Map-making and myth-making in Broad Street: the London cholera epidemic, 1854

Brody, Howard; Rip, Michael Russell; Vinten-Johansen, Peter; Paneth, Nigel; Rachman, Stephen

doi:10.1016/s0140-6736(00)02442-9

Cited by 139 publications

(72 citation statements)

References 2 publications

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“…To find the optimum over the whole arrangement A, we need to determine Formula 3 for every cell, which means that we determine the six a i and the six b i coefficients. Of course, we can just do this from scratch for each cell individually, but that could require up to O(N) time per cell, since the cluster window belonging to each cell could intersect up to O(N) edges of P. 3 This then leads to a total of O(NK) time (where K is the complexity of the arrangement). We can do better by traversing the cells of the arrangement from cell to adjacent cell while maintaining some information.…”

Section: Computing the Optimal Placement In The Homogeneous Modelmentioning

confidence: 99%

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Processing aggregated data: the location of clusters in health data

Buchin

Kreveld

et al. 2011

Geoinformatica

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498Geoinformatica (2012) 16:497-521 data is given as a subdivision into regions with two values per region, the number of cases and the size of the population at risk. We formulate the problem as finding a placement of a cluster window of a given shape such that a cluster function depending on the population at risk and the cases is maximized. We propose area-based models to calculate the cases (and the population at risk) within a cluster window. These models are based on the areas of intersection of the cluster window with the regions of the subdivision. We show how to compute a subdivision such that within each cell of the subdivision the areas of intersection are simple functions. We evaluate experimentally how taking aggregation into account influences the location of the clusters found.

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Section: Computing the Optimal Placement In The Homogeneous Modelmentioning

confidence: 99%

“…Analyzing the geographic nature of disease cases has been a key factor in finding the source of many outbreaks. Since the famous case of geographical analysis of John Snow in 1854 [3,29], numerous examples have been documented in the literature of several fields like epidemiology, public health, preventive medicine, and medical geography.…”

Section: Introductionmentioning

confidence: 99%

Processing aggregated data: the location of clusters in health data

Buchin

Kreveld

et al. 2011

Geoinformatica

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“…The original impetus for the sewer network was likely driven by overblown rhetoric and unfair criticism of urban institutions by individuals such as Edwin Chadwick, and relied on an entirely incorrect understanding of disease vector biology [6,7]. While there are heroes that emerge from the story, scientists who analyzed the data and made correct determinations (e.g., John Snow), the methodology and influence attributed to them has become apocryphal [6,8,9]. When first applied, the retrospectively "right" decision, isolating humans from their waste, was undermined by the fact that dramatically improving hydrologic connectivity between London privy pots and the Thames introduced much more sewage to drinking water sources.…”

Section: Human Decisions and Water Usementioning

confidence: 99%

Historical Legacies, Information and Contemporary Water Science and Management

Bain

Arrigo

Green

et al. 2011

Water

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Hydrologic science has largely built its understanding of the hydrologic cycle using contemporary data sources (i.e., last 100 years). However, as we try to meet water demand over the next 100 years at scales from local to global, we need to expand our scope and embrace other data that address human activities and the alteration of hydrologic systems. For example, the accumulation of human impacts on water systems requires exploration of incompletely documented eras. When examining these historical periods, basic questions relevant to modern systems arise: (1) How is better information incorporated into water management strategies? (2) Does any point in the past (e.g., colonial/pre-European conditions in North America) provide a suitable restoration target? and (3) How can understanding legacies improve our ability to plan for future conditions? Beginning to answer these questions indicates the vital need to incorporate disparate data and less accepted methods to meet looming water management challenges. OPEN ACCESSWater 2011, 3 567

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“…The history of medicine is replete with astute clinical observations that have led to major scientific breakthroughs. The classic example in public health is one physician's observation that cholera cases in London were directly related to the distance a person lived from a certain public well (2) . This observation was made at a time when microbiology did not yet exist.…”

Section: International Journal Of Therapeutic Massage and Bodywork-vomentioning

confidence: 99%

From the Editor-in-Chief’s Perspective . . . .

Findley¹

2008

IJTMB

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Map-making and myth-making in Broad Street: the London cholera epidemic, 1854

Cited by 139 publications

References 2 publications

Processing aggregated data: the location of clusters in health data

Processing aggregated data: the location of clusters in health data

Historical Legacies, Information and Contemporary Water Science and Management

From the Editor-in-Chief’s Perspective . . . .

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