Global Spatial Autocorrelation of Corn-Belt Farmland Values and Rental Rates

2021 ◽  
Author(s):  
Allan Pinto ◽  
Terry Griffin
Keyword(s):  
Spatial Autocorrelation ◽  
Corn Belt ◽  
Farmland Values ◽  
Global Spatial Autocorrelation

Accuracy Analysis of DEM Based on Coons Surface

2011 ◽  
Vol 65 ◽  
pp. 214-217
Author(s):  
Yao Ge Wang ◽  
Peng Yuan Wang
Keyword(s):  
Spatial Pattern ◽  
Spatial Autocorrelation ◽  
Boundary Curve ◽  
Digital Elevation ◽  
Grid Points ◽  
Elevation Model ◽  
Global Spatial Autocorrelation ◽  
Core Problem ◽  
Dem Model ◽  
Better Than

Interpolation is the core problem of Digital Elevation Model (DEM). The Coons DEM model is better than bilinear interpolation and moving surface fitting. It is constructed by grid boundary curve, the curve interpolates by some adjoining grid points. Its spatial pattern of error is random in global area, there is no significant global spatial autocorrelation, but it is an increasing trend along with the terrain average gradient increases.There is significant local spatial autocorrelation, the spatial pattern of error converges strongly in local areas.

scholarly journals How Far Has China’s Urbanization Gone?

2018 ◽  
Vol 10 (8) ◽  
pp. 2953 ◽  
Author(s):  
Yiping Xiao ◽  
Yan Song ◽  
Xiaodong Wu
Keyword(s):  
Urban Development ◽  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Spatial Association ◽  
Early Years ◽  
Spatial Data Analysis ◽  
Rapid Urbanization ◽  
Urban Rural ◽  
Global Spatial Autocorrelation ◽  
Over Time

China’s rapid urbanization has attracted wide international attention. However, it may not be sustainable. In order to assess it objectively and put forward recommendations for future development, this paper first develops a four-dimensional Urbanization Quality Index using weights calculated by the Deviation Maximization Method for a comprehensive assessment and then reveals the spatial association of China’s urbanization by Exploratory Spatial Data Analysis. The study leads to three major findings. First, the urbanization quality in China has gradually increased over time, but there have been significant differences between regions. Second, the four aspects of urbanization quality have shown the following trends: (i) the quality of urban development has steadily increased; (ii) the sustainability of urban development has shown a downward trend in recent years; (iii) the efficiency of urbanization improved before 2006 but then declined slightly due to capital, land use, and resource efficiency constraints; (IV) the urban–rural integration deteriorated in the early years but then improved over time. Third, although the urbanization quality has a significantly positive global spatial autocorrelation, the local spatial autocorrelation varies between eastern and western regions. Based on these findings, this paper concludes with policy recommendations for improving urbanization quality and its sustainability in China.

Sensitivity Analysis of Spatial Autocorrelation Using Distinct Geometrical Settings

2016 ◽  
Vol 7 (1) ◽  
pp. 65-77 ◽  
Author(s):  
António Manuel Rodrigues ◽  
José António Tenedório
Keyword(s):  
Sensitivity Analysis ◽  
Spatial Autocorrelation ◽  
Count Data ◽  
Data Aggregation ◽  
Scientific Field ◽  
Social Phenomena ◽  
Resident Population ◽  
Geometrical Data ◽  
Proximity Measures ◽  
Global Spatial Autocorrelation

Inferences based on spatial analysis of areal data depend greatly on the method used to quantify the degree of proximity between spatial units - regions. These proximity measures are normally organized in the form of weights matrices, which are used to obtain statistics that take into account neighbourhood relations between agents. In any scientific field where the focus is on human behaviour, areal datasets are greatly relevant since this is the most common form of data collection (normally as count data). The method or schema used to divide a continuous spatial surface into sets of discrete units influences inferences about geographical and social phenomena, mainly because these units are neither homogeneous nor regular. This article tests the effect of different geometrical data aggregation schemas - administrative regions and hexagonal surface tessellation - on global spatial autocorrelation statistics. Two geographical variables are taken into account: scale (resolution) and form (regularity). This is achieved through the use of different aggregation levels and geometrical schemas. Five different datasets are used, all representing the distribution of resident population aggregated for two study areas, with the objective of consistently test the effect of different spatial aggregation schemas.

scholarly journals Spatial cluster and Heterogeneity Research on Energy-Related Carbon Emissions in Districts and Counties of Guangdong Province

2019 ◽  
Vol 79 ◽  
pp. 03019
Author(s):  
Wenxiu Wang ◽  
Shangjun Ke ◽  
Daiqing Zhao ◽  
Guotian Cai
Keyword(s):  
Spatial Autocorrelation ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Guangdong Province ◽  
Total Carbon ◽  
Low Carbon ◽  
Cluster Area ◽  
Reduce Emissions ◽  
Global Spatial Autocorrelation ◽  
High Cluster

Energy-related carbon emissions in districts and counties of Guangdong province from 2005 to 2016 are researched based on spatial econometrics method in this article, and significance cluster area and heterogeneity area are precise pinpointed. Conclusions are as follows: (1) total carbon emissions and per capita carbon emissions exist significance global spatial autocorrelation in the year 2005-2016, and formed significance high-high cluster area in districts and counties of Guangzhou city, Shenzhen city and Dongguan city. It also formed three significance low-low cluster areas in districts and counties of eastern, western and northern of Guangdong province. Low-high heterogeneity area and high -low heterogeneity area often appears in the scope of high-high cluster area and low-low cluster area. (2)Carbon emission intensity not exist significance global spatial autocorrelation, but exist significance cluster area and heterogeneity area in the ecological development areas of eastern, western and northern of Guangdong province. In the end, the paper puts forward the regional and detailed policy recommendations for efficient carbon emission reduction for each cluster type region: carbon high-high cluster areas are priority reduce emissions area, heighten energy saving technology and optimize industrial structure are two grippers to reduce emissions. Low - low carbon emissions concentrated area in western of Guangdong should primarily develop high and new technology industry. Low low carbon emissions concentrated areas and high - high carbon emissions intensity concentrated area for eastern and northern of Guangdong province should try hard to wins ecological compensation at the same time focus on developing ecological tourism.

scholarly journals Spatial determinants of farmers’ interest in European Union’s pro-investment programs in Poland

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248059
Author(s):  
Ewa Kiryluk-Dryjska ◽  
Barbara Więckowska ◽  
Arkadiusz Sadowski
Keyword(s):  
Spatial Autocorrelation ◽  
Rural Areas ◽  
Agricultural Development ◽  
Historical Context ◽  
Local Version ◽  
Cohesion Policy ◽  
Basic Assumptions ◽  
Global Spatial Autocorrelation ◽  
The Eu ◽  
Spatial Autocorrelation Index

The purpose of the paper is to investigate spatial determinants of farmers’ interest in pro-investment programs co-financed by the EU, by identifying and describing the territorial clusters of rural areas in Poland where the applications rates for these programs were above or below the national average. We tested for spatial autocorrelation using Moran’s global spatial autocorrelation index, while the search for clusters was done using a local version of Moran’s statistics. The results show significant regional variation in the farmers’ interest in these programs in Poland. This interest was higher in regions with a greater level of agricultural development and better agrarian structure. In Poland, both of these factors are related not only to natural conditions, but also to strong historical context. We conclude that the pro-investment programs contribute to the deepening of development differences in Polish agriculture in the territorial dimension, which is not in line with the basic assumptions of cohesion policy.

scholarly journals Spatial pattern of foot-and-mouth disease in animals in China, 2010–2016

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4193 ◽  
Author(s):  
Jun Ma ◽  
Jianhua Xiao ◽  
Xiang Gao ◽  
Boyang Liu ◽  
Hao Chen ◽  
...  
Keyword(s):  
Spatial Autocorrelation ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Economic Losses ◽  
Distribution Analysis ◽  
Serotype A ◽  
Serotype O ◽  
Foot And Mouth ◽  
Considerable Length ◽  
Global Spatial Autocorrelation

Foot-and-mouth disease (FMD) is a highly contagious disease of cloven-hoofed animals. An outbreak of FMD can produce devastating economic losses for a considerable length of time. In order to investigate the distribution characteristics of FMD in China, data from 2010 to 2016 were collected, including information on 65 outbreaks of FMD (25 by serotype A and 40 by serotype O), and 5,937 diseased animals (1,691 serotype A and 4,284 serotype O cases). Spatial autocorrelation, including global spatial autocorrelation and local spatial autocorrelation, as well as directional distribution analysis, were performed. Global spatial autocorrelation analysis of FMD cases from 2010 to 2016 did not show clustering (P > 0.05). In 2013 and 2014, the FMD serotype A hotspots areas were Tibet (Z = 3.3236,P < 0.001 in 2013;Z = 3.2001,P < 0.001 in 2014) and Xinjiang provinces (Z = 4.2113,P < 0.001 in 2013;Z = 3.9888,P < 0.001 in 2014). The FMD serotype O hotspots areas were: Xinjiang (Z = 2.5832,P = 0.0098) province in 2010; Tibet (Z = 3.8814,P < 0.001) and Xinjiang (Z = 4.9128,P < 0.001) provinces in 2011; and Tibet (Z = 3.0838,P = 0.0020), Xinjiang (Z = 3.8705,P < 0.001) and Qinghai (Z = 2.8875,P = 0.0039) provinces in 2013. The distribution of FMD cases from 2010 to 2016 showed a significant directional trend (northwest-southeast). In conclusion, our findings revealed the spatial patterns of FMD cases, which may provide beneficial information for the prevention and control of FMD.

scholarly journals Epidemiology and spatial distribution of bluetongue virus in Xinjiang, China

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6514 ◽  
Author(s):  
Jun Ma ◽  
Xiang Gao ◽  
Boyang Liu ◽  
Hao Chen ◽  
Jianhua Xiao ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Autocorrelation ◽  
Bluetongue Virus ◽  
Large Scale ◽  
Economic Losses ◽  
Random Pattern ◽  
Diurnal Range ◽  
Precipitation Seasonality ◽  
Risk Areas ◽  
Global Spatial Autocorrelation

Bluetongue (BT) is a non-contagious disease affecting domestic and wild ruminants. Outbreaks of BT can cause serious economic losses. To investigate the distribution characteristics of bluetongue virus (BTV), two large-scale censuses of BTV prevalence in Xinjiang, China were collected. Spatial autocorrelation analysis, including global spatial autocorrelation and local spatial autocorrelation, was performed. Risk areas for BTV occurrence in Xinjiang were detected using the presence-only maximum entropy model. The global spatial autocorrelation of BTV distribution in Xinjiang in 2012 showed a random pattern. In contrast, the spatial distribution of BTV from 2014 to 2015 was significantly clustered. The hotspot areas for BTV infection included Balikun County (p< 0.05), Yiwu County (p< 0.05) and Hami City (p< 0.05) in 2012. These three regions were also hotspot areas during 2014 and 2015. Sheep distribution (25.6% contribution), precipitation seasonality (22.1% contribution) and mean diurnal range (16.2% contribution) were identified as the most important predictors for BTV occurrence in Xinjiang. This study demonstrated the presence of high-risk areas for BTV infection in Xinjiang, which can serve as a tool to aid in the development of preventative countermeasures of BT outbreaks.

scholarly journals Spatial Statistics and Influencing Factors of the Epidemic of Novel Coronavirus Pneumonia 2019 in Hubei Province, China

2020 ◽  
Author(s):  
Yongzhu Xiong ◽  
Yunpeng Wang ◽  
Feng Chen ◽  
Mingyong Zhu
Keyword(s):  
Spatial Autocorrelation ◽  
Influencing Factors ◽  
Hot Spot ◽  
Pearson Correlation ◽  
Hubei Province ◽  
Land Area ◽  
Novel Coronavirus ◽  
And Control ◽  
Global Spatial Autocorrelation ◽  
County Scale

Abstract An in-depth understanding of spatiotemporal dynamic characteristics of infectious diseases could be helpful to an epidemic prevention and control. Based on the novel coronavirus pneumonia (NCP) data published on official websites, GIS spatial statistics and Pearson correlation methods were used to analyze the spatial autocorrelation and influencing factors of the 2019 NCP epidemic from January 30, 2020 to February 18, 2020. The results of the study showed that: (1) During the study period, Hubei Province was the only significant cluster area and hot spot of the cumulative cases confirmed with the NCP infection in China on the provincial scale; (2) The epidemic of the NCP infection in China on the prefecture-city scale had a very significant global spatial autocorrelation, and Wuhan had always been the significant hot spot and cluster city of the cumulative cases confirmed with the NCP infection in the whole country; (3) The cumulative cases confirmed with the NCP infection in Hubei Province on the county scale had a very significant global spatial autocorrelation, and the county-level districts under the jurisdiction of Wuhan and its neighboring Huangzhou district in Huanggang City were the significant hot spots and spatial clusters of the cumulative cases confirmed with the NCP infection; (4) Based on Pearson correlation analysis, the number of the accumulative cases confirmed with the NCP infection in Hubei Province on the prefecture-city scale and also on the county scale had very significant and positive correlations (p < 0.01) with the four indexes of population of registration population, resident population, regional GDP and total retail sales of consumer goods, respectively, during the study period; (5) The number of the cumulative cases confirmed with the NCP infection in Hubei Province on the prefecture-city scale also had a very significant and positive correlation (p < 0.01) with Baidu migration index and population density, respectively, but not with land area, whereas that in Hubei Province on the county scale had a significant and positive correlation (p < 0.05) with land area, but not with population density from January 30, 2020 to February 18, 2020. It is found that the NCP epidemic in Hubei Province has the distinctive characteristics of significantly centralized outbreak, significantly spatial autocorrelation and complex influencing factors and that the spatial scale has a significant effect on the global spatial autocorrelation of the NCP epidemic. The findings help to deepen the understanding of spatial distribution patterns and transmission trends of the NCP epidemic and may also benefit scientific prevention and control of epidemics such as NCP 2019.

scholarly journals Geographical Disparities in HIV Prevalence among Men Who Have Sex with Men and People Who Inject Drugs in Nigeria

2020 ◽  
Author(s):  
Amobi Onovo ◽  
Abiye Kalaiwo ◽  
Moses Katbi ◽  
Otse Ogorry ◽  
Antoine Jaquet ◽  
...  
Keyword(s):  
Hiv Infection ◽  
Spatial Autocorrelation ◽  
People Who Inject Drugs ◽  
Hiv Prevalence ◽  
Geographical Information ◽  
North Central ◽  
Hiv Test ◽  
Sex With Men ◽  
Global Spatial Autocorrelation ◽  
Central South

AbstractBackgroundAssessment of geographical heterogeneity of HIV among men who have sex with men (MSM) and people who inject drugs (PWID) can usefully inform targeted HIV prevention and care strategies. We aimed to measure HIV prevalence and identify hotspots of HIV infection among MSM and PWID in Nigeria.MethodsWe included all MSM and PWID accessing HIV testing services across seven prioritized states (Lagos, Nasarawa, Akwa Ibom, Cross Rivers, Rivers, Benue and the Federal Capital Territory) in three geographic regions (North Central, South South, South West) between Oct 1, 2016 and Sept 30, 2017. We extracted data from national testing registers, georeferenced all HIV test results aggregated at the level of Local Government Areas (LGAs), and calculated HIV prevalence. We calculated and compared HIV prevalence from our study to the integrated biological and behavioral surveillance survey (IBBSS) 2014 and used global spatial autocorrelation and hotspot analysis to highlight patterns of HIV infection, and to identify areas of significant clustering of HIV cases.FindingsA positive HIV test was reported in 12.1% (95%CI 9.7-13.1) and 11.8% (95%CI 9.3-12.7) of the 26,423 MSMs and 9,474 PWIDs, respectively. Global spatial autocorrelation Moran’s I statistics revealed a clustered distribution of HIV infection among MSMs and PWIDs with a <5% and <1% likelihood that this clustered pattern could be due to chance respectively. Significant clusters of HIV infection (Getis-Ord-Gi* statistics) confined to the North Central, South-South regions were identified among MSM and PWID. Compared to the 2014 IBBSS our results suggest an increased HIV prevalence among PWID and a substantial decrease among MSM.InterpretationThis study identified geographical areas to prioritize for control of HIV infection among MSM and PWID, thus demonstrating that geographical information system technology is a useful tool to inform public health planning for interventions for epidemic control of HIV infection.FundingData used for this study was collected from Key Population program in Nigeria through PEPFAR/USAID. OK was funded by the Swiss National Science Foundation (grant no 163878).

scholarly journals Spatial autocorrelation analysis in plant population: An overview

2015 ◽  
Vol 7 (1) ◽  
pp. 501-513 ◽  
Author(s):  
Manish Mathur
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Association ◽  
Plant Population ◽  
Moran’S I ◽  
Moran's I ◽  
Entire Study ◽  
Cold Spots ◽  
The Many ◽  
Global Spatial Autocorrelation ◽  
Entire Study Area

Analysis of spatial distribution in ecology is often influenced by spatial autocorrelation. In present paper various techniques related with quantification of spatial autocorrelation were categorized. Three broad categories namely global, local and variogram were identified and mathematically explained. Local measurers captures the many local spatial variation and spatial dependency while global measurements provide only one set of values that represent the extent of spatial autocorrelation across the entire study area. Global spatial autocorrelation measures the overall clustering of data and it included six well defines methods, namely, Global index of spatial autocorrelation, Joint count statistics, Moran’s I, Geary’s C ration, General G-statistics and Getis and Ord’s G. The study revealed that out of the six methods Moran’s I index was most frequently utilized in plant population study. Based on their similarity degree, local indicator of spatial association (LISA) can differentiate the neighbors in to hot and cold spots. Correlogram and variogram approaches are also given.

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