Web-based analytical tools for the exploration of spatial data

Anselin, Luc; Kim, Yong-Wook; Syabri, Ibnu

doi:10.1007/s10109-004-0132-5

Cited by 76 publications

(32 citation statements)

References 29 publications

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“…It was calculated as the number of LGAs within each State with HPAI outbreaks between 2005 and 2008 over the total number of LGAs in that State and corrected or smoothed using the spatial rate procedure in OpenGeoDa 0.9.9.12 (1998-2011, GeoDa Center for Geospatial Analysis and Computation, Tempe, AZ, USA). In the spatial rate procedure, the spatial neighbours and the underlying spatial risk in each State are considered for a smoothed risk calculation (Anselin et al, 2004). Thereby, common State boundaries identified neighbouring States and were expressed as a Rook contiguity matrix with one order of adjacency (Anselin et al, 2004).…”

Section: Smoothed Incidence Risk Calculationmentioning

confidence: 99%

“…In the spatial rate procedure, the spatial neighbours and the underlying spatial risk in each State are considered for a smoothed risk calculation (Anselin et al, 2004). Thereby, common State boundaries identified neighbouring States and were expressed as a Rook contiguity matrix with one order of adjacency (Anselin et al, 2004). The shape file of the administrative boundaries of Nigeria was obtained from the International Institute of Tropical Agriculture, Geographical Information System Department, Ibadan, Nigeria.…”

Section: Smoothed Incidence Risk Calculationmentioning

confidence: 99%

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Incidence of Highly Pathogenic Avian Influenza H5N1 in Nigeria, 2005-2008

Henning¹,

Bett

Okike

et al. 2012

Transboundary and Emerging Diseases

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Outbreaks of highly pathogenic avian influenza (HPAI) H5N1 occurred in Nigeria between December 2005 and July 2008. We describe temporal and spatial characteristics of these outbreaks at State and Local Government Area (LGA) levels. A total of 25 of 37 States (67.6%; Exact 95% CI: 50.2-82.0%) and 81 of 774 LGAs (10.5%; Exact 95% CI: 8.4-12.8%) were affected by HPAI outbreaks over the period from 2005 to 2008. The incidence risk of HPAI outbreak occurrence at the State level was 5.6% (0.7-18.7%) for 2005, 50.0% (30.7-69.4%) for 2006, 54.5% (29.9-80.3%) for 2007 and 0% for 2008. Only very few LGAs experienced HPAI outbreaks within the affected States. The incidence risk of HPAI outbreak occurrence on a LGA level was 0.3% (0.0-0.9%) for 2005, 6.6% (4.9-8.6%) for 2006, 4.2% (2.9-6.0%) for 2007 and 0% for 2008. The mean period between farmers noticing HPAI outbreaks and reporting them to veterinary authorities, and between reporting HPAI outbreaks and the depopulation of infected premises, was for both 4.5 days; both periods also had medians of 1 day. We have estimated the spatially smoothed incidence risk for the whole outbreak period and identified the existence of a large corridor in the western part of Nigeria and a smaller corridor in south-eastern part, where the risk of HPAI occurrence was lower than in the rest of the country. The effect of HPAI control policies on the outbreaks patterns are discussed, as well as possible reasons why HPAI did not become endemic in Nigeria.

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Section: Smoothed Incidence Risk Calculationmentioning

confidence: 99%

Section: Smoothed Incidence Risk Calculationmentioning

confidence: 99%

Incidence of Highly Pathogenic Avian Influenza H5N1 in Nigeria, 2005-2008

Henning¹,

Bett

Okike

et al. 2012

Transboundary and Emerging Diseases

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“…An adjusted Moran's I was proposed based on EB smoothing by Assunção and Reis (1999), while another smoothing method was developed by Kafadar (1996) to consider varied demographic compositions of areas by age and race. Marshall's EB rate is incorporated in web-based analytical tools for mapping and spatial autocorrelation and GeoDa (Anselin, Kim, and Syabri 2004;Anselin, Syabri, and Kho 2006).…”

Section: Heteroscedasticity-consistent Empirical Bayes Methodsmentioning

confidence: 99%

Spatial Autocorrelation Statistics of Areal Prevalence Rates under High Uncertainty in Denominator Data

Jung

Thill

Issel

2018

Geographical Analysis

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We propose a new estimator of spatial autocorrelation of areal incidence or prevalence rates in small areas, such as crime and health indicators, for correcting spatially heterogeneous sampling errors in denominator data. The approach is dubbed the heteroscedasticity‐consistent empirical Bayes (HC‐EB) method. As American Community Survey (ACS) data have been released to the public for small census geographies, small‐area estimates now form the demographic landscape of neighborhoods. Meanwhile, there is growing awareness of the diminished statistical validity of global and local Moran’s I when such small‐area estimates are used in denominator data. Using teen birth rates by census tracts in Mecklenburg County, North Carolina, we present comparisons of conventional and new HC‐EB estimates of Global and Local Moran’s I statistics created on ACS data, along with estimates on ground truth values from the 2010 decennial census. Results show that the new adjustment method dramatically enhances the statistical validity of global and local spatial autocorrelation statistics.

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“…Rate smoothing is a technique used to: 1) stabilize rates based on small numbers and 2) reduce noise in rates caused by different population sizes [30, 31]. In the context of CER, especially with new drug introductions where study populations are smaller, rate smoothing allows us to examine data at a finer spatial resolution (e.g., zip codes) rather than aggregating data to larger regions (e.g., counties).…”

Section: Methodsmentioning

confidence: 99%

Adjusting for geographic variation in observational comparative effectiveness studies: a case study of antipsychotics using state Medicaid data

Root

Thomas

Campagna

et al. 2014

BMC Health Serv Res

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BackgroundArea-level variation in treatment and outcomes may be a potential source of confounding bias in observational comparative effectiveness studies. This paper demonstrates how to use exploratory spatial data analysis (ESDA) and spatial statistical methods to investigate and control for these potential biases. The case presented compares the effectiveness of two antipsychotic treatment strategies: oral second-generation antipsychotics (SGAs) vs. long-acting paliperiodone palmitate (PP).MethodsA new-start cohort study was conducted analyzing patient-level administrative claims data (8/1/2008–4/30/2011) from Missouri Medicaid. ESDA techniques were used to examine spatial patterns of antipsychotic prescriptions and outcomes (hospitalization and emergency department (ED) visits). Likelihood of mental health-related outcomes were compared between patients starting PP (N = 295) and oral SGAs (N = 8,626) using multilevel logistic regression models adjusting for patient composition (demographic and clinical factors) and geographic region.ResultsESDA indicated significant spatial variation in antipsychotic prescription patterns and moderate variation in hospitalization and ED visits thereby indicating possible confounding by geography. In the multilevel models for this antipsychotic case example, patient composition represented a stronger source of confounding than geographic context.ConclusionBecause geographic variation in health care delivery is ubiquitous, it could be a comparative effectiveness research (CER) best practice to test for possible geographic confounding in observational data. Though the magnitude of the area-level geography effects were small in this case, they were still statistically significant and should therefore be examined as part of this observational CER study. More research is needed to better estimate the range of confounding due to geography across different types of observational comparative effectiveness studies and healthcare utilization outcomes.Electronic supplementary materialThe online version of this article (doi:10.1186/1472-6963-14-355) contains supplementary material, which is available to authorized users.

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Web-based analytical tools for the exploration of spatial data

Cited by 76 publications

References 29 publications

Incidence of Highly Pathogenic Avian Influenza H5N1 in Nigeria, 2005-2008

Incidence of Highly Pathogenic Avian Influenza H5N1 in Nigeria, 2005-2008

Spatial Autocorrelation Statistics of Areal Prevalence Rates under High Uncertainty in Denominator Data

Adjusting for geographic variation in observational comparative effectiveness studies: a case study of antipsychotics using state Medicaid data

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