GeoDa: An Introduction to Spatial Data Analysis

Anselin, Luc; Syabri, Ibnu; Kho, Youngihn

doi:10.1007/978-3-642-03647-7_5

Cited by 851 publications

(981 citation statements)

References 39 publications

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“…All models and estimations were computed using R [17]. We used GeoDa [18] software and the R package SPDEP [19] to create the contiguity matrix and compute Moran's I and LISA.…”

Section: Resultsmentioning

confidence: 99%

Micro and macro population effects in disease transmission: the case of varicella

et al. 2009

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SUMMARYProfiles of varicella-zoster virus (VZV) seroprevalence have shown large variability in European countries in which vaccination has not been implemented. Differences in micro and macro population structures (e.g. household and municipality, respectively) may explain such variability, which is the focus of a population-based study of varicella in 12 000 children from 7800 French households in Corsica. The cumulative incidence was 89 % at age 11 years, as the median age at infection was 5 years in first-born children, but decreased to 3 . 9 years in younger siblings. Hazard of infection in households increased as the first-born child initially enrolled in primary school. Age at infection was higher in less populated areas. Household attack rates increased with age (55 % in <6 months, >90 % in >3 years) and household secondary attack rate was y70 %. Several levels of population structure independently affect age at varicella infection.

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“…All models and estimations were computed using R [17]. We used GeoDa [18] software and the R package SPDEP [19] to create the contiguity matrix and compute Moran's I and LISA.…”

Section: Resultsmentioning

confidence: 99%

Micro and macro population effects in disease transmission: the case of varicella

et al. 2009

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“…For example, the indicator values in one area will be affected by the surrounding areas. SLM is expressed as follows (Anselin et al, 2006):…”

Section: Spatial Econometric Regression Analysismentioning

confidence: 99%

“…Compared with SLM, the spatial dependence in the SEM enters through the errors rather than through the neighboring values of the dependent variable in the SLM (Anselin et al, 2006). SEM is specified as:…”

Section: Spatial Econometric Regression Analysismentioning

confidence: 99%

Land use transitions and their dynamic mechanism: The case of the Huang-Huai-Hai Plain

Liu

Long

2016

J. Geogr. Sci.

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Land use transition refers to changes in land use morphology, including dominant morphology and recessive morphology, of a particular region over a period of time driven by various factors. Recently, issues related to land use transition in China have attracted interest among a wide variety of researchers as well as government officials. This paper examines the patterns of land use transition and their dynamic mechanism in the Huang-Huai-Hai Plain during 2000-2010. First, the spatio-temporal patterns of land use transition, their characteristics and the laws governing them were analyzed. Second, based on the established conceptual framework for analyzing the dynamic mechanism of land use transition, a spatial econometric regression analysis method was used to analyze the dynamic mechanism of the five types of major land use transition in the Huang-Huai-Hai Plain at the county level. Land use pattern changes in the study area were characterized by an increase in construction land, water body and forested land, along with a decrease in farmland, unused land and grassland. The changes during 2000-2005 were much more significant than those during 2005-2010. In terms of factors affecting land use transitions, natural factors form the basis, and they have long-term effects. Socio-economic factors such as population and GDP, however, tend to determine the direction, structure, size and layout of land use transition over shorter time periods. Land law and policy factors play a mandatory guiding and restraining role in land use transitions, so as to improve the overall efficiency of land use. Land resource engineering is also an important tool to control land use transitions. In general, the five types of major land use transition were the result of the combined action of various physical, social and economic factors, of which traffic condition and location condition had the most significant effects, i.e. they were the common factors in all land use transitions. Understanding the spatio-temporal process of land use transitions and their dynamic mechanisms is an important foundation for utilizing land resources, protecting regional ecological environment and promoting sustainable regional socio-economic development. 516Journal of Geographical Sciences

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“…GeoDa software (Anselin et al, 2006) was used to detect spatial autocorrelation. Global spatial autocorrelation, calculated by Moran's I (Moran, 1950), was used to explore the overall clustering of annual malaria cases from 2009 to 2014 in Zambia.…”

Section: Spatial and Statistical Analysismentioning

confidence: 99%

“…Using this approach, districts with small populations at risk tend to have their observed rates adjusted more heavily towards the neighbourhood average than for more densely populated districts. The annual, average rate of cases diagnosed clinically from 2009-2014 was spatially smoothed by employing an empirical Bayes spatial smoothing procedure using GeoDa software, version 0.9.5-i (Anselin et al, 2006).…”

Section: Spatial and Statistical Analysismentioning

confidence: 99%

Diagnostic approaches to malaria in Zambia, 2009-2014

et al. 2015

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Malaria is an important health burden in Zambia with proper diagnosis remaining as one of the biggest challenges. The need for reliable diagnostics is being addressed through the introduction of rapid diagnostic tests (RDTs). However, without sufficient laboratory amenities in many parts of the country, diagnosis often still relies on non-specific, clinical symptoms. In this study, geographical information systems were used to both visualize and analyze the spatial distribution and the risk factors related to the diagnosis of malaria. The monthly reported, district-level number of malaria cases from January 2009 to December 2014 were collected from the National Malaria Control Center (NMCC). Spatial statistics were used to reveal cluster tendencies that were subsequently linked to possible risk factors, using a non-spatial regression model. Significant, spatio-temporal clusters of malaria were spotted while the introduction of RDTs made the number of clinically diagnosed malaria cases decrease by 33% from 2009 to 2014. The limited access to road network(s) was found to be associated with higher levels of malaria, which can be traced by the expansion of health promotion interventions by the NMCC, indicating enhanced diagnostic capability. The capacity of health facilities has been strengthened with the increased availability of proper diagnostic tools and through retraining of community health workers. To further enhance spatial decision support systems, a multifaceted approach is required to ensure mobilization and availability of human, infrastructural and technological resources. Surveillance based on standardized geospatial or other analytical methods should be used by program managers to design, target, monitor and assess the spatio-temporal dynamics of malaria diagnostic resources country-wide.

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GeoDa: An Introduction to Spatial Data Analysis

Cited by 851 publications

References 39 publications

Micro and macro population effects in disease transmission: the case of varicella

Micro and macro population effects in disease transmission: the case of varicella

Land use transitions and their dynamic mechanism: The case of the Huang-Huai-Hai Plain

Diagnostic approaches to malaria in Zambia, 2009-2014

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