THE LINK:
THE LINK:
[[Spatial epidemiology]] can be made more accurate with the integrated mapping of incidence and uncertainty <ref>{{cite journal |last1=De Cola |first1=Lee |title=Spatial forecasting of disease risk and uncertainty |journal=Cartography and Geographic Information Science |date=2002 |volume=29 |issue=4 |page=363-380 |doi=10.1559/152304002782008413 |url=https://www.tandfonline.com/doi/abs/10.1559/152304002782008413|url-access=subscription }}</ref>.
[[Spatial epidemiology]] can be made more accurate with the integrated mapping of incidence and uncertainty<ref>{{cite journal |last1=De Cola |first1=Lee |title=Spatial forecasting of disease risk and uncertainty |journal=Cartography and Geographic Information Science |date=2002 |volume=29 |issue=4 |page=363-380 |doi=10.1559/152304002782008413 |url=https://www.tandfonline.com/doi/abs/10.1559/152304002782008413|url-access=subscription }}</ref>.
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Adding a citation
THE WIKIPEDIA LINK:
https://en.wikipedia.org/wiki/Help:Introduction_to_referencing_with_Wiki_Markup/2
THE CITATION:
De Cola, L. (2002). ”Spatial forecasting of disease risk and uncertainty.” Cartography and Geographic Information Science 29(4), pp. 363-380.
THE LINK:
Spatial epidemiology can be made more accurate with the integrated mapping of incidence and uncertainty[1].
CO2 growth estimation
Based on monthly Mauna Loa CO2 data from 1958 March to 2013 May the mean yearly value for year t is can be estimated as
- y ( t ) − 255 = e x p ( − 27.2 + .0160 t ) , {\displaystyle y(t)-255=exp(-27.2+.0160t),}
which corresponds to exponential growth of 1.6% per year and implies a “historical” value of about 255 ppmv.[1]
DENSITY OF WATER
(can’t figure out how to get the column headings to span columns…)
| LENGTH colspan=2 | VOLUME | MASS | |||||
|---|---|---|---|---|---|---|---|
| MULTIPLY | NAME | ABBREVIATION | MULTIPLY | NAME | ABBREVIATION | NAME | ABBREVIATION |
| 103 = 1000 | kilometer | km | 109 = 1,000,000,000 | cubic kilometer | km3 | gigatonne | Gt |
| 101 = 10 | meter | m | 103 | cubic meter | m3 | tonne | t |
| 101 = 10 | decimeter | .1 m | 103 | liter | (.1 m)3 | kilogram | kg |
| 101 = 10 | centimeter | cm | 103 | milliliter | cm3 | gram | g |
| 100 = 1 | millimeter | mm | 1 | microliter | mm3 | milligram | mg |
Decomposition of time series
GRAPHIC – noaa_co2_seasonal_decompose.png
“C:\0ldecola\projects\wikipedia\co2\noaa_co2_seasonal_decompose.png”
CAPTION: Atmospheric CO2 measured at Mauna Loa Hawaiʻi in parts per million (ppm) 1959-2024.
DESCRIPTION: The data are provided by the National Oceanic and Atmospheric Administration
[1] in several forms, including monthly average
The data were seasonally decomposed so that the underlying ‘trend’ can be seen, with the seasonal component shown in an inset.
TEXT: A particularly good example of seasonal decomposition is appied to the daily collection of dry air CO2 by the US National Oceaning and Atmospheric Administration (NOAA). Averages were computed for each month, so that the data show a typically ‘sawtooth’ appearance which can be removed to show the underlying, roughly exponential, trend, along with a box and wisker plot
REFERENCES HOW TO FORMAT, LINK, ETC.?
Venables, W. N. and B. D. Ripley (1997). Modern applied statistics with S-Plus. New York, Springer.
“How we measure background CO2 levels on Mauna Loa.’ [[2]]
