dhis2-devs team mailing list archive

[Ola Hodne Titlestad <olati@xxxxxxxxxx>]

Hi,

Search for onchange in this page:
http://208.76.222.114/confluence/display/DHIS1/DHIS+1.4
<http://208.76.222.114/confluence/display/DHIS1/DHIS+1.4>
----------------------------------
Ola Hodne Titlestad (Mr)
HISP
Department of Informatics
University of Oslo

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2010/11/18 Lars Helge Øverland <larshelge@xxxxxxxxx>

>
>
> 2010/11/18 Bob Jolliffe <bobjolliffe@xxxxxxxxx>
>
> Can someone define what exactly is an onChange period type?  Its a
>> term I have heard referred to a lot but I must confess I don't know if
>> I properly understand what it means.  Do we simply mean an arbitrary
>> period of time (without frequency) ie. it has start and end date only,
>> but no inherent frequency characteristic?  So start date would equal
>> the end date of the previous OnChange period for the same series of
>> datavalues.  Or is it an instant ie. an observation captured at a
>> moment in time with no inherent interval implied?  So
>> startdate=enddate?
>>
>> One case I recall hearing onChange periods being discussed was regard
>> staffing data in the context of data import from iHRIS.  The number of
>> doctors might change infrequently so we might capture number of
>> doctors only when there is a change, rather than capturing the same
>> numbers each month.  Either way it raises an interesting problem. I
>> think we define the aggregation "operator" (sum, average etc) per data
>> element.  But it seems to me, looking at this particular example, that
>> you would actually want to aggregate differently depending on which
>> axis (time and space) that you were aggregating along.  So for example
>> if I have datavalues for "number of doctors" which I collect on a
>> monthly report, it makes sense to sum across the spatial axis and
>> average across the time axis
>>
>>               clinicA     clinicB   DistrictTotal
>> Jan           5              7            12
>> Feb           5              6            11
>> Mar           5              5            10
>>
>> Quarter      5             6             11
>>
>> Maybe there are other such cases where the aggregation operator would
>> differ across different axes.  So there are maybe two ways to
>> rationalise this: one is to not capture the data monthly, but rather
>> "onChange".  This does have an implication for things like monthly
>> data collection instruments, because I suspect frequently there will
>> be a requirement to capture such data routinely.  The other is for the
>> dataelement to capture the change rather than the cumulative count -
>> like we would do with "new malaria cases" for example.  In this case
>> we'd capture "new doctors".  In that case we could sum across both
>> axes and get a sensible result, but one which would only be useful
>> with a sort of opening balance for the dataelement.   So it is not so
>> much the period type which is onChange but the dataValue type.  The
>> datavalue either reflects what has happened within that period or what
>> the cumulative status is at the end of that period.
>>
>> This is maybe confusing two related issues, but the other approach
>> which comes to mind is to have two aggregation operators for the data
>> element - the time aggregation operator and the space aggregation
>> operator.  This might lead a simple way out of the contradictions and
>> complications above.  For many, perhaps most, these would both be
>> "sum".  But for others, like the above, we would "sum" over space and
>> "average" over time.  I am sure there might be other examples where
>> the reverse is true.
>>
>> I am also sure these are all well known and well solved problems, so
>> apologies if I am being ignorant.
>>
>> Cheers
>> Bob
>>
>> Philosophical aside: Frederick Engels in Anti-Durhring, defines time
>> as that quantity required in order for things to change.  I always
>> like that definition :-)
>>
>
>
> Quick comment: Currently the aggregation operator property applies to the
> time axis only, space axis is always sum.
>
>
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