Compensating for the Impact of Incoherent Noise in the Spatial Autocorrelation Microtremor Array Method

2018 ◽  
Vol 109 (1) ◽  
pp. 199-211 ◽  
Author(s):  
Ikuo Cho
Keyword(s):  
Spatial Autocorrelation ◽  
The Impact ◽  
Incoherent Noise

The impact of spatial heterogeneity on selection: a case study on Pinus pinaster breeding seedling orchards

2008 ◽  
Vol 38 (1) ◽  
pp. 114-124 ◽  
Author(s):  
Rafael Zas
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Genetic Gain ◽  
Pinus Pinaster ◽  
Spatial Models ◽  
Selection Methods ◽  
Family Selection ◽  
The Impact

Although failure to account for spatial autocorrelation has been dramatic in some forest progeny trials, little attention has been paid to how this issue may affect selections within the trials. The effects of spatial autocorrelation of height growth on the estimation of genetic gain and on the spatial distribution of the selected trees were studied in four Pinus pinaster Ait. progeny trials that were rogued using different selection methods and intensities. When selections are based on unadjusted original values, selected trees tend to be located in the best microsites and are unlikely to be the most genetically superior. This resulted in a loss of genetic gain that varied between 10% and 20% and sometimes exceeded 30%. Differences in the loss of gain among different selection methods and intensities were minor and followed no clear pattern. Selecting on the basis of a conventional model resulted in spatial patterns of the retained trees that were clearly aggregated in all cases. However, selections based on spatially adjusted data resulted in random spatial patterns, except with family selection because of the use of multiple-tree plots. Because clumping of the retained trees may seriously affect the quantity and quality of the seed crop, breeders are strongly encouraged to use appropriate spatial models for roguing breeding seedling orchards.

scholarly journals Spatial Variation and Factors Impacting Grassland Circulation Price in Inner Mongolia, China

2018 ◽  
Vol 10 (12) ◽  
pp. 4381 ◽  
Author(s):  
Jing Zhang ◽  
Colin Brown
Keyword(s):  
Quantile Regression ◽  
Spatial Autocorrelation ◽  
Inner Mongolia ◽  
Local Market ◽  
High Demand ◽  
Grazing Land ◽  
Regression Methods ◽  
Price Distribution ◽  
Quantile Regression Analysis ◽  
The Impact

As the circulation of grassland use rights in China increases, relatively little is known about the factors that influence circulation price. This paper examines the spatial distribution of grassland circulation prices and the impact of various attributes on grassland circulation prices in Inner Mongolia Autonomous Region (IMAR). Spatial autocorrelation tests and quantile regression methods are applied to data from an online land-circulation website covering the period from January to October 2017. The spatial analysis found that grassland circulation price does vary greatly throughout IMAR but that no significant spatial autocorrelation is evident. The quantile regression analysis revealed significant, though varied, quantile effects across the price distribution indicating that local market structures, strong demand for grazing land in desert steppe, high demand of poor herders for smaller plots, and high demand of richer herders for larger plots all play an important role in determining circulation prices. These nuanced findings should enable policy makers, grassland users, and other grassland actors to better understand how grassland price is determined with respect to a range of factors across the quantiles of price as well as the spatial pattern of price characteristics. This information and understanding are a crucial step in improving grassland circulation.

scholarly journals The Spatiotemporal Interaction Effect of COVID-19 Transmission in the United States

2021 ◽  
Vol 10 (6) ◽  
pp. 387
Author(s):  
Lingbo Liu ◽  
Tao Hu ◽  
Shuming Bao ◽  
Hao Wu ◽  
Zhenghong Peng ◽  
...  
Keyword(s):  
United States ◽  
Population Density ◽  
Spatial Autocorrelation ◽  
Low Income ◽  
Pearson Correlation ◽  
The United States ◽  
Time Varying ◽  
Ols Regression ◽  
Highly Educated ◽  
The Impact

(1) Background: Human mobility between geographic units is an important way in which COVID-19 is spread across regions. Due to the pressure of epidemic control and economic recovery, states in the United States have adopted different policies for mobility limitations. Assessing the impact of these policies on the spatiotemporal interaction of COVID-19 transmission among counties in each state is critical to formulating epidemic policies. (2) Methods: We utilized Moran’s I index and K-means clustering to investigate the time-varying spatial autocorrelation effect of 49 states (excluding the District of Colombia) with daily new cases at the county level from 22 January 2020 to 20 August 2020. Based on the dynamic spatial lag model (SLM) and the SIR model with unreported infection rate (SIRu), the integrated SLM-SIRu model was constructed to estimate the inter-county spatiotemporal interaction coefficient of daily new cases in each state, which was further explored by Pearson correlation test and stepwise OLS regression with socioeconomic factors. (3) Results: The K-means clustering divided the time-varying spatial autocorrelation curves of the 49 states into four types: continuous increasing, fluctuating increasing, weak positive, and weak negative. The Pearson correlation analysis showed that the spatiotemporal interaction coefficients in each state estimated by SLM-SIRu were significantly positively correlated with the variables of median age, population density, and proportions of international immigrants and highly educated population, but negatively correlated with the birth rate. Further stepwise OLS regression retained only three positive correlated variables: poverty rate, population density, and highly educated population proportion. (4) Conclusions: This result suggests that various state policies in the U.S. have imposed different impacts on COVID-19 transmission among counties. All states should provide more protection and support for the low-income population; high-density populated states need to strengthen regional mobility restrictions; and the highly educated population should reduce unnecessary regional movement and strengthen self-protection.

Influence Models of Urban Road Network Operation Performance Base on Spatial Autocorrelation Theory

2012 ◽  
Vol 253-255 ◽  
pp. 1922-1929
Author(s):  
Jian Cheng Weng ◽  
Wen Jie Zou ◽  
Jian Rong
Keyword(s):  
Spatial Autocorrelation ◽  
Road Network ◽  
Road Networks ◽  
Operation Performance ◽  
Urban Road Network ◽  
Spatial Characteristics ◽  
Urban Road ◽  
The Road ◽  
Network Operation ◽  
The Impact

In order to better identify the spatial influence between adjacent parts of road networks, the paper introduces the spatial autocorrelation theory in evaluating the operation performance of urban road networks. The research proposes several spatial correlation validation indicators to verify the spatial characteristics among the road networks. Based on the analysis of spatial characteristics, the relationship between operation performance and influencing factors under the impact of spatial effect is examined. Furthermore, a spatial autocorrelation based influence models at three traffic flow levels is developed by using the data from a partial urban road network in Beijing. The model analysis shows that the spatial autocorrelation model is more effective in analyzing the urban road network operation performance under the influence of various factors. This model will be beneficial in identifying traffic network problems and improving traffic operations of the urban road network.

The impact of spatial autocorrelation on CPUE standardization between two different fisheries

2018 ◽  
Vol 36 (3) ◽  
pp. 973-980 ◽  
Author(s):  
Luoliang Xu ◽  
Xinjun Chen ◽  
Wenjiang Guan ◽  
Siquan Tian ◽  
Yong Chen
Keyword(s):  
Spatial Autocorrelation ◽  
Cpue Standardization ◽  
The Impact

scholarly journals Spatial Clustering of Dengue Fever Incidence and Its Association with Surrounding Greenness

2018 ◽  
Vol 15 (9) ◽  
pp. 1869 ◽  
Author(s):  
Chi-Chieh Huang ◽  
Tuen Tam ◽  
Yinq-Rong Chern ◽  
Shih-Chun Lung ◽  
Nai-Tzu Chen ◽  
...  
Keyword(s):  
Land Use ◽  
Spatial Autocorrelation ◽  
Dengue Fever ◽  
Vegetation Index ◽  
Green Space ◽  
Hot Spot ◽  
Southern Taiwan ◽  
The Impact ◽  
The Relationship ◽  
Green Land

With more than 58,000 cases reported by the country’s Centers for Disease Control, the dengue outbreaks from 2014 to 2015 seriously impacted the southern part of Taiwan. This study aims to assess the spatial autocorrelation of the dengue fever (DF) outbreak in southern Taiwan in 2014 and 2015, and to further understand the effects of green space (such as forests, farms, grass, and parks) allocation on DF. In this study, two different greenness indexes were used. The first green metric, the normalized difference vegetation index (NDVI), was provided by the long-term NASA MODIS satellite NDVI database, which quantifies and represents the overall vegetation greenness. The latest 2013 land use survey GIS database completed by the National Land Surveying and Mapping Center was obtained to access another green metric, green land use in Taiwan. We first used Spearman’s rho to find out the relationship between DF and green space, and then three spatial autocorrelation methods, including Global Moran’s I, high/low clustering, and Hot Spot were employed to assess the spatial autocorrelation of DF outbreak. In considering the impact of social and environmental factors in DF, we used generalized linear mixed models (GLMM) to further clarify the relationship between different types of green land use and dengue cases. Results of spatial autocorrelation analysis showed a high aggregation of dengue epidemic in southern Taiwan, and the metropolitan areas were the main hotspots. Results of correlation analysis and GLMM showed a positive correlation between parks and dengue fever, and the other five green space metrics and land types revealed a negative association with DF. Our findings may be an important asset for improving surveillance and control interventions for dengue.

scholarly journals Spatial Determinants of Land Conversion for Various Urban Use: A Case Study of Beijing

2020 ◽  
Vol 9 (12) ◽  
pp. 708
Author(s):  
Daquan Huang ◽  
Erxuan Chu ◽  
Tao Liu
Keyword(s):  
Land Use ◽  
Spatial Autocorrelation ◽  
Driving Forces ◽  
Municipal Government ◽  
Land Use Changes ◽  
Construction Land ◽  
Different Types ◽  
Negative Impacts ◽  
The Impact

Studying the factors that influence the expansion of different types of construction land is instrumental in formulating targeted policies and regulations, and can reduce or prevent the negative impacts of unreasonable land use changes. Using land use survey data of Beijing (2001 and 2010), an autologistic model quantitatively analyzed the leading driving forces and differences in four types of construction land expansion (industrial, residential, public service, and commercial land types), focusing on the impact of spatial autocorrelation. The results showed that the influencing factors vary greatly for different types of construction land expansion; the same factor may have a different impact on different construction land, and both planning factors and spatial autocorrelation variables have a significant positive effect on the four types. Accordingly, the municipal government should consider the differences in the expansion mechanisms and driving forces of different construction land and formulate suitable planning schemes, observe the impact of spatial autocorrelation on construction land expansion, and guide spatial agglomeration through policies while appropriately controlling the scale of expansion. The methods and policy recommendations of this research are significant for urban land expansion research and policy formulations in other transition economies and developing countries.

scholarly journals Airbnb Offer in Spain—Spatial Analysis of the Pattern and Determinants of Its Distribution

2019 ◽  
Vol 8 (3) ◽  
pp. 155 ◽  
Author(s):  
Czesław Adamiak ◽  
Barbara Szyda ◽  
Anna Dubownik ◽  
David García-Álvarez
Keyword(s):  
Spatial Analysis ◽  
Spatial Autocorrelation ◽  
Regression Models ◽  
Peer To Peer ◽  
Tourism Demand ◽  
Property Market ◽  
Large Cities ◽  
Tourism Sector ◽  
The Impact

The rising number of homes and apartments rented out through Airbnb and similar peer-to-peer accommodation platforms cause concerns about the impact of such activity on the tourism sector and property market. To date, spatial analysis on peer-to-peer rental activity has been usually limited in scope to individual large cities. In this study, we take into account the whole territory of Spain, with special attention given to cities and regions with high tourist activity. We use a dataset of about 250 thousand Airbnb listings in Spain obtained from the Airbnb webpage, aggregate the numbers of these offers in 8124 municipalities and 79 tourist areas/sites, measure their concentration, spatial autocorrelation, and develop regression models to find the determinants of Airbnb rentals’ distribution. We conclude that apart from largest cities, Airbnb is active in holiday destinations of Spain, where it often serves as an intermediary for the rental of second or investment homes and apartments. The location of Airbnb listings is mostly determined by the supply of empty or secondary dwellings, distribution of traditional tourism accommodation, coastal location, and the level of internationalization of tourism demand.

scholarly journals Measurement of spatial equity : a case study of nursing institution

2019 ◽  
Author(s):  
Xin Sui ◽  
◽  
Yifan Yu ◽  
Liu Huhui ◽  
◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Autocorrelation ◽  
Supply And Demand ◽  
The Elderly ◽  
Spatial Dimension ◽  
Spatial Equity ◽  
Public Resources ◽  
Local Moran ◽  
The Impact

Equity and justice have always been important norms in the field of urban planning. With the gradual deepening of understanding of residential environment, the research context of equity and justice related to location is becoming more and more sophisticated. Recently, varieties of subjects Including Public Health and Geography focus on the inequity of public resources in spatial distribution and how to measure the degree of this gap. In general, the mainstream measurement methods can be summarized into two categories: (1) The description of phenomenon caused by the spatial inequities, and accessibility is a typical method of this type. (2) the direct quantification of inequity, such as Gink Coefficient which is originated from the economics field and introduced into the measurement of health equity, and Getis-Ord General G, together with Moran’ index is the most commonly method used into the general spatial autocorrelation. In this paper, based on the overall literature review of the concept of equity in the study using these methods and a summary of their specific context of the measurement using, nursing institution in Shanghai, China are regarded as a typical case to practice these methods and compare the differences in using. Meantime, the impact of the politics and planning related to this special facility is also been considered. Results show that, accessibility of nursing institution among elderly groups is much different under different research distance, and the overall trend seems like the research units in suburb appears higher accessibility than those in highly urbanized area. And Gink Coefficient helps us determine the proportion of the elderly population in different reachable areas in Shanghai is within a reasonable range. However, Global Moran’ index provide reliable evidence that the existence of the aggregation combined by the high-value units. It indicates that there are inequities among the distribution of aged-nursing resources, and Local Moran I （LISA）help us to find the specific boundaries of these areas. In general, in the study of the equity related to location, accessibility can only reflect the differences phenomenon in distribution, but it is not clear to describe this gap to what extent, and it’s difficult to achieve the possibility of comparison among different periods and different subjects. The Gini coefficient often focuses on the unfairness of the distribution of people, but ignored the aggregation characteristics of the spatial dimension, which the analysis of spatial autocorrelation can make up. All these methods proved that it’s necessary to consider both the spatial distribution of supply and demand. And the discussion about equity related to location should be strictly qualified in study.

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