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A comparison of spatial heterogeneity with local cluster detection methods for chronic respiratory diseases in Thailand

https://doi.org/10.12688/f1000research.12128.2
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Applied Spatial Statistics for Public Health Data
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The spatial distribution of atopic dermatitis cases in a population of insured Swedish dogs
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Spatial scaling of stable night lights
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Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data
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A technique for using composite DMSP/OLS “City Lights” satellite data to map urban area
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Radiance Calibration of DMSP-OLS Low-Light Imaging Data of Human Settlements
inferredcites →https://doi.org/10.1016/s0167-9473(02)00160-3openalex-snapshot
Power comparisons for disease clustering tests
inferredcites →https://doi.org/10.1016/s0277-9536(01)00203-9openalex-snapshot
Alcohol mortality: a comparison of spatial clustering methods
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Global Metropolis: Assessing Economic Activity in Urban Centers Based on Nighttime Satellite Images
inferredcites →https://doi.org/10.1080/01431160802430693openalex-snapshot
Modelling the population density of China at the pixel level based on DMSP/OLS non‐radiance‐calibrated night‐time light images
inferredcites →https://doi.org/10.1080/01431160903261005openalex-snapshot
The use of night-time lights satellite imagery as a measure of Australia's regional electricity consumption and population distribution
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Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption
inferredcites →https://doi.org/10.1080/03610929708831995openalex-snapshot
A spatial scan statistic
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Light at Night Co‐distributes with Incident Breast but not Lung Cancer in the Female Population of Israel
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Remote sensing of human beings – a perspective from nighttime light
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Spatial Analysis in Epidemiology
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The rise of the mega-region
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Breast Cancer Clusters in the Northeast United States: A Geographic Analysis
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GeoDa : An Introduction to Spatial Data Analysis
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The Analysis of Spatial Association by Use of Distance Statistics
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Local Indicators of Spatial Association—LISA
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A Comparison of Three Exploratory Methods for Cluster Detection in Spatial Point Patterns
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Identifying High‐risk Geographic Areas for Cardiac Arrest Using Three Methods for Cluster Analysis
inferredcites →https://doi.org/10.1126/science.1210554openalex-snapshot
Explaining Seasonal Fluctuations of Measles in Niger Using Nighttime Lights Imagery
inferredcites →https://doi.org/10.1186/1476-072x-2-3openalex-snapshot
Local clustering in breast, lung and colorectal cancer in Long Island, New York
inferredcites →https://doi.org/10.1186/1476-072x-2-9openalex-snapshot
Power evaluation of disease clustering tests
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Spatial and temporal structure of typhoid outbreaks in Washington, D.C., 1906–1909: evaluating local clustering with the G i * statistic
inferredcites →https://doi.org/10.1198/016214506000000618openalex-snapshot
Tests of Spatial Randomness Adjusted for an Inhomogeneity
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A Bright Idea for Measuring Economic Growth
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Measuring Economic Growth from Outer Space
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COPD in Asia
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Night-time Imagery as a Tool for Global Mapping of Socioeconomic Parameters and Greenhouse Gas Emissions
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Night-time Imagery as a Tool for Global Mapping of Socioeconomic Parameters and Greenhouse Gas Emissions
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The Night Light Development Index (NLDI): a spatially explicit measure of human development from satellite data

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/s10708-022-10707-3openalex-snapshot
Comparing geospatial clustering methods to study spatial patterns of lung cancer rates in urban areas: A case study in Mashhad, Iran
inferred← citeshttps://doi.org/10.1016/j.sste.2022.100534openalex-snapshot
Spatiotemporal clusters and the socioeconomic determinants of COVID-19 in Toronto neighbourhoods, Canada
inferred← citeshttps://doi.org/10.1093/ofid/ofaa088openalex-snapshot
Getting Personal: How Childhood Vaccination Policies Shape the Landscape of Vaccine Exemptions
inferred← citeshttps://doi.org/10.1109/icdm59182.2024.00008openalex-snapshot
LISA: Learning-Integrated Space Partitioning Framework for Traffic Accident Forecasting on Heterogeneous Spatiotemporal Data
inferred← citeshttps://doi.org/10.1111/ctr.14821openalex-snapshot
Geographic hot spots of kidney transplant candidates wait‐listed post‐dialysis
inferred← citeshttps://doi.org/10.1111/rssc.12480openalex-snapshot
Bayesian Modelling for Spatially Misaligned Health Areal Data: A Multiple Membership Approach
inferred← citeshttps://doi.org/10.1186/s12874-023-02135-9openalex-snapshot
Evaluation and comparison of spatial cluster detection methods for improved decision making of disease surveillance: a case study of national dengue surveillance in Thailand
inferred← citeshttps://doi.org/10.2196/45870openalex-snapshot
Epidemic Characteristics, Spatiotemporal Pattern, and Risk Factors of Other Infectious Diarrhea in Fujian Province From 2005 to 2021: Retrospective Analysis
inferred← citeshttps://doi.org/10.22266/ijies2019.0630.11openalex-snapshot
Spatial Data Modeling on GIS for Classification of Measles-prone Region Using Multiple Attribute Decision Making
inferred← citeshttps://doi.org/10.24057/2071-9388-2021-090openalex-snapshot
Using Moran’s I For Detection And Monitoring Of The Covid-19 Spreading Stage In Thailand During The Third Wave Of The Pandemic
inferred← citeshttps://doi.org/10.3390/su17178002openalex-snapshot
Spatial Effects of Air Passenger Location Entropy on Airports’ Passenger Throughputs: A Case Study of Multi-Airport System in the Yangtze River Delta Region, China, with Implications for Sustainable Development
inferred← citeshttps://doi.org/10.4081/gh.2018.608openalex-snapshot
Prevalence of hypertension in Thailand: Hotspot clustering detected by spatial analysis
inferred← citeshttps://doi.org/10.4081/gh.2020.856openalex-snapshot
Modelling and analyzing spatial clusters of leptospirosis based on satellite-generated measurements of environmental factors in Thailand during 2013-2015
inferred← citeshttps://doi.org/10.4081/gh.2023.1203openalex-snapshot
Spatial pattern and heterogeneity of chronic respiratory diseases and relationship to socio-demographic factors in Thailand in the period 2016 to 2019
inferred← citeshttps://doi.org/10.4081/gh.2025.1329openalex-snapshot
Spatial association of socioeconomic and health service factors with antibiotic self-medication in Thailand
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