Visualizing the impact of space-time uncertainties on dengue fever patterns

Delmelle, Eric; Dony, Coline; Casas, Irene; Jia, Meijuan; Tang, Wenwu

doi:10.1080/13658816.2013.871285

Cited by 81 publications

(55 citation statements)

References 71 publications

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“…Previous research has identified flower pots, water storage containers, impervious surface catchments, and other anthropogenic water sources as opportunities for Aedes aegypti larval development [1,6]. These features are common to domesticated or developed areas, perpetuating the likelihood of the Aedes aegypti mosquito to be found in urban/populated environments, and increasing the opportunity for mature specimens to feed on human populations to disperse the virus [4,6,20].…”

Section: Introductionmentioning

confidence: 99%

Exploratory Analysis of Dengue Fever Niche Variables within the Río Magdalena Watershed

2016

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Previous research on Dengue Fever have involved laboratory tests or study areas with less diverse temperature and elevation ranges than is found in Colombia; therefore, preliminary research was needed to identify location specific attributes of Dengue Fever transmission. Environmental variables derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) satellites were combined with population variables to be statistically compared against reported cases of Dengue Fever in the Río Magdalena watershed, Colombia. Three-factor analysis models were investigated to analyze variable patterns, including a population, population density, and empirical Bayesian estimation model. Results identified varying levels of Dengue Fever transmission risk, and environmental characteristics which support, and advance, the research literature. Multiple temperature metrics, elevation, and vegetation composition were among the more contributory variables found to identify future potential outbreak locations.

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Section: Introductionmentioning

confidence: 99%

Exploratory Analysis of Dengue Fever Niche Variables within the Río Magdalena Watershed

2016

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“…calls in/out, and total Internet traffic -in a space-time cube framework, following , and using the visualization package Voxler: the Xand Y-axis denote the geographic space, while the Z-axis is the temporal axis, as presented in Delmelle et al (2014). Volume rendering reflects the strength of mobile phone usage.…”

Section: Incoming Outgoingmentioning

confidence: 99%

Mapping collective human activity in an urban environment based on mobile phone data

Sagl

Delmelle

2014

Cartography and Geographic Information Science

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Identifying and characterizing variations of human activity -specifically changes in intensity and similarity -in urban environments provide insights into the social component of those eminently complex systems. Using large volumes of usergenerated mobile phone data, we derive mobile communication profiles that we use as a proxy for the collective human activity. In this article, geocomputational methods and geovisual analytics such as self-organizing maps (SOM) are used to explore the variations of these profiles, and its implications for collective human activity. We evaluate the merits of SOM as a cross-dimensional clustering technique and derived temporal trajectories of variations within the mobile communication profiles. The trajectories' characteristics such as length are discussed, suggesting spatial variations in intensity and similarity in collective human activity. Trajectories are linked back to the geographic space to map the spatial and temporal variation of trajectory characteristics. Different trajectory lengths suggest that mobile phone activity is correlated with the spatial configuration of the city, and so at different times of the day. Our approach contributes to the understanding of the spacetime social dynamics within urban environments.

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“…Although several studies have been reported on this issue in modern demography and epidemiology (e.g., Zandbergen 2007Zandbergen , 2009Griffith et al 2007;Mazumdar et al 2008;Vieira et al 2010), this issue is a rather neglected topic that requires additional attention (Jacquez 2012). In addition, with a few exceptions (e.g., Delmelle et al 2014) little attention has been focused on the temporal quality of the geocoding of longitudinal data. Such quality might represent the temporal accuracy of the reference data, e.g., how close each start and end date of a property unit stored in an object-lifeline representation is to the ''true'' time period for which the property unit existed.…”

Section: Geocoding Of Demographic Databasesmentioning

confidence: 99%

Importance of the Geocoding Level for Historical Demographic Analyses: A Case Study of Rural Parishes in Sweden, 1850–1914

et al. 2017

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Geocoding longitudinal and individual-level historical demographic databases enables novel analyses of how micro-level geographic factors affected demographic outcomes over long periods. However, such detailed geocoding involves high costs. Additionally, the high spatial resolution cannot be properly utilized if inappropriate methods are used to quantify the geographic factors. We assess how different geocoding levels and methods used to define geographic variables affects the outcome of detailed spatial and historical demographic analyses. Using a longitudinal and individual-level demographic database geocoded at the property unit level, we analyse the effects of population density and proximity to wetlands on all-cause mortality for individuals who lived in five Swedish parishes, 1850-1914. We compare the results from analyses on three detailed geocoding levels using two common quantification methods for each geographic variable. Together with the method selected for quantifying the geographic factors, even small differences in positional accuracy (20-50 m) between the property units and slightly coarser geographic levels heavily affected the results of the demographic analyses. The results also show the importance of accounting for geographic changes over time. Finally, proximity to wetlands and population density affected the mortality of women and children, respectively. However, all possible determinants of mortality were not evaluated in the analyses. In conclusion, for rural historical areas, geocoding to property units is likely necessary for fine-scale analyses at distances within a few hundred metres. We must also carefully consider

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Visualizing the impact of space-time uncertainties on dengue fever patterns

Cited by 81 publications

References 71 publications

Exploratory Analysis of Dengue Fever Niche Variables within the Río Magdalena Watershed

Exploratory Analysis of Dengue Fever Niche Variables within the Río Magdalena Watershed

Mapping collective human activity in an urban environment based on mobile phone data

Importance of the Geocoding Level for Historical Demographic Analyses: A Case Study of Rural Parishes in Sweden, 1850–1914

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