A Local Indicator of Multivariate Spatial Association: Extending Geary's <i>c</i>

Anselin, Luc

doi:10.1111/gean.12164

Cited by 182 publications

(153 citation statements)

References 49 publications

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“…We attempted to analyze local spatial autocorrelation in a multivariate context with the PCA data using Geary's C [67]. Anselin [67,68] extends the application of the local Geary C statistic to a multivariate context based on PCA components, where statistical inference is estimated using a conditional permutation approach. Geary's C calculation is similar to Moran's I.…”

Section: Methodsmentioning

confidence: 99%

“…Table 2 shows the PCA 1 and 2 loadings which account for the variance. We employ local Geary's C in a multivariate setting, as described by Anselin [68] to compare geographical neighbors with neighbors in multi-attribute space. We map the first PCA in Figure 7a using natural breaks to show distribution.…”

Section: Spatial Analysis Of Social Demographics Housing and Behavmentioning

confidence: 99%

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Characterizing the Spatial Determinants and Prevention of Malaria in Kenya

Gopal

Chen

et al. 2019

IJERPH

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The United Nations’ Sustainable Development Goal 3 is to ensure health and well-being for all at all ages with a specific target to end malaria by 2030. Aligned with this goal, the primary objective of this study is to determine the effectiveness of utilizing local spatial variations to uncover the statistical relationships between malaria incidence rate and environmental and behavioral factors across the counties of Kenya. Two data sources are used—Kenya Demographic and Health Surveys of 2000, 2005, 2010, and 2015, and the national Malaria Indicator Survey of 2015. The spatial analysis shows clustering of counties with high malaria incidence rate, or hot spots, in the Lake Victoria region and the east coastal area around Mombasa; there are significant clusters of counties with low incidence rate, or cold spot areas in Nairobi. We apply an analysis technique, geographically weighted regression, that helps to better model how environmental and social determinants are related to malaria incidence rate while accounting for the confounding effects of spatial non-stationarity. Some general patterns persist over the four years of observation. We establish that variables including rainfall, proximity to water, vegetation, and population density, show differential impacts on the incidence of malaria in Kenya. The El-Nino–southern oscillation (ENSO) event in 2015 was significant in driving up malaria in the southern region of Lake Victoria compared with prior time-periods. The applied spatial multivariate clustering analysis indicates the significance of social and behavioral survey responses. This study can help build a better spatially explicit predictive model for malaria in Kenya capturing the role and spatial distribution of environmental, social, behavioral, and other characteristics of the households.

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Section: Methodsmentioning

confidence: 99%

Section: Spatial Analysis Of Social Demographics Housing and Behavmentioning

confidence: 99%

Characterizing the Spatial Determinants and Prevention of Malaria in Kenya

Gopal

Chen

et al. 2019

IJERPH

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show abstract

“…Finally, in cases where no association seems to exist between a location and nearby locations, the data exhibit zero spatial autocorrelation. Spatial autocorrelation can be measured both globally (for the entire dataset) and locally (for neighborhoods) [37]. Global indices estimate spatial autocorrelation by a single value for the entire study area, whereas local spatial autocorrelation indices calculate the value of the index for each single location.…”

Section: Methodsmentioning

confidence: 99%

“…The optimal fixed distance band is observed at 444,633.46 m (first peak in Figure 2) with a statistically significant z-score and a Moran's I index value of 0.34. An index score larger than 0.3 is an indication of strong positive autocorrelation [37]. For more meaningful results 450 km is retained as the optimal fixed distance band for this analysis, which is a plausible distance to form neighborhoods (needed for spatial statistics) at the NUTS 2 regional level.…”

Section: Setting the Neighborhood Size: Incremental Spatial Autocorrementioning

confidence: 99%

Further Widening or Bridging the Gap? A Cross-Regional Study of Unemployment across the EU Amid Economic Crisis

Grekousis

2018

Sustainability

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Abstract:The 2008 global economic crisis led to a sharp increase in unemployment with an estimated 210 million people being unemployed worldwide by 2010. This study analyzes the spatio-temporal distribution of unemployment in the European Union (EU) at a cross-regional level between 2008 and 2013 to identify if spatio-temporal patterns of unemployment exist, and if the European regions have suffered similarly during the study period. Various local spatial autocorrelation techniques are applied and results show that unemployment is highly polarized across the EU regions. Portugal, Spain, Italy, and Greece are experiencing high rates of unemployment forming clusters in space and time. By contrast, Germany, Austria, and nearby regions are more resilient to the economic crisis strains thus creating spatial clusters of low rates of unemployment. Spatial autocorrelation increased considerably in 2013 compared to 2008, indicating further polarization of unemployment and a widening gap between the south and the central-north, showcasing that the severe austerity measures imposed in the beginning of the crisis on some countries did not have any positive effect on unemployment mitigation. The paper also discusses interesting cross-regional patterns to assist policymakers and planners to better understand how high rates of unemployment are spreading geographically and thus take preventive measures to alleviate the implications of the phenomenon. The proposed analysis delves deeper into comprehending geographies of change, and related findings can support spatial planning for achieving society's sustainability.

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“…Land-use change factors, as a comprehensive reflection of human activities and one of the key features of these hotspot cities, were used to further explore the changes and uncertainties related to the local highresolution spatiotemporal patterns of China's FFCO 2 emissions. Here we introduce the local Geary c i a univariate statistic, but instead apply it to a multivariate context (Anselin 2019). The long-term series 300 m spatial resolution LC images consistent with the 22 IPCC classes benefits data exploration between LC and FFCO 2 emissions by using a multivariate statistic.…”

Section: Multivariate Local Gearymentioning

confidence: 99%

High-resolution spatiotemporal patterns of China’s FFCO₂ emissions under the impact of LUCC from 2000 to 2015

Zhao

Cohen

Chen

et al. 2020

Environ. Res. Lett.

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Fossil fuel carbon dioxide (FFCO 2 ) emissions have become a principal driver behind the increase of atmospheric CO 2 concentration and spatiotemporal variations of atmospheric CO 2 in the urban surface layer. This study quantifies the 2000-2015 urban high-resolution spatiotemporal patterns of China's FFCO 2 emissions under the impact of the land-use and land-cover change. Multi-source data were used together with various up-to-date geostatistics and spatial analysis methods. FFCO 2 emissions were determined to rise over the 15 years in the highest emitting cities in the South and East of China. The high-value clusters inside of all cities expanded outward from their city centers and in some cases transferred to economic development zones or new city centers, while the expansion speeds and variation time were found to differ significantly. We found further that then FFCO 2 emissions spatial distribution is interconnected with diverse factors: urbanization, and either croplands (rainfed, irrigated, and post-flooding) or native vegetation, being the two most important. As expected, the increase in urban areas was associated with increased FFCO 2 emissions, while the wettability in croplands or the increase in native vegetation have an association with the decrease of FFCO 2 emissions. Unlike previous studies, we have found no change associated with changes in water cover. Finally, while the primary source of FFCO 2 emissions is still coal, there has been a gradual move to cleaner energy (natural gas in Beijing) or more efficient industrial processes (Wuxi and Dalian), although diverse industrial structures and energy efficiencies exist. Over time, the current spatial patterns of FFCO 2 emissions in China will conflict with these trends at the macroscale.

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A Local Indicator of Multivariate Spatial Association: Extending Geary's c

Cited by 182 publications

References 49 publications

Characterizing the Spatial Determinants and Prevention of Malaria in Kenya

Characterizing the Spatial Determinants and Prevention of Malaria in Kenya

Further Widening or Bridging the Gap? A Cross-Regional Study of Unemployment across the EU Amid Economic Crisis

High-resolution spatiotemporal patterns of China’s FFCO₂ emissions under the impact of LUCC from 2000 to 2015

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A Local Indicator of Multivariate Spatial Association: Extending Geary's c

Cited by 182 publications

References 49 publications

Characterizing the Spatial Determinants and Prevention of Malaria in Kenya

Characterizing the Spatial Determinants and Prevention of Malaria in Kenya

Further Widening or Bridging the Gap? A Cross-Regional Study of Unemployment across the EU Amid Economic Crisis

High-resolution spatiotemporal patterns of China’s FFCO2 emissions under the impact of LUCC from 2000 to 2015

Contact Info

Product

Resources

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High-resolution spatiotemporal patterns of China’s FFCO₂ emissions under the impact of LUCC from 2000 to 2015