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About 10,000 papers for “Spatial dependence”

Spatial dependence test.

Hao Wang (39217), Zhiying Huang (591505), Yanqing Liang (11160416) et al. · Figshare · 2023

<div><p>Urban infrastructure resilience is an important perspective for measuring the development quality of resilient cities and an important way to measure the level of infrastructure development. This paper uses the kernel density estimation, exploratory spatial data analysis, and spatial econometric models to analyze the characteristics of dynamic evolution and the spillover effects of the infrastructure resilience levels in 283 prefecture-level and above cities in China from 2010 to 2019. Our results are as follows. (1) The overall level of urban infrastructure resilience increased. The eastern region had a higher level than the national average. In contrast, the central, western and north-eastern regions had a slightly lower level than the national average. (2) The areas with high and higher resilience levels were mostly cities with more developed economic and social conditions in Eastern China. The areas below moderate resilience levels show a certain degree of clustering and mainly include some cities in Central, Western, and Northeast China. (3) The national level of urban infrastructure resilience shows significant spatial clustering characteristics, and the spatial pattern from coastal to inland regions presents a hotspot-subhotspot-subcoldspot-coldspot distribution. (4) There is a differential spatial spillover effect of national urban infrastructure resilience, which is gradually strengthened under the role of the economy, financial development, population agglomeration and government funding and weakened under the role of urbanization, market consumption and infrastructure investment. By exploring the dynamic evolution of infrastructure resilience in cities at the prefecture level and above and its spatial spillover effects, we provide a scientific basis for avoiding the siphoning effect among cities, improving the level of infrastructure resilience, and guiding the construction and development of resilient cities.</p></div>

Cited by 0Open access

Modeling Spatial Dependence

Michael Coppedge, Amanda B. Edgell, Carl Henrik Knutsen et al. · 2022

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Cited by 1

Spatial Fixed Effects and Spatial Dependence

Luc Anselin, Daniel Arribas‐Bel, Luc Anselin et al. · 2011

We investigate the common conjecture in applied econometric work that the inclusion of spatial fixed effects in a regression specification re- moves spatial dependence. We demonstrate analytically and by means of a series of simulation experiments how evidence of the removal of spatial autocorrelation by spatial fixed effects may be spurious when the true DGP takes the form of a spatial lag or spatial error dependence. In addition, we also show that only in the special case where the dependence is group-wise, with all observations in the same group as neighbors of each other, do spatial fixed effects correctly remove spatial correlation.

Cited by 11

Parrondo games with spatial dependence

S. N. Ethier, Jiyeon Lee · arXiv (Cornell University) · 2012

Toral introduced so-called cooperative Parrondo games, in which there are N players (3 or more) arranged in a circle. At each turn one player is randomly chosen to play. He plays either game A or game B. Game A results in a win or loss of one unit based on the toss of a fair coin. Game B results in a win or loss of one unit based on the toss of a biased coin, with the amount of the bias depending on whether none, one, or two of the player's two nearest neighbors have won their most recent games. Game A is fair, so the games are said to exhibit the Parrondo effect if game B is losing or fair and the random mixture (1/2)(A+B) is winning. With the parameter space being the unit cube, we investigate the region in which the Parrondo effect appears. Explicit formulas can be found if N=3,4,5,6 and exact computations can be carried out if N=7,8,9,...,19, at least. We provide numerical evidence suggesting that the Parrondo region has nonzero volume in the limit as N approaches infinity.

Cited by 0Open access

Measuring Spatial Dependence with Semivariograms

Ricardo A. Olea · Bulletin (Kansas Geological Survey) · 1977

The semivariogram is a measure of statistical dependence and is closely related to the autocorrelogram. It is particularly suitable for analysis of spatial functions that describe natural phenomena characterized by a geographic distribution and variations which arc in part continuous and in part stochastic. If the phenomenon is sampled at regular intervals along lines, important statistical parameters can be estimated from the semivariogram.

Cited by 12Open access

Spatial dependence of extreme rainfall

Noor Fadhilah, Ahmad Radi, Roslinazairimah, Zakaria, Siti Zanariah, Satari et al. · UMP Institutional Repository (Universiti Malaysia Pahang) · 2017

This study aims to model the spatial extreme daily rainfall process using the max-stable model. The max-stable model is used to capture the dependence structure of spatial properties of extreme rainfall. Three models from max-stable are considered namely Smith, Schlather and Brown-Resnick models. The methods are applied on 12 selected rainfall stations in Kelantan, Malaysia. Most of the extreme rainfall data occur during wet season from October to December of 1971 to 2012. This period is chosen to assure the available data is enough to satisfy the assumption of stationarity. The dependence parameters including the range and smoothness, are estimated using composite likelihood approach. Then, the bootstrap approach is applied to generate synthetic extreme rainfall data for all models using the estimated dependence parameters. The goodness of fit between the observed extreme rainfall and the synthetic data is assessed using the composite likelihood information criterion (CLIC). Results show that Schlather model is the best followed by Brown-Resnick and Smith models based on the smallest CLIC’s value. Thus, the max-stable model is suitable to be used to model extreme rainfall in Kelantan. The study on spatial dependence in extreme rainfall modelling is important to reduce the uncertainties of the point estimates for the tail index. If the spatial dependency is estimated individually, the uncertainties will be large. Furthermore, in the case of joint return level is of interest, taking into accounts the spatial dependence properties will improve the estimation process

Cited by 0

Nonparametric Monitoring of Spatial Dependence

Adämmer, Philipp, Wittenberg, Philipp, Weiß, Christian H. et al. · Figshare · 2025

In process monitoring, it is common for measurements to be taken regularly or randomly from different spatial locations in two or three dimensions. While there are nonparametric methods for process monitoring with such spatial data to detect changes in the mean, there is a gap in the literature for nonparametric control charting methods developed to monitor spatial dependence. This study considers streams of regular, rectangular data sets using spatial ordinal patterns (SOPs) as a nonparametric method to detect spatial dependencies. We propose novel, distribution-free SOP control charts. To uncover higher-order dependencies, we develop a new class of statistics that combines SOPs with the Box-Pierce approach. An extensive simulation study demonstrates the superiority and effectiveness of our proposed charts over traditional parametric approaches, particularly when the spatial dependence is nonlinear or bilateral or when the spatial data contains outliers. The proposed SOP control charts are illustrated using real-world datasets to detect (i) heavy rainfall in Germany, (ii) war-related fires in (eastern) Ukraine, and (iii) manufacturing defects in textile production. This wide range of applications and findings demonstrates the broad utility of the proposed nonparametric control charts. In addition, all methods in this study are provided as a publicly available Julia package on GitHub for further implementations.

Cited by 0Open access

Spatial Dependence in Asylum Migration

Fabian Barthel, Eric Neumayer · Journal of Ethnic and Migration Studies · 2014

Existing refugees in a destination country from the same source country reduce the uncertainty faced by subsequent asylum migrants since existing refugees can provide information and assistance. We argue that such network effects extend beyond the borders of specific source countries. Potential asylum migrants might also be able to draw on networks from geographically proximate as well as linguistically similar countries and from countries having previously been colonised by the same destination country, thus creating spatial dependence in asylum migration among source countries. Many destination countries meanwhile aspire to reduce the inflow of migrants by tightening their asylum policies. Target countries which restrict their policies relatively more than other destinations deflect some asylum migrants to geographically proximate destination countries, thus creating spatial dependence among target countries. We find evidence for both types of spatial dependence in our global analysis of asylum migration. However, while statistically significant, the degree of spatial dependence among target countries is modest. On the source side, there is evidence for modest spatial dependence among linguistically similar countries and no evidence for spatial dependence among countries which were previously colonised by the same destination country. By contrast, we find substantial spatial dependence among geographically proximate source countries.

Cited by 0Open access