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1.
An empirical test of the competing destinations model 总被引：2，自引：0，他引：2
It has long been believed that properties of spatial structure have a strong effect on trip distribution, which thus leads
to a bias in the estimated distance decay parameters of spatial interaction models. This paper is an attempt to identify to
what extent the spatial structure effect affects the trip distribution and determine whether the incorporation of a term to
account for the relative location of destinations into the conventional gravity models, results in a model that can more correctly
represent the actual trip distribution. The main focus is on the comparison of the origin–specific estimates of the distance
decay parameter, calibrated from the traditional production-constrained model and the production-constrained competing destinations
model. The results show that the competing destinations model is superior to the conventional model in both reproducing the
interaction flows and giving behavioral explanation to the distance decay parameters, but the essential aim of the competing
destinations model to remove the map pattern from the distance decay parameters of the conventional model has not been identified.
Received: 5 September 2001 / Accepted: 17 June 2002
We are grateful to Gloria. A. Swieczkowski for kindly providing the migration data. The authors also gratefully acknowledge
the comments of the referees. 相似文献
2.
Modeling spatial autocorrelation in spatial interaction data: empirical evidence from 2002 Germany journey-to-work flows 总被引：3，自引：3，他引：0
Daniel A. Griffith 《Journal of Geographical Systems》2009,11(2):117-140
Since before the inception of work by Okabe, the intermingling of spatial autocorrelation (i.e., local distance and configuration)
and distance decay (i.e., global distance) effects has been suspected in spatial interaction data. This convolution was first
treated conceptually because technology and methodology did not exist at the time to easily or fully address spatial autocorrelation
effects within spatial interaction model specifications. Today, however, sufficient computer power coupled with eigenfunction-based
spatial filtering offers a means for accommodating spatial autocorrelation effects within a spatial interaction model for
modest-sized problems. In keeping with Okabe’s more recent efforts to dissemination spatial analysis tools, this paper summarizes
how to implement the methodology utilized to analyze a particular empirical flows dataset.
相似文献
| Daniel A. GriffithEmail: |
3.
Modeling network autocorrelation within migration flows by eigenvector spatial filtering 总被引：8，自引：5，他引：3
Yongwan Chun 《Journal of Geographical Systems》2008,10(4):317-344
Although the assumption of independence among interaction flows frequently is engaged in spatial interaction modeling, in
many circumstances it leads to misspecified models and incorrect inferences. An informed approach is to explicitly incorporate
an assumed relationship structure among the interaction flows, and to explicitly model the network autocorrelation. This paper
illustrates such an approach in the context of U.S. interstate migration flows. Behavioral assumptions, similar to those of
the intervening opportunities or the competing destinations concepts, exemplify how to specify network flows that are related
to particular origin–destination combinations. The stepwise incorporation of eigenvectors, which are extracted from a network
link matrix, captures the network autocorrelation in a Poisson regression model specification context. Spatial autocorrelation
in Poisson regression is measured by the test statistic of Jacqmin-Gadda et al. (Stat Med
16(11):1283–1297, 1997). Results show that estimated regression parameters in the spatial filtering interaction model become
more intuitively interpretable.
相似文献
| Yongwan ChunEmail: |
4.
地理加权回归分析是对普通线性回归模型的扩展,将空间数据的地理位置嵌入线性回归参数之中,以此来研究空间关系的空间异质性或空间非平稳性,属于局部空间分析模型.通过地理加权回归分析可以确定两种或两种以上变量间相互依赖的定量关系,局部区域的参数估计可以得到地理空间存在的不同空间关系,核函数的选取规则和带宽参数的验证方法也是本文研究的内容. 相似文献
5.
6.
ABSTRACT Spatial heterogeneity represents a general characteristic of the inequitable distributions of spatial issues. The spatial stratified heterogeneity analysis investigates the heterogeneity among various strata of explanatory variables by comparing the spatial variance within strata and that between strata. The geographical detector model is a widely used technique for spatial stratified heterogeneity analysis. In the model, the spatial data discretization and spatial scale effects are fundamental issues, but they are generally determined by experience and lack accurate quantitative assessment in previous studies. To address this issue, an optimal parameters-based geographical detector (OPGD) model is developed for more accurate spatial analysis. The optimal parameters are explored as the best combination of spatial data discretization method, break number of spatial strata, and spatial scale parameter. In the study, the OPGD model is applied in three example cases with different types of spatial data, including spatial raster data, spatial point or areal statistical data, and spatial line segment data, and an R “GD” package is developed for computation. Results show that the parameter optimization process can further extract geographical characteristics and information contained in spatial explanatory variables in the geographical detector model. The improved model can be flexibly applied in both global and regional spatial analysis for various types of spatial data. Thus, the OPGD model can improve the overall capacity of spatial stratified heterogeneity analysis. The OPGD model and its diverse solutions can contribute to more accurate, flexible, and efficient spatial heterogeneity analysis, such as spatial patterns investigation and spatial factor explorations. 相似文献
7.
A spatial panel ordered-response model with application to the analysis of urban land-use development intensity patterns 总被引：1，自引：0，他引：1
This paper proposes and estimates a spatial panel ordered-response probit model with temporal autoregressive error terms to analyze changes in urban land development intensity levels over time. Such a model structure maintains a close linkage between the land owner’s decision (unobserved to the analyst) and the land development intensity level (observed by the analyst) and accommodates spatial interactions between land owners that lead to spatial spillover effects. In addition, the model structure incorporates spatial heterogeneity as well as spatial heteroscedasticity. The resulting model is estimated using a composite marginal likelihood (CML) approach that does not require any simulation machinery and that can be applied to data sets of any size. A simulation exercise indicates that the CML approach recovers the model parameters very well, even in the presence of high spatial and temporal dependence. In addition, the simulation results demonstrate that ignoring spatial dependency and spatial heterogeneity when both are actually present will lead to bias in parameter estimation. A demonstration exercise applies the proposed model to examine urban land development intensity levels using parcel-level data from Austin, Texas. 相似文献
8.
Instability in spatial error models: an application to the hypothesis of convergence in the European case 总被引：1，自引：1，他引：0
This paper focuses on the hypothesis of stability in the mechanisms of spatial dependence that are usually employed in spatial
econometric models. We propose a specification strategy for which the first step is to solve a local estimation algorithm,
called the Zoom estimation. The aim of this stage is to detect problems of heterogeneity in the parameters and to identify
the regimes. Then we resort to a battery of formal Lagrange Multipliers to test the assumption of stability in the processes
of spatial dependence. The alternative hypothesis consists of the existence of several regimes in these parameters. A small
Monte Carlo serves to confirm the behaviour of this strategy in a context of finite size samples. As an illustration, we solve
an application to the case of the hypothesis of convergence for the per capita income in the European regions. Our results
reveal the existence of a strong Centre-Periphery dichotomy in which instability extends to all the elements (coefficients
of regression as well as parameters of spatial dependence) that intervene in a classical conditional β-convergence model. 相似文献
9.
《地理信息系统科学与遥感》2013,50(4):426-442
Based on remote sensing and GIS, this study models the spatial variations of urban growth patterns with a logistic geographically weighted regression (GWR) technique. Through a case study of Springfield, Missouri, the research employs both global and local logistic regression to model the probability of urban land expansion against a set of spatial and socioeconomic variables. The logistic GWR model significantly improves the global logistic regression model in three ways: (1) the local model has higher PCP (percentage correctly predicted) than the global model; (2) the local model has a smaller residual than the global model; and (3) residuals of the local model have less spatial dependence. More importantly, the local estimates of parameters enable us to investigate spatial variations in the influences of driving factors on urban growth. Based on parameter estimates of logistic GWR and using the inverse distance weighted (IDW) interpolation method, we generate a set of parameter surfaces to reveal the spatial variations of urban land expansion. The geographically weighted local analysis correctly reveals that urban growth in Springfield, Missouri is more a result of infrastructure construction, and an urban sprawl trend is observed from 1992 to 2005. 相似文献
10.
Francesco Lagona 《Journal of Geographical Systems》2002,4(1):53-68
Markov Random Fields, implemented for the analysis of remote sensing images, capture the natural spatial dependence between
band wavelengths taken at each pixel, through a suitable adjacency relationship between pixels, to be defined a priori. In most cases several adjacency definitions seem viable and a model selection problem arises. A BIC-penalized Pseudo-Likelihood
criterion is suggested which combines good distributional properties and computational feasibility for analysis of high spatial
resolution hyperspectral images. Its performance is compared with that of the BIC-penalized Likelihood criterion for detecting
spatial structures in a high spatial resolution hyperspectral image for the Lamar area in Yellowstone National Park.
Received: 9 March 2001 / Accepted: 2 August 2001 相似文献
11.
Seong-Hoon Cho Dayton M. Lambert Seung Gyu Kim Roland K. Roberts William M. Park 《Journal of Geographical Systems》2011,13(4):393-414
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. 相似文献
12.
A Network Model for Dispersion of Communicable Diseases 总被引：3，自引：0，他引：3
The spread of communicable diseases through a population is an intrinsic spatial and temporal process. This paper presents an individual‐based analytical framework for modeling the spatial and temporal heterogeneity in the disease transmission. The framework specifies a network model structure and six associated parameters. These parameters describe the properties of nodes, the properties of links, and the topology of the network. Through this model structure and associated parameters, this framework allows the representation of discrete individuals, individualized interactions, and interaction patterns in a network of human contact. The explicit representation of the spatial distribution and mobility of individuals in particular facilitates the modeling of spatial heterogeneity in the disease transmission. 相似文献
13.
Daniel A. Griffith 《Journal of Geographical Systems》2002,4(1):43-51
As either the spatial resolution or the spatial scale for a geographic landscape increases, both latent spatial dependence
and spatial heterogeneity also will tend to increase. In addition, the amount of georeferenced data that results becomes massively
large. These features of high spatial resolution hyperspectral data present several impediments to conducting a spatial statistical
analysis of such data. Foremost is the requirement of popular spatial autoregressive models to compute eigenvalues for a row-standardized
geographic weights matrix that depicts the geographic configuration of an image's pixels. A second drawback arises from a
need to account for increased spatial heterogeneity. And a third concern stems from the usefulness of marrying geostatistical
and spatial autoregressive models in order to employ their combined power in a spatial analysis. Research reported in this
paper addresses all three of these topics, proposing successful ways to prevent them from hindering a spatial statistical
analysis. For illustrative purposes, the proposed techniques are employed in a spatial analysis of a high spatial resolution
hyperspectral image collected during research on riparian habitats in the Yellowstone ecosystem.
Received: 25 February 2001 / Accepted: 2 August 2001 相似文献
14.
R. G. V. Baker 《Journal of Geographical Systems》2005,7(3-4):361-379
The Internet has been publicly portrayed as a new technological horizon yielding instantaneous interaction to a point where
geography no longer matters. This research aims to dispel this impression by applying a dynamic form of trip modelling to
investigate pings in a global computer network compiled by the Stanford Linear Accelerator Centre (SLAC) from 1998 to 2004.
Internet flows have been predicted to have the same mathematical operators as trips to a supermarket, since they are both
periodic and constrained by a distance metric. Both actual and virtual trips are part of a spectrum of origin–destination
pairs in the time–space convergence of trip time-lines. Internet interaction is very near to the convergence of these time-lines
(at a very small time scale in milliseconds, but with interactions over thousands of kilometres). There is a lag effect and
this is formalised by the derivation of Gaussian and gravity inequalities between the time taken (Δt) and the partitioning of distance (Δx). This inequality seems to be robust for a regression of Δt to Δx in the SLAC data set for each year (1998 to 2004). There is a constant ‘forbidden zone’ in the interaction, underpinned by
the fact that pings do not travel faster than the speed of light. Superimposed upon this zone is the network capacity where
a linear regression of Δt to Δx is a proxy summarising global Internet connectivity for that year. The results suggest that there has been a substantial
improvement in connectivity over the period with R
2 increasing steadily from 0.39 to 0.65 from less Gaussian spreading of the ping latencies. Further, the regression line shifts
towards the inequality boundary from 1998 to 2004, where the increased slope shows a greater proportional rise in local connectivity
over global connectivity. A conclusion is that national geography still does matter in spatial interaction modelling of the
Internet. 相似文献
15.
Manfred M. Fischer 《Journal of Geographical Systems》2002,4(3):287-299
In this paper we view learning as an unconstrained non-linear minimization problem in which the objective function is defined
by the negative log-likelihood function and the search space by the parameter space of an origin constrained product unit
neural spatial interaction model. We consider Alopex based global search, as opposed to local search based upon backpropagation
of gradient descents, each in combination with the bootstrapping pairs approach to solve the maximum likelihood learning problem.
Interregional telecommunication traffic flow data from Austria are used as test bed for comparing the performance of the two
learning procedures. The study illustrates the superiority of Alopex based global search, measured in terms of Kullback and
Leibler's information criterion.
Received: December 2001 / Accepted: August 2002
The author gratefully thanks Martin Reismann (Department of Economic Geography & Geoinformatics) for his valuable research
assistance. 相似文献
16.
17.
The Spatial Interaction Model proposed by Alonso as “Theory of Movements” offers an innovative specification of spatial origin-destination
flow models. Equations for flows between regions, total outflow from and total inflow to a region are linked by balancing
factors. This paper presents a consistent formulation of Spatial Interaction Models in the Wilson tradition and Alonso's Theory
of Movements. The paper is intended as an introduction to the model and a review of␣the state of the art. Besides it is argued
that simultaneous equation techniques are required to estimate the so-called systemic parameters.
Received: 21 May 2000 / Accepted: 18 January 2001 相似文献
18.
This study analyses the relationship between fire incidence and some environmental factors, exploring the spatial non-stationarity
of the phenomenon in sub-Saharan Africa. Geographically weighted regression (GWR) was used to study the above relationship.
Environment covariates comprise land cover, anthropogenic and climatic variables. GWR was compared to ordinary least squares,
and the hypothesis that GWR represents no improvement over the global model was tested. Local regression coefficients were
mapped, interpreted and related with fire incidence. GWR revealed local patterns in parameter estimates and also reduced the
spatial autocorrelation of model residuals. All the covariates were non-stationary and in terms of goodness of fit, the model
replicates the data very well (R
2 = 87%). Vegetation has the most significant relationship with fire incidence, with climate variables being more important
than anthropogenic variables in explaining variability of the response. Some coefficient estimates exhibit locally different
signs, which would have gone undetected by a global approach. This study provides an improved understanding of spatial fire–environment
relationships and shows that GWR is a valuable complement to global spatial analysis methods. When studying fire regimes,
effects of spatial non-stationarity need to be incorporated in vegetation-fire modules to have better estimates of burned
areas and to improve continental estimates of biomass burning and atmospheric emissions derived from vegetation fires. 相似文献
19.
Peter M. Atkinson 《International Journal of Applied Earth Observation and Geoinformation》2004,5(4):277-291
A simple approach for incorporating a spatial weighting into a supervised classifier for remote sensing applications is presented. The classifier modifies the feature-space distance-based metric with a spatial weighting. This is facilitated by the use of a non-parametric (k-nearest neighbour, k-NN) classifier in which the spatial location of each pixel in the training data set is known and available for analysis. A remotely sensed image was simulated using a combined Boolean and geostatistical unconditional simulation approach. This simulated image comprised four wavebands and represented three classes: Managed Grassland, Woodland and Rough Grassland. This image was then used to evaluate the spatially weighted classifier. The latter resulted in modest increase in the accuracy of classification over the original k-NN approach. Two spatial distance metrics were evaluated: the non-centred covariance and a simple inverse distance weighting. The inverse distance weighting resulted in the greatest increase in accuracy in this case. 相似文献
20.
基于表层卫星遥感观测的中深层海洋遥感对于了解海洋内部异常及其动力过程有重要意义。如何从现有的海洋表层遥感观测资料提取海洋内部关键动力环境信息场是具有挑战性的海洋遥感技术前沿。本文采用支持向量回归(SVR)方法,通过卫星遥感观测获取的多源海表参量(海表高度异常(SSHA)、海表温度异常(SSTA)、海表盐度异常(SSSA)和海表风场异常(SSWA)),选择最优参量输入组合,感知海洋次表层温度异常(STA),并用实测Argo数据作精度验证。结果表明SVR模型可准确估算全球尺度的STA(1000 m深度以浅);当SVR输入变量为2个(SSHA、SSTA)、3个(SSHA、SSTA、SSSA)、4个(SSHA、SSTA、SSSA、SSWA)时对应的平均均方差(MSE)分别为0.0090、0.0086、0.0087,平均决定系数(R2)分别为0.443、0.457、0.485。因此,除了SSHA和SSTA外,SSSA与SSWA的输入对SVR模型的估算有积极影响,有助于提高STA的估算精度。在全球增暖与减缓背景下,该研究可为从表层卫星遥感观测提取海洋内部热力异常信息研究提供重要技术支持,有利于拓展卫星对海观测范围。 相似文献