Spatial datasets in support of decision making

StGroup Conference – 15 September 2015

Overview
• Introduction to GEOTERRAIMAGE (Pty) Ltd
• Analysis and reporting requirements
• African and South African datasets
• Spatial data products
• Spatial data services
• Summary

GEOTERRAIMAGE (Pty) Ltd
• Established in 1999
• Clear focus on information provision:
– Remote sensing / Image interpretation
– Field work
– GIS Data fusion / Spatial modeling
• Other GISc services
– GISc Services & consultancy
• Over 200 years of experience

Products / Services

Data Clients and Projects
• Vodacom
• MTN
• Cell-C
• Nokia Siemens Networks
• Telkom / Telkom Mobile
• Telco Network Operators and
Service Providers across Africa
• ESKOM
• AskAfrica
• Aqynt
• AfricaScope
• Peace Parks Foundation
• African Parks Network
• Gauteng City Region Observatory
• Statistics South Africa
• Human Science Research Council
• CSIR
• CRUISE (Stellenbosch University)
• Housing Development Agency
• Standard Bank
• ABSA Bank
• City of Johannesburg
• City of Tshwane
• TomTom
• Nokia / HERE.com

Partnerships
• Spatial Technologies
• TomTom South Africa
• AfricaScope
• SiQ
• MetroGIS
• Geo Data Design
• Airbus (EADS)

Analysis and
Reporting
Requirements
Unleashing the power of imagery, improving your business intelligence

Analysis and Reporting
• Highlight where growth has occurred
• Where has densification occurred?
• Where has land use changed?
Present
Future
Past

• Indicate Day-time versus night-time
population
• Where is present and future demand?
• What is the potential for change in a
given area?
Present
Future
Past

• Is there any indication of planned
developments in an area?
• What is the demographic composition
of an area?
• Where have landscapes or areas been
transformed?
Present
Future
Past

Southern African Land Cover
7 land cover classes

Southern African Land Cover
No Class Area (km²) Percentage
1. Water 14,949.6 1.9%
2. Bare earth 957.1 0.1%
3. Low vegetation (Grassland) 153,009.7 19.1%
4. Tree / Bush dominated areas 386724.8 48.1%
5. Tree dominated areas 246,116.4 30.6%
6. Cultivated (Commercial) 1,419.3

Sudan Land Cover

African settlements
Urban / Village / Built-up

African settlements
Urban / Village / Built-up
No Class Area (km²)
1. Urban / Villages 8,614.8
2. Central Business Districts 1.5
3. Industrial 146.9

Updating African settlements
Average visible light

Updating African settlements
Stable visible light

African population distribution

African population distribution
No Class Area (km²) Population
1. Concentrated 1,193.3 16,562,536
2. Scattered 211,295.4 20,196,760
3. Water 52,621.2 0

African clutter datasets (40m)

African cities clutter datasets (10m)

African cities datasets
• There is limited detailed information available
• The existing data is costly and may be of
limited value
• We are working towards identifying the
relevant information requirements for major
cities in Africa
• Dataset need to be sustainable and consistent
across the continent

Area based land use
Gauteng (ex Smallholdings) – 1990/2013
Land-use 1990 (km²) 2013 (km²) Growth
Built-up 180.2 213.2 18.3%
Commercial 120.8 144.5 19.6%
Industrial 129.3 153.7 18.9%
Informal 83.1 222.2 167.4%
Residential 848.3 1022.0 20.5%
Townships 165.8 361.9 118.3%

Area based land use
Thulamela Local Municipality – 1990/2013

National land cover dataset

Land cover change detection
Emalahleni (1990/2013)

Urban land cover (2010)

Urban land cover (2014)

New Developments (2013)

New Developments (2014)

Building based land use (2012)
Residential
1,014,320
Non-residential
86,365

Building based land use (2012)
Informal
157,674
Backyard
294,116

Building based land use
Residential analysis (2001/2012)
Gauteng Residential Land Uses Formal Informal Backyard
2001
1,195,031 558,927 266,584
59.1% 27.7% 13.2%
1,195,031 825,511
59.1% 40.9%
2012
1,604,249 593,158 816,283
53.2% 19.7% 27.1%
1,604,249 1,409,441
53.2% 46.8%

2000 2009
Diepsloot densification

Building based land use
Residential versus Non-Residential (2001/2012)

Super EA Summary Dataset
• Provide socio-economic, demographic and
activity indicators
– Integrating diverse datasets
• Consistent and easy to use
reporting environment
– Using the StatsSA 2011 EAs
– All data standardised and
reported at this level

Historical
Growth
Future
Growth
Population
Demographics
Lifestyle
Residential
Land Use
Building
Footprints
Landscape

Super EA Based Indicators
No Class Value
1. Total Population 539
2. Total Households 233
3. Daytime population 500 to 1,000
4. Dominant LSM 10
5. Dominant Age class 30 to 34
6. Dominant Race Black
7. Dominant Gender Male
8. Primary Land Use Residential
9. Secondary Land Use Commercial
No Class Value
10. Size of Commercial
buildings
10,500m³
to 31,800m³
11. Major change has occurred since
2001
12. Change has been in residential land
use
13. Chance of change in
the future
67%

Building Based Land Use (National)
No Class % of Residential
1. Informal 12.0%
2. Formal Residential 18.2%
3. Township 17.7%
4. Villages 52.1%

Population distribution dataset

Summary
Unleashing the power of imagery, improving your business intelligence

Summary
• Imagery is under utilised as an additional
source of information
• We are able to quantify historical land use
change
• We are able to assist in forecasting change in
the future
• Datasets can be developed for areas where
imagery is available

Summary
• We are striving to provide South African
equivalent data for Africa
• We are working to understand the economies
of scale and the role-players in Africa
• Working closely with existing and new
business partners to provide a complete
solution

Spatial datasets in support of decision making

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