Processing aggregated data: the location of clusters in health data

Buchin, Kevin; Buchin, Maike; Kreveld, Marc van; Löffler, Maarten; Luo, Jun; Silveira, Rodrigo I.

doi:10.1007/s10707-011-0143-6

Cited by 8 publications

(5 citation statements)

References 29 publications

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“…This will help policymakers analyse spatial risk factors to figure out the way to move forward of health care strategies for health services preparation and implementation. Areal interpolation has been demonstrated to be a promising method for defining endemic disease clusters [30][31][32][33].The projection of areal interpolation moves the complicated structure from high dimensional regions into lower dimensional clusters, which is essential to cluster endemic disease areas based on the neighbourhood relations. The integration of local interpolation and GIS is designed successfully to produce dynamic visualisation, which in turn helps public health officials to decide Covid management in a timely manner.…”

Section: Discussionmentioning

confidence: 99%

Spatio-temporal analysis of COVID-19 in India – a geostatistical approach

Bhunia¹,

Roy

Shit³

2021

Spat. Inf. Res.

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Coronavirus (Covid) is a severe acute respiratory syndrome infectious disease, spreads primarily between human beings during close contact, most often through the coughing, sneezing, and speaking small droplets. A retrospective surveillance research is conducted in India during 30th January-21st March 2020 to gain insight into Covid's epidemiology and spatial distribution. Voronoi statistics is used to draw attention of the affected states from a series of polygons. Spatial patterns of disease clustering are analyzed using global spatial autocorrelation techniques. Local spatial autocorrelation has also been observed using statistical methods from Getis-Ord G i *. The findings showed that disease clusters existed in the area of research. Most of the clusters are concentrated in the central and western states of India, while the northeastern countries are still predominantly low-rate of clusters. This simulation technique helps public health professionals to identify risk areas for disease and take decisions in real time to control this viral disease.

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Section: Discussionmentioning

confidence: 99%

Spatio-temporal analysis of COVID-19 in India – a geostatistical approach

Bhunia¹,

Roy

Shit³

2021

Spat. Inf. Res.

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“…By contrast, several applications such as facility location planning [45] and traffic monitoring [7,31,39,41] do not need to work with individual data. In fact, in order to meet the third-party requirement and the user's privacy concerns, aggregation techniques [8,33] as a privacy-aware method could be applied [7,39]. In these types of examples, moving objects' trajectories need to be pre-processed.…”

Section: Related Workmentioning

confidence: 99%

The CASE histogram: privacy-aware processing of trajectory data using aggregates

et al. 2015

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Due to the high uptake of location-based services (LBSs), large spatio-temporal datasets of moving objects' trajectories are being created every day. An important task in spatial data analytics is to service range queries by returning trajectory counts within a queried region. The question of how to keep an individual user's data private whilst enabling spatial data analytics by third parties has become an urgent research direction. Indeed, it is increasingly becoming a concern for users. To preserve privacy we discard individual trajectories and aggregate counts over a spatial and temporal partition. However the privacy gained comes at a cost to utility: trajectories passing through multiple cells and re-entering a query region, lead to inaccurate query responses. This is known as the distinct counting problem. We propose the Connection Aware Spatial Euler (CASE) histogram to address this long-standing problem. The CASE histogram maintains the connectivity of a moving object path, but does not require the ID of an object to distinguish multiple entries into an arbitrary query region. Our approach is to process trajectories offline into aggregate counts which are sent to third parties, rather than the original trajectories. We also explore modifications of our aggregate counting approach that preserve differential privacy. Theoretically and experimentally we demonstrate that our method provides a high level of accuracy compared to the best known methods for the distinct counting problem, whilst preserving privacy. We conduct our experiments on both synthetic and real datasets over two competitive Euler histogram-based methods presented in the literature. Our methods enjoy improvements to accuracy from 10% up to 70% depending on trip data and query region size, with the greatest increase seen on the Microsoft T-Drive real dataset, representing a more than tripling of accuracy.

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“…Some data (e.g., number of diseases, population at risk) are summarized by regions or administrative units, i.e., in spatial clusters. In this context, new methods to compute clusters in spatially aggregated health data and identify multiple variable associations have been recently proposed [54,55]. For TB data analysis, this kind of spatial clustering may ease the identification of "hot spots" (e.g.…”

Section: Analysis: How To Spatially Cluster Health Datamentioning

confidence: 99%

Geospatial information infrastructures to address spatial needs in health: Collaboration, challenges and opportunities

Granell

Fernández

Díaz

2014

Future Generation Computer Systems

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Abstract:Most health-related issues such as public health outbreaks and epidemiological threats are better understood from a spatial-temporal perspective and, clearly demand related geospatial data sets and services so that decision makers may jointly make informed decisions and coordinate response plans. Although current health applications support a kind of geospatial features, these are still disconnected from the wide range of geospatial services and datasets that geospatial information infrastructures may bring into health. In this paper we are questioning the hypothesis whether geospatial information infrastructures, in terms of standards-based geospatial services, technologies, and data models as operational assets already in place, can be exploited by health applications for which the geospatial dimension is of great importance . This may be certainly addressed by defining better collaboration strategies to uncover and promote geospatial assets to health community. We discuss the value of collaboration, as well as the opportunities that geographic information infrastructures offer to address geospatial challenges in health applications.

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Processing aggregated data: the location of clusters in health data

Cited by 8 publications

References 29 publications

Spatio-temporal analysis of COVID-19 in India – a geostatistical approach

Spatio-temporal analysis of COVID-19 in India – a geostatistical approach

The CASE histogram: privacy-aware processing of trajectory data using aggregates

Geospatial information infrastructures to address spatial needs in health: Collaboration, challenges and opportunities

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