[OSGeo Africa] Request for HIV Prevalence for North West Province - Ngaka Modiri Molema District Municipality

Tue Aug 2 03:06:40 PDT 2016

Good day Colleagues
Can someone assist with HIV Prevalence data for Ngaka Modiri Molema District Municipality in the North West Province? Either spreadsheet or dataset will be appreciated.
Regards,
Flora Makgale

Sent from my Samsung Galaxy smartphone.<div>
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</div><!-- originalMessage --><div>-------- Original message --------</div><div>From: Calle Hedberg <chedberg at telkomsa.net> </div><div>Date: 01/08/2016  23:05  (GMT+02:00) </div><div>To: 'Africa local chapter discussions' <africa at lists.osgeo.org> </div><div>Subject: Re: [OSGeo Africa] Recent main place or sub-place spatial data       for     SA </div><div>
</div>
Francois,

What layer to use depends on the purpose.

I’ve looked at all the three layers Helene sent me now:

-          Main place layer with around 14,000 polygons, including names

-          Sub-place layer with over 22,000 polygons, including names and including population count (although 1.3 mill less than the Small Area Layer)

-          The Small Area Layer with over 90,000 polygons, without names but including population count (1.3 mill more than SP layer).

For the purpose of establishing a standard list of know “localities” that patients can use to describe where they live, SAL is useless – it only have numbers/codes. The SP layer is far better than the MP layer here, because the “main places” are too generic/large in many cases, especially urban areas. Take a look at this example from Tshwane – it should be obvious that the SP-names (sub-places) are far more useful as location names than the main place name (Akasia). People will know they live in Akasia, of course, but they will more typically say they live in Amandasig or Heather View or Rosslyn if asked where they live. Since the main purpose of capturing their address and “location” would be to possible send out case investigators to check for other disease cases or look for nearby ponds etc where the mosquitos breed, the more local the better.

_SP11_Pop

SP_CODE

SP_NAME

MP_CODE

MP_NAME

799037017

Amandasig

799037

Akasia

799037010

Chantelle

799037

Akasia

799037012

Clarina

799037

Akasia

799037018

Heather View

799037

Akasia

799037016

Heatherdale AH

799037

Akasia

799037014

Hesteapark

799037

Akasia

799037011

Karenpark

799037

Akasia

799037004

Klerksoord

799037

Akasia

799037019

Ninapark

799037

Akasia

799037003

Onderstepoort Nature Reserve

799037

Akasia

799037002

Rosslyn

799037

Akasia

799037001

Rosslyn Industrial

799037

Akasia

799037005

The Orchards

799037

Akasia

799037007

The Orchards Ext

799037

Akasia

799037009

The Orchards Ext 11

799037

Akasia

799037006

The Orchards Ext 21

799037

Akasia

799037008

The Orchards Ext 24

799037

Akasia

799037015

Theresapark

799037

Akasia

799037013

Winternest AH

799037

Akasia

In cases where you want e.g. population break-downs for non-standard areas – like health sub-districts in the metros, which often don’t follow ward boundaries – the SAL is clearly superior. So it’s all about purpose....

Again, my apologies if the thread is getting into too much detail – but these challenges are common when trying to use generic spatial data sets of varying granularity and with varying degree of attribute data for special purposes like tracking diseases...

Regards

Calle

From: Venter, Francois (GPHEALTH)
Sent: Monday, August 01, 2016 8:50 PM
To: Calle Hedberg (calle at hisp.org)
Subject: RE: [OSGeo Africa] Recent main place or sub-place spatial data for SA

Hi Calle,

I wanted to reply this morning just before I saw Helene’s reply to you.  I will also suggest the small area layer as a working layer, seeing that it is ever smaller than the sub-place layer.  However just a quick heads-up, I am not sure what the layer looks like from Helene, but if it is the same as the one I have, then you will see that there are a lot of “islands” in it which is blank.  Those are excluded for the sake of reserving people’s confidentiality.  You can confirm from Helene but as far as I understand, you can allocate a value of 0 to those areas, because StatsSA already assigned those values to the surrounding areas closest to those.

I hope this helps.

Regards

Francois

From: Africa [mailto:africa-bounces at lists.osgeo.org] On Behalf Of Calle Hedberg
Sent: Monday, August 01, 2016 8:16 PM
To: 'Africa local chapter discussions'
Subject: Re: [OSGeo Africa] Recent main place or sub-place spatial data for SA

UID09duf63i2bd

Hi,

Just a follow-up of my inquire about this earlier today:

1. Helene Verhoef from StatsSA provided me with the relevant layers earlier this afternoon. EXCELLENT service - my type of GIS expert :-)

2. She also told me that anybody visiting a StatsSA office can get a copy of the Small Areas data set, released in 2013, onto their laptops (my impression was that it's been distributed internally on 3 DVDs, which probably means that it is too much data for normal downloads). Furthermore, something of high relevance for e.g. the Malaria program and others working with local communities and residences/dwellings: StatsSA has coordinates for around 14 mill dwellings in the country - probably around 90%+ of the total - and successfully used some months back for the Community Survey 2016. Bona Fide users (like other government departments) can thus use the same data set as a basic framework for their work with households/dwellings.

3. Finally, Helen also told me that AfriGIS has digitized all the postal areas in the country - I will contact them about access/cost.

If all the above is known stuff, I apologise - but if not, others might find the info useful.

Regards
Calle

-----Original Message-----
From: Africa [mailto:africa-bounces at lists.osgeo.org] On Behalf Of Calle Hedberg
Sent: 01 August 2016 09:44 AM
To: 'Africa local chapter discussions'
Subject: [OSGeo Africa] Recent main place or sub-place spatial data for SA

Hi,

Disease surveillance, and in particular the Malaria program, has for many years used a set of "localities" (villages, suburbs, etc) to determine the origin or residence of e.g. malaria cases.

That level of detail/granularity is similar to the so called "sub-place" layer released by StatsSA in 2003 - containing around 21,500 local areas (although a number of them are sparsely populated areas with no name).

We are now revamping and consolidating the system for disease surveillance, which require a spatial data set with similar granularity but preferably newer than 2001/2003. I already have the ward layers for 2011 and 2016 - using wards is too coarse for our purposes, and most patients would not easily use wards as a reference to where they live.

1.
StatsSA said 2-3 years ago that they had updated the so called "main place" layer, but I cannot find any download links on their website. Anybody knows where to get that layer?

2.
There was no mention in that news item of the "sub-place" layer - anybody knows if a new (well, probably based on Census 2011) sub-place layer is available anywhere?

3.
Another option would be to build a clustered Enumeration Area layer - is the Census 2011 EA layer available from anywhere?

4.
Final question: the list of postal codes from SAPO is at the same level of granularity as the sub-place layer. I know SAPO never developed or at least released spatial data for their postal areas - have anybody else attempted to do that work for them?

Best regards
Calle Hedberg
Cape Town

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