understanding people and place within metropolitan context

Understanding People And Place within Metropolitan

Context

Dr Pali Lehohla

Statistician General

Migration to and between Metropolitan areas

is a feature of the South African landscape

The migrants are attracted to the economic

opportunities within these metropolitan

areas, along with the existing inhabitants

the urban form shapes their access to

improving their livelihoods

Source: CS 2016

Strong inflows to Gauteng

and Western Cape

Source: CS 2016

Metro

36%

Non

Metro

64%

Metro

40%Non

Metro

60%

Metropolitan areas are

increasing their share

of the South African

population from

36% in 2011 to

40% in 2016

20012011

Source: CS 2016 and Census 2011 (boundaries aligned)

0% 5% 10% 15% 20% 25% 30% 35% 40%

0–14 (Children

15–34 (Youth)

35–64 (Adults)

65+ (Elderly)

Metro Non Metro

Metro

Metro

Metro

Metro

Population Structure Metro vs Non Metro CS 2016

Significantly higher

proportion of adults

for Metropolitan

areas

Source: CS 2016

South Africa's towns and cities are highly fragmented,

imposing high costs on households and the economy.

Since 1994, densities have increased in some urban

areas and there has also been partial regeneration of

inner cities, coupled with the growth of housing

ownership but, overall, little progress has been made

in reversing apartheid geography.

NDP 2030

Economic activities are largely

concentrated in the metropolitan

areas which benefit from

economies of agglomeration

Spatial balances / imbalances in the SA urban system 2006

Commerce Commerce Services Services

Industry Industry Finance Finance

Economic balance / imbalance

This imbalance is also reflected in

Metropolitan spending and revenue.

Operating municipal spending: 2014 and 2015 (Metros)

Source Financial Census of

Municipalities for 2015

Province

Type of municipality

Metros Districts Locals Total

2014* 2015 2014* 2015 2014* 2015 2014* 2015

Western Cape 29 242 28 675 2 105 2 504 16 950 17 208 48 297 48 387

Eastern Cape 12 534 13 493 4 886 5 605 13 964 13 830 31 384 32 928

Northern Cape 0 0 769 640 9 089 8 630 9 858 9 270

Free State 6 256 7 803 546 588 14 427 13 162 21 229 21 553

KwaZulu-Natal 26 655 24 725 5 575 6 375 21 170 22 572 53 400 53 672

North West 0 0 1 571 2 234 13 839 14 385 15 410 16 619

Gauteng 78 819 78 567 1 137 1 149 13 691 14 012 93 647 93 728

Mpumalanga 0 0 548 658 14 961 16 304 15 509 16 962

Limpopo 0 0 4 465 4 749 12 449 12 691 16 914 17 440

South Africa 153 506 153 263 21 602 24 502 130 540 132 794 305 648 310 559

Employment by type* of municipality : 2014 & 2015

*Including: full –time + part-time + vacant + managerial positions

**See: Financial census of municipalities 2014, P9114 for more detail Source Financial Census of


ProvinceType of municipality

Metros Districts Locals Total

2014* 2015 2014* 2015 2014* 2015 2014* 2015

Western Cape 8 691 8 178 492 546 3 360 3 642 12 542 12 366

Eastern Cape 2 896 3 368 1 232 1 514 2 326 2 558 6 454 7 440

Northern Cape 0 0 176 187 1 524 1 625 1 700 1 812

Free State 1 109 1 261 206 225 2 231 2 418 3 547 3 904

KwaZulu-Natal 6 894 7 158 1 268 1 430 3 821 4 209 11 983 12 796

North West 0 0 520 589 2 612 2 858 3 132 3 447

Gauteng 19 661 20 723 369 382 2 170 2 361 22 201 23 466

Mpumalanga 0 0 216 253 3 177 3 521 3 393 3 774

Limpopo 0 0 1 091 1 188 2 585 2 855 3 677 4 042

South Africa 39 251 40 688 5 571 6 313 23 806 26 047 68 628 73 048

Employee-related costs*: 2014 & 2015 (R million)

*excludes remuneration of boards of directors & councillors Source Financial Census of


Vacancy rates in metropolitan municipalities per

department: 2015

Electricity

Sport &recreation

Waste watermanagement

Road transport

Public safety

Environmental protection

Waste management

Finance & administration

Community &Social services Health

Water

10%vacancy rate in all departments

5%

7%

7%

7% 8%

10%

10%

11%

20%

28%

4%

Excludes managerial positions & other.

Understanding the apartheid legacy on

our spatial form

Living on the periphery of core areas of

economic activity plays out in increased

transport costs and commute times

Food and non-alcoholic beverages,

12.9%

Housing water

electricity gas and

other fuels,

32.6%

Miscellaneous goods and services,

14.7%

*Other unclassified Expenses, 0.1

Clothing and footwear, 4.8

Communication, 3.4

Furnishings household equipment and

routine maintenance of the house, 5.2

Recreation and culture,

3.8

Transport,

16.3%

Alcoholic bev. tobacco and

narcotics,

0.9

Ed

uc

ati

on

, 2

.5

Health,

0.9

Re

sta

ura

nts

an

d h

ote

ls,

2.1

*

Current Spending Patterns In South Africa:2015

For Urban Informal

this figures rises to

18,9%

Source: Living Conditions Survey 2014/2015

18.9 18 18.6 32.2 12.30

5

10

15

20

25

30

35

Before 6am 6am-6:29am 6:30 to 6:59am 7am to 7:59 8am or later

2003 2013

Percentage of workers in metropolitan areas by leaving time to place of work, 2003 and 2013

Early morning commutes

to work are rising

Source: National Household Travel Survey 2013

%

5.96.8 6.6 7 7.1

14.3

21.1

13.4

10.511.4

0

5

10

15

20

25

Food or Grocery Shop Other Shops Medical Service Post office Police Station

2003 2013

Percentage of metropolitan households who travel more than 60 minutes

to selected services, 2003 and 2013

Larger percentages of Households spending

more time traveling to reach a variety of

services


%

Workers living in metropolitan areas were more

likely to use taxis (29,6%) than trains (9,2%) and

buses (6,3%).

The three main sources of dissatisfaction with taxi

services was Taxi Rank Facilities (54,9%) Fares

(51,1%) and Safety from accidents (45,8%)

Transportation insights

Most important factors influencing household's choice of

mode of travel as selected by the household within

Metros was Safety from Accidents in 2003 in 2013 the

most important factor was Travel Time

*Multiple response allowed


Has policy intentions translated into new spatial realities

examination of spatio-cultural and temporal dimensions of measurement

within selected metropolitan areas

The 2011 settlement patterns illustrate that policy intentions and public action are at variance

with densification on the margins

Census 2001 Census 2011

Census 2011 shows

increasing urban sprawl

on the periphery instead

Source: Internal research

Highest spending in the central city, Nasrec, parts of Soweto (Diepkloof, Bara), parts of Lenasia, Cosmos City, Fleurhof and Diepsloot.

Source: Stats SA internal research

City of Joburg population and spending patterns


Highest spending for the total 5 years were in Pretoria West and the Pretoria North areas like Ga-Rankuwa, Soshanguve, Mabopane, Temba,

and in the east i.e. Mamelodi, most of these been their high priority areas in the municipality.

Tshwane population and spending patterns


Highest spending for the total 5 years were in and around the OR Tambo International Airport (parts of Kempton Park), in and around the Rand

Airport (parts of Germiston) and the Rietspruit area (parts of Katlehong).

Ekurhuleni population and spending patterns


Newly announced projects for

Mega Housing settlements follow

a similar trajectory

Source: GCRO

Where housing is delivered satisfaction levels are variable, KZN Citizen Satisfaction Survey highlights these issues

Type A

Type B1

Type B2

Type B3

Type B4

-10% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Outright Satisfaction with services provided

B3 and B4

Municipality

have

particular

concerns with

Quality of

water

provision

Affordable

Housing

ranks lowest

amongst all

MIIF

categories

High

Satisfaction

with

Electricity

services

almost

universal

Percentage Outright Satisfied

(Ekurhuleni)

Source: KZN Citizen Satisfaction Survey

What are the key perceived municipal

challenges as seen by households?

Access to reliable and

safe water

Lack of/ Inadequate

employment

opportunities

Cost of electricity

Top 3 Perceived municipal challenges

Source: Community Survey 2016

Educations as a catalyst for change

Age structure based on CS 2016


First demographic wave: Children of 1996

The life circumstances of first demographic

wave have not achieved full potentialHigh Unemployment/Poor Educational outcomes

Second demographic wave

Need to invest in

second demographic

wave to achieve

outcomes not seen in

their parents

generation

Age structure based on CS 2016


Main contributors to poverty amongst Youth (15-24)

2.3

2.9

3.2

3.6

3.9

4.7

5.1

8.0

14.6

16.4

35.5

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0

Energy for lighting

Dwelling type

Assets

Energy for cooking

Water

Energy for heating

Sanitation

General health and functioning

NEET

Adult unemployment

Educational attainment

%

The major contributor to the poverty

situation of the youth in South Africa

is educational attainment.


Stats SA has also been working on being

more relevant to the needs of Metropolitan

and Local Municipalities through

partnerships and targeted research in

topical areas.

THE CENTRE FOR REGIONAL & URBAN INNOVATION & STATISTICAL EXPLORATION (CRUISE)

Statistics South Africa

Stellenbosch University

Local government as the cornerstone of change and service delivery

plays a major role in the NDP. As our contribution to policy position

formulation, Statistics SA established a Chair for CRUISE at

Stellenbosch University in 2009 producing over 67 students have

either qualified or are in the process of obtaining a degree as urban

and regional scientists, with several hailing from different parts of

Africa.

Developing estimates for South African local municipality socio-economic models: applications of the rank-size rule 2007 to 2013

Zipf’s rule has proven empirical performance internationally, with

consistently high statistical inference properties.

Image Credit

https://people.hofstra.edu/geotrans/eng/c

h2en/conc2en/centralplacestheory.html

Ex ante performance

Zipf’s rule can help generate useful estimates to bridge the statistics gap in the development of local municipality socio-economic models – i.e. IDPs, SDFs and LEDs.

Source: Developing estimates for South African local municipality socio-

economic models: applications of the rank-size rule 2007 to 2013

Hlabi Morudu Stats SA

0

2

4

6

8

10

12

14

16

18

20

Ca

md

eb

oo

Ma

ka

na

Ba

via

an

s

Mb

ha

she

Am

ah

lath

i

Nx

ub

a

Ink

wa

nc

a

Em

ala

hle

ni

Elu

nd

ini

Ga

rie

p

Ny

an

de

ni

Ma

tati

ele

Nta

ba

nk

ulu

Le

tse

me

ng

Na

led

i

Tsw

elo

pe

le

Se

tso

to

Ma

luti

a P

ho

fun

g

Mo

qh

ak

a

Ma

fub

e

Mid

va

al

Ra

nd

fon

tein

Ek

urh

ule

ni

Vu

lam

eh

lo

UM

uzi

wa

ba

ntu

uM

shw

ath

i

Imp

en

dle

Ric

hm

on

d

Um

tsh

ezi

En

du

me

ni

Um

vo

ti

Da

nn

ha

use

r

Ab

aq

ulu

si

Um

hla

bu

ya

lin

ga

na

Hla

bis

a

uM

hla

thu

ze

Mth

on

jan

en

i

Kw

aD

uk

uza

Ing

we

Ub

uh

leb

ezw

e

Gre

ate

r G

iya

ni

Ba

-Ph

ala

bo

rwa

Mu

tale

Blo

ub

erg

Po

lok

wa

ne

Le

ph

ala

le

Be

la-B

ela

Eli

as

Mo

tso

ale

di

Gre

ate

r T

ub

ats

e

Mk

ho

nd

o

Dip

ale

sen

g

Em

ala

hle

ni

Th

em

bis

ile

Mb

om

be

la

Bu

shb

uc

kri

dg

e

Ka

mie

sbe

rg

Kh

âi-

Ma

Em

tha

nje

ni

Th

em

be

lih

le

Mie

r

!Kh

eis

So

l P

laa

tjie

Ph

ok

wa

ne

Ga

ma

ga

ra

Ru

ste

nb

urg

Ra

tlo

u

Dit

sob

otl

a

Ma

mu

sa

Ka

gis

an

o/M

olo

po

Cit

y o

f M

atl

osa

na

Ce

de

rbe

rg

Sw

art

lan

d

Ste

lle

nb

osc

h

Th

ee

wa

ters

klo

of

Sw

ell

en

da

m

Mo

sse

l B

ay

Bit

ou

Pri

nc

e A

lbe

rt

NU

MB

ER

OF

PR

OT

ES

TS

LOCAL MUNICIPALITY

Protests2013 versus predicted protests2013

Protests2013 Pred(Protests2013)

Protest predictions (Pred(Protests2013)) emanating from protests registered in 2013 (Protests2013). All major protests areas (Johannesburg, Ethekwini and Cape Town) could be adequately predicted.

Source : Service Delivery Protests in South African Municipalities: an exploration using Principal

Component Regression and 2013 data Dr Hlabi Morudu Stats SA

To alleviate service delivery protests, the findings suggest more needs to be done in expanding the provision of

electricity, sewerage and sanitation, the number of schools, nurseries, crèches and hospitals. Expenditures on new

construction works in residential buildings and electricity infrastructure also need to be extended to significantly

contribute towards reducing the number of service delivery protests.

But is Statistical information being used

optimally ?

SOURCES

Population size and spatial distribution by administrative unit and

locality

Census, Population

projections

Age-sex structure of the population for identification of various

categories, especially the vulnerable groups

Census, Population

projections

Socioeconomic characteristics of the population (literacy, economic

activity, etc.)

Census

Socio-cultural characteristics of the population (ethnic group,

language, religion, etc.)

Census

Location and other details on the basic social infrastructure Census mapping,

Administrative sources

Reproductive behaviour patterns of the population (fertility,

contraceptive practice, family size, household size and composition,

etc.)

Surveys, Census

Income levels and basic indicators of well-being and vulnerability Surveys, Census

Level of indicators and data sources for IDP in-depth analysisIN

DIC

AT

OR

S

General Observations from a sample of Districts

These IDPs may not provide enough statistical information to substantiate the claims that have been made in the documents.

The qualitative and quantitative analysis of the IDPs is lacking. Documents mention some areas as being problematic without providing context and explanation to justify the claim.

Indicators are not standardised no classified in such away that relevant outputs can be linked to outcomes. This leads to difficulty in implementing monitoring and evaluation (M&E)

Metropolitan areas are a important

part of the broader South African and

International economic landscape

Sustainability and Linkages

Funds received by local and metropolitan municipalities in the form of property rates 2015 (% of total income)

Sustainability and connection between

Metro and Non metro areas needs to

be considered

Source: Financial Census of Municipalities for 2015