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CIPH Rwanda CSMT_ENACT falciparum TempCondProb: Percent Occurrence of Temperature Condition in Historical Record data
Percent Occurrence of Temperature Condition in Historical Record from CIPH Rwanda CSMT_ENACT falciparum: Climatic Suitability for a Malaria Transmission.Independent Variables (Grids)
- Month
- grid: /T (months since 01-Jan) periodic (Jan) to (Dec) by 1.0 N= 12 pts :grid
- Longitude (longitude)
- grid: /X (degree_east) ordered (28.525E) to (30.975E) by 0.05000002 N= 50 pts :grid
- Latitude (latitude)
- grid: /Y (degree_north) ordered (2.975S) to (1.025S) by 0.05 N= 40 pts :grid
Other Info
- CE
- 100.0
- CI
- 25.0
- CS
- 0.0
- datatype
- realarraytype
- file_missing_value
- -9999
- fnname
- flagge
- maxncolor
- 254
- missing_value
- NaN
- scale_max
- 100.0
- scale_min
- 0.0
- units
- unitless
- history
- CIPH Rwanda CSMT_ENACT falciparum TempCondProb
- Boxes with less than 0.0% dropped
Averaged over T2[1981, 2010] minimum 0.0% data present
- Boxes with less than 0.0% dropped
- colorscale
References
Grover-Kopec, E. K., M. B. Blumenthal, P. N. Ceccato, T. Dinku, J. Omumbo, S. J. Connor, 2006: Web-based climate information resources for malaria control in Africa. Malaria Journal, 5:38.
Last updated: Wed, 26 Jun 2024 17:08:06 GMT
Data Views
Views
data as colors
data as contours
colors with land
contours with land
colors with coasts| T | X | Y | |
 CIPH Rwanda CSMT_ENACT falciparum[AllCondProb
Annual_Sum
PcpnCondProb
RHCondProb
TempCondProb
] | |||
| grid: /X (degree_east) ordered (28.5375E) to (30.975E) by 0.0375 N= 66 pts :grid | D | ||
|---|---|---|---|
| grid: /Y (degree_north) ordered (2.9625S) to (1.05S) by 0.0375 N= 52 pts :grid | D | ||
| grid: /T (months since 01-Jan) periodic (Jan) to (Dec) by 1.0 N= 12 pts :grid | M | ||
| CIPH Rwanda CSMT_ENACT falciparum PcpnCondProb[ X Y T |] | M | D | D |
| CIPH Rwanda CSMT_ENACT falciparum AllCondProb[ X Y T |] | M | M | M |
 CIPH Rwanda CSMT_ENACT falciparum TempCondProb[ X Y T |] | M | M | M |
Filters
Here are some filters that are useful for manipulating data. There are actually many more available, but they have to be entered manually. See Ingrid Function Documentation for more information.- Monthly Climatology calculates a monthly climatology by averaging over all years.
- anomalies calculates the difference between the (above) monthly climatology and the original data.
- Integrate along X Y T
- Differentiate along X Y T
- Take differences along X Y T
RMS (root mean square with mean *not* removed) over X Y T | X Y X T Y T | X Y T |
RMSA (root mean square with mean removed) over X Y T | X Y X T Y T | X Y T |
Maximum over X Y T | X Y X T Y T | X Y T |
Minimum over X Y T | X Y X T Y T | X Y T |
Detrend (best-fit-line) over X Y T | X Y X T Y T | X Y T |
Note on units