With over 200 years of experience GEOTERRAIMAGE (Pty) is the preferred service provider in the GIS and Remote sensing industry.
We have been providing geospatial services and products to a wide range of public and commercial sectors in support of business intelligence and planning decisions since 1999.
“Unleashing the power of imagery, improving your business intelligence” www.geoterraimage.com
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Overview
• Introduction to GEOTERRAIMAGE (Pty) Ltd
• Analysis and reporting requirements
• African and South African datasets
• Spatial data products
• Spatial data services
• Summary
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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
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GEOTERRAIMAGE (Pty) Ltd
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
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GEOTERRAIMAGE (Pty) Ltd
Partnerships
• Spatial Technologies
• TomTom South Africa
• AfricaScope
• SiQ
• MetroGIS
• Geo Data Design
• Airbus (EADS)
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Analysis and
Reporting
Requirements
Unleashing the power of imagery, improving your business intelligence
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Analysis and Reporting
• Highlight where growth has occurred
• Where has densification occurred?
• Where has land use changed?
Present
Future
Past
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• Indicate Day-time versus night-time
population
• Where is present and future demand?
• What is the potential for change in a
given area?
Analysis and Reporting
Present
Future
Past
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• 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?
Analysis and Reporting
Present
Future
Past
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Southern African Land Cover
7 land cover classes
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Southern African Land Cover
7 land cover classes
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
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African settlements
Urban / Village / Built-up
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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
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Updating African settlements
Average visible light
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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
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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
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Building based land use
Residential versus Non-Residential (2001/2012)
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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
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Historical
Growth
Future
Growth
Population
Demographics
Lifestyle
Residential
Land Use
Building
Footprints
Landscape
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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%
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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%
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Summary
Unleashing the power of imagery, improving your business intelligence
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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
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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