The method is illustrated by applying it to a well-known case study in spatial analysis.     

基于GIS的农村医疗设施空间可达性分析——以河南省兰考县为例

针对农村医疗设施空间分布的公平性问题,提出了使用GIS技术和空间可达性指标评估医疗设施的区域分布特征。以兰考县乡级以上卫生院为例,建立人口分布、医疗设施位置、行政区域等地理数据库,选择人均医疗资源分配、就医的最近距离、选择医院的机会、重力模型及改进的重力模型5个空间可达性模型,计算了各乡镇、各行政村的医疗设施可达性指标,并制作了相应的专题地图;在此基础上对医疗设施的空间布局进行了分析。空间可达性指标全面地反映医疗设施的空间分布特征,鉴别出资源分配较薄弱的区位,是农村医疗改革中设施规划和资源分配的重要依据。     

Heterogeneity of Attribute Sampling Error in Spatial Data Sets

This paper considers the standard error of the estimate of the mean of a spatially correlated variable in the case where data are obtained by a process of random sampling. Two distinct mean estimation problems are identified: estimating the area mean and estimating the population mean. Methods are described for obtaining standard error estimates in the two cases and, within the limits of publicly available information, the methods are implemented on average household income data at the census tract scale for Syracuse, New York. The purpose of the paper is to draw attention to issues of data precision in relation to sampled geographic information on averages and in particular to consider the problems of estimating standard errors using such data. The paper also examines the extent to which standard errors vary between census tracts.