Prioritisation of Bulk Water Services:

Obtaining the Base Data

Stats SA’s IsiBalo ConferenceBreak-Away 3: Service Delivery in a Municipal Context

Moses Mabhida StadiumDurban

13 September 2013

Alka RamnathUmgeni Water

(The views expressed in this paper are that of the author anddo not necessarily represent the views of Umgeni Water.)

Purpose

• The purpose of the overall study was to assess the status of water

services and prioritise the provision of bulk water services in Sisonke

District Municipality.

• The purpose of this paper is to discuss the verification exercise

undertaken and how the status of water services were determined.

Problem Statement

Methodology: Verification

Thiessen Polygon Analysis on 2009 Eskom Building Count Dataset

Urban and Rural Settlements Identified by DRDLR (2009)

Legal Boundaries

2008 Land Cover (EKZN-W)

Example Showing Requirement for Amendments of

Boundaries

• The attribute fields were compared with each other to determine if the

information correlated.

• The attribute information was compared to the Census 2011 (Statistics

SA 2012).

• Using the comparison of the attribute information, the fields were

simplified.

• Challenge – At time of exercise, Census 2011 only available at ward

level. Density distribution was therefore used.

Results

Ward

Number

Water

Service

Provider

Scheme Name StatusSource of

Water

Water Source

Name

Type of

TreatmentWTP Name

Connection

Type

Designed For

(l/person/day)

Ward 1

Sisonke Stepmore Existing Unknown Unknown Unknown Standpipe 60

Sisonke Cavernfalls Existing Unknown Unknown Unknown Unknown 0

Sisonke

Mqatsheni

Stepmore Existing Spring Unknown Unknown Unknown 60

Sisonke Maguzwana Existing Unknown Unknown Unknown Standpipe 60

Sisonke Pitella Existing Borehole Chlorination N/A Standpipe 60

Ward 2

Sisonke Emakholweni Existing Borehole Unknown Unknown Standpipe 60

Sisonke Himeville Existing River

uMzimkhulu

River WTP

Underberg

WTP

House

Connection 250

Sisonke

South-west of

Okhalweni

Station Existing Unknown Unknown Unknown Unknown 0

Ward 3

Sisonke Underberg Existing River

uMzimkhulu

River WTP

Underberg

WTP

House

Connection 160

Table 1 Attributes captured per footprint (after Umgeni Water 2012)

Table 2 Access to water source per footprint in KwaSani Municipality

(Census 2011 and Eskom 2009).

WardName of

Scheme

Number of

Buildings

(Eskom

2009)

Number of

Households

(Census

2011)

Regional/

local water

scheme

Borehole Spring

Rain

water

tank

Dam/ pool/

stagnant

water

River/

stream

Water

vendor

Water

tankerOther

Ward 1 796 661

Maguzwana 282 234 0 11 140 1 1 33 0 47 0

Mqatsheni

Stepmore 77 64 0 3 38 0 0 9 0 13 0

Stepmore 44 37 0 2 22 0 0 5 0 7 0

Pitella 74 61 0 3 37 0 0 9 0 12 0

Cavernfalls 105 87 0 4 52 0 0 12 0 18 0

Remainder of

Ward 1 214 178 0 9 106 1 1 25 0 36 0

Ward 2 900 1131

Himeville 394 495 255 23 36 3 71 52 3 39 15

Emakholweni 118 148 76 7 11 1 21 15 1 12 4

South-west of

Okhalweni

Station 18 23 12 1 2 0 3 2 0 2 1

Remainder of

Ward 2 370 465 239 21 34 2 67 49 3 36 14

Ward 3 955 1101

Underberg 685 790 671 14 36 3 19 33 3 6 6

Remainder of

Ward 3 270 311 264 6 14 1 7 13 1 2 2

Ward 4 994 780

Ward 4 994 780 86 267 132 37 118 84 12 26 18

Table 3 Access to water per footprint in KwaSani Municipality (Census

2011 and Eskom 2009).

Ward Name of Scheme

Number of

Buildings

(Eskom 2009)

Number of

Households

(Census 2011)

Piped (tap)

water inside

dwelling/

institution

Piped

(tap)

water

inside yard

Piped (tap) water

on community

stand: distance

less than 200m

from dwelling/

institution

Piped (tap) water

on community

stand: distance

between 200m and

500m from

dwelling/

institution

Piped (tap) water on

community stand:

distance between 500m

and 1000m (1km) from

dwelling /institution

Piped (tap) water on

community stand:

distance greater

than 1000m (1km)

from

dwelling/institution

No access

to piped

(tap)

water

Ward 1 796 661

Maguzwana 282 234 33 23 12 2 1 4 159

Mqatsheni Stepmore 77 64 9 6 3 1 0 1 43

Stepmore 44 37 5 4 2 0 0 1 25

Pitella 74 61 9 6 3 1 0 1 42

Cavernfalls 105 87 12 8 4 1 0 1 59

Remainder of Ward 1 214 178 25 17 9 2 1 3 121

Ward 2 900 1131

Himeville 394 495 259 182 13 3 5 2 32

Emakholweni 118 148 78 54 4 1 1 1 10

South-west of Okhalweni

Station 18 23 12 8 1 0 0 0 1

Remainder of Ward 2 370 465 243 171 12 2 5 2 30

Ward 3 955 1101

Underberg 685 790 496 258 6 4 2 0 24

Remainder of Ward 3 270 311 195 101 2 2 1 0 10

Ward 4 994 780

Ward 4 994 780 200 402 90 20 9 12 47

Table 4 Access to sanitation per footprint in KwaSani Municipality

(Census 2011 and Eskom 2009).

Ward Name of Scheme

Number of

Buildings

(Eskom 2009)

Number of

Households

(Census 2011)

None

Flush toilet

(connected to

sewerage

system)

Flush toilet

(with septic

tank)

Chemical

toilet

Pit toilet

with

ventilation

(VIP)

Pit toilet

without

ventilatio

n

Bucket

toiletOther

Ward 1 796 661

Maguzwana 282 234 4 5 4 11 140 71 0 0

Mqatsheni

Stepmore 77 64 1 1 1 3 38 19 0 0

Stepmore 44 37 1 1 1 2 22 11 0 0

Pitella 74 61 1 1 1 3 37 19 0 0

Cavernfalls 105 87 1 2 2 4 52 26 0 0

Remainder of

Ward 1 214 178 3 3 3 8 106 54 0 0

Ward 2 900 1131

Himeville 394 495 3 158 106 8 78 44 75 21

Emakholweni 118 148 1 47 32 2 23 13 23 6

South-west of

Okhalweni

Station 18 23 1 8 10 0 0 1 2 0

Remainder of

Ward 2 370 465 7 86 74 3 27 103 8 13

Ward 3 955 1101

Underberg 685 790 18 279 372 3 5 21 86 6

Remainder of

Ward 3 270 311 7 110 147 1 2 8 34 3

Ward 4 994 780

Ward 4 994 780 18 208 180 8 65 251 19 31

Lessons Learnt

• Importance of clear methodology before data capture commences.

• Mistrust in the Census data but it is useful and correlates with other

datasets in certain situations.

• Fallacy of assuming a 1 – 1 relationship between a “building” and a

“household”.

• Usefulness of the “dwelling unit type” in the Census.

• Evidence-based planning, users appear more comfortable with a Building

Count dataset – “seeing is believing!”.

• To avoid resource waste, need to stop “trying to re-invent the wheel”

and need to start using all available datasets together with their

methodology reports and their metadata. The failure of datasets to have a

proper methodology and this documented is hampering service delivery.

Thank you!